organizing collaborative innovation

I

Organizing Collaborative Innovation Studying the Process of Intermediaries for Open Innovation

Von der Fakultät für Wirtschaftswissenschaften der

Rheinisch-Westfälischen Technischen Hochschule Aachen

zur Erlangung des akademischen Grades eines Doktors der

Wirtschafts- und Sozialwissenschaften genehmigte Dissertation

vorgelegt von

Kathleen Diener

Berichter: Univ.-Prof. Dr.rer.pol. Frank Thomas Piller

Univ.-Prof. Dr.rer.pol.habil. Malte Brettel

Tag der mündlichen Prüfung 15.12.2014

Diese Dissertation ist auf den Internetseiten der Universitätsbibliothek online verfügbar.

V

Thesis abstract

This thesis intends to contribute to the debate about what really constitutes open innovation.

Specifically, the results of the study shall help to sharpen the meaning of open innovation on a

theoretical, as well as on a practical, level. In addition to distinguishing open innovation from

other concepts of cooperation within innovation, this thesis wants to provide insight into the

organization of such collaboration. In doing so, its objective is to isolate the central parame-

ters of organization and how these interact.

Innovation and cooperation are strongly interlinked concepts with a long research history.

Simply defined, innovation means of the creation of new knowledge. Especially in technology

intensive fields, external knowledge sources are of importance because expertise is widely

distributed, which causes a shift in the locus of innovation away from the firm into the net-

work. Scholars arguing from this knowledge-based perspective highlight the role of inter-

organizational collaboration. With Chesbrough’s (2003) introduction of the open innovation

paradigm the collaboration concepts have gained a new facet. The concept of collaboration

expands beyond inter-organizational networks in terms of collaborating with all different

kinds of actors, like customers, suppliers, users etc. The scientific relevance of this topic is

strongly supported by the exponential growth of associated work in scientific literature data-

bases, such as EBSCO and ISI Web of Science within the past decade. The growing number

of articles reviewing the current state of the literature show an interest in identifying princi-

ples behind open innovation and its correlation with innovation performance. However, the

recent conceptual ambiguity prevents a full understanding of open innovation and there re-

mains a need to better differentiate practices of open innovation. Academics have further

agreed that there is a paucity of research considering the organization and costs of open inno-

vation; so far organizational aspects are a minor researched issue. When talking about costs of

openness, scholars see a particular need to investigate the costs for coordination and competi-

tion. Once the increasing and decreasing effects of openness are known the true benefit of

open innovation can be determined.

This thesis seeks to cease the opportunity that this gap in the research on organizing (open)

innovation collaboration provides. By adopting the theory of organizational learning and the

resourced-based view, it investigates which aspects describe the organization of innovation

collaboration. The focus, therefore, lies especially on new forms of collaboration, which de-

scribe a distributed innovation process. By performing three single studies this thesis intends

to answer the following research questions:

VI

1. How do new forms of collaboration differ from traditional forms in terms of their for-

malization and proximity?

2. What are beneficial structural requirements in the organization of collaboration with

external partners?

3. What is the function behind for open innovation?

The empirical data of the studies are based on surveying intermediaries in the field of open

innovation. In total, two market studies analyzing open innovation intermediaries were per-

formed. By doing so, this research suggests a framework which allows for the arrangement of

different existing concepts of collaboration as an interaction between two dimensions: the

degree of collaboration formalization and the degree of collaboration proximity. In general,

results show that the governance of ‘open innovation’-related collaboration forms are charac-

terized by an informal interaction that takes place further away from the boundaries of a firm.

The first study outlines the research into, and practice of, open innovation and suggests a con-

ceptual framework to differentiate this concept from others. The conceptual framework of this

study is an interaction model which reflects the process of generating and transferring

knowledge. This framework allows facets of openness that distinguish between configuration

types of collaboration to be derived. The second study adds to the literature on open innova-

tion by opening the ‘black box’ of collaboration. The results advance the understanding of the

relationship between external knowledge sourcing and organization structure to process in-

formation. The conceptual model of this study hypothesizes a mediating role of the

knowledge transfer process between cooperation and coordination costs. The type of media-

tion indicates the degree of decomposability of the knowledge transfer process. Results reveal

that collaboration starting with broadcast search the knowledge transfer process is decompos-

able whereas collaboration starting with direct search comprises more highly interdependent

activities which make the transfer process complex and not decomposable. The comparison of

broadcast search and direct search showed that broadcast search is more efficient but is mod-

erated by the size of the involved community. The third study empirically investigates the

understanding of the term ‘open innovation’ and how open innovation methods can be struc-

ture. Open innovation methods can be described according to the type of information they

intend to generate and the mechanism through which collaboration between the diverse part-

ners is initiated. After successful collaboration, achieved through calling for participation or

searching for adequate partners a third mechanism was identified – selective call. The frame-

work derived from this allows for the classification of six open innovation methods, ranging

from different contest formats to workshops and market research.

VII

Kurzzusammenfassung der Arbeit

Diese Dissertation befasst sich mit Kooperationsformen im Bereich Innovationsmanagement.

Ziel der Arbeit ist zum einen die Abgrenzung von Zusammenarbeit im Rahmen des Open

Innovation Konzepts gegenüber klassischen Zusammenarbeitsformen und zum anderen die

Analyse organisationaler Aspekte, welche für die Durchführung von Zusammenarbeit not-

wendig sind. Die beiden Konzepte Kooperation und Innovation sind stark miteinander ver-

bunden. Innovieren im Allgemeinen bedeutet, die Erschaffung neuen Wissens. Relevantes

Wissen, insbesondere in technologieintensiven Umfeldern, liegt dabei oft verteilt und immer

öfter nicht innerhalb des eigenen Unternehmens vor. Daher sind insbesondere externe Quellen

für den Innovationsprozess von Bedeutung. Dies führt zu einer Verschiebung des Ursprungs

von Innovationen aus dem Unternehmen heraus hinein ins Netzwerk. Mit der Einführung des

Open Innovation Konzepts durch Chesbrough (2003) erlebte das Thema Zusammenarbeit eine

Renaissance. Das Konzept erfährt eine Ausdehnung über die inter-organisationale Kooperati-

on hinaus. Weitere externe Akteure wie Universitäten, Kunden, Wettbewerber werden als

mögliche Interaktionspartner in Betracht gezogen. Die wissenschaftliche Relevanz des The-

mas Open Innovation zeigt sich unter anderem besonders im exponentiellen Anstieg von Pub-

likationen in diesem Bereich. Es besteht ein gesteigertes Interesse darin, zugrundeliegende

Zusammenhänge zwischen Open Innovation und Innovationsleistung zu verstehen. Jedoch ist

ein besseres Verständnis aktueller Open Innovation Praktiken durch die konzeptionelle Mehr-

deutigkeit behindert. Diese konzeptionelle Mehrdeutigkeit zu beheben, sehen Forscher als

eine wesentliche Aufgabe aktueller Forschung. Darüber hinaus besteht Einigkeit über vorlie-

gende Forschungslücken bzgl. der Organisation und den daraus resultierenden Kosten von

Open Innovation. Insbesondere Kosten für Koordination von Open Innovation Projekten sind

für die Erfassung des Gesamtnutzens sowie zur situativen Bewertung von Open Innovation

notwendig.

Diese Arbeit greift die dargestellten Forschungslücken auf und versucht die Frage nach der

Organisation von Open Innovation Kooperationen mit Hilfe der Theorie des organisationalen

Lernens und des ressourcenorientierten Ansatzes zu beantworten. Der Fokus der Arbeit liegt

dabei auf den Innovationsbeziehungen, die einen stärker verteilten Innovationsprozess be-

schreiben. Es werden drei Studien zur Beantwortung der folgenden Forschungsfragen durch-

geführt.

1. Worin unterscheiden sich neue Kooperationsformen von klassischen Formen der Zu-

sammenarbeit auf den Dimensionen Formalität und Proximität?

VIII

2. Was sind vorteilhafte Organisationsstrukturen bei der Zusammenarbeit mit externen

Akteuren?

3. Wie gestaltet sich das Funktionsprinzip hinter Open Innovation?

Die Befragung von Open Innovation Intermediären bildet die empirische Grundlage der Ar-

beit. Informationen aus zwei aufeinanderfolgenden, eigens durchgeführten Marktstudien sind

in die Analysen eingegangen. Die Analyse hat grundsätzlich ergeben, dass die Organisation

der Zusammenarbeit im Bereich Forschung und Entwicklung durch das Zusammenspiel zwei-

er Dimensionen beschrieben werden kann: den Grad der Formalisierung und den Grad der

Nähe. Open Innovation Kooperationsformen werden dabei als informale und von der Unter-

nehmensgrenze weiter entfernt stattfindende Zusammenarbeitsformen klassifiziert. Die Er-

gebnisse der Einzelstudien vertiefen das Verständnis des abgeleiteten Rahmenkonzepts. Die

erste Studie gibt einen Überblick über Kooperationsformen, die unter das Open Innovation-

Verständnis fallen und grenzt diese gegenüber klassischen Zusammenarbeitsformen ab. Ein

Interaktionsmodell des Wissenstransfers bildet den konzeptionellen Rahmen für diese Be-

trachtung. Des Weiteren gelingt die Ableitung von Facetten, die zu einer differenzierenden

Beschreibung von Offenheit sowie zur Unterscheidung von Kooperationsformen dienen kön-

nen. Eine zweite Studie analysiert den Zusammenhang zwischen externer Wissensbeschaf-

fung und Organisationsstrukturen der Informationsverarbeitung. Das konzeptionelle Modell

nimmt an, dass der Wissenstransferprozess die Beziehung zwischen Kollaboration und Koor-

dination mediiert. Der Mediationstyp (partiell vs. voll) gibt Auskunft über die Modularisie-

rung des Wissenstransferprozesses. Ergebnisse zeigen, dass Kollaborationen, die „broadcast

search“ beinhalten, im Gegensatz zu Kollaborationen, die durch direkte Suche nach externen

Wissensquellen gekennzeichnet sind, einen zerlegbaren Wissenstransferprozess aufweisen.

Diese indirekte Suchstrategie ist bzgl. der Koordination der Zusammenarbeit, unter Berück-

sichtigung der Communitygröße, mit geringeren Kosten verbunden. Die dritte Studie unter-

sucht zum einen das Verständnis von Open Innovation und zum anderen wie Open Innovati-

on-Methoden gruppiert werden können. Grundsätzlich können die Methoden bzgl. der zu ge-

nerierenden Informationsart und des Mechanismus, wie die Interaktion initiiert wird, be-

schrieben werden. Es zeigt sich, dass neben dem Ausrufen eines Kollaborationsinteresses und

des Suchen eines konkreten Partners ein dritter, hybrider Mechanismus existiert. In den ab-

leitbaren Bezugsrahmen lassen sich abschließend in Abhängigkeit der Kollaborationsform

und der Offenheit der Suche sechs Open Innovation-Methoden, wie verschiedene Wettbewer-

be, Workshops oder Marktforschungsansätze, einsortieren.

XI

Table of Contents

Thesis abstract ........................................................................................................................... V

Kurzzusammenfassung der Arbeit .......................................................................................... VII

List of Figures ....................................................................................................................... XIII

List of Tables .......................................................................................................................... XV

Acronyms ............................................................................................................................. XVII

Part A. Introduction and summary of the dissertation project .............................................. XIX

1 Introduction ..................................................................................................................... 1

2 Theoretical and conceptual background of innovation collaboration ............................. 5

2.1 Method: Co-citation analysis of literature on open innovation ............................... 8

2.2 Data analysis .......................................................................................................... 15

2.3 Results of the literature analysis ............................................................................ 17

2.4 A conceptual framework for organizing innovation collaboration ........................ 23

3 Research design ............................................................................................................. 26

3.1 Intermediaries in innovation .................................................................................. 27

3.2 Data basis ............................................................................................................... 29

4 Introduction and summary of the research articles ....................................................... 34

4.1 Study 1: Facets of open innovation: Development of a conceptual framework .... 35

4.2 Study 2: Organizing collaboration: The costs of innovative search ...................... 38

4.3 Study 3: The market for intermediation for open innovation ................................ 40

5 Conclusion and general discussion ............................................................................... 43

5.1 Summary of study results ....................................................................................... 43

5.2 Theoretical implications ......................................................................................... 48

5.3 Managerial implications ......................................................................................... 51

5.4 Limitations and outlook ......................................................................................... 53

6 References ..................................................................................................................... 56

7 Appendix (A) ................................................................................................................. 73

Appendix (A) 1: Most cited articles (top five) for each factor ......................................... 73

Appendix (A) 2: Publication sources and their rankings .................................................. 74

Appendix (A) 3: Keywords clustered by hierarchical cluster analysis (compared with factor belonging) ............................................................................................................... 83

Appendix (A) 4: Rotated factor matrix with loadings of keywords ................................. 84

Appendix (A) 5: Survey form of market study I ............................................................... 85

.................................................................................................................................................. 97

Appendix (A) 6: Survey form of market study II ............................................................. 98

XII

Part B. Research papers .......................................................................................................... 133

Study 1: Facets of Open Innovation: Development of a Conceptual Framework .............. 135

Study 2: Organizing collaboration: The costs of innovative search ................................... 195

Study 3: The market for intermediation for open innovation ............................................. 249

XIII

List of Figures

Part A

Figure (A) 1: Development of open innovation related concepts .............................................. 6

Figure (A) 2: Publication rate for “open innovation” between the years 2003-2013* .............. 7

Figure (A) 3: Multidimensional Scaling – A map of innovation collaboration ....................... 16

Figure (A) 4: Type of publication per factor in percent ........................................................... 19

Part B

Study 1

Figure (B1) 1: Conceptual framework: A model of collaboration for open innovation ........ 144

Figure (B1) 2: Stage structure of the conceptual model ......................................................... 150

Figure (B1) 3: OIIs and their industry focus .......................................................................... 157

Study 2

Figure (B2) 1: Three-step model of knowledge transfer according to von Krogh and Koehne

(1998, p. 238) ......................................................................................................................... 200

Figure (B2) 2: Conceptual model of open innovation collaboration ..................................... 208

Figure (B2) 3: Final conceptual model of open innovation collaboration ............................. 210

Figure (B2) 4: Average extent of search activities within open innovation methods ............ 232

Study 3

Figure (B3) 1: Population growth of intermediaries for open innovation .............................. 261

Figure (B3) 2: Geographical market coverage of the intermediaries (N=59) ........................ 265

Figure (B3) 3: Project market development as indicated by expected revenue growth of OI

intermediaries (N=59) ............................................................................................................ 266

Figure (B3) 4: Distribution of collaboration initiation mechanism regarding type of

information ............................................................................................................................. 268

Figure (B3) 5: Partner configurations intermediaries interact throughout the project (N=64)

................................................................................................................................................ 271

Figure (B3) 6: Interaction format across partner configurations (N=64) ............................... 272

XV

List of Tables

Part A

Table (A) 1: Keyword list used for literature search in ISI ........................................................ 9

Table (A) 2: Publications to identify potential search keywords ............................................. 10

Table (A) 3: Main publication sources ..................................................................................... 13

Table (A) 4: Publications with multiple factors belonging ...................................................... 17

Table (A) 5: Types of organizing innovation collaboration ..................................................... 25

Table (A) 6: The data basis of the research papers .................................................................. 35

Table (A) 7: Main research results of my thesis answering the research questions ................. 48

Part B

Study 1

Table (B1) 1: Mode of communication and the degree of integration ................................... 154

Table (B1) 2: Segmentation for the degree of integration ..................................................... 155

Table (B1) 3: Definition of the residual categories on the bases of empirical data ............... 159

Table (B1) 4: Number of collaboration types found among the 43 OIIs ............................... 160

Table (B1) 5: Cross tab of variables

Initiation and Constitution in Stage 1 Forming (N=65) ......................................................... 160

Table (B1) 6: Cross tab of variables mode of communication and direction of communication

(N=65) .................................................................................................................................... 161

Table (B1) 7: Cross tab including all content variables (N=65) ............................................ 162

Table (B1) 8: Cross tab including the degree of integration (N=65) ..................................... 163

Table (B1) 9: Configuration types of open innovation collaboration .................................... 165

Table (B1) 10: R&D collaboration forms along the dimension of “openness” ..................... 175

XVI

Study 2

Table (B2) 1: Method categories for analysis ........................................................................ 216

Table (B2) 2: Variable definitions ......................................................................................... 217

Table (B2) 3: Descriptive statistics and correlations ............................................................. 218

Table (B2) 4: Results of OLS regression of control variables predicting search effort ......... 220

Table (B2) 5: Comparing search mechanisms – results of regression for path c’ in mediation

model – direct search ............................................................................................................. 222

Table (B2) 6: Comparing search mechanisms – results of regression for path c’ mediation

model – indirect search .......................................................................................................... 223

Table (B2) 7: Results of regression for comparing

the direct and indirect search mechanism .............................................................................. 224

Table (B2) 8: Results of regression for comparing the direct and indirect search mechanism

including interaction ............................................................................................................... 225

Table (B2) 9: Summary of findings ....................................................................................... 227

Study 3

Table (B3) 1: A structure of mechanisms to broker knowledge in open innovation ............. 258

Table (B3) 2: Frequency of theoretical derived open innovation approaches (N=64) ........... 268

Table (B3) 3: Extended structure of OI approaches and frequencies observed in sample

(N=64) .................................................................................................................................... 270

Table (B3) 4: Regression results: number of partners on open innovation understanding .... 273

Table (B3) 5: Types of open innovation reach (frequency of occurrence, N=64) ................. 275

Table (B3) 6: Regression results: Community reach on open innovation understanding ...... 275

XVII

Acronyms

ACAP Absorptive capacity

ANOVA Analysis of variance

B-to-B Business-to-Business

B-to-C Business-to-Consumer

CA Cluster Analysis

CEO Chief Executive Officer

FA Factor Analysis

FFE Fuzzy front end

FMCG Fast-moving-consumer-goods

ICC Intraclass correlation coefficients

ICT Information and communication technologies

IP Intellectual property

IPR Intellectual property rights

KIBS Knowledge Intensive Business Service

LOG function Logarithmic function

LU method Lead user method

MDS Multidimensional Scaling

NGO Non-profit organization

NIH Not invented here

NPD New product development

OI Open innovation

OII Open innovation intermediaries

OLS regression Ordinary least squares regression

R&D Research & Development

RBV Resource-based view

RSQ R-squared

SMEs Small and medium-sized enterprises

TCE Transaction cost economics

VHB Verband der Hochschullehrer für Betriebswirtschaft

VKB Virtual knowledge broker

WU Wien Vienna University of Economics and Business Administration

XIX

Part A. Introduction and summary of the dissertation project

In Part A of my thesis I introduce the theoretical and practical relevance of my research on

innovation collaboration. I perform a large-scale systematic literature review in order to pro-

duce a conceptual framework capable of providing a firm foundation for my empirical re-

search on the organizational requirements of especially new collaboration forms. Part A clos-

es with an overall discussion of the research results and their implication for theory and prac-

tice.

In Chapter 1, the introduction, I motivate the topic of open innovation and its organization as

a distributed innovation process. I show the general relevance of this topic based on prior re-

search and demonstrate the gaps in the research that remain. Additionally, I relate my disser-

tation project to the underlying theoretical background of organizational learning and the re-

sourced-based view, which explains the behavioral perspective I take on in my research. The

chapter concludes by outlining the main contribution of the thesis to the field of open innova-

tion.

Chapter 2 comprises the review of literature on collaboration in the innovation process. It first

shows the problem of selecting the right literature due the recent plethora of publications and

the conceptual ambiguity surrounding the term ‘open innovation’. In the section following

this, I explain my review method of a modified co-citation analysis. Based on N=1,305 publi-

cations, I perform a factor analysis and a multidimensional scaling to derive a thematic map

of innovation collaboration which is displayed in the section of data analysis. In the results

section I describe and interpret the factors identified. Chapter 2 closes with the conceptual

framing of collaboration types and the deduction of the research question.

Chapter 3 presents the research design which underlies my empirical studies. It describes the

sample of innovation intermediaries that were chosen and introduces the two questionnaires

used to collect data within the two separate surveys.

In Chapter 4 I provide a brief overview of my three research papers. They form the main body

of my thesis and are presented in Part B. The chapter outlines the motivation, analytic proce-

dure, core results and their meaning for the theory and practice of each paper.

Chapter 5 concludes by supplying a summary of all research results and answers to the re-

search questions. This is followed by a general discussion of the theoretical and managerial

implications, before finishing with suggestions for future avenues of research.

Introduction

1

“A firm's portfolio of collaborations is both a resource

and a signal to markets, as well as to other potential partners, of

the quality of the firm's activities and products.”

(Powell, 1998, p. 231)

1 Introduction

My thesis intends to contribute on the debate about what really constitutes open innovation. In

particular, my research shall help to understand the meaning of open innovation on a theoreti-

cal as well as on a practical level. Additionally, my work offers a starting point in distinguish-

ing open innovation from other concepts of cooperation within innovation, focusing especial-

ly on the arrangement of such collaboration. In doing so, my objective is to isolate the central

parameters in organizing open innovation and identify their relationships.

With Chesbrough’s (2003) introduction of the open innovation paradigm, traditional inter-

firm cooperation concepts underwent a revival. For example, scientists now explore inter-firm

alliances from a relational perspective and use open innovation as a theoretical foundation

(e.g. Faems et al., 2010). More importantly, though, the concept of collaboration in innovation

gained a new focus. The diversity partners and the form of interaction across distances be-

came central (Rosenkopf and Nerkar, 2001). As well as traditional, inter-firm cooperation,

other collaboration partners like customers, universities, users etc. have come to receive in-

creased attention. Open innovation expands the pool of potential cooperation partners beyond

the immediate environment of the focal firm. West and Gallagher (2006, p. 320) therefore

define open innovation as “[…] exploring a wide range of […] external sources for innovation

opportunities […]”. It seems, then, that open innovation is a further concept linking innova-

tion and cooperation.

However, an increasing number of articles reviewing the current body of literature indicate a

certain relevance of this topic (Dahlander and Gann, 2010; Huizingh, 2011; Lichtenthaler,

2011; Trott and Hartmann, 2009; Van de Vrande et al., 2010; Elmquist et al., 2009; West and

Bogers, 2013). These reviews predominantly structure the research field according to its main

research issues and central findings. Researchers agree that open innovation can be performed

in two ways: as in-bound and out-bound innovation. Basically, this structure describes the

direction of the knowledge flow from either the outside to the inside of the firm or vice versa.

Furthermore, scholars are showing a joint interest in understanding the principles behind open

innovation and its correlation with innovation performance, such as the scope of searching for

Introduction

2

external sources. Academics also agree upon existing research gaps. For example, organiza-

tional aspects of open innovation have received only minor attention, even though the imple-

mentation of open innovation in the organizational context poses a challenge to innovation

management (Lichtenthaler, 2011, p. 80). According to Dahlander and Gann (2010, pp. 701-

706), there is, firstly, the need to better understand different practices of open innovation. The

authors request greater precision of open innovation concepts, with the assumption that this

will enable firms to successfully adopt innovation management strategies. The current con-

ceptual ambiguity prevents a full understanding of the underlying principles of open innova-

tion. Secondly, they see a need in further examining the costs of openness for coordination

and competition. Knowing their effects on innovation performance would allow for determin-

ing the true benefit of an open innovation strategy.

In summary, the reviews suggest that open innovation embraces more than just collaboration

with an increased number and type of external sources. Rather, it seems that the way in which

these sources are explored in terms of their interaction is central. In my thesis I intend to ex-

plore the research gaps that have been identified and begin to fill them by analyzing open in-

novation interaction.

The literature provides a variety of terms describing this interaction amongst diverse actors,

for example, crowdsourcing and co-creation (Howe, 2006; Prahalad and Ramaswamy, 2000).

Thus, the kind of mass collaboration implies that a behavioral component is inherent in open

innovation. Focusing on the behavioral aspect of open innovation, two main theories can

serve as theoretical anchorage – organizational learning, which views an organization as a

problem solver (Cyert and March, 1963; Huber, 1991; Katila and Ahuja, 2002), and the re-

source-based view (RBV), with its two specifications: the knowledge-based view and the rela-

tional-view. Theorists regard knowledge as an essential resource and firms as distributers of

knowledge (Barney, 1991; von Krogh et al., 2001; Powell et al., 1996). Both theoretical per-

spectives share an understanding of innovation as the creation of new knowledge (Nelson and

Winter, 1982).

First, from the organizational learning perspective, knowledge creation means the ability to

search for adequate knowledge outside of an organization. Katila (2002) describes the search

behavior as one core element of a firm’s innovative capabilities. Thus, the behavioral perspec-

tive (Simon, 1955; Cyert and March, 1963) appears to be most suitable in the context of or-

ganizing innovation collaboration. Fundamental for the behavioral theory of the firm is the

prediction of a firm’s decision regarding output, resource allocation, price etc. (Cyert and

March, 1963, p. 19). Furthermore, this theory includes the question of how organizations

Introduction

3

search for information, which is central when examining the topic of innovation collaboration.

Organizational learning theorists regard the search activity as a core process for firms to adapt

to changing environments (Cyert and March, 1963; Huber, 1991). Thus, the innovation pro-

cess resembles problem solving (Katila and Ahuja, 2002). The basic argumentation of my

thesis is that firms engage in collaboration because they search for information to an innova-

tion problem (Katila and Ahuja, 2002; Katila and Chen, 2008; Laursen and Salter, 2006). This

corresponds with the theoretical concept of problemistic search introduced by Cyert and

March (1963). Indeed, in his recent study, Salge (2012, p. 721) has explicitly linked innova-

tive search to the key concepts of problemistic search.

Second, the RBV (Barney, 1991) argues that “sustained competitive advantage derives from

the resources and capabilities a firm controls that are valuable, rare, imperfectly imitable, and

not substitutable.” (Barney et al., 2001, p. 625). In the context of innovation collaboration,

resources incorporate organizational processes and routines, but also a firm’s knowledge and

information. In their review of the RBV, Barney et al. (2001, p. 630) state that resources and

knowledge are related to each other. The role of knowledge as a strategic resource (Grant,

1996) has already been explicated by Kogut and Zander (1992) in their examination of a

knowledge-based organization. Especially in the context of innovation, scholars highlight the

role of inter-organizational collaboration to create new knowledge (e.g. Hargadon and Sutton,

1997; Powell et al., 1996; Powell, 1998 Gulati, 1995). The knowledge-based perspective of

the RBV proposes that “inter-firm collaborative arrangements are efficient mechanisms to

transfer and integrate explicit knowledge […]” (Grant and Baden-Fuller, 1995, p.19). Powell

(1998) argues that knowledge-seeking and knowledge-creation are fundamental abilities of a

firm to achieve competitive advantage. In technology intensive fields in particular, external

knowledge sources are of importance because expertise is widely distributed, leading Powell

to conclude that this causes a shift in the locus of innovation away from the firm and into the

network.

This knowledge-based understanding adopts a network approach which forms the last theoret-

ical component for analyzing innovation collaboration in my thesis. Scholars who research

external knowledge sourcing from the perspective of the network (e.g. Gulati, 1995; Ahuja,

2000; Katila and Ahuja, 2002; Rosenkopf and Almeida, 2003; Borgatti and Cross, 2003;

Salge et al. 2012) typically apply the relational view, another facet of the RBV. According to

Dyer and Singh (1998), this view regards the relations to external actors as a source of com-

petitive advantage. Dyer and Singh (1998, p. 662) describe this source as ‘relational rent’

which is defined as “a supernormal profit jointly generated in an exchange relationship that

Introduction

4

cannot be generated by either firm in isolation and can only be created through the joint idio-

syncratic contributions of the specific alliance partners”. The main objective of innovation

collaboration lies in the intention of a firm to create and recombine knowledge to enrich their

own knowledge stock with the help of the network (Salge et al., 2012, p. 5).

In this regard, I am interested in how far firms profit from leveraging the network by analyz-

ing the effort to coordinate such collaboration venture. Barney et al. (2001) highlight the rela-

tionship between the RBV and corporate governance as a fruitful path in research. According

to the authors, organizational issues are not necessarily a source for competitive advantage,

but an adequate organization fully acknowledges the resource benefits (p. 632). Research on

alliances place a special interest on this relationship. For example, Oxley and Sampson (2004)

examined the role of governance structures when balancing competing goals, like open

knowledge share and controlling knowledge flow during R&D alliances. McGill (2007), on

the same issue, found a curvilinear relationship between technological similarity of partners

and the form of alliance governance. A moderate similarity goes along with a more integrative

form of control. Stieglitz and Billinger (2007) suggest that the demand for coordination in-

creases with more organizational search for information.

My thesis connects with the research on organizing innovation collaboration. I focus on the

relationship between the degree of openness and the effort to coordinate cooperation of part-

ners with diverse characteristics. By linking the behavioral (Simon, 1955; Cyert and March,

1963) and the resourced-based view, with special focus on the relational view (Dyer and

Singh, 1998), this thesis investigates which aspects describe the organization of innovation

collaboration. I approach this through the perspective of the innovating firm and their possi-

bilities of engaging in diverse innovation collaboration forms. Doing so, my research creates a

framework which enables the mapping out of different existing concepts of collaboration.

Based on that framework and my empirical findings, I propose a conceptual distinction be-

tween open innovation and other collaboration forms, and suggest a more specific meaning of

open innovation. Furthermore, this thesis extends the literature on open innovation by opening

the ‘black box’ of collaboration in order to better analyze the underlying process. The results

advance the understanding of the relationship between external knowledge sourcing and or-

ganization structure to process information. In summary, my thesis contributes to the under-

standing of the open innovation phenomenon and provides managerial implications for its

organization.

Theoretical and conceptual background of innovation collaboration

5

In the following section, I will review the broad literature on innovation collaboration to de-

velop a conceptual framework and derive my research questions. This forms the basis of my

thesis and guides my research. To do so, I first run co-citation analysis, modified based on

search keywords (Section 2.2). This method allows me to review the literature on a large scale

and helps to create a thematic landscape of innovation collaboration in Section 2.3. This map

is the starting point for establishing the conceptual framework to describe the organization of

innovation collaboration. Moreover, three research questions are derived to identify crucial

aspects in organizing innovation collaboration. Section 3 discusses the general research de-

sign underpinning the three papers that build the main body of my thesis. It outlines the em-

pirical foundation by explaining the database and study samples. Section 4 briefly introduces

these papers by discussing their objectives, motivations, results and major contributions. Fi-

nally, Section 5 presents all research findings in order to answer the research questions of my

thesis. The implications for theory and practice are discussed, as well as limitations and ideas

for future research.

2 Theoretical and conceptual background of innovation collaboration

Different definitions of the term ‘open innovation’ have caused conceptual ambiguity, as a

consequence of which researchers have asked for greater precision in conceptualizing open

innovation. (Dahlander and Gann, 2010, pp. 705-706). Moreover, there are several terms ex-

isting that are associated with open innovation, each describing a form of distributed innova-

tion process. Figure (A) 1 displays the development of such terms. Bogers and West (2012;

2013) provide a comprehensive overview of this literature. The authors isolate similarities and

differences of the various approaches, derive implications for theory and practice and promote

a more integrative view of these concepts.


6

Figure (A) 1: Development of open innovation related concepts

Interestingly, the literature on distributed innovation in general spans a time period that al-

ready starts before the term open innovation was coined in the year 2000. This demonstrates

that the identification of all concepts is required in order to fully understand open innovation

and its organization. The key challenge is to resolve the existing conceptual ambiguity, mak-

ing a broad and comprehensive literature review necessary. Such a review faces one chal-

lenge, though – a plethora of potentially relevant articles.

The concept of open innovation rapidly diffused into research as well as practice. Early re-

search mainly consists of only a few cases studies from companies successfully applying open

innovation (Chesbrough, 2006; Dodgson et al., 2006; Huston and Sakkab, 2006; Rohrbeck et

al., 2009). However, the topic of open innovation soon found broad academic reception. Thus,

within the past decade, associated literature exponentially increased in scientific literature

1980 ----- 1985 ----- 1990 ----- 1995 ----- 2000 ----- 2005 ----- 2010 ----- 2013 Time

Lead user (von Hippel, 1986)

Cumulative innovation (Allen, 1983)

User innovation (von Hippel, 1986)

Distributed innovation (von Hippel, 1988)

Open source software (Raymond, 1999)

Co-creation (Prahalad and Ramaswamy, 2000)

Open innovation (Chesbrough, 2003)

Wisdom of crowds (Surowiecki, 2004)

Peer-production (Benkler, 2005)

Crowdsourcing (Howe, 2006)


7

databases like EBSCO and ISI Web of Science (Figure (A) 2). Now, when searching “open

innovation” related articles, nearly 2,500 publications are listed.

Figure (A) 2: Publication rate for “open innovation” between the years 2003-2013*

*search results from October 2013

In addition, the number of articles increases further when terms being associated with open

innovation are taken into consideration (Figure (A) 1). As a consequence of this increase in

literature, identifying the right publications needed to analyze the theoretical background of

innovation collaboration is difficult. West and Bogers (2013) took the top 25 most-cited tech-

nology and management journals to select publications from and analyzed 287 articles in their

review. However, their literature selection is biased towards the rankings of journals, which

frequently change. Like searching for solutions to technical problems in innovation, literature

search in academia is biased in several ways.

First, searching is affected by the citation bias. One’s search results are completed by check-

ing reference lists of well-known and topic relevant articles. This behavior leads to a bias in

filtering literature supporting one’s own view and research results and ignoring other perspec-

tives of the same phenomenon simply by overlooking them. Second, the prestige of a journal

leads to a selection bias. Mainly, articles published in highly ranked journals are favored to

support one’s own research. Likewise, author’s guidelines in a journal frequently demand

using the “relevant literature”, which means citing literature commonly used in journal publi-

cations. Third, bias can be found in the database used for the literature search, with search

results being strongly dependent on the quality of the database.

0

500

1000

1500

2000

2500

3000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

EBSCOISI


8

To reduce the bias in my own literature search, I followed a systematic search approach. My

review of the literature on innovation collaboration has been done by the bibliometric method

of co-citation analysis. The idea behind this method is that documents which are often co-

cited are related and show a similar research focus (White and Griffith, 1981, p. 163). For the

purpose of the thesis, I adapt the principle of the co-citation analysis, analyzing co-usage of

certain topic-related terms, instead of co-citations. This method helps me to create an over-

view which allows for an unbiased evaluation and organization of the research topic accord-

ing to the position of different literature streams (Di Stefano et al., 2012, p. 1284). Further-

more, it enables me to establish my conceptual framework for investigating the organization

of innovation collaboration within my thesis.

2.1 Method: Co-citation analysis of literature on open innovation

Even though I use keywords in the literature search, instead of co-citation information, the

data handling follows the suggested procedure in literature (White and Griffith, 1981; Di

Stefano et al., 2013). I performed the following steps in detail: (1) selection of publications as

units of analysis; (2) retrieval of frequencies of publication co-occurrence regarding the same

keywords; (3) compilation of a raw co-occurrence matrix; (4) conversion of the raw data into

a correlation matrix; (5) data analysis by multivariate techniques; (6) interpretation and vali-

dation of the results. For interpreting the findings, I used the most frequently cited articles,

based on the assumption that they represent the importance and influence of a certain research

field (Di Stefano et al., 2013, p. 1284). I collected data from the Thomson-ISI Web of Science

database. Only articles were selected; books, book chapters, proceeding papers and working

papers were not considered. The retrieved data cover a time period of 42 years. The oldest

publication is from 1961 and the latest from May 2013.

In order to select the relevant literature I followed the search procedure of Di Stefano et al.

(2013, p. 1284). I also defined subsets of words to trigger a relevant paper selection. First, I

determined the search domain of innovation. Only studies contributing to that topic should be

listed. Secondly, to ensure that publications focus on collaboration within the field of innova-

tion, I created a list of 27 search keywords (Table (A) 1). I decided that this keyword list

should roughly capture three aspects of the literature. Firstly, the keywords should ensure that

papers reporting on traditional inter-firm collaboration concepts like alliances, networks etc.

are contained in the literature search. Their inclusion should allow for investigating the con-

nection between these traditional conceptions and the new notion of open innovation. Second-


9

ly, keywords should trigger publications that specifically grasp the behavioral aspect of col-

laboration and describe such activities. Thirdly, search keywords should be able to identify

publications on open innovation, as well as articles on associated concepts like user innova-

tion.

Table (A) 1: Keyword list used for literature search in ISI

Traditional innovation collaboration concepts 1 alliances 5 strategic alliances 2 joint venture 6 strategic networks 3 networks 7 university-industry 4 R&D cooperation Activities during innovation collaboration 8 knowledge transfer 9 technology transfer New innovation collaboration concepts 10 broadcast search 19 customer integration 11 collaboration 20 distributed innovation 12 collaborative innovation 21 lead user 13 collective intelligence 22 open innovation 14 community 23 open source software 15 co-creation 24 peer production 16 cumulative innovation 25 user-driven 17 crowdsourcing 26 user innovation 18 customer-active 27 wisdom of the crowds

A good starting point for developing the keyword list was the origin articles which introduced

new concepts like user innovation, crowdsourcing, co-creation etc., as well as review articles

(Table (A) 2). I derived a list of potential keywords by scanning these publications for words,

guided by three factors: traditional innovation collaboration concepts, collaboration activities

and new innovation collaboration concepts.


10

Table (A) 2: Publications to identify potential search keywords

Author Publications Extracted keywords

Allen, R. C. Collective invention. Journal of Econom-ical Behavior and Organization, 4, 1, 1–24, (1983).

Cumulative innovation, collective invention,

Benkler, Y. Common wisdom: Peer production of educational materials. (2005.)

Commons-based peer-production, open source software, collaboration, open development

Borgatti, S. P.; Fos-ter, P. C.

The network paradigm in organizational research: A review and typology. Journal of Management, 29, 6, 991-1013 (2003).

Social capital, ties, network organiza-tion, joint ventures, inter-firm allianc-es, experience management, knowledge management

Chesbrough, H.

Open Innovation: The New Imperative for Creating and Profiting from Technol-ogy. Harvard Business School Press, Boston, MA, (2003).

Open innovation, collectively, univer-sity-industry, collaborative

Dahlander, L.; Gann, D. M.

How open is innovation? Research Poli-cy, 39, 699-709 (2010).

Collaboration pattern, user innova-tion, absorptive capacity, selective revealing, licensing,

Elmquist et al. Exploring the field of open innovation. European Journal of Innovation of Man-agement, 12, 3, 326-345 (2009).

Open innovation, open source soft-ware, openness, knowledge transac-tions, selective revealing, user innova-tion

Gulati, R. Alliances and networks. Strategic Man-agement Journal, 19, 293-317 (1998).

Strategic alliances, alliances, co-development, networks, transactions, knowledge acquisition, technology partnerships

Howe, J. The rise of crowdsourcing. Wired maga-zine, 14, 6, 1-4, (2006).

Crowdsourcing, user-generated, knowledge network, ties, collabora-tion, community

Jiang, X.

Theoretical perspectives of strategic alliances: A literature review and an integrative framework. International Journal of Information Technology and Management, 10, 2/3, 272-295 (2011).

Alliances, alliance formation, strate-gic alliances, knowledge transaction

Lichtenthaler, U.

Open innovation: Past research, current debates, and future directions. Academy of Management Perspectives, 25, 1, 75-93 (2011).

Commercialize knowledge, strategic alliances, inter-organizational rela-tionships, external knowledge explo-ration, technology transfer, user inno-vation


11

Author Publications Extracted keywords

Osborn, R. N.; Hage-doorn, J.

The institutionalization and evolutionary dynamics of inter-organizational alliances and networks. Academy of Management Journal, 40, 2, 261-278 (1997).

Inter-firm alliances, mode of coopera-tion, inter-organizational networks, R&D cooperation, collective, tech-nology development,

Prahald, ; Ramaswa-my,

Co-opting customer competence. Harvard Business Review, 78, 1, 79-88, (2000)

Co-creation, customer, user, commu-nity involvement, self-selected

Raymond, E.

The cathedral and the bazaar: Musings on Linux and open source by an accidental revolutionary.

O’Reilly, Sebastopol, CA, (1999).

Open source software, community, peer-evaluation, co-developer

Surowiecki, J. The wisdom of crowds. Random House LLC, (2004).

Wisdom of crowds, crowd intelli-gence, ties, prediction markets, col-lectively

Von Hippel, E. The Sources of Innovation. Oxford Uni-versity Press, New York, (1988).

Distributed innovation, informal knowledge-trading, cooperative R&D, user-developed

von Hippel, E. Lead users: A source of novel product concepts. Management Science, 32, 7, 791-805, (1986).

Lead user, user innovation

West, J.; Bogers, M.

Leveraging external sources of innova-tion: A review of research on open inno-vation. Journal of Product and Innovation Management, 31, 4, 18 pages (2013).

Strategic alliances, joint ventures, university-industry, co-creation, crowdsourcing, user innovation, open source software, innovation commu-nities, knowledge sharing,

For the next step I discussed this list with five scholars in the field of innovation management

research. They were asked to select and/or add those keywords they associate with the topic

of innovation collaboration, before sorting words into the three categories. The keywords on

which all five researchers came to an agreement were added to the final list. In the end I ob-

tained seven words indicating traditional inter-firm collaboration concepts within the field of

innovation. Knowledge and technology transfer were regarded as the main activities during

collaboration. In total, 18 keywords were selected to be associated with the new concept of

open innovation. At this point it should be noted that I do not consider the keyword list to be

exhaustive, but rather sufficiently comprehensive to guide the initial literature search.

Having identified the keywords, I exported the list of search results as BibTeX files and saved

them using their keyword, e.g., open innovation. A self-programmed script read out the ex-

ported data files and extracted publication related information like author, title, year of publi-


12

cation etc. into an a CSV-file.1 Following this, the script coded the information using the

keyword the publication was found under. For example, an article that appeared under the

search keyword “open innovation” was indicated by the number 1, otherwise it received 0.

The final data set consists of 1,305 publications, published from 1961-2013. To evaluate the

quality of the search results I used the publication rating provided by the Vienna University of

Economics and Business Administration (WU Wien) and the ranking developed on behalf of

the Association of University Professors of Business in German speaking countries (Verband

der Hochschullehrer für Betriebswirtschaft - VHB). The WU ranking ranges from A+ to D,

with an A+ publication indicating the highest possible quality. The evaluation scale of the

VHB ranking ranges from 1 (very low) to 10 (very high). I took the recent ratings of the jour-

nals from the 48th edition of the Journal Quality List. The descriptive analysis regarding the

publication source (Table (A) 3)2 of the selected articles revealed that only studies published

in high ranked journals were extracted. Journals like R&D Management, Technovation and

Research Policy are among the top ten outlet journals for research on innovation collabora-

tion. This is in line with findings of Dahlander and Gann (2010, p. 701) and offers a good

quality of the sub-set of studies for further analysis.

After selecting the publications, I counted the number of articles that share two keywords,

based on the assumption that a publication listed under different keywords may be indicative

of a thematic relationship between the keywords. The resulting raw co-occurrence3 matrix

was converted into a matrix of Pearson’s correlation coefficients (Di Stefano et al., 2013, p.

1285) for reasons of normalizing frequency data and consequently eliminating scale effects

(White and Griffith, 1981, p. 165). Positive correlation coefficients represent the similarity

between two keywords based on their underlying publications (White and Griffith, 1981, p.

168; Di Stefano et al., 2013, p. 1285). The correlation matrix formed the input for the multi-

variate analysis, such as Factor Analysis (FA), Cluster Analysis (CA) and Multidimensional

Scaling (MDS) (Di Stefano et al., 2013, p. 1285).

1 The huge amount of data requests to realize the program as an event-related parser. The program reads each row of the input file and captures the relevant information to save it into the final data structure. The data import produces a matrix consisting of keywords and publications. For the matrix, the keywords are extracted from the imported information and saved as 0-1 matrix. 2 For a full overview regarding the commonly used publication sources please refer to Appendix (A) 2. 3 The main diagonal is treated as missing values as suggested by Di Stefano et al. (2013).

Tabl

e (A

) 3: M

ain

publ

icat

ion

sour

ces

Fac

tor

1 F

acto

r 2

Fac

tor

3 F

acto

r 4

Fac

tor

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acto

r 6

Fac

tor

7 F

acto

r 8

K

eyw

ords

load

ing

on

Fac

tor

broa

dcas

t se

arch

; cr

owds

ourc

ing;

cu

mul

ativ

e in

nova

tion;

di

strib

uted

in

nova

tion;

op

en

inno

vatio

n;

open

sour

ce

softw

are;

pee

r pr

oduc

tion;

us

er

inno

vati

on*

colla

bora

tion;

ne

twor

ks;

univ

ersi

ty-

indu

stry

; te

chno

logy

tra

nsfe

r;

co-c

reat

ion

allia

nces

; kn

owle

dge

trans

fer;

stra

tegi

c al

lianc

es

cust

omer

in

tegr

atio

n;

lead

use

r*;

cust

omer

-ac

tive

com

mun

ity;

lead

use

r*;

user

in

nova

tion

*

colla

bora

tive

inno

vatio

n;

stra

tegi

c ne

twor

ks

join

t ve

ntur

e;

R&

D

coop

erat

ion

Col

lect

ive

inte

llige

nce

T

otal

no

of p

aper

s 37

4 43

495

6829

534

102

14

M

ean

cita

tion

fr

eque

ncy

95.4

0 23

.26

13.1

425

.12

20.9

99.

9116

.93

1.71

M

in c

itat

ion

freq

uenc

y 0

00

00

00

0

M

ax c

itat

ion

freq

uenc

y 29

,972

1,

799

522

429

1,52

793

253

7

N

o of

pap

ers

sele

cted

fo

r in

terp

reta

tion

13

58

1417

5010

25

7

V

aria

nce

expl

aine

d by

F

acto

r 21

.40

14.4

010

.73

8.20

7.61

6.04

5.99

4.

27

Jou

rnal

R

ank

ing

VH

B

Ran

kin

g W

U

N

um

ber

of

pap

ers

Su

m

Tot

al %

Res

earc

h Po

licy

A

A

27

62

14

1810

122

9,34

9Jo

urna

l of P

rodu

ct

Inno

vatio

n M

anag

emen

t A

A

9 6

714

181

554,

215

Inte

rnat

iona

l Jou

rnal

of

Tec

hnol

ogy

Man

agem

ent

C

10

157

55

15

1 49

3,75

5

Tech

nova

tion

D

9 24

43

62

1

493,

755

Man

agem

ent S

cien

ce

A+

A+

15

5

24

5

312,

375

R&

D M

anag

emen

t C

12

7

12

62

30

2,29

9Jo

urna

l of T

echn

olog

y Tr

ansf

er

B

1 20

22

25

1,91

6

Org

aniz

atio

n Sc

ienc

e A

A

+

10

31

82

1 25

1,91

6C

reat

ivity

&

Inno

vatio

n M

anag

emen

t C

4

31

56

191,

456

Inte

rnat

iona

l Jou

rnal

of

Indu

stria

l O

rgan

izat

ion

B

A

2

310

151,

149


13

Fac

tor

1 F

acto

r 2

Fac

tor

3 F

acto

r 4

Fac

tor

5 F

acto

r 6

Fac

tor

7 F

acto

r 8

K

eyw

ords

load

ing

on

Fac

tor

broa

dcas

t se

arch

; cr

owds

ourc

ing;

cu

mul

ativ

e in

nova

tion;

di

strib

uted

in

nova

tion;

op

en

inno

vatio

n;

open

sour

ce

softw

are;

pee

r pr

oduc

tion;

us

er

inno

vati

on*

colla

bora

tion;

ne

twor

ks;

univ

ersi

ty-

indu

stry

; te

chno

logy

tra

nsfe

r;

co-c

reat

ion

allia

nces

; kn

owle

dge

trans

fer;

stra

tegi

c al

lianc

es

cust

omer

in

tegr

atio

n;

lead

use

r*;

cust

omer

-ac

tive

com

mun

ity;

lead

use

r*;

user

in

nova

tion

*

colla

bora

tive

inno

vatio

n;

stra

tegi

c ne

twor

ks

join

t ve

ntur

e;

R&

D

coop

erat

ion

Col

lect

ive

inte

llige

nce

Jou

rnal

R

ank

ing

VH

B

Ran

kin

g W

U

N

um

ber

of

pap

ers

Su

m

Tot

al %

Long

Ran

ge P

lann

ing

B

A

7

11

23

141,

073

Res

earc

h-Te

chno

logy

M

anag

emen

t D

3

61

13

141,

073

Tech

nolo

gica

l Fo

reca

stin

g an

d So

cial

C

hang

e B

A

4 3

24

130,

996

Indu

stria

l and

C

orpo

rate

Cha

nge

C

6

22

1

110,

843

IEEE

Tra

nsac

tions

on

Engi

neer

ing

Man

agem

ent

B

A

1

51

21

10

0,76

6

Indu

stry

& in

nova

tion

B

5 2

12

100,

766

*key

wor

ds w

ith h

igh

cros

s-lo

adin

gs in

FA

indi

cate

to b

ridge

diff

eren

t res

earc

h pe

rspe

ctiv

e

14



15

2.2 Data analysis

Figure (A) 3 shows a two-dimensional map as the result of the MDS. According to Di Stefano

et al. (2013, p. 1288), the positioning of the keywords towards each other indicates their con-

ceptual proximity, and, conversely, a great distance between keywords within a group stands

for a high consistency of their conceptual domain. The evaluation of the fit criteria of the

MDS result is acceptable regarding the bi-dimensional arrangement of the research field fo-

cusing on innovation collaboration (McCain, 1989, p. 679). The configuration explains 84

percent of the variance in the data (R-squared (RSQ)), with a Kruskal’s Stress value of 0.17.

For a better interpretation of the map, groups of keywords are identified by running a FA4.

The FA allows me to identify the research perspective that lies behind a certain keyword.

Moreover, the factors form the bases to differentiate whether two keywords describe different

theoretical concepts or are used synonymously for the same research aspect. Finally, the iden-

tified groups and the factor interpretation allow me to determine the meaning of the vertical

and horizontal axis of the MDS map.

According to Di Stefano et al. (2013, p. 1286), I applied principal component analysis and the

varimax rotation as the extraction algorithm. The final factor solution was determined by us-

ing the Kaiser’s criterion and the scree test. Eight factors were extracted explaining 78.64

percent of the variance, whereby the first three factors account for nearly 50 (46.53) percent

of the total explained variance (Appendix (A) 4). Factor loadings of 0.40 and higher were

considered. As a consequence of this lower threshold, the keywords ‘user-driven’ and ‘wis-

dom of the crowds’ were excluded. On average, I obtained factor loadings higher than 0.70,

indicating that keywords reliably represent the factor. Two keywords, ‘user innovation’ and

‘lead user’, showed high cross-loadings. Following Di Stefano et al. (2013, p. 1286), such

loadings indicate the potential of the research behind the search keywords bridging different

research perspectives. Appendix (A) 4 summarizes the keywords representing the factors and

their loadings.

4 A Cluster Analysis was performed to identify sets of similar used keywords. The result of the CA supported the factor solution I found (Appendix (A) 3). Like Di Stefano et al. (2013, p. 1286) suggest, I present the results of the FA since they allow for a better interpretation of the grouping due to displayed factor loadings.


16

Figure (A) 3: Multidimensional Scaling – A map of innovation collaboration

To interpret the factors, the publications listed under the keywords of the factor were collected

and organized according to their citation frequency. The citation frequency functions as an

indicator of the importance of a publication within a research area (Di Stefano et al., 2013, p.

1284). All publications showing a citation value between the factor’s mean value and the

maximum value were examined according to the research question posed, the research type,

the core findings and contributions. This procedure generated a subsample of 194 publications

(Table (A) 3). According to Table (A) 4, 26 articles were assigned to more than one of which

Factor 8

Col

labo

rati

on f

orm

aliz

atio

n

info

rmal

form

al

Collaboration proximity

low high

Factor 1

Factor 1

Factor 2

Factor 2

Factor 3

Factor 4 Factor 6

Factor 7

Factor 5

Factor 5


17

24 had two different factors, one publication (Faems et al., 2005)5 belonged to three factors

(F2/3/5) and one article (Morrison et al., 2000)6 could be found on four factors (F1/2/4/5).

The most common cross-categorization of papers happened at factor four and five (n=10) and

factor one and five (n=8). This finding supports the assumption that keywords with the cross-

loadings, user innovation and lead user, bridge different research areas.

Table (A) 4: Publications with multiple factors belonging

Factor 1 Factor 2 Factor 3 Factor 4 Factor 2 1 Factor 3 0 2 Factor 4 2 2 0Factor 5 8 2 0 10Factor 6 0 0 1 0Factor 7 0 0 2 0

Appendix (A) 1 displays the most cited papers regarding different aspects of innovation col-

laboration. These publications are, furthermore, typical representatives of particular factor

meaning.

2.3 Results of the literature analysis

Using the bibliographic approach described in the last section, I identified the following eight

research areas of innovation collaboration: (Factor 1) organizational aspects of collaboration;

(Factor 2) structural characteristics of collaboration; (Factor 3) performance indicators of

collaboration; (Factor 4) characteristics of collaboration participants; (Factor 5) partner inte-

gration in collaboration; (Factor 6) management of collaboration; (Factor 7) capabilities to

profit from collaboration; and (Factor 8) software supporting collaboration.

Factor 1 explains most of the variance (21.40%). Research questions within this group of

papers mainly address organizational aspects of collaboration. As objects of their inquiry,

many authors use either open source software projects (e.g. von Hippel and von Krogh, 2003;

5 Faems and his colleagues (2005) examined in how far inter-organizational collaboration supports the effective-ness of innovation strategies. They found a positive relationship between collaboration and innovation perfor-mance. They further specified that performance depends on the nature of partner involved in collaboration. 6 In their study, Morrison et al. (2000) investigated innovation characteristics of the OPAC information system in Australia. They showed that product modifications were made by users and additionally product information was freely shared among users.


18

Lakhani and von Hippel, 2003; Morrison et al., 2000; Franke and von Hippel, 2003; West,

2003) or virtual communities (e.g. Sawhney and Prandelli, 2000; Jeppesen and Frederiksen,

2006; Shah, 2006). In specific, few papers focus on how distributed knowledge contributes to

the innovation performance of a firm (e.g. Sawhney and Prandelli, 2000; Lee and Cole, 2003).

A second topic within this group of papers investigates the motivation behind joining a com-

munity and contributing to the product development process (e.g. von Krogh et al., 2003;

Jeppesen and Frederiksen, 2006; Shah, 2006; Roberts et al., 2006). Research results show that

organizations can profit from distributed knowledge without giving away control over the

innovation object. The analysis of the research type revealed that the most influential papers

discussing this factor are of a conceptual nature (Figure (A) 4), describing a new form of col-

laboration by peer-to-peer interaction.

Factor 2 focuses attention on the structural parameters of collaboration, studying mainly uni-

versity-industry cooperation, alliances, or networks. This factor encompasses the highest

number of publications and explains 14.40 percent of the variance. The main drivers behind

articles of this factor are the description and investigation of parameters of knowledge transfer

and organizational learning (e.g. Teece, 1986; Powell et al., 1996). The first set of studies

uses certain types or institutions of collaboration to deepen the understanding of cooperation

interests (e.g. Siegel et al., 2003; Colombo and Delmastro, 2002). The second, and even larger

set of papers, investigates single collaboration parameters like type of relationship (formal vs.

informal) (e.g. Nambisan and Nambisan, 2008; Nambisan and Baron, 2009; Smith et al, 1991;

Thursby and Thursby, 2004), motives and incentives for cooperation activities (e.g. Prager

and Omenn, 1980; Feller et al., 2002), tools and channels to leverage cooperation (e.g. Bek-

kers and Freitas, 2008; Mueller, 2006; Carayannis et al., 2000), knowledge characteristics

(ACAP) (e.g. Knudsen and Roman, 2004), transfer activities (contracting, interaction, search,

signaling, negotiating, etc.) (e.g. Laursen and Salter, 2004, 06; De Luca and Atuahene-Gima,

2007; D’Este and Patel, 2007; Fontana et al., 2006), and network structure (spatial extension,

boundaries, proximity, ties etc.) (e.g. Ahuja, 2000; Schilling and Phelps, 2007; Pond et al.,

2007). Large empirical designs are used to study the correlations among these parameters and

how they affect innovative performance (e.g. Faems et al., 2005; Bourgrain and Haudeville,

2002).

Factor 3, investigating performance indicators of collaboration, shows a separation into two

sub-sets. One set of publications again investigates the relation between collaboration parame-

ter and innovative performance by using mainly quantitative studies. In comparison with Fac-

tor 2 studies, the attention here shifts to the cooperation structure in terms of distance and


19

proximity (e.g. Phelps, 2010; Tracy and Clark, 2003). The other set of articles focus on the

capabilities to transfer knowledge, highlighting the methods of organizing such transfer (e.g.

Gerwin and Ferris, 2004; Lichtenthaler and Lichtenthaler, 2009; Gilbert and Cordey Hayes,

1996; Nielsen and Nielsen, 2009). Even though this factor revealed a smaller number of arti-

cles in comparison with the first two factors, it still accounts for 10.73 percent of the entire

variance.

Figure (A) 4: Type of publication per factor in percent

Factor 4, which explains 8.20 percent of the variance, explores the interface between a firm

and its customers. Central to this is the question of how the interactive product development

partnership can be organized (Fueller and Matzler, 2007; Piller et al., 2004; Koufteros et al.,

2005), for example, by applying toolkits (Franke and Piller, 2003). The factor is mainly char-

acterized by literature on the lead user theory. The publications range from describing the

phenomenon (e.g. Lilien et al., 2002; Herstatt and von Hippel, 1992; von Hippel et al., 1999)

to extending the theory through confirmatory studies (e.g. Morrison et al., 2004; Franke et al.,

2006).

Factor 5 focuses on aspects of partners engaging in cooperation. Like the first two factors, it

contains the most publications, but explains only 7.61 percent of the variance, which is proba-

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8

review/conceptualquantitativequalitative


20

bly due to its heterogeneous nature. Its common denominator is the study of all kinds of

communities, ranging from general (inter-) organizational communities (e.g. Brown and

Duguid, 1991; Drazin and Schoonhoven, 1996; Lynn et al., 1996) to over communities of

practice (Swan et al., 2002) and open innovation communities (e.g. Fleming and Waguespeck,

2007; Henkel, 2006; von Hippel, 2001; Fueller et al., 2007; West and Lakhani, 2008). One

reason for the heterogeneity of this factor is the overlap of publications with Factor 1 and 4.

In total, 33 articles focus on either lead user (Factor 4) or open source software (Factor 1)

research. These papers predominantly investigate research questions regarding the integration

of different actors when organizing collaboration (e.g. von Hippel and von Krogh, 2003;

Lakhani and von Hippel, 2003; Lee and Cole, 2003; Franke and von Hippel, 2003; Sawhney

and Prandelly, 2000; Franke et al., 2006). Other papers discussing the factor examine the role

of communities during diffusion of innovation or technology commercialization (e.g. Lynn et

al., 1996; Mahajan and Muller, 1994; West and Lakhani, 2008).

Factor 6 represents literature that discusses networks as a source for external relationships

more generally (Heikkinen et al., 2007; Presutti et al., 2007; Lamming et al., 2000). Here

scholars try to answer questions related to managerial aspects like tool application, partner

selection, or different managing roles by applying case study designs (Figure (A) 4)

(Thorgren et al., 2009; Prandelli et al., 2006; Hinterhuber, 2002). Together, this factor ex-

plains 6.04 percent of the variance.

Factor 7 also reveals a heterogeneous picture similar to Factor 5. This factor is comprised of

more than one hundred publications but it explains only 5.99 percent of the variance. Articles

loading on this factor cover various topics. Most of the case studies are used to describe the

functional principles behind joint ventures as a cooperation form, like knowledge flow, or

different types of joint ventures (research, international etc.) (e.g. Park and Ungson, 1997;

Vandeven and Polley, 1992; Park and Kim, 1997; Rosenkranz, 1995). The NUMMI alliance

project between Toyota and GM (1984-2010) is one typical case study. Furthermore, scholars

study the strategic decisions to enter a joint venture (Zhao and Aram, 1995; Firth, 1996;

Zhang et al., 2007). Reasons for failures or success of joint ventures are addressed by examin-

ing the relationship between characteristics of the partnership (network size, partner types,

level of trust etc.) and performance (e.g. Fosfuri and Tribo, 2008; Zhao et al., 2005; Plambeck

and Taylor, 2005; Adler, 1993). A few researchers devote their work to investigate the issue

of knowledge spill-over effect when operating in joint ventures or R&D collaborations, and

how this phenomenon influences managerial decisions (e.g. Zajac et al., 1991; Vonortas,


21

1997). The final stream of literature adopts an entrepreneurial perspective on the topic of joint

venture, or networking, to answer predominant questions regarding the advantages of entering

into such cooperation (e.g. Adler, 1993; Sicotte et al., 1998; Link and Scott, 2005).

Factor 8 constitutes the smallest group, with only 14 selected publications. It accounts for

only 4.27 percent of the total variance and represents just one key term “collective intelli-

gence”. This term is associated with articles describing the phenomenon itself (e.g. Bothos et

al., 2009; Komninos, 2004) and reviewing software programs which support joint work (e.g.

Levy, 2010; Gouarderes et al., 2005; Arnal, 2007). Noticeable within this work is the general

theoretical foundation provided by collective intelligence in Nonaka’s and Takeushi’s (1995)

organizational learning within communities.

Across all factors, one dominant topic can be identified – the organization of innovation col-

laboration. Research questions considering the configuration of collaborative work between

different parties are reflected in nearly every factor. The most obvious focus on this topic was

revealed in Factor 1 by directly examining the organizational aspect. Factor 2 also acknowl-

edged organizational issues through a micro-perspective in the form of analyzing the single

structural parameter of collaboration, like relationships or knowledge transfer activities. Fac-

tor 3 grasped this aspect in particular by investigating knowledge transfer. The meaning of

tool application or design of virtual user communities, captured by Factor 4, described the

organization of collaboration with customers specifically. Factor 8 further considered how

tools could foster collaborative work 8, whilst Factor 5 answered organizational questions by

containing publications from Factors 1 and 4.

Factors 6 and 7 shifted their attention more towards describing characteristics of certain col-

laboration forms, like joint ventures or networks. The MDS map (Figure (A) 3) reveals that

the geographical distribution of Factor 6 and 7 differs from the rest in that these factors are

not vertically stretched. The Factors 1, 2, and 5 yield the largest vertical expansion whereas

the Factors 3 and 4 are limited in their spread. Thus, moving top-down on the map represents

papers focusing on different organizational structures of innovation collaboration, like open

source software, alliances, networks, or crowdsourcing (Alexy et al. 2013, p. 282). The locali-

zation of the keywords along this y-axis, coupled with the results from analyzing the factors

regarding their content with respect to organizational aspects, indicates that this axis describes

the degree of formalization of innovation collaboration. Organizational literature describes

formalization as “…the proportion of codified jobs …” (Hall et al., 1967, p. 906) which en-

compasses several indicators such as roles (job descriptions, defined positions), authority rela-


22

tions (hierarchy), communication (channels, process), or norms (written rules, policies) (see

Hall et al., 1967, pp. 906-907). Consequently, the y-axis spans a continuum, ranging from

formal organization of innovation collaboration at the top to informal organization at the bot-

tom. For example, taking the literature on open source software specifically, scholars found,

despite the lack of formal control, that such development communities reveal quite a formal

structure for organizing their tasks (Lerner and Tirole, 2002; Waguespack and Fleming, 2009,

2007; Johnson, 2006). In contrast, the literature discussing distributed innovation focuses on

profiting from the rather informal, organized interaction between users or customers in com-

munities (e.g. Sawhney and Prandelli, 2000; Nambisan, 2002). The position of Factors 6 and

7 are very close to the origin of the y-axis, explaining why publications appearing under these

factors do not show a special interest in answering research questions of organizing collabora-

tion. Yet, their focus on certain cooperation types sheds light on the meaning of the horizontal

axis of the MDS map (Figure (A) 3). The right hand side of the map contains all the keywords

referring to rather traditional concepts or innovation collaboration (Table (A) 1). When mov-

ing along the axis to the left hand side, concepts considered as new forms of innovation col-

laboration appear.

The results of the content analysis of the factors suggest that the x-axis describes proximity of

innovation collaboration (Figure (A) 3). The term proximity does, however, have several

meanings. In literature it is used to describe geographical, organizational, social or cognitive

nearness (Mattes, 2012, p. 1086). In innovation literature the geographical proximity is usual-

ly termed the locus of innovation (Fredberg et al., 2008; Ogawa, 1998; von Hippel, 1994;

Powell et al., 1996). For the aspect of social proximity in collaboration literature, which refers

to the relationship amongst the participating individuals, especially on their alliances, there is

another term – partner similarity (Luo and Deng, 2009; Robson et al., 2008). In the context of

collaboration, the geographical, as well as the social, proximity, range on scale of close to far

away from the focal firm. Thus, the x-axis of the MDS maps shows the development of inno-

vation collaboration from traditional concepts, which happen in the nearer surroundings of a

firm, to newer forms, like user innovation, peer production, or the integration of a community,

which take place further away from the organizational boundaries. This conclusion is in line

with findings from the literature on open or distributed innovation (von Hippel, 2005; Fred-

berg et al., 2008; Afuah and Tucci, 2012; Jeppesen and Lakhani, 2010), which highlight the

advantage of new collaboration forms because of the profit that can be gained from innova-

tion sources located further away from the focal firm.


23

Taking both dimensions together, the formalization and proximity of innovation collaboration

reflect the development of the research field of innovation collaboration. More recent research

tends to proceed in the direction of the lower left quadrant, where new forms of collaboration

describe a rather informal interaction process with partners that are distributed further apart.

2.4 A conceptual framework for organizing innovation collaboration

The procedure of co-citation analysis with search keywords enabled me to provide a detailed

review of the academic literature dealing with innovation collaboration in general. Since liter-

ature review nowadays is hampered by exponentially increasing rates of publication, identify-

ing relevant literature is a core challenge in the scientific process. The systematic approach to

the taken to the literature review led to four main results.

First, it shed light on the general arrangement of the research on innovation collaboration.

This research field can be organized according to proximity and formalization collaboration

characteristics, both of which are being investigated by scholars. Regarding new forms of

collaboration, such as open innovation and its associated terms, I have identified a clear trend

whereby scientists focus on research questions that examine the role of somewhat informal

and more distant relationships within the innovation process. Concentrating on proximity, I

found that the topic of open innovation marks the transition from traditional inter-firm coop-

eration to new collaboration forms. According to Figure (A) 3 the keyword is located between

conventional concepts, like strategic alliances, R&D cooperation, and new ideas, like distrib-

uted innovation, broadcast search and crowdsourcing. Thus, open innovation characterizes

collaboration that is still close to the focal firm, but already far enough away to fall into very

classical cooperation arrangements. In his first book on open innovation, Chesbrough (2003)

used the cases of IBM’s and Intel’s university-industry cooperation to explain the principles

of the new paradigm. By doing so, Chesbrough included traditional forms of integrating ex-

ternal knowledge in his definition of openness. However, he argues from the perspective of

the relationships between collaboration partners when demonstrating the benefits of openness.

Other scientists have followed this idea in their research. For example, Perkmann and Walsh

(2007) review research on university-industry cooperation in the context of open innovation.

They found that “specificities and roles of networked inter-organizational relationships” (p.

272) are central when investigating openness. Faems et al. (2010) investigated that correlation

between technology alliances and innovation performance from the viewpoint of open innova-

tion. Their results also point to the effect of external relationships on internal innovation ef-


24

forts (p. 793), highlighting in particular the role of informal external relationships. This ele-

ment of informality in relationships is captured specifically by the other dimension of the MD

scaling, which I labeled ‘collaboration formalization’. This dimension characterizes the bor-

der between rather formal and informal interactions between cooperation partners. Informality

primarily concerns the ways in which new knowledge is generated or the collaboration partic-

ipants solve tasks. The keyword ‘open innovation’ appears on the informal side of this dimen-

sion.

Second, in order to further structure the research field, the descriptions of the dimensions sup-

port my working hypothesis which states that new forms of collaboration show clearly the

different requirements for organizations within the innovation context, due to their main char-

acteristics like partner types, location of the interaction, channel to leverage the collaboration,

etc. The content analysis of the factors has revealed that across all factors the investigation of

organizational issues played a central role, even if the perspective on these issues varied.

Third, the literature review helped me to focus on the relevant literature streams for the pur-

poses of my research. Factor 1 and 2 appear to contain the most promising foundation for

answering my research question. Moreover, Factor 2 also includes literature which discusses

the theoretical foundation of the collaboration topic (e.g. Teece, 1986; Powell et al., 1996;

Ahuja, 2000). This finding lends support for adopting the RBV as the theoretical anchor for

the thesis.

Fourth, this systematic overview has enabled me to identify and develop the conceptual

framework for the thesis. The analysis revealed that the organization of innovation collabora-

tion could be characterized by two components: the degree of collaboration formalization and

the degree of collaboration proximity. A 2x2-matrix describing organizational types for inno-

vation collaboration (Table (A) 5) has been produced by integrating both dimensions.


25

Table (A) 5: Types of organizing innovation collaboration

Collaboration proximity

Close Far

Collaboration formalization

Formal

Type A Rights and duties are clearly distributed among local collab-oration participants. The focal firm holds the lead. Project-oriented division of labor with traditional coordination in-struments.

Type B Project tasks are clearly dis-tributed among participants of a temporarily collabora-tion. Project activities are coordinated by the help of ICT.

Informal

Type C Collaboration is directed to-ward general strategic objec-tives in the future. Participants indirectly focus their activities on achieving joint goals.

Type D Mutual agreement regarding rights and duties to achieve project goal. Collaboration participants make joint deci-sions. Individuals rather un-known to each other collabo-rate.

Based on this framework I derive three questions that guide my research. They help me to

better understand the organization of open innovation and to distinguish the open innovation

concept from other collaboration forms.

1. How do new forms of collaboration differ from traditional forms in terms of their for-

malization and proximity?

2. What are beneficial structural requirements in the organization of collaboration with

external partners?

3. What is the function behind for open innovation?

Research design

26

3 Research design

The empirical core of this thesis is based on a survey of intermediaries operating in the field

of open innovation chosen because their business model is directed towards connecting firms

with external actors on a large scale. Two earlier publications (Diener and Piller, 2010; 2013)

have already disseminated the results of the empirical surveys underlying my research to a

management audience. This chapter partly builds on these publications.

The basic idea behind the sample choice is that intermediaries function as a proxy to reflect a

firm’s decision according to their engagement in a certain collaboration form. Conventionally,

innovation collaboration is investigated at the level of the firm. Three problems are associated

with adopting firm cooperation projects as a unit of analysis. First, the identification of firms

explicitly running open innovation collaboration projects is difficult and runs the risk of not

generating enough cases with sufficient variance of the core measurements. Second, when

identifying firms performing open innovation projects, clearly isolating this specific project

structure from surrounding organizational routines of sourcing external knowledge can be

problematic. Third, when investigating firms’ projects it is often difficult to identify the key

informant, something which is necessary to generate the valid information required for map-

ping out the detailed project procedure. These problems would lead to biases in the interpreta-

tion of results. In contrast, examining intermediaries has the advantage of overcoming these

problems associated with firm cooperation projects. Intermediaries form a neutral entity in the

landscape of innovation collaboration. Thus, the acquisition of a true open innovation project

is guaranteed when surveying open innovation intermediaries. A successful business model

indicates that the intermediary applies a collaborative approach which is requested by the

market and therefore reflects validity. Because of the intermediary position between external

actors and a firm, they have a broader perspective on the phenomenon of open innovation

collaboration in particular. And, finally, such intermediary firms are usually agencies with a

small number of permanent employees - on average they employ 30 people7. Consequently,

there is a great likelihood that the survey respondent can provide detailed project-related in-

formation.

To answer the research questions of this thesis, two surveys of open innovation intermediaries

were conducted between 2009 and 2012. Besides generating the data base for my research,

the surveys intended to create an overview of the recent open innovation market in terms of

collaboration forms and the service provider. The following section briefly introduces the role 7 Numbers based on information provided by the intermediaries participating in the first survey Appendix (A) 5 section I, question 5).

Research design

27

of intermediaries within innovation, before explaining the data basis through a description of

the applied questionnaires and sample compositions of the two surveys.

3.1 Intermediaries in innovation

Currently, intermediaries in the context of innovation are attracting special attention in re-

search as well as practice. Two aspects have caused this development: the emphasis on the

role of networks to search and profit from distributed knowledge (e.g. Chesbrough, 2003;

Gulati, 1995; Ahuja, 2000; Katila and Ahuja, 2002; Rosenkopf and Almeida, 2003; Borgatti

and Cross, 2003) and the recent technological possibilities to access this knowledge by means

of new ICTs (Pisano and Verganti, 2008). Further still, the convergence of industries and

markets creates a demand for more diverse sources of knowledge (Prahalad and Ramaswamy,

2004). As a result of these trends, sufficient knowledge management, which enables firms to

identify, mobilize, and apply knowledge for economic returns (e.g. Stewart, 1997; Eisenhardt

and Martin, 2000; Kogut and Zander, 1992), is a prerequisite for any successful organization.

Since knowledge management gets more complex and cannot take place in a vacuum, inter-

mediaries join the knowledge transfer process (Zhang and Li, 2010). These agents act as rela-

tively autonomous entities and perform intermediate tasks on the behalf of their client organi-

zations (Datta, 2007).

However, innovation intermediaries are not a new phenomenon and have been the object of

research for many years. Four major fields of literature pick up on the topic. The first focuses

on diffusion and technology transfer where the intermediary firm is relevant to an organiza-

tion for speed of diffusion and new product uptake (Haegerstrand, 1952; Rogers, 1962), as

well as negotiation and contractual skills (Shohert and Prevezer, 1996). The second involves

innovation management and focuses on an intermediary’s activities and how it can be inte-

grated into an organization's innovation process. Of special interest is the role of intermediar-

ies as facilitators of the knowledge transfer process between the actors taking part in the inno-

vation process (Hargadon and Sutton, 1997). The third is literature on systems and networks,

which investigates the influence of intermediaries with respect to the entire innovation sys-

tem. Intermediaries are seen as linking and transforming relations within the network or inno-

vation system, supporting the information flow (Lynn et al., 1996). The final strand of litera-

ture, which has emerged as a standalone avenue of research, is the analysis of intermediaries

in the context of service innovation (Knowledge Intensive Business Service – KIBS). This

concentrates on the continuous interaction between the intermediary firm and an organization

(Howells 2006).

Research design

28

Scholars examine intermediaries from different perspectives to describe either their role or

their function within innovation, which is reflected in the variety of terms used. They are

called intermediary firms (Stankiewicz, 1995), innovation intermediation (Wolpert, 2002;

Howells, 2006), bridgers (Bessant and Rush, 1995; McEvily and Zaheer, 1999), brokers (Har-

gadon and Sutton, 1997), information intermediaries (Popp, 2000) or superstructure organiza-

tions (Lynn et al., 1996). Howells (2006) provides the most comprehensive literature review

on innovation intermediaries and identifying, for example, functions such as testing, validat-

ing, regulating, or protecting. The brokering job of an intermediary, then, is rather complex,

requiring the translation of information, coordination, and the alignment of different perspec-

tives (Datta, 2007).

When aggregating all functions on a more general level, two main roles of an intermediary

can be extracted:

Scanning and gathering information.

Facilitating communication and knowledge exchange.

Furthermore, intermediaries differ in their service focus depending on the innovation process

stage they offer assistance on (evaluation, validation, exploitation, adoption, diffusion) and

the stakeholder group they concentrate on (SMEs, science networks, consumers).

Taken together, across diverse fields of research, knowledge remains the trading good of an

intermediary (Nonaka and Takeuchi, 1995). From a network perspective, intermediaries con-

nect an actor with different sources of knowledge. Consequently, they make the organiza-

tion’s localized and embedded “knowledge stock” actionable (Choo, 1998) because they op-

erate across multiple clusters of specialization and practice. Therefore, my thesis follows the

intermediary definition provided by Howells (2006, p.720) whereby intermediaries are de-

fined as “an organization or body that acts as agent or broker in any aspect of the innovation

process between two or more parties”.

As mentioned in the beginning of this section the role of innovation intermediaries has been

changed recently. From the perspective of social capital, new IC technologies have increased

the number of possible transaction partners tremendously (Arora et al. 2002; Nambisan and

Sawhney, 2007; Lopez-Vega, 2009; Howells, 2006). This has increased the opportunity to

network on a large scale, as facilitated by social network technologies like LinkedIn or Face-

book, and has strongly reduced the effort needed to find new contacts or interesting new

sources of information. Yet, this makes it difficult for companies to maintain an overview of

the market for potential interaction partners. Responding to this need, there is a growing num-

Research design

29

ber of intermediaries that focus on structuring possible interactions and creating more trans-

parency and trust. Engaging with technological developments, these intermediaries conduct

their business in a virtual environment, which has led them to labeled as ’virtual knowledge

brokers’ (Verona et al., 2006). Consequently, we can observe the advent of a new market for

intermediaries. Especially in the last decade, a large number of entrepreneurs created such

virtual knowledge broking firms focused on generating knowledge through new methodologi-

cal approaches, e.g., crowdsourcing idea jams, innovation contests, lead user workshops,

technology scouting etc. In the literature, these companies are positioned as intermediaries in

the context of open innovation (e.g. Verona et al., 2006; Sieg et al., 2010; Dodgson et al.,

2006; Gassmann et al., 2011). According to Chesbrough’s (2003) open innovation definition,

interacting with such an intermediary can, therefore, increase the likelihood of receiving the

required knowledge and the chance to find and make use of the right channel for bringing a

technology to market. They help companies to design permeable organizational boundaries

and to benefit from different knowledge sources, while simultaneously providing advice,

managing risk, tracking IP, educating participants, and monitoring performance.

3.2 Data basis

The data for answering the research questions was generated on the basis of surveying inter-

mediaries since they are highly associated with open innovation per se (e.g. Verona et al.,

2006; Sieg et al., 2010; Dodgson et al., 2006; Gassmann et al., 2011; Chesbrough, 2006) and

demonstrate a dedicated approach to organizing such new forms of collaboration. In the fol-

lowing section I introduce two market studies which form the data basis for analysis.

3.2.1 Market study I

I conducted the first set of data regarding OII in 2009. At this time, open innovation was be-

coming a major buzzword in innovation management. The primary intention of this study was

to gain an exploratory overview of the recent market of open innovation intermediaries. The

study was part of the research project focusing on new ways of integrating customers in the

development process, supported by the Stiftung Industrieforschung8.

8 Stiftung Industrieforschung is a German foundation which sponsors research of young scholars. They especially support research on SMEs.

Research design

30

The survey included two major steps. For the first step, a written questionnaire was sent to the

sample of intermediaries in order to get basic information about their business. After receiv-

ing a response, the second step was a telephone interview to verify and clarify answers and

gather further information, if necessary. By using this procedure, I could fully understand the

working concept of each open innovation intermediary.

The questionnaire consisted of five parts (Appendix (A) 5).

1. The first part asked for information about the intermediary’s business model and which

stage of the innovation process they targeted.

2. The second asked for specific information about their service offerings and the meth-

ods of integrating the input of external actors in the innovation process of their clients.

3. The third part of the questionnaire requested information about the structure and man-

agement of a typical client project.

4. The fourth part focused on the external actors and their integration in the process.

5. The last part surveyed the market the intermediary is operating in and asks some addi-

tional questions on their business model.

All questions were either coded by pre-defined answer sets or had open answer boxes. As

most of the intermediaries are rather small companies, the survey was addressed to the CEO,

who was often the founder of the company. Filling out the survey form took approximately 35

minutes.

The follow-up interviews forming the second step of the survey were conducted by one inter-

viewer. On average, the interviews took between 30 and 240 minutes each, with a mean of 60

minutes. The interviewer went over all the answers to the questions that the interviewee had

given in the survey. Often the interviewee completed the answers and explained their mean-

ing, especially with those questions containing a free answer space. Data was collected be-

tween October 2008 and June 2009. During the first phase (October 2008 to January 2009),

two reminders were sent out to increase the overall response rate.

In order to select the sample and ensure enough data was gathered for the analysis, five steps

were performed. The first (1), and introductory, step was a wide-ranging search on the Inter-

net. An intermediary who claimed to offer a method or service to support an organization’s

intention to engage with open innovation was selected as an open innovation intermediary. To

receive a large variability in the data, the threshold for being classified as an open innovation

intermediary was set fairly low. Second, (2) a networking approach was applied, asking key

informants to name companies helping others with "open innovation". This search generated a

Research design

31

set of 36 intermediaries. Once a new intermediary was identified, the key informant was

asked for their main competitors or market alternatives to identify further candidates for the

study. Simultaneously, the online search for further intermediaries continued.

Ultimately, this allowed for the surveying of 47 open innovation intermediaries. Of those, 24

intermediaries completed the survey form fully. This equates to a response rate of 51.1%.

About 15 (31.9%) of them also joined (3) the follow up interview in what was a third step.

These response rates would have been acceptable had there already been a deeper understand-

ing of our research topic, but due to its explorative nature, data quality needed to be improved.

Therefore, in a fourth step, (4) a second round of internet research began. Intermediaries’

shares were found using secondary data information – accessed through the internet, whitepa-

pers, company documentations, company blogs, or public information like press reports or

research papers - allowing the survey forms to be self-completed for 23 intermediaries. In a

fifth step, (5) company profiles, including key figures and a brief description of business,

were sent out for final evaluation by the intermediary, asking them to confirm the accuracy of

the representation of their company. Twenty (42.6%) modified or confirmed profiles were

returned. By the end of the data-gathering period in June 2009, the sample consisted of 43

selected open innovation intermediaries. They formed the starting point for the analysis made

within Study I.

3.2.2 Market study II

The second market study was published in April 2013. Again, the intention was to create an

overview of the market of open innovation intermediaries that help client organizations to

integrate external partners into all stages of the innovation process. Based on feedback re-

ceived from the 2009 study participants, the survey’s structure and questions were revised and

updated.

The survey is based on an extensive online questionnaire to fully understand the business

concept of each intermediary. An invitation email with an individual participation web link to

the survey was sent to all contacts that could be identified for each intermediary. Most of the

invitations were targeted to the founder of the business or other key executives for marketing,

new business development and innovation management. Multiple respondents for one inter-

mediary were desired in order to control for the response bias and also to receive more com-

plete picture of the intermediary’s business.

Research design

32

The questionnaire consisted of eight sections (Appendix (A) 6):

1. The first section asked general questions about the intermediary’s business model and

its business environment, such as industry branch and geographical market orientation.

2. The second section focused on the intermediaries’ productivity with regards to revenue

and number of projects. Questions on key clients and competitors were included, as

well as assessments of current and future market volumes, to provide a fuller picture of

the intermediary and its competitive environment. Additionally, to determine the in-

termediaries’ understanding of the term open innovation, a definition and an evaluation

of how intermediaries consider themselves as part of the open innovation community

was requested.

3. Section three was dedicated to the intermediary’s service proposal and their included

methods. One insight from the study conducted in 2009 was that intermediaries com-

monly offer more than one service approach to their customers. Learning from this, the

third section functions as a key navigation point through the rest of the survey.

Here the intermediary had to self-categorize their business into four broad service cate-

gories which roughly described the central approach to generate information: work-

shops, contests, market search, and technical search.

The following sections four to six are service specific. Depending on how many services the

intermediary indicated in section three, these service sections were presented again. Take, for

example, an intermediary that answered run contests and market search. First, the intermedi-

ary would answer all questions in the following sections according to the contest service. Fol-

lowing this, a question asks if the intermediary is interested in answering these questions

again for their second indicated service, market search.

4. Section four consisted of questions on the detailed project specifics – such as project

experience, average duration, cost range and objectives – related to one specific service

indicated in the section above.

5. Section five asked questions regarding community characteristics, including size of

community, legal participation terms, reach, and mix of expertise. Additionally, it was

asked if the intermediary further selects dedicated project teams from their community.

To gain detailed information on the intermediary’s approach and, again, building on the

results of the first market study, it was of interest to ask what kind of information or

knowledge (technological vs. market-related information) is commonly generated for

their clients and how collaboration interest is signaled to the community members (di-

Research design

33

rect vs. indirect invitation). This information was crucial for the framework used to

categorize the different intermediary approaches, undertaken in Study III.

6. Sections six observed more closely the communication structure and the coordination

effort between the intermediary, the client and the community of (external) partici-

pants. A rather basic phase model to differentiate project initiation (phase 1, including

planning activities), project execution (phase 2, in which the project outcome is creat-

ed) and project finalization (phase 3, including review and evaluation processes) was

introduced. The focus was to reveal common communication and coordination patterns

along these three generic project phases and how collaboration between the involved

parties took place.

7. Section seven, which concluded the survey, tasked the intermediary with producing a

small Wikipedia-like entry for the company profile page, using a maximum of 600

words.

8. If the intermediary integrated any software solution into their service offering, the sur-

vey then concluded with an eighth section on the functionalities of this software solu-

tion.

Answers to the question were not mandatory except for those which were crucial in opening

up the next consequential question, like the self-categorization into a service category. Rating

and multiple choice answer formats were primarily used. In the case of multiple choice, sev-

eral answers were possible. The underlying constructs passed validity and reliability tests. To

increase the response rate and encourage the completion of the survey, questions were dis-

played with a sign to indicate the sensitivity of the answer (either information is published in

the market study or data are used for research purposes only). For example, detailed sales data

or market volume estimates were represented in an aggregated manner and not ascribable to a

specific intermediary. Data collection took place between May and August 2012. Reminder

email were sent out three to six weeks after the intermediary was invited to take part in the

survey,

The first market study served as a starting point for the sampling strategy. The published re-

port from 2010 formed a great incentive to join the second round of the study, with 44.19 per-

cent of the intermediaries in the 2009/10 panel participating in the survey again. The set of

intermediaries was expanded with companies that had recently been analyzed in academic

publications, working papers, and other studies on open innovation intermediaries (Mortara

2010, Bakici et al. 2010, Lopez-Vega and Vanhaverbeke 2009, Lopez-Vega and

Vanhaverbeke 2010). Additionally, steps one (online search) and two (network approach) of

Introduction and summary of the research articles

34

the successful data gathering procedure applied during the first study were repeated. Thus, the

sample list was supplemented with companies identified using a broad and extensive online

search. For example, the website crowdsourcing.org, which offers landscape of firms engaged

in crowdsourcing activities, served as a source to identify potential intermediaries. All firms

mentioned on this site were further analyzed in detail to determine whether they offered any

open innovation service propositions according to the definitions of the four service catego-

ries. The network approach was again used to enrich the sample of open innovation interme-

diaries. Key informants were asked to name firms and competitors operating in this field and

they were added them to the sample list. Finally, companies that had noticed the first edition

of the report, and subsequently requested to take part in the survey, were added to the list.

This elaborate sampling strategy resulted in a list of 162 intermediaries which were then in-

vited to fill in the web-based questionnaire. From 59 intermediaries data sets with sufficient

information for analysis were received, which equals a response rate of 36.4 percent. Howev-

er, when reviewing the results it should be recognized that a survey of the total population of

open innovation intermediaries was the intention because the number of well-established in-

termediary firms remains small.

4 Introduction and summary of the research articles

In this section I present my research papers. I conducted three studies. Table (A) 6 displays

which data set I based my analysis on. For Study 1 the paper, which uses data from the first

market study, is exploratory in character as I try to identify potential characteristics of open

innovation. Studies 2 and 3 are both developed from the data provided by the second market

study. The rich data set enables me to investigate costs of openness in Study 2 and provides an

insight into the operational principles of diverse intermediaries, which I analyze in Study 3. In

the following section I briefly outline the motivation, analytical procedure, core results and

their meaning for theory and practice of each research paper.


35

Table (A) 6: The data basis of the research papers

Data basis Research paper Publication

Market study I

Study 1 Facets of open innova-tion: Development of a conceptual framework

Conference paper and presented at: Academy of Management Annual Meeting: Green Management Matters, Chicago, IL, USA, 7-11 August 2009. University of Twente, 16th International Prod-uct Development and Management Conference: Managing Dualities in the Innovation Journey, Enschede, The Netherlands 7-9 June 2009.

Submission intended to: International Journal of Technology Manage-ment

Market study II

Study 2 Organizing collaboration: The costs of innovative search

Accepted for presentation at: 12th Annual Open and User Innovation Con-ference, Boston, MA, USA, 28-30. July, 2014

Submission intended to: Management Science

Study 3 The market for interme-diation for open innova-tion

Conference paper and presented at: Intermediaries for Open Innovation, Presenta-tion on the European Innovation Forum, Am-sterdam, The Netherlands, 15 January, 2014, (on invitation by Prof. Henry Chesbrough)

Submission intended to: R&D Management

4.1 Study 1: Facets of open innovation: Development of a conceptual framework

The objective of this paper is to map the landscape of research and practice in "open innova-

tion" and to suggest a conceptual framework, as well as understanding its underlying internal

logic. I intend to gain a deeper understanding of what openness means and where a meaning-

ful distinction can be made between a new paradigm of open innovation and the perspective

of classical innovation management. This shall also help me to develop a measure open inno-

vation. Finally, I want to suggest an operational model which allows for a standardized inves-

tigation of the open innovation phenomena along the various stages of the innovation process.

This research is urgently needed as the term "open innovation" has emerged as a major

buzzword in the innovation management literature and practice, now encompassing a broad

and diverse set of understandings and interpretations. For example, a search in "Business

Source Elite", a large bibliographic database in the field of management research, lists 180


36

scholarly papers on the topic, with more than 60% published in the last two years (as of April

2010). Yet open innovation is just one term for a phenomenon that has been described much

earlier in the research on user innovation. Together, this earlier research provides a very het-

erogeneous picture that I intend to clarify in this paper.

Based on an empirical study of intermediary firms which considered themselves to be con-

ducting practices of open innovation, I distinguished the different types of collaboration

which incorporate external input to solve a given innovation problem. The conceptual

framework of this study is an interaction model that reflects the process of generating and

transferring knowledge. This framework allows me to derive facets of openness that distin-

guish between configuration types of collaboration, focusing particularly on ways to initiate

collaboration and how groups of interacting partners might be composed. The study combines

qualitative and quantitative research techniques in its design. Informed by a literature review

of distributed innovation, I gather the activities that can be used to share knowledge during

collaboration with external actors to describe the single facets of openness.

Based on data from the market study I, I then analyze 43 open innovation intermediaries. In

total, these intermediaries offered 65 collaboration models. I tested to see if I can confirm the

previously identified activities per facet. Residual categories are defined to explore further

potential interaction patterns. I counted the frequencies of dominant facet designs and com-

bined them into collaboration types.

To summarize the main results, this paper revealed eight dominant patterns of collaboration

between a firm and external actors in the innovation process, with the majority of the 65 col-

laboration models capable of being assigned to these eight patterns. An important content-

based observation was how differently the collaboration between an organization and its ex-

ternal environment was initiated across the eight classes, ranging from contracting, searching,

open calling, or targeted calling. The favored way to initiate collaboration was to set an open

or targeted call, which is typically the case when applying a form of innovation contest. Ac-

cording to the literature, running innovation contests is a method of performing open innova-

tion. Another distinguishing feature of the eight classes is the way in which collaboration

partners interact. I found the case of no interaction at all. Thus, collaboration models vary

according to the integrated knowledge object - either just an information artefact or the holder

of the information. Interestingly, I found that even very traditional concepts of R&D coopera-

tion are incorporated into the understanding of open innovation collaboration. The final major

finding was that when a measure to examine the degree of integration of external actors into


37

the innovating firm was applied, it did not reveal a systematic association in relation to pat-

terns of collaboration. Taking the results together, I assume that in the process of pursuing

“openness”, companies give away their ‘control’ of parameters of knowledge acquisition and

integration processes. According to my findings, I suggest two parameters: (a) the way inter-

est in collaboration is signaled by activities like calling or searching and (b) the locus where

collaboration takes place by means of regulating access to the cooperation.

My paper contributes to the existing literature in various respects. First, I present a model that

integrates the heterogeneous understandings of the term "open innovation" that can be found

in the literature. This model can provide a starting point to discover the core differentiator of

the open innovation concept from earlier models of collaborations in the innovation process. I

propose that it is not simply the interaction with external actors or the transfer of external in-

formation into the innovation process, but the "how", i.e. the nature of the activities which

constitute this collaboration. Second, my research identifies different modes of practicing

open innovation, which can be represented by different configurations of my collaboration

model. Understanding these modes helps to map the open innovation landscape. Third, my

empirical study and the detailed analysis of 43 intermediaries explicitly offering "open inno-

vation methods" provides a rich and interesting insight into the practice of this phenomenon.

A fruitful task for further research is to align the eight configuration types along a dimension

of “openness”. My study provides a good starting point to discuss a few commonly spread

indicators for openness. For example, some previous studies used the degree of integration to

operationalize and investigate the phenomenon of open innovation. My study, however, re-

vealed that the degree of integration plays a minor role in describing different open innovation

models, and as a consequence, there is reason to question if this measurement is indeed a suf-

ficient factor for explaining open innovation. Another indicator for openness could be the

non-directional way of starting collaboration, e.g., problem broadcasting targets an undefined

big group for potential interaction. Still, the non-directional mode does not sufficiently ex-

plain the nature of collaboration within open innovation, which gives me reason to assume

that there must be another underlying mechanism. A deeper analysis of my eight configura-

tion types leads me to the conclusion that the core of the new paradigm for open innovation

lies in a different mechanism of how firms organize their acquisition and assimilation process

of new knowledge.


38

4.2 Study 2: Organizing collaboration: The costs of innovative search

The objective of the paper is to understand how innovation collaboration can be organized. I

intend to investigate the relationship between two different forms of innovative search and the

effect of this behavior on the total effort to coordinate such a collaboration project. Addition-

ally, I will examine the correlation between the two search forms and the associated organiza-

tion structure that supports these activities. In the context of open innovation, the process cap-

tures the integration of external knowledge by a firm, which is mainly characterized by un-

conventional forms of collaboration. As of yet, little research has been undertaken to investi-

gate procedural differences in innovation collaboration and its effect on coordination costs.

The way knowledge is transferred from outside of the company into the firm is influenced by

aspects of organizational structure, which in turn affect costs. In order to plan and run cooper-

ation with external actors it is, therefore, necessary to understand structural requirements for

organization and costs to select the suitable collaboration strategy.

To answer my research question I apply a process-view of collaboration. This process starts

with the decision made by the firm to engage in collaboration with external actors, which also

implies the selection of a certain process-design to transfer knowledge from the external to the

internal. As a consequence of this, and since new knowledge is necessarily required for gen-

erating innovation the conceptual framework for my study draws upon the process of

knowledge transfer . I focus on the activities used to transfer knowledge, structuring the pro-

cess into three basic phases: initiation, generation and transfer. In particular, I am interested in

investigating phase one since it influences the subsequent phases. In doing so, I focus on the

activity of searching which usually takes place at the beginning of the transfer process. In my

understanding, searching is a behavior that is directed toward finding alternative solutions to a

given problem within innovation. In the context of open innovation, searching focuses on

identifying mainly external sources of solution information. A broad literature describes ways

of performing such a search, from which I have identified two major forms: (1) a direct

search, which actively searches for external sources, and (2) an indirect search (also known as

a broadcast search or crowdsourcing), which is rather passive in comparison.

Finally, in my model I regard the process of knowledge transfer as an element connecting

cooperation and coordination costs. I open the ‘black box’ of collaboration by analyzing the

effect of two different search forms used during the first process phase, concentrating on two

aspects of this. First, I hypothesize a mediating role of the knowledge transfer process be-

tween cooperation and coordination costs. The type of mediation indicates the degree of de-


39

composability of the knowledge transfer process in how far the three steps proposed can be

separately performed. In the case of direct search, I expect to find a full mediation effect and

therefore a non-decomposable knowledge transfer process. On the contrary, for indirect

search, I assume that it partially mediates the relation between cooperation form and coordi-

nation costs and, as a consequence, allows the knowledge transfer process to be decomposed

into single steps. I subsequently interpret decomposability as being related to the centrality of

the underlying organization structure, as already suggested in the literature. Second, consider-

ing the cost of organizing open innovation collaboration and drawing upon the literature, I

theorize that indirect search produces fewer costs in comparison to direct search.

By surveying open innovation intermediaries during the second round of the market study II I

intend to answer my research questions. I used open innovation intermediaries because they

reflect a variety of different open innovation collaboration forms and therefore show a varia-

tion in their search activity. The final data set is based on answers from 59 open innovation

intermediaries, which results in 64 projects for analysis. Summarizing my results, I found in

general that open innovation projects run by innovation intermediaries were collaboration

intensive ventures. The findings support the assumption that the first phase of knowledge

transfer is crucial for companies choosing to engage in collaboration, Collaboration approach-

es applied by intermediaries contain both search behaviors directly and indirectly in terms of

broadcast search, but to different extends, which affects the process decomposability and the

corresponding coordination costs. Interpreting my results in light of process decomposability,

I observe that the knowledge transfer process that begins with broadcast search is capable of

being divided into several stages, whereas the process that starts with direct search comprises

more highly interdependent activities which make the structure complex and not decomposa-

ble. This means the collaboration process needs to be organized differently depending on the

search behavior. For a broadcast search, a central information processing structures is benefi-

cial, whereas a direct search for external sources is best supported by more decentralized in-

formation processing. The comparison of the two search activities revealed broadcast search

to be more efficient than direct search since it produces less overall self-perceived coordina-

tion effort. Unexpectedly though, I also discovered that community size affects coordination

costs, when it was observed that interacting with a large community leads to an increase of

coordination costs when applying broadcast search. Therefore, I can confirm that a modular

structure of the knowledge transfer process saves coordination costs.

Through this paper I contribute primarily to the search literature by comparing two estab-

lished search behaviors. I provide empirical evidence that organizational structure appears to


40

be a contingency factor when planning and organizing open innovation collaboration, as well

as shedding light on the debate concerning the cost of openness. To my knowledge this is the

first study attempting to analyze the underlying structure of open innovation collaboration.

Despite all the constraints inherent in the design of the study, I was able to empirically com-

pare two different mechanisms of search and the effect of open innovation collaboration on

coordination costs. Nevertheless, future research should focus on the interplay of the single

process stage of knowledge transfer. In particular, the role of the last stage – the transfer of

the external generated knowledge into the firm by means of realized absorptive capacity –

should be considered as another factor adding to the cost of collaboration. It is at this point in

the process that important activities, like negotiating the application of the external

knowledge, take place.

4.3 Study 3: The market for intermediation for open innovation

With this paper I want to provide an overview of the market of intermediaries supporting open

innovation. A descriptive analysis of the market and the intermediaries’ practices shall help to

better understand the collaboration process between a focal firm and its external environment.

Such an overview allows firms to compare different intermediaries’ practices for a better

partner selection, which finally supports the management of open innovation projects. Open

innovation conceptualize the beneficial use of widely distributed knowledge for innovation.

To successfully perform openness, a firm needs to first search for external knowledge. Sec-

ond, relevant information has to be selected and acquired, and third, to become integrated in

its own R&D efforts.

Intermediaries are important actors in the field of innovation. They are specialized entities

that support firms in performing the three key steps of openness, acting as brokers between

the focal firm and the external knowledge stocks. The rise of the Internet provoked a profound

change of the intermediaries’ brokering function. The costs of trading distributed knowledge

and connecting previously unconnected actors decreased tremendously as a result of ad-

vancements in information and communication technologies. A new form of intermediaries

arose, facilitated by a dedicated technological infrastructure and a set of corresponding tools

to execute knowledge brokerage and problem solving by integrating a larger group of external

entities into the open innovation initiatives of their clients. These open innovation intermedi-

aries (OII) have been identified as a core actor in an open innovation ecosystem.


41

To successfully engage with an OII a firm needs to understand the OIIs’ practices, especially

knowledge about service differences which can help the firm to select the right partner for a

given task contingency. So far, research on OIIs either intends to develop typologies of inter-

mediaries based on their function and role or deepen the understanding of a certain intermedi-

ary type by performing in-depth case studies. Thus, the work has failed to provide guidance

for selecting and navigating the space of OI intermediaries, something, which I seek to rectify

through my own research. In order to better understand open innovation practices, I apply a

conceptual model for interactive coupled open innovation between firms and users. The mod-

el combines three existing interaction models and considers the recent literature on organizing

contest-based crowdsourcing for ideation and technical problem solving. The model entails

four major steps to initiate co-creation: (1) defining a project task/problem and rules of en-

gagement, (2) finding participants, (3) collaborating to create knowledge (process govern-

ance), (4) leveraging and exploiting the knowledge (integration and commercialization).

To understand the recent market for intermediation in open innovation, I conducted an empir-

ical study of OI intermediaries (market study II). I identified key informants in 162 firms and

invited them to participate in my survey. I received sufficient data for analysis from 59 inter-

mediaries. The database that was generated allowed me to describe the recent open innovation

market. Additionally, I analyzed three key characteristics of intermediaries that guided a

firm’s OII selection process: mechanisms to initiate collaboration, partner integration and

bridging boundaries. Summarizing my results, I found that the market for open innovation is

maturing but is still not consolidated. The OIIs’ self-assessment of the current market volume

and its development indicates a promising market, particularly the contest format. In terms of

their offered services, OIIs can be divided into two groups: running projects on behalf of cli-

ents and providing a solution to a given task or helping clients in building own open innova-

tion competences to engage in direct collaboration with external entities. In both cases, dedi-

cated software is a prerequisite for intermediaries to integrate a large number of participants at

relatively low cost.

Furthermore, I have shown that applied open innovation methods can be described according

to the type of information they intend to generate (need vs. solution information) and the

mechanism of how collaboration between the diverse partners is initiated. As well as collabo-

ration arising from either calling for participation or searching for adequate partners, I found a

third mechanism – selective calling. A selective call addresses an already pre-selected audi-

ence of potential cooperation partners, but the final participation is a choice based on self-

interest and the motivation of the partner. Both dimensions, information type and initiation


42

mechanism are correlated. A selective call is the mechanism of choice when aiming to gener-

ate solution information. The 3x2 matrix created when crossing the two dimensions, allows

the classification of existing open innovation methods ranging from different contest formats

to workshops or market research. Open innovation methods mainly integrate an external part-

ner during the generation of content and transfer into the intermediary’s client firm. The com-

position of an intermediary's pool of potential external partners for a specific client project

can be seen as a core asset of an intermediary. Intermediaries differ significantly with regard

to their community composition. A regression analysis of the self-reported "open innovation

understanding" of the familiarity and localization of external partners for an OI project indi-

cated that open innovation corresponded to collaborating with “unobvious others”.

In conclusion, my study extends the theoretical perspective of “coupled” OI. The results indi-

cate that there also is an interactive collaboration where innovative outputs are being created

in a collaborative activity of all parties together. Thus, in contrast to the prediction of the

model of absorptive capacity, I find that in the latter project stages of evaluation, selection,

and transfer of contributions many external actors are involved. Connecting this with manage-

rial practice, my results can be summarized and transferred into three questions that ought to

guide managers when selecting an OI intermediary for an open innovation project: (1) What is

the expected outcome? (2) What is the project infrastructure of the OI intermediary? (3) What

are the characteristics of the intermediary's community?

With this paper I move beyond the existing literature by providing a comprehensive overview

of the market for open innovation support, as offered by dedicated open innovation intermedi-

aries. I open the ‘‘black box’’ of OI intermediaries by studying their intermediation and

knowledge brokerage function in greater detail. I discover and describe a new, hybrid form of

initiating collaboration in open innovation, a "selective open call". In addition to this, I con-

tribute to the development of sustainable business models for OI intermediaries. My paper

provides insights into key design elements of the business models of OI intermediaries, which

may serve as a template for future research, but also the strategic planning of an intermediary.

Taken on the whole, then, my results contribute to the ongoing debate about what characteriz-

es "openness" for innovation. Crossing organizational and cognitive boundaries to connect a

firm with "obvious others", seems to constitute the true character of open innovation. Never-

theless, future research is very much needed to analyze an intermediary’s contribution to the

innovation success of its clients. Further research should also study open innovation and its

Conclusion and general discussion

43

outcome in secondary fields of application, since the core pattern of OI to ‘look outside the

box’ seems to be transferrable to a variety of tasks along the value chain.

5 Conclusion and general discussion

The objective of this thesis was to offer a deeper understanding of the organization of open

innovation collaboration and to finally distinguish the concept of open innovation from other

collaboration forms. Collaborating with externals becomes necessary for firms to increase

their own competitive advantage (Chesbrough, 2003). Firstly, the search for external infor-

mation to innovation problems stimulates organizational learning (Gavetti et al., 2012, pp. 6,

17) and improves innovativeness and productivity, especially when dealing with uncertainties

in technologies and markets (Dodgson, 1993, p. 378). Secondly, cooperative effort leads to

shared risks and resources, which again is beneficial in reducing existing uncertainties (Pow-

ell et al., 1996, p. 117).

5.1 Summary of study results

Powell et al. (1996) argue that utilizing external information to enlarge the knowledge base

shifts the locus of innovation towards the network. Engaging in this form of cooperation al-

lows a firm to access distributed knowledge which would be otherwise unavailable (p. 119).

Consequently, an effective governance structure to organize collaboration with external part-

ners is necessary in order to achieve competitive advantage (Dyer and Singh, 1998, p. 660).

The demand for such organizational capability is further enhanced by recent rapid progress in

the development of interaction and communication technologies which allow knowledge to

circulate faster and at a lower cost (Verona et al., 2006, pp. 771-772), whilst simultaneously

enlarging the pool of potential interaction partners. This development accompanies a variety

of new possibilities to interact with diverse external partners, examples of which caught my

attention in both research, (e.g. Jeppesen and Lakhani, 2010; Laursen and Salter, 2006; Bou-

dreau et al., 2011; Afuah and Tucci, 2012), as well as in practice (e.g. Boudreau and Lakhani,

2013; Pisano and Verganti, 2008; Huston and Sakkab, 2006; Nambisan and Sawhney, 2007)

A systematic literature review revealed that existing research either focuses on aspects that

influence the innovative performance of a firm or examines the interplay of single collabora-

tion parameters. Yet, there is an absence of an overall framework to interpret findings in light

of the organization handling of these new collaboration forms. As a result, I have produced


44

such an interpretative framework using the systematic literature review. According to this

framework, the organization of collaboration can be described as an interaction between the

degree of collaboration formalization and the degree of collaboration proximity. I found that

the governance of newer collaboration forms has to deal with the issue of interaction taking

place further away from the boundaries of a firm. The communication at this large distance

can be either coordinated by formal or informal organizational structures.

To deepen the understanding of the interrelation between formalization and proximity when

investigating new collaboration forms, I conducted three studies. In the first study (Study 1),

which was explorative and mainly qualitative, the general landscape of open innovation col-

laboration was examined, with the specific intention of identifying the characteristics of

openness. Literature (Chesbrough et al., 2006; Baldwin et al., 2006; Lettl et al., 2008; Lettl,

2007) suggests an observation of the level of activities which describe interactions. Thus, a

model of interaction was established which maps the way participants get involved in the co-

operation arrangement. The analysis of business models of intermediaries operating in the

field of open innovation showed that the interaction model could be executed in eight differ-

ent ways. The majority of collaboration forms could not be specified prior to data analysis,

indicating an extension of the open innovation understanding. Nonetheless, it is obvious that

open innovation collaboration is mainly characterized by signaling collaboration interest

through posting the collaboration task. The extent of the integration of external actors as a

measure of governance (Lichtenthaler and Ernst, 2006; Dahlander and Gann, 2010) did not

distinguish between the different interaction formats. In contrast, activities in early stages of

interaction did determine how potential participants found each other in order to cooperate

and allow for better differentiation. The regulation of access to the collaboration was identi-

fied as the organizational practice that controls the degree of proximity on the one hand and

the degree of formalization on the other hand.

Recruiting criteria were applied to set limits to aspects of distance, such as knowledge do-

mains or socio-demographical backgrounds. No access restriction at all expresses an informal

way of engaging in cooperation since participants can select for themselves according to their

individual motives (Lakhani and Panetta, 2007, pp. 104-107). For example, the type 2 collab-

oration identified explicit searches for external actors to integrate into a formally structured

joint development process (e.g. lead user method). In contrast, the type 4 cooperation mode

intended to invite an undefined number of individuals to contribute to problem solving in a

rather informal manner (e.g. brainstorming communities). Type 3 created a special form

where no direct interaction takes place, just information gathering through observations. Ac-


45

cordingly, the formalization dimension of organizing collaboration does not matter. Only

proximity is defined, which connects perfectly with the literature of innovative search in

terms of search scope (e.g. Katila and Ahuja, 2002; Laursen and Salter, 2006; Rosenkopf and

Nerkar, 2001; Katila and Chen, 2008; Maggitti et al., 2012). In conclusion, all eight collabora-

tion forms fit into the suggested framework of organizing innovation collaboration (Table (A)

5). Based on the accumulation of rather informally organized collaboration forms, as well as

those further away from the firm boundary, the study discovered a trend of firms giving away

direct control over process parameters of knowledge transfer. This trend can be represented

by a diagonal line across the framework coming from the upper left to the lower right quad-

rant.

The second study (Study 2) focused its attention on the first phase of the interaction process

where potential cooperation partners find one another. Two different mechanisms used for

selecting and inviting potential participants were quantitatively examined in detail according

to their effect on the organization structure and overall coordination effort. Analysis showed

that a direct search and selection of partners (Laursen and Salter, 2006; Katila and Ahuja,

2002; Huber, 1991) resulted in a higher coordination effort to run the collaboration project

than an indirect search, in terms of broadcasting the collaboration intention and dependence

upon the self-selection of individuals (Jeppesen and Lakhani, 2010; Alexy et al., 2013; Bou-

dreau et al., 2011). The cost advantage of the broadcast mechanism lasted only if the pool of

potential participants was not too large. Standing in opposition to the published findings of

the positive effect of absolute open calls to signal collaboration interest, the study results indi-

cate that the determination of collaboration proximity is somehow an element in controlling

project costs. Furthermore, the mechanism of initiating collaboration goes along with a pref-

erence for the organizational structure of such a venture. Decentralized structures, which are

used to process the generated knowledge, support the collaboration formats that are based on

direct searching for, and selecting of, partners. In contrast, when engaging in collaboration by

broadcasting, centralized organization structures are preferred in order to adequately process

the distributed knowledge.

Both core results contribute to an understanding of the proximity dimension of organizing

innovation collaboration. For one, both initiation mechanisms mainly influence the aspects of

proximity like the geographic, social, cognitive etc. The indirect search activities, like broad-

cast search, further extend the reach of collaboration since direct search is usually limited to a

firm’s own experiences and knowledge (Cohen and Levinthal, 1990, p.p. 135-138). Yet ulti-

mately, determining the cooperation reach, which is often used to operationalize openness in


46

innovation literature (Laursen and Salter, 2006; Jeppesen and Lakhani, 2010; Salge et al.,

2013), is a trade-off decision based on the costs that arise.

The third study (Study 3) investigated the aspects to consider when engaging with intermedi-

aries in the field of open innovation. The analysis focused predominantly on issues concern-

ing the applied approach, such as identification of collaboration participants and their integra-

tion. One major finding was that methods like ideation contests, creativity workshops, or

online market places could be classified according to the mechanism used to initiate collabo-

ration and the type of information that was requested to solve the innovation problem. There

was a correlation between both aspects in the way that the information type determined which

initiation mechanism was applied. Technological solutions were mainly generated within

smaller groups of experts, which are identified by a selective call, whereas new market trends,

or needs, are identified either by open ideation contests or market research methods. The ex-

ternal community of the intermediary is the key resource in the business model. Interestingly,

the analysis of partner integration regarding the understanding of open innovation showed that

an intense interaction during the middle phase of a project, as well as an integration of the

community in late project phases, was especially strongly associated with openness. This re-

sult runs contrary to the prediction of the model of absorptive capacity in the context of

knowledge acquisition in innovation, which expects less external partners during transfer ac-

tivities. Additionally the investigation of the bridging function revealed that the collaboration

with the so called “unobvious others” is understood as open innovation. These “unobvious

others” are individuals far away from the organizational boundary and unknown to members

of the firm. The trend towards mass collaboration with fairly unknown and distant individu-

als, again, emphasizes that new collaboration forms are organized rather informally and are

located further inside the network. The results of this study revealed that methods of integrat-

ing external actors into a project are good instruments to determine the degree of proximity

and, consequently, the degree of formalization too. For example, the literature on ideation

contests (Piller and Walcher, 2006) examines features needed to design a contest. Elements

like toolkits are instruments to coordinate the interface between the external and internal firm

environment, particularly for the scope of communication and interaction among the partici-

pants.

Taken together, the results of the three studies fit perfectly into the MDS map of innovation

collaboration (Figure (A) 3). They mainly contribute to the literature collected under Factors

1 and 2, which clearly showed an organizational focus and consideration on collaboration

parameters. The conceptual framework that has been derived (Table (A) 5) supports an inte-


47

grative interpretation of the results and helps to answer my research questions. Considering

my first question, which asked how new collaboration forms differ from conventional forms

in terms of their formalization and proximity, my systematic literature review revealed that

collaborations associated with open innovation are characterized by informal relationships

between cooperation partners. Moreover, partners in the network are widely distributed and

have to bridge large distances. I could further support this finding by the results of Study 3,

where collaboration with unknown individuals further away from the focal firm was associat-

ed with openness. For organizations to benefit from such new collaboration forms my re-

search revealed that the organization structure plays a central role. However, in answering my

second research question, Study 2 has shown that an adequate governance structure depends

on the type of search selected to find new knowledge. A summary of the total results helps to

suggest a more detailed definition of open innovation. The term open innovation does not

describe a highly specific collaboration form; rather, it defines and emphasizes the type of

relationship underlying the cooperation. Openness in this sense means the organization of

collaboration partnerships according to formalization and proximity. The concept of open

innovation incorporates all forms of collaboration between a firm and its environment, and, as

a consequence, existing cooperation concepts can be subsumed under this general topic of

open innovation. My findings, summarized in Table (A) 7, lead to the following theoretical

and practical implications.


48

Table (A) 7: Main research results of my thesis answering the research questions

Research question Study Findings

How do new forms of collabora-tion differ from traditional forms on the dimension of formaliza-tion and proximity?

Synopsis Study 3

New collaboration forms are located further away from the focal firm and are characterized by loose and rather informal relationships between part-ners.

What are beneficial structural requirements to the organization of collaboration with external partners?

Study 2

The optimal organizational structure depends on the interdependency of tasks during knowledge transfer and the resulting decomposability of the process. The interdependency is main-ly influenced by the applied search activity. A broadcasted search is sup-ported by a centralized organization structure whereas a more direct search for external partners profits from a decentralized organization.

What is the function behind open innovation?

Synopsis Study 1-3

The term open innovation is a generic term describing collaboration in new product development. Yet, it is mainly associated with collaboration forms where the first stage of knowledge transfer is decoupled and handed to a community (e.g. broadcast search). This leads to an increase in efficiency when acquiring external information.

5.2 Theoretical implications

The results of my thesis provide insights into the field of innovation collaboration. In specific,

the findings deepen the understanding of newer collaboration forms from the perspective of

how they are organized. In general, when talking about collaboration in the context of open

innovation the academic community needs to extend the differentiation of collaboration types.

Distinguishing between in-, outbound, or integrated innovation is too broad to capture the

underlying nature of collaboration (Dahlander and Gann, 2010; Lichtenthaler, 2011;

Gassmann and Enkel, 2004). First, the studies, especially Studies 1 and 2, revealed that within

open innovation alone, at least eight different types could be distinguished. A comparison

between the different modes showed that the shape of the first phase of collaboration- how

collaboration participants are identified and invited to join together- was particularly influen-

tial in characterizing the nature of cooperation. This fact should be considered in future re-

search when investigating the effects of certain open innovation collaboration forms on inno-


49

vation performance. So far, scholars have just compared traditional in-house R&D coopera-

tion with one form of open innovation collaboration to prove that open innovation outper-

forms close innovation approach (Lakhani and Panetta, 2007; Huston and Sakkab, 2006; Al-

mirall and Csadesus-Masanell, 2010).

Second, when this broad understanding of open innovation collaboration is taken, it only co-

vers the direction of knowledge transfer, thus ignoring the transfer style. Study 2 provided

evidence that the way external knowledge sources are identified and triggered plays a crucial

role when it comes to calculating costs of collaboration. I was able to illuminate the debate on

the cost of open innovation (Dahlander and Gann, 2010, p. 705; Dahlander and Magnusson,

2008, p. 646), which is an important avenue of discussion since the cost-perspective is neces-

sary to evaluate the effects of open innovation collaboration on innovative performance. I

found that different search activities to acquire knowledge for an innovation can have oppo-

site effects on costs. Furthermore, my results demonstrated that, depending on the search ac-

tivity, a certain organization structure is associated with the cost of coordinating transfer ac-

tivities. For example, Laursen and Salter’s (2006) proclaimed inverted u-shape relation be-

tween search and performance should be explained by increasing cost for coordinating such

kind of search. Therefore, future research should apply the level of innovation activities for

analysis when comparing different innovation approaches according to their production costs.

To examine the activities for communicating knowledge, the transaction cost economics

(TCE) theory would provide an adequate theoretical lens. This theory has already been adopt-

ed by the traditional collaboration literature, like on alliances, to explain effective inter-firm

collaboration (e.g. Dyer, 1997; Oxley, 1997; Gerwin and Ferris, 2004), especially with respect

to governance-related issues (e.g. Argyres and Liebeskind, 1999; Pisano, 1991). Bogers

(2011, p. 96) transfers this argument into open innovation by stating that the “outcome of col-

laborative agreements depends on the effectiveness of the governance structure”. So far, in the

field of open innovation TCE theory only finds application when examining outbound innova-

tion, highlighting aspects of technology transfer (Lichtenthaler, 2009; Baldwin, 2007).

Thirdly, Study 2 generated valuable knowledge regarding the controversial debate surround-

ing which organizational structure best supports open innovation ventures. Scholars found

evidence that centralization, as well as decentralization, supports the integration of external

knowledge (Zhang et al., 2007; Jansen et al., 2006). I found both governance structures are

applicable in the context of open innovation. My study results showed that which is best de-

pends on the sourcing mechanism- the way firms design the search for external partners.

Siggelkow and Levinthal (2003) therefore suggest a balance between centralized and decen-


50

tralized structures that should depend on the interdependencies of relevant tasks (p. 665). Fur-

ther research on organization structure should take parameters of the knowledge transfer pro-

cess as influential variables when investigating open innovation collaboration.

My thesis also contributes to the conceptual understanding of the term open innovation. Re-

cent literature reviews on open innovation outline a broad scope of application of open inno-

vation collaboration (e.g. Dahlander and Gann, 2010; Huizingh, 2011; Lichtenthaler, 2011).

The term open innovation is used in very generic form which is also reflected by study results.

Integrating the results of Study 1 into the conceptual framework (Table (A) 5) showed that

various types of organizing innovation collaboration were found in open innovation practice.

Interestingly, also very traditional forms like Type A (Table (A) 5) were among the eight col-

laboration models. By his definition of open innovation, Chesbrough (2003) still incorporates

traditional cooperation, like university-industry interaction. Further support revealed the MDS

map (Figure (A) 3) of the research field on collaboration. Here, the keyword open innovation

is located on the proximity dimension between traditional cooperation concepts, like alliances

and networks, and new collaboration formats, like distributed innovation or lead users. How-

ever, the formalization dimension already indicates the informal aspect of interaction within

the context of open innovation. Based on the findings I can clearly isolate the new forms of

organizing innovation collaboration. Study 3 revealed that the term open innovation is mainly

associated with collaboration between a firm and vaguely known individuals far away from

the firm’s boundary. This finding is in line with recent literature on innovation contests,

which focuses on profiting from marginal individuals (Jeppesen and Lakhani, 2010) or unob-

vious others (Piller et al., 2011). Thus, the Type D (Table (A) 5) of organizing innovation col-

laboration reflects what is generally associated with open innovation. Moreover, Study 3

found that such collaboration reveals certain patterns of integrating external actors along the

process of knowledge transfer. Connecting this finding to the perspective of open innovation

as a contingent phenomenon (Huizingh, 2011; Salge et al., 2013), I can confirm that open

innovation does not mean a general opening of a firm’s boundaries from the beginning until

the end of the development process. According to the study results, open innovation collabo-

ration is characterized by incorporating external participants mainly in the middle and end

phase of the process. Gassman (2006, pp. 223-224) asks for a contingency approach to apply

open innovation effectively. The effect of this specific openness pattern on innovative per-

formance and the isolation of factors leading to this pattern cannot be answered within the

scope of my thesis, but it provides a starting point for future research. Salge and colleagues


51

(2013), for example, adopt a contingency view on openness. The authors are, in general, able

to confirm an inverted u-shaped relationship between openness and new product performance,

though found specifically that openness during ideation depends on several further conditions,

like project type or project environment.

5.3 Managerial implications

In practice, the design of innovation collaboration, particularly when directed towards profit-

ing from external sources, can have many faces. This thesis reveals various aspects managers

should consider when organizing such cooperation. First and foremost, project leaders have to

make decisions regarding the two dimensions of organizing innovation collaboration – prox-

imity and formalization. In doing so, they need to decide upon a certain organizational mode

(Table (A) 5). Study 1 highlights that access regulation is an instrument to determine proximi-

ty. Managers can define a set of criteria to select suitable participants for collaboration. Such

criteria can cover social proximity aspects, like what type of external actor, e.g., supplier or

customer, geographical proximity, like regional specifications, or cognitive proximity, like

knowledge background, e.g., same or different knowledge domains of expertise. Study 3 in

particular provides insights into how managers can design the second important dimension –

the degree of formalization of innovation collaboration. Here, the method approach is the cho-

sen instrument to integrate external actors like workshops, contests, or market places etc. Se-

lecting a method allows project leaders to customize the interface to the external firm envi-

ronment. Rules for participation and rights and duties for the joint work can also be deter-

mined.

By choosing a method to integrate external sources, managers are also making a decision

based on, firstly, what type of innovation problem needs to be solved and, secondly, how po-

tential participants are identified and invited to collaborate. The type of innovation task is

related to the kind of information which needs to be acquired by the firm. At the same time,

this aspect can be associated with the degree of maturity of the requested knowledge. For ex-

ample, solution information can be knowledge about complex technological applications, in

which case project leaders should prefer to signal collaboration interests by a selective call

since they will want to receive a manageable amount of contributions. By posting an innova-

tion problem to a certain target group only, a firm can determine the scope of collaboration

especially regarding cognitive issues like knowledge expertise. Reports from practice includ-

ing the Proctor and Gamble case (Huston and Sakkab, 2006) have shown that contributions


52

made by externals have to be tested by internal R&D employees to assess how well they satis-

fy their requirements. On the other hand, when projects are intended to generate need infor-

mation, managers should have a broad circulation for invitations to contribute to the project

topic or engage in a wide search for relevant information. This type of knowledge usually has

the status of raw ideas. The contributions can more easily processed and aggregated into more

sophisticated idea concepts, for example, regarding a new product.

However, the decision to initiate collaboration is not trivial. Study 2 demonstrates that differ-

ences in the initiation mechanism are related to the organization structure which is supposed

to facilitate the process of integrating external knowledge and affect the cost of collaboration

projects. For managers, this highlights the fact that the initiation mechanism should be tai-

lored to the existing structure of internal information processing. A decentralized organization

structure supports direct search activities, whereas a more central structure is beneficial when

applying indirect search in the form of broadcasting a problem by either an open or selective

call. The nature of the search activities is causal for this differentiation. When managers pur-

sue a direct search, they should nominate employees that show competence in identifying,

selecting, but also negotiating with, relevant external sources. In a direct search setting, the

single process steps of transferring knowledge are difficult to separate. A better division of

labor allows collaboration initiation by indirect search. When following the call approach pro-

ject, leaders can distribute tasks of knowledge transfer according to individual competences of

employees. For example, evaluating and selecting suitable contributions to solve the innova-

tion task demands abilities other than negotiating the application of the new acquired

knowledge. Thus, costs and resources of the collaboration project can be easier distributed

when signaling collaboration interest by a call.

Furthermore, Study 2 reveals that such collaboration initiation produces less overall project

costs than an explicit search for relevant external sources. This is mainly due to the fact that

organizations save resources when qualifying potential external sources by indirectly asking

for collaboration. Yet, managers need to understand that openness in the form of extended

cooperation negatively affects the project costs. That means, when, for example, applying a

contest format or another virtual mass collaboration method, project leaders should consider

the size of the participant community. Unlimited project participation increases the effort of

screening, evaluating, and selecting, and outweighs the positive effects on costs of the initia-

tion mechanism. In general, direct and indirect search activities are included in every method

of integrating external actors. Relevant for managers is to be aware of the trade-off relation

between both activities when selecting a certain approach.


53

Regarding open innovation collaboration in particular, this thesis shows that the Type D

(Table (A) 5) format is associated with what the term open innovation expresses – collaborat-

ing with unknown individuals in an informal way. It is important for project leader to recog-

nize that this particular kind of openness means opening the firm boundaries during the phase

of generating content and keeping them open until the knowledge transfer has ended. This

joint work resembles a mass collaboration, characterized by bidirectional, real-time interac-

tion. By doing so, managers have the choice to either integrate the knowledge asset or the

holder of the information. For example, techniques like prediction markets or idea rankings

deliver easy information about the potential of contributions. In contrast, a further joint devel-

opment with an external expert, like within the lead user approach, allows a quite formal inte-

gration of external knowledge. In conclusion, managers should be aware of the fact that open-

ness and ‘entering the unknown’ is a part of sharing control over parameters of the knowledge

transfer process. Dealing with these issues demands a new set of capabilities. The investiga-

tion of open innovation intermediaries showed that firms have the choice between either

building up these open innovation competences or engaging with an intermediary that runs the

collaboration project on behalf of the organization.

5.4 Limitations and outlook

In sum, my thesis provides valuable insights into the organization of innovation collaboration

in general and ‘open innovation’ in specific. The systematic structure of the research field and

the study results empirically emphasize the trend towards a democratized innovation process,

as proclaimed by von Hippel (2005). Within new forms of collaboration, the locus of innova-

tion has shifted more onto the network. However, this democratization produces costs of co-

ordinating collaborative activities. Future research should, therefore, focus on more compara-

tive research of different collaboration forms with respect to their costs and benefits for inno-

vation performance. Moreover, my thesis has opened the ‘black box’ of innovation collabora-

tion and investigated functional principles which particularly affect organizational issues like

structure and effort. Again, the identified correlations should be further examined to deter-

mine how far they influence innovation performance. A detailed cost-performance analysis

with respect to the applied collaboration governance structure should reveal the point where

open innovation collaboration starts to become beneficial. The systematic literature review

contributes to the arrangement of the research field and illustrates point of connection where

the traditional literature on R&D collaboration meets the new, distributed forms of collabora-


54

tion. The identified structure forms an easier basis for transferring already consolidated re-

search findings from one field to another. Finally, the findings contribute to the debate sur-

rounding the meaning of the term open innovation. My thesis provides empirical evidence

that open innovation is not “old wine in new bottles” (Trott and Hartmann, 2009), especially

when speaking of Type D (Table (A) 5) forms of organizing collaboration. What is new is the

highly collaborative and democratic interaction between a firm and the rather unknown envi-

ronment on a neutral platform outside of its boundary.

Besides the limitations of the single studies there are more general limitations of this thesis

worth noting. First, the selected research subject means that all studies are based on survey

data generated from intermediaries in the field of open innovation. The basic assumption be-

hind them this is that they must have a dedicated understanding and business model when

operating in this highly specialized area. A further assumption relates to the transferability of

perspective. I assume that the project characteristic an intermediary provides is comparable to

a similar project type within a larger organization. These assumptions affect the generalizabil-

ity of results. Future research should seek to find the identified collaboration types within or-

ganizations claiming to have implemented open innovation. This would allow for the exist-

ence of different forms of open innovation to be proven, and their relevance to the field ex-

plained. More importantly, it can help answer the question of whether intermediaries are an

inherent part of open innovation, like some scholars claim (Chesbrough, 2006, p. 136; Lopez-

Vega and Vanhaverbeke, 2009).

Second, a further effect of this specific data source is the rather small sample size I generated.

In terms of surveying the market of open innovation intermediaries I was able to cover most

parts of it. The sample included many of the well-known and often cited companies like Inno-

centive or Yet2.com. Still, findings based on observations from fewer than 150 cases are dif-

ficult to interpret according to their general validity. Furthermore, the validity is influenced by

the variety of different types of project types I mapped. To answer the research questions I

needed that variety, but, as a consequence, it was not possible to conduct a statistical compar-

ative analysis of the different collaboration forms due to the small case numbers when split-

ting the sample. Future comparative research should, therefore, focus on only a very small

number of collaboration forms selected on the basis of a specific research question but with a

greater number of observations.

Finally, the systematic literature review at the beginning of the thesis is biased in that I was

the only evaluator interpreting the factors and MDS dimensions. Involving more raters would


55

increase the reliability and validity of results. Furthermore, an additional co-citation analysis,

as undertaken by Di Stefano et al. (2013) in their study, would make the findings more com-

plete and precise in the relations between the single research fields of innovation collabora-

tion. In future research, the connectedness among scholars and their influence on the devel-

opment of research fields could be investigated. It would be interesting to examine if single

research areas really do represent clearly defined phenomena, or if terms like open innovation,

broadcast search or crowdsourcing are just a result of labeling one’s own scientific work. De-

spite the limitations, I believe that this work has been able to deepen our understanding of

organizing innovation collaboration. Furthermore, it suggests characteristics which can ex-

plain the difference between open innovation collaboration and other forms of cooperation.

Notwithstanding the developments in our knowledge of collaboration, the types of collabora-

tion an organization is able to engage in still forms an essential resource in furthering compet-

itive advantage. As stated by Powell in 1998, a firm’s collaboration portfolio is an essential

resource. In particular, this collaborative competence serves as a signal to the market and

helps to attract potential partners for future joint ventures (p. 231).

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Appendix (A)

73

7 Appendix (A)

Appendix (A) 1: Most cited articles (top five) for each factor

Factor Reference Citation frequency

Factor 1 Sheldrick, G. M. (2008) 29,972 Saldanha, A.J. (2004) 637 von Hippel, E and von Krogh, G. (2003) 293 Lakhani, K.R. and von Hippel, E. (2003) 280 Lee, G.K. and Cole, R.E. (2003) 143Factor 2 Teece, D.J. (1986) 1,799 Powell, W.W.; Kogut, K.W. and Smith Doerr, L. (1996) 1,763 Ahuja, G. (2000) 734 Etzkowitz, H. and Leydesdorff, L. (2000) 608 Siegel, D.S.; Waldman, D. and Link, A. (2003) 235Factor 3 Tsai, W.P. (2001) 552 Stuart, T.E. (2000) 358 Faems, D.; van Looy, B. and Debackere, K. (2005) 116 Gilbert, M. and Cordey Hayes, M. (1996) 87 Sampson, R.C. (2007) 84

Factor 4 Tennenhouse, D.L.; Smith, J.M.; Sincoskie, W.D.; Wetherall, D.J. and Minden, G.J. (1997)

429

Lilien, G.L.; Morrison, P.D.; Searls, K.; Sonnack, M. and von Hip-pel, E. (2002)

149

Herstatt, C. and von Hippel, E. (1992) 128 Morrison, P.D.; Roberts, J.H. and von Hippel, E. (2000) 108 Koufteros, X.; Vonderembse, M. and Jayaram, J. (2005) 94Factor 5 Brown, J.S. and Duguid, P. (1991) 1,527

Tennenhouse, D.L.; Smith, J.M.; Sincoskie, W.D.; Wetherall, D.J. and Minden, G.J. (1997)

429

von Hippel, E. and von Krogh, G. (2003) 293 Lakhani, K.R. and von Hippel, E. (2003) 280

Lilien, G.L.; Morrison, P.D.; Searls, K.; Sonnack, M. and von Hip-pel, E. (2002)

149

Factor 6 Lamming, R.; Johnsen, T.; Zheng, J.R. and Harland, C. (2000) 93 Garcia-Pont, C. and Nohria, N. (2002) 55 Hinterhuber, A. (2002) 32 Stuart, T.E. and Sorenson, O. (2007) 27 Presutti, M.; Boari, C. and Fratocchi, L. (2007) 23Factor 7 Park, S.H. and Ungson, G.R. (1997) 253 Park, S.H. and Russo, M.V. (1996) 200 Vandeven, A.H. and Polley, D. (1992) 117 Zhao, L.M. and Aram, J.D. (1995) 86 Sampson, R.C. (2007) 84Factor 8 Levy, P. (2010) 7 Degreene, K.B. (1993) 5 Bothos, E.; Apostolou, D. and Mentzas, G. (2009) 4 Bear, J.B. and Woolley, A.W. (2011) 3 Gouarderes, G.; Saber, M.; Nkambou, R. and Yatchou, R. (2005) 2

App

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Appendix (A)

75

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79

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Appendix (A)

80

Jou

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Appendix (A)

81

Jou

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Appendix (A)

82

Appendix (A)

83

Appendix (A) 3: Keywords clustered by hierarchical cluster analysis

(compared with factor belonging)

Cluster Factor (according to FA)

broadcast search 2 F1 crowdsourcing 2 F1 cumulative innovation 2 F1 distributed innovation 2 F1 open innovation 2 F1 open source 2 F1 peer production 2 F1 collaboration 3 F2 networks 3 F2 university-industry 3 F2 technology transfer 3 F2 co-creation 3 F2 alliances 1 F3 knowledge transfer 1 F3 strategic alliances 1 F3 customer integration 7 F4 customer-active 7 F4 lead user 6 F4/5 user innovation 6 F1/5 community 6 F5 collaborative innovation 4 F6 strategic networks 4 F6 joint venture 8 F7 R&D cooperation 8 F7 collective intelligence 5 F8 user-driven 4 wisdom of the crowds 5

Appendix (A)

84

Appendix (A) 4: Rotated factor matrix with loadings of keywords

1 2 3 4 5 6 7 8

broadcast search 0.817 crowdsourcing 0.721 cumulative inno-vation

0.786

distributed inno-vation

0.774

open innovation 0.767 open source 0.903 peer production 0.796 collaboration 0.938 networks 0.760 university-industry

0.639

technology trans-fer

0.711

co-creation 0.935 alliances 0.928 knowledge trans-fer

0.778

strategic alliances 0.930 customer integra-tion

-0.849

customer-active -0.877

lead user -0.505 0.555 user innovation 0.602 0.603 community 0.772 collaborative in-novation

0.810

strategic net-works

0.731

joint venture 0.819 R&D cooperation 0.754 collective intelli-gence

-0.855

user-driven wisdom of the crowds

Extraction method: Principal component analysis Rotation method: Varimax with Kaiser normalization Rotation converged in 16 iterations

Appendix (A)

85

Appendix (A) 5: Survey form of market study I

RWTH Technology and Innovation Management Group tim.rwth-aachen.de

Open Innovation Accelerator Survey 2007

The objective of this survey is to provide an overview of the market of open innovation accelerators.

The target audience of this report will be managers in organizations who want to start an open

innovation initiative and are seeking professional support for this task. If not otherwise indicated by

you, all information given here may be published in the final report.

Please answer all questions as openly and truthfully as possible, and only address services you

will be offering in Q4/2007 and Q1/2008 and not those you plan to offer in the future.

Important definitions

Open innovation describes the use of: “[..] inflows and outflows of knowledge to accelerate in-

ternal innovation [..].” (Chesbrough et al., 2006). It opens the internal innovation process of an organ-

ization to input from external sources with the objective to enhance the efficiency and/or effectiveness

of the firm’s innovation process. A central source providing important input for innovation is the user or

customer of a product.

Open innovation accelerators are companies that offer services to generate external knowledge on

behalf of their client organizations to support them in their innovation challenges.

“Services offered” refers to the entire product or solution package your company offers to your clients

with.

Appendix (A)

86

I Questions referring your companies’ positioning

1. What are you name and your responsibilities?

Name

Responsibility

2. When was your company founded?

3. Please summarize your company’s philosophy / core offering or posi-

tion in brief (about 20 words)

4. What would you consider as your company’s core competencies?

5. How many employees are working in your company?

6. How many people are working in an average project team?

Appendix (A)

87

II Questions referring your services and methods to accelerate open innovation at your clients

This graphic below is an example for a generic innovation process with different stages. It shall help you to answer the following questions.

7. At which stage of the innovation process does your company offer methods

for open innovation? Please also name / describe briefly your offering at this stage (or refer us to a more detailed description).

Idea generation

Idea evaluation

Concept development

Prototyping

Product/market test

Launch

Appendix (A)

88

If your company works with a different innovation process (less, more or other stages) please use the graphic below to show us your company’s process.

Appendix (A)

89

8. For each method, please rate the degree of involvement of external actors? Please use a

scale from 1 = very low to 6 = very high!! Very low Very high

Idea generation

Idea evaluation

Concept development

Prototyping

Product/market test

Launch

9. Please characterize the nature of your business: Would you consider your company primary

as an open innovation consulting firm offering highly customized solutions for your clients’ open innovation initiative, or are you offering a predefined method / platform / process you replicate for different clients? Please rate the following statements with regard to your core offerings on a scale from 1 = I do not agree at all to 6 = I totally agree

I do not agree at all I totally agree

We use an established platform / software to acceler-ate the open innovation process of our clients.

Each project involves a highly customized consulting and method creation process for our clients.

We have an established community (panel) of exter-nal actors (e.g. users, experts) which we integrate into problem solving for diverse clients.

We offer standard software / web services for open innovation.

We consider us as an open innovation consultancy.

10 What are the methods offered by your company to accelerate open innovation?

Important Definition

A community is a group that primarily or initially communicates or interacts via internet. The community is not defined by physical boundaries but the interest of its members. Different kinds of communities exist depending on what they aiming for. Discussion forum is a web application for holding discussions and posting user generated content. A panel is a group of people gathered to judge, interview, and discuss etc. a distinct topic.

a) (Online) Communities

What kind of communities are you working with?

Discussion forum Panel

Others

How can new members join the community?

Open to everyone

By invitation only

Restricted access by expertise

demography

Others

Appendix (A)

90

b) Idea Contests

c) Workshops

Lead User Method

Brainstorming and creativity workshop

Others

d) Software Tools

Please specify:

Important Definition

Virtual concept testing is the testing of virtual prototypes with customers in a media-rich presentation environ-ment, rather than creating expensive physical prototypes.

e) Toolkits for User Innovation

f) Configuration Tools

g) Virtual concept testing

h) Others

11 While the focus of this survey is the innovation process, users and external experts also can be integrated into other processes of a firm. Does your company also accelerate the integra-tion of external input for …

Business model development

Marketing / Sales

Customer Service (after sales)

Others

If you offer such a service for other processes, please provide more information on the nature of this service (or add a document with a more detailed description and examples):

Appendix (A)

91

III Questions referring the users and actors for integration

The following questions address the community your company works with. If you had checkmarked Question 10 a) Communities, please answer the following questions; if not skip this part and continue with question 27.

12. How is the innovation community composed that is integrated in the innovation pro-

cess of your clients? (please tick all relevant classes)

Recent customers (of client)

Potential customers

Non-customers

Students

Scientists

Others

13. How does your company build the community?

Build every time new

How long does a build up take (in days)?

We have an existing and established community

(panel) we use for several clients

We work with existing external communities

Others

14. How and where do you recruit new community members? Do you allow for self-nomination of new members?

15. Do you have specific requirements for selecting new members of the community?

Yes No

If YES, what are these requirements?

Appendix (A)

92

IV Questions referring the operation of a typical client project

The following questions are supposed to gather information about how you approach a new project and what your stages of processing are.

16. How many projects are you running in a typical year?

17. How many projects have your company completed yet?

18. What is the average duration of a typical project? (in months)

19. What is your fee structure?

Product – licence fee

Consulting – person days

Others

20. What is the average project cost range in US$?

21. Can you estimate the return on investment you create for your clients?

Yes, it is in the range of … %

What are other performance indicators your clients real-ize after a typical project (e.g., shorter time to market, num-ber of radical new ideas, cost saving compared to internal development, customer satisfaction …)?

22. Please name and describe one or two important projects you had within the last two years? (Please add a document if you need more space or you have an existing client case study.)

Appendix (A)

93

23. Could you characterize a typical project structure?

24. Does your company integrate employees from the client company in your process?

Yes No

If YES, how do you integrate them? How much time and effort do they have to invest? What are their tasks?

25. In which form do you present the output for your client? (e.g., Idea books, tables, re-ports, online database, presentation etc.)

Appendix (A)

94

26. Do you have mechanisms to monitor the quality and performance of your services?

Yes No

If YES, how do you evaluate/measure your project outcome and performance?

Appendix (A)

95

V Questions referring work environment and markets

The following questions are supposed to give us information on which markets you are operating and who your company addresses in first place.

27. Would you consider the focus of your services more for clients operating in B-to-B or B-to-C markets?

B-to-B B-to-C both

In which markets have been the majority of your existing projects in the last two years?

B-to-B B-to-C both

28. Please name major clients your company has already worked with.

29. Who are your main competitors? (in case you don’t have a direct competitor, please name the open innovation accelerators you consider as leading in the field)

30. In which sectors/industries is your company operating?

Automotive (Consumer) Electronics

Engineering Fast-Moving-Consumer-Goods

Tele-Communications Health/Medicine

Computer/IT Chemical / Pharmaceutical

Design/Product Design Others

31. On which geographical market are you acting?

North America

Middle America

South America

Europe

Asia (Russia, China, Japan etc.)

Oceania (Australia, New Zealand, Hawaii)

Africa

Appendix (A)

96

32. Is there any aspect you consider as important and which we did not ask yet?

33. Your company’s contact data to publish in the final report:

34. To which e-mail address shall we send your personal copy of the final report?

Thank you very much for participation!!!!

Appendix (A)

97

Appendix (A)

98

Appendix (A) 6: Survey form of market study II

(Text in introduction)

We invite you to present your company (name was automatically filled in) in the upcoming edi-

tion of our report targeting managers in global organizations responsible for innovation and

technology management, R&D, marketing, new business development, market research, and

strategy.

NOTE: The survey needs your full attention for a minimum of 40 and a maximum of 85 minutes,

depending on the scope of your company's offerings. But you can pause answering at any time

and complete your survey later.

Before the results are published, you will receive your profile for verification. Of course, your

company also will receive a complimentary copy of the report (an 800 Euro value).

Thank you very much for participating!

Frank Piller & Kathleen Diener,

Contact for any questions: +49 241 809 6576 or [email protected]

----------------------------------------------------------------------

Technology and Innovation Management Group

RWTH Aachen University

School of Business and Economics

www.tim.rwth-aachen.de

(further information to understand the logic of the survey)

The signal light next to the question on the right side is a help to indicate the sensitivity of the

information that is asked for. Green means low sensitivity and red means high sensitivity.

Mandatory questions are marked with a star (*).

(Lis

t of i

tem

s an

d th

eir

answ

er o

ptio

ns)

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

1 W

hat i

s the

nam

e of

you

r com

pany

? In

serta

nt fo

r fol

low

ing

ques

tions

to p

ose

ques

tion

dire

ctly

to th

e co

mpa

ny.

Long

free

text

2 Pl

ease

indi

cate

you

r nam

e an

d pr

imar

y co

ntac

t inf

orm

atio

n.

Lo

ng fr

ee te

xt

3 W

hat i

s you

r com

pany

's fo

undi

ng y

ear?

Num

eric

al in

put

4 Pl

ease

nam

e yo

ur c

ompa

ny's

cont

act d

ata

to

be p

ublis

hed

in th

e fin

al re

port.

Long

free

text

5 In

whi

ch g

eogr

aphi

cal m

arke

t(s) i

s you

r co

mpa

ny ru

nnin

g pr

ojec

ts?

Nor

th A

mer

ica

Mid

dle

Am

eric

a So

uth

Am

eric

a Eu

rope

A

fric

a A

sia

Oce

ania

Mul

tiple

cho

ice

nom

inal

6 W

hich

are

typi

cal p

roje

ct ty

pes t

hat y

ou

supp

ort a

t you

r clie

nts?

R&

D p

roje

cts

Des

ign

proj

ects

B

usin

ess d

evel

opm

ent p

roje

cts

Mar

ketin

g / s

ales

/ br

andi

ng p

roje

cts

Cus

tom

er se

rvic

e pr

ojec

ts

HR

pro

ject

s M

arke

t res

earc

h

Mul

tiple

cho

ice

nom

inal

Appendix (A)

99

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

7 R

egar

ding

you

r ave

rage

clie

nts,

in w

hich

se

ctor

s/in

dust

ries a

re th

ey o

pera

ting?

Agr

icul

ture

A

utom

otiv

e in

dust

ry

Bui

ldin

g in

dust

ry

Che

mic

al &

Pha

rmac

eutic

al

Engi

neer

ing

(Tel

e)C

omm

unic

atio

n/IT

(C

onsu

mer

) Ele

ctro

nics

Fi

nanc

e H

ealth

/ M

edic

ine

Mul

tiple

cho

ice

nom

inal

8 D

o yo

u ha

ve c

lient

s fro

m th

e pu

blic

sect

or?

Y

es/N

o bi

nary

9 Pl

ease

nam

e up

to 3

clie

nts f

rom

the

publ

ic

sect

or.

M

ultip

le sh

ort t

ext

10

Do

you

have

exp

erie

nce

with

clie

nts f

rom

no

n-pr

ofit

orga

niza

tions

/ N

GO

's?

Y

es/N

o bi

nary

11

Plea

se n

ame

up to

3 c

lient

s (N

GO

, non

-pr

ofit)

.

Mul

tiple

shor

t tex

t

12

Plea

se n

ame

3 of

you

r top

refe

renc

e cl

ient

s.

Mul

tiple

shor

t tex

t

13

Plea

se n

ame

3 of

you

r mai

n co

mpe

titor

s or

alte

rnat

ives

to y

our b

usin

ess.

M

ultip

le sh

ort t

ext

14

Rel

ativ

e to

you

r mai

n co

mpe

titor

s /

alte

rnat

ives

, ple

ase

rate

…

reve

nue

proj

ect v

olum

e cu

stom

er b

ase

repu

tatio

n Th

e de

gree

to w

hich

you

r clie

nts u

se th

e ge

nera

ted

idea

s/so

lutio

ns

Mul

tiple

num

eric

al in

put

(slid

er)

sam

e as

com

petit

ors /

ve

ry m

uch

high

er th

an

com

petit

ors

met

rical

(0

-100

;0.0

0)

Appendix (A)

100

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

15

Plea

se d

escr

ibe

the

over

all d

evel

opm

ent o

f sa

les i

n %

bet

wee

n th

e ye

ars 2

009

and

2011

.

Num

eric

al in

put

met

rical

(%)

16

How

man

y of

you

r pro

ject

s are

from

re

curr

ing

clie

nts i

n 20

11?

W

hat d

o yo

u ex

pect

for 2

012?

2011

20

12

Mul

tiple

num

eric

al in

put

met

rical

(cou

nt)

17

If y

ou c

an sp

ecify

, how

hig

h w

as th

e ov

eral

l gr

owth

of y

our c

ompa

ny b

etw

een

the

year

s 20

09 a

nd 2

011

and

how

hig

h is

the

proj

ecte

d gr

owth

till

2012

?

2009

-201

1 20

12

Mul

tiple

num

eric

al in

put

met

rical

(%)

18

Plea

se c

hoos

e a

curr

ency

, US

$ or

€?

Inse

rtant

for f

ollo

win

g qu

estio

ns th

at a

sk fo

r co

sts e

tc.

Shor

t fre

e te

xt

19

Inno

vatio

n in

term

edia

ries a

re a

hot

bus

ines

s id

ea to

day.

H

ow la

rge

is y

our e

stim

ate

of th

e to

tal

(glo

bal)

volu

me

of y

our m

arke

t (in

€) t

oday

?

curr

ent m

arke

t vol

ume

in M

io €

ex

pect

ed m

arke

t vol

ume

in 3

yea

rs in

Mio

€

Mul

tiple

num

eric

al in

put

(slid

er)

met

rical

(cou

nt)

20

Gen

eral

ly sp

oken

, wou

ld y

ou a

gree

that

you

r cl

ient

s val

ue y

our s

ervi

ces?

Mul

tiple

num

eric

al in

put

(slid

er)

stro

ngly

dis

agre

e /

stro

ngly

agr

ee

met

rical

(0

-100

;0.0

0)

21

Plea

se p

rovi

de u

s the

def

initi

on o

f ope

n in

nova

tion

used

in y

our c

ompa

ny!

Plea

se g

ive

a br

ief d

escr

iptio

n!

Lo

ng fr

ee te

xt

In

the

foll

owin

g w

e pr

esen

t you

dif

fere

nt s

tate

men

ts r

egar

ding

ope

n in

nova

tion

. P

leas

e ra

te to

wha

t ext

ent y

ou a

gree

.

Appendix (A)

101

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

22

It w

as e

asy

for m

e to

gen

erat

e th

at d

efin

ition

of

ope

n in

nova

tion

abov

e.

Mul

tiple

num

eric

al in

put

(slid

er)

stro

ngly

dis

agre

e /

stro

ngly

agr

ee

met

rical

(0

-100

;0.0

0)

23

Our

com

pany

per

form

s ope

n in

nova

tion

acco

rdin

g to

its c

omm

on u

nder

stan

ding

.

Mul

tiple

num

eric

al in

put

(slid

er)

stro

ngly

dis

agre

e /

stro

ngly

agr

ee

met

rical

(0

-100

;0.0

0)

24

Our

com

pany

is a

ver

y go

od e

xam

ple

of

open

inno

vatio

n.

Mul

tiple

num

eric

al in

put

(slid

er)

stro

ngly

dis

agre

e /

stro

ngly

agr

ee

met

rical

(0

-100

;0.0

0)

25

We

from

you

r co

mp

any

(nam

e w

as

disp

laye

d) a

re p

roud

to b

e pa

rt of

the

open

in

nova

tion

com

mun

ity.

Mul

tiple

num

eric

al in

put

(slid

er)

stro

ngly

dis

agre

e /

stro

ngly

agr

ee

met

rical

(0

-100

;0.0

0)

26

With

wha

t ser

vice

(s) d

oes y

our

com

pany

supp

ort c

lient

's pr

ojec

t(s)?

Pl

ease

cho

ose

from

the

follo

win

g ca

tego

ries.

We

supp

ort o

ur c

lient

's pr

ojec

t(s) b

y …

wor

ksho

ps

cont

ests

m

arke

t res

earc

h br

oadc

ast s

earc

h In

serta

nts f

or fo

llow

ing

met

hod

spec

ific

ques

tions

.

Mul

tiple

cho

ice

nom

inal

27

Wha

t asp

ects

are

incl

uded

in y

our s

ervi

ce?

(mat

rix)

(r

ow)

wor

ksho

ps

Arr

ays (

Num

eric

al)

nom

inal

Appendix (A)

102

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

co

ntes

ts

mar

ket r

esea

rch

broa

dcas

t sea

rch

(col

umn)

so

ftwar

e an

d/or

web

solu

tion

onlin

e pl

atfo

rm fo

r (ex

tern

al) a

ctor

s of

fline

net

wor

k of

(ext

erna

l) ac

tors

co

nsul

tanc

y pa

ckag

e da

taba

se w

ith te

chni

cal i

nfor

mat

ion

28

Do

you

have

a c

erta

in n

ame

for t

he se

rvic

es

you

offe

r?

wor

ksho

ps

cont

ests

m

arke

t res

earc

h br

oadc

ast s

earc

h se

rvic

e na

me

Arr

ays (

Text

) w

ordi

ng

29

Plea

se ra

nk th

e se

rvic

es c

ateg

orie

s acc

ordi

ng

to th

eir i

mpo

rtanc

e fo

r you

r bus

ines

s!

wor

ksho

ps

cont

ests

m

arke

t res

earc

h br

oadc

ast s

earc

h

ra

nkin

g

30

How

wel

l doe

s the

term

ope

n in

nova

tion

fit

with

you

r bus

ines

s mod

el /

serv

ice?

Mul

tiple

num

eric

al in

put

(slid

er)

fits v

ery

poor

ly /

fits

very

wel

l

met

rical

(0

-100

;0.0

0)

31

You

cho

se –

(m

etho

d na

me

was

dis

play

ed) -

as

you

r mos

t im

porta

nt se

rvic

e ca

tego

ry.

How

man

y pr

ojec

ts h

ave

you

been

runn

ing

in th

e pa

st 3

yea

rs?

2009

-201

1 M

ultip

le n

umer

ical

inpu

t m

etric

al (c

ount

)

Appendix (A)

103

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

32

Wha

t is t

he a

vera

ge d

urat

ion

of a

typi

cal

proj

ect f

rom

the

star

t unt

il tra

nsfe

rrin

g th

e re

sults

into

the

clie

nt o

rgan

izat

ion?

Pl

ease

spec

ify in

mon

th.

N

umer

ical

inpu

t m

etric

al (c

ount

)

33

Wha

t is y

our a

vera

ge p

roje

ct c

ost r

ange

(in

€)?

Shor

t fre

e te

xt

met

rical

(in

terv

al)

34

In p

erce

nt h

ow h

igh

is th

e se

rvic

e's sh

are

in

tota

l rev

enue

rega

rdin

g th

e la

st 3

yea

rs

(200

9-20

11)?

Num

eric

al in

put

met

rical

(%)

35

You

are

in th

e se

rvic

e ca

tego

ry: (

met

hod

nam

e w

as d

ispl

ayed

) A

t the

beg

inni

ng o

f a ty

pica

l pro

ject

, ob

ject

ives

are

def

ined

with

repr

esen

tativ

es

of th

e cl

ient

com

pany

. H

ow o

ften,

rang

ing

from

har

dly

ever

to v

ery

freq

uent

, doe

s you

r co

mp

any

(nam

e w

as

disp

laye

d) ru

n pr

ojec

ts w

ith th

e fo

llow

ing

obje

ctiv

es?

(sol

utio

n in

form

atio

n)

Find

a te

chno

logy

to sa

tisfy

clie

nt's

exis

ting

requ

irem

ents

. Fi

nd id

eas/

oppo

rtuni

ties f

or a

giv

en

solu

tion/

prod

uct.

Find

exp

erts

(e.g

. lea

d us

er) w

ho a

ctiv

ely

inno

vate

. Fi

nd a

tech

nica

lly te

st p

roce

dure

. Fi

nd p

artie

s int

eres

ted

in fu

rther

join

t de

velo

pmen

t of c

lient

's te

chno

logy

. (n

eed

info

rmat

ion)

Find

mar

ketin

g id

eas/

oppo

rtuni

ties f

or a

giv

en

solu

tion/

prod

uct.

Iden

tify

mar

ket/f

utur

e tre

nds a

nd/o

r cus

tom

er

need

s. G

ener

ate

info

rmat

ion

abou

t per

sona

l opi

nion

s an

d/or

eva

luat

ion

of p

rodu

ct c

once

pts.

Dev

elop

idea

s abo

ut p

rodu

ct im

prov

emen

t, ne

w p

rodu

cts a

nd/o

r new

serv

ices

.

Mul

tiple

num

eric

al in

put

(slid

er)

hard

ly e

ver /

ver

y fr

eque

nt

met

rical

(0

-100

;0.0

0)

Appendix (A)

104

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

Fi

nd le

adin

g ed

ge c

usto

mer

s/ea

rly a

dopt

ers.

36

Wha

t doe

s you

r com

pany

cal

l suc

h a

pote

ntia

l par

ticip

ant p

ool?

N

OTE

: If

you

don

’t ha

ve a

cer

tain

nam

e,

plea

se ty

pe (c

opy

past

e) in

par

ticip

ant p

ool.

Inse

rtant

for f

ollo

win

g qu

estio

ns re

gard

ing

appl

ied

com

mun

ity.

Shor

t fre

e te

xt

wor

ding

37

On

aver

age,

how

man

y in

divi

dual

s doe

s yo

ur

par

tici

pan

t p

ool h

old?

In

serta

nt fo

r fol

low

ing

ques

tions

rega

rdin

g na

me

for a

pplie

d co

mm

unity

. N

umer

ical

inpu

t m

etric

al (c

ount

)

38

How

was

the

deve

lopm

ent o

f ne

w p

arti

cip

ant

poo

l (na

me

was

di

spla

yed)

mem

bers

ove

r the

pas

t 3 y

ears

? Pl

ease

des

crib

e th

e to

tal c

hang

e in

per

cent

(%

) bet

wee

n th

e ye

ars 2

009

and

2011

.

2009

-201

1 M

ultip

le n

umer

ical

inpu

t m

etric

al (%

)

39

Plea

se d

eter

min

e th

e ge

nera

l flu

ctua

tion

rate

in

% o

f you

r par

tici

pan

t p

ool.

Fl

uctu

atio

n ra

te %

= lo

st m

embe

rs /

tota

l m

embe

rs o

f par

tici

pan

t p

ool *

100

%

N

umer

ical

inpu

t m

etric

al (%

)

40

Wha

t leg

al c

ondi

tions

do

your

mem

bers

of

the

par

tici

pan

t p

ool h

ave

to a

ccep

t?

Acc

ept o

ur g

ener

al te

rms a

nd c

ondi

tions

by

regi

stra

tion

Sign

a n

ondi

sclo

sure

agr

eem

ent

Sign

a fo

rmal

con

tract

with

spec

ific

right

s and

du

ties

We

don'

t nee

d ag

reem

ents

in le

gal c

ondi

tions

. Th

ere

is a

mut

ual u

nder

stan

ding

rega

rdin

g ou

r co

de o

f con

duct

.

Mul

tiple

cho

ice

nom

inal

Appendix (A)

105

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

41

How

wou

ld y

ou d

escr

ibe

the

prim

ary

reac

h of

you

r par

ticip

ant p

ool f

rom

the

pers

pect

ive

of y

our c

lient

? Pl

ease

spec

ify 'r

each

' on

a sc

ale

reac

hing

fr

om v

ery

orga

niza

tiona

l int

erna

l to

very

or

gani

zatio

nal e

xter

nal a

nd o

n sc

ale

reac

hing

fr

om v

ery

wel

l kno

wn

to v

ery

unkn

own

.

Mul

tiple

num

eric

al in

put

(slid

er)

very

org

aniz

atio

nal

inte

rnal

/ ve

ry

orga

niza

tiona

l ext

erna

l ve

ry w

ell k

now

n / v

ery

unkn

own

met

rical

(0

-100

;0.0

0)

42

Plea

se d

eter

min

e th

e m

ix o

f exp

ertis

e in

yo

ur p

artic

ipan

t poo

l. D

ispe

nse

100%

.

Soci

al sc

ienc

es (h

isto

ry, l

aw, e

cono

mic

s, ps

ycho

logy

) N

atur

al sc

ienc

es (c

hem

istry

, mat

hs, p

hysi

cs)

App

lied

scie

nces

(eng

inee

ring

scie

nce)

A

rts (d

esig

n, p

erfo

rmin

g, fi

ne a

rts, m

usic

)

Mul

tiple

num

eric

al in

put

met

rical

(%)

43

In a

vera

ge, w

hat i

s the

gen

eral

leve

l of

expe

rtise

in y

our p

arti

cip

ant

poo

l (na

me

was

dis

play

ed)?

Pl

ease

spec

ify o

n a

scal

e re

achi

ng fr

om

begi

nner

/nov

ice

to a

dvan

ced/

expe

rt .

Mul

tiple

num

eric

al in

put

(slid

er)

begi

nner

/nov

ice

/ ad

vanc

ed/e

xper

t

met

rical

(0

-100

;0.0

0)

44

In g

ener

al d

o yo

u ha

ve re

stric

tions

to q

ualif

y fo

r/joi

n yo

ur p

arti

cip

ant

poo

l (na

me

was

di

spla

yed)

? Pl

ease

cho

ose

from

bel

ow.

Age

Ed

ucat

ion

Dem

ogra

phy

Geo

grap

hy

Dom

ain

of e

xper

tise

We

don'

t hav

e re

stric

tions

to a

cces

s our

pa

rtici

pant

poo

l. O

ur se

lect

ion

crite

ria a

re p

roje

ct d

epen

dent

.

Mul

tiple

cho

ice

Appendix (A)

106

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

45

Reg

ardi

ng a

typi

cal c

lient

's pr

ojec

t(s) o

f the

se

rvic

e ca

tego

ry -

(met

hod

nam

e w

as

disp

laye

d),

do y

ou fu

rther

sele

ct p

artic

ipan

ts?

Y

es/N

o bi

nary

46

Plea

se n

ame

a fe

w se

lect

ion

crite

ria.

Lo

ng fr

ee te

xt

47

Do

you

have

a c

erta

in n

ame

for t

he se

lect

ed

smal

l par

ticip

ant g

roup

? In

serta

nt fo

r fol

low

ing

ques

tions

rega

rdin

g ap

plie

d pr

ojec

t tea

m.

Yes

/No

bina

ry

48

Wha

t do

you

nam

e th

at g

roup

?

Shor

t fre

e te

xt

wor

ding

49

How

man

y m

embe

rs o

f the

par

tici

pan

t p

ool (

nam

e w

as d

ispl

ayed

) us

ually

pa

rtici

pate

in a

pro

ject

? Pl

ease

giv

e an

ave

rage

.

N

umer

ical

inpu

t

50

How

man

y in

divi

dual

s fro

m

your

par

tici

pan

t p

ool (

nam

e w

as

disp

laye

d) re

peat

pro

ject

par

ticip

atio

n?

Plea

se sp

ecify

in p

erce

nt.

N

umer

ical

inpu

t

51

Doe

s you

r co

mp

any

(nam

e w

as

disp

laye

d) u

se th

e sa

me

sele

cted

gr

oup

(na

me

was

dis

play

ed)

for s

ever

al

proj

ects

?

Y

es/N

o bi

nary

Appendix (A)

107

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

52

To fi

nd th

e rig

ht e

xter

nal p

artic

ipan

t fro

m

your

par

tici

pan

t p

ool (

nam

e w

as d

ispl

ayed

) to

ach

ieve

the

proj

ect o

bjec

tive(

s),

how

ofte

n do

es y

our

com

pan

y (n

ame

was

di

spla

yed)

use

the

follo

win

g ap

proa

che(

s)?

We

post

requ

ests

to o

ur p

artic

ipan

t poo

l AN

D

ask

for p

artic

ipat

ion

(ran

dom

cho

ice)

. In

divi

dual

s of o

ur p

artic

ipan

t poo

l sel

ect

them

selv

es to

pro

ject

requ

est(s

) we

post

to

them

. W

e po

st re

ques

ts to

indi

vidu

als i

n ou

r pa

rtici

pant

poo

l and

invi

te o

nly

a fe

w o

f the

m

for p

artic

ipat

ing

(sys

tem

atic

cho

ice)

. W

e po

st re

ques

ts to

spec

ifica

lly p

re-s

elec

t in

divi

dual

s fro

m o

ur p

artic

ipan

t poo

l fitt

ing

the

proj

ect r

equi

rem

ent(s

). Th

en th

ey se

lf-se

lect

to p

artic

ipat

e.

We

defin

e se

arch

are

as to

iden

tify

sing

le

indi

vidu

als f

ittin

g th

e ta

sk re

quire

men

t(s)

AN

D a

sk fo

r par

ticip

atio

n.

We

defin

e se

arch

are

as fo

r ide

as, s

ugge

stio

ns,

solu

tions

on

the

proj

ect r

eque

st(s

). W

e w

idel

y se

arch

for s

ingl

e or

a g

roup

of

indi

vidu

als f

ittin

g th

e ta

sk re

quire

men

t(s)

AN

D a

sk fo

r the

ir pa

rtici

patio

n.

We

perf

orm

an

unlim

ited

sear

ch fo

r ide

as,

sugg

estio

ns, s

olut

ions

on

the

proj

ect

requ

est(s

).

Mul

tiple

num

eric

al in

put

(slid

er)

hard

ly e

ver /

ver

y fr

eque

nt

met

rical

(0

-100

;0.0

0)

Appendix (A)

108

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

53

Wha

t typ

e of

info

rmat

ion/

know

ledg

e do

es y

our

com

pan

y (n

ame

was

di

spla

yed)

typi

cally

gen

erat

e in

a p

roje

ct?

Mul

tiple

num

eric

al in

put

(slid

er)

rath

er te

chno

logi

cal

info

rmat

ion/

know

ledg

e /

rath

er m

arke

t/cus

tom

er

orie

nted

in

form

atio

n/kn

owle

dge

met

rical

(0

-100

;0.0

0)

54

To su

m u

p, h

ow w

ould

you

des

crib

e th

e co

mpo

sitio

n of

you

r par

tici

pan

t p

ool (

nam

e w

as d

ispl

ayed

)

Mul

tiple

num

eric

al in

put

(slid

er)

rath

er h

omog

eneo

us /

rath

er h

eter

ogen

eous

met

rical

(0

-100

;0.0

0)

55

To su

m u

p, h

ow w

ould

you

des

crib

e th

e co

mpo

sitio

n yo

ur se

lect

ed p

arti

cip

ant

grou

p (n

ame

was

dis

play

ed)

Mul

tiple

num

eric

al in

put

(slid

er)

rath

er h

omog

eneo

us /

rath

er h

eter

ogen

eous

met

rical

(0

-100

;0.0

0)

56

Whi

ch a

ppro

ach

does

you

r co

mp

any

(nam

e w

as d

ispl

ayed

) fo

llow

in a

typi

cal p

roje

ct fo

r ac

hiev

ing

proj

ect o

bjec

tives

?

Mul

tiple

num

eric

al in

put

(slid

er)

We

rath

er C

ALL

for

indi

vidu

als j

oini

ng

proj

ect t

o so

lve

task

s/re

ques

ts. /

W

e ra

ther

SEA

RC

H fo

r in

form

atio

n/in

divi

dual

s jo

inin

g pr

ojec

t to

solv

e ta

sks/

requ

ests

.

met

rical

(0

-100

;0.0

0)

Appendix (A)

109

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

57

You

are

in th

e se

rvic

e ca

tego

ry: (

met

hod

nam

e w

as d

ispl

ayed

) W

ould

you

agr

ee to

the

follo

win

g st

atem

ent:

With

in a

pro

ject

we

inte

ract

(in

term

s of

activ

ities

) at l

east

with

one

oth

er p

arty

(c

lient

or p

artic

ipan

t poo

l/gro

up).

Y

es/N

o bi

nary

58

On

aver

age,

can

you

thin

k of

any

inte

ract

ion

that

all

thre

e pa

rties

(y

our

com

pan

y (n

ame

was

dis

play

ed),

your

clie

nt, a

nd y

our p

arti

cip

ant

poo

l/gr

oup

) joi

n at

the

sam

e tim

e?

Plea

se d

ecid

e fo

r eac

h pr

ojec

t pha

se!

(ans

wer

rel

ated

to in

tera

ctio

n re

lati

on)

(row

) i5

=int

erac

tion

betw

een

all t

hree

par

ties

(col

umn)

pr

ojec

t ini

tiatio

n pr

ojec

t exe

cutio

n pr

ojec

t fin

aliz

atio

n do

es n

ot a

pply

Arr

ays (

Num

eric

al)

bina

ry

59

Plea

se d

ecid

e fo

r the

follo

win

g pa

irs in

w

hich

pro

ject

pha

se th

ey p

oten

tially

co

llabo

rate

!

(ans

wer

rel

ated

to in

tera

ctio

n re

lati

on)

(row

) i1

=int

erac

tion

betw

een

your

com

pany

(nam

e w

as d

ispl

ayed

) and

you

r clie

nt

i2=i

nter

actio

n be

twee

n yo

ur c

ompa

ny (n

ame

was

dis

play

ed) a

nd y

our p

artic

ipan

t po

ol/g

roup

i3

=int

erac

tion

betw

een

your

clie

nt a

nd y

our

parti

cipa

nt p

ool/g

roup

(nam

e w

as d

ispl

ayed

) i4

=int

erac

tion

betw

een

mem

bers

of y

our

Arr

ays (

Num

eric

al)

bina

ry

Appendix (A)

110

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

pa

rtici

pant

poo

l/gro

up (n

ame

was

dis

play

ed)

(col

umn)

pr

ojec

t ini

tiatio

n pr

ojec

t exe

cutio

n pr

ojec

t fin

aliz

atio

n do

es n

ot a

pply

60

Dur

ing

a ty

pica

l pro

ject

, how

wou

ld y

ou

desc

ribe

the

inte

nsity

of

inte

ract

ion/

com

mun

icat

ion

betw

een

the

follo

win

g pa

rties

? N

ote

: fre

quen

cy o

f in

tera

ctio

n =

tota

l nu

mbe

r of i

nfor

mat

ion

exch

ange

(pho

ne

calls

, em

ail,

mee

ting,

con

fere

nce,

blo

g et

c.)

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

i1=i

nter

actio

n be

twee

n yo

ur c

ompa

ny (n

ame

was

dis

play

ed) a

nd y

our c

lient

i2

=int

erac

tion

betw

een

your

com

pany

(nam

e w

as d

ispl

ayed

) and

you

r par

ticip

ant

pool

/gro

up

i3=i

nter

actio

n be

twee

n yo

ur c

lient

and

you

r pa

rtici

pant

poo

l/gro

up (n

ame

was

dis

play

ed)

i4=i

nter

actio

n be

twee

n m

embe

rs o

f you

r pa

rtici

pant

poo

l/gro

up (n

ame

was

dis

play

ed)

Mul

tiple

num

eric

al in

put

(slid

er)

very

low

freq

uenc

y /

very

hig

h fr

eque

ncy

met

rical

(0

-100

;0.0

0)

Appendix (A)

111

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

61

In a

vera

ge w

hat i

s the

inte

ract

ion

form

at fo

r co

mm

unic

atio

n be

twee

n th

e pa

rtici

patin

g pa

rties

?

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

(row

) i1

=int

erac

tion

betw

een

your

com

pany

(nam

e w

as d

ispl

ayed

) and

you

r clie

nt

i2=i

nter

actio

n be

twee

n yo

ur c

ompa

ny (n

ame

was

dis

play

ed) a

nd y

our p

artic

ipan

t po

ol/g

roup

i3

=int

erac

tion

betw

een

your

clie

nt a

nd y

our

parti

cipa

nt p

ool/g

roup

(nam

e w

as d

ispl

ayed

) i4

=int

erac

tion

betw

een

mem

bers

of y

our

parti

cipa

nt p

ool/g

roup

(nam

e w

as d

ispl

ayed

) (c

olum

n)

they

inte

ract

one

-to-o

ne

they

inte

ract

in sm

all g

roup

s th

ey a

ll in

tera

ct w

ith e

ach

othe

r

Arr

ays (

Num

eric

al)

nom

inal

62

Wha

t is t

he d

irect

ion

of c

omm

unic

atio

n be

twee

n th

e pa

rtici

patin

g pa

rties

?

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

(row

) i1

=int

erac

tion

betw

een

your

com

pany

(nam

e w

as d

ispl

ayed

) and

you

r clie

nt

i2=i

nter

actio

n be

twee

n yo

ur c

ompa

ny (n

ame

was

dis

play

ed) a

nd y

our p

artic

ipan

t po

ol/g

roup

i3

=int

erac

tion

betw

een

your

clie

nt a

nd y

our

parti

cipa

nt p

ool/g

roup

(nam

e w

as d

ispl

ayed

) i4

=int

erac

tion

betw

een

mem

bers

of y

our

Arr

ays (

Num

eric

al)

nom

inal

Appendix (A)

112

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

pa

rtici

pant

poo

l/gro

up (n

ame

was

dis

play

ed)

(col

umn)

un

idire

ctio

nal

bidi

rect

iona

l del

ayed

bi

dire

ctio

nal r

eal-t

ime

63

To w

hat e

xten

t are

the

follo

win

g to

pics

ob

ject

of c

omm

unic

atio

n be

twee

n th

e th

ree

parti

es?

Plea

se d

ecid

e fo

r eac

h co

mbi

natio

n!

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

i1=i

nter

actio

n be

twee

n yo

ur c

ompa

ny (n

ame

was

dis

play

ed) a

nd y

our c

lient

i2

=int

erac

tion

betw

een

your

com

pany

(nam

e w

as d

ispl

ayed

) and

you

r par

ticip

ant

pool

/gro

up

i3=i

nter

actio

n be

twee

n yo

ur c

lient

and

you

r pa

rtici

pant

poo

l/gro

up (n

ame

was

dis

play

ed)

i4=i

nter

actio

n be

twee

n m

embe

rs o

f you

r pa

rtici

pant

poo

l/gro

up (n

ame

was

dis

play

ed)

Join

t pro

blem

solv

ing

Off

er su

ppor

t/ans

wer

que

stio

ns

Prov

ide

feed

back

Pr

ovid

e ev

alua

tion

Dis

cuss

and

agr

ee o

n ge

nera

l pr

ojec

t iss

ues

Dis

cuss

pro

ject

spec

ifica

tions

D

iscu

ss le

gal a

spec

ts

Mul

tiple

num

eric

al in

put

(slid

er)

hard

ly e

ver /

ver

y fr

eque

nt

met

rical

(0

-100

;0.0

0)

Appendix (A)

113

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

In th

e fo

llow

ing

we

pres

ent a

ctiv

itie

s to

you

whi

ch c

an b

e pe

rfor

med

wit

hin

a pr

ojec

t. P

leas

e de

cide

from

you

r pe

rspe

ctiv

e fo

r ea

ch p

arty

(yo

ur c

ompa

ny (n

ame

was

dis

play

ed),

you

r cl

ient

, and

you

r pa

rtic

ipan

t poo

l/gro

up

(nam

e w

as d

ispl

ayed

)) th

e co

ordi

nati

on e

ffor

t and

mon

itor

ing

effo

rt fo

r th

ose

acti

viti

es.

We

wil

l arr

ange

the

acti

viti

es a

long

the

thre

e pr

ojec

t pha

ses

you

alre

ady

know

: pr

ojec

t ini

tiat

ion,

pro

ject

exe

cuti

on, a

nd p

roje

ct

fina

liza

tion

We

are

star

ting

wit

h P

hase

1, p

roje

ct in

itia

tion

64

The

follo

win

g ac

tiviti

es c

an b

e pe

rfor

med

in

Phas

e 1,

pro

ject

initi

atio

n, w

hen

the

gene

ral

scop

e of

the

proj

ect i

s bei

ng o

utlin

ed.

Plea

se d

ecid

e fo

r the

se a

ctiv

ities

if th

ey a

re

rele

vant

to y

ou in

an

aver

age

proj

ect o

f the

se

rvic

e ca

tego

ry: (

met

hod

nam

e w

as

disp

laye

d)

Act

ivity

1 F

orm

ulat

ing

the

task

/pro

blem

st

atem

ent

Act

ivity

2 D

efin

ing

the

form

al ru

les o

f pa

rtici

patio

n (e

.g.,

IP ru

les,

term

s, et

c.)

Arr

ays

rele

vant

vs.

not r

elev

ant

nom

inal

if

parti

cipa

nt g

roup

sele

cted

(nam

e w

as

disp

laye

d)

Act

ivity

3 D

efin

ing

the

crite

ria h

ow to

recr

uit

exte

rnal

par

ticip

ants

to sp

ecifi

c pr

ojec

ts

Arr

ays

rele

vant

vs.

not r

elev

ant

nom

inal

P

leas

e ra

te fo

r th

e in

volv

ed p

arti

es th

e ef

fort

that

is p

ut in

coo

rdin

atin

g ac

tivi

ties

(e.

g., t

ask

assi

gnin

g, p

lann

ing

task

s) a

nd th

e ef

fort

to

mon

itor

ach

ieve

men

t of o

bjec

tive

s (e

.g.,

com

pare

pla

nned

and

rea

lize

d pe

rfor

man

ce, t

ime

keep

ing)

!

Appendix (A)

114

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

65

Act

ivity

1 F

orm

ulat

ing

the

task

/pro

blem

st

atem

ent

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

Mul

tiple

num

eric

al in

put

(slid

er)

i1=c

oord

inat

ion

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur c

lient

i2

=coo

rdin

atio

n yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

pa

rtici

pant

poo

l/gro

up

(nam

e w

as d

ispl

ayed

) i3

=coo

rdin

atio

n yo

ur

clie

nt /

your

par

ticip

ant

pool

/gro

up (n

ame

was

di

spla

yed)

i1

=mon

itorin

g yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

clie

nt

i2=m

onito

ring

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur

parti

cipa

nt p

ool/g

roup

(n

ame

was

dis

play

ed)

i3=m

onito

ring

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

met

rical

(0

-100

;0.0

0)

Appendix (A)

115

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

66

Act

ivity

2 D

efin

ing

the

form

al ru

les o

f pa

rtici

patio

n (e

.g.,

IP ru

les,

term

s, et

c.)

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

Mul

tiple

num

eric

al in

put

(slid

er)

i1=c

oord

inat

ion

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur c

lient

i2

=coo

rdin

atio

n yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

pa

rtici

pant

poo

l/gro

up

(nam

e w

as d

ispl

ayed

) i3

=coo

rdin

atio

n yo

ur

clie

nt /

your

par

ticip

ant

pool

/gro

up (n

ame

was

di

spla

yed)

i1

=mon

itorin

g yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

clie

nt

i2=m

onito

ring

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur

parti

cipa

nt p

ool/g

roup

(n

ame

was

dis

play

ed)

i3=m

onito

ring

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

met

rical

(0

-100

;0.0

0)

Appendix (A)

116

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

67

Act

ivity

3 D

efin

ing

the

crite

ria h

ow to

re

crui

t ext

erna

l par

ticip

ants

to sp

ecifi

c pr

ojec

ts

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

Mul

tiple

num

eric

al in

put

(slid

er)

i1=c

oord

inat

ion

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur c

lient

i2

=coo

rdin

atio

n yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

pa

rtici

pant

poo

l/gro

up

(nam

e w

as d

ispl

ayed

) i3

=coo

rdin

atio

n yo

ur

clie

nt /

your

par

ticip

ant

pool

/gro

up (n

ame

was

di

spla

yed)

i1

=mon

itorin

g yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

clie

nt

i2=m

onito

ring

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur

parti

cipa

nt p

ool/g

roup

(n

ame

was

dis

play

ed)

i3=m

onito

ring

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

met

rical

(0

-100

;0.0

0)

Appendix (A)

117

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

Y

ou a

re n

ow in

Pha

se 2

, pro

ject

exe

cuti

on

68

The

follo

win

g ac

tiviti

es c

an b

e pe

rfor

med

in

Phas

e 2,

pro

ject

exe

cutio

n, w

hen

the

actu

al

cont

ent i

s bei

ng g

ener

ated

. Pl

ease

dec

ide

for t

hese

act

iviti

es if

they

are

re

leva

nt to

you

in a

n av

erag

e pr

ojec

t of t

he

serv

ice

cate

gory

: (m

etho

d na

me

was

di

spla

yed)

Act

ivity

1 D

istri

butin

g pr

oble

m/ta

sk to

you

r pa

rtici

pant

poo

l/gro

up

Act

ivity

2 G

ener

atin

g th

e co

ntrib

utio

n (s

olut

ions

/idea

s) e

tc.

Act

ivity

3 Im

prov

ing,

iter

atin

g or

com

men

ting

on so

lutio

ns o

r ide

as

Arr

ays

rele

vant

vs.

not r

elev

ant

nom

inal

69

Act

ivity

1 D

istri

butin

g pr

oble

m/ta

sk to

you

r pa

rtici

pant

poo

l/gro

up

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

Mul

tiple

num

eric

al in

put

(slid

er)

i1=c

oord

inat

ion

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur c

lient

i2

=coo

rdin

atio

n yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

pa

rtici

pant

poo

l/gro

up

(nam

e w

as d

ispl

ayed

) i3

=coo

rdin

atio

n yo

ur

clie

nt /

your

par

ticip

ant

pool

/gro

up (n

ame

was

di

spla

yed)

i1

=mon

itorin

g yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

clie

nt

i2=m

onito

ring

your

met

rical

(0

-100

;0.0

0)

Appendix (A)

118

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

com

pany

(nam

e w

as

disp

laye

d) /

your

pa

rtici

pant

poo

l/gro

up

(nam

e w

as d

ispl

ayed

) i3

=mon

itorin

g yo

ur

clie

nt /

your

par

ticip

ant

pool

/gro

up (n

ame

was

di

spla

yed)

70

Act

ivity

2 G

ener

atin

g th

e co

ntrib

utio

n (s

olut

ions

/idea

s) e

tc.

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

Mul

tiple

num

eric

al in

put

(slid

er)

1=co

ordi

natio

n yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

clie

nt

i2=c

oord

inat

ion

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur

parti

cipa

nt p

ool/g

roup

(n

ame

was

dis

play

ed)

i3=c

oord

inat

ion

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

i1=m

onito

ring

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur c

lient

i2

=mon

itorin

g yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

pa

rtici

pant

poo

l/gro

up

met

rical

(0

-100

;0.0

0)

Appendix (A)

119

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

71

Act

ivity

3 Im

prov

ing,

iter

atin

g or

co

mm

entin

g on

solu

tions

or i

deas

(a

nsw

er r

elat

ed to

pri

or in

dica

ted

inte

ract

ion

rela

tion

)

Mul

tiple

num

eric

al in

put

(slid

er)

1=co

ordi

natio

n yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

clie

nt

i2=c

oord

inat

ion

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur

parti

cipa

nt p

ool/g

roup

(n

ame

was

dis

play

ed)

i3=c

oord

inat

ion

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

i1=m

onito

ring

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur c

lient

i2

=mon

itorin

g yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

pa

rtici

pant

poo

l/gro

up

(nam

e w

as d

ispl

ayed

) i3

=mon

itorin

g yo

ur

clie

nt /

your

par

ticip

ant

pool

/gro

up (n

ame

was

di

spla

yed)

met

rical

(0

-100

;0.0

0)

Y

ou a

re n

ow in

Pha

se 3

, pro

ject

fina

liza

tion

Appendix (A)

120

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

72

The

follo

win

g ac

tiviti

es c

an b

e pe

rfor

med

in

Phas

e 3,

p

roje

ct fi

naliz

atio

n, w

hen

the

proj

ect

outc

ome

is b

eing

sele

cted

and

eva

luat

ed, a

nd

trans

ferr

ed to

the

clie

nt's

com

pany

. Pl

ease

dec

ide

for t

hese

act

iviti

es if

they

are

re

leva

nt to

you

in a

n av

erag

e pr

ojec

t of t

he

serv

ice

cate

gory

: (m

etho

d na

me

was

di

spla

yed)

Act

ivity

1 E

valu

atin

g th

e co

ntrib

utio

ns

(sol

utio

ns/id

eas)

A

ctiv

ity 2

Sel

ectin

g so

lutio

ns/id

eas

Act

ivity

3 T

rans

ferr

ing

and/

or im

plem

entin

g so

lutio

ns/id

eas i

n ou

r clie

nt's

com

pany

Arr

ays

rele

vant

vs.

not r

elev

ant

nom

inal

73

Act

ivity

1 E

valu

atin

g th

e co

ntrib

utio

ns

(sol

utio

ns/id

eas)

(a

nsw

er r

elat

ed to

pri

or in

dica

ted

inte

ract

ion

rela

tion

)

Mul

tiple

num

eric

al in

put

(slid

er)

1=co

ordi

natio

n yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

clie

nt

i2=c

oord

inat

ion

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur

parti

cipa

nt p

ool/g

roup

(n

ame

was

dis

play

ed)

i3=c

oord

inat

ion

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

i1=m

onito

ring

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur c

lient

i2

=mon

itorin

g yo

ur

met

rical

(0

-100

;0.0

0)

Appendix (A)

121

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur

parti

cipa

nt p

ool/g

roup

(n

ame

was

dis

play

ed)

i3=m

onito

ring

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

74

Act

ivity

2 S

elec

ting

solu

tions

/idea

s (a

nsw

er r

elat

ed to

pri

or in

dica

ted

inte

ract

ion

rela

tion

)

Mul

tiple

num

eric

al in

put

(slid

er)

1=co

ordi

natio

n yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

clie

nt

i2=c

oord

inat

ion

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur

parti

cipa

nt p

ool/g

roup

(n

ame

was

dis

play

ed)

i3=c

oord

inat

ion

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

i1=m

onito

ring

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur c

lient

i2

=mon

itorin

g yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

pa

rtici

pant

poo

l/gro

up

met

rical

(0

-100

;0.0

0)

Appendix (A)

122

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

(n

ame

was

dis

play

ed)

i3=m

onito

ring

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

75

Act

ivity

3 T

rans

ferr

ing

and/

or im

plem

entin

g so

lutio

ns/id

eas i

n ou

r clie

nt's

com

pany

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

Mul

tiple

num

eric

al in

put

(slid

er)

1=co

ordi

natio

n yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

clie

nt

i2=c

oord

inat

ion

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur

parti

cipa

nt p

ool/g

roup

(n

ame

was

dis

play

ed)

i3=c

oord

inat

ion

your

cl

ient

/ yo

ur p

artic

ipan

t po

ol/g

roup

(nam

e w

as

disp

laye

d)

i1=m

onito

ring

your

co

mpa

ny (n

ame

was

di

spla

yed)

/ yo

ur c

lient

i2

=mon

itorin

g yo

ur

com

pany

(nam

e w

as

disp

laye

d) /

your

pa

rtici

pant

poo

l/gro

up

(nam

e w

as d

ispl

ayed

) i3

=mon

itorin

g yo

ur

clie

nt /

your

par

ticip

ant

met

rical

(0

-100

;0.0

0)

Appendix (A)

123

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

po

ol/g

roup

(nam

e w

as

disp

laye

d)

76

For t

he p

artic

ipat

ing

parti

es p

leas

e de

cide

ho

w th

ey a

re in

volv

ed in

sele

ctin

g an

d ev

alua

ting

cont

ribut

ions

mad

e by

pa

rtici

pant

s?

Plea

se d

iffer

entia

te b

etw

een

join

tly v

s. in

depe

nden

tly p

erfo

rmed

act

iviti

es.

(ans

wer

rel

ated

to p

rior

indi

cate

d in

tera

ctio

n re

lati

on)

i1=i

nter

actio

n be

twee

n yo

ur c

ompa

ny (n

ame

was

dis

play

ed) a

nd y

our c

lient

i2

=int

erac

tion

betw

een

your

com

pany

(nam

e w

as d

ispl

ayed

) and

you

r par

ticip

ant

pool

/gro

up

i3=i

nter

actio

n be

twee

n yo

ur c

lient

and

you

r pa

rtici

pant

poo

l/gro

up (n

ame

was

dis

play

ed)

i4=i

nter

actio

n be

twee

n m

embe

rs o

f you

r pa

rtici

pant

poo

l/gro

up (n

ame

was

dis

play

ed)

Mul

tiple

cho

ice

jo

intly

vs.

Inde

pend

ently

bina

ry

77

Plea

se ra

te th

e de

gree

of s

tand

ardi

zatio

n fo

r yo

ur p

roce

ss o

f sel

ectin

g an

d ev

alua

ting

cont

ribut

ions

dur

ing

a pr

ojec

t!

Mul

tiple

num

eric

al in

put

(slid

er)

non-

stan

dard

ized

/ fu

lly

stan

dard

ized

met

rical

(0

-100

;0.0

0)

78

How

ofte

n do

es y

our

com

pan

y (n

ame

was

di

spla

yed)

use

the

follo

win

g m

echa

nism

s for

se

lect

ing

and

eval

uatin

g

cont

ribut

ions

?

sele

ctio

n an

d ev

alua

tion

happ

ens b

y us

ing

certa

in c

riter

ia

parti

cipa

nt-b

ased

sele

ctio

n an

d ev

alua

tion

sele

ctio

n an

d ev

alua

tion

happ

ens t

hrou

gh a

pr

edic

tion

mar

ket:

Mul

tiple

num

eric

al in

put

(slid

er)

hard

ly e

ver /

ver

y fr

eque

nt

met

rical

(0

-100

;0.0

0)

79

IF y

ou u

se p

re-d

efin

ed se

lect

ion

and

eval

uatio

n cr

iteria

ple

ase

nam

e a

few

!

Long

free

text

Appendix (A)

124

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

80

Plea

se ra

te th

e ef

fort

to m

onito

r act

iviti

es o

f go

al a

chie

vem

ent y

our

com

pan

y (n

ame

was

di

spla

yed)

has

dur

ing

...

proj

ect i

nitia

tion

(pla

nnin

g)

proj

ect e

xecu

tion

(run

ning

) pr

ojec

t fin

aliz

atio

n (e

valu

atin

g, tr

ansf

errin

g)

Mul

tiple

num

eric

al in

put

(slid

er)

very

low

/ ve

ry h

igh

met

rical

(0

-100

;0.0

0)

81

Plea

se ra

te th

e ef

fort

to m

onito

r act

iviti

es o

f go

al a

chie

vem

ent y

our

com

pan

y (n

ame

was

di

spla

yed)

has

with

you

r clie

nt d

urin

g ...

proj

ect i

nitia

tion

(pla

nnin

g)

proj

ect e

xecu

tion

(run

ning

) pr

ojec

t fin

aliz

atio

n (e

valu

atin

g, t

rans

ferr

ing)

Mul

tiple

num

eric

al in

put

(slid

er)

very

low

/ ve

ry h

igh

met

rical

(0

-100

;0.0

0)

82

... a

nd w

ith y

our p

arti

cip

ant

poo

l/gr

oup

(n

ame

was

dis

play

ed)

proj

ect i

nitia

tion

(pla

nnin

g)

proj

ect e

xecu

tion

(run

ning

) pr

ojec

t fin

aliz

atio

n (e

valu

atin

g, tr

ansf

errin

g)

Mul

tiple

num

eric

al in

put

(slid

er)

very

low

/ ve

ry h

igh

met

rical

(0

-100

;0.0

0)

83

Whe

n do

you

con

side

r a c

lient

's pr

ojec

t ob

ject

ive(

s) a

chie

ved?

M

ultip

le a

nsw

ers a

re p

ossi

ble

Whe

n ra

w so

lutio

ns/id

eas/

conc

epts

are

tra

nsfe

rred

into

our

clie

nt's

com

pany

. W

hen

soph

istic

ated

con

cept

s are

tran

sfer

red

into

our

clie

nt's

com

pany

. W

hen

prot

otyp

es a

re tr

ansf

erre

d in

to o

ur

clie

nt's

com

pany

. W

hen

a fo

rmal

con

tract

bet

wee

n ou

r clie

nt a

nd

mem

ber(

s) o

f par

ticip

ant p

ool/g

roup

is

final

ized

. W

hen

IPs a

re b

eing

tran

sfer

red.

Arr

ays (

Num

eric

al)

yes /

no

nom

inal

Appendix (A)

125

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

84

To w

hat e

xten

d w

ould

you

agr

ee to

the

follo

win

g st

atem

ent.

Con

cern

ing

the

qual

ity o

f the

pro

ject

ou

tcom

e, a

ll pa

rtici

patin

g pa

rties

mus

t m

utua

lly u

nder

stan

d an

d ag

ree

the

gene

ral

code

of c

ondu

ct.

Mul

tiple

num

eric

al in

put

(slid

er)

stro

ngly

agr

ee /

stro

ngly

di

sagr

ee

met

rical

(0

-100

;0.0

0)

85

I wou

ld li

ke to

con

tinue

the

surv

ey w

ith o

ur

seco

nd/th

ird/fo

urth

impo

rtant

serv

ice

cate

gory

.

Yes

/No

86

Plea

se su

mm

ariz

e yo

ur

com

pan

y's (

nam

e w

as d

ispl

ayed

) pro

file

in W

ikiS

tyle

re

gard

ing

follo

win

g po

ints

: bus

ines

s mod

el,

serv

ice,

and

act

iviti

es su

ppor

ting

clie

nts'

proc

esse

s. Th

e de

scrip

tion

of y

our

com

pan

y's (

nam

e w

as d

ispl

ayed

) pro

file

is in

tend

ed to

be

publ

ishe

d in

the

final

repo

rt. (u

p to

600

ch

arac

ters

)

Lo

ng fr

ee te

xt

Appendix (A)

126

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

F

B S

oftw

are

87

Plea

se se

lect

from

the

list b

elow

feat

ures

yo

ur so

ftwar

e/w

eb to

ol o

ffer

s in

the

phas

e of

id

ea g

ener

atio

n!

Tool

to p

rom

ote

new

task

s K

ey w

ord

depe

ndin

g di

spla

y of

idea

s in

the

syst

em

Cla

ssifi

catio

n of

con

tribu

tions

mad

e by

use

rs

(tags

) Te

mpo

raril

y st

orag

e of

idea

s bef

ore

publ

ishi

ng U

ploa

d fu

nctio

n fo

r add

ition

al m

ater

ial

(gra

phic

s, do

cum

ents

etc

.) To

ol fo

r col

labo

rativ

e id

ea g

ener

atio

n In

tegr

atio

n of

ext

erna

l use

rs

Dis

play

idea

stat

us (r

aw id

ea, d

etai

led

idea

, id

ea c

once

pt e

tc.)

Dis

play

act

iviti

es o

n id

eas (

num

ber o

f hits

, ra

nkin

g, c

omm

ents

etc

.) In

terf

ace

to o

ther

(mob

ile) e

lect

roni

cal d

evic

es

Mul

tiple

cho

ice

nom

inal

Appendix (A)

127

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

88

Plea

se se

lect

from

the

list b

elow

so

ftwar

e/w

eb fe

atur

es re

gard

ing

the

phas

e of

id

ea e

valu

atio

n!

Idea

mat

chin

g/id

entif

icat

ion

of si

mila

r ide

as

(dat

a m

inin

g, O

CR

) Id

ea e

valu

atio

n sy

stem

(rat

ing,

like

vs.

disl

ike,

et

c.)

Indi

vidu

al d

efin

ition

of s

elec

tion

crite

ria

Indi

vidu

al d

efin

ition

of s

elec

tion

proc

ess

Com

men

ting

func

tion

Hit

list o

f ide

as a

nd u

sers

In

tegr

atio

n of

ext

erna

l eva

luat

ors

Supp

ort o

f gro

up e

valu

atio

n

Mul

tiple

cho

ice

nom

inal

9 Fo

r the

pha

se o

f ide

a se

lect

ion,

ple

ase

sele

ct

from

the

list b

elow

feat

ures

you

r sof

twar

e/

web

tool

off

ers!

Idea

cat

egor

izat

ion

by c

riter

ia

Ran

king

list

s Ev

alua

tion

stat

istic

s C

onfig

urat

ion

of e

valu

atio

n st

atis

tics

Mul

tiple

cho

ice

nom

inal

Appendix (A)

128

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

90

In th

e fo

llow

ing,

ple

ase

indi

cate

whi

ch

gene

ral f

eatu

re y

our t

ool p

rovi

des?

Rol

e ba

sed

user

and

secu

rity

conc

epts

(use

r, m

oder

ator

, adm

in e

tc.)

Dou

ble

role

ass

ignm

ent (

e.g.

mod

erat

or a

nd

user

) In

tegr

atio

n of

ext

erna

l use

rs (c

row

dsou

rcin

g)

Gra

phic

al p

rese

ntat

ion

of u

nder

lyin

g pr

oces

s st

ruct

ure

(idea

/inno

vatio

n m

anag

emen

t pr

oces

s etc

.) In

tegr

ated

stag

e-ga

te p

roje

ct m

anag

ing

proc

ess

Exis

tenc

e of

an

idea

dat

a ba

se (a

rchi

ve)

Tim

etab

le/c

alen

dar f

unct

ion

(sch

edul

ing,

co

mm

unic

atin

g ev

ents

) LO

G fu

nctio

n C

usto

miz

able

star

ting

page

(fitt

ing

corp

orat

e de

sign

etc

.) H

isto

ry o

f ide

a ge

nera

tion

Mul

tiple

cho

ice

nom

inal

91

Whi

ch la

ngua

ge(s

) pro

vide

s the

use

r in

terf

ace?

Shor

t fre

e te

xt

Appendix (A)

129

No

Item

A

nsw

er o

pti

ons

An

swer

typ

e S

cale

92

Wha

t fea

ture

s of c

omm

unic

atio

n do

es y

our

softw

are/

web

solu

tion

offe

r?

Gen

eral

com

mun

icat

ion

tool

s lik

e em

ail

Furth

er c

onne

ctio

n to

oth

er c

omm

unic

atio

n ch

anne

ls li

ke tw

itter

, Fac

eboo

k et

c.

Not

ifica

tion

optio

ns (w

hen

new

ent

ries,

chan

ges e

tc.)

Bui

ldin

g co

mm

uniti

es, p

anel

s, di

scus

sion

fo

rum

s

Mul

tiple

cho

ice

nom

inal

93

How

doe

s you

r co

mp

any

(nam

e w

as

disp

laye

d) a

rran

ge it

s sup

port

serv

ice?

(mat

rix)

(r

ow)

Perm

anen

t (24

/7)

Dur

ing

regu

lar w

orki

ng h

ours

(8/5

) (c

olum

n)

Tele

phon

e/ho

tline

supp

ort

Onl

ine

supp

ort

Face

-to-f

ace

supp

ort

Arr

ays

nom

inal

94

Wha

t kin

d of

cos

ts g

o al

ong

with

you

r se

rvic

e?

Lice

nsin

g co

sts (

rang

e) in

€

Impl

emen

ting

cost

s (pe

rson

day

s, in

per

cent

of

licen

se e

tc.)

in €

M

aint

enan

ce c

osts

(per

son

days

, in

perc

ent o

f lic

ense

etc

.) in

€

Add

ition

al c

osts

(per

son

days

, in

perc

ent o

f lic

ense

etc

.) in

€

Mul

tiple

cho

ice

with

te

xt fi

eld

nom

inal

Appendix (A)

130

Appendix (A)

Appendix (A)

Part B. Research papers

Study 1: Facets of open innovation: Development of a conceptual framework

Study 1 is an explorative study investigating operational modes of open innovation by survey-

ing open innovation intermediaries. The research aims to offer a conceptual framework for

understanding openness. This framework distinguishes between the various configuration

types of collaboration with different kinds of external actors. Study 1 deepens the understand-

ing of openness.

Submission intended to: International Journal of Technology Management

Study 2: Organizing collaboration: The costs of innovative search

Study 2 investigates the organization of innovation collaboration with a focus on procedural

differences in search activities. Utilizing a survey of innovation intermediaries, the costs of

collaboration and aspects of organizational structure are analyzed. Study 2 shows that the col-

laboration process needs to be organized differently depending on the search behavior.

Submission intended to: Management Science

Study 3: The market for intermediation for open innovation

Study 3 generates an overview of the recent market of intermediaries supporting open innova-

tion. A descriptive analysis sheds light on the collaboration process between a focal firm and

its external environment. Study 3 provides a basis for the better selection of a project partner

that ultimately supports the management of open innovation projects.

Submission intended to: R&D Management

Study 1: Facets of Open Innovation: Development of a Conceptual Framework

135


Abstract

The term "open innovation" is becoming a buzzword in the innovation management literature,

representing diverse understandings and interpretations. Given that the term represents a new

phenomenon, the heterogeneous conceptualizations of its construction prevent researchers

from aggregating and comparing existing studies. In this paper, we provide a contribution to

theory development by suggesting a conceptual framework for open innovation. We intend to

gain a deeper understanding of what openness means and where a meaningful difference be-

tween a new paradigm of open innovation and the classical perspective of innovation net-

works can be found. Based on an empirical study of firms that self-identify as conducting

practices of open innovation, we develop a framework that maps different approaches and

concepts of collaboration and knowledge transfer. We derive specifications of openness to

distinguish between various configuration types of collaboration with different kinds of exter-

nal actors. These configuration types differentiate ways of incorporating external input to

solve a given technical problem. We especially focus on ways to initiate the collaboration,

compose groups of problem solvers, and constitute interaction with the group. Our methodo-

logical approach is twofold. From a literature review, we gather measurements of "openness"

that have been used in previous studies of distributed innovation. In our own survey, we then

analyze 43 intermediaries offering open innovation. In this paper, we present the results of an

explorative analysis of this data to reveal eight dominant patterns of collaboration between a

firm and external actors in the innovation process.


136

1 Introduction

The objective of this paper is to map the landscape of research and practice in "open innova-

tion" and to suggest a conceptual framework to derive suggestions for its operationalization

and measurement in large empirical research. Previous research on open innovation empha-

sizes the notion of a component of interaction and active collaboration between diverse partic-

ipants within the innovation process. This interaction goes beyond a sheer transfer of infor-

mation from one source to a receiver (Piller and Walcher, 2006; von Hippel and von Krogh,

2006; Fredberg et al., 2008; Lichtenthaler and Ernst, 2006; Pisano and Verganti, 2008). It

resembles more a joint knowledge creation process which is often described as a ‘coupled

process’ of open innovation by some researchers (Gassmann and Enkel, 2004). We regard this

notion of collaboration and interaction to be a core characteristic of open innovation; conse-

quently it will become a lead element in the construction of our research model in this paper

(section 2.2). We follow a qualitative research approach which allows us to understand the

underlying internal logic of open innovation. We intend to isolate characteristics of what

openness means and where a meaningful difference can found between a new paradigm of

open innovation and the classical perspective of innovation cooperation. Finally, we suggest

an operational model which allows for a standardized investigation of the open innovation

phenomena on the basis of collaboration and interaction.

This research is urgently needed as the term "open innovation" has emerged as a major

buzzword in innovation management literature and practice, addressing a broad and diverse

set of understandings and interpretations. The term first emerged in an online discussion paper

by Clancy (1999) in the context of open source software development, but only became popu-

lar with the publication of Chesbrough’s book with the same title in 2003. Today, a Google

search lists more than 60 million pages using this term (as of April 2010). Even Google

Scholar quotes 2.48 million scientific papers which inclue the term open innovation. A search

in "Business Source Elite", a large bibliographic database in the field of management re-

search, lists 889 scholarly papers about the topic, with more than 60% of them having been

published in the last two years (as of November 2013). Yet open innovation is just one term

for a phenomenon that has been described much earlier in research on user innovation (as

summarized in von Hippel, 2005) and on Open Source Software Development (e.g. Raymond,

1999; Bergquist and Ljungberg, 2001; Lakhani and von Hippel, 2003; Lerner and Tirole,

2005). Together, this research provides a very heterogeneous picture that we intend to bring

greater clarity and cohesion to in this paper.


137

Nowadays, there is a common understanding that firms rarely innovate alone and that the in-

novation process can be seen as an interactive relationship among producers, users and many

other different institutions. Thus, innovative performance has become the ability of an innova-

tive organization to establish networks with external entities. The main effect of including

external information is to enlarge the base of information that can be utilized for the innova-

tion process (Lakhani, Jeppesen, Lohse and Panetta, 2006).

In a conventionally "closed" system of innovation, only knowledge in the domain of the man-

ufacturer is being used as creative input for the innovation process, a problem that has been

called the "local search bias" (Stuart and Podolny, 1996). In an innovation system that is more

open to external input, the knowledge of the firm is extended by the large base of information

about needs, applications, and solution technologies that reside in the domain of customers,

retailers, suppliers, and other external parties (Verona et al., 2006, p. 766). The interaction

with the external firm environment redefines its boundary, making the firm more porous and

embedded in loosely coupled networks of different actors, collectively and individually work-

ing toward commercializing new knowledge (Laursen and Salter, 2006). The fact that interac-

tion and cooperation matters for innovation is hardly a new fact. There is a broad literature on

networks and alliances for innovation (Hagedoorn, 1993; Hagedoorn and Schakenraad, 1993;

Kotabe and Swan, 1995; Harbison and Pekar, 1997; Narula and Hagedoorn, 1999; Hage-

doorn, 2002; Contractor and Lorange, 2002), but how does open innovation differ from this

older literature?

To answer this question, a general conceptualization of an open innovation construct is help-

ful. So far only a few reviews have tried to systemize the research field of open innovation.

Chesbrough et al. (2006), for example, mentioned five levels of analysis for open innovation:

the individual participant, the organization, the network, the industry, and national institu-

tions. However, these do not provide a detailed map of the existing literature and research

along this classification. Fredberg, Elmquist, and Ollila (2008), for example, offer a more

detailed literature review and conclude that the literature revolves around seven categories:

the notion that open innovation exists; business models for open innovation; literature on or-

ganizational design and boundaries of the firm; leadership and culture for open innovation;

tools and technologies enabling open innovation; IP, patenting and appropriation; and the ef-

fect of open innovation on industrial dynamics and manufacturing. They conclude that the

dimension of collaboration is a central aspect in describing the research landscape (p.36).


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In general, these review articles show that research on open innovation spans a wide field and

incorporates adjoining topics, such as user innovation (von Hippel, 1998); Baldwin et al.,

2006; von Hippel, 1998), open source software (West and Gallagher, 2006; Dahlander and

Magnusson 2008; von Hippel and von Krogh, 2003), co-creation (Prahalad and Ramaswamy,

2000, 2004; Zwass, 2010), and crowdsourcing (Howe, 2006). Despite differences in the indi-

vidual research perspective, we found a common understanding of what constitutes open in-

novation. Central is the innovating firm’s integration of internal and external knowledge

(Grant, 1996; Lichtenthaler, 2005; West and Gallagher, 2006; Lakhani et al., 2006; Boudreau

et al., 2008) to achieve more radical innovation (Chesbrough, 2003b, Chesbrough et al., 2006;

Laursen and Salter, 2006; Gassmann, 2006; Lettl et al., 2006). A common assumption is that

the knowledge needed to solve a related problem is regarded as widely distributed and diffi-

cult to locate (Hayek, 1945; Smith, 2000; Kogut, 2000; Lee and Cole, 2003; Chesbrough et

al., 2006; Lakhani et al., 2006). To access the knowledge, new forms of collaboration, in

terms of partner type and communication pattern, are applied (Piller and Walcher, 2006; Pisa-

no and Verganti, 2008).

To answer the question of what drives openness we develop a framework where we place the

collaboration used to transfer knowledge at the center of consideration. The resulting interac-

tion model forms the basis for our empirical study. In order to study expressions of that inter-

action model in the field of open innovation we surveyed and interviewed intermediaries. We

selected those which are called virtual knowledge brokers (VKBs, Verona et al., 2006), like

InnoCentive or Yet2.com, conducting practices of open innovation according to their own

account. We assume, since these intermediaries focused their business model on bridging

knowledge gaps by linking different parties through open innovation, that they have devel-

oped a specific approach to collaboration. Assigning activities of interaction intermediaries

perform to our model, allows us to map different approaches and concepts of open innovation

collaboration. These configuration types differentiate ways of incorporating external input to

solve a given innovation problems. To make this distinction between various configuration

types of collaboration we derive facets of openness.

Our paper contributes to the existing literature in various ways. First, we present a model that

integrates the heterogeneous understandings of the term "open innovation" found in the litera-

ture. This model provides a starting point to create a measure of openness in future research.

We propose that it is not the sheer interaction with external actors or the transfer of external

information into the innovation process which constitutes this collaboration, but the "how",


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i.e. the nature of the activities. Second, our research identifies different modes of practicing

open innovation, which can be represented by different configurations of our collaboration

model. Understanding these modes helps us to map the open innovation landscape. Third, our

qualitative study and the detailed analysis of 43 open innovation intermediaries provide a rich

and interesting insight into the recent practice of this phenomenon.

The remainder of this paper develops as follows. In the next section, we review the literature

and derive our conceptual model of collaboration that underlies our understanding of open

innovation and provides the structure for the discussion. Our research method and empirical

setting are then described and the steps of analysis explained. Finally, we present and discuss

our results, before offering a summary and outlook on future research questions in the con-

cluding paragraph.

2 Literature and conceptual model

2.1 Open innovation as a new form of collaboration

Recent reviews of open innovation have outlined several research themes, but the most com-

mon is the notion of collaboration as a constituting factor of openness. In their book,

Chesbrough et al. (2006, pp. 285-308) state five levels of investigating the phenomenon: (1)

the individual participant, (2) the organization, (3) the network, (4) the industry, and (5) na-

tional institutions. In light of collaboration, the authors ask for more research to be done on

business models that help to convert individual creativity into a commercially relevant inno-

vation (p. 289). Putting the dyadic level- in terms of examining the interaction between two

parties- into the center of consideration would allow for a better understanding of the collabo-

ration activities like search, negotiation and contractual implementation (p. 294). The authors

regard activities that concentrate on identifying potentially relevant external sources and their

effect on IP management as an especially important aspect of future research.

Fredberg et al. (2008) provided a detailed literature review and analyzed papers that were

published between 2003 and 2007 using the term open innovation. The authors structured

future research possibilities according to two dimensions: the locus of the innovation process

and the extent of collaboration (p. 36). In their opinion, these are the main two characteristics

“that distinguish open innovation processes from other innovation processes” (p. 36). They

argue that focusing on the locus of innovation would automatically lead the research effort to


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opening the ‘black box’ on how the innovation process happens, especially in terms of coor-

dinating collaboration. Fredberg and colleagues state a continuous collaboration extension

depending on the number of individuals participating in collaboration. A fruitful avenue for

future research is thought to be the investigation of how collective intelligence can be orga-

nized (p. 37). By applying a “firm’s process perspective” (p. 312), Enkel et al. (2009) orga-

nized their review of the recent literature on open innovation according to three core process-

es: the outside-in, inside-out, and coupled process. The coupled process in particular captures

the collaborative aspect of open innovation. The authors refer to this process as the phenome-

non of co-creation which is directed toward a joint development of innovation (p. 313). They

found that this process type attracted significant research attention in the past (p. 311), which

indicates that the underlying collaborative nature is a central characteristic of the new phe-

nomenon of open innovation.

Dahlander and Gann (2010) emphasize collaboration in their review since recent technologies

have opened up new ways to interact across large geographical distances (p. 699). They sum-

marize the literature that describes two directions of collaboration – outbound and inbound

innovation. For example, they cite Henkel’s (2006) work on the activity of firm’s selectively

revealing their own technology as a way to signal an interest in collaboration to a firm’s ex-

ternal environment (p. 703). Huizingh (2011), in his review of open innovation, explicitly

devotes an entire chapter to open innovation practices and the question of how to achieve

openness (p. 6). He regards the answers to questions like “When, how, with whom, with what

purpose, and in what way should [firms] cooperate with outside parties?” to be central man-

agement decisions. He concludes from the review that an integrated framework for describing

open innovation practices is still missing (p. 7). In summary, all reviews on open innovation

highlight its interest and the need for further research investigating how open innovation col-

laboration can be best described regarding parameters like partner selection, process organiza-

tion, knowledge creation etc.

For the next step we reviewed the broader open innovation literature, including literature on

user innovation, open source software, innovation search, etc., in varying detail according to

how far they had already tackled collaborative aspects. We found that literature on user inno-

vation and innovation search highlight the relevance of external information sources and their

characteristics. Several studies that had a particular focus on the nature of the external source

and its effect on innovative performance were of interest (Herstatt and Hippel, 1992; Morri-

son et al. 2000; Morrison et al., 2004; Luethje, 2004; von Hippel, 1988; Katila and Ahuja,


141

2002; Laursen and Salter, 2006). Other researchers regard the organizational set-up and the

structure of a firm in which collaboration is embedded as the key variable influencing open

innovation performance (Allen and Cohen, 1969; Tushman and Katz, 1980; Chesbrough,

2003b; Simard and West, 2006; Lichtenthaler and Ernst, 2006). However, we also found that

a constant theme in the existing literature on open innovation is the transfer of knowledge as a

core activity (Chesbrough, 2003a; von Hippel, 1994, 1998; Szulanski, 2003; Foss et al., 2005;

Lichtenthaler, 2006; Lettl et al., 2006). Researchers highlight that organizations need to re-

structure their process of exchanging knowledge on their periphery to a new focus on

knowledge acquisition, knowledge integration and knowledge exploitation (Lichtenthaler and

Ernst, 2006; Chesbrough and Crowther, 2006). Solving any innovation problem demands the

execution of these three activities. Exchanging knowledge simultaneously implies that the

organization has a practice of interacting with its environment. There is, for example, a very

rich body of open innovation literature describing different forms, activities, and intensities of

collaboration between the manufacturer and user (Baldwin et al., 2006; Lettl et al., 2008;

Lettl, 2007).

To conclude, our literature review provides evidence that the nature of collaboration forms the

core characteristic of open innovation. We found that recent open innovation research focuses

on single collaborative elements like features of the external source or mechanisms to profit

from external information. Less research, however, has been done on how this new form of

collaboration functions and there is currently a paucity of work considering how open innova-

tion takes place from the process perspective (Fredberg et al., 2008, p. 37). Identifying major

elements that describe open innovation collaboration would allow for the deduction of the

crucial functional principles that distinguish open innovation from traditional cooperation

concepts such as strategic alliances or networks. Pisano and Verganti (2008) call this set of

elements the collaborative architecture behind the relationship (p. 80) and stress two major

points which characterize this architecture: the access control of collaboration network (closed

vs. open) and the governance form (hierarchical vs. flat). They propose four types of collabo-

ration based on these two dimensions (p. 82): innovation mall (hierarchical, open), elite circle

(hierarchical, closed), innovation community (flat, open), and consortium (flat, closed). Con-

sidering the advantages and disadvantages of each collaboration type, the authors provide

managerial guidance for picking the right collaboration form in practice. For the purpose of

our paper we intend to follow an integrated approach that applies a process view of interaction

to understand how open innovation collaboration happens. By doing so we aim to derive the

main determinant of open innovation.


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2.2 Conceptual framework of open innovation collaboration

We develop a conceptual framework representing open innovation collaboration. The funda-

mental element of our framework is an understanding of collaboration between at least two

different parties in the innovation process – the organization and an actor from its entire ex-

ternal environment – as a precondition to create and transfer knowledge (Grant, 1996; Teece,

1998). Consequently, our model has to meet the following three major requirements:

1. The framework shall represent the process of knowledge exchange between an or-

ganization and its environment.

2. It integrates activities that support the knowledge exchange process in an interac-

tive and collaborative manner.

3. The knowledge exchange process is not limited to a single transmitter and a single

receiver, but is valid for diverse participating parties.

To model the first component, the knowledge transfer, we build on a three-step process model

by Krogh and Koehne (1998), who differentiate three major stages: initiation, knowledge

flow, and integration. The knowledge transfer process is designed to assure that the created

knowledge is disseminated. Von Krogh, Nonaka and Aben (2001, pp. 422-426) state that the

process starts with identifying the knowledge that is intended to be transferred. This happens

by signaling the benefit of the transfer to the potential receiver or sender. Consequently, the

receiver and transmitter assess the value of the transfer in calculating potential loss and gain.

To finally transfer the knowledge, it needs to be adequately packed and dispatched to the re-

ceiver. Only if this step is successful, the adaption and integration of new knowledge can take

place. The literature of knowledge management offers a variety of models describing the

knowledge process, often adopting the Shannon-Weaver model of communication (Shannon,

1948; Weaver, 1949). Recent models can be differentiated according to their focus of process

intention, e.g. knowledge driven models, push/pull knowledge models (Mahé and Rieu, 1998;

Hartlieb, 2002), knowledge transfer influencing factors, e.g. stickiness of knowledge (Szulan-

ski, 2000) during problem solving, motivation, or capabilities (Teece, 2007; Zahra and

George, 2002).

The second component of our conceptual model is collaboration. To conceptualize collabora-

tion, we refer to the frameworks proposed in literature. Pisano and Verganti (2008) describe

collaboration through the access control to the collaboration network (closed vs. open) and the

governance form (hierarchical vs. flat) (p. 82). Early literature on user innovation has already


143

clearly emphasized a collaborative component, for example von Hippel (1978) with the idea

of the customer-active paradigm where the customer is a valuable source for innovation and

firms profit from cooperating. Benkler (2002) has also coined the term ‘peer-production’,

which again presents an interaction model characterized by a novel way to coordinate division

of labor in an innovation project. This kind of self-determined collaboration among peers in

absence of specific market or hierarchy mechanism allows firms to fully benefit from their

human capital (p. 5).

For our conceptual framework we integrate both perspectives: knowledge transfer and collab-

oration. We combine the single stages of knowledge transfer (von Krogh and Koehne, 1998)

with respective activities to organize interaction between individuals (Pisano and Verganti,

2008; Benkler, 2002; von Hippel, 1978). For the understanding of our paper we therefore de-

fine open innovation collaboration as a process of knowledge transfer that contains a set of

activities which are targeted to identify and connect several knowledge sources and to distrib-

ute and share information among them. The process starts with the intention to transfer infor-

mation and ends with the utilization of it. An essential characteristic of the process is that the

stages are not necessarily consecutive (von Krogh and Koehne, 1998). Repetitions, as well as

jumping back to one of the earlier stages, are possible, so that knowledge transfer corresponds

to “an iterative process of exchange” (Szulanski, 1996, p. 28). Together, we suggest three

major steps to describe open innovation collaboration: (1) forming, (2) generating, and (3)

exploiting (see Figure (B1) 1).

This broad structure provides an outline for the entire interaction process of knowledge trans-

fer. The sheer fact that knowledge is transferred into an organization from its periphery is not

a distinct aspect of open innovation, but a well-known basic principle of every innovation

process. We propose that the consideration of "how" aspects of collaboration take place with-

in this knowledge transfer between the firm and external actors represents the differentia of

open innovation, (i.e. its distinguishing feature which marks it off from other forms of

knowledge transfer in the context of open innovation) (Jeppesen and Frederiksen, 2004;

Berkhout et al., 2006).


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Figure (B1) 1: Conceptual framework: A model of collaboration for open innovation

(1) Forming. During forming two central activities take place: the Initiation of the coopera-

tion and the Constitution of the actual project team. The Initiation activity comprises the defi-

nition of the collaboration objective (von Korgh and Koehne, 1998, p.239) and signaling col-

laboration interest to the external environment (Fontana et al., 2006, p. 312). The second ac-

tivity, Constitution, is how potential collaboration partners come to be realized partners for

interaction within a concrete open innovation project. Selecting the right partners is necessary

to profit from collaboration (Dyer and Singh, 1998, p. 661), which is executed by regulating

the access to the project team (Pisano and Verganti, 2008, p. 80).

(2) Generating. The second facet of our collaboration model focuses on how information is

processed to solve the innovation task or problem. It maps how existing knowledge is shared,

validated, and composed into new knowledge. The key element is the organization of collabo-

ration to create innovation relevant solutions (Prahalad and Ramaswamy, 2004, p. 6; Pisano

and Verganti, 2008, p. 80).

(3) Exploiting. Exploitation activities are necessary in order to finally profit from collabora-

tion. This phase maps the process of handling the outcome in terms of how a governance

mechanism is constructed to regulate the terms of utilizing the generated knowledge by the

(1) Forming

(2) Generating

(3) Exploiting

Kno

wle

dge

tran

sfer

Initiation

Constitution

Degree of integration

Exploitation


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participants for individual purposes. To adopt the results, firms have to overcome internal

resistance similar to the ‘not invented here’ (NIH) syndrome (Chesbrough and Crowther,

2006, p. 234).

One restriction of our model should be mentioned at this point. Although we do not intend to

perform correlation analysis within this study, we assume that the stages and their specifica-

tion are not independent from one another. However, such correlations among specifications

influence the number of potential observable collaboration patterns.

In the remaining sections of this paper, we will map out the realization of open innovation

strategies that we found in a large empirical sample of intermediaries which self- identified as

"doing open innovation". Applying our conceptual framework to describe their business mod-

el from the interactive perspective allows us to find specific configurations of the single stag-

es and representations that would correspond to one "mode" or "type" of open innovation.

Thus we propose:

Proposition 1

Different types of open innovation can be derived from preferred configu-

rations of the specifications of our collaboration model. A configuration

type represents a specific cluster of collaboration activities.

A second conclusion from the discussion which led to our framework refers to the organiza-

tion of collaboration between the participants. The more an organization intensifies its rela-

tionship with external actors, the more these external partners are integrated into the innova-

tion (value creation) process. In the strategic management literature, there is a stream that

looks at the re-design of value chains when external actors are integrating in the value chain

of an organization (Ramírez, 1999; Prahald and Ramaswamy, 2000, 2004). The authors sug-

gest a correlation between the governance structure and the degree of integrating collabora-

tion partners. For example, Ramírez states that low hierarchies foster co-production and inte-

gration (1999, p. 56). In Pisano’s and Verganti’s (2008) framework of collaboration, this kind

of governance plays an important role for the collaboration architecture. Thus, we assume that

the way the interaction of the collaboration partners is organized during the phase of generat-


146

ing content is associated with different degrees of integrating these partners. Consequently we

suggest:

Proposition 2

Specific types of open innovation differ with regard to the degree of inte-

gration of external actors in the innovation process. The degree of integra-

tion is associated with the form in which collaboration is organized.

In the next section we present our methodology and empirical context that helps us to answer

our propositions.

3 Research design

3.1 Methodology

In order to answer our propositions we adopted a mixed methods research approach which

combines qualitative and quantitative methods. Since our research is explorative in nature and

the complexity of the research objective cannot be fully understood at the point of investiga-

tion, a mixed method approach is recommended to answer our research question (Johnson and

Onwuegbuzie, 2004, p. 23; Bryman, 2006, pp. 105-106). For the first step, we used a ques-

tionnaire, informed by our framework, to gather basic information about open innovation col-

laboration in practice. Having received the response, a telephone interview was scheduled to

verify and clarify answers, as well as to encourage respondents to elaborate further with the

hope of gathering more information that could extend our proposed framework. Through this

procedure, we tried to fully plot out the working concept of open innovation collaboration.

When searching for an empirical sample to test our theoretical model of open innovation col-

laboration, we faced a challenge: the motivation behind our research was the broad heteroge-

neity in the use of the term open innovation found in the literature. Our underlying assump-

tion was that open innovation differs from traditional forms of collaboration and exchange of

information in the innovation process, thus justifying a new term. We further discovered that

innovation can be considered a rather new phenomenon, where practical implementation has

just started. This makes it rather difficult to find a sample of companies that could be used to

validate our model. To overcome this challenge, we used a proxy. Instead of companies ap-

plying open innovation for their innovation projects, we used intermediaries which explicitly


147

offer "open innovation" as a service to innovating firms. Observing intermediaries allowed us

to get a deeper understanding of their collaboration models for open innovation since this is

the core of their business.

3.2 Sample

Intermediaries are a well-known actor in an innovation process. For a long time innovating

firms have used the services of such consultancies, offering brainstorming techniques for

ideation, market research firms to do concept testing, or knowledge brokers to obtain and sell

licenses (Blondel, 1995; Sawhney et al., 2003). A rich scientific literature describes and ana-

lyzes intermediaries from diverse perspectives. As a result, the term intermediary denotes

different kinds of agents performing a variety of tasks within the innovation process for their

clients. These firms are called bridgers, (Bessant and Rush, 1995; McEvily and Zaheer, 1999),

brokers (Hargadon and Sutton, 1997), information intermediaries (Popp, 2000) or superstruc-

ture organizations (Lynn et al., 1996) etc. Howells (2006) presents a comprehensive overview

of research in the field of intermediaries by reviewing different streams of literature, includ-

ing, for example diffusion and technology transfer, system and networks and service innova-

tion etc. Intermediaries help to connect, transform, and translate knowledge (Nonaka and

Takeuchi, 1995).

The work of Bessant and Rush (1995) provides details on the activities intermediaries per-

form. Intermediaries tend to be specialized into the areas of articulation and selection of new

technology options; scanning and locating of sources of knowledge; building linkages be-

tween external knowledge providers; development and implementation of business and inno-

vation strategies. In summary, when reflecting on all these functions at a more general level,

two main roles of an intermediary can be extracted: scanning/gathering information and facili-

tating communication for knowledge exchange. Activities within these two major tasks can be

associated with the first two stages of our collaboration model (Figure (B1) 1) – Stage 1,

Forming, and Stage 2, Generating,.

In the last few years we have observed the emergence of a new generation of intermediaries,

manifesting as the classical knowledge broker in a virtual environment (Verona et al., 2006).

The development of new information and communication technologies (ICTs) has increased

the number of possible transaction partners tremendously. This makes it difficult for organiza-

tions to maintain an overview of the market and potential external knowledge sources. Virtual


148

knowledge brokers (VKBs) take advantage of the global medium that is the Internet to inte-

grate a large number of individuals in a collaborative manner for generating content. Commu-

nication and interaction becomes more cost-effective and also seems to diminish the trade-off

between richness and reach of information (Evans and Wurster, 1997). New knowledge gen-

erated in such a way usually helps the client organization to accelerate their innovation pro-

cess (Nambisan and Sawhney, 2007). The advent of these intermediaries is strongly associat-

ed with the open innovation concept (Vanhaverbeke et al., 2008, p. 6) and, accordingly, we

use the term “open innovation intermediaries” (OII). To achieve market success, OIIs mostly

specialize on one or a few methods of open innovation. They organize idea contests, search

for lead users, or organize innovation communities on the internet. We assume that these

companies are highly specialized in their methods and therefore must have a sophisticated

collaboration model to be competitive in this domain. This justifies their use as our empirical

basis and should help us to get richer data on the effects we want to study.

3.3 Data gathering and sampling

To identify OIIs for our sample, we used a broad online research. An intermediary who

claimed to offer a method or service to support an organizations' open innovation intention

was selected as an open innovation intermediary. We decided to adopt a very low threshold

for the classification of an OII, because getting a large variability in the data set supports our

objective of mapping the open innovation collaboration landscape. Furthermore, we applied a

networking approach, asking key informants to name companies helping others with "open

innovation". This search generated a set of 36 OIIs. Once we identified a new OII, we also

asked its managers for main competitors or market alternatives to identify further candidates

for our study. This allowed us to finally survey 47 OIIs. Of those, 24 intermediaries fully

completed our survey form, which equals a response rate of 51.1%. About 15 (31.9%) of them

also participated in a follow up interview.

These response rates would have been acceptable if we already had a deeper understanding of

our research topic, but due to its explorative character, we needed to improve the data quality.

Therefore we started a second round of internet research and self-completed the survey forms

of the remaining 23 research subjects in our sample, based on the information they share on

the internet, whitepapers, company documentations, company blogs, or public information

like press reports or research papers. For an additional evaluation of this further sample set,


149

we sent out those profiles for confirmation of the correctness of information gathered about

the company. We received 20 (42.6%) modified or confirmed profiles. After finishing the

data gathering, four accelerators were excluded from further analysis because of inconsistency

in the information. Our final set of OIIs totals 43 companies (Appendix (B1) 1), which is an

acceptable size for an initial explorative and descriptive analysis – especially given the highly

specialized population of our research.

The questionnaire given to the OIIs to answer consists of five parts. A first part asks for in-

formation about the company’s business model and the stage of the innovation process they

target. The second part requests specific information about their service offerings and the

methods to integrate the input of external actors in the innovation process of their clients. The

third part of the questionnaire is intended to provide us with information about the structure

and management of a typical client project. In the fourth part we focus on the external actors

and their integration into the process. The last part of the survey addresses the market in

which the intermediary is operating.

We used pre-defined answer sets for questions that are aimed to quantitatively confirm col-

laboration forms, ex ante, proposed by our conceptual framework. To extend our framework

and identify new collaboration forms we provided free answer space. As most of the interme-

diaries are rather small companies, the survey was addressed to the CEO, who was often the

founder of the company. The survey was presented in pdf-format and emailed to the key in-

formant identified. Answering the survey took about 40 to 50 minutes. The follow-up inter-

view was conducted by one interviewer and took between 30 and 240 minutes each, with a

mean of 60 minutes. The interviewer reviewed all the questions and answers the interviewee

had given. Often the interviewee completed the answers and explained their meaning, espe-

cially those questions with a free answer space. Data was collected between October 2008 and

June 2009 (9 months).

3.4 Coding scheme to translate data into the conceptual framework

To analyze our data we developed a coding system to match our conceptual model (Figure

(B1) 1) with the observations in the sample. Three sources of information formed our data

basis: the intermediary’s website9, the survey, and the interview. To operationalize our model

9 We took the website of the open innovation intermediaries for gathering secondary data. On their webpages intermediaries mainly presented their services highlighting their core competence. Furthermore, in “terms of use” we found valuable information about rights and duties of participating parties. In most cases, we were able to derive information about how collaboration with external actors is arranged and organized.


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stages, we explored the literature on open innovation, R&D cooperation and networks, with a

particular focus on identifying concrete activities which are performed throughout an interac-

tive process. Each stage is translated into a specific activity for which we provide a definition

to the coders. Since we follow an explorative approach, we defined a residual category for

every stage to collect further information which could not clearly be assigned to one of the

stage activities derived from theory. We explicitly instructed coders to look for this residual

information in order to extend our conceptual framework to reflect recent practices from in-

dustry. The development of the final coding system (Appendix (B1) 2) resembles an iterative

procedure which we adapted from social science literature (Weston et al., 2001). In the next

section we introduce each of the single stages of the framework, which follows the structure

shown in Figure (B1) 2.

Figure (B1) 2: Stage structure of the conceptual model


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3.4.1 Stage 1: Forming

For Initiation our literature review revealed three mechanisms explaining how the initiation

for interaction is communicated to potential external parties (sources): contracting, searching,

and an open call.

Contracting mirrors the conventional approach of R&D collaborations (Håkanson, 1993, p.

273; Hurmelinna et al., 2005, p. 375). An organization as the initiator of the interaction has a

problem to solve during its innovation process. Based on a legal contract, it assigns this task

to an external partner regarded as the being best suited to deliver the solution. The definition

of open innovation by Chesbrough (2003b) is addressing exactly this case by saying: “[The

paradigm] assumes that firms can and should use external ideas as well as internal ideas, and

internal and external paths to market as they look to advance their technology”.

Searching is the second mechanism used to start collaboration. In comparison to the contract-

ing activity, it refers to a broad search for information needed and relevant sources. Some

authors consider the breadth and the depth of search as a crucial aspect when performing this

activity (Katila and Ahuja, 2002; Laursen and Salter, 2006). For example, netnography

(Kozinets, 2002), a qualitative, interpretive research methodology used to study online cul-

tures and communities, provides a wide search of the internet for certain information and

sources. A broader search for collaboration partners also helps to overcome the local search

problem that firms face while innovating (Rosenkopf and Almeida, 2003, p. 751).

An open call is the third possible sub-activity to begin collaboration. Here, problems and

tasks are broadcast by an organization to an unknown (rather large) group of potential recipi-

ents. Potential contributors screen the task and self-select whether they want to become en-

gaged in the collaboration (Benkler and Nissenbaum, 2006; Lakhani et al., 2006).

To determine an intermediary’s specification during Initiation, we coded information from

their website about services offered and details gathered from the interview where we verified

our web search results. Furthermore, we drew upon answers given in the survey to the follow-

ing questions: ‘Please summarize your company’s philosophy / core offering or position in

brief.’ ‘What would you consider as your company’s core competencies?’ (Part A, Appendix

(A) 5; Q 3, 4)

Constitution is the second activity within Stage 1, Forming. It captures the way individuals

identified in the previous activity function together during their collaboration and address the

issue of coordinating the division of labor when problem solving. As before, we derive activi-


152

ties from the existing literature on how groups for open innovation are constituted. We opera-

tionalize the realization of the actual project team through regulating access to it (Pisano and

Verganti, 2008, p. 80). According to Pisano and Verganti (2008, p. 80), access can be either

“open” to everyone or “closed” through access permission for selected individuals. Conse-

quently, we can identify two mechanisms from the literature which are consistent with this

activity: self-selection and task assignment, also referred to as the push (self-selection) and

pull (task assignment) principle of knowledge transfer (Mahé and Rieu, 1998, Hartlieb, 2002).

Self-selection is an organizing principle first described in the literature on open source soft-

ware (Lakhani and Panetta, 2007, p. 105). It allows the individual to decide for themselves

whether to become involved in a task or problem. Self-selection usually does not happen in

the domain of the initiator of the collaboration but rather in the domain of the potential col-

laboration partner. Individuals either select a task based on their previous expertise in the

field, giving them a cost advantage, or based on their intrinsic motivation to become engaged

in the task- they really want to do it (Benkler and Nissenbaum, 2006, p. 400).

Task assignment, on the contrary, describes a mode where the initiator assigns a task to a po-

tential contributor. This assignment is either based on screening the capabilities of potential

contributors or signaling activities to the contributors. Task assignment is the standard way of

organizing division of labor in an enterprise (Ancona and Caldwell, 1989, pp. 5-6; Brower et

al., 2000, p.235).

We acquired the information regarding the design of the access regulation from the survey by

asking respondents: ‘Do you allow for self-nomination of new members?’ ‘Do you have spe-

cific requirements for selecting new members of the community?’ (Part A, Appendix (A) 5; Q

14, 15)

3.4.2 Stage 2: Generating

The central objective of the Stage 2 Generating is the creation of new knowledge. As de-

scribed in our conceptual framework, organizing this joint knowledge creation is the primary

activity at this stage. We apply the degree of integration, understood as the communication

structure among collaboration participants (Mohr et al., 1996, p. 105), to analyze governance

structure, which refers to how hierarchical or flat the collaboration is organized. According to

literature, communication is regarded as an underlying process of coordination (Malone and

Crowston, 1990, pp. 364-365). Also, the innovation management literature highlights the im-


153

portance of communication for successful product development (Griffin and Hauser, 1992, p.

360). Based on prior research of communication typology, we define two specifications for

communication activities: the mode of communication (Gales and Mansour-Cole, 1995) and

the direction of communication (Moreau, 2003).

The mode of communication is associated with the depth to which an information artifact is

elaborated upon. Our understanding of the depth of elaboration is that the number of partici-

pating actors in an exchange enhances the quality of the exchange itself (Gruner and Hom-

burg, 2000, p.3). The more contributors to a conversation, the better the content can be delib-

erated, and the more knowledge can be generated. In turn, we expect a higher degree of inte-

gration (Mohr et al., 1996, p. 103). Moreover, studies of communities and practice demon-

strated that collaborative work creates new, more complex knowledge, and at a faster, than

individual problem solving because actors learn from each other and can build on one anoth-

er’s contributions (Wenger and Snyder, 2000; Brown and Hagel, 2006). Further support for

this assumption can be found in Surowiecki’s (2004) concept of “the wisdom of crowds”. He

argues that the aggregation of information, which happens in groups, results in decisions that

are often better than could have been made by a single member of the group (p. 190). Con-

cluding from this literature, we define three values the mode of communication can obtain.

Collaboration can occur between dyadic communication [1:1], small group communication,

and collaborative communication [n:n] where members are able to communicate to every oth-

er member of the group without limits.

The second specification regarding interaction, direction of communication, grasps the time

component in information processing, addressing the form in which information is passed

between the participants – vertical vs. horizontal. We operationalize the direction of commu-

nication with the availability of feedback. Receiving feedback on one’s work helps to improve

the outcome (Carlsson et al., 2002, pp. 234-235; Ritter and Gemuenden, 2003, pp. 747-4751)

and support the vertical movement of information (Mohr et al., 1996, p. 109). Furthermore,

we include a time-dependent aspect of communication which accounts for the quality of the

knowledge outcome due to the organization of information flow. For example, research has

found that asynchronous communication leads to disjointed discussions and information over-

load (Montoya-Weiss et al., 2001, p. 1252). Again, we differentiate three values for the direc-

tion of communication. A unidirectional communication does not provide any feedback. Bidi-

rectional asynchronous communication provides feedback, but after a delay in time. Bidirec-

tional real-time feedback is provided without any delay.


154

To determine an intermediary’s specification of the communication pattern we coded infor-

mation from the website mainly provided in the section “terms of use” and information from

the interviews used to validate our findings. Both sides involved in the interaction (the organi-

zation and the external actors) have very similar possibilities for interacting: single, many, or

collaborative many. By taking the cross product of the options, nine interacting patterns (3x3)

for each party arise. Also, considering the direction of the communication, the number of pos-

sible combinations has to be multiplied by three potential communication directions. In total,

we differentiated 27 different forms of communication. To receive the degree of integration

between actors, which maps the governance structure of open innovation collaboration, we

converted the two previously specified communication aspects into one new variable. Based

on our literature review, we allocate values of integration to the single specifications. Thus,

for the first step we assign three values, low (1), medium (2), and high (3) integration to the

mode of communication. Table (B1) 1 shows the combined specifications which result.

Table (B1) 1: Mode of communication and the degree of integration

Internal/ external Dyadic Small group Collaborative Dyadic Low (1) degree of

integration Low (1) degree of integration

Medium (2) degree of integration

Small group Low (1) degree of integration

Medium (2) degree of integration

High (3) degree of inte-gration

Collaborative Medium (2) degree of integration

High (3) degree of integration

High (3) degree of inte-gration

In the next step, we used the same conversion for the aspect of communication direction.

Again, based on our knowledge from the literature review on the correlation between com-

munication and coordination of information flow, we evaluated the resulting degree of inte-

gration. The unidirectional interaction equals a low (1) degree of integration, whereas a bidi-

rectional time-delayed communication forms a medium (2), and a bidirectional real-time

communication produces a high (3) degree of integration. For the final step we integrated both

specification of communication and their translation into one measure by simply using the

cross product to obtain 3x3x3 new observable integration values. Table (B1) 2 shows the


155

combination of the two initial variables and the final segmentation we used to get different

characteristics of the integration degree (low (1-3), medium (4-6), and high (7-9)).10

Table (B1) 2: Segmentation for the degree of integration

Mode of communication Direction of communication

Internal / external Dyadic Small group Collaborative

Unidirectional Dyadic Low Low Low Small group Low Low Medium Collaborative Low Medium Medium

Bidirectional

Time-delayed

Dyadic Low Low Medium Small group Low Medium High Collaborative Medium High High

Bidirectional

Real-time

Dyadic Medium Medium High Small group Medium High High Collaborative High High High

3.4.3 Stage 3: Exploiting

The events in this phase reflect the exploitation activities of the new knowledge. In general,

we assume that from this point well-known practices of managing external knowledge occur,

such as knowledge unpacking, recombination, and integration (von Krogh et al., 2001, p. 424;

Abou-Zeid, 2005, pp. 150-151). Furthermore, we presume from the fact that the new

knowledge was generated by an open innovation approach that this approach serves to act like

a moderator on those activities. This leads us to conclude that one last key difference between

an open innovation approach and traditional R&D cooperation is located in the strategy to

protect intellectual property (IP). The open usage of new generated knowledge can be a policy

of the company (Chesbrough, 2003b, p. 155; West and Gallagher, 2006, p. 328) and the litera-

ture on open source software development, in particular, suggested different IP strategies for

innovation success (Lerner and Tirole, 2005; West and Gallagher, 2006). This explains why

we operationalize the exploitation activities as follows: granting an open license, using a crea-

tive commons license, or applying for forms of intellectual property protection via patents or

copyright.

10 The product of the translated two initial variables communication mode and communication direction into the degree of integration was taken for the final segmentation. E.g., one-to-one communication (dyadic) has a low degree of integration but the bidirectional communication indicates a medium integration. The product of both results in a low integration degree.


156

At this point it should be noted that the role of this stage is not discussed further, mainly due

to our empirical findings. We had to learn that our empirical approach does not allow us to

make a statement about the shape of this parameter. Stage 3 Exploitation takes place in the

domain of the OII's client. Thus, all intermediaries had a fixed agreement, described in the

“terms of use”, on how to deal with any intellectual property created during the process and

they all operated within traditional regulations. Due to this lack of variability, stage 3 is ex-

cluded from further analysis, though it should be investigated in more detail in future studies.

3.5 Data analysis

Three coders were assigned to analyze the raw data. One coder had the status of an expert and

was also involved in the development of the coding scheme. To control for the bias of the

main coder, two further people were familiarized with the procedure and coded the same ma-

terial. All coders underwent training with an extra sample and practiced the coding process at

one sample subject together. Afterwards, four further samples served as training material to

get familiar with the coding system. Once the training was completed, coders met and dis-

cussed questions or comments about the coding scheme. Overall, we achieved an inter-coder

reliability of 0.84 using Scott’s pi for more than two coders (composite reliability), a value

that can be considered good (Scott, 1955). For the next stage of analysis, we took a closer

look at the residuals. Based on the information in the residual categories, the coders interpret-

ed the content and discussed it, with the help of literature, to define more specifications for

the stages (new specifications are marked * in the coding scheme, Appendix (B1) 2).

In a second step, we performed cross tabs to count frequencies of occurring combinations.

Initially, we looked at each model stage separately. For Stage 1, Forming, we crossed the

specifications Initiation and Constitution, and for Stage 2, Generating, the specifications mode

and direction of communication. For Stage 2 we obtained, according to the procedure de-

scribed, the degree of integration from our observations regarding the communication pattern.

After doing so, we then combined both process stages and counted frequencies of identified

configurations. Due to our model restriction of potential interdependencies between single

specifications, we expect the number of observable configurations to be lower than the num-

ber of theoretically possible configurations.

To investigate our first propositions, we interpreted our findings of the cross tab analyses to

determine if we could find preferred combinations representing a configuration type of open


157

innovation collaboration. To answer our second proposition, we examined the performance

and change in the degree of integration over these different types.

4 Results

4.1 Descriptive Data

We begin our data analysis with a brief description of the sample in order to better understand

the foundation of our data basis11. We found a general and interesting trend while analyzing

the founding years of the sample subjects- most of the open innovation intermediaries are

quite new on the market. The increase in emergence of new intermediaries in the past ten

years is remarkable, with more than three quarters of them (81.4%) not in existence before the

year 2000 and only three companies working in this specific field of innovation before the

1990s. Intermediaries in the field of open innovation operate globally, 22 OIIs answered that

they act on several continents. We also found a wide distribution of this business form across

diverse industry sectors (Figure (B1) 3).

Figure (B1) 3: OIIs and their industry focus

11 Please refer to Diener and Piller (2010) for a comprehensive overview of intermediaries in the open innovation market.

0 10 20 30 40 50 60 70

chemical/pharmaceuticalhealth/medicine

FMCG(consumer) electronics

design/product designcomputer/IT

telecommunicationengineering

automotive

percent of OIIs

ind

ust

ry s

ecto

r


158

In answer to our question about what stage of the innovation process the intermediaries oper-

ate, the majority (87.2%) regarded themselves predominantly as contributors to the early

phases of the innovation process ("Fuzzy frond end", FFE), by supporting the process of idea

generation and evaluation. Though 48.7% still offer services during the stage of new product

development (NPD), whilst just a third (33.3%) operate in the stages of commercialization.

To get an idea of the services such intermediaries offer, we asked them to describe the nature

of their business (Part A, Appendix (A) 5; Q9). Furthermore, they were requested to indicate

which kind of open innovation method they apply (Part Appendix (A) 5; Q10). Respondents

could choose between community application, idea contests, workshops, or other software

tools. Combining and analyzing the answers to these two questions revealed that OIIs can be

classified according to three major service types: community managers, software provider,

and innovation consultants.

We found 36 OIIs were community managers, making them the largest group. They use either

discussion forums or user panels to generate new content. Six OIIs provided dedicated soft-

ware for open innovation (e.g. social software), often in the form of a web-service. The sec-

ond largest group of intermediaries (25 OIIS) operated as consultants and provided a custom-

ized and integrated service in the client's innovation process. For the proceeding stage of

analysis, we applied our coding system to analyze the collaboration form of the selected OIIs

in greater detail.

4.2 Analysis results of the applied coding scheme

We found that most of the information from the survey and interviews could be clearly as-

signed to one of the pre-determined categories of the coding scheme. Yet, we observed rather

large occurrences of information provided in our “others” answer fields. Such information not

fitting our proposed categories was put into the residual categories. Having done so, we inter-

preted the content of the residual categories and found five additional stage specifications

which are displayed in Table (B1) 3. Within Stage 1, Forming, we discovered residuals within

Initiation and Constitution. The analysis of the initiation-residual category reveals a fourth

option to initiate the collaboration; a modified form of the calling option. In this case the call

is not openly addressing all possible external actors, but is targeted to a pre-selected group.

The firm determines a certain group of individuals in advance, to which they post a problem

or task. We name this new possibility of initiating collaboration a ‘targeted open call’.


159

Table (B1) 3: Definition of the residual categories on the bases of empirical data

Stage Specification Operationalization 1 Forming Initiation Targeted call Innovation problems or tasks are

posted to a limited audience

Constitution

Combined self-selection and task-assignment No constitution

The self-selection of the external actors is followed by another se-lection process performed by the organization representative or vice versa A group of external actors already exists, an active constitution is not taking place

2 Generating Mode of communication

No mode of commu-nication

No direct communication be-tween participating parties takes place

Direction of communication

No direction of com-munication

No direct communication be-tween participating parties takes place

Regarding the specification Constitution, the residual category shows two new cases. The first

describes a double selection recruitment procedure of potential collaboration participants. The

constitution of the project team is neither solely self-selection nor conventional task assign-

ment; rather, it seems to be a combination of the two. Either potential external actors select a

problem or task and then the organization decides who they assign the task to or, vice versa,

the organization gives a problem to a selected group and the group members self-select who

works on solving it. We called the new specification a combined self-selection and task as-

signment. The second case reflects the fact that no constitution took place. A few of the OIIs

offer a method where no interaction between different parties is required (see Table (B1) 9).

Consequently, a similar specification has to be understood for Stage 2, Generating. If no di-

rect interaction between the organization and the external actors is intended, then there will be

no mode and no direction of communication. For the degree of integrating collaboration part-

ners that means that no integration occurs.

After revising the coding scheme, we could identify all collaboration models which were of-

fered by the OII in our sample. We found a total of N=65 collaboration types (Table (B1) 4),

which means that some of our 43 intermediaries offered their clients more than one method to


160

perform open innovation, or one method being applied in different ways. Interestingly, inter-

mediaries that explicitly claimed to be specialized in one method offered more than one con-

figuration according to our coding scheme.

Table (B1) 4: Number of collaboration types found among the 43 OIIs

one collabora-tion model

two collabo-ration models

three collabo-ration models

six collabo-ration mod-

els

Total

OIIs 26 15 1 1 43Collaboration models

26 30 3 6 65

Our cross tab analysis of the first model Stage 1, Forming, revealed that only 7 out of 16 theo-

retically possible observations occur in the data (Table (B1) 5). The maximum frequency of

24 for a certain observed pattern within Stage 1 represents collaboration models which are

initiated by a targeted call and are constituted by a combination of self-selection and task-

assignment.

Table (B1) 5: Cross tab of variables Initiation and Constitution in Stage 1 Forming (N=65)

Initiation / Constitution

Self-selection

Task as-signment

Combined self-selection and task assignment

No constitution

Total

Contracting * 8 * * 8Searching * 2 6 7 15Open call 17 * 0 * 17Targeted call 1 * 24 * 25Total 18 10 30 7 65* combinations expected to not occur

At the minimum, we find only one case where collaboration starts with a targeted call, ad-

dressed to a pre-selected group of potential external actors and where the members select

themselves for the final team. Starting off collaboration with an absolute open call and the

formation of the interacting parties through a self-selection mechanism occurred in 17 cases.

Since existing theorists explored whilst constructing our conceptual framework did not con-

sider a two-step procedure for Stage 1, forming a group, it is surprising that 30 observations

fell into this class.


161

After analyzing patterns of stage (1) forming, we did the same with stage (2) generating. The

cross tab analysis yielded just 9 possibilities out of 28 potential combinations12 between mode

and direction of communication could be observed (Table (B1) 6). We found 46 observations

in the category of one individual of an organization communicating with the external envi-

ronment, twelve in the category of interaction between many collaborative individuals, and

seven cases of no communication. We did not discover the communication mode for many

members of an organization interacting with external actors. It should be noted that we attrib-

ute this result to the fact that we did not get sufficient information our investigation of inter-

mediaries to distinguish between the number and structure of participating individuals from

the firm. Another result that stands out is that most of the client organizations of the OIIs pre-

ferred to interact with external actors who were collaborating with one another as well (n=35).

The most frequent (2.2) direction of communication was a real-time bidirectional one (n=41).

Table (B1) 6: Cross tab of variables mode of communication and direction of communication (N=65)

Mode of communication

Direction of communication

External Dyadic

Small group

Collabora-tive

No communi-cation direc-tion

Internal Total

Unidirectional Dyadic 0 2 6 * 8Small group 0 0 0 * 0Collaborative 0 0 0 * 0

Bidirectional

Time-delayed

Dyadic 0 5 4 * 9Small group 0 0 0 * 0Collaborative 0 0 0 * 0

Bidirectional

Real-time

Dyadic 6 10 13 * 29Small group 0 0 0 * 0Collaborative 0 0 12 * 12

No communication mode

* * * 7 7

Total 6 17 35 7 65

* combinations expected to not occur

At this point in analysis we included all variables in the cross tab to evaluate the most fre-

quent combination patterns. Table (B1) 7 shows the 22 configurations found. In general, these

results support our previous findings and interpretations. Regarding the cases of collaboration

12 28 potential combinations have resulted from 3x3 possible communication modes (one-to-one, one-to-many etc.) plus the case of no communication (see Table (B1) 6).


162

initiated by contracting, we revealed an accumulation of a dyadic bidirectional interaction

(n=5).

Table (B1) 7: Cross tab including all content variables (N=65)

Initiation Constitution

Direction of Communication

Uni

Directional

Bidirec-tional time-delayed

Bidirec-tional real-time

No di-rection Mode of Commu-

nication

Internal External

Contract-ing

Task as-signment

Dyadic Dyadic 0 0 5 *

Dyadic Small group

0 0 2 *

Dyadic Collab-orative

0 0 1 *

Search-ing

Task as-signment

Dyadic Dyadic 0 0 1 *


0 0 1 *

Combined self-selection and task assign-ment


0 0 4 *

Collab-orative

Collab-orative

0 0 2 *

No constitu-tion

No communication mode

0 0 0 7

Open call

Self-selection

Dyadic Small group

0 3 2 *


3 2 4 *

Collab-orative

Collab-orative

0 0 3 *

Targeted call

Self-selection

Dyadic Small group

1 0 0 *


Dyadic Small group

1 2 6 *


3 2 3 *

Collab-orative

Collab-orative

0 0 7 *

*combinations expected to not occur

Furthermore, we found a favored combination of open call and targeted open call in Stage 1,

Forming, followed by more collaborative interaction patterns in Stage 2, Generating. Out of

35, 27 cases fell into these two classes. Generally, these two categories showed more variabil-

ity in combinations. Within the class we also found a preferred combination of starting col-

laboration by searching for external knowledge. It was observed that Searching occurred with

either a two-step actor selection process and rather collaborative information processing


163

(n=4), or with no direct communication at all (n=7). The conversion of the communication

pattern into the degree of integrating external actors, according to the scheme introduced in

the section on data analysis (Table (B1) 2:), produced the following results (Table (B1) 8). As

expected, the cross tab reduced the variability of configuration types found in Table (B1) 7,

and therefore reveals a clearer picture of configuration types in our open innovation collabora-

tion model.

In total, we could observe 14 configurations of open innovation collaboration approaches. We

did not analyze single occurrences any further because we were primarily interested in domi-

nant patterns. The most common model (n=12) starts with a targeted call, which is followed

by a two-step procedure of selecting potential external actors.

Table (B1) 8: Cross tab including the degree of integration (N=65)

Degree of integration

Initiation Constitution (1) Low

(2) Me-dium

(3) High

No Total

Contracting Task assignment 7 1 0 * 8

Searching Task assignment 1 1 0 * 2


0 4 2 * 6

No constitution

0 0 0 7 7

Open call

Self-selection

8 6 3 * 17

Targeted call

Self-selection

1 0 0 * 1

Combined self-selection and task assignment

12 5 7 * 24

Total 29 17 12 7 65* combinations expected to not occur

This type showed a low degree of integration. Since the average number of observations per

configuration is n=4.5 (median), we decided to take those configuration types showing a fre-

quency equal to and higher than four as the dominant patterns in our empirical sample. Thus,

we excluded six collaboration models from further discussion.


164

In Table (B1) 9, the final eight configuration types of open innovation are displayed. The ta-

ble summarizes the single stage specifications. Additionally, the table provides an exploratory

description of the different collaboration modes, based on the aggregation of the gathered

information about the OIIs business model and typical representatives from practice. It is

worth noting that all collaboration forms differ only in regard to their degree of integration,

except the collaboration type starting with contracting for information. Only collaboration

forms including a targeted call and a two-step selection process show a high integration of

external actors.

Tabl

e (B

1) 9

: Con

figur

atio

n ty

pes o

f ope

n in

nova

tion

colla

bora

tion

Sta

ge I

S

tage

I

S

tage

II

Init

iati

on

Con

stit

uti

on

Mod

el t

ype

Deg

ree

of in

tegr

atio

n E

xplo

rato

ry

des

crip

tion

M

eth

od

Exa

mp

le

Con

tract

ing

Task

ass

ignm

ent

1

Low

inte

grat

ion

(e.g

. bid

irect

iona

l rea

l tim

e co

mm

unic

atio

n be

twee

n co

mpa

ny

and

man

y ex

tern

al a

ctor

s)

Spec

ific

resi

dual

form

: C

olla

bora

tion

rese

mbl

es m

ore

a fo

rm

of c

ontra

ct re

sear

ch

than

eng

agin

g w

ith a

n in

term

edia

ry.

Partn

er fi

rm p

ursu

its a

n ow

n op

en in

nova

tion

appr

oach

in o

rder

to

gene

rate

requ

este

d in

form

atio

n.

Inno

vatio

n co

nsul

tanc

y,

desi

gn a

genc

y

IDEO

Fr

onte

er

Gen

3 Pa

rtner

s

Sear

chin

g C

ombi

ned

self-

sele

ctio

n an

d ta

sk

assi

gnm

ent

2

Med

ium

inte

grat

ion

(e.g

. bid

irect

iona

l rea

l tim

e co

llabo

rativ

e w

ork

betw

een

com

pany

and

ext

erna

l act

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Inte

rmed

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s sea

rch

for a

cer

tain

solu

tion

open

ly w

ith a

few

pr

esum

ptio

ns w

here

to

loca

te th

e in

form

atio

n.

They

inte

grat

e th

e ex

tern

al h

olde

r of t

he

solu

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info

rmat

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and

wor

k co

llabo

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on

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ing

the

prob

lem

.

LU m

etho

d,

inno

vatio

n co

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uniti

es

Gur

u H

yve

Idea

wic

ket


165

Sta

ge I

S

tage

I

S

tage

II

Init

iati

on

Con

stit

uti

on

Mod

el t

ype

Deg

ree

of in

tegr

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ry

des

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M

eth

od

Exa

mp

le

N

o co

nstit

utio

n 3

Rat

her

no

inte

grat

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(no

dire

ct in

tera

ctio

n)

Inte

rmed

iarie

s ope

nly

sear

ch fo

r inf

orm

atio

n,

mai

nly

on th

e In

tern

et.

Ther

e ar

e ju

st a

few

pr

esum

ptio

ns a

bout

w

here

to fi

nd th

e co

ncre

te k

now

ledg

e an

d ho

w it

is m

aybe

co

mpo

sed.

Th

ey in

tend

no

dire

ct

inte

ract

ion

with

ex

tern

al a

ctor

s.

Net

nogr

aphy

(o

bser

ving

on

line

com

mun

ities

)

Hyv

e Sp

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n ca

ll Se

lf-se

lect

ion

4

Low

inte

grat

ion

(e.g

. one

-to-m

any

bidi

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iona

l tim

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laye

d, c

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bora

tive

wor

k bu

t uni

dire

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nal c

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166

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167


168

5 Discussion

Our objective of the paper was to survey the open innovation landscape. We intended to get a

better insight into, and understanding of, the open innovation phenomenon by investigating

recent practices. By performing an exploratory study, we aimed to derive hypotheses about

elements and characteristics that might be central for constructing open innovation. We sur-

veyed intermediaries in the field of open innovation as a proxy for mapping the form of col-

laboration needed to transfer knowledge between an organization and its external environ-

ment.

We assumed that intermediaries, since their core competence lay in the creation and transmis-

sion of knowledge (Bessant and Rush, 1995; Sawhney et al., 2003; Howell, 2006), must have

a dedicated open innovation model. Indeed, we were able to identify 43 intermediaries focus-

ing their service explicitly on open innovation. The services the intermediaries offered are

proof of the authenticity of our sample. We could classify the identified methodological ap-

proaches as clear open innovation methods, e.g. idea contest (Piller and Walcher, 2006; Ter-

wiesch and Xu, 2008), lead user method (Urban and von Hippel, 1988; Lilien et al., 2002),

and problem broadcasting platforms (Lakhani et al., 2006). From the rich information we

were able to gather regarding the business strategy, methodologies and focus of service, we

have a good data basis for applying the model of cooperation developed (Figure (B1) 1).

Our descriptive data analysis showed that the market for open innovation intermediaries is

quite young. We identified two peaks, with a lot of knowledge brokers either founded after

the year 2000 or after 2006. We assume that this is due to the growing attention that the open

innovation phenomena received after it was named and published by Chesbrough in 2003.

The latter wave of growing demand for open innovation services post-2006 was probably

triggered by best practice cases, for example Proctor and Gamble, which appeared in the press

(Dodgson et al., 2006; Huston and Sakkab, 2006). The distribution between industry sectors

revealed that open innovation is not specific to certain branches of industries. Thus, open in-

novation’s functional principles seem to apply in a variety of fields. Surprisingly, 48.8% of

open innovation services lie in the engineering industry. Usually, this sector is known to be

more conservative in its choice of innovation strategy. On the contrary, the results for the

health and medicine sectors, Fast-Moving-Consumer-Goods (FMCG), and (consumer) elec-

tronics are not surprising. On average 56.9% of the OIIs offer an open innovation method for

supporting innovation in these branches. From previous empirical research, we know that

these industries particularly, bear a high potential for successfully innovating with external


169

actors, especially with users and customers (Urban and von Hippel, 1988; Luethje et al., 2002;

Franke and Shah, 2003; Luethje, 2003/04).

From 43 intermediaries we extracted 65 collaboration models representing different ways of

interacting between an organization and its external environment. We found that most of the

information from the survey could be clearly assigned to one of our pre-determined categories

in the coding scheme. Even though some OIIs offered only one method, they were revealed to

have more than one collaboration model. Thus, we conclude that it is not the method, but the

type, of collaboration that is an indicator for openness. As a consequence, our conceptual

model, which mirrors the existing literature, provides a good representation of the scope of

open innovation in practice. Yet, we had to make some additional specifications regarding

activities concerning Stage 1, Forming a group, and Stage 2, Generating content. We called

the new specification within stage one a targeted call. The additional specification we found

for constitution is a combination of self-selection and task assignment. Stage 1 of the coopera-

tion does not have to be a one-step sequential procedure, but can occur as a process of itera-

tions. Based on this finding we discovered evidence that the stages and their specifications are

not necessarily independent from each other. Here, constitution appears to be technically as-

sociated with initiation. Inducing a certain mechanism to initiate collaboration seems to be

conditional for a mechanism to explain how participants come together for collaboration. Al-

ready, the knowledge transfer models suggested by von Krogh and Koehne (1998, p.242) and

Szulanski (1996, p. 28) allows for feedback loops and the jumping back into earlier stages of

the knowledge transfer process.

Another specification concerns the observation in Stage 1, Forming, that the formation of the

actual project team does not take place and subsequently no interaction happens in Stage 2,

Generating. We revealed intermediaries that offered practices which were clearly labeled as

"open innovation" whilst not showing activities aimed at an active collaboration between or-

ganization and external actors. This observation is contrary to the common interpretation in

the literature that open innovation demands involvement of, and active interaction with, ex-

ternal actors (Gassmann and Enkel, 2004, p.7-8; von Hippel and von Krogh, 2006, pp.300-

302; Lichtenthaler and Ernst, 2006, pp. 376-369; Fredberg et al., 2008, pp.19-20). Thus, we

either have to extend our understanding of open innovation, or, we, by mistake, nominated

these intermediaries as OIIs. From the eight observed collaboration types we were able to

predict three models. Five types were specified ex post by analyzing the residual information.

Thus, we prefer to support an extension of our understanding of open innovation.


170

The Type 1 collaboration model (Table (B1) 9) offers itself as a very surprising case within

the continuum of open innovation practices. We interpret this finding as an expression of the

existing confusion surrounding the understanding of the term open innovation. The Type 1

model describes collaboration with an external partner assigned to generate knowledge as

input for the internal innovation process of a firm. In general, this is the basic motivation for a

firm to engage with an open innovation intermediary. Yet, in contrast to an open innovation

intermediary, this Type 1 cooperation partner does not offer a specific knowledge brokering

service, such as connecting the client organization’s domain with another external knowledge

domain. Within this collaboration form, the partner applies open innovations on their own to

generate the requested knowledge. Design offices like Fronteer or IDEO are typical represent-

atives of this class. To conclude, this procedure resembles a traditional and much formalized

contract research activity of a firm. Arguing from the competence-based perspective (Pra-

halad and Hamel, 1990), a firm hires a partner to carry out an R&D activity which is not part

of the core competence of the firm (Haour, 1992). Literature on the management of coopera-

tion like alliances and focusing on legal aspects of such cooperation (e.g. Howells, 2010;

Hagedoorn, 1993; Oxley, 1997; Cassiman and Veugelers, 2002; Blomquist et al., 2005), pro-

vide a well-researched foundation of the collaboration type initiated by a contract. Even

though these kinds of intermediaries are promoted as using an open innovation approach, their

collaboration models suggested the opposite. It appears that 12% of all collaboration models

demonstrated a very traditional approach of cooperation in the innovation process.

The collaboration Types 2 and 3 (Table (B1) 9) describe open innovation approaches where

collaboration is initiated by a search activity. Such search mainly follows a broad scope

which is characteristic of open innovation (Laursen and Salter, 2006; Katila and Ahuja, 2002).

Comparing both types, it seems that an organization has a choice regarding the object of

knowledge which they prefer to integrate; they can either be the holder of the information

(Type 2) or just the piece of information (Type 3). We found that Type 2 intermediaries usual-

ly apply the lead user method. Here a firm is interested to collaborate with external actors that

hold knowledge they do not possess, in order to solve internal innovation problem. Thus, the

organization engages with an intermediary who identifies a suitable collaboration partner up-

on their request. Since this lead user procedure is an accepted form of collaboration within the

open innovation literature (Fredberg et al., 2008), the approach expresses a form of recent

open innovation practices.


171

The counterpart to Type 2 of collaboration is Type 3. The third type is also initiated by search

but describes the case where no interaction takes place; it is a mere search for information. In

the literature focusing on marketing and consumer insights, we found a method like netnogra-

phy, a qualitative, interpretive research methodology that adapts the traditional, in-person

ethnographic research techniques of anthropology to the study of the online cultures and

communities formed through computer-mediated communications (Kozinets, 2002). An or-

ganization doing netnography performs a wide search of the Internet for certain information

and sources. Neither presumptions about concrete information, nor the source of the infor-

mation, is clear to the initiator of the collaboration. This kind of search process reflects the

two characteristics of an open search – depth and breadth (Laursen and Salter, 2006; Katila

and Ahuja, 2002). Furthermore, Type 3 is a typical representation of collaboration within the

co-creation approach. Literature on co-creation (Prahalad and Ramaswamy, 2004) focuses on

capturing value by integrating the customer into the production process. This rich body of

work describes, in particular, several ways to realize this integration (e.g. Nambisan and Bar-

on, 2009; Piller and Walcher, 2000; Fueller et al., 2009).

The largest group (n=17) of collaboration types is comprised of those firms starting coopera-

tion with an open call. Types 4 and 5 (Table (B1) 9) are what we expected to find within the

open innovation landscape since they can be predicted from theory. The content-based analy-

sis of the intermediaries assigned to those categories revealed that they characterize a class of

innovation contests. We can confirm the findings from the recent literature that, in an innova-

tion contest, a company calls on its customers, users, or experts in the general public to dis-

close innovative ideas and suggestions for product improvement (Lakhani et al., 2006; Piller

and Walcher, 2006; Walcher, 2007; Afuah and Tucci, 2012; Boudreau et al., 2011). These

types of competitions resemble open calls for cooperation in its purest form, as they seek in-

put from actors a company does not know.

Type 4 and 5 only vary in the extent to which they integrate external actors. Comparing these

two modes, we assume that the distinguishing element is the task of the contest. The charac-

teristics of such a contest task seem to influence the self-selection process of potential partici-

pants and the collaboration process that follows. The innovation literature provides the con-

cept of need and solution information to grasp this aspect (Ogawa, 1998; von Hippel, 1998).

This means there can be a very broad call for contributions concerning a general innovation

question directed towards an unspecified audience (e.g. (potential) customers of the company)

(Type 4), or an organization can ask for a very specific solution for a dedicated (technical)


172

innovative task (Type 5). The Type 4 model, represented for example by the OII Brainstorm-

ing Exchange, is tasked to generate as many ideas as possible and we do not find a deeper

interaction between the firm and external actor. In contrast, the Type 5 intends to find a more

sophisticated solution to a specific technical problem, which requires some agreement be-

tween the firm and external actors. The OII InnoCentive can be seen to execute this collabora-

tion type.

Among the ex post defined collaboration models, we found three interaction modes where an

organization follows a two-step procedure of selecting external actors to participate in their

innovation task. Mainly, these intermediaries offer platforms for running idea contests as well

as working with expert communities, but with a clear regulation of access to those contests. A

similar differentiation is provided by Terwiesch and Ulrich (2009) who distinguish between

open and closed contests (p.23). A comparison of the collaboration Types 6, 7, and 8 (Table

(B1) 9) show that they differ exclusively in their degree of integration of external actors. A

typical representative for Type 6 collaboration was the intermediary Idea Crossing, who is

organizing innovation challenges with a certain type of MBA students. A good example of

Type 7 is Nine Sigma, who offers a select expert community solving specific technological

problems. Collaboration Type 8 captures a version of contests that is highly collaborative. For

example, the OII Brain Reactions organizes online brainstorming sessions where employees

and external actors work together in real-time, in a virtual or physical environment, when

solving an innovative task.

To sum up our core findings regarding recent open innovation practices, we found evidence of

the dominance of the collaboration method innovation contest as suggested by literature

(Piller and Walcher, 2006; Lakhani et al., 2006). Our analysis revealed five collaboration

forms originating from this method. As already stated by Ernst (2004, pp.192-193), idea con-

tests come in many forms. Furthermore, it seems that, in general, organizations favored col-

laboration with a pre-selected group of external actors. Regardless of which way collaboration

was initiated (calling or searching), we discovered that, when constituting the final project

team, there is a kind of regulation of access. Thus, we found empirical support for distinguish-

ing open innovation collaboration according to that parameter, as proposed by Pisano and

Verganti (2008). Also in line with recent open innovation research, we could clearly identify

the case of a collaborative interaction (Fredberg et al., 2008). Organizations seem to profit

from intermediaries (e.g. Hyve) offering a highly interactive community. However, we ob-

served unexpected collaboration forms where no direct interaction between organization and


173

external environment took place. Interestingly, even very traditional concepts of R&D coop-

eration are incorporated into the open innovation understanding. As a consequence, we con-

clude that the broad open innovation definition provided by Chesbrough (2003b) obviously

includes a great variety of open innovation practices.

The analysis of the degree of integration, which we derived from the interaction pattern,

showed no systematic in the allocation to certain collaboration models. This is interesting

because the degree of integration was used in other studies to investigate the open innovation

phenomena (Lichtenthaler and Ernst, 2006). We regarded the degree of integration as an ex-

pression of the collaboration governance. Again, our results cannot confirm that the type of

governance distinguishes between different collaboration modes, as suggested by Pisano and

Verganti (2008). Rather, it seems that the degree of integration of external actors is a second-

ary parameter that organizations can use to better shape a selected open innovation approach.

6 Implications and conclusion

The intention of our paper was to map out the landscape of open innovation. We proposed a

framework based on the open innovation literature, which models the knowledge transaction

between an organization and its environment as an interaction process. Our collaboration

model identified forms which were obviously associated with the paradigm of open innova-

tion in the existing literature. Isolated model specifications allowed us to get an idea of what

lies behind open innovation. According to our findings, openness of collaboration is defined

by controlling the flow of knowledge through the boundary of the firm. The permeability of

the firm’s boundary is central to Chesbrough’s definition of open innovation (2003b, p. xxiv).

We regard the way collaboration is formed to be the main driver for the permeability of a

firm’s boundary.

When considering the single observed model specifications of open search, organizations

planning an open innovation venture have to make a decision regarding the object to be inte-

grated. The firm can either integrate solely the information artifact, and therefore chose a col-

laboration model with no direct integration of external actors, or they can integrate the exter-

nal actor who holds the requested information. Furthermore, the community composition

seems to depend on the innovation task the company intends to solve. Thus, an organization

needs to make the decision whether they would like to work with experts, for example when


174

solving technological problems, or non-experts, such as when answering market-oriented

questions. Other studies have found that “… specialized knowledge workers (e.g., freelancers,

consultants or part-time engineers) make a living as portfolio workers, offering their services

to different organizations by joining different communities” (Gassmann, 2006).

Taking our findings together, we can confirm our first proposition. We derived eight types of

open innovation from preferred configurations of the specifications of our collaboration mod-

el (Figure (B1) 1). A configuration type represents a specific cluster of collaboration activi-

ties. For our second proposition we found limited support. The project governance can be

described through the integration of external actors, but does not seem to be a distinguishing

parameter. The degree of integration that flows from the communication pattern between the

actors involved does not differ according to the specific type of open innovation. Yet it is dis-

tinct within classes of single open innovation approaches, like those initiated by an open call

or search. Whilst we based our measure only on one dimension, future research should apply

a multidimensional measure of the integration degree that also captures efforts to distribute

tasks among participants and reintegrate generated information. This more sophisticated con-

struction would allow differences to be identified between diverse open innovation collabora-

tion forms.

The motivation for this paper was to identify characteristics that constitute an open innovation

construct and help to distinguish this form from traditional concepts of R&D cooperation.

When reviewing our stage specifications and the identified collaboration models, we discov-

ered that the most distinguishable feature of open innovation, in comparison to more tradi-

tional concepts of collaboration, is that an organization avoids the active search for infor-

mation with a clear presumption about location and composition (as it is the underlying logic

of classical innovation networks). The finding that five out of eight collaboration models rep-

resent a form of innovation contest, which is clearly an open innovation method (Piller and

Walcher, 2006, Walcher, 2009), highlights the trend of signaling collaboration interest by

posting an innovation problem to the outside of the firm. Recent literature describes this activ-

ity of broadcasting a problem to the external as central for distributed innovation (Lakhani et

al., 2006; Howe, 2006; Boudreau et al., 2011). Traditionally, an organization defines an inno-

vation problem and searches for the ideal partners with solving competences (Katila and Ahu-

ja, 2002). This behavior is anchored in the theory of organizational learning (Huber, 1991;

March, 1991). In this case, the organization is able to control search fields, evaluations and

selection processes. However, we found that executing search for external knowledge is also


175

part of open innovation that is reflected in the collaboration Types 2 and 3. As a result of this

discovery, broadcasting a task and searching for external knowledge, as two different options

in designing the openness of a firm, should be the subject of further research. Identifying con-

tingency factors for the application of these activities would contribute to the understanding of

the relationship between openness and innovation performance. So far a comparative analysis

is missing; research has focused on either examining the broadcast mechanism (Afuah and

Tucci, 2012; Boudreau et al., 2011; Jeppesen and Lakhani, 2010) or the innovation search

(Laursen and Salter, 2006; Katila and Chen, 2008).

Another aspect revealed through our explorative study is that through regulating access not

only the governance of the collaboration is determined, but also the distance from the organi-

zation’s boundary where the collaboration takes place. The locus of innovation as characteris-

tic for open innovation has already been mentioned by Fredberg and his colleagues (2008).

With no restrictions on access it enables individuals to select themselves to be involved in co

operations. As a result of this, participants could stem from different technology domains, as

well as from different geographic regions. In their recent work, scholars isolate the positive

effect of such a distant search and the quality of knowledge on innovation performance that

can result from it (Jeppesen and Lakhani, 2010; Afuah and Tucci, 2012).

According to our findings, we suggest that two parameters constitute openness: (a) the way

collaboration interest is signaled and (b) the locus where collaboration takes place. Combining

both parameters results in a 2x2 matrix describing R&D collaborations based on openness

(Table (B1) 10).

Table (B1) 10: R&D collaboration forms along the dimension of “openness”

Locus of collaboration

Close to firm bounda-ry

Far away from firm boundary

Signaling collabora-tion interest

Direct Co-location Consortium

Indirect Technology clus-ter/parks

Open innovation

Open innovation is defined as a collaboration form where an organization does not directly

control who participates and where collaboration takes place. Open innovation with all the

facets described in our study is distinct from traditional concepts of cooperation, like co-


176

location (Reichhart and Holweg, 2008), technology parks (Loefsten and Lindeloef, 2002), or

consortium (Freeman, 1999; West and Gallagher, 2006), which appear to be more formalized

in their organization. This leads us to the conclusion that on the dimension of openness, com-

panies are giving away ‘control’ over the two parameters of knowledge acquisition and inte-

gration. This assessment is in line with the published lessons of West and Gallagher (2006) in

their investigation of open source software development, where they reported that a lack of

control and governance in the innovation process are characteristics that are not usually found

in traditional industries (p. 319). Table (B1) 10 can serve as a starting point for further re-

search since it introduces for the first time a distinction between open innovation and other

concepts of R&D cooperation. The objective for future research should be to clearly opera-

tionalize single grades of openness. It would be interesting to see how far specific open inno-

vation collaboration forms differ in their openness and the effects that this has on the organi-

zation of collaboration.

Even though our study is explorative in nature and our model makes some limited assump-

tions, our proposed framework seems to be fruitful for mapping the landscape of open innova-

tion. It remains necessary, though, to undertake more confirmatory research to validate our

results in the future, especially regarding the construction of the concept ‘openness’. An inter-

esting task for further research would be to align the eight configuration types along a dimen-

sion of “openness”. Our study provides a good starting point to discuss several commonly

spread indicators for openness. For example, some previous studies have used the degree of

integration to operationalize and investigate the phenomenon of open innovation. However,

our study revealed that the degree of integration plays only a minor role in explaining differ-

ent open innovation models, raising the question of whether this measurement is indeed a suf-

ficient factor for explaining open innovation. Another indicator for openness could be the

non-directional way of initiating collaboration, for example, problem broadcasting, which

targets a large, undefined group for potential interaction. Notwithstanding this, the non-

directional mode does not sufficiently explain the nature of collaboration within open innova-

tion, which gives us reason to assume that there must be another underlying mechanism. A

deeper analysis of our eight configuration types leads us to the conclusion that the core of the

new paradigm of open innovation lies in a different mechanism explaining how firms organ-

ize their acquisition and assimilation process of new knowledge.

In summary, future research ought to focus its efforts on defining a consistent construct of

openness. Our research indicates that openness is a multidimensional construct. We have al-

ready identified two potential dimensions: the locus of innovation and the organization of the


177

how collaboration is initiated. Future research should focus on a detailed investigation of the

openness construct, which would serve to contribute to the understanding of open innovation

and support, in particular, the connection with existing literature on R&D cooperation. Final-

ly, understanding openness better would help managers to extend their firm’s collaboration

portfolio and provide a good basis for selecting a suitable collaboration strategy.


178

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8 Appendix (B1)

Appendix (B1) 1: OIIs survey in the study

No. Open Innovation Accelera-tor

Homepage

1 99 Designs www.99designs.com 2 Big Idea Group www.bigideagroup.net

3 Brain Reactions www.brainreactions.com / brainreac-tions.net

4 Brainfloor www.brainfloor.com 5 Brainstorm Exchange www.brainstormexchange.com/ 6 Cassiber www.corporate.cassiber.com/de/home 7 Communispace www.communispace.com 8 Crowdspirit www.crowdspirit.com 9 Crowdspring www.crowdspring.com 10 Elance www.elance.com 11 Elephant Design www.elephant-design.com 12 Elephant Design + Strategy www.elephantdesign.com 13 Favela Fabric www.favelafabric.com 14 Fellow Force www.fellowforce.com 15 Fronteer www.fronteerstrategy.com 16 Future Lab Consulting www.futurelab.de 17 Gen 3 Partners www.gen3partners.com 18 Guru www.Guru.com 19 Hype www.make-ideas-work.com 20 Hyve www.hyve.de 21 IBM www.collaborationjam.com 22 Idea Crossing www.ideacrossing.com 23 Idea Connection www.ideaconnection.com 24 Ideas To Go www.ideastogo.com 25 Idea Tango www.ideatango.com 26 Ideawicket www.ideawicket.com 27 InnoCentive www.innocentive.com 28 Innovation Framework www.innovation-framework.com 29 Invention Machine www.invention-machine.com 30 Kluster www.kluster.com/home/people

31 LEAD Innovation Manage-ment

www.lead-innovation.com

32 NineSigma www.ninesigma.com 33 Openad www.openad.net 34 Redesign Me www.redesignme.org 35 Rent-a-coder / Top Coder www.rentacoder.com 36 Sitepoint www.contests.sitepoint.com 37 Spigit www.spigit.com 38 Venture2 www.venture2.net 39 Verhaert www.verhaert.com


191

No. Open Innovation Accelera-tor

Homepage

40 VOdA www.vo-agentur.de 41 Wilogo www.wilogo.com 42 Yet2.com www.yet2.com 43 Your Encore www.yourencore.com

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195


Abstract

Open innovation is a new understanding of how to acquire external knowledge, with the pro-

cess being mainly characterized by unconventional forms of collaboration. There is a paucity

of research investigating procedural differences in open innovation collaboration. How

knowledge is transferred from outside of the company into the firm may be influenced by

aspects of organizational structure, and causes differences in costs of coordination. Based

upon a survey of innovation intermediaries, we analyze the costs of their knowledge transfer

activities, with specific focus on the applied search mechanism. We find that variations in

search behavior lead to different coordination efforts. Indirectly searching for information, in

terms of broadcasting a problem and call for possible solutions, produces less coordination

costs than directly searching and determining the breadth and depth of the desired search

field. Furthermore, the extent of communication effort required to perform search, in compar-

ison to the rest of knowledge transfer activities, informs us of how the information processing

is configured. To organize collaboration, we find the first phase of knowledge transfer, initia-

tion of collaboration, to be crucial. Our results demonstrate that depending on the search be-

havior, the collaboration process needs to be organized differently. In an indirect search be-

havior central information processing structures are beneficial, whereas in a direct search

more decentralized information processing is most supportive.


196

1 Introduction

“Open innovation is a paradigm that assumes that firms can and should use external ideas […]

and external paths to market, as firms look to advance their technology” (Chesbrough, 2003a,

p. XXIV). The start of the open innovation era led to a revival of the notion of external

knowledge for a firm’s innovation process. Various studies have shown that firms profit from

the input of several external sources, such as advanced users, customers, experts etc. (Luethje

and Herstatt, 2004; Walcher, Fueller, 2010; Fueller et al., 2013; Piller and Walcher, 2006;

Sakkab, 2002; etc.). After a decade of practicing and researching open innovation, scholars

are now able to characterize the main aspects of this new innovation concept, which include:

types of innovation (in- and outbound, open source, open vs. closed innovation), the effec-

tiveness in terms of what influences performance, and open innovation practices and their

organization (Dahlander and Gann, 2010; Lichtenthaler, 2011; Gassmann et al., 2010;

Lichtenthaler, 2011; Huizingh, 2011). In summary, this literature distinguishes two character-

istics of open innovation that are primarily responsible for the change in how firms innovate

today – the collaboration format and the sourcing mechanism (Fredberg et al., 2008, p. 36-38;

Bingham and Spradlin, 2011, pp. 26-30; Afuah and Tucci, 2012, p. 356; Lakhani, 2006, pp.

23-25; Laursen and Salter, 2006, p. 136).

Taking the collaboration aspect first, scholars report that interaction within open innovation

differs from traditional cooperation within alliances or networks. Terms like crowdsourcing

(Howe, 2006) and mass collaboration (Fredberg et al., 2008; Walcher and Piller, 2006) are

strongly associated with open innovation, and capture a new method whereby numerous ac-

tors come together to share and create new knowledge (Boudreau and Lakhani, 2013). In par-

ticular, the literature discussing distributed innovation and open source software communities

investigates how such collaboration is motivated and organized (Lakhani et al., 2007; Howe,

2006; Harhoff et al., 2003; von Hippel and von Krogh, 2003; West and Gallagher, 2006). This

body of work emphasizes that the behavior of free revealing, driven by prospects for reputa-

tional gain (Allen, 1983, p. 18), community and reciprocity (Henkel, 2008, p. 439), is a pow-

erful mechanism in innovation. Another effective behavior to organize mass collaboration in

this context is the self-selection of individuals to a task (Lakhani et al., 2007, p. 11-13; Poetz

and Schreier, 2012, pp. 205-251).

Second, in order to profit from external sources, scholars identified that searching beyond

organizational boundaries is a beneficial sourcing mechanism (Rosenkopf and Nerkar, 2001;

Laursen and Salter, 2006; Cassiman and Veugelers, 2006; Jeppesen and Lakhani, 2010; etc.).


197

Searching in general is an important behavior that an organization needs to perform to ad-

vance their technologies or their capabilities (Katila, 2002, p. 996). Search supports the crea-

tion of valuable new knowledge (Kogut and Zander, 1992, p. 391; Katila and Chen, 2008, p.

594; Dosi and Marengo, 2007, p. 494) and is an important mechanism for organizational

adaption (Salge, 2012, p. 720) and progress (Greve, 2003, p. 690). Within the understanding

of a learning organization, search plays a central role (Huber 1991, p. 97-99). The recent

search literature in the context of innovation focuses primarily on parameters to configure a

successful external innovation search, e.g. search scope, search timing, or characteristics of

the problem (e.g. Laursen and Salter, 2004, 2006; Rosenkopf and Nerkar, 2001; Katila and

Ahuja, 2002; Katila and Chen, 2008; Afuah and Tucci, 2012). In line with the behavioral the-

ory of the firm (Cyert and March 1963), results from studies have advanced the understanding

of the functional principles behind search and provided advice for adapting search strategies

towards organizational needs. An optimal mix between a wide-open search and a deep explo-

ration of external sources has been found to be positively related to innovation success

(Laursen and Salter, 2006; Chiang and Hung, 2010; Katila and Ahuja, 2002).

The two aspects of collaboration and search are neatly connected by the literature on distrib-

uted problem solving search. This particular literature stream focuses on investigating the

benefits of open innovation collaboration. In general, the research on innovation tournaments

highlights the ways in which large virtual communities efficiently solve the difficult innova-

tion problems of a firm based on their existing knowledge diversity (Boudreau et al., 2011;

Jeppesen and Lakhani, 2010). Afuah and Tucci (2012), for example, emphasize that due to the

progress in information and communication technologies (ICT), virtuality actually turns a

distant search for new knowledge into a local search. They demonstrate that this type of

crowdsourcing is a great mechanism for search at relatively low cost. Many scholars regard

the use of the Internet and the rapid progress in ICT as the cause for the new efficient and

effective collaboration and search activities (Verona et al., 2006; Sawhney et al., 2005; Bald-

win and von Hippel, 2011).

Recent review articles show that a considerable amount is already known when it comes to

identifying what is driving success in open innovation (Dahlander and Gann, 2010; Lichten-

thaler, 2011; Huizingh, 2011). Most empirical studies elaborate on the way specific influential

factors affect the outcome variable. These kinds of studies regard the elapsing process as a

‘black box’. Yet, in order to evaluate the pros and cons of open innovation, it is important to

understand the process within open innovation collaboration (West and Bogers, 2011, p. 32).

Differences in cooperation formats and specific search behavior may affect the organization


198

of open innovation collaboration, especially regarding its cost of coordination. In their review,

Dahlander and Gann (2010) note that costs emerging from collaboration with external actors

are insufficiently investigated (p. 705), especially conditions of openness (p. 707). In order to

plan and manage cooperation with external actors, it is necessary to understand structural re-

quirements for its organization and the costs incurred in selecting the suitable collaboration

strategy (Dahlander and Magnusson, 2008, p. 646).

Our paper provides us with an opportunity to close this gap. The research is based on a survey

of 59 open innovation intermediaries, which results in 65 projects for analysis. By surveying

open innovation intermediaries, we intend to answer how Cooperation and search behavior

affect overall project Coordination effort. Some scholars regard intermediaries as an inherent

part of open innovation (Chesbrough, 2006, p. 136). Moreover, these open innovation inter-

mediaries have established their entire business model around acquisition and creation of

knowledge through open innovation methods (Sawhney et al., 2003). They provide a great

variety of search activities, helping companies design permeable organizational boundaries

and to benefit from different knowledge sources, for example, filtering and selecting the re-

quired information, (Howell, 2006, p. 716). Today, intermediaries are the curators of infor-

mation (Diener and Piller, 2013, p. 17). Interacting with an intermediary can increase the like-

lihood of receiving the required knowledge or can increase the likelihood of finding and using

the right channel to bring self-developed technology to market (Chesbrough, 2003a). Thus, it

allows us to analyze different approaches of search, as well as forms of cooperation.

We find that Coordination effort is caused by intense Cooperation. The kind of search influ-

ences the degree Coordination effort that results. We differentiated between direct and indi-

rect search behavior, which show opposite effects on Coordination costs. Initiating search

directly through scanning activities, leads to a higher Coordination effort than using the indi-

rect search mechanism, like broadcasting innovation problems. Moreover, results indicate

structural differences within the knowledge transfer process with respect to the alignment of

information processing steps. The decomposability of the transfer process functions as an in-

dicator that can be used to draw implications about the suitable organization structure for

open innovation collaboration. We find that centralized processing structures best support

broadcasting activities, whereas decentralized structures are more beneficial for the applica-

tion of direct search activities.

Based on our findings, we offer a valuable contribution to the literature on innovation search

in several ways. First, we take a differential perspective on search by comparing two kinds of


199

search. Second, we open up the ‘black box’ of search to investigate how this activity influ-

ences the relationship between Cooperation and Coordination effort. This means that by put-

ting the knowledge transfer process into the center of analysis, we are able to infer how inter-

action produced by different search activities create cost for open innovation collaboration.

Finally, we provide empirical evidence that organizational structure is a contingency factor

when planning and organizing open innovation collaboration.

This paper is organized as follows. In Section 2 we explore the relevant literature and derive

our research hypotheses. Section 3 describes the empirical backdrop and our approach to data

analysis. Our findings are reported in Section 4. And, finally, we discuss the results and draw

conclusions for research and managerial practice.

2 Literature review and hypothesis development

2.1 Innovation search as an activity within knowledge transfer

Search is an essential part of the innovation process (Laursen and Salter 2006, p. 131), while

generating innovation itself can be understood as a process of knowledge transfer (Grant,

1996, p.120; Teece, 1998, p. 55). Szulanski (1996, p. 28) defines transfer as “dyadic exchang-

es of organizational knowledge between a source and a recipient unit […]”. Argote and In-

gram (2000) agree and consider the knowledge transfer as "the process through which one

unit (e.g. group, department, or division) is affected by the experience of another" (p. 151). In

later work, Szulanski (2000, p. 10) extends his definition and describes knowledge transfer as

“a process in which an organization recreates and maintains a complex, causally ambiguous

set of routines in new settings”. From the perspective of knowledge transfer, search can be

regarded as an activity of solving innovation problems (Szulanski, 1996, p. 26, 2000, p. 13;

Katila and Ahuja, 2002, p. 1184). Have defined an innovation problem, it invites the selec-

tion of a search strategy to find suitable solutions. Rooted in the context of the learning organ-

ization, search practices and rules are part of exploring new possibilities to produce innova-

tions (March, 1991, p. 71). We know from the literature that diverse activities for integrating

external knowledge exist. Such differences in knowledge acquisition behavior stimulate dif-

ferent modes of organizational learning, which, in turn, influences the degree of innovation.

Studies show that the type of search influences how radical the innovation is (Chiang and

Hung, 2010, p. 293; Eisenhardt and Martin, 2000, p. 1109).


200

In our paper we examine search for external knowledge. Conceptualizing innovation search as

a knowledge transfer process allows us to investigate our main concepts from the perspective

of a process. Several scholars have researched components of the process and identified the

central activities and challenges that characterize each process stage. Szulanski (1996, pp. 28-

29; 2000, pp.12-16) suggests four stages in a transfer process: initiation, implementation,

ramp-up, and integration. Szulanski (1996) understands the knowledge transfer process as a

sequence of activities, like problem recognition, problematic or slack search, solution evalua-

tion, information exchange between source and recipient, testing and finally implementing the

knowledge for routine usage. Von Krogh and Koehne (1998, p. 238) break this process down

into three stages: initiation, generation, and transfer, all activities which are performed

throughout the process (Figure (B2) 1). Primarily, they focus on activities such as identifying

sources relevant for knowledge transfer, determining search scope, exchanging knowledge by

communication and interaction (von Krogh and Koehne, 1998, p. 239), or evaluating and im-

plementing new knowledge (von Krogh and Koehne, 1998, p. 241). Comparing both process

models shows that Szulanski’s and von Krogh’s and Koehne’s models differ only in the label-

ing and number of their phases. Szulanski’s implementation and ramp-up phase constitutes

von Krogh’s and Koehne’s generating stage. Thus, for our later argumentation we chose the

three-phase model by von Krogh and Koehne (Figure (B2) 1). Both processes have in com-

mon their involvement of search behavior which takes place at the beginning of the

knowledge transfer process.

Figure (B2) 1: Three-step model of knowledge transfer according to von Krogh and Koehne (1998, p. 238)

Whilst the initiating phase plays an important role, the transfer process should not be dis-

missed as a strictly sequential running process, but rather recognized as iterative in its phase

progression (von Krogh and Koehne, 1998, p. 242). A central aspect of this phase is the iden-

tification and activation of relevant knowledge sources (von Krogh and Koehne, 1998, p.

239). The influence of the attributes of the potential information source on the search behavior

is especially strong in this phase. Szulanski (2000, p. 13) states that “[…], the influences of

the attributes of the source are expected to diminish as the transfer unfolds”. In the context of

open innovation, these source attributes can be understood as diverse cooperation partners,

Initiation Transfer Generation


201

along with their characteristics (Dahlander and Gann, 2010, p. 699; Laursen and Salter, 2006,

p. 131). This suggests that the cooperation type shapes the knowledge transfer process.

In conclusion, we define innovation search as a behavior at the beginning of the knowledge

transfer process whereby an organization strives to solve their innovation problems. Relevant

to open innovation are the activities of identifying and activating the external knowledge

source to release their knowledge for transferring into the firm. Thus, we focus our research

on different search activities that initiate knowledge transfer.

2.2 Search in open innovation

As already mentioned in the previous section, search is an activity within the innovation pro-

cess which is directed towards generating information to solve a given innovation problem

(Szulanski, 1996, p. 26, 2000, p. 13; Katila and Ahuja, 2002, p. 1184). The studies to date

have concentrated on identifying parameters that improve such innovation search. Katila and

Chen (2008) investigated the search timing and found that innovative firms adapt their search

timing according to the competitors’ search behavior. Thus, the activity of search is not inde-

pendent but instead related to the behavior of other actors in the innovation network.

Rosenkopf and Almeida (2003) also inquire into the relational aspect of search. They extend

the research by isolating two mechanisms, alliances and mobility, which help firms to reach

beyond their existing search fields. The authors demonstrate that acting in alliances and with

increased mobility, which facilitates the knowledge flow, enables firms to bridge larger tech-

nological distances. Consequently, these two mechanisms help firms to overcome the problem

of a localized search (Lakhani et al., 2007; Stuart and Podolny, 1996). At the level of indi-

viduals, Borgatti and Cross (2003) identify relational characteristics that predict search behav-

ior. They declare that the decision to seek information depends on the type of knowledge a

person holds and how valuable and accessible that knowledge is. Research that distinguishes

between the different types of knowledge search found that particular categories affect prod-

uct innovation. For example, Koehler et al. (2012) research the impact of science vs. market-

driven search and reveal that science-driven search stimulates new-to-market innovations (p.

1349). Adopting the process perspective of search, Knudsen and Levinthal (2007) investigate

the generation and evaluation of search alternatives and showed an order effect of alternatives

on evaluation. The authors conclude that the starting position of search matters. The tradi-

tional search literature covers a range of important aspects that extend the understanding of

organizational learning within networks. The literature of technology transfer adds an im-


202

portant aspect regarding the organization of search. This body of works understands search

through the decision to either make (internal R&D) or buy (external knowledge acquisition)

technology to increase innovative performance (Cassiman and Veugelers, 2006; Arora and

Gambardella, 2010; von Hippel, 1988; Veugelers and Cassiman, 1999). Results of the studies

are consistent in their finding that internal and external innovation search activities should be

balanced to receive the best outcome for a firm.

Open innovation offers the external perspective on the role of search within the innovation

process. Laursen and Salter (2006), with their work which defines openness as the balance

between search breadth and depth, were the first researchers connecting open innovation to

the traditional search literature (Cassiman and Veugelers, 2006; Rosenkopf and Nerkar, 2001;

Katila and Ahuja, 2002; Katila and Chen, 2008) and continued the work of Katila and Ahuja

(2002) on search depth and scope. In the context of open innovation it is central to determine

a search scope wide enough to allow access to external, radical, and new knowledge. Such an

optimal structure of organizing search is the response to the local search problem (Laursen,

2012, p. 1195), which constitutes a major challenge within innovation (Rosenkopf and

Nerkar, 2001; Rosenkopf and Almeida, 2003; Stuart and Podolny, 1996). Theorists assume

that a firm’s R&D activity is influenced by previous activities (Rosenkopf and Nerkar, 2001,

p. 287) and therefore their search behavior is technologically and geographically constrained

(Rosenkopf and Almeida, 2003, p. 751).

Other scholars examine a further approach of search used to overcome the problem of local

information seeking. They consider the firm’s possibility to interact with a community of di-

verse actors. Such interaction with a community is regarded as a new form of collaboration

(Pisano and Verganti, 2008), since firms can take advantage of crowdsourcing (Howe, 2006)

and co-creation (Zwass, 2010) effects when seeking information. Different literature streams

look at the benefit of community involvement during the innovation process. A first insight

into new possibilities to organize innovation is provided by the literature on open source

software. Researchers found that the joint effort involved in solving an innovation problem

constitutes a major benefit of community membership (Franke and Shah, 2003, p. 164). In-

formation seeking is supported by the mechanism of free revealing information by the com-

munity members (von Hippel and von Krogh, 2003, p. 209; von Hippel, 2001, pp. 7-8; Mahr

and Lievens, 2012, p. 169). Such free sharing of information causes the necessary spillovers

to create innovations (Harhoff et al., 2003, pp. 1767-1768) and is seen as a common driver

within these specific user communities. Firms adapt this mechanism of openness to their suit-


203

ed purpose and selectively reveal their technology to encourage a form of informal develop-

ment collaboration (Henkel, 2006, p. 953).

A second literature stream incorporating the benefits of community interaction when organiz-

ing innovation search constitutes the broad research on innovation contests (Terwiesch and

Xu, 2008; Jeppesen and Lakhani, 2010; Afuah and Tucci, 2012; Boudreau et al., 2011; Fuel-

ler, 2010; Bullinger et al., 2010). According to Afuah and Tucci (p. 355), who provide a defi-

nition of contest generally accepted among scholars in this area, contests are based on the

technique of crowdsourcing. A task is outsourced to a “crowd” in form of an open call. Con-

sequently, “each agent from the crowd self-selects to work on its own solution to the problem,

and the best solution is chosen as the winning solution”. Features like the fixed time span and

defined award for the winning contribution create the competitive nature that is inherent in

contests and the main characteristic of this approach. Regarding the target of a contest, the

literature distinguishes between market-driven and science-driven contests (Koehler et al.

2012). Whereas the former is directed towards identifying market trends and new product

ideas (e.g. Piller and Walcher, 2006; Fueller, 2010; Bullinger et al., 2010), the latter focuses

on generating specific solutions to precise technological problems (e.g. Terwiesch and Xu,

2008; Jeppesen and Lakhani, 2010; Afuah and Tucci, 2012; Boudreau et al., 2011).

In particular, the literature on science-driven contests, or so called ‘tournament-based

crowdsourcing’ (Terwiesch and Xu, 2008; Jeppesen and Lakhani, 2010; Afuah and Tucci,

2012; Boudreau et al., 2011), focuses on researching the functional principles that lie behind a

contest and their effect on innovation performance. One aspect of this considers how the con-

test format changes the search for new knowledge. Concerning the search scope especially,

scholars investigate how particular features of a community affect the diversity of generated

knowledge. They show that a large pool of contestants increase the innovation performance,

particularly through tapping into unknown knowledge domains achieved by bridging large

distances (Boudreau et al., 2011; Jeppesen and Lakhani, 2010; Terwiesch and Xu, 2008).

Firms profit from the relational aspect behind this search form, which describes how commu-

nity members collaborate and interlink (Pisano and Verganti, 2008; Franke and Shah, 2003;

von Hippel and von Krogh, 2003; Mahr and Lievens, 2012). Bianchi et al. (2011) showed that

especially weak ties are beneficial to enter new knowledge fields (p. 31).

Concluding from the literature review, search in the light of open innovation has many facets.

Traditionally innovation search is seen as a part of organizational learning (Huber, 1991, p.

90). Furthermore, in the innovation context search is a problem-solving activity seeking to


204

advance technology (Katila and Ahuja, 2002, p. 1184), which is also the case in open innova-

tion (Afuah and Tucci, 2012; Boudreau et al., 2011). What is original in open innovation ap-

pears to be the organization of search provoked by recent technological development. The

literature review showed that the notion of open innovation is often related to the technologi-

cal progression in recent decades in the field of interaction and communication (Wittke and

Hanekop, 2011, p. 36-37; Sawhney et al., 2005, pp. 6-7). The trend of digitalization leads to

easy access to new knowledge, especially at low costs (Pisano and Verganti, 2008, p. 78;

Mahr and Lievens, 2012, p. 169). Additionally, collaborative software tools allow for higher

numbers of participants to join projects (Sawhney et al., 2005, p. 6-7). Taken together, this

enables knowledge to travel faster and across greater distances by intentionally provoked in-

formation spillover effects (Harhoff et al., 2003, p. 1767).In the next paragraph we identify

how these changes in innovation search are related to the organization of collaboration with

new product development.

2.3 Organizing search in innovation

Pisano (1991) stated that changes in cooperation forms affect the organization of innovation

activities (p. 237). Two dimensions have been researched in this context: (1) the governance

form of collaboration and (2) the associated costs for Coordination.

Regarding the governance of collaboration, the literature on alliances and networks describes

how traditionally innovative activities, like search, happen within administrative hierarchies

(Pisano, 1990, p. 237). However, a recent trend of involving more diverse partners into coop-

eration (Pisano and Verganti, 2008, p. 78) demands an adaption of governance structures to

profit from these new collaboration forms. Pisano and Verganti (2008) highlighted that col-

laboration networks differ in their governance form. They found that aside from a hierarchical

governance form a rather flat organization structure occurs (p. 80). Transferring this finding to

the topic of search, managers have to decide who defines problems that need to be solved and

who chooses the solutions.

In a different body of literature this issue of organizational structure is termed ‘centralization’

(Mansfield, 1973; Argyres and Silverman, 2004). Scholars capture this notion of centraliza-

tion in the context of innovation search. Siggelkow and Levinthal (2003), for instance, focus

on how to manage exploration and exploitation by organizational design. They found that

organizational structure is also the subject of balance. The authors suggest a temporal se-

quencing of decentralized and centralized organizational structures, since this can lead to


205

higher performance (p. 665). They adopt the perspective of interdependencies between divi-

sions. For example, in the context of innovation search this means that a high interdependency

calls for a more decentralized search, whereas centralization allows for the refinement of

search results and its coordination across divisions (p. 665).

A final avenue of literature dealing with the topic of organizing search to solve innovation

problems analyzes organizational design and its modularity (Baldwin, 2007; Brusoni et al.,

2007; Baldwin and von Hippel, 2011), as well interlinking organizational structure and asso-

ciated costs for coordination. The primary subjects of analysis are decisions or tasks and their

dependencies (Baldwin, 2007, p. 162). Theorists take the problem-solving perspective, with

their core element in determining organizational design being the modularity of problems.

Modularity means the decomposability of problems into independent sub-problems. Problem

modularity is associated with an increased speed of search for solutions (Brusoni et al., 2007,

p. 130) and also affects coordination effort. In high modular organizational designs, costs for

coordination are reduced since coordination across divisions is unnecessary (Sanchez and

Mahoney, 1996, p. 68; Baldwin and von Hippel, 2011, p. 7; Argyres and Silverman, 2004, p.

930), which can be explained by the absence of interdependencies which organization design

scholars define as coordination costs (Gulati and Singh, 1998, p. 785). For example, coordina-

tion costs arise from identifying the right external partners and aligning them to jointly solve

the innovation task (Gulati and Singh, 1998, p. 782). In sum, this research shows that the

characteristic of process decomposability is an important aspect when analyzing governance

form and cost of collaboration.

The work of Alexy and his colleagues (2013) demonstrates the effect of decomposability on

the organization and cost of coordination for open innovation. They specifically discuss bene-

fits of the open innovation collaboration forms that a firm elicits by selectively revealing

knowledge. They argue that the costs of forming and managing such collaboration forms are

assumed to increase non-linearly (p. 274). However, mechanisms to reduce costs are neces-

sary in order to benefit from broad search scopes (p.276), especially in cases of open innova-

tion where the number of potential collaboration partners is increased. The authors differenti-

ate four revealing strategies, each demanding different organizational structures. For instance,

revealing a problem in the form of an open research call is organized as crowdsourcing (p.

282). When using this specific revealing strategy, which usually is performed when applying

a type of contest, Alexy and his colleagues state an initial cost benefit. However, the authors

explain that additional costs for coordinating knowledge acquisition arise by contracting ac-

tivities occurring later in the knowledge transfer process (p. 276). Pursuing this revealing


206

strategy establishes the knowledge transfer process as a decomposable task. Activities, such

as defining problems and selecting solutions, within the process can be performed inde-

pendently and, consequently, the governance form can be adapted to a particular sub-task.

2.4 Conceptual model and hypotheses

In this paragraph we integrate the results of the previous literature review on innovation

search as a knowledge transfer activity and its organization in the light of open innovation.

Based on this integrative perspective, we derive our conceptual model guiding the empirical

analysis. Our objective is to understand how open innovation collaboration can be organized

against the background interplay between Cooperation, innovation search and Coordination.

Search is an activity at the beginning of the knowledge transfer process that is directed to-

wards finding alternative solutions to a given problem. In the context of open innovation,

search activities focus on identifying mainly external sources holding alternatives to a recent

innovation task. So far, the work on open innovation search has focused on investigating prin-

ciples of search, for instance, search scope, or different search types which are defined by

their purpose, e.g., science/market-driven search. Characteristic of open innovation search is

the reach far beyond a firm’s boundaries achieved by bridging large distances to acquire new

technology. To profit from the variety of external partners, we find that the organization of

search in terms of its governance structure and associated costs is crucial, particularly mecha-

nisms within search activities which initiate the transfer of knowledge from the external

source into the firm.

We intend to investigate the relationship between mechanisms of search and the effect on the

effort to coordinate an open innovation project. For a comprehensive understanding of collab-

oration, Gulati et al. (2012, p. 568) demand a balanced view of the two facets, Cooperation

and Coordination. They argue that even in situations of perfect Cooperation between part-

ners, Coordination is necessary to successfully complete a joint task (p. 533). The cooperation

perspective captures the relational component, the alignment of interests including a common

understanding of the project task, share of knowledge and contributions etc. (p. 533). On the

other hand, the Coordination perspective places its focus on technical and administrative con-

cerns. Central to this is the management of process activities in terms of division of labor,

which means achieving alignment and adjustment of partners’ actions (p. 531). Gulati et al.

(2012), furthermore, propose concepts about how both facets are interdependent (pp. 555-

566), regarding Cooperation and Coordination to be complementary (p. 559). We follow Gu-


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lati et al.’s (2012) request to consider cooperation and coordination when analyzing collabora-

tion.

In this setting the Cooperation facet mirrors how partners of the collaboration interact with

each other. Here the quantity and quality of interaction mainly influences the creation of valu-

able knowledge (Mahr and Lievens, 2012, p. 169). Particularly, solving innovation related

tasks demands intense communication (Grant, 1996, p. 115), since they are characterized by

high uncertainty (Anderson et al., 2001, p. 134). The effort to achieve the project objectives

reflects the Coordination facet. Task distribution and reintegration, as well as task uncertainty

and the degree of interdependencies between the involved partners, mainly influence the Co-

ordination effort (Argote, 1982; Galbraith, 1977; Thompson, 1967 in: Gulati et al., 2012, p.

538). As Gulati et al. (2012) note, both collaboration components are naturally interlinked.

Researchers show that cooperation involves information processing which consequently cre-

ates costs incurred from coordinating activities (Puranam et al., 2012, p. 419).

In our model we regard the process of knowledge transfer as a connecting element between

Cooperation and Coordination. Scholars show that communication between different parties

is the basis to retrieve and transfer relevant knowledge (Kogut and Zander, 1992, p. 389;

Grant, 1996, p. 117). Increased communication across collaboration participants is necessary

to coordinate activities for achieving such shared knowledge (Kogut and Zander, 1992,

p.389). Furthermore, researchers relate activities within the knowledge transfer process, like

problem definition, alternative search, selection, and evaluation, to produce Coordination

costs (Pisano and Verganti, 2008; Alexy et al., 2013). For instance, the search scope influ-

ences costs in that costs for screening external sources increase with a growing network size

(Pisano and Verganti, 2008, p. 80). Alexy et al. (2013, p. 276) state that emerging screening

costs can be reduced by an open offer to collaborate. For our conceptual model we therefore

assume a mediating role performed by the knowledge transfer process between Cooperation

and Coordination within open innovation collaboration (Figure (B2) 2). By adopting a pro-

cess view we are able to derive implications regarding the organizational structure to arrange

open innovation collaboration. Literature has shown that we can achieve this through an anal-

ysis of its decomposability (Siggelkow and Levinthal, 2003, p. 651; Sanchez and Mahoney,

1996, pp. 64-69).


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Figure (B2) 2: Conceptual model of open innovation collaboration

Earlier we introduced a three-stage process model for knowledge transfer (Figure (B2) 1) con-

sisting of the phases Initiation, Generation, and Transfer. We already highlighted the special

role of the first stage (Szulanski, 2000, p. 13) with innovation search as the major activity

(von Krogh and Koehne, 1998, p. 239). Furthermore, this stage forms the starting point for

collaboration. Therefore, we concentrate our analysis primarily on Stage Initiation. An inte-

grative analysis of search practices (in section 2.2) and its organization (in section 2.3) iso-

lates two contrasting search mechanisms.

The first one we term direct search (Gulati et al., 2012, p. 577). The mechanism of direct

search describes the search as a decision about boundaries of the search field. This direct

search mechanism is operationalized by possible requirements to qualify for the actual col-

laboration by defining criteria like expertise, experience etc. The regulation of openness to

join collaboration directly influences the governance arrangements (Gulati et al., 2012, p.

577).

The second mechanism is incorporated into practices like broadcasting an innovation problem

within an innovation contest (Lakhani et al., 2007; Jeppesen and Lakhani, 2010; Afuah and

Tucci, 2012; Boudreau et al., 2011) or selectively revealing information about a problem or a

solution to a problem (Henkel, 2006; Alexy et al., 2013). Both activities describe individuals

who are motivated to self-determined participate in collaboration (Alexy et al., 2013, p. 276).

In the following we call this mechanism indirect search, as the opposite of direct search.

Here, the initiator of the search indirectly signals the requirements to join collaboration by the

broadcasted innovation task. An active search for suitable partners does not take place; rather

a firm passively selects individuals in contrast to a direct search for partners.

To answer the question of how search within open innovation is being organized, we need to

bring together all components of the model. The interplay between Cooperation, Coordina-

tion costs, and Search activities and its effect on the design of the knowledge transfer process

answers the question considering the organizational structure for open innovation collabora-

tion. Within this, the decomposability of the knowledge transfer process plays a crucial role.

We regard knowledge transfer as a problem-solving process, which, consequently, consists of

Knowledge transfer process Cooperation Coordination


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decomposable sub-tasks. We hypothesize a relationship between the search mechanism and

decomposability of the process because literature has already demonstrated the process shap-

ing effect of search (Szulanski, 2000, p. 13). As the literature review revealed, Alexy and his

colleagues (2013), for example, describe decoupled transfer activities when choosing the indi-

rect search mechanism. They argue that the activity of selectively revealing an innovation

problem is separate from contracting potential solutions to the problem (p. 276). Thus, the

authors regard the knowledge transfer process to be decomposable and therefore flexible

enough to embed into a specific organizational structure. The aspect of task modularity con-

nects perfectly with the literature on centralization of organization. Siggelkow and Levinthal

(2013) propose a balance of a centralized and decentralized structure to enable a firm to also

balance their exploration, such as search for technologies, and exploitation activities, such as

integrating and applying new technologies (p. 650). According to their findings, a temporal

sequencing of organizational structures can lead to higher performance. They regard the initial

phase of knowledge transfer as being particularly associated with higher costs, leading them

to suggest a decentralized organization structure at this point (p. 665). The authors focused

their research on the direct search mechanism in Stage Initiation.

Consequently, we suppose the following hypotheses, which are summarized in the final con-

ceptual model (Figure (B2) 3). Regarding the organizational structure, we hypothesize that in

the case of direct search we expect no decomposability of the knowledge transfer process

because sub-tasks, like defining recruitment criteria, evaluating the quality of the knowledge

the potential partner provides and contracting them into collaboration, are highly interdepend-

ent. That means the relationship between Cooperation, the intensity of interaction during the

entire project, and Coordination should be fully explained by the presence of search activity.

In contrast, we assume we will find a partial mediation effect of search activity in the case of

indirect search because activities such as broadcasting an innovation problem, evaluating and

selecting contributions are independent tasks. Considering the cost of organizing open innova-

tion collaboration, we can theorize based on the literature that the indirect search activities,

due to their modularity, produce fewer costs than the direct search activities.


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Figure (B2) 3: Final conceptual model of open innovation collaboration

* In this paper, subject of analysis is the Stage Initiation only.

H1: Organizational structure

The mediation effect of the search mechanism between cooperation frequency and

coordination effort indicates the decomposability of the knowledge transfer process.

H1a: The direct search mechanism fully mediates the relationship between cooperation fre-

quency and coordination effort and indicates a non-decomposability of the knowledge

transfer process.

H1b: The indirect search mechanism partially mediates the relationship between coopera-

tion frequency and coordination effort and indicates a decomposability of the

knowledge transfer process.

H2: Organizational costs

Ceteris paribus, the direct search mechanism results in a higher coordination effort

than the indirect mechanism of search.

Initiation

indirect search vs. direct search

Generation* (transform)

Transfer* (select)

Cooperation

Interaction to ex-change information

Coordination

Effort to monitor and coordinate project progress

c

Controls

Method / Service Community size Information type

a b


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3 Data and Methods

3.1 Sample and procedure

To empirically test our hypotheses on the organization of open innovation our sample needs

to satisfy several requirements. Conventionally, open innovation projects are observed within

and/or across organizations. The major disadvantages of this approach are, firstly, that it does

not generate enough cases and, therefore, insufficient variance according to the core variables.

Secondly, open innovation projects within a firm cannot be clearly isolated from their sur-

rounding organizational structures to properly interpret resulting correlations. Finally, we

need an analysis perspective that allows a neutral description of recent open innovation prac-

tices in terms of a detailed insight into how firms get connected with their external environ-

ment and how the knowledge acquisition process is organized.

3.1.1 Sample characteristics

For the purpose of our paper we translate collaboration within open innovation by investigat-

ing intermediaries in this field. Our analysis is based on the assumption that a firm’s choice

for a specific open innovation broker equates to the choice for certain open innovation collab-

oration types. The intermediaries’ services mainly consist of the methodological integration of

various kinds of actors to generate knowledge that is requested by the firm. It is broadly ac-

cepted, and published, that intermediaries core functions are the generation and transfer of

knowledge, especially from external sources (Nambisan and Sawhney, 2007; Lopez-Vega,

2009; Howells, 2006; Verona et al., 2006). Their entire business model comprises dedicated

methodological approaches, for instance, crowdsourcing idea jams, innovation contests, lead

user workshops, technology scouting etc., to organize and profit from open innovation collab-

oration. Analyzing the intermediaries’ projects allows us to get an insight into search activi-

ties, as well as aspects of cooperation and coordination.

Since innovation intermediaries are a very specific market, we strived for a nearly complete

sample. We performed different steps to compose our sample. We had an initial list of open

innovation intermediaries who participated in an earlier version of this survey (Diener and

Piller, 2010). We added further intermediaries that were listed in recent academic publica-

tions, working papers, or other studies on open innovation (Mortara, 2010; Bakici et al., 2010;

Lopez-Vega and Vanhaverbeke, 2009; 2010). After doing so, we identified potential respond-

ents by extensive online research, starting from websites like crowdsourcing.org. Finally, we

conducted a network approach to enrich our sample. Key informants were mainly founders of


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the intermediary firm. To further assure the representativeness of our sample, participating

firms had to confirm that they operate in an open innovation context13.

From the 162 firms invited we received sets with sufficient answers for analysis in 59 cases.

This equals a 36.4 percent response rate, which is the average reported in literature (Shih and

Fan, 2008, p. 257) and is quite a good average with respect of achieving a complete sample.

Moreover, response rates for online-surveys are generally lower than response rates achieved

by other modes (Manfreda et al., 2008, p. 97). Regarding the non-response bias, the response

rate is sufficient for data analysis (Hansen and Smith, 2012, p. 256). The online survey was

designed to describe the landscape of open innovation intermediaries, therefore participants

were asked to describe their business model, outlining the services they offered, including

how they collaborate with communities and general project characteristics. Each service they

named stood for a single project type of how open innovation collaboration could be orga-

nized. Thus, raw data from the survey were prototypical projects of open innovation collabo-

ration the intermediaries were engaged in and they formed the input for our statistical analy-

sis. Intermediaries had the chance to describe more than one service, yet the majority (55 cas-

es) answered in regards to their main offer, and therefore their main approach when interact-

ing with a community to create knowledge. Three intermediaries referred their answers to two

different services, and one intermediary even answered the survey for three different types of

open innovation collaboration. In total our final sample contained 64 open innovation project

types of which 52 provided sufficient information for analysis.

The descriptive analysis of our sample showed that intermediaries had been in business for

7.5 years on average. The development of that market showed two peaks in total. Around the

year 2000 the first and still established open innovation intermediaries emerged. The second

peak between 2007/08 brought about intermediaries focussing on co-creation. This finding is

in line with the publishing period of research on such kind of intermediaries (Verona et al.,

2006; Sawhney and Prandelli, 2000; Nambisan and Sawhney, 2007). Aside from the age of

the intermediary’s business, the number of projects can be considered as an indicator for

experience. On average, intermediaries performed 90 projects in a year, with a typical

duration of three months. Whithin this time, the project objective was to create ideas or

sophisticated concepts for a certain task in 52.3 percent of all cases. The average share in total

13 We asked three questions regarding the intermediaries belonging to open innovation. (1) Our company per-forms open innovation according to its common understanding? (2) Our company is a very good example of open innovation? (3) We are proud to be a part of the open innovation community? (Appendix (A) 6; Q 23-25) For all three questions we found an affirmation between 70-91%.


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revenue of the collaboration approach the intermediaries reported to predominantly apply was

42.7 percent.

3.2 Measurements

In this section we describe how we measured our key variables. In general, we measured the

main constructs by applying slider-scales instead of presenting Likert-scales. The underlying

scale reached from 0-100 with two decimal points precision, which allowed us to continuous-

ly measure individual preferences. Sliders were presented in the middle of the scale and re-

spondents did not receive numeric feedback when they were moving them. The system recog-

nized if sliders were touched.

3.2.1 Dependent measure of Coordination cost

Different bodies of literature offer varying perspectives on costs for Coordination, ranging

from modeling transaction costs in the economic literature (Gulati and Singh, 1998), to meas-

uring project specific resources (Jeppesen and Lakhani, 2010; Baldwin and von Hippel,

2011)14 to capture individual perceived effort estimates (Zoogah et al., 2011) in the human

resource literature. The choice for a specific cost measure strongly depends on the research

context and objective. In the present study we are interested in how Cooperation influences

Coordination cost within open innovation collaboration. Key figures like project duration or

average project costs are too approximate estimates to detect differences in open innovation

collaboration. Consequently, we focus on the efficiency aspect of Coordination, the relative

cost for performing coordination (Gulati et al., 2012, p. 537). In our setting, the best predictor

for costs is a self-evaluation of perceived Coordination effort. We asked respondents to rate

the effort to monitor15 and coordinate16 activities of goal achievement on one item. They had

to give an estimate for each of the three stages of knowledge transfer process (see Figure (B2)

1). The continuous answer scale ranged from “very low” (0) to “very high” (100) effort (Part

A, Appendix (A) 6; Q 80). For analysis we used the aggregated mean value of Coordination

effort across all phases.

14 Baldwin and von Hippel (2011) in their work take costs for design, communication, production, and transac-tion into consideration (pp. 1403-1404). In the setting of problem solving, Jeppesen and Lakhani (2010) use the award values for winning problem solution and the time that was invested to generate the solution to measure the seeker and solvers effort (pp. 1023-1025). 15 We provided the following explanation on the level of activities for monitoring: Monitoring effort – compar-ing planed and realized performance, time keeping. 16 We provided the following explanation on the level of activities for coordination: Coordination effort – assign tasks to parties, integrate results, plan activities for participating parties.


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3.2.2 Cooperation

Cooperation between collaboration partners is essential for the success of a joint project (Gu-

lati et al., 2012, p. 532). To ensure interests align and knowledge exchange information is

shared are necessary conditions (Gulati et al., 2012, p. 569; Kogut and Zander, 1992, p. 389;

Grant, 1996, p. 117). To determine the extent of Cooperation between different parties within

an open innovation project, we chose to take the frequency of interaction as an appropriate

indicator. It is a commonly used indicator when measuring interaction degrees. For example,

the literature on team creativity in new product development describes it as communication

density (Leeders et al., 2003, p. 77). Marketing literature applies this measurement, for in-

stance, to investigate effects of different communication modes (e.g. telephone, face-to-face)

on collaborative communication (Mohr et al., 1996, pp. 107-108).

We take communication intensity measured by one item to operationalize Cooperation within

open innovation collaboration. Respondents had to answer the question “How would you de-

scribe the intensity of interaction/communication?” by moving a slider ranging from “very

low frequency” (0) to “very high frequency” (100) (Part A, Appendix (A) 6; Q 60). Addition-

ally, we provided a short explanation of how to understand interaction frequency, which is the

total number of exchanges of information, using phone calls, email, meetings, conferences,

blogs etc. Again, respondents had to give an estimate of interaction frequency for each phase

of the knowledge transfer process (initiation, generation, and transfer). For the analysis, we

used the aggregated mean value over all phases.

3.2.3 Mechanism of search as mediator

To measure search, we asked for activities that would indicate the two different mechanisms

we found in our literature review: direct search (Gulati et al., 2012; Laursen and Salter, 2006;

Katila and Ahuja, 2002; Knudsen and Levinthal, 2007) and indirect search (Jeppesen and

Lakhani, 2010; Bourdreau et al., 2011, Alexy et al., 2013). Researching behavior has a tradi-

tion in consumer research. Here, for example, individual search behavior is examined (Moore

and Lehman, 1980, p. 298; Johnson et al., 2004, pp. 302-304). Since scholars in innovation

management primarily focus on the organizational level, search is mostly operationalized by

determining parameters like breadth, depth, or distance through patent citations, number of

external sources, or patent portfolio (Laursen and Salter, 2006; Katila and Ahuja, 2002;

Laursen et al., 2010).


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In this paper we focus on different open innovation collaboration forms and we are therefore

interested in how search is performed at the level of activities. As a result, we asked questions

that would suggest the frequency of use, ranging from hardly ever (0) to very frequent (100),

for two possible search activities: (1) we widely search for single or a group of individuals

fitting the task requirement(s) and ask for their participation (direct search); (2) individuals of

our community select themselves to project request(s) we post to them (indirect search) (Part

A, Appendix (A) 6; Q 52). A Pearson correlation of both items with one another demonstrated

that we could not assume a relationship between both mechanisms (ρ=-0.115, p=0.368); they

appear to be independent from each other.

We validated our assumption by correlating both items with the question, an approach which

is followed in the average project, functioning as an external criterion for search behavior.

Respondents had to move a slider ranging from “we rather call17 for individuals joining pro-

jects to solve requests/tasks” to “we rather search for information/individuals joining project

to solve requests/tasks” (Part A, Appendix (A) 6; Q 56). We found significant correlations

between our items and the corresponding pole of the external criterion (direct search ρ=-

0.458, p=0.000; indirect search ρ=0.288, p=0.022). Thus, we can confirm that both search

activities can be performed in open innovation collaborations.

3.2.4 Control variables

Variables that might be correlated with effort to coordinate open innovation collaboration

were included as controls. First, the Coordination effort is influenced by the number of part-

ners that are involved in collaboration (Gulati and Singh, 1998, pp. 781-782). Research on

innovation teams uses the team size as a standard control variable (Bercovitz and Feldman,

2011, p. 86; Hoegl and Proserpio, 2004, p. 1158). In the context of open innovation, size is

represented by the scope of the external network. Members of the external network are fre-

quently called “community”. Consequently, we use the community size as an indicator of

collaboration size (Part A, Appendix (A) 6; Q 37). To measure community size we adopted the

natural logarithm of the total number of community members because of a high skew and

large interval when case numbers were small (Rosenkopf and Almeida, 2003, p. 760). Inter-

mediaries differ in the service they offered to connect firms and potential external partners.

The methodological approaches behind these services form a second control variable since the

method comprises the kind of search activity that is executed to generate knowledge. Our lit- 17 The term “call” is used to describe the broadcasting mechanisms in journals addressing practitioners (Howe, 2006; Boudreau and Lakhani, 2009; Reichwald and Piller, 2009; Afuah and Tucci, 2012) and is therefore ade-quate to use in our sample.


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erature review revealed that, depending on the purpose of search, the method to organize

search can vary. In the field of customer integration, for instance, we reveal new market re-

search methods, ranging from idea or design contests over lead user workshops to observing

consumer behavior online (Kozinets, 2002; Piller and Walcher, 2006; Luethje and Herstatt,

2004). We assume that a lead user workshop requests a different organization of collaboration

than an online design contest with customers. Correspondingly, we expect that characteristics

like task distribution, content generation and evaluation of the different methods might influ-

ence the effort of coordinating such a project. To determine methodological approaches that

were used to organize open innovation collaboration, we asked our respondents to self-

categorize their service into pre-established method categories: (1) contests (2) workshops (3)

market and technological search18 (Table (B2) 1) (Part A, Appendix (A) 6; Q 26).

We deliberately left the description statements broad enough and provided keywords so that

intermediaries gained an understanding of the particular approaches and were able to classify

their service. To control for biases arising from our method, we validated the self-

categorization with a prior self-performed assignment of invited intermediaries to those meth-

od categories. The inter-coder reliability revealed an almost perfect agreement (Cohen’s Kap-

pa=0.919, p<0.01) (Landis and Koch, 1977). We dummy coded the three categories for later

regression analysis.

Table (B2) 1: Method categories for analysis

Method category Coding N=64

Workshops DUMMY_1 18

Contests DUMMY_2 32

Search (technological and market) DUMMY_3 14

As a last control we consider the type of information that is intended to be generated within

open innovation collaboration. The literature offers two different types: need and solution

information (Ogawa 1998, p.778; von Hippel 1998, p. 630). Need information relates to

knowledge about preferences, desires etc. of customers, while solution information refers to 18 Initially we offered separate categories for market and technological search. Yet due to low sample sizes in the single search method categories (nmarket search =8; ntechnological search =6) and the common underlying methodological approach of active searching we merged both categories to one called “search”.


217

knowledge needed to solve technological problems. Both information types are needed to

overcome uncertainties within the innovation process (Cassiman and Veugelers, 2006;

Thomke, 2003) and are relevant at different points in the innovation process. Whereas the

right need information answers the question of what to innovate, the right solution infor-

mation supports an efficient realization of the innovation task (Piller et al., 2008, p. 56). Thus,

we expect that the handling of these two types of information affect the Coordination of the

collaboration differently. The literature suggests that need information is characterized by its

‘stickiness’, which makes it costly to transfer (von Hippel, 1998, p. 630; Ogawa, 1998, p.

778). We measured the information type by applying a single-item scale asking for what type

of information is typically generated in a project. The answer scale ranged from “rather tech-

nological” (0) to “rather market/customer oriented information” (100) (Part A, Appendix

(A) 6; Q 53).

Descriptions of all of the variables used in our analysis are provided in Table (B2) 2, and de-

scriptive statistics and correlations are in Table (B2) 3.

Table (B2) 2: Variable definitions

Variable Definition

(1) Coordination Effort of monitoring and coordinate activities for goal achieve-ment ranging from low to high effort

(2) Cooperation Interaction frequency as total number of information exchange ranging from low to high frequency

(3) Direct search Extend of behavior of actively searching for alternatives

(4) Indirect search Extend of behavior of passively searching, mainly by broadcasting an innovation task, for alternatives

(5) Information type In a project generated information ranging from technologi-cal/solution to market/need information

(6) Community size Natural logarithm of the number of community members

(7) Method category Intermediaries classification to a specific method type: work-shops, contests, search (technological and market search)


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Table (B2) 3: Descriptive statistics and correlations

Variable Mean Std. dev. (1) (2) (3) (4) (5) (6)

(1) Coordination 66.81 26.40

(2) Cooperation 72.23 14.80 0.317**

(3) Direct search 59.25 25.52 0.360*** 0.507***

(4) Indirect search 56.93 35.76 -0.260* -0.235* -0.115

(5) Information type

57.40 22.96 0.030 0.351** -0.189 -0.230*

(6) Community size

67.58 107.09 0.260* -0.228 0.014 0.191 -0.302**

(7) Method catego-ry

1.938 0.710 0.065 0.227 0.079 0.277 0.158 0.494

DUMMY_1 0.027 0.011 0.049 -0.086 -0.017 -0.327**

DUMMY_2 0.030 -0.183 -0.079 0.262** -0.116 0.492***

DUMMY_3 -0.064 0.210 0.042 -0.221 0.154 -0.236*

*p<0.10; **p<0.05; ***p<0.01

3.3 Analysis method

To statistically test our model, we followed the mediation analysis procedure suggested by

Baron and Kenny (1986). Since all variables had the same statistical properties, we employed

ordinary least squares (OLS) regression analysis by using SPSS.19 We conducted several sep-

arate regressions. Data sets with missing values were case-wise excluded from analysis; ex-

plaining why we operate with different sample sizes. In our first model we include the control

variables (information type, community size, and method category). Subsequent models out-

line the Baron and Kenny (1986, p. 1176-1177) procedure to test for a mediation effect. The

mediation analysis follows the analyses of the indirect effects. We deploy the Sobel test to

examine whether there is a significant change in the effect of our independent variable after

including the mediators in the model. Since we have to deal with a rather small sample size in

our analyses, we also use the non-parametric procedure to estimate indirect effects suggested

by Preacher and Hayes (2004). The estimation is based on 10,000 bootstrapped samples. For

the comparison of the two search mechanisms, direct and indirect search, we also performed

OLS regression. For all regressions we evaluated the post hoc test power by using G*Power

and we calculated effect sizes (f2) (Faul et al., 2009). For regression analyses the effect sizes

are based on the r-square values (r2/1-r2). For the evaluation of the effect size we used Cohen

19 All assumptions for the OLS model were satisfied. Data showed no multicollinearity. The values for tolerance were always above >0.25 and the VIF<5.0 (Urban & Mayerl, 2006, p. 232). Furthermore we found that errors are homoscedastic and serially uncorrelated. Diagrams of residuals confirmed a normal distribution and linearity (compare Field, 2005, pp.146-217).


219

(1987, pp. 413-414), whereby a small effect is indicated by an f2-value of 0.02, a medium

effect size at f2=0.15 and large effect at f2=0.35.

We controlled for non-response bias by comparing early and late respondents (Sheikh and

Mattingly, 1981; Armstrong and Overton, 1977). The t-test showed no statistical differences

between answers regarding these two groups (average p=0.302). To avoid common method

bias, we integrated our constructs into the broader context of performing a market study on

open innovation intermediaries and their business models (Podsakoff et al., 2003). Thus, rele-

vant measures were not presented in immediate distance. In addition, we assessed the inter-

rater reliabilities for six firms where we obtained data from multiple respondents. The intra-

class correlation coefficients for the scales (average ICC=0.745) exhibit the requested level of

ICC>0.72 in social sciences (Shrout and Fleiss, 1979). Although method variance cannot be

ruled out completely (Richardson et al., 2009, p. 797-798), these results suggest it was not a

major problem. Lastly we classified participating intermediaries into the given method cate-

gories prior to the start of the survey on the basis of secondary data from web search. The

concordance between our external categorization and the intermediaries’ self-categorizations

was 91% (κ=0.919).


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4 Results

At the beginning of our analysis, in our baseline Model 1, we observed whether our control

variables show an effect on the dependent variable, Coordination (Table (B2) 4). We did not

obtain a significant model fit (F=.1306; p=0.282). Coordination effort cannot be explained by

only the control variables information type, community size, or applied method.

Table (B2) 4: Results of OLS regression of control variables predicting search effort

Dependent variable Coordination

Model 1

B SE Beta LLCI 95% ULCI95%

Explanatory variables

control variables (5) information type 0.064 0.118 0.083 -0.174 0.303 (6) community size 3.790** 1.796 0.371 0.175 7.405 Dummies for methods (7) DUMMY_1 8.335 10.255 0.143 -12.307 28.977 (7) DUMMY_2 -0.790 9.912 -0.015 -20.741 19.161

Constant 25.144 21.096 -17.320 67.608

R-squared 0.102

Adjusted R-squared 0.024

F-value 1.306

Observations (N) 51

*p<0.10; **p<0.05; ***p<0.01

In Model 1, the coefficient for community size contributed significantly (β=0.371, p=0.040).

To keep enough statistical power for the analysis, all control variables proving insignificant

(type of information or the method category) were not included in the following regression

analyses.

In the second step of the analysis, to test our Hypothesis 1, we ran the procedure of Baron and

Kenny (1986, p. 1176) to test the mediating effect of the two search mechanisms. For the first

step, the procedure requires the establishment of a significant relationship between the predic-

tor variable (Cooperation) and dependent variable (Coordination). The regression Model 2a

(see Appendix (B2) 1) is significant (F=3.684, p=0.033), meaning that interaction frequency

during open innovation collaboration causes Coordination effort (β=0.343, p=0.018). The

path c for a mediation analysis according to Baron and Kenny (1986) is established. The


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community size did not show a significant contribution (β=0.234, p=0.100). A post hoc analy-

sis of the test power required an F-value of 4.038, which is a bit higher than the actual F-value

of the regression. We achieved a medium effect size of f2=0.157.

Two separate regressions in Model 2b test whether Cooperation causes differences in the two

search mechanisms. We found a highly significant relationship between Cooperation and the

direct search mechanism (F=17.645, p=0.000) and a large effect size of f2=0.346. A signifi-

cant relationship between Cooperation and the indirect search mechanism could only be ob-

served at the 10 percent level (F=2.992, p=0.090). We still accept this relationship as being in

existence due to the small sample size and its effect on the statistical power20. The post hoc

power analysis revealed a required F-value of 4.030 to demonstrate that the indicated relation

exists in the population. Furthermore, we only found a small effect of f2=0.058. Although

statistical support is weak, for completing the mediation analysis we were able to establish the

path between the predictor variable and mediators (see Appendix (B2) 2). Yet, comparing the

coefficients, we found that Cooperation is related in different directions with search depend-

ing on the mechanism. The relationship between Cooperation and indirect search was nega-

tive (β=-0.235; p=0.090), and the relationship between Cooperation and direct search was

positive (β=0.507; p=0.000). As a result, less interaction intensity indicates indirect search

behavior in the initial stage of knowledge transfer.

In the third step, Model 2c, we associated the two mediator variables with the outcome varia-

ble to establish the necessary path b of mediation (see Appendix (B2) 3). In both cases we

could establish a significant relationship between the mediators and Coordination effort (di-

rect search β=0.359; p=0.014; F=6.422; p=0.003; indirect search β=-0.241; p=0.079;

F=3.853; p=0.028). The effect sizes for this path are moderate f2direct search=0.263 and f2

indirect

search=0.157. In the case of the regression testing the relation between indirect search and Co-

ordination effort, we did not obtain enough test power (Frequired-value=4.052). The control

community size revealed a significant effect in the regression testing the relation of indirect

search and Coordination (β=0.320; p=0.021). Comparing the standardized coefficients of

indirect search and community size, it seems that an indirect search activity reduces the Co-

ordination effort, but a greater number of integrated community members increases the effort.

The fourth and final step, Model 3, 3’, the relevant regression to answer our Hypothesis 1 in-

cludes interaction intensity measuring Cooperation and search mechanism as indicators pre-

dicting Coordination effort. To establish a partial mediation effect, the coefficient for Coop-

20 A bootstrapping with a sample of 10,000 supports this assumption and indicates the tendency of a significant relationship between Cooperation and indirect search mechanism (p=0.079).


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eration controlling for search initiation should remain significant. For full mediation this coef-

ficient should become insignificant (see Baron and Kenny, 1986, p. 1176). In the case of di-

rect search (Table (B2) 5), we found a significant regression (F=7.890, p=0.005) but a non-

significant coefficient for Cooperation (β=0.117; p=0.461). The effect size (f2=0.326) re-

vealed an almost large effect (f2=0.35). Statistical power for this regression was sufficient

(Frequired-value=3.204).

Table (B2) 5: Comparing search mechanisms – results of regression for path c’ in mediation model – direct search


Model 3

Step 4 B SE Beta LLCI 95% ULCI95% Explanatory variables (2) Cooperation 0.173 0.233 0.117 -0.297 0.644 (3) Direct search 0.316** 0.124 0.393 0.066 0.565 Control variable (6) community size 1.431 1.088 0.178 -0.760 3.622

Constant 25.784 20.051 -14.600 66.169

R-squared 0.246 Adjusted R-squared 0.196 F-value 4.890*** Observations (N) 49 *p<0.10; **p<0.05; ***p<0.01

This indicates a full mediation of the relationship between Cooperation and Coordination. All

interaction that causes Coordination effort is due to the direct search activity during

knowledge transfer, which is actually located in the first process stage. The regression Model

3’ for indirect search (Table (B2) 6) was also significant (F=4.100; p=0.012) at sufficient test

power (Frequired-value=3.204), yielding a medium effect size (f2=274).


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Table (B2) 6: Comparing search mechanisms – results of regression for path c’ mediation model – indirect search


Model 3’

Step 4 B SE Beta LLCI 95% ULCI95% Explanatory variables (2) Cooperation 0.420** 0.204 0.283 0.010 0.830 (3) Indirect search -0.174** 0.082 -0.287 -0.339 -0.008 Control variable (6) community size 2.251** 1.097 0.279 0.042 4.461

Constant 28.911 21.012 -13.410 71.232

R-squared 0.215 Adjusted R-squared 0.162 F-value 4.100** Observations (N) 49 *p<0.10; **p<0.05; ***p<0.01

Here, all predictor variables and the control variable remain significant (Cooperation

β=0.283, p=0.045; indirect search β=-0.287, p=0.041; community size β=0.279; p=0.046). In

this situation we were able to establish a partial mediation of Cooperation and Coordination

by an indirect search mechanism. Both regression models (Table (B2) 5, Table (B2) 6)

achieved a significant change in R-square, which demonstrates the better fit of the mediation

model (direct search: R-square change = 0.109, p=0.014; indirect search: R-square

change=0.078, p=0.041).

Finally, to answer our Hypothesis 1, we estimated the significance of the mediation effects21.

The classical Sobel test (Baron and Kenny, 1986, p. 1176) was used to confirm whether the

difference in beta weights for the effect were significant. The test indicated a significant full

mediation effect for the direct search mechanism (effect=0.321; SE=0.134; z=2.336;

p=0.019). The total effect (effect = 0.494, SE=0.207, p=0.021) was significant whereas the

direct effect did not achieve a significant level (effect=0.321, SE=0.233, p=0.461). We ob-

tained the same result by bootstrapping the effect as the non-parametric equivalent to the So-

bel test (effect=0.321; SE=0.132; LL 95% CI=0.088, UL 95% CI=0.611) (Preacher and

Hayes, 2004, p. 720). The effect ranged in the middle of the confidence interval. Thus, the

21 For the estimation of the significance of the effects we applied the suggested procedure by Preacher Hayes (2004). On their website (http://afhayes.com/spss-sas-and-mplus-macros-and-code.html) the authors offer a syntax file for calculating the effects within SPSS. Yet this procedure produces slightly different coefficients which result in discrepancies between the mediation effects found by single performed regressions and the re-gressions performed by the downloaded script. However, both procedures reveal the same result pattern.


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indirect effect (0.321) of search makes 64.98% of the total effect (0.494). Only 35.02% of the

relationship between Cooperation and Coordination can be explained by the direct path.

Taken together, the results support our Hypothesis 1a: when applying a search behavior, fol-

lowing a direct search mechanism, the knowledge transfer process is designed as being rather

decomposable. The evaluation of the mediation effect of indirect search initiation showed a

non-significant Sobel test (effect=0.076; SE =0.083; z=1.247; p=0.212). The non-parametric

bootstrap procedure supported this finding (effect=0.092; SE=0.077; LL 95% CI =-0.057, UL

95% CI=0.243). The total effect (effect=0.494; SE=0.207; p=0.021) and the direct effect (ef-

fect=0.420; SE=0.203; p=0.044) were significant. The indirect effect (0.076) comes up to

15.39% of the total effect (0.494). These findings indicate that the relation between Coopera-

tion and Coordination is mainly of direct nature. Cooperation due to the applied indirect

search behavior causes only a sixth of the overall Coordination effort. In sum, we find some

support for the partial mediation effect of indirect search initiation. Consequently, the first

phase of knowledge transfer seems to be excluded from causing Coordination effort and

therefore is decoupled from the rest of the process. We can partially accept Hypothesis 1b.

To test our Hypothesis 2, we compare the Coordination effort that is produced by applying

the different search mechanisms. Our descriptive data (Table (B2) 3) revealed that both

mechanisms were almost evenly applied in collaboration. It seems that the intermediaries use

both within the one collaboration form.

Table (B2) 7: Results of regression for comparing the direct and indirect search mechanism


Model 4

B SE Beta LLCI 95% ULCI95% Explanatory variables (3) Direct search 0.353*** 0.124 0.355 0.104 0.601 (3) Indirect search -0.179* 0.096 -0.234 -0.373 0.014 Control variable (6) Community size 3.026** 1.291 0.295 0.430 5.622 Constant 27.780 14.825 -2.028 57.587



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Therefore, we performed a regression analysis which estimates the different effects of the

search mechanisms on Coordination (Model 4, Table (B2) 7).

Results indicated a trade-off relationship between the search mechanisms. Applying direct

search leads to an increase in Coordination effort (β=0.355, p=0.006), whereas the use of

indirect search decreases Coordination effort (β=-0.234, p=0.069). However, the increase in

Coordination effort is 1.97 times higher after adding one unit of direct search than the poten-

tial reduction in Coordination effort caused by one more unit of indirect search. Two units

more indirect search outweigh the effort increasing effect of one unit direct search. This ef-

fect is of a fairly large size (f2=0.353).

Furthermore, we found that community size did matter when searching, which leads us to as-

sume an interaction between search mechanisms and community size. The results of the re-

gression analysis revealed an interaction in case of the indirect search mechanism (F=5.352,

p=0.001) (Model 4’, Table (B2) 8). The effect due to interactions was very large (f2=0.582).

Thus, with a greater number of community members, the positive effect of an indirect search

mechanism reducing the overall Coordination effort diminishes (β=0.295, p=0.021).

Table (B2) 8: Results of regression for comparing the direct and indirect search mechanism including interaction

Dependent variable Coordination Model 4’

B SE Beta LLCI 95% ULCI95% Explanatory variables (3) Direct search 0.426*** 0.120 0.429 0.185 0.668 (3) Indirect search -0.186* 0.093 -0.243 -0.374 0.002 (6) Community size 2.994** 1.233 0.292 0.513 5.476 Direct search x Commu-nity size

-2.943 3.585 -0.103 -10.160 4.273

Indirect search x Com-munity size

9.620** 4.026 0.295 1.516 17.724

Constant 22.866 14.121 -5.558 51.289



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Yet, even when taking into account the moderating effect of the size of the community in-

volved, indirect search produces less Coordination effort than direct search. Taking our re-

sults as a whole, we find support for our Hypothesis 2.

5 Discussion

Open innovation is increasingly applied in practice. It can be performed in many different

ways- licensing technology, performing innovation or idea contests, observing online con-

sumer communities, or running workshops with lead users. Firms are faced with the challenge

of organizing such open innovation collaboration. To shed light on the understanding of how

firms can plan open innovation ventures, this study investigates the relationship between dif-

ferent Cooperation forms of open innovation and their effect on Coordination costs. In order

to do so, we analyzed different collaboration approaches applied by intermediaries in the field

of open innovation. We argued that the approaches used by such open innovation intermediar-

ies differ in the way that collaboration between the participating parties is organized. To

deepen our understanding, we deployed a process view of the underlying knowledge transfer

process, which allowed us to isolate the effects of transforming and transferring knowledge on

the effort to coordinate external knowledge integration projects.

In our study we focus especially on search activity, which is located at the beginning of a

knowledge transfer process. The literature identifies search as a highly relevant activity in the

success of a collaboration venture since it determines the scope of further knowledge transfer

(Szulanski, 2000, p. 14). Drawing upon the literature, we distinguished between two different

mechanisms within search behavior that presumably influence the way cooperation partners

get involved and, therefore, the way in which collaboration is designed: an indirect search

mechanism, which is called broadcasting (Jeppesen and Lakhani, 2010) or selective revealing

(Alexy et al., 2013), and a direct searching for alternatives (Laursen and Salter, 2006; Katila

and Ahuja, 2002; Huber, 1991).

In our empirical study we examined how much of the required Coordination effort, caused by

Cooperation between the partners, can be explained through the kind of search activity per-

formed in the first stage of knowledge transfer. We interpreted the size of this mediation ef-

fect as an indicator for the decomposability of the underlying knowledge transfer process. The

focus on processes and activities of innovation search allowed us to contribute to the debate


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surrounding the cost of openness (Dahlander and Gann, 2010, p. 707). Our paper also extents

the literature of innovation search by adding a further parameter of search. Our results con-

firm that search scope, in terms of the number of external individuals involved, influences the

search outcome (Katila and Ahuja, 2002; Laursen and Salter, 2006). However, this effect de-

pends on the type of search activity that is performed. In addition to the depth and breadth of

search, the type of search matters. Table (B2) 9 summarizes our main findings and their im-

plications.

Table (B2) 9: Summary of findings

Investigation Findings Implications

Organizational structure

Applying direct search behavior demands intense cooperation be-tween collaboration parties during the initial knowledge transfer phase. Thus the task of information pro-cessing appears to be rather in-decomposable.

A decentralized information pro-cessing structure is recommended as well as individuals with great abilities in scanning, evaluating, and selecting relevant knowledge.

Applying indirect search behavior does not demand a frequent coop-eration between collaboration par-ties during the first stage of knowledge transfer. Thus the task of information pro-cessing appears to be rather de-composable.

A centralized information pro-cessing structure is recommended and the process should be divided in subtasks. Individual abilities of knowledge transfer should be dis-tributed over the entire knowledge transfer process.

Organizational cost

Direct search behavior produces more Coordination costs for col-laboration than indirect search be-havior.

Activities of both search activities should be balanced to achieve optimal Coordination costs for open innovation collaboration.

The effort reducing effect of indi-rect search diminishes with grow-ing community size.

Bridging larger distances by in-volving a large community in-creases the Coordination costs. Thus, knowledge diversity needs to be trade off against the costs to acquire it.

We found a partial mediation effect in case of indirect search. This indirect effect contributes

to about 19% of the total effect. Yet, this effect is insignificant, which means that indirect

search behavior, like broadcasting or selective revealing, does not explain the relationship

between interactions throughout the project and the self-evaluated effort to coordinate it. It


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seems that the first phase is isolated from the other process stages. Thus, the effort which

causes interaction and communication to transfer the knowledge appears to take place during

later phases of the knowledge transfer process. This finding is consistent with the open inno-

vation literature, which states that Coordination costs occur in later phases when contract ne-

gotiation about the external knowledge arises (Alexy et al., 2013, p. 276).

In contrast to this, we were able to confirm a full mediation effect of the direct search behav-

ior. In this case, almost all project relevant interaction used to transfer the knowledge that is

causing Coordination effort can be explained by the activity in the first process phase. Identi-

fying potential external sources and selecting the relevant sources for collaboration appear to

be communication intensive activities, which again is in line with the literature (Kogut and

Zander, 1992, p. 389; Anderson et al., 2001, pp. 148-149).

First, interpreting our results in light of process decomposability, we observe that the

knowledge transfer process, involving indirect search activity in the beginning, can be de-

composed into several stages, whereas the process starting with direct search comprises more

highly interdependent activities which make the structure complex and not decomposable

(Baldwin and von Hippel, 2011, pp. 1401-1402; Sanchez and Mahoney, 1996, p. 64;

Siggelkow and Levinthal, 2003, p. 651). Within organizational theory, decomposability is

associated with the centralization of a firm’s internal process (Siggelkow and Levinthal, 2003,

p. 651; Sanchez and Mahoney, 1996, p. 64). So far, scholars disagree whether decentralization

or centralization of organizational structure supports openness. For example, Zhang and col-

leagues (2007) argue that centralization enables firms to benefit from external relationships

(p. 518). Other researchers like Jansen et al. (2006) claim that a high level of centralization

inhibits exploratory innovation. They suggest that decentralization is necessary to initiate ide-

as for new products (pp. 1669-1670) since communication channels in particular are not so

narrow. Following Siggelkow and Levinthal (2003), they suggest a balance between central-

ized and decentralized structure depending on interdependencies of relevant tasks (p. 665).

Consequently, neither an exclusively centralized nor decentralized organization of the

knowledge transfer process is the only governance choice a firm has. Rather, collaboration

initiated by direct search benefits from a more decentralized structure of processing the in-

formation, whereas a centralized information processing structure supports the application of

the indirect search mechanism.

In the case of direct search, tasks of screening potential sources, evaluating them according to

the knowledge they can provide and finally selecting partners, are highly interdependent. On


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the other hand, in the setting up of indirect search, the task of defining the problem and eval-

uating contributions are independent from each other since potential external partners select

themselves for the published problem. This finding contributes to the literature discussing the

optimal organization of open innovation processes. Interpreting these findings in the sense of

innovation search, we are able to connect our work to the study of Knudsen and Levinthal

(2007) and refer to the relevance of individual search abilities. Knudsen and Levinthal (2007,

pp. 40-50) found that the structure of information-processing is associated with the search

abilities of the screening individual. Their outcome indicates that centralized information-

processing is useful when imperfect screeners search for information and, conversely, decen-

tralized information-processing is recommended to organizations with highly accurate screen-

ers. Thus, future research may wish to examine situational aspects of search. The correlation

between organizational structure and innovation problem, existing knowledge base of the firm

and the targeted innovation degree may influence the type of search. For example, a firm

striving for radical innovation may indirectly search for problem solutions. Since the firm’s

knowledge about the information needed is imperfect, a centralized organization of search

might be beneficial. In contrast, if an incremental improvement of a given technology is

planned, a firm might profit from a decentralized organization of information processing,

since employees are aware of the knowledge they need to directly search for.

Second, analysis revealed that the different modularity of the knowledge transfer process

structure affects the overall cost for coordinating open innovation collaboration. Comparing

the two search mechanisms, we discovered indirect search to be a more efficient mechanism

than direct search, since it produces less overall Coordination effort. This indicates that or-

ganizational structure appears to be a contingency factor influencing open innovation cost. In

the case of indirect search, where alternative evaluation and selection takes place at a later

point in the process, the basis for negotiation between partners is shaped differently. Negotia-

tion about already proven external knowledge alternatives, for instance, concrete solutions to

a problem provided by externals (Lakhani et al., 2007, p. 4), produces less Coordination costs

(Alexy et al.; 2013, p. 276) in comparison to the negotiation of less precise external

knowledge in the scenario of direct search (Gulati and Singh, 1998, p. 782). Here, a possible

explanation could be that the knowledge seeking company is faced with the problem of ‘stick-

iness’ when trying to transfer complex knowledge (Szulanski, 2000, p. 11). Stickiness of

knowledge is associated with increasing costs of problem solving activities (von Hippel,

1994, p. 429; von Hippel, 1998, p. 630). An increased information exchange is required to


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evaluate potential external solution alternatives to an internal problem (Szulanski, 2000, p.

14).

It seems, then, that the indirect search approach is basically superior to direct search. Howev-

er, we found that the control variable, community size, yielded an influence. The number of

external partners involved moderates the effect of indirect search on Coordination effort. We

saw that interacting with a large community leads to an increase of Coordination costs within

indirect search. Apparently the number of external interaction partners adds complexity to the

relationship between search activity and Coordination costs. The cost reducing effect of indi-

rect search diminishes when the number of external partners potentially involved increases.

As a result of this, we suppose that community characteristics form an important cost driver

of open innovation. For example, the more individuals participate, the more contributions

have to be reviewed and discussed by the focal firm. Additionally, connectedness positively

influences knowledge transfer (Jansen et al., 2006, p. 1663) but demands effort in managing

interaction. Therefore, future research should focus on investigating the influence of commu-

nity characteristics, like social relationships between members or the formalization of com-

munity interaction, on the costs of open innovation collaborations initiated by the different

search mechanisms.

Reviewing the result together, the organizational structure should play a role when determin-

ing the type of search activity and the extent to which the search activity is performed. We

can confirm that a modular structure of the knowledge transfer process reduces Coordination

costs, which is in line with the literature. Grant (1996, p. 115) has already proposed a se-

quencing of knowledge transfer, which allows the integration of specialized knowledge by

minimal Coordination effort. For the purpose of our study, by analyzing the effects of differ-

ent search activities on the structure of the knowledge transfer process, we looked separately

at these activities. However, in practice firms select a methodological approach, like innova-

tion workshops or idea contests to integrate external knowledge, rather than consciously

choosing a specific search mechanism. Our research subjects- open innovation intermediar-

ies- offered services that differ due to underlying methods of collaboration. Even though the

control variable, method, showed no significant influence on the correlation between search

and Coordination, cost remained insignificant, indicating independency of both activities.

This leads us to assume that direct, as well as indirect search, are both executed when apply-

ing an open innovation method, but to differing extents.


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Going back into our survey data, we find evidence for this assumption. Figure (B2) 4 illus-

trates this aspect of the discussion. For instance, the contest format shows more indirect

search activity by its applied mechanism of broadcast search (Jeppesen and Lakhani, 2010) in

comparison to workshops. In workshops, access to the collaboration is more restricted and,

therefore, the direct search activity of scanning and selecting partners is more present (Pisano

and Verganti, 2008)22. Since the search mechanisms yielded opposite effects on Coordination

costs, we suppose that a trade-off exists between direct and indirect search. Future research

should further investigate the trade-off between the search mechanisms, especially the mar-

ginal effects on Coordination costs which result. Moreover, costs of open innovation projects

should be compared to the innovation performance of these projects. Doing so provides a ba-

sis to evaluate the meaning of certain amounts of cost, which is a necessary condition for the

practical management of open innovation. Figure (B2) 4 shows the extension of search activi-

ties which results when subtracting the indirect search from the direct search. Accordingly,

methods like market research or technology scouting show distinct active search behavior in

comparison to methods like contests, where indirect search is the dominant activity. Future

research should continue to explore the trade-off between the search mechanisms and, in par-

ticular, marginal effects on Coordination costs. Moreover, the resulting costs of open innova-

tion projects should be compared to the innovation performance of these projects. Doing so

provides a basis to evaluate the meaning of certain amounts of cost, which is a necessary con-

dition for managerial practice of open innovation.

In conclusion, our findings have implications for planning open innovation collaboration ven-

tures. Aspects of organizational structure concerning the type, and the extent, of search be-

havior need to be taken into consideration, especially when planning capacity for information

processing. Managers should become aware of the ratio of the two search activities that are

incorporated in the open innovation method chosen to collaborate with external partners. A

ratio for the benefit of indirect search allows reducing costs for coordinating such open inno-

vation projects. In general, when talking about open innovation, researchers regard mecha-

nisms like crowdsourcing or broadcast search as being representative practices for this con-

cept (Jeppesen and Lakhani, 2010; Afuah and Tucci, 2012; Boudreau et al., 2011). Literature

also highlights further advantages of such indirect search, such as dealing with highly uncer-

tain problems (Boudreau et al. 2011, pp. 860-861) or bridging large distances in search

22 Even though the open innovation methods should incorporate the search activities our regression analyzes did not yield an effect of the control variable method. Also a performed ANOVA did not find any significant differ-ences within the search activities due to the applied method (direct search F=0.181, p=0.835; indirect search F=2.419, p=0.098; direct minus indirect search F=2.213, p=0.118).


232

(Jeppesen and Lakhani 2010, p. 1018-1020; Afuah and Tucci 2012, p. 364-365). The self-

selection of potential external partners supports the accumulation of diverse and marginal

knowledge.

Figure (B2) 4: Average extent of search activities within open innovation methods

However, project leaders have to consider the cost of increasing the effect of community

openness during these sorts of crowdsourcing activities. Instead, we recommend adding some

direct search activity in the form of defining recruitment criteria or further selecting individu-

als that answered a project call. The intermediary Nine Sigma, for example, operates along

this basis, posting innovation tasks to a pre-selected audience. Doing so enables firms to con-

trol their costs of coordinating open innovation projects.

Along with determining the extent of certain search activities, managers should adapt the

structure of information processing in the project team according to the search approach and

corresponding interdependency of tasks involved. If a firm directly searches for suitable ex-

ternal partners, this collaboration is organized in small teams with high search abilities. The

employees have expertise in searching, evaluating, selecting, and negotiating relevant external

sources (Knudsen and Levinthal, 2007, p. 41). In the case of indirect search in the form of

broadcasting a problem, a centralized structure for transferring the external knowledge into

0

10

20

30

40

50

60

70

80

90

100

Workshops Contests Technologicaland market

search

Ave

rage

ext

end

of

sear

ch a

ctiv

itiy

Open innovation methods

direct search

indirect search

-50

-40

-30

-20

-10

0

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20

30

40

50

Workshops Contests Technologicaland market

search

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Open innovation methods

direct minus indirect search(-100/100)


233

the firm is beneficial. Major project costs arise at a later point in the process, i.e., in the phase

of negotiating the knowledge transaction (Alexy et al., 2013, p. 276). Consequently, capabili-

ties to integrate external knowledge can be distributed more precisely and should therefore be

governed from the top down. At the beginning of the transfer process, employees with great

abilities to formulate innovation problem and to place them at the right spot are recommend-

ed. In the step of knowledge generation, evaluation abilities in form of expertise and experi-

ence are needed to select the right alternatives (Sieg et al., 2010, pp. 287-288).

6 Limitations and future research

Several limitations of our study should be acknowledged. We chose a process view to allow

us to derive implications regarding the decomposability of the knowledge transfer process

and, consequently, to make reference to the underlying organizational structures (Siggelkow

and Levinthal, 2003, p. 651; Sanchez and Mahoney, 1996, pp. 64-69). We translate the pro-

cess perspective on open innovation collaboration by mapping activities involved in transfer-

ring external knowledge into a firm. However, we concentrated mainly on the first stage of

knowledge transfer, the initiation stage, which includes the search activities.

The first limitation of our study concerns the mapping of the single process stages. Due to the

reliance on data from intermediaries, our process view is constrained to the first two stages of

knowledge transfer- initiation and generation. The nature of the intermediary’s business fo-

cuses on creating knowledge on the behalf of a client firm (Chesbrough, 2006, 136; Diener

and Piller, 2013, p. 17). The implementation into a company’s internal structure is not part of

the intermediary’s service. Thus, after knowledge is generated, it is passed over to the client

firm for their own use. As a result, the company itself can only provide data for the last pro-

cess stage. This constraint affects the robustness of our implications of the organizational de-

sign derived from potential decomposability of the knowledge transfer process. The analysis

of how Cooperation activities cause Coordination effort due to the requirements of all three

single process stages would deliver even more valid results regarding organizational design.

This approach, in particular, would allow for considering the effect of the process stage and

their potential interdependence.

The second limitation is related to the operationalization of our core variables by single-item

scales. Psychometric literature argues that these measures have the disadvantage of having


234

scales that are too low in quality, especially in terms of validity. In our study we followed

Bergvist and Rossiter (2007), who showed that variables which do not form any abstract con-

structs, by means of higher order constructs consisting of several components, can be meas-

ured by single items. Our constructs are based on performed activities which are simple to

observe, but scales consisting of several activities that describe one behavior would probably

increase the validity of results. Direct search behavior, for example, can be described by a set

of different activities (Gulati et al., 2012, p. 577). The same applies to our independent varia-

ble of cooperation. Here, more activities than just the intensity of interaction are necessary to

make sure that knowledge travels from source to recipient (Dahlander and Magnusson, 2008,

p. 646; Puranam et al. 2012, p. 419). A broader set of activities would be beneficial to dis-

criminate the Cooperation component from the Coordination component within the examina-

tion of collaboration. Considering the dependent variable of Coordination effort, we applied a

measure of self-evaluating the effort that occurs when coordinating open innovation collabo-

ration. Self-perceived and real effort could diverge meaningful from each other. For example,

a self-perceived significant change in effort does not necessarily correspond with a significant

change in more objective cost measures, like transaction costs etc. For future research, cost

measure based on facts, like time needed to finish an activity, should be considered.

The third possible issue with our study is its weakness arising from the small sample size we

used for analyses. Even though we achieved a 36.4% response rate, the actual case number is

below 100. The small sample size affects the statistical power of analysis. The post hoc G

power analysis produced mainly sufficient test power. Yet, at points of testing the mediation

effect of indirect search, a higher test power would be desirable. Although we used the boot-

strap procedure and elevated the alpha level, a higher sample size should be used in future

research.

The final limitation concerns the generalizability of our results. We have examined a very

diverse field of open innovation application by surveying open innovation intermediaries.

Even though intermediaries in this field are a special feature of this study, since they allow a

better insight into collaboration process than investigated innovation projects within firms, we

did not control for the industry sector the intermediaries mainly operate in. In future research,

branch specific differences should be considered because we can assume that there is a rela-

tion between the type of problem and the industry sector. Furthermore, we relied on interme-

diaries’ self-selection and interest to present their own business within a market study to par-

ticipate in our study. This approach could provoke biases in their answers which can lead to

an over- or underrepresentation of the investigated parameter. Self-marketing, as an incentive


235

to participate in the study, was effective but not, admittedly, very helpful in furthering result

validity.

Despite these limitations, our paper is to our knowledge the first study analyzing the underly-

ing structure of open innovation collaboration. We were able to empirically compare two dif-

ferent mechanisms of search and the effect of open innovation collaboration on Coordination

costs. We hope that our findings provide a helpful basis for future research.


236

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8 Appendix (B2)

Appendix (B2) 1: Results of regression for path c in mediation model

Dependent variable Coordination Model 2a Step 1 B SE Beta LLCI 95% ULCI95% Explanatory variables (2) Cooperation 0.506** 0.206 0.343 0.092 0.920 Control variable (6) Community size 1.851 1.104 0.234 -0.370 4.071 Constant 16.322 20.635 -25.190 57.834 R-squared 0.136 Adjusted R-squared 0.099 F-value 3.684** Observations (N) 50 *p<0.10; **p<0.05; ***p<0.01

Appendix (B2) 2: Results of regressions for path a in mediation model23

Dependent variable Direct search

Model 2b

Step 2 B SE Beta LLCI 95% ULCI95%


(2) Cooperation 0.878*** 0.209 0.507 0.513 1.261 Constant -3.524 15.435 -34.744 25.97 R-squared 0.257 Adjusted R-squared 0.242 F-value 17.645*** Observations (N) 53 *p<0.10; **p<0.05; ***p<0.01

Dependent variable Indirect search

Model 2b

Step 2 B SE Beta LLCI 95% ULCI95%


(2) Cooperation -0.558* 0.322 -0.235 -1.198 1.261 Constant 98.876*** 23.800 51.823 25.97 R-squared 0.055 Adjusted R-squared 0.037 F-value 2.992* Observations (N) 53 *p<0.10; **p<0.05; ***p<0.01

23 The control variable community size was excluded from the regression for reason of statistical power. Inclu-sion in both regressions produced insignificant results (direct search model: β=0.171, p=0.185; indirect search model: β=0.121, p=0.412) which decreased the overall model fit.


248

Appendix (B2) 3: Results of regressions for path b in mediation model

Dependent variable Coordination Model 2c Step 3 B SE Beta LLCI 95% ULCI95% Explanatory variables (3) Direct search 0.357*** 0.127 0.359 0.103 0.612 Control variable (6) Community size 2.640** 1.306 0.258 0.016 5.265

Constant 20.493 14.654 -8.955 49.941 R-squared 0.208 Adjusted R-squared 0.175 F-value 6.422*** Observations (N) 52 *p<0.10; **p<0.05; ***p<0.01


Model 2c Step 3 B SE Beta LLCI 95% ULCI95% Explanatory variables (4) Indirect search -0.185* 0.103 -0.241 -0.392 0.022

Control variable

(6) Community size 3.279** 1.379 0.320 0.509 6.049

Constant 46.469*** 14.231

R-squared 0.136 Adjusted R-squared 0.101 F-value 3.853** Observations (N) 49 *p<0.10; **p<0.05; ***p<0.01


249


Abstract

Open innovation strives to make external, distributed knowledge available for innovation. To

profit from OI, a firm, firstly, needs to identify potentially relevant external knowledge, sec-

ondly, this knowledge has to be selected and acquired and, thirdly, the knowledge needs to be

integrated in internal R&D efforts. Intermediaries (brokers, platforms, facilitators, consult-

ants) for open innovation, which broker between the focal firm and external knowledge

stocks, have been identified as important actors in the performance of these activities.

Our paper provides an in-depth insight into the market of intermediaries supporting open in-

novation. Extending previous research that has predominantly structured OI intermediaries

into different types according to their business models or structural firm data, our objective is

to help firms to understand and compare the practices of different types of intermediaries, in

order to select an intermediary that best fits a given task in an open innovation project. Con-

sequently, our focus is on the collaboration and interaction model of these intermediaries with

both the firm and external knowledge providers.

We build on primary data from an extensive survey of 59 different open innovation interme-

diaries. This data allows us to describe the scale and scope of the market for intermediaries in

open innovation, but particularly to provide insight into the collaboration models underlying

different types of intermediaries. We identify a new mechanism to initiative such collabora-

tion, called selective open call, and look into different forms of collaboration along the typical

stages of an innovation project. We also find that intermediaries differ significantly with re-

gard to their community composition.


250

1 Introduction

Previous research has frequently demonstrated that it is mandatory for firms to identify, mobi-

lize, and apply external knowledge for economic returns (Stewart, 1997; Eisenhardt and Mar-

tin, 2000; Kogut and Zander, 1992), especially knowledge relevant for innovation activities

and strategic renewal of a firm, which is widely dispersed outside the firm (West and Bogers,

2013; Chesbrough, 2006). In turn, the concept of open innovation has been conceptualized as

a strategy to facilitate “the use of purposive inflows … of knowledge to accelerate internal in-

novation” (Chesbrough, 2006, p. 1). Such a model combines externally and internally devel-

oped technologies to produce an offering that is commercialized by the focal firm. Key steps

in this approach include: searching for external knowledge (or actors which can become

sources of knowledge), selecting and acquiring suitable knowledge stocks, and integrating

them into the firm’s R&D efforts (West and Bogers, 2013).

Important actors in this process are the specialized entities that act as brokers or intermediar-

ies operating between the focal firm and the external knowledge stocks (Zhang and Li, 2010;

Datta, 2007). While the study of intermediaries has a long tradition in innovation research

(Howells, 2006), modern information and communication technologies (ICT) facilitated a

profound change of intermediation for the innovation process. The basic function of an inter-

mediary, brokering distributed knowledge and connecting previously unconnected actors, is

strongly supported by lower transaction cost resulting from modern ICT (Chesbrough, 2006;

Arora et al., 2002; Sawhney et al. 2005, p. 5; Gordon et al., 2008, p. 57). As a consequence, a

new form of intermediaries arose, facilitated by a dedicated technological infrastructure and a

set of corresponding methods to execute knowledge brokerage and problem solving by inte-

grating a larger group of external entities into the open innovation initiatives of their clients.

These open innovation intermediaries have been identified as a core part of an open innova-

tion ecosystem (West and Bogers, 2013; Piller and West, 2014; Gassmann et al, 2011; Co-

lombo et al., 2014). Previous research on intermediaries in the context of open innovation has

structured the field by developing typologies of intermediaries based on their role and func-

tion (Nambisan et al., 2012, Lopez-Vega, 2009; Howells, 2006; Verona et al., 2006;

Gassmann et al., 2011). Other research considers managerial challenges of organizations

when engaging with an intermediary (Boudreau and Lakhani, 2009; Sieg et al., 2010; Bessant

and Rush, 1995; Lüttgens et al., 2014). To understand the business model of intermediaries,

several researchers have also conducted in-depth case studies of well-established representa-

tives, like InnoCentive or Yet2.com (Lopez-Vega and Vanhaverbeke, 2010; Tran et al., 2011;


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Benassi and Di Mini, 2009; Huston and Sakkab, 2006; Verona et al., 2006). A final avenue of

research studies the methodological approaches applied by intermediaries to perform their

knowledge brokering function, for instance investigating the design of ideation contests by an

intermediary (Jeppesen and Lakhani, 2010; Afuah and Tucci, 2012; Kozinets, 2002; Nam-

bisan and Sawhney, 2007).

However, we still have rather limited knowledge about the concrete implications for manag-

ing projects with an OI intermediary. Our research has been motivated by an exploratory

study of this market by Diener and Piller (2010), who, in 2009, identified more than 100 in-

termediaries advertising their services explicitly as "support or facilitation of open innova-

tion". We were interested how this market has developed and how different models of these

intermediaries have prospered, five years later in the adoption process of OI strategies in or-

ganization. We found that the number of OI intermediaries has multiplied within the last few

years and organizations often lack an overview of the market of open innovation intermediar-

ies. Previous studies have failed in providing a guideline in selecting and navigating the space

of OI intermediaries in order to select the right intermediary for a given task contingency. Our

research strives to close this gap.

The contributions of this paper are fourfold. First, we provide a structural overview of the

market for open innovation intermediation. Given the large number of original companies

occupying this niche, we expect that a descriptive exploration of this market in the form of a

comprehensive market study will already be a helpful contribution. Secondly, we open the

‘black box’ of OI intermediaries by studying their intermediation and knowledge brokerage

function in greater detail. Using a model of collaboration in open innovation by Piller and

West (2014), we support firms seeking to utilize external knowledge for their innovation pro-

cess by demonstrating how different intermediaries vary with regard to the stages of the col-

laboration process (defining, finding participants, collaborating, leveraging). This analysis

also provides further insights in the genus of open innovation and its distinctive elements in

comparison to other forms of collaboration in the innovation process (conventional contract

research, R&D alliances, or R&D networks). Thirdly, we discover and describe a new, hybrid

form of initiating collaboration in open innovation, a "selective open call". Different to the

traditional mechanisms of call or search, here firms identify the characteristics of suitable

participants a priori (e.g. market segment, field of ex-pertise, revenue potential by customers),

and then limit their call for collaboration to that select list. Fourthly, we contribute to the de-

velopment of sustainable business models for OI intermediaries. Earlier research has shown


252

that open innovation intermediaries systematically provide value for an innovating firm (Bou-

dreau et al., 2011; Morgan and Wang, 2010; Sieg et al., 2010; Fueller et al., 2011; Poetz and

Schreier, 2012). Hence, there should be a general interest (even for policy makers) to under-

stand the conditions of successful business models for OI intermediaries. Our paper provides

insights into key design elements of these business models, which may serve as a template for

future research, but also strategic planning for an intermediary.

The paper is organized as follows. In the next section, we will provide a brief literature review

of the concept of open innovation and the related approaches which seek to fulfill its objec-

tive. We will show how intermediaries perform an important brokerage function for open in-

novation. Section three then introduces our data set and the methodology of our survey of

open innovation intermediaries. Section four will present the results of our analysis, covering

some descriptive data on the market size and structure, the services offered by the intermedi-

aries, and a detailed assessment of the collaboration and brokerage mechanisms observed in

the market for open innovation support today. Section five discusses the implications of these

findings for research and management.

2 Literature review and background

2.1 A process view of open innovation

With Chesbrough’s (2003) introduction of the open innovation paradigm, our traditional un-

derstanding of cooperation forms for innovation has been extended, including not only strate-

gic alliances or R&D networks among firms, but also more informal collaborations with a

large range of partners (West and Gallagher, 2006). We will not review the rich literature on

open innovation at this point (for comprehensive reviews, see Fredberg et al., 2008; Dahland-

er and Gann, 2010; Huizingh, 2011; Lichtenthaler, 2011; Trott and Hartmann, 2009), but fo-

cus on the notion of an open innovation process in order to provide a structure for the analysis

of typical activities of an open innovation intermediary in the next section. Combining the OI

interaction model by Diener and Piller (2010) and the inbound OI models of West and Gal-

lagher (2006) and West and Bogers (2013), Piller and West (2014) suggest four stages of a

typical open innovation process:


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1. Defining. The firm needs to define the problem that it is seeking to address via engaging

external partners in the co-creation effort (von Krogh et al., 2012). It depends on institutions

and rules of the engagement, be it the rules of communities that it creates or might join (West

and O’Mahony, 2008), or broader appropriability rules of the society or economy (Teece,

1986; West, 2006). Finally, the firm needs to determine the resources that it is willing to pro-

vide and, more broadly, its level of strategic commitment to the collaboration process (Lazza-

rotti and Manzini, 2009).

2. Finding Participants. A major theme of open innovation research has been on searching for

suitable external partners with the right knowledge that is relevant for the firm’s needs (see

West and Bogers, 2013 for a summary). Both the search for, and the acquisition of, such

knowledge will depend on understanding and strengthening the motivations of external part-

ners to create and share their knowledge (West and Gallagher, 2006; Antikainen et al., 2010).

3. Collaborating. A core element of open innovation is an interactive collaboration process

that creates new innovations. This includes creating and implementing the processes for col-

laboration (Prahalad and Ramaswamy, 2004), as well as providing suitable tools (such as IT-

enabled platforms) to facilitate the collaboration process. Finally, firms face the daunting

challenge of selecting the most promising input (Terwiesch and Xu, 2008). Such external in-

teractions assume that the firm is willing to open itself to the external partners: the risk of

leakage of internal firm insights must be weighted against the new insights gained by empow-

ering external collaborators (Enkel et al., 2009).

4. Exploiting. Even if these collaborations are successful in creating new knowledge or inno-

vations, there is no guarantee of success flowing from such efforts. Internal open innovation

advocates must overcome suspicion and other resistance to externally sourced ideas by their

colleagues, whether it is an overt culture of “Not Invented Here” or structural barriers that

impair collaboration (Chesbrough and Crowther, 2006; Dodgson et al., 2006; Schiele, 2010).

We will apply this process model in the following study to differentiate the core activities of

intermediaries supporting and facilitating an open innovation project on behalf of their client.

We argue that OI intermediaries provide input for all four stages of this model.


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2.2 Intermediaries for open innovation and mechanisms for knowledge brokerage

A core actor described in the open innovation literature is the intermediary, or knowledge

broker (Verona et al., 2006; Lopez-Vega and Vanhaverbeke, 2009; West and Bogers, 2013).

Intermediaries, however, are not a new element in the innovation system; rather they have

been the objects of research for many years. Generally speaking, an innovation intermediary

is "an organization or body that acts as agent or broker in any aspect of the innovation process

between two or more parties" (Howells, 2006, p. 720). Previous research has studied innova-

tion from different perspectives, which is reflected in the variety of terms there are for this

economic actor, including intermediary firms (Stankiewicz, 1995), innovation intermediation

(Wolpert, 2002; Howells, 2006), bridgers (Bessant and Rush, 1995; McEvily and Zaheer,

1999), brokers (Hargadon and Sutton, 1997), or superstructure organizations (Lynn et al.,

1996). All perspectives on innovation intermediaries have in common their focus on the role

of an innovation intermediary to make a localized and embedded knowledge stock actionable

for other organizations, because they operate across multiple clusters of specialization and

practice (Choo, 1998). In doing so, intermediaries perform two main tasks: (i) scanning and

gathering information, and (ii) facilitating communication and knowledge exchange. As the

result of a comprehensive literature review, Howells (2006) identifies the activities of an in-

novation intermediary to be the translation of information, coordination, and the alignment of

different perspectives, as well as functions like testing, validating, regulating, or protecting

knowledge.

Older literature focused on intermediation for technology transfer and how intermediaries

expedite the exploitation of a technology (Haegerstrand, 1952; Rogers, 1962) and contribute

negotiation and contractual skills (Shohert and Prevezer, 1996). Other work in this area has

studied how intermediaries facilitate a knowledge transfer process between different actors

focused at the frontend of an innovation process (Hargadon and Sutton, 1997). Network litera-

ture has investigated how intermediaries influence an innovation system by linking and trans-

forming relations within a network (Lynn et al., 1996). Lately, a stream of research has

emerged analyzing intermediaries in the context of service innovation (Howells, 2006). This

body of work not only looked upon dedicated methods and functions for this domain, but also

placed a special emphasis on the interaction between the intermediary and its clients (the fo-

cal, innovating firm). This research also studied the impact of new information and communi-

cation technologies (ICT) on the innovation intermediation function. New ICT has increased

the number of possible transaction partners tremendously (Arora et al. 2002; Nambisan and


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Sawhney, 2007; Lopez-Vega, 2009), enabling networking on a large scale and strongly de-

creasing the effort to find new, interesting sources of information.

Current research on intermediaries for open innovation follows this latter understanding. In

the last ten years, numerous entrepreneurs created intermediaries explicitly positioned in the

context of open innovation and building upon new methodological approaches like

crowdsourcing, idea jams, innovation contests, lead user workshops, open technology scout-

ing, etc. (e.g. Verona et al., 2006; Sieg et al., 2010; Dodgson et al., 2006; Gassmann et al.,

2011). The value proposition of these open innovation intermediaries is to increase the likeli-

hood of identifying and acquiring useful external knowledge stocks (input open innovation),

finding appropriate users for a firm's own technologies (outbound open innovation), or to find

interaction partners for an ongoing collaboration in an innovation project (coupled open inno-

vation). OI intermediaries help companies to build permeable boundaries and to benefit from

different external knowledge sources, while at the same time providing advice, managing risk,

tracking IP, educating participants, and tracking performance.

Applying Piller and West's (2014) process model of open innovation as described in the last

section, we can distinguish the following activities of an open innovation intermediary.

1. Defining. Intermediaries contribute dedicated skills and experience in defining a task (Lu-

ettgens et al., 2014). This includes advice on terms and conditions of participation, and defin-

ing (and later monitoring) duties for both parties in open innovation collaboration. Frequently,

however, selecting one specific intermediary also means selecting a (given) specific set of

rules and conditions, as these remain constant for all innovation tasks offered to a community

of participants by an intermediary.

2. Finding Participants. A core activity of open innovation intermediaries is searching for

suitable external partners with the right knowledge, relevant to the firm’s needs (West and

Bogers, 2013). As we will discuss below, intermediaries use dedicated approaches to perform

this search activity.

3. Collaborating. A key activity in open innovation is an interactive collaboration between

participants, the intermediary, and the client firm. The degree of interaction, however, de-

pends on the method and approach followed during the project, as we will discuss below. In-

termediaries provide specific methodological knowledge and software platforms, as well as

other IT tools, to facilitate this collaboration. They also offer dedicated support in selecting a

valuable contribution for their client's innovation task and in informing the participants of the


256

collaboration as to whether their contribution has been selected by their client or not. A core

role performed by an OI intermediary during the interaction process it to secure proprietary

information of the client so that no internal firm insights are leaked. At the same time, inter-

mediaries also provide trust and security for external participants by reassuring them that their

contributions are not utilized by a client without adequate reward.

4. Exploiting. Finally, intermediaries support the acquisition and transfer of the external

knowledge into the client firm. This includes legal advice, licensing templates, and other sup-

port for IP transfer, including brokering the negotiation of IP transfer fees or contractual con-

ditions.

In the following part of the discussion, we want to look a bit further into the activities of find-

ing participants and organizing the collaboration. Here, we can distinguish two fundamentally

different approaches to reach the external participants in order to establish the collaboration.

Conventionally, organizations have started collaboration with an external partner by perform-

ing a search for potential partners, screening their abilities, and then contracting a service or

collaboration agreement. Search refers to a wide and deep search for information and sources

(Laursen and Salter, 2006; Katila and Ahuja, 2002). An open innovation intermediary ap-

proaches a search for certain information without too many pre-assumptions about the source

and/or location of the information, and crosses the organizational and cognitive boundaries of

its clients (Howells, 2006). A core procedure when open innovation intermediaries perform

such an "open search" is to actively seek potential external information or contributors using

advanced sampling methods, engaging in pre-screening specific characteristics and using so-

cial networks or network analysis.

In open innovation, this search activity, however, is being supplemented by a call activity. An

"open call" refers to a problem statement that is publicly announced, directed to a heterogene-

ous, and generally large, network of external actors. The idea behind this approach is to

spread the problem statement as widely as possible, allowing previously unknown outsiders to

contribute to its solution. Potential solution providers ("solvers") decide via self-selection

whether or not they want to participate in the process of finding a solution to the respective

problem. The "seeker", i.e. the entity issuing the call, then selects the best-submitted solutions

and either awards a pre-defined incentive to the winning solver or engages in collaboration

with the identified solution provider (Terwiesch and Xu, 2008; Benkler and Nissenbaum,

2006; Lakhani et al., 2006; Müller-Seitz and Reger, 2010). This mechanism of open call is

also the idea underlying the concept of crowdsourcing (Howe, 2006).


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In addition to the differentiation between call and search as means to identify potential con-

tributors to an innovation challenge by an intermediary's client firms, an important second

criterion which characterizes an activity of an open innovation intermediary is the kind of

knowledge being sought. In general, firms seek external knowledge for an innovation endeav-

or to reduce uncertainties or gain missing information to solve a (technical) problem. Follow-

ing Thomke (2003), we differentiate uncertainties of an innovation project into technical, pro-

duction, need, and market uncertainty. To reduce these uncertainties, firms need to access and

transfer two different types of information (Cassiman and Veugelers 2006): need (market)

information and solution (technological) information. Both kinds of information answer the

two central questions of every innovation process: (1) what should be innovated? I.e. what is

the latent need or opportunity requiring an innovation? and (2) how should this need be solved

using adequate (technical) solutions? In a generic framework, this information can be divided

into two groups (Ogawa 1998; von Hippel 1998):

(1) Information on customer and market needs (“market information” or, synonymously,

"need information") This is information about preferences, needs, desires, satisfaction, mo-

tives, etc. of customers and users of a new product or a new service offering. Better access to

sufficient need-related information from customers is increasing the effectiveness of the inno-

vation activities and reduces the risk of failure (von Hippel and Katz, 2002; Lüthje et al.,

2005).

(2) Information on (technological) solution possibilities ("technological information" or,

synonymously, "solution solution") This concerns information about how to best apply a

technology to transform customer needs into new products and services. Access to technolog-

ical/solution information is primarily addressing the efficiency of the innovation process

(Piller et al., 2008). Better technological/solution information enables product developers to

engage in more directed problem-solving activities in the innovation process.

When we cross the type of information required (need or solution information) and the meas-

ure to initiate the interaction (call versus search), we can produce a 2x2-matrix as shown in

Table (B3) 1. An interaction with an intermediary can either be driven by the task to gain ac-

cess to market (need) information or technological (solution) information, and it can be initi-

ated by either "calling" for participants (posting a problem statement as wide as possible so

that willing participants self-select) or searching for them by dedicated search methods. This

table also allows us to map and describe four offerings of how intermediaries provide their

knowledge brokering function of connecting a client firm (or the task of their client) with an


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external community of potential participants and knowledge providers, as described in previ-

ous literature (Nambisan and Sawhney, 2007; Nambisan et al., 2012, Lopez-Vega, 2009;

Howells, 2006; Gassmann et al., 2011).

Table (B3) 1: A structure of mechanisms to broker knowledge in open innovation

Mechanism to initi-ate collaboration

Type of information requested

Market information Technological information

Call Ideation contests Solution contests (Tournament-based crowdsourcing)

Search Market research methods; Analysis of social media streams (Netnography)

Technology scouting, search for lead users

(1) Call/Market information: Ideation (or co-creation) contests strive to generate innovative

ideas or designs at the frontend of the innovation process (Piller and Walcher, 2006; Hutter et

al., 2011; Poetz and Schreier, 2012). Generating market-related information, such a contest,

broadly taps into customer needs. Participants are identified by a typical crowdsourcing

mechanism: the intermediary (on behalf of a client) publishes and publicizes a task statement

("challenge") to generally a very large and undefined pool of potential participants.

(2) Call/Technological information: Solution contests work similar to an ideation contest,

but seek input on pre-defined technical challenges in the development stage of an innovation

project. Also called tournament-based crowdsourcing (Afuah and Tucci, 2012; Jeppesen and

Lakhani, 2010), an intermediary sends a request for proposals or solutions to a pool of spe-

cialized actors who may have knowledge in the general domain of the task.

(3) Search/Market information: This category captures approaches that intend to generate

market related information by searching and/or observing participants in a pool of (potential)

customers and/or users. Complementing conventional forms of observation or market inquiry,

especially the analysis of social media streams has gained special attention of open innovation

intermediaries (Fueller et al., 2007, Mahr and Lievens, 2012). Netnography (Kozinets, 2002),

is a typical method in this quadrant. In a Netnography study, an online community of users in

a particular field is screened for innovative ideas or even prototypes shared by users.


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(4) Search/Technological information: Finally, technological knowledge can also be identi-

fied via dedicated search activities. The intention here is to either identify IP or other forms of

documented technological information relevant to a client task or to find experts that may

bear this knowledge or are able to generate it. This method can be seen as the origin of open

innovation intermediaries when new ICT enabled the creation of platforms which function as

virtual market places. These market places connect providers and seekers of technologies or

skills (Lichtenthaler and Ernst 2008).

2.3 The role of communities in the intermediary’s business

By definition, intermediaries interact with different parties to generate, share, and transfer

knowledge (Howells, 2006). On the one hand, there is the focal firm seeking external

knowledge (in the case of inbound open innovation), which typically is the client of an open

innovation intermediary. On the other hand, there is a pool of potential external knowledge

sources or contributors, often called the "community" of an intermediary (Kogut and Zander,

1992, p. 389; Islam et al., 2012, p.145; Sawhney and Prandelli, 2000; Jeppesen and Freder-

iksen, 2006; Shah, 2006; Fleming and Waguespeck, 2007; Henkel, 2006; von Hippel, 2001;

Fueller et al., 2007; West and Lakhani, 2008). Note that the term community here has a very

wide understanding, and is not understood in the same way as it was in earlier literature in

(relationship) marketing or information systems, which used it in a more narrow and focused

sense (Swan et al., 2002; McAlexander et al., 2002; Schau et al., 2009). In the understanding

of open innovation intermediaries, the participant community is one or several pools of poten-

tial participants, which often are not connected to each other; neither do they share a common

goal or objective.

A core asset of an open innovation intermediary is the composition of its existing participant

communities (with regard to size, cultural background, level of expertise), but also its ability

to recruit new participants for a specific client project. Its clients hire an intermediary explicit-

ly to gain access to these participants and their knowledge stocks. Due to this involvement of

an external participant community, the number of potential interaction partners in an open

innovation project facilitated by an intermediary is considerably larger compared to traditional

R&D cooperation in form of strategic networks or R&D alliances.

The larger an intermediary’s community and/or the more heterogeneous their composition,

the greater the likelihood of finding individuals that can significantly contribute to a suitable


260

problem through their diverse knowledge background (Jeppesen and Lakhani 2010. This

mechanism of an intermediary's community can be described well by the theory of structural

holes (Burt, 1992; Ahuja, 2000) or the concept of ties (Granovetter, 1973). Burt’s (1992) the-

ory rests on the basic assumption that communication in terms of circulation and diffusion

takes time. Structural holes are gaps in the social network that act like a buffer – information

cannot cross the gap and they separate different information flows from each other. Structural

holes are thus characterized by weaker ties between individuals (Granovetter, 1973, p. 1366)

and information can cross a greater social distance when passing through weak ties. Bridging

these holes results in a connection and, consequently, an extension of the information diversi-

ty and volume. As a result, broking activities, like spanning networks across such structural

holes, create a competitive advantage by increasing the value of cooperation.

Gaining access to marginal individuals, especially, has been demonstrated to be rich for weak

ties, which in turn increases the productivity of problem solving in an innovation process

(Jeppesen and Lakhani, 2010). Hence, the characteristics of the community of an open inno-

vation intermediary, and its approach to building and extending such a community, are a core

differentiator when studying different forms of intermediaries.

3 Research Methodology

3.1 Data

To understand the recent market for intermediation in open innovation, we conducted an em-

pirical study of OI intermediaries. We composed our sample using four steps. We started with

the list of intermediaries participating in the 2009 survey by Diener and Piller (2010). We

added further intermediaries named in recent publications in the domain (Mortara, 2010;

Bakici et al., 2010; Lopez-Vega and Vanhaverbeke, 2009; 2010). In our third step, we identi-

fied additional potential respondents through an extensive online research, starting from web-

sites like crowdsourcing.org. Finally, we conducted a networking approach (Poetz and Pruegl,

2010) to continuously enrich our sample by asking respondents to name the core competitors.

We only included firms operating for at least one full year. We also included only firms which

had at least a section of their website in English, meaning we probably missed some local

intermediaries, especially in Asia. Overall, we could identify 162 OI intermediaries. In com-


261

parison with the earlier studies and reports on intermediaries for open innovation, this is a

surprisingly large number, indicating a dynamic growth in the market (Figure (B3) 1).

Figure (B3) 1: Population growth of intermediaries for open innovation

Notes: Data shows cumulative number of intermediaries present on the market based on the year when the re-spective company has been founded. Median of the population is in 2006. We can observe a first peak of market development (+11%) in 2000, and a second peak (+54%) between 2006 and 2009.

We identified key informants in these 162 firms and invited them to participate in our survey

(in the first quarter of 2013). From 59 intermediaries we received nearly completed24 data sets

(for study participants, refer to Appendix (B3) 1), equaling a response rate of 36.4%. We

asked each intermediary to answer our survey with regard to their core offering in open inno-

vation (referring to the mechanisms or methods identified in our literature review, as dis-

cussed in the previous section). For firms that had more than one core offering, we asked

them to fill in a survey questionnaire for each offering independently. This resulted in a final

data set of 64 types of intermediation for open innovation.

However, for all 162 identified companies, we gathered secondary data from the intermedi-

ary's website, press reports, and other sources. This allowed us to get a rather full picture of

24 We decided to make items not mandatory as respondents often reject to provide sensitive data like revenue or market growth. For this paper, our data set compromises all responses with full answers to the items used in this study.


262

some descriptive elements, like services offered, geographical scope, or industries served. In

addition, we conducted a series of follow-up interviews with representatives from about 30%

of the responding organizations (in general with the CEO or another member of the leadership

team of the intermediary) to gain more insight into the structure and dynamics of the OI in-

termediary’s market and business models, and to validate our observations from the data by

managerial insights.

One of our core concerns was to solely study open innovation intermediaries, rather than in-

novation consultants or service providers more generally. Hence our survey contained a num-

ber of control items to identify the self-perception of an intermediary within a distinct market

category of "open innovation intermediation" (Navis and Glynn, 2010, p. 441).25 Scale relia-

bility of these items proved to be very good (α=0.845). Thus, we used the mean value of these

items as an indicator for the composition of our sample.

3.2 Measures

In this section we introduce the measurements used in our study. Our survey consisted of

open questions, simple check boxes and scale-based items.26 Respondents started the survey

by providing general information about their business environment and company. A core con-

cern at the beginning of the survey was to classify the open innovation approach applied by

the intermediary. We used three indicators: (1) a self-categorization into the four method cat-

egories described above (hosting contests ("crowdsourcing"), performing search for market

information, performing search for technical information, organizing workshops) in order to

identify the central brokering mechanism of an intermediary (2) items regarding the process

stages of OI as described before and (3) an open field where we asked respondents to provide

a short verbal description of their firm ("Wikipedia style"). We used this information to au-

tomatically customize the remainder of the survey by focusing it on the primary method of-

fered (in cases where an intermediaries offered two or more services, the entire survey was

replicated for all services).

25 These items were as follows: (1) Our company is a very good example of open innovation. (2) We are proud to be part of the open innovation community. (3) The term open innovation fits with our business model. Scales were “strongly disagree” to “strongly agree” for Items 1 and 2 and “fits very poorly” to “fits very well” for Item 3. 26 For all items with rating scales, respondents had to use a "slider" reaching from 0 to 100 with two decimal points precision. Exact measures were not visible to the respondent. The slider starting position was in the mid-dle of the scale.


263

The verbal self-description of the business served as a source for evaluating the adequateness

of the brokerage mechanism (open innovation method) selected by the respondent. Using

three coders to assess the open statements of the respondents, we confirmed the selected

mechanism.27 The correlation between the self-categorization and our re-classification was

significantly high (ν= 0.917, p=0.000, N=64), indicating the correct assignment of open inno-

vation methods by the intermediaries. This is crucial for the following analysis of our data.

To assess the activities performed in the different stages of an open innovation project with a

client, we used several items. With regard to the mechanism of how collaboration is initiated

and primarily which kind of external knowledge is being sought, we asked two questions:

Which approach does your company follow in a typical project for achieving project ob-

jectives?

Respondents could move a slider ranging from "we rather CALL for individuals joining a

project to solve tasks/requests" to "we rather SEARCH for information/individuals joining

a project to solve tasks/requests".

What type of information/knowledge does your company typically generate in a project?

Respondents were asked to move a slider ranging from “rather technological infor-

mation/knowledge” to “rather market/customer oriented information/knowledge”.

A median split on each scale allowed us to create a 2x2-matrix of open innovation approaches

derived from open innovation principles, as indicated in the literature review. The results are

presented in the next section.

To analyze how intermediaries integrate different actors, we distinguished three stages: pro-

ject initiation (defining the task and finding external participants), collaboration and interac-

tion for task fulfillment, and leveraging (exploiting) the identified knowledge within the client

firm. Intermediaries were asked to indicate the main parties involved at each stage: the inter-

mediary itself, personnel from the client, and actors from the participant community. Combin-

ing these actors into groups resulted in five possible partner configurations, i.e. parties inter-

acting during one stage of the project: (1) intermediary and client (2) intermediary and com-

munity (3) client and community (4) community members among each other (5) all three

partners together.

27 To validate the brokerage mechanism by firms, we used statements from the self-descriptions like “…we are able to solve technology related requests ...”; “…, our portal offers a marketplace for design.”; “…crowdsourcing innovation problems … people who compete to provide ideas and solutions …”; or “… our technology search process …”.


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In sum, a maximum of 15 possible interaction models are possible within an open innovation

(3 stages * 5 partner configurations). As a measure for partner integration, we simply counted

the specified number of configurations in each phase (count variable between 1 and 5). In

addition, however, we asked the respondents to indicate the typical number of actors from

each party for each project configuration: Do actors from typically communicate in dyads

(one-to-one), in small groups, or in large groups without clear group limits. We use this cate-

gorical variable to investigate group differences between the partner configurations.

Finally, we were interested in the cognitive and organizational distance of a community in

relation to the domain of an average client. Here, we used the following items:

How would you describe the primary reach of your company from the perspective of your

client? Two scales, one reaching from “very organizational internal“ to “very organiza-

tional external” and on scale ranging from “very well known” to “very unknown”.

4 Analysis and Results

Our data analysis has been based mainly on established methods to generate descriptive statis-

tics. We calculated correlations using the coefficient of contingency to determine the relation-

ship between the open innovation approaches based on the open innovation principles and the

reported open innovation methods. To investigate the extent to which partner integration and

community reach influence the label open innovation, we applied simple OLS regression

analysis. All prerequisites for regressions are satisfied by the data.

4.1 Market size and structure

Our descriptive analysis indicates that the market for open innovation is maturing. On aver-

age, an OI intermediary has already conducted a high number of client projects, many of them

with 200 or more (mean = 177.43; min = 10; max = 1,200). We also discovered a broad dis-

tribution of open innovation projects over various industry sectors (e.g. agriculture, finance,

building industry, FMCG, automotive etc.). Evaluating the geographical coverage reported by

our respondents (Figure (B3) 2), i.e. the countries which they offer their services, we found

that almost all intermediaries are operating in Europe, with 80% of the entire intermediary


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population reporting the ability to perform a project in North America, while South America,

Africa, and Oceania are less well covered.

Figure (B3) 2: Geographical market coverage of the intermediaries (N=59)

In addition, the market for open innovation intermediation shows continuous growth. A self-

assessment by the OIAs in our study provides an estimated current market volume of €2.7

billion (revenues generated by the intermediaries via client fees and licenses for their services;

this does not include any awards or license fees of technology exchange or knowledge bro-

kerage between an innovating firm and an external solution provider). The respondents in our

sample expect that this volume will double by 2015 to €5.5 billion. The strongest growth is

reported in applications to provide access to market data (need information), including, in

particular, a string of expected growth of Netnography as a method of open innovation.

Performing contests was seen as the most promising open innovation format, covering almost

80% of the entire open innovation market (Figure (B3) 3). Yet the market is far away from

being consolidated. Despite this dynamic, we found that about 20 percent of the open innova-

tion intermediaries that were part of our original sample composition (documented in the

sources described above) went out of business or had been merged with another intermediary

at the time when we conducted your survey in 2013. Judging from the comments of our re-

Geographical market coverage of OIIs in %


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spondents in the open fields on market dynamics, we expect an even stronger wave of acquisi-

tions and mergers in the coming years.

Figure (B3) 3: Project market development as indicated by expected revenue growth of OI intermediaries (N=59)

4.2 Service offerings

We discovered that open innovation intermediaries can be distinguished into two groups, rep-

resenting their business model. The first group runs an open innovation project on behalf of

their clients and provides a solution to a given task as the project output. These firms act as

traditional knowledge brokers, following a model of an agency that provides a service for a

client in return for a defined fee. The second group helps their clients in building own open

innovation competences to engage in direct collaboration with external entities. These firms

can be considered as infrastructure providers, equipping their clients with a platform, soft-

ware, or access to a community, but not performing the OI project on their behalf. The first

group best represents the market for open innovation support, with 85% of the 162 intermedi-

aries offering to generate a requested knowledge stock for their clients.

In both cases, dedicated software is a prerequisite for intermediaries seeking to integrate a

large number of participants at a relatively low cost. In our survey, 90% of all respondents

claimed to have a piece of proprietary software supporting the OI function. This is not surpris-

ing considering the background on open innovation as a brokerage function enabled by new


267

ICT (Pisano, 1991; Dodgson et al., 2006; Arora and Gambardella, 2010; Verona et al., 2006).

At the same time, however, the main driver of project costs of an intermediary is personnel

capacity. The average fee for an OI project charged by an OIA is €43,000, but project costs

differ widely, ranging from €12 (for a basic monthly description of an OIA web-service) to

€164,000 (for an OI consulting service). Despite the importance of dedicated software to per-

form the initiation or collaboration in an OI project, though, OIAs in general are not pure IT

services or "self-service internet platforms", but knowledge-intensive service businesses. Re-

cruiting experienced project managers and analysts becomes a major challenge for many OI-

As, as several respondents reported.

Costs strongly depend on the entire service package that is offered. We isolated five revenue

models. Intermediaries offering a software solution usually charged a product license fee.

Almost all intermediaries billed person days for consulting activities. Furthermore, we found

that at predominantly web-based services, either subscription, success (6-12%), or posting

fees were charged.

4.3 Open innovation approaches and initiating the collaboration

Our literature review developed a basic structure of four types of OI methods applied by in-

termediaries, which could be distinguished on the basis of need versus solution information,

and the mechanism of Call versus Search to find participants. Within our sample, we find no

dominant method when looking at the descriptive statistics of the frequencies of the four types

(Table (B3) 2). This is consistent with the distribution over the four method categories as a

result of the self-categorization of open innovation service methods by the respondents at the

beginning of the survey: workshops (26.5%), contests (29.4%), market search (22.5%) and

technical search (21.6%) are almost evenly named as the "main" method by the respondents.

Interestingly, however, a match between the four open innovation types and the self-

categorization of open innovation methods only revealed a weak correlation of CC=0.417

(p=0.050; N=64), corresponding to a concordance of 41.7%. A closer look at our data re-

vealed that this rather high unexplained variance could, to a large extent, be explained by in-

termediaries offering a workshop as their core service offering. For this category, the 2x2 ma-

trix, based on the type of information and the initiation mechanism of collaboration, did not

seem to provide a suitable categorization.


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Table (B3) 2: Frequency of theoretical derived open innovation approaches (N=64)

Our data analysis allowed us to examine the distribution of the originally continuous variable

"collaboration initiation activity". A scatterplot indicated a concentration in the middle section

(Figure (B3) 4). This led us to the conclusion that a third initiation mechanisms exists next to

the principles of call and search to find participating actors. This third mechanism, which we

refer to a selective call, seems to be a hybrid, combining elements from the call and search

approach.

Figure (B3) 4: Distribution of collaboration initiation mechanism regarding type of infor-mation


Mechanism to ini-tiate collaboration

Market (need) in-formation

Technological (solu-tion) information

Call n=18 (28%) n=17 (27%)

Search n=15 (23%) n=14 (22%)


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For a selective open call, intermediaries follow a two-step procedure. They can first identify

the characteristics of suitable participants a priori (e.g. market segment, field of expertise,

revenue potential by customers), and then limit their call for participation in a specific client’s

project to that select list. Or, vice versa, intermediaries first perform an open call and ask for

general interest in participating in a specific project, but select only a few individuals for the

project team. When integrating this hybrid form into our framework (resulting in a 3x2 struc-

ture), we could better classify the workshop offers of some intermediaries’ (Table (B3) 3).

Appendix (B3) 2 matches the respondents from our sample into this new structure.

The following examples hope to illustrate the principle of a selective call. While traditional

focus groups or "creativity workshops" with customers are, for example, recruited by a search

process based on pre-defined criteria, workshops in the cluster of "a selective call" find their

participants among active contributors who followed the open call to participate at an ideation

contest. Similar recruitment strategies could also be found for solution workshops, where an

intermediary first hosted an open concept generation contest to identify participants with lead

user characteristics, and then recruited a selected group of these participants for internal tech-

nical problem solving activities. The mechanism of a selective call does, however, also work

for methods other than workshops. A typical application of this initiation strategy is to define

a clear set of criteria for contributing participants first, and then "broadcast" and invite to a

crowdsourcing contest those community members only. Other intermediaries perform a

Netnography study (open search), first to identify communities with relevant content in a

problem domain, and then recruit participants for an OI initiative in these communities only.

A cross tab analysis of the observed data allowed us to further show significant differences

between the methods due to their underlying open innovation principle. We discovered that

the initiation mechanism is related to the type of information sought in a project (CHI2=5.992,

p=0.05). Projects generating technological information usually start with a selective call,

whereas need information was either retrieved by an open call or explicit search. The re-

assessed concordance between the six open innovation approaches and the self-categorization

of the intermediaries (CC=0.687, p=0.007, N=64) yields a significant increase to nearly 70%.


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Table (B3) 3: Extended structure of OI approaches and frequencies observed in sample (N=64)

Mechanism to ini-tiate collaboration


Market information Technological information

Open call Co-creation contests, e. g. design or ideation contests n=10 (15%)

Crowdsourcing tournaments (also: "broadcast search", solu-tion contests),* e.g. problem solving platforms n=13 (20%)

Selective call

Workshops with a pre-selected target group, e.g. online co-design panels or concept genera-tion workshops with winners of an ideation contest n=15 (24%)

Workshops with pre-selected experts, e.g. lead user workshops n=5 (9%)

Open search

Market research methods, e.g. community observations (netnography) n=8 (12%)

Technology search, e.g. online market places n=13 (20%)

Note: Shown are those methods dominant for a cell. Workshops also can be initiated by a pure search process – the conventional process for customer workshops, where participants are recruited following specific sampling criteria. At the same time, participants for ideation or solution contests can also be recruited via a selected call.

4.4 Open innovation partner integration

A central criterion to differentiate intermediaries is the number of external partners involved

in each stage of the OI process. Using the McNemar test (McNemar, 1947; Norusis, 2012),

we analyzed the pattern of interaction regarding the number and type of partners involved.28

This analysis of partner integration and collaboration format reflected the brokering function

of an OI intermediary. An intermediary selectively interacts with the client as well as with the

community. Yet throughout the entire project, the most frequent interaction interestingly oc-

curs between the intermediary and its client (Figure (B3) 5). These meetings usually take

place in a one-to-one environment, i.e. typically client meetings or phone calls (average test

value=17.557, p=0.005). The community, as a key resource of the intermediary’s business,

showed, as expected, the highest involvement during project execution (Figure (B3) 5). Con-

28 The McNemar test was selected as our data obviously has dependencies between different observations. We always study the collaboration patterns with the different stages of an open innovation project for the same in-termediary. Hence, we need an analysis that accounts for these dependencies (Norusis, 2012).


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trary to our expectations, we found that even in the finalization phase of the project, the com-

munity is still involved into the process. Here contributions are evaluated, selected and trans-

ferred into the client’s organization.

Figure (B3) 5: Partner configurations intermediaries interact throughout the project (N=64)

Interestingly, we found that the scale (number) and scope of partner involvement in an OI

projects strongly correlates with the self-perception of an intermediary to follow "open inno-

vation". Intermediaries who evaluate themselves as more open integrate more partners during

project execution and finalization, but show a particularly intense collaboration in the core

project phase of collaboration and task fulfillment (Table (B3) 4). Involving many external

partners and putting them in a string of interactions with each other, as well as with the client

and the intermediary, signals a strong understanding of the nature of open innovation.

The dominant format of interaction among community members was one that resembles mass

collaboration – communication with no group limits (average test value =4.974, p=0.037)

(Figure (B3) 6). Curiously, respondents could not give a preferred format of community

members for their communication with the intermediary (average test value =1.801, p=0.268).

0

10

20

30

40

50

60

initiation execution finalization

Nu

mb

er o

f ca

ses

Partner configurations

intermediary*clientintermediary*communityclient*communitycommunity memberintermediary*client*community


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The client tended to prefer to interact with community members in a one-to-one format, i.e.

via direct chat, e-mails, or phone calls (average test value =3.838, p=0.057). In the case of

meetings with all three partners (intermediary, client, community), we found two dominant

interaction patterns: for technical solution tasks, often a team of one representative from each

partner met to discuss exploitation issues after a technical solution has been identified. In

these instances, open innovation turns into more conventional modes of coordinating innova-

tion projects. The other pattern consisted of a larger group of participants from all three par-

ties, a typical setting being a follow-up workshop (average test value =3.774, p=0.056).

Figure (B3) 6: Interaction format across partner configurations (N=64)

05

1015202530354045

one-to-one small group no group limits

Nu

mb

er o

f ca

ses

Partner configurations

intermediary*clientintermediary*communityclient*communitycommunity memberintermediary*client*community


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Table (B3) 4: Regression results: number of partners on open innovation understanding

Self perception of following "Open innovation"

B (SE) Beta p-value

Number of partners

… in initiation 0.587 (1.829) 0.042 0.749

… in execution 2.874 (1.064) 0.340 0.009

… in finalization 2.483 (1.161) 0.273 0.037

Constant 67.231 (3.911) 0.000

F-value 6.573***

R2 0.264

Adjusted R2 0.224

Number observations 64

*p<0.10; **p<0.05; ***p<0.01

4.5 Bridging organizational and cognitive boundaries

As discussed above, a core function of an intermediary is bridging the organizational (spatial)

and cognitive distance between the innovating firm and potential contributors around its pe-

riphery. Intermediaries, however, differ in the extent to which they bridge these boundaries.

This is shown both in the different scale and scope of their participant communities, as well as

the mechanisms applied to identify participants for a client project. We first found that inter-

mediaries vary significantly with regard to their community composition. On average, inter-

mediaries have an existing pool of 20,000 members. When specializing on ideation or tech-

nical solution contests, however, communities often contain more than 100,000 members. To

join the community, prospective participants in most instances have to accept some general

terms and conditions, but, typically, they do not sign a formal contract. This is a fundamental

difference of open innovation through such intermediaries compared to traditional forms of

R&D networks or alliances, where participation generally involves an explicit legal arrange-

ment. On average, 200 to 300 participants contribute to a particular project. Depending on the

project objective, communities differ strongly in their general level of expertise. In the case of

creating raw ideas or concepts, broad and very heterogeneous communities are sought. For

technologically oriented projects, we observed mainly smaller networks of high-level experts.


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Beyond the size of the community, intermediaries vary considerably with regard to the kind of

participants they recruit for a client’s project, as measured by the cognitive distance of these

participants in relation to the knowledge domain of their client. Some intermediaries are fo-

cused on providing access to community members who are fairly well known to the client

firm, in the sense that they can be found inside or close to the organizational boundaries and

may be considered to be within the cognitive domain of their client. Consider, for example,

measures to include large numbers of employees or existing customers into the innovation

process. Other intermediaries specialize in providing access to individuals, who are a large

organizational and cognitive distance from the firm, i.e. participants from different industries

or fields of expertise. Connecting clients with the right community, by matching their infor-

mation request with the characteristics of community members, is a core capability of an OI

intermediary. In our analysis, we differentiated four forms of "distance" between an innovat-

ing firm and a community of participants (Table (B3) 5) the localization of the (potential)

participants can be inside or outside the organizational boundaries of their client and firms can

cross or remain within the cognitive boundaries of their industry. Appendix (B3) 3 places the

intermediaries in our sample within these categories, based on their self-assessment of a typi-

cal client project.

Open innovation intermediaries fill the full spectrum, from operating at a very close distance

to the client’s organization, to connecting far out to the boundaries of the organizational pe-

riphery. Especially large companies already profit from applying open innovation to reach

broad pools of their own employees for an innovation project (Huston and Sakkab, 2006;

Rohrbeck et al., 2009). Similarly, firms use open innovation to engage with a group of known

suppliers, retailers, or other value chain partners for a dedicated innovation project. In both

cases, the collaboration goes beyond routine exchanges of knowledge and refers to topics and

tasks that, conventionally, not all of the participants would have been included in. Consider

the case of Siemens, who included more than 20,000 employees in an "innovation jam" (an

online tool to co-create openly in a short time frame around a given task) for enhancing the

piracy protection of their goods and components.

The original understanding of open innovation, however, is a firm connecting with an "unob-

vious others", i.e. actors external to the client firm and from communities, sectors, or

knowledge domains not familiar to them. Here, participants from the community came from

beyond the client’s organizational periphery and domain of awareness. Finding solution pro-

viders for technical challenges via the method of "broadcast search", as offered by intermedi-


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aries like InnoCentive or NineSigma, is a typical example of this approach. A regression

analysis of the self-reported "open innovation understanding" with the familiarity and locali-

zation of participants of an OI project indicated that open innovation for the respondents also

corresponded with collaborating with “unobvious others” (see Table (B3) 6).

Table (B3) 5: Types of open innovation reach (frequency of occurrence, N=64)

Localization of par-ticipants (organiza-tional boundaries)

Familiarity with participants (cognitive boundaries)

known unknown

external

Collaborating with net-works and alliances part-ners n=16 (25%)

Collaborating with “un-obvious” others n=16 (25%)

internal Collaborating with broad pools of employees n=19 (29.7%)

Collaborating with value chain partners n=13 (20.3%)

Table (B3) 6: Regression results: Community reach on open innovation understanding

Self perception of following "Open innova-tion"

B (SE) Beta p-value

Community reach 0.154 (0.061) 0.307 0.014

Constant 67.826 (85.272) 0.000

F-value 6.454**

R2 0.094

Adjusted R2 0.080

Number observations 64

*p<0.10; **p<0.05; ***p<0.01


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5 Discussion and Implications

Our analysis contributes to the understanding of the practice of open innovation. We extend

the existing literature by providing a comprehensive overview of the market for open innova-

tion support, as offered by dedicated open innovation intermediaries. Our analysis has a num-

ber of theoretical and practical implications, which we discuss in the following section.

5.1 Theoretical implications

Previous research on open innovation has identified collaboration with actors from the pe-

riphery of the innovating firm as a core characteristic of this approach (Chesbrough et al.;

2006; Gassmann and Enkel, 2004, p. 2; Dittrich and Duysters, 2007, p. 512; West and Gal-

lagher, 2006, p. 320; Terwiesch and Xu, 2008, p. 1529). By studying intermediaries for open

innovation, we have opened the ‘black box’ of open innovation and identified a number of

practices and patterns which reveal how this collaboration takes place. Based on respondent’s

self-assessment of whether their firms offers "open innovation services" or conventional bro-

kerage services in the innovation process, we demonstrated a clear correlation between organ-

izational and cognitive openness (bridged distance) and the perceived nature of being an open

innovator or not. In the OI literature, this understanding closely resembles the model of “cou-

pled” OI, identified by Gassmann and Enkel (2004; Enkel et al., 2009), as a third mode of

open innovation, beyond the original inbound and outbound processes outlined by

Chesbrough (2003). Defining coupled OI as “working in alliances with complementary part-

ners”, Gassmann and Enkel propose that in a coupled OI process, the outside-in process (to

gain external knowledge) with the inside-out process (to bring ideas to market) is being com-

bined. As suggested by Gassmann and Enkel, the concept focused on the traditional perspec-

tive of firm alliances but has had limited theoretical development, despite its potential wide-

spread application to open innovation research (Piller and West, 2014).

However, our study of the intermediaries and their collaboration models indicate that there is

also an interactive collaboration between two actors that is qualitatively and quantitatively

different from the bidirectional form. Instead of just "importing" knowledge to augment a

firm’s internal innovation process, in the interactive approach the knowledge creation takes

place outside a particular firm, facilitated by an intermediary. Innovative outputs are being

created together in collaborative activity with all parties (Piller and West, 2014). It is this lat-

ter understanding of coupled open innovation as an interactive, collaborative process of joint


277

value creation that is the focus of open innovation activities, as offered by the intermediaries

in this domain.

A further theoretical perspective to shed light on the results of our study is provided by the

absorptive capacity approach. It has been widely accepted that absorptive capacity follows a

funnel model (Lane and Klavans, 2005, p. 185), starting with a large breath of exploration of

external knowledge (Cohen and Levinthal, 1990, p.129) to fill the funnel. Along the

knowledge acquisition process, however, this broad external knowledge has to be filtered and

refined to build realized absorptive capacity (similar to exploitation or leveraging in our OI

model) (Zahra and George, 2002, p. 185). Our results confirm a broad input into the first stage

of an OI project, when diverse community members screen a task or deliver initial input. But

in contrast to the perspective of absorptive capacity, we find that in many cases (and especial-

ly those self-identified as "open innovation" by the intermediaries) even in the latter project

stages of evaluation and selection of contributions; many external actors are still involved. We

also discovered a surprisingly high participation of individuals in the last project phase when

knowledge is transferred into the client’s organization. In intermediary-facilitated open inno-

vation, search scope and processing of the acquired knowledge do not seem to be independ-

ent. Further research should investigate mirroring competences in knowledge acquisition and

assimilation in more detail in order to modify the concept of absorptive capacity to reflect the

realities of open innovation.

Our study also contributes to the notion of network research (Lee et al., 2010; Mahr and

Lievens, 2012). Our data indicates that crossing a broad distance by collaborating with un-

known individuals is strongly associated with a central understanding of open innovation.

This supports earlier studies which reveal that operating in a network increases a firm's

knowledge stock for innovation (Ahuja, 2000, p. 430). Furthermore, a higher number of col-

laborative relationships have been shown to be positively related to innovation success (Shan,

Walker and Kogut, 1994, p. 387). Intermediaries for open innovation are performing exactly

this role. Bridging a broad distance between the innovating firm and external cooperation

partners helps organizations to overcome the problem of local search and increases the likeli-

hood of radical innovation (Afuah and Tucci, 2012, p. 369; Jeppesen and Lakhani, 2010, p.

1030). Intermediaries have frequently developed dedicated information technology and soft-

ware platforms to perform such a distant search at a low cost (Afuah and Tucci, 2012, p. 356).

However, we find a large variation in collaboration patterns in our sample of OI intermediar-

ies. Even though our research subjects were self-appointed experts on open innovation, not all


278

of them provided the same networking and bridging functions. This calls for research on the

contingencies of an innovation task as a characteristic to select an intermediary with the ap-

propriate collaboration model. This is a core competence that may constitute a firm's capabil-

ity to profit from open innovation.

On this theme, we also believe that our results contribute to the ongoing debate surrounding

what characterizes "openness" for innovation (Laursen and Salter, 2006; Dahlander and Gann,

2010; Chesbrough, 2003). Collaboration with a number of external individuals is not a

unique, defining characteristic of open innovation. Rather, providing access to a large com-

munity of potential participants with specific knowledge stocks and engaging them in interac-

tive collaboration to achieve a set innovation task for a firm seems to be a pattern that is char-

acteristic of open innovation compared to conventional forms of collaboration in innovation,

like strategic alliances or contract research. Crossing organizational and cognitive boundaries

to connect a firm with "obvious others" from these domains seems, to us, to constitute the true

character of open innovation.

5.2 Managerial implications

For an innovating firm, our research can guide managers when selecting an OI intermediary

for an open innovation project. There appears to be at least three questions managers ought to

be aware of:

(1) What is the expected outcome?

First of all, managers have to consider the type of task and the nature of their innovation prob-

lem. Not all intermediaries are suited for every open innovation challenge (even if some ad-

vertise so). OIAs differ with regard to the breadth, scope, and structure of their pool of poten-

tial participants, as well as the opportunities for clients to control access to this pool and the

interaction within a given project. An important differentiator is the information required: we

found that intermediaries were clearly split into those providing access to need (market) in-

formation and those providing access to solution (technical) innovation.

In addition, managers should remember that open innovation means mass collaboration. It is a

highly interactive process where an undefined number of individuals are communicating in-

tensively and which requires cooperation and exchange. External actors are often integrated

from the start to the end of a project. Clients of intermediaries thus have to engage in monitor-


279

ing the interaction across the entire project, which can generally be structured along three

stages: ideation, concept generation, and launch. Intermediaries provide support for each

phase, ranging from ideas to IP. Selecting an intermediary for open innovation also depends,

therefore, on the preferred degree of outsourcing the OI function (and control) to the interme-

diary (Lee et al., 2010; Afuah and Tucci, 2012; Sawhney and Prandelli, 2000).

(2) What is the project infrastructure of the OI intermediary?

Communication technology and software infrastructure to perform the knowledge brokerage

function plays an essential part in any open innovation venture. Web 2.0 and social software

technologies allow open innovation intermediaries to operate globally and integrate large

numbers of participants without high transaction cost. Our analysis showed that in 90% of all

cases, intermediaries offer a distinct software solution. Consequently, selecting an intermedi-

ary also means deciding whether the software solution should be implemented in-house (fol-

lowing a traditional license model) or whether a web-service or hosted service of the interme-

diary should be used. However, our research also revealed that intermediation for open inno-

vation is a knowledge-intensive activity that demands considerable experience and capabili-

ties from the project managers responsible. This is particularly the case in projects seeking

technological information via search or an open call in a contest environment (Luettgens et

al., 2014).

(3) What are the characteristics of the intermediary's community?

Open innovation intermediaries build on the involvement of a community (in the broader un-

derstanding of the term) and, through these communities, connect their clients with a variety

of external actors, who often are new and unknown to the client (this idea of "looking outside

of the box" is exactly the value of open innovation). Our analysis, though, has shown that

open innovation intermediaries differ significantly in their community composition. The typi-

cal expertise of a community is highly influenced by the service offerings of the intermediary.

Services such as technical search involve individuals with expertise preferably in natural and

applied sciences, and less in the social sciences or arts. On the other hand, design or ideation

contests are offered by intermediaries who can provide access to communities with experi-

ence in the arts or social sciences. Managers of client firms are advised to inquire into the

community composition of an intermediary and its ability to recruit new participants or make

connections with new communities.


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Organizations can further influence their search scope prior to the project beginning. We

found that intermediaries ask clients to specify criteria for selecting project participants, for

example, socio-demographic aspects or level of expertise, resulting in a selective call. In addi-

tion, our results indicated that the search field can be defined by means of choosing the cogni-

tive and organizational distance between a targeted community and the client firm's internal

boundaries. Organizations can decide to collaborate within familiar surroundings, but also to

cross the border to completely unknown domains. This decision should be based on the de-

sired degree of control of the project, with regard to the client's ability to control and coordi-

nate the flow of incoming knowledge. The greater the desire of a firm for control, the more

familiar the targeted community should be.

6 Limitations and further research

Of course our analysis is not without limitations. It operates at a rather aggregate level, de-

scribing the market and basic functional principles of the business models of open innovation

intermediaries. Furthermore, we focused on inbound (coupled) and not outbound open inno-

vation, thus neglecting an important part of the discussion. Client firms of intermediaries,

however, do often become innovation problem solvers themselves, i.e., contribute with their

knowledge to an OI project of another firm. We believe that the factors identified in our study

also help potential solvers to identify the intermediaries who offer a promising platform for

exploiting existing technical knowledge. We call for further research to study this participant

(solver) perspective of open innovation more extensively.

Our results have focused on describing the market for open innovation intermediaries in

greater detail and identifying correlations between the design elements of the business model

of an intermediary. Our data does not, however, enable us to make predictions regarding ei-

ther the financial performance of an intermediary or its contribution to innovation success of

its clients. This kind of analysis is still very much needed (Lichtenthaler and Ernst, 2008) and

should become a focus of future endeavors in this field. Nonetheless, we want to report one

final interesting observation from our study. When analyzing the client industries of the in-

termediaries and the reported scope of innovation tasks brought to them, we observed that

open innovation projects increasingly extend to functions and tasks beyond product, service,

and process development. We found new fields of application like marketing, customer ser-


281

vice, recruitment, knowledge management, and even HR. The core pattern of open innovation

to "look outside the box" through engaging with a wide, undefined network of people seems

to be transferrable to a variety of tasks along the value chain. This can be regarded as a strong

indicator of the (perceived) value that open innovation intermediaries can create for their cli-

ents. But again, further research is needed to study open innovation and its outcome in these

secondary fields of application.


282

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8 Appendix (B3)

Appendix (B3) 1: List of open innovation intermediaries participating in the survey

No Open innovation intermediary

1 100%Open 2 Atizo 3 blur Group 4 BrainJuicer GmbH 5 Brightidea 6 ChallengePost 7 Chaordix 8 Communispace 9 CREAX Projects 10 crowd-creation 11 CrowdWorx 12 Cuuso Systems 13 Daily Grommet 14 Deutscher Technologiedienst GmbH 15 Dialego AG/ Hello!nnovation 16 Enterprise Europe Network 17 eYeka 18 Favela Fabric 19 Fronteer Strategy 20 Gerson Lehrman Group (HighTable.com) 21 Grow VC 22 HYPE Innovation 23 Hypios 24 Hyve AG 25 IBM (JAM) 26 Idea Bounty 27 IdeaConnection 28 ideaken 29 Ideas To Go 30 ikom Unternehmensberatung 31 Induct Software 32 InnoCentive 33 Innoget 34 innosabi GmbH 35 Innovation Exchange 36 Innovation Framework Technologies 37 InnovationXchange (IXC UK Ltd) 38 Inova Software 39 Jovoto GmbH 40 KENFORX 41 Lumenogic


290

No Open innovation intermediary

42 Market of Innovation 43 Mutopo 44 NineSigma 45 Nosco 46 One Billion Minds 47 Owela VTT 48 Presans 49 Promise Corporation 50 Skild 51 SpecialChem 52 Spigit 53 The Bnet Ants S.L. 54 Userfarm 55 Van Berlo 56 Veeel designers 57 Vocatus AG 58 Vworker 59 Yet2.com


291

Appendix (B3) 2: Open innovation intermediaries organized by their offered approaches C

OL

LA

BO

RA

TIO

N I

NIT

IAT

ION

ME

CH

AN

ISM

Sear

ch

CREAX Projects Hypios

Hyve AG Ideaken

InnovationXchange (IXC UK Ltd) NineSigma

BrainJuicer GmbH Dialegeo AG/ Hello!nnovation

Favela Fabric Grow AG Hyve AG

innosabi GmbH Innovation Framework Technologies

Promise Corporation Skild

Veeel designers Vocatus

Sele

ctiv

e ca

ll

Crowd-creation Deutscher Technologiedienst GmbH

Fronteer Strategy Gerson Lehrman Group (HighTable.com)

Innoget KENFORX

Market of Innovation Presans

Veeel designers Vworker Yet2.com

100%Open blur Group Chaordix

Daily Grommet Enterprise Europe Network

Ideas To Go Lumenogic

Cal

l

100%Open ChallengePost

Deutscher Technologiedienst GmbH HYPE Innovation IdeaConnection

InnoCentive Inova Software

One Billion Minds SpecialChem

The Bnet Ants S.L.

Atizo CrowdWorx

eYeka Hyve AG

Idea Bounty Innovation Exchange

Jovoto GmbH Owela VTT

Userfarm

Technological information Market information

INFORMATION TYPE

Note: Before publishing results intermediaries verified their classification with the option to change their place-ment due to changes in their offerings or simply due to marketing reasons. This may causes discrepancies be-tween statistical results and this figure.


292

Appendix (B3) 3: Open innovation intermediaries organized by their community reach O

RG

AN

IZA

TIO

NA

L B

OU

ND

AR

IES

exte

rnal

100%Open

Atizo blur Group

ChallengePost Chaordix

Daily Grommet Fronteer Strategy

Gerson Lehrman Group (HighTable.com) Grow VC

IdeaConnection ideaken

innosabi GmbH InnovationXchange (IXC UK Ltd)

NineSigma Userfarm

BrainJuicer GmbH eYeka

Gerson Lehrman Group (HighTable.com) Hypios

Hyve AG ideaken

Ideas To Go Innovation Exchange

InnovationXchange (IXC UK Ltd) Jovoto GmbH

NineSigma One Billion Minds

Owela VTT Skild

SpecialChem Vocatus AG

Inte

rnal

Brightidea crowd-creation

CrowdWorx Dialego AG/ Hello!nnovation

Deutscher Technologiedienst GmbH Favela Fabric IBM (JAM) Idea Bounty

Innoget Innovation Framework Technologies

Inova Software KENFORX Lumenogic

Nosco Promise Corporation

Vworker Yet2.com

100%Open CREAX Projects

Deutscher Technologiedienst GmbH Enterprise Europe Network

HYPE Innovation Idea Bounty InnoCentive

Jovoto GmbH Lumenogic

Market of Innovation The Bnet Ants S.L.

Veeel designers Yet2.com

Very known Very unknown

COGNITIVE BOUNDARIES

Note: Before publishing results intermediaries verified their classification with the option to change their place-ment due to changes in their offerings or simply due to marketing reasons. This may causes discrepancies be-tween statistical results and this figure.

organizing collaborative innovation

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