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An Empirical Investigation into the Antecedents of Knowledge

Dissemination at the Strategic Business Unit Level

Hans van der Bij, X. Michael Song, and Mathieu Weggeman

Knowledge dissemination is of crucial importance for the strategic planning in new

product development. Many new ideas stem from recombination of previously

successful, disseminated actions, and knowledge dissemination offers a clear

overview of market needs, technology developments, and competitors’ actions.

Moreover, in dynamic environments, where strategic planning has to be added by

some kind of improvisation, knowledge dissemination leads to a high quality of

improvisation. It leads to a quick awareness of external or internal surprises, gives

an opportunity to learn quickly from the past, and compensates for a coordination

mechanism instead of planning.

The dissemination of knowledge does not always happen spontaneously.

Especially, people with a technical background often are highly individualistic

and do not disseminate knowledge naturally. So, this must be fostered by the

organization. In management research, particularly on technology and innovation

management, many facilitating factors have been identified that enhance

communication. Intuitively, they also would seem useful in enhancing knowledge

dissemination; however, these factors have not been tested empirically for this

specific use. Research on knowledge and its management has not given muchattention to the way knowledge in an organization is disseminated and the factors

that can facilitate it. If such factors are mentioned, they are not tested empirically

and their relative impact is not addressed.

In this study we identified 17 important factors in enhancing knowledge

dissemination and validated 10 of these factors empirically and determined their

relative impact. We focused on technological knowledge in new product

development—not on the project level but on the level of the strategic business unit.

The field research comprised three parts. In the first step, we conducted in-depth

interviews with research and development (R&D) managers and their supervisors

to select the most important potential facilitating factors. In the second step, in-

depth interviews with senior executives, information technology (IT) officers, and

R&D experts were conducted to determine whether the constructs regardingknowledge dissemination and the potential facilitating factors had face validity.

Finally, the potential factors were tested empirically in 277 U.S. high-technology

firms at the strategic business unit (SBU) level. It was our intention to examine

potential factors beyond the level of the particular project, so we looked for

antecedents in an SBU environment with a longer-term impact.

Address correspondence to Michael Song, 309 Mackenzie Hall,Box 353200, University of Washington, Seattle, WA 98195-3200.Phone: (206) 543-4587. Fax: (530) 706-5432; E-mail: [email protected]

J PROD INNOV MANAG 2003;20:163–179r 2003 Product Development & Management Association


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Our results indicate that individual commitment to the firm is very important to

facilitate knowledge dissemination as well as organizational crises and risk-taking

behavior. Individual commitment was found to have the greatest impact on the level

of knowledge dissemination, followed by organizational crisis and risk-taking

behavior. It is thus up to management to find new ways to control individual

commitment. More research, however, is required to better understand the ways bywhich managerial interventions stimulates knowledge dissemination.

Introduction

Knowledge dissemination is defined as the

process and extent of technological infor-

mation exchange within a given organiza-

tion (adopted from [13,33]). The information

exchange can occur both formally and informally

and both horizontally (i.e., interdepartmental) and

vertically within the organization. Management

literature has defined knowledge in several ways

(e.g., [14]). Following Nonaka [46] and the epistemo-

logical tradition [3], we define knowledge as justified

true belief. So knowledge here refers to information

that has been validated by experience that has enteredhuman belief systems as rules for guiding actions and

that has proven beneficial to firm performance. In this

paper, we sometimes use the concept of knowledge

creation, which is a broader concept than knowledge

dissemination and includes knowledge generation,

dissemination, and application [11].

Knowledge dissemination is important for the

strategic planning of new product development. First,

knowledge dissemination determines the quality of

strategic planning. Many new ideas stem from

recombination of previously successful (and dissemi-

nated) actions [42]. Moreover, a higher level of

knowledge dissemination leads to a clear overview

of market needs, technological developments, and

competitors’ actions within the organization. Thus, a

higher level of knowledge dissemination will increase

the quality trade-off decisions in strategic planning.

Second, improvisation has to be prompted in addition

to or as a substitute for strategic planning in dynamic

environments [42]. A high level of knowledge

dissemination leads to a quick awareness of external

or internal surprises. Moreover, it compensates for a

coordination mechanism instead of planning [42],

while it gives the opportunity to learn from former

improvisations [19].

Past research has identified many factors in

enhancing (cross-functional) communication and

organizational learning in new product development.

Facilitating factors mentioned in the new product

research probably can be used to enhance knowledge

dissemination in new product development; however,

most of the new product literature neither refers to

knowledge dissemination explicitly nor has tested

BIOGRAPHICAL SKETCHES

Dr. J.D. (Hans) van der Bij is assistant professor of organization

science in the Department of Technology Management atEindhoven University of Technology (EUT) in The Netherlands.

He studied applied mathematics at Groningen State University and

gained his Ph.D. from EUT on the subject of manpower planning.

His current research interests are in quality management in

professional service firms and in innovation and knowledge

management in high-technology firms. He is associate fellow of

the research institute BETA of EUT, and in 2001 he had a visiting

associate professorship at the University of Washington in Seattle.

He publishes in books and articles on various topics regarding

quality management, knowledge management, and business re-

search methods.

Dr. X. Michael Song holds the Michael L. and Myrna Darland

Distinguished Chair in Entrepreneurship at the University of

Washington. He also serves as research professor in the Eindhoven

Center for Innovation Studies at EUT. He received an M.S. fromCornell University and an M.B.A. and Ph.D. in business admin-

istration from the Darden School at University of Virginia. Dr.

Song’s current research interests include start-up high-tech firms,

valuation of technology and new ventures, new product develop-

ment, project risk assessment and management, entrepreneurship in

high-technology environments, measuring values of technology and

R&D projects, and technology portfolio evaluation. He is a

frequent keynote speaker at international conferences, and his

research articles have appeared in numerous journals and con-

ference proceedings.

Dr. Mathieu C.D.P. Weggeman is professor of organization science

and innovation management in the Department of Technology

Management at EUT in The Netherlands. He holds a Ph.D. in

strategic management from the Catholic University of Brabant

(The Netherlands). His primary expertise lies in the field of

organizational knowledge creation in the early stages of the

innovation process, and he is engaged actively in teaching and

conducting research in this area. A second area of interest concerns

the design of organizations in which professionals are motivated to

high performance. As a project leader he conducted several large-

scale projects in R&D environments, geared to major structural and

cultural change. He is member of the Eindhoven Center of

Innovation Studies at EUT. He has published books and articles

in the fields of participative strategy development, knowledge

management in professional organizations, and the concept of

knowledge.

164 J PROD INNOV MANAG2003;20:163–179

H. VAN DER BIJ ET AL.


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these factors empirically for this specific use. Research

on knowledge management itself has been focused on

the object of knowledge; on defining it [3]; on

distinguishing it from other dimensions, whether

explicit or tacit, individual or collective (e.g.,

[50,20,38]); and on distinguishing it from the objectof information (e.g., [18]). Little research has been

focused on knowledge dissemination and the factors

that can promote it. Moreover, if such factors are

mentioned, they are not tested empirically (e.g., [46,

11]), and their relative importance is not addressed.

In this paper we identify several factors for

enhancing knowledge dissemination in new product

development, empirically validate these factors, and

assess their relative impact. We began by deducing 17

potential facilitating factors from management litera-

ture. Then we executed field research in seven

knowledge-intensive organizations (IBM, Intel,Merck, Microsoft, Motorola, Philips, and Sony).

During the first step of the field research we

conducted 10 in-depth interviews with research and

development (R&D) managers and their supervisors.

The field research was designed to reduce the number

of potential factors and select the 10 most important

ones in the eyes of the interviewees. During the

second step of the field research we conducted in-

depth interviews with 22 senior executives, informa-

tion technology (IT) officers, and R&D experts. The

objective was to see whether the constructs regarding

knowledge dissemination and the 10 potential facil-

itating factors could be understood (face validity) and

whether the accompanying scale items were clear and

complete. Next, we tested the 10 potential factors

empirically in 277 U.S. high-technology firms at the

strategic business unit (SBU) level. It was our

intention to examine potential factors beyond the

level of the particular project, so we looked for

antecedents in an SBU-environment with a longer-term impact.

We present our findings as follows. First, we offer a

rationale for the identification and selection of the 10

potential factors. Then we present our conceptual

framework and our research propositions. After-

wards, we discuss the research design and present

the analysis and the results. Finally, we offer

conclusions and implications of our research and

discuss limitations and directions for future research.

Identification and Selection of PossibleAntecedents

To identify potential antecedents for knowledge

dissemination, we reviewed articles published in 17

top-management journals over the last 15 years. We

reviewed articles not only on knowledge management

but also on organizational learning, individual learn-

ing, innovation management, R&D management,

technology management, information systems, hu-

man resource management, and strategic manage-

ment. Finally, without claiming to be exhaustive, we

identified 17 potential antecedents as the most

interesting and having the highest potential contribu-

tion to the literature. They are summarized in Table 1.

Table 1. Factors Mentioned in Literature for Enhancing the Level of Knowledge Dissemination

Factors Authors

Co-location Coombs and Hull [11], McDonough III et al. [39], Moenaert and Caldries [40]

Teams* Matusik and Hill [38], Nonaka [46]

Information Technologies Huber [26], Kendall [29], Lucas [35], Warkentin et al. [61]

Lead user and supplier networks Dodgson [16], Gemu ¨ nden et al. [21], Nonaka [46]

Formal rewards Matusik and Hill [38], Mueller and Dyerson [44]

Job rotation*

Bird [5], Moenaert and Souder [41] Individual commitment Nonaka [46], Polanyi [50]

Feedback mechanisms* Coombs and Hull [11], Matusik and Hill [38]

Post-project evaluation* Busby [7]

R&D budget* Dodgson [16], Hausman et al. [25], Kamien and Schwarz [27]

Long-term orientation Dodgson [16], Souder [57]

Asset specificity* Christensen [8]

Organizational redundancy Nonaka [46]

Goal congruency* Ginn and Rubenstein [22], Song et al. [56]

Organizational crisis Drazin et al. [17], Kim [30], Nonaka [46]

Risk-taking behavior Sitkin [52], Sitkin and Pablo [53]

Management support Brown and Eisenhardt [6], Song et al. [56], Van de Ven [60]

* Note: * means that the factor has not been selected in the field research.

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After our literature review, during the first step of

our field research, we conducted 10 in-depth inter-

views with R&D managers and their supervisors in

seven knowledge-intensive organizations, including

IBM, Intel, Merck, Microsoft, Motorola, Philips, and

Sony. We followed the standard format of thestructured open-response interview that ‘‘uses an

interview schedule which is in format rather like the

structured interview, with questions included in a set

of order. However, some questions were open-ended,

and there were flexibility to allow variation in the

order in which groups of questions are asked’’ [31].

We listed the potential antecedents and asked

the managers to indicate a rank order of the

importance for the factors identified in the literature

reviews. Next, in a less structured way, we asked for

stories of success and failure in the management of

knowledge dissemination and its consequences for thefirm. After all interviews were conducted, we analyzed

the results and selected 10 factors with the highest

rank order scores as most interesting and important

in their impact on knowledge dissemination (see

Table 1).

Respondents regarded physical co-location and

virtual co-location through information technologies

as important in enhancing knowledge dissemination.

Several companies regularly invite their current

and potential suppliers and lead users to parti-

cipate in retreat conferences to discuss their current

technological and new product development

problems. Suppliers and customers make notes,

ask questions, and talk with each other at the

conferences. Often participants come back with

proposals for solving perceived problems. Companies

co-develop with those participants whose proposals

look promising. In general, respondents did not

mention the use of teams to foster knowledge

dissemination.

Respondents also identified formal rewards and

individual commitment as important. Despite their

importance, respondents noted that formal reward

systems for knowledge dissemination are rare in high-

technology companies. Most appraisal forms do not

use the criterion ‘‘shares knowledge with others,’’

and the common criterion ‘‘is able to work

independently’’ actually discourages knowledge dis-

semination. Respondents considered the use of feed-

back relatively unimportant for knowledge

dissemination.

The usefulness of measures on top-management

level was confirmed in the field research. Respondents

favored the stability of the budget for important

research areas over years rather than the size of

the budget. European respondents favored the use

of organizational redundancy and organizational

crisis (either real or generated intentionally by top

management) to enhance knowledge dissemination.However, these measures are not used on a large

scale. They also favored a risk-taking behavior

and management support; however, they did not

mention asset specificity and goal congruency. Results

from the field research indicate that, since people

with a technical background tend to be more

individualistic than those with a nontechnical back-

ground, technicians do not give high priority to

knowledge dissemination. Therefore management

should encourage engineers to leave their silos in

latter stages of the development process to work

together. It is the only way to keep the developmentprocess within 48 weeks, according to one of the

respondents.

Conceptual Framework and ResearchPropositions

Conceptual Framework

The conceptual framework guiding this study is

presented in Figure 1. The framework is a result of

both literature review and the field research described

above. Briefly, the model focuses on 10 antecedents

that may influence knowledge dissemination posi-

tively. To reflect the potential influence of the external

environment, we incorporated a set of control

variables.

Similar to Kohli and Jaworski [32], the results from

our field research interviews confirm our expectation

that effective knowledge dissemination and informa-

tion exchange require the participation of virtually all

departments in an organization (e.g., R&D, market-

ing, manufacturing, etc).

Effective innovation processes require the collec-

tion of information about new technology and new

knowledge development for several reasons. First,

greater dissemination of knowledge leads to a better

understanding of technology capabilities and trends.

This knowledge helps guiding engineering design and

contributes to better technical development and

manufacturing-process designs. Moreover, informa-

tion about customers, competitors, and manufactur-

ing capabilities is essential in determining product

features and specifications. Since such technological




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and market information is equally valuable for other

functional areas, increasing knowledge dissemination

and information exchange becomes particularly im-

portant in assuring an effective and timely generation

and dissemination of technology, market, and com-

petitive intelligence.

Second, higher levels of knowledge dissemination

and information exchange significantly reduce

both marketing- and technical-related uncertainties

in the innovation process, thereby improving a

company’s ability to develop a product that provides

superior technical performance and meets consumers’

needs.

Third, knowledge dissemination between technical

and marketing departments can increase marketing’s

understanding of technical development and manu-

facturing-process designs. At the same time, market-

ing’s information about the market and the

competition can be used to determine desirable

features and specifications and hence can improve

the chances of developing a successful product. A

high degree of exchange of information and knowl-

edge by marketing and technical people also can

minimize the need for costly redesigns and respecifi-

cations while maximizing the possibility of meeting

customer needs. Furthermore, it can ease R&D’s task

of designing and redesigning product features, man-

ufacturing’s task of planning production schedules

and reducing product costs, and marketing’s task of

positioning and differentiating the product in the

global marketplace [48,55].

Finally, information exchange and knowledge

dissemination are also important for new product

selection and for product introduction decisions.

Effective product introduction requires a greater

information flow from marketing to manufacturing

(e.g., sales forecasts), marketing to R&D (e.g.,

product modifications), and a greater knowledge flow

from R&D to marketing (e.g., product support

services). The results from our field research suggest

that higher levels of information exchange and

knowledge dissemination are owing partly to the

process by which decisions about the development of

innovative products are made. Decisions about when

and how a highly innovative new product is to be

introduced into or withdrawn from the marketplace

often are made only after a consensus is reached

among R&D, manufacturing, and marketing. Thus,

greater levels of information exchange and knowledge

dissemination within an organization increase the

likelihood that the new product will be positioned in

the right market segments and will be introduced at

an optimal time, thereby improving the chances of

product success or minimizing financial loss.

Co-location

Information Technologies

Lead user and supplier networks

Formal rewards

Individual commitment

Long-term orientation

Organizational redundancy

Organizational crisis

Risk-taking behavior

Management support

Level of Knowledge

Dissemination

Figure1. A conceptual framework for studying potential antecedents of the level of knowledge dissemination


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Research Propositions

In this section 10 research propositions are developed

for each of the 10 antecedents of knowledge

dissemination. These antecedents are co-location of

R&D personnel, IT, lead user and supplier networks,formal rewards, individual commitment, long-term

orientation, organizational redundancy, organiza-

tional crisis, risk-taking behavior, and management

support.

Co-location of R&D personnel . Moenaert and Cael-dries [40] found that, contrary to expectations, placing

R&D professionals in closer proximity to one another

did not increase technological learning in the organiza-

tion, but it improved market learning and product

innovativeness. McDonough III et al. [39] studied the

performance of co-located, virtual, and global NPD

teams. They found that co-located NPD teams per-

formed significantly better than virtual and global NPD

teams due to less behavioral and project management

problems of co-located NPD teams, which enhances the

quality of communication, the interpersonal relation-

ships, and the level of project agreement. Since knowl-

edge dissemination occurs through communication,

these arguments also hold for knowledge dissemination.

Moreover, examining knowledge management practices,

Coombs and Hull [11] found that physical clustering of

R&D projects in cognate technological areas has a

profound effect on the generation and dissemination of

technological and market knowledge. Thus, we proposethe following:

P1: Co-location of R&D personnel has a positive

influence on the level of knowledge dissemination.

Information Technologies (IT). Typically, research-ers classify IT by technological functions [29]. Huber

[26] defines advanced IT to include computer-assisted

communication technologies (e.g., email, video confer-

encing) and computer-assisted decision-aiding technol-

ogies (e.g., decision support systems, expert systems).

Kendall [29] proposes a classification that includes

production-oriented technologies, coordination-or-iented technologies, and organizational-oriented tech-

nologies. We concentrate on two types of IT:

communication technologies (ITc) and decision-aiding

technologies (ITd). ITc supports and enhances the

communication-related activities of organization mem-

bers. It helps to overcome space and time constraints in

communication; to increase the range and depth of

information access; to target groups more precisely; and

ultimately to enable knowledge to be shared more

rapidly, more conveniently, and yet less expensively

(e.g., [35,61]). Whereas ITc is concerned with commu-

nication, ITd is concerned with tasks. It helps indivi-

duals or organizations create models and develop

alternatives and solutions for their tasks. ITd usually

requires standard forms of input and produces standard

reports that are readily understandable to users. In

addition, graphic display functions in many ITdprograms replace text and tables with charts and graphs,

which further facilitates knowledge dissemination

among different departments who often use different

functional languages [23]. ITd also builds an ‘‘informa-

tion center’’ for organization members to store, share,

and retrieve information [34, 58]. Increased accessibility

to stored information improves absorptive capacity of

recipients [10] and thus enhances knowledge dissemina-

tion. Therefore, we propose the following:

P2: Use of Information Technologies has a positive


Lead user and supplier networks. Dodgson [16]concludes from a literature review that lead users and

suppliers are important sources of learning for innova-

tion in firms. Experience of others enables people to

acquire large, integrated patterns of behavior without

having to form them gradually by tedious trial and

error. According to Gemu ¨ nden et al. [21], lead users

represent an important source of technological know-

how. They can let the organization participate in their

knowledge of future trends in new product requirements

or by suggestions of improving the products already

existing. Nonaka [46] argues that sharing tacit knowl-edge with suppliers or customers through co-experience

and creative dialogue plays a critical role in creating

relevant knowledge. Collaboration to exchange ideas

through shared narratives and ‘‘war stories’’ can provide

an important platform on which to construct shared

understanding out of conflicting and confused data.

Following the literature and considering that informa-

tion from lead users and suppliers is probably so

accurate and interesting that it is worthwhile to

disseminate, we propose the following:

P3: Lead user and supplier networks have a positive


Formal rewards. According to Matusik and Hill [38],the relationship between organizational knowledge and

competitive advantage is moderated by the firm’s ability

to integrate and to apply knowledge. Firms use formal

and informal integrating mechanisms in order to

facilitate the transfer of existing knowledge to different

areas of the firm. One of the mechanisms that stems

from product development research is the use of rewards

for integrating information. This mechanism also is




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mentioned by Mueller and Dyerson [44], who notice that

much more research has to be conducted to specify the

exact nature of the interplay between managerial

decisions and the creation of a superior and distinctive

base of organizational knowledge. Although we fully

agree with these authors that the exact mechanism how

formal rewards influence knowledge dissemination is

not examined yet, we propose the following:

P4: Use of formal rewards as an integrating mechan-

ism will positively influence the level of knowledge

dissemination.

Individual commitment. The prime movers in theprocess of organizational knowledge creation are the

individual members of an organization. Individuals are

committed continuously to recreate the world in

accordance with their own perspectives [46]. As Polanyi

[50] and Nonaka [46] note, commitment is based on

three factors: intention, autonomy, and environmental

fluctuations. Intention regards the way people approach

the world and try to make sense of it. Autonomy leads

to greater flexibility in acquiring, relating, and inter-

preting information. Environmental fluctuations gener-

ate new patterns of interaction between people and their

environment (see also [37, 62]). Why would commitment

enhance the dissemination of knowledge? Probably,

identification with and involvement in the organization

also means communication with the people in the

organization—at least for some. And knowledge dis-

semination occurs through communication. Moreover,

environmental fluctuations give this communication thepurpose of identifying and explaining these fluctuations.

We propose the following:

P5: Individual commitment has a positive influence on

the level of knowledge dissemination.

Long-term orientation. Souder [57] found that animportant requirement for successful innovation is an

organization that fosters long-term commitments to

technology. The most innovative firms, he studied,

exhibited a quality of patience in permitting ideas to

germinate and gestate. The ‘‘‘let alone and something

good will happen’’ philosophy was not applied, butrather there was a definite decisiveness in controlling the

amount of time ideas spent gestating. After a reasonable

time, decisions were made and some ideas were selected,

while others were abandoned. Dodgson [16] also

pinpoints the importance of a long-term orientation to

create organizational structures and cultures that

encourage learning: ‘‘The costs of learning are immedi-

ate, and the benefits are long-term.’’

In our view, a long-term orientation offers a stable

strategic direction, implemented by a steadily growing

number of organization members. While following

the same strategy together, people become more

involved with each other and are more willing to

disseminate knowledge. Though too much common-

ality might decrease the number of subjects to

disseminate, clearly there usually is enough dissim-ilarity to promote the dissemination of ideas. There-

fore, we propose the following:

P6: A long-term orientation of the firm has a positive


Organizational redundancy. An important principlefor managing organizational knowledge is redundancy,

i.e., the conscious overlapping of company information,

business activities, and management responsibilities

[46]. Redundant information can be instrumental in

speeding up concept creation. When organization

members share overlapping information, they can

sense what others are trying to articulate. Especially

in the concept development stage, it is critical to

articulate images rooted in tacit knowledge. When

people share overlapping information, they can enter

each other’s area of operation and can provide advice

from new and different perspectives. So, redundant

information can stimulate the exchange of nonredun-

dant information; in other words, it can stimulate

knowledge dissemination. However, too much overlap

might decrease the incentives to share knowledge and

may have a negative influence on knowledge dissemina-

tion. Following the ideas of Nonaka [46], we proposethe following:

P7: Organizational redundancy has a positive influence

on the level of knowledge dissemination.

Organizational crisis. The positive influence of orga-nizational crisis on organizational learning is posited by

Kim [30]. Crises may stem from external sources. In

response to these external developments, top managers

can construct a crisis internally. But they also deliber-

ately can construct an internal crisis in the absence of an

external one. The shared sense of an internally

constructed crisis among organization members intensi-fies their efforts to expedite learning and thus the

absorptive capacity of the organization. According to

Nonaka [46] and Drazin et al. [17], disruptive events

may lead to the demolition of existing frames of ideas

and beliefs and so offer the opportunity to build new

ones. So, following the literature we can argue that

organizational crises offer the opportunity to shape new

ideas and beliefs and that the increased loyalty to the

organization stimulates the dissemination of this knowl-

edge. We propose the following:


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P8: An organizational crisis has a positive influence on

the level of knowledge dissemination.

Risk-taking behavior. The greater the degree of short-term success, the more unquestioningly members

of the organization will follow standard operational

routines. If failures arise, confidence in standardprocedures decreases and experiments and learning

grow more likely. Not all failures are equally likely to

facilitate learning. Sitkin [52] defined criteria for so-

called intelligent failure as well as conditions that

facilitate such failure. These include emphasis on

processes instead of outcomes, legitimization of intelli-

gent failure, development and maintenance of individual

commitment to intelligent failure through organiza-

tional culture and design, and emphasis on failure

management systems instead of individual failures. All

of these conditions incorporate risk-taking behavior

[53]. In fact, the conditions encourage the building of

new ideas and beliefs through experiments, and the

dissemination of these. So, we propose the following:

P9: Risk-taking behavior has a positive influence on the

level of knowledge dissemination.

Management support. Management literature hasshown the influence of management style on organiza-

tion behaviors. For instance, Van de Ven [60] found top-

management support to be vital to a climate favorable

to innovation, while Song et al. [56] found senior-

management support to be important for the success of

cross-functional integration among marketing, R&D,and manufacturing in Hong Kong and Japan. Senior-

management support includes providing clear objectives

and appropriate organizational structures for integra-

tion. The chances that integration efforts will succeed

are increased, not only by providing necessary financial

and political resources but also by signaling that the

organization values cooperation [6]. The same argu-

ments can be applied to the influence of management,

supporting the generation, dissemination, and applica-

tion of knowledge. Since we restrict this study to

knowledge dissemination, we propose the following:

P10: Management support has a positive influence onthe level of knowledge dissemination.

Methodology

Research Instrument Development Procedure

We used existing scales wherever possible and under-

took the following six steps to develop the new scales.

First, we conducted a literature review and identified

a pool of items for each of the constructs from the

existing literature. We tried to generate items that tap

the domain of each construct as closely as possible [9].

In the second step of our field research, we

conducted in-depth interviews to check whether the

constructs defined could be understood (face validity)and that the accompanying scale items were clear and

complete. A total number of 22 senior executives, IT

officers, and R&D experts were interviewed during

this phase of the field research. The interviews

followed a standard protocol, and they consisted of

two parts. The first part of the interviews was

designed to elicit salient scale items for our constructs

and definitions of those items. The second part of the

interviews addressed perceptions of the relevance and

completeness of constructs and scale items drawn

from our literature review and the current and earlier

case studies.Third, we performed a content analysis using the

procedure recommended by Kassarjian [28]. The

objective was to standardize the outcomes of the

different interviews from the field research. All

measurement items generated from the above two

steps were given a unique code. Five researchers with

adequate knowledge in the field of knowledge

management independently verified for all issues

how they could be positioned in the developed

research instrument. Four researchers compared their

outcomes and discussed any differences. In one

measurement item where consensus could not be

reached, the fifth researcher served as a referee and

determined the final positioning.

Fourth, using the measurement items generated,

we developed the first draft of our research instru-

ment. We discussed this first draft with a representa-

tive panel of experienced IT officers and R&D

managers from the companies. This helped us to

refine a number of the items included in the first draft

of our research instrument. We then followed the

recommendations of Churchill [9] and identified

subsets of items that were unique and possessed

‘‘different shades of meaning’’ to informants.

We submitted a list of constructs and corres-

ponding measurement items to a panel of four

academic ‘‘experts’’ for critical evaluation and sugges-

tions. We constructed a questionnaire based on those

items judged to have high consistency and face

validity.

Fifth, we pretested the survey for clarity and

appropriateness using the participants of the field

research. The participants were asked to indicate




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any ambiguity or difficulties they experienced in

responding to the items. Based on the feedbacks

from the participants, we eliminated some items and

modified other items with which managers either had

difficulties or found to be ambiguous.

Sixth, the final research instruments were subjectedto additional pretests involving personal interviews

with six executives in Motorola, Microsoft, and IBM.

We asked these executives to complete the survey as

they applied to their business unit. At this stage, this

pretest resulted only in minor refinements on two

measurement items.

Measures

The constructs and accompanying scale items are

listed in the Appendix.

Dependent variable. Since the scale for knowledgedissemination was not available in the literature, we

developed the scale using the research instrument

development procedure discussed in the earlier sec-

tion. Knowledge dissemination is defined as the

process and extent of technological information

exchange within a given organization. Several man-

agers noted that for an organization to be competitive

in the knowledge intensive economy, knowledge must

be communicated and disseminated to relevant

departments and individuals in the organization.

R&D directors and marketing managers in both

Philips and Sony developed procedures for periodi-

cally circulating documents (e.g., reports, newsletters)

that provide new knowledge created and the progress

of technology development activities. Thus, we

developed a four-item scale that measures the extent

the company periodically circulates documents (e.g.

reports, newsletters) that provide new knowledge

created, the extent data on technology development

are disseminated at all levels in the company on a

regular basis, the extent information about successful

and unsuccessful technology development is commu-

nicated freely across all business functions, and the

extent of cross-functional communication concerning

technology developments in the company.

Independent variables. Co-location of R&D person-

nel is defined as the location of different departments

and offices of R&D personnel in close proximity to

each other [49]. The three-item scale was adopted

from [49].

Information Technologies. Information technolo-

gies refer to the availability, level of investment in,

and usage of state-of-the-art computer-assisted com-

munication technologies and decision-aid informa-

tion technologies [26,29,51]. The four-item scale was

adopted from [51].

Lead user and supplier networks are defined as the

pattern of relations among the organizations’ mem-

bers and its lead users and suppliers through which anorganization member seeks advice from a lead user or

supplier or vice versa (from: [2]). The two-item scale

was adopted from [2].

Formal rewards is defined by the extent to which

knowledge dissemination is a major component of the

organizations’ performance evaluation; the one-item

scale was adopted from [54].

Individual commitment is defined as the employer’s

identification with and involvement in a particular

organization [43] and was measured by a five-item

scale adopted from [1].

Long-term orientation is defined as the expectationthat the current direction of R&D efforts and expenses

will continue in the future [36]. It was measured by a

four-item scale that was adopted from [36].

Organizational redundancy is defined as the con-

scious overlapping of company information, business

activities, and management responsibilities [46] and

was measured by a three-item scale adopted from [24].

Organizational crisis refers to perceived disconti-

nuities in technologies, markets, or other environ-

mental conditions [30, 59]. A three-item scale that is

based on the field research was used. It measures the

extent to which top management intentionally creates

organizational crises, the frequency of organizational

crises in the organization, and the extent to which

organizational crisis is a characteristic of the firm.

Risk-taking behavior is defined as taking decisions

with uncertain expected outcomes, with decision goals

that are hard to achieve, and with a potential

outcome set that includes some extreme consequences

[53]. It was measured by a three-item scale, adopted

from [54].

Management support is defined as the creation of

an environment that directly facilitates the genera-

tion, dissemination, and application of knowledge

[56]. The one-item scale was adopted from [54].

To control for possible industry and firm effects,

we included eight variables: (1) Buyer power (BPOW)

measures the extent to which the customers of the

firm are able to negotiate lower prices from it; (2)

Supplier power (SPOW) measures the extent to which

the firm is able to negotiate lower prices from its

suppliers; (3) Seller concentration (CONC) measures

the percentage of total sales accounted for by the four


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competitors with the largest sales; (4) Ease of entry

(ENTRY) measures the likelihood of a new compe-

titor being able to earn satisfactory profits in the

firm’s principal served market segment within three

years after entry; (5) Market growth (MGRO)

measures the average annual growth rate of totalsales in an SBU’s principal served market segment

over the past three years; (6) Technological change

(TCHG) measures the extent to which production/

service technology in an SBU’s principal served

market segments has changed over the past three

years; (7) Relative size (RSIZE) measures the size of

an SBU’s sales revenues in its principal served

market segment in relation to those of its largest

competitor; and (8) Relative cost (RCOST) measures

the SBU’s average total operating costs (adminis-

trative, production, marketing/sales, etc.) in relation

to those of its largest competitor in its principal servedmarket segment. These control variables were adopted

from [45].

Data Collection

The data were collected using mail surveys. The

sampling frame consisted of the companies listed in

the High-Technology Industries Directory, all of which

were sent a mailing. After initial contacts to identify

appropriate informants, we narrowed the original list

to 686 firms that had valid contact information for

the final survey. Phone calls were made to verify the

contact information. In administering each of the

mail surveys, we followed the total design method for

survey research [15]. The first mailing packet included

a personalized letter, an express postage-paid envel-

ope with an individually typed return-address label,

and the questionnaires. We sent out three follow-up

letters. We resent the questionnaire, together with a

reminder letter, to each firm that did not respond

after three weeks. We also resent the questionnaire

with the second reminder letter. To increase the

response rate, we supplemented our extensive perso-

nal contacts and networking efforts with numerous

incentives.

From the 686 firms, we collected complete data

from 277 firms (a 40 percent response rate). These

companies are operating in the following businesses:

telecommunications equipment; semiconductors and

computer-related products; software-related pro-

ducts; Internet-related services and equipments; in-

struments and related products; electronic and

electrical equipment; pharmaceutical, drugs, and

medicines; and industrial machinery and equipment.

The average age of the respondents is 43. The average

number of employees is 2,406 and ranges from 490 to

4,300.

To test for possible nonresponse bias, we com-

pared early (first wave of mailing) with late responseson the level of knowledge dissemination of the firm.

The results indicated no significant differences at a 95-

percent confidence interval. We also collected addi-

tional financial data from secondary sources such as

CompuStat and company annual reports to compare

respondent with nonrespondent firms on annual

sales and number of employees. The results indi-

cated that there were no significant differences

between the responding and nonresponding firms

at a 95-percent confidence interval. Thus, we con-

clude that there is no nonresponse bias and that the

results may be generalized to the firms that did notrespond.

Analysis

We performed a factor analysis using Varimax

rotation. The factor loadings are reported in

Table 2. Loadings range from 0.52 to 0.90, suggesting

a high level of validity for all constructs. The total

variance explained by the factors is 0.77.

To test propositions, the measure on each multiple-

item scale was obtained by a simple average of the

individual scale items. In Table 3, we present

construct reliabilities on the diagonal, and correla-

tions on the off diagonal. The reliability of all

measures is found to exceed the 0.70 thresholds

recommended by Nunnally [47], hence implying a

high level of scale reliability.

Ordinary least-squares technique was employed for

estimating model parameters. Results for the regres-

sion are presented in Table 4. F-statistic was 18.62

(po.0001); R-square and adjusted R-square were

0.57 and 0.53, respectively.

To address problems associated with multicolli-

nearity, an application of the Belsley et al. [4]

multicollinearity diagnostic test was executed; results

indicated no serious multicollinearity problems.

Results

Table 4 shows that our findings confirm the value of

eight of the 10 potential enhancing factors. In

particular, it shows that P1 is confirmed at an alpha




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level of 0.01, suggesting that co-location of R&D

personnel has a positive association with the level of

knowledge dissemination. So, living in close proxi-

mity of each other makes it easier for people to

disseminate knowledge. P2 predicts that the use of IT

has a positive influence on the level of knowledge

dissemination. However, we did not find empirical

support for this proposition.

P3 is confirmed at an alpha level of 0.05, indicating

that lead user and supplier networks positively

influence the level of knowledge dissemination.

Having strong networks of lead users and suppliers

enhances the dissemination of knowledge. P4 pertain-

ing the use of formal rewards is also confirmed, again

at an alpha level of 0.05. Individual commitment is

found to have a positive influence on the level of

knowledge dissemination at an alpha level of 0.01,

thus confirming P5.

As predicted by P6, the positive effect of long-term

orientation on the level of knowledge dissemination is

confirmed at an alpha level of 0.01. So, long-term

strategic plans and investments, as well as top

management’s believe that R&D efforts will benefit

in the long run, will stimulate people to disseminate

knowledge. P7, predicting a positive influence of

organizational redundancy on the level of knowledge

dissemination, could not be supported by our

empirical findings. However, we did find a positive

influence of organizational crisis on the level of

knowledge dissemination at an alpha level of 0.01—

thus confirming P8—and a positive influence of risk-

taking behavior and management support on the level

Table 2. Factor Loadings with Varimax Rotation

Factor Loadings*

Items F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11

KDIS2 0.84 0.10 0.07 0.04 0.11 0.28 0.01 0.11 0.06 0.09 0.05

KDIS4 0.76 0.09 0.09 0.06 0.12 0.25 0.05 0.23 0.18 0.14 0.02KDIS1 0.72 0.34 0.12 0.03 0.15 0.26 0.07 0.10 0.11 0.03 0.00

KDIS3 0.52 0.29 0.02 0.20 0.25 0.10 0.12 0.06 0.17 0.16 0.36

CL1 0.10 0.79 0.22 0.00 0.25 0.10 0.06 0.01 0.19 0.14 0.01

CL3 0.22 0.79 0.09 0.03 0.09 0.17 0.03 0.19 0.14 0.13 0.16

CL2 0.19 0.74 0.06 0.05 0.28 0.09 0.09 0.08 0.09 0.13 0.06

UIT1 0.14 0.02 0.82 0.14 0.00 0.02 0.06 0.07 0.18 0.04 0.07

UIT4 0.00 0.03 0.81 0.02 0.03 0.03 0.01 0.12 0.05 0.18 0.02

UIT2 0.05 0.14 0.77 0.07 0.04 0.22 0.01 0.01 0.15 0.09 0.05

UIT3 0.01 0.19 0.56 0.01 0.04 0.05 0.14 0.11 0.28 0.23 0.33

NETW1 0.01 0.00 0.01 0.90 0.05 0.06 0.03 0.00 0.00 0.10 0.14

NETW2 0.12 0.02 0.18 0.84 0.07 0.10 0.10 0.04 0.04 0.03 0.12

REWARD1 0.14 0.05 0.06 0.01 0.84 0.03 0.09 0.17 0.13 0.02 0.10

COMMIT4 0.08 0.08 0.07 0.08 0.08 0.85 0.01 0.05 0.08 0.12 0.05

COMMIT1 0.14 0.03 0.09 0.05 0.02 0.84 0.14 0.02 0.04 0.21 0.14

COMMIT3 0.21 0.04 0.04 0.01 0.03 0.74 0.20 0.11 0.02 10.31 0.17

COMMIT2 0.20 0.12 0.08 0.10 0.03 0.73 0.01 0.13 0.20 0.08 0.14

COMMIT5 0.28 0.27 0.04 0.05 0.12 0.67 0.03 0.12 0.09 0.34 0.02

LTO4 0.18 0.03 0.06 0.02 0.03 0.03 0.87 0.04 0.07 0.11 0.11

LTO3 0.05 0.01 0.01 0.01 0.03 0.02 0.87 0.09 0.09 0.08 0.13

LTO1 0.09 0.02 0.13 0.13 0.24 0.19 0.73 0.00 0.05 0.06 0.07

LTO2 0.33 0.09 0.08 0.01 0.12 0.05 0.68 0.09 0.09 0.13 0.23

OR3 0.14 0.10 0.06 0.06 0.09 0.00 0.04 0.89 0.01 10.15 0.11

OR2 0.01 0.19 0.08 0.00 0.11 0.01 0.01 0.87 0.09 0.09 0.04

OR1 0.20 0.04 0.02 0.11 0.00 0.09 0.03 0.83 0.05 0.02 0.15

ORGC3 0.21 0.13 0.15 0.04 0.12 0.11 0.06 0.08 0.78 0.18 0.09

ORGC1 0.03 0.16 0.02 0.07 0.05 0.12 0.07 0.05 0.78 0.19 0.16

ORGC2 0.14 0.09 0.34 0.08 0.16 0.01 0.09 0.02 0.74 0.25 0.03

RISKB1 0.03 0.08 0.04 0.01 0.03 0.30 0.24 0.06 0.00 0.82 0.02

RISKB3 0.04 0.11 0.14 0.01 0.16 0.11 0.06 0.09 0.09 0.79 0.16

RISKB2 0.09 0.17 0.09 0.10 0.12 0.28 0.09 0.20 0.06 0.75 0.10

MS1 0.13 0.14 0.07 0.03 0.18 0.08 0.06 0.37 0.09 0.12 0.74

* Items identified as eleven factors: F15knowledge dissemination; F25co-location of R&D personnel; F35Information Technologies; F45leaduser and supplier networks; F55formal rewards; F65individual commitment; F75long-term orientation; F85organizational redundancy;F95organizational crisis; F105risk- taking behavior; F115 management support.Note: black numbers indicate items that load highly for each of the 11 factors.


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of knowledge dissemination at an alpha level of 0.01,

confirming P9 and P10 respectively.

Moreover, the control variables supplier power,

seller concentration, ease of entry, relative size, and

relative cost were significant at an alpha level of 0.05

or 0.01.

Conclusions and Implications

We developed antecedents for knowledge dissemina-

tion and tested them for their significance and their

impact on the level of knowledge dissemination. We

suggested that long-term orientation and risk-taking

behavior affect the level of knowledge dissemination.

Although some antecedents are mentioned explicitly

or implicitly in the knowledge management literature,

none of them have been tested empirically for itssignificance and impact. Since knowledge dissemina-

tion is crucial to the quality of the strategic planning

of new products, we also contributed to the theory on

this topic.

All propositions but two have been confirmed,

indicating that knowledge dissemination is at the

heart of the organization and that many interventions

can increase the level of knowledge dissemination. In

fact (the good news of this study), all important

antecedents identified are people-related factors and

basically are changeable.

Two propositions pertaining IT and organizational

redundancy have not been confirmed. The most

surprising nonsignificant factor perhaps is IT. In

their study on media richness, Daft and Lengel [12]

label IT as relative ‘‘lean’’ media by which it is

difficult to transfer rich information (i.e., information

regarding ambiguous issues stemming from different

frames of reference). Since the information in the new

product development area often is rich, this might

explain the nonsignificant influence of IT. Although

nonsignificant, the effect of organizational redun-

dancy on the level of knowledge dissemination

appeared to be negative. This is contrary to existing

research and is a warning that the management of

knowledge dissemination is complex and is not

always straightforward. Evidently, overlapping skills,

resources and business activities across different

divisions/departments will lead to too much similar-

ity, thus weakening the incentives to disseminate

knowledge.

Individual commitment has the greatest impact on

the level of knowledge dissemination, followed by T a b l e 3 .

M e a s u r e m e n t I n f o r m

a t i o n

M e a n

S . D .

K D

C L

U I T

N E T W

C O M M I T

R E W A R D

L T O

O R

O R G C

R I S K A

M A

K n o w l e d g e d i s s e m i n a t i o n

4 . 9

1

2 . 3

1

0 . 8

4

C o - l o c a t i o n

5 . 6

0

2 . 2

2

0 . 4

6 * *

0 . 8

0

I n f o r m a t i o n T e c h n o l o g i e s

6 . 0

5

1 . 9

6

0 . 2

2 * *

0 . 2

5 * *

0 . 7

8

L e a d u s e r a n d s u p p l i e r n e t w o r k s

2 . 8

2

2 . 4

7

0 . 1

4 *

0 . 0

4

0 . 1

7 * *

0 . 7

2

I n d i v i d u a l c o m m i t m e n t

5 . 9

4

2 . 2

4

0 . 4

7 * *

0 . 3

5 * *

0 . 1

9 * *

0 . 0

1

0 . 8

9

F o r m a l r e w a r d s

7 . 2

3

2 . 7

7

0 . 2

6 * *

0 . 1

7 * *

0 . 1

0

0 . 0

1

0 . 0

9

L o n g - t e r m o r i e n t a t i o n

4 . 6

6

2 . 6

1

0 . 1

2 *

0 . 0

0

0 . 1

1

0 . 1

1

0 . 1

7 *

N A

0 . 8

2

O r g a n i z a t i o n a l r e d u n d a n c y

3 . 6

7

2 . 3

4

0 . 3

4 * *

0 . 2

1 * *

0 . 1

1

0 . 0

6

0 . 1

0

0 . 1 3 *

0 . 0

3

0 . 8

8

O r g a n i z a t i o n a l c r i s i s

5 . 3

7

2 . 5

2

0 . 3

1 * *

0 . 3

9 * *

0 . 4

1 * *

0 . 0

7

0 . 2

2 * *

0 . 2 3 * *

0 . 0

5

0 . 1

4 *

0 . 7

7

R i s k - t a k i n g b e h a v i o r

3 . 0

8

2 . 7

6

0 . 0

3

0 . 2

9 * *

0 . 2

5 * *

0 . 0

7

0 . 4

4 * *

0 . 2 3 * *

0 . 2

8 * *

0 . 1

8 * *

0 . 1

8 * *

0 . 8

5

M a n a g e m e n t s u p p o r t

6 . 1

0

2 . 5

3

0 . 1

5 *

0 . 1

0

0 . 0

7

0 . 0

6

0 . 0

9

0 . 0 2

0 . 0

8

0 . 3

6 * *

0 . 0

7

0 . 2

4 * *

N A

N o t e s : *

p o 0 . 0

5 ; * *

p o 0 . 0

1

N o t e : T h e C r o n b a c h ’ s c o e f fi c i e n t a l p h a f o r e a c h m e a s u r e i s o n t h e d i a g o n a l i n i t a

l i c s ; t h e i n t e r c o r r e l a t i o n s a m o n g t h e m e a s u r e s a r e o n t h e o f f d i a g o n a l .




13/17

organizational crisis and risk-taking behavior. Un-

fortunately (the bad news in our study), individual

commitment is not the easiest factor to control.

Management should find new ways to control this

factor. Possibly, it would be helpful to look at the

commonalities between the three factors mentioned

here. All factors have in common that in one way oranother, the occurrence of disruptive events is

stimulated. These events may lead to higher loyalty

to the organization and its members and to new

knowledge that is worthwhile to disseminate. Dis-

ruptive events may either be created by environmental

fluctuations [46], by so-called intelligent failures [52],

or by setting ambitious goals [30]. The impact of

disruptive events on the level of knowledge dissemi-

nation may be strengthened by autonomy [46] or by

the conditions that facilitate intelligent failure [52],

mentioned before. Of course far more research is

required to understand the mechanisms by whichmanagerial interventions lead to an increased level of

knowledge dissemination.

Limitations and Future Research

This study has several limitations. First, two of our

antecedents, formal rewards and management sup-

port, are only one-item scales. Second, we studied the

dissemination of technological knowledge in new

product development and validated our model using

data collected from high-technology U.S. industries.

Future research may include knowledge of markets

and industries, as well as other countries and other

knowledge processes such as knowledge application

[11]. Moreover, future research may include the studyof the impact of antecedents under different technol-

ogy and market conditions. For instance, being a

follower or a leader in innovation and working under

high- or low-market uncertainty may influence the

impact of the antecedents.

Third, our theoretical framework did not include

all possible antecedents. We focused only on those

that managers in our field research regarded as

important. For instance, we did not study the

influence of teams; job rotation; feedback mechan-

isms, including post-project evaluations; R&D bud-

get; asset specificity; and goal congruency onknowledge dissemination although these factors are

named in past research [5,7,8,11,16,22,25,27,38,

41,46,56]. Future research may include these ante-

cedents as well as a more thorough analysis of the

mechanisms leading to an increased level of knowl-

edge dissemination.

All authors contributed equally to this manuscript, and the

authors are arranged in alphabetical order. The authors wish to

Table 4. Regression Analysis: The Level of Knowledge Dissemination as a Dependent Variable

Coefficient Standard Error Significance Level t-Value Standardized Coefficient

Intercept 1.62 0.91 * 1.78 0

Co-location of R&D personnel 0.21 0.05 ** 4.03 0.21

Information Technologies 0.10 0.06 ns 1.59 0.09

Lead user and supplier networks 0.08 0.04 * 2.05 0.09Formal rewards 0.08 0.04 ** 2.09 0.10

Individual commitment 0.41 0.06 ** 7.21 0.39

Long-term orientation 0.17 0.04 ** 3.93 0.19

Organizational redundancy 0.07 0.05 ns 1.46 0.07

Organizational crisis 0.21 0.05 ** 3.94 0.23

Risk-taking behavior 0.19 0.05 ** 3.96 0.23

Management support 0.13 0.05 ** 2.88 0.15

Buyer power 0.03 0.03 ns 0.77 0.04

Supplier power 0.18 0.04 ** 4.20 0.22

Ease of entry 0.10 0.04 ** 2.49 0.11

Seller concentration 0.06 0.03 ** 1.93 0.10

Market growth 0.02 0.03 ns 0.59 0.03

Technological change 0.03 0.03 ns 1.16 0.05

Relative size 0.21 0.06 ** 3.37 0.19

Relative cost

0.09 0.05 *

1.75

0.10F-value 18.62

R2 0.57

Adjusted R2 0.53

Note: * po0.05; ** po0.01; ns indicates that the coefficient is not significant at 95% confidence level using one tail t-test.


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acknowledge the financial support provided by the faculty of

technology management at the Eindhoven University of Technol-

ogy and the Michael L. and Myrna Darland Distinguished Chair

Endowment.

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Appendix. Constructs, Measurement Items, and Construct Reliabilities

Knowledge dissemination (Construct reliability: 0.84) (new scale)

Our company periodically circulates documents (e.g., reports, newsletters) that provide new knowledge created.

Data on technology development are disseminated at all levels in our company on a regular basis.

We freely communicate information about our successful and unsuccessful technology development across all

business functions.

There is a lot of cross-functional communication concerning technology developments in our company.

Co-location (Construct reliability: 0.80) (adopted from [49])

The physical distance between the different departments of the R&D is (05none; 105very far).

The offices of R&D personnel are located in close proximity to each other (Anchor: 05strongly disagree;

105strongly agree) (R).

It is easy for the R&D personnel to travel to meet (Anchor: 05strongly disagree; 105strongly agree) (R).

Information Technologies (IT) (Construct reliability: 0.78) (adopted from [51])

Relative to the industry norm/standard, the level of the investment in information technologies in this

organization is (Anchor: 05much lower than the industry norm/standard; 55the same as the industry norm/

standard; 105

much higher than the industry norm/standard).Our information technologies systems are easy to use (Anchor: 05very easy to use; 105very difficult to use).

The availability of the information technologies systems to our employees (Anchor: 05none; 105everyone).

The level of usage of our information technologies systems in this organization (Anchor: 05very low; 105very

high).

Lead user and supplier networks (Construct reliability: 0.72) (adopted from [2])

Relative to our major competitors, our company has a stronger network of suppliers.

Relative to our major competitors, our company has a stronger network of lead users.


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Formal rewards (adopted from [54])

Knowledge creation is a major component of our performance evaluation.

Individual commitment (Construct reliability: 0.89) (adopted from [1])

People defend our company when others criticize the company.Generally speaking, there isn’t much personal loyalty to this organization (R).

People are not very committed to this company (R).

People are expected to work with the company for some time.

Many people are continually on the lookout for the opportunity to work with the other companies (R).

Long-term orientation (Construct reliability: 0.82) (adopted from [36])

Renewal of the R&D budget is virtually automatic in our organization.

Our top management believes that our R&D effort will benefit us in the long run.

We are quite willing to make long-term investments in R&D.

In this organization, the strategic plans of R&D are long-term oriented.

Organizational redundancy (Construct reliability: 0.88) (adopted from [24])Organizational redundancy is a characteristic of our firm.

The degree of overlapping of skills and resources in this organization is (05none; 105very high).

The degree of overlapping of business activities across different divisions/departments in our company is

(05none; 105very high).

Organizational crisis (Construct reliability: 0.77) (new scale)

Our top management sometimes intentionally creates organizational crises.

We tend to have frequent organizational crises in this organization.

Organizational crisis is a characteristic of our firm.

Risk-taking behavior (Construct reliability: 0.85) (adopted from [54])

Our senior management has a strong desire for high-risk, high-return investments.

In this company management provides enough incentives to work on new ideas despite the uncertainty of their

outcomes.

If people fail in the process of creating something new, our top senior management often encourages them to

keep trying.

Management support (adopted from [54])

Top management formally promotes knowledge generation, knowledge dissemination, and knowledge

application in our organization.

Control Variables (adopted from [45])

Buyer power (BPOW)

The extent to which the customers of the firm are able to negotiate lower prices from it (0–10 scale).

Supplier power (SPOW)

The extent to which the firm is able to negotiate lower prices from its suppliers (0–10 scale).

Seller concentration (CONC)

In an SBU’s principal served market segment, the percentage of total sales accounted for by the four

competitors with the largest sales (including the SBU if appropriate) (0–10 scale).




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Ease of entry (ENTRY)

The likelihood of a new competitor being able to earn satisfactory profits in the firm’s principal served market

segment within three years after entry (0–10 scale).

Market growth (MGRO)Over the past three years, the average annual growth rate of total sales in an SBU’s principal served market

segment (0–10 scale).

Technological change (TCHG)

The extent to which production/service technology in an SBU’s principal served market segments has changed

over the past three years (0–10 scale).

Relative size (RSIZE)

The size of an SBU’s sales revenues in its principal served market segment in relation to those of its largest

competitor (0–10 scale).

Relative costs (RCOST)An SBU’s average total operating costs (administrative, production, marketing/sales, etc.) in relation to those of

its largest competitor in its principal ser

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