inter-organizational information systems adoption – a configuration analysis approach

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OPINION PIECE

Inter-organizational information systems

adoption – a configuration analysis approach

Kalle Lyytinen1

and Jan Damsgaard2

1Case Western Reserve University, U.S.A.;2Copenhagen Business School, Denmark &

Curtin University of Technology, Australia.

Correspondence: Jan Damsgaard,Copenhagen Business School, Howitzvej 60,DK 2000 Copenhagen, Denmark.Tel: þ45 2242 0082;E-mail: damsgaard@cbs.dk

Received: 9 June 2009Revised: 18 February 20102nd Revision: 25 July 20103rd Revision: 29 November 2010Accepted: 7 December 2010

AbstractIn this article we propose a new complementary approach to investigateInter-Organizational Information Systems (IOIS) adoption called configuration

analysis. We motivate the need for a new approach by the common

observation that the structure and the strategy of an IOIS are interdependent

and that the IOIS adoptions consequently cluster orderly. For example, anIOIS setup with a powerful customer as a hub and many suppliers as spokes

frequently surfaces across diffusion studies. Yet, this fact has not been

integrated into existing analyses, and its implications have not been fullydeveloped. We propose that IOIS scholars need to look beyond the single

adopting organization in IOIS adoption studies and in contrast consider

adoption units what we call an adoption configuration. Each such configura-tion can be further characterized along the following dimensions: (1) vision,

(2) key functionality, (3) mode of interaction, (4) structure and (5) mode of

appropriation. In addition, these dimensions do not co-vary independently. For

example, a particular organizing vision assumes a specific inter-organizationalstructure. A typology of IOIS configurations for adoption analysis is laid out

consisting of dyadic, hub and spoke, industry and community configurations.

Specific forms or adoption analysis are suggested for each type of configura-tion. Overall, configuration analysis redirects IOIS adoption studies both at the

theoretical and the methodological level, and a corresponding research agenda

is sketched.European Journal of Information Systems (2011) 20, 496–509.

doi:10.1057/ejis.2010.71; published online 18 January 2011

Keywords: inter organizational information systems; configuration analysis; diffusionand adoption; typology; multi-level theory

IntroductionInter-organizational Information Systems (IOIS) founded on commoninformation technology (IT) that facilitates business transactions betweenorganizations have existed at least for half a century. Initially, they weremainly used to automate portions of order fulfillment cycles in longstanding customer-supplier relationships. Recently, however, IOIS haveadvanced to encompass completely new functionalities so that theyenable, for example, the formation of new market places or sophisticatedauctions. At the same time, the continued slow and sometimes painfuladoption of IOIS constitutes a challenge for both industry and academia(Reimers et al., 2008). Critical industry observers raise concerns aboutoverall IOIS benefits and their low rate of utilization (Nagy, 2006), whileacademia raises concerns about inadequate models and frameworks tounderstand and manage IOIS adoption (Reimers et al., 2008; Reimers &Johnston, 2008).

European Journal of Information Systems (2011) 20, 496–509

www.palgrave-journals.com/ejis/

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In this article we address this conundrum from anacademic, theoretical angle by arguing that one reasonfor the continued theoretical inadequacy is the deploy-ment of too ‘coarse’ models to explain IOIS adoption.Received from ‘classic’ diffusion theory the deployedmodels focus plainly on explaining a single adopter’sbehavior (or multiple single adopter’s behavior in gametheoretic models). At the same time, the students ofIOIS adoption have long been cognizant that a ‘hub andspoke’ IOIS setup is a frequently occurring phenomenon(Iacovou et al., 1995; Kumar & van Dissel, 1996; Hart &Saunders, 1997; Manabe et al., 2005; Nagy, 2006) and insuch situations there is a need for ‘alignment’ betweenthe vision of one powerful customer and several ‘obedi-ent’ suppliers that subsequently influences the structureand functionality of the IOIS. Yet, the theoretical andmethodological implications of this observation havenot been scrutinized. Following IOIS diffusion, analysesoften fail to account for the presence of such an ‘alignment’among a set of factors that explain successful adoption andare likely to miss an important theoretical narrative. If,indeed, aligning the organizing vision and the structure ofan IOIS in the hub and spoke situation is necessary foreffective IOIS adoption, it needs to be reckoned in theapplied research models and methodologies.

In light of this observation a way forward is torecognize the distinct nature of organized clusters of IOISadopters, where the cluster needs to be adopted as a wholein order to make the adoption outcome viable (Ragin,1987; Klein & Kozlowski, 2000). Such clusters we calladopter configurations. Consequently, we propose IOISadoption investigators need to probe, in addition toindividual adopters, the structure and behaviors of theadopter configuration in which a particular individualadoption decision takes place. The concept of adopterconfigurations shifts scholars’ attention away from theatomistic view of singular and independent adopters (asreflected in associated statistical analyses) to molar viewswhere investigators examine families of interdependentorganizations with distinct technological capabilities, andtheir strategic and structural arrangements as wholes.

The idea of a configuration suggests a holistic ‘emer-gent’ analysis level for the IOIS adoption (Ragin, 1987;Klein & Kozlowski, 2000). This is nothing new per se inacademic endeavor and it has been fundamental for thesignificant evolutionary steps in science. In physics, forexample, physicists moved from analyses of ‘configura-tions’ of atoms, to configurations of particles, and then toconfigurations of quarks. Similarly, organizational scho-lars and economists have admitted for a while that socialworlds are constituted by inter-connected structures – orconfigurations – such as organizations (Coase, 1937) ormarkets (Williamson, 1979). Configuration analysisunderlies also Mintzberg’s logic of structures in fives,where a multitude of organizational features are clusteredinto five archetypical forms like machine bureaucracy(Mintzberg, 1983). Finally configuration analysis isprevalent in the new discipline of service science, as it

seeks to identify in a holistic manner re-configurations ofservice networks by combining business, technology,organizations and innovations (Chesbrough & Spohrer,2006). In all these forms of configuration analysis,one important outcome is the identification of a stableand relevant set of configurations, such as Mintzberg’sfive archetypes or Porter’s three generic strategies (Porter,1985).

Armed by the theoretical logic and insight offered bythe configuration concept our goal in this paper is toexamine the following questions in the context of IOISadoption:

(1) How can we define the concept of an adopterconfiguration?

(2) Does the concept of adopter configurations offer afruitful theoretical lens in explaining IOIS adoption?

(3) What are appropriate ways, if any, to classify IOISadopter configurations?

(4) How does the concept of adopter configurations helpexplain in a different way IOIS diffusion dynamics?and

(5) What are the theoretical and methodological impli-cations of using the adopter configuration concept inIOIS adoption studies?

The remainder of the article is organized as follows.In section 2, we define IOIS and discuss its nature. Insection 3, we formulate our key concepts – such asadopter configuration – and illustrate the benefits ofconfiguration analysis, addressing goals 1–3. In section 4,we formulate a research agenda for advancing configura-tion analysis in IOIS studies to address research questions4 and 5. Finally, in section 5, we reiterate main findingsand propose new research avenues for analyzing thediffusion of IOIS.

Inter-organizational information systemsIOIS can be defined as an information system used jointlyby at least two autonomous organizations that drawupon common and/or shared IT capabilities. An IOIS istypically built around shared (having similar function-ality), or common (the same) IT capabilities that facilitatethe creation, storage, transformation and transmission ofinformation across organizational boundaries ( Johnston& Vitale, 1988). The definition is by purpose broadand covers among others: traditional Value Added Net-work based Electronic Data Interchange (EDI), Internetbased EDI, Extranets, B2B exchanges and electronicsupply chain management, product life cycle manage-ment and design support systems among others. Mostearly IOISs were proprietary, that is, operated using acommon technology and operated under singular own-ership including American Hospital Supply Systems,(Sviokla & Marshall, 1994) and SABRE (Copeland &McKenney, 1988). Most IOISs of today, however, arebased on open standards and their ownership is shared.In the remainder of this article, we focus exclusively onthese systems.

Inter-organizational information systems adoption Kalle Lyytinen and Jan Damsgaard 497

European Journal of Information Systems

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In open and shared IOIS, the creation, transformation,manipulation and transmission of data are governed byopen data and process standards that define the format,structure and semantics of data flowing across organiza-tional borders and the exchange choreographies thatconstitute valid business transactions. Such standardsallow business level interoperability across separate andheterogeneous systems (Damsgaard & Truex, 2000).Standard openness implies that neither the user nor thestandard creator has complete control over the standardspecification or application. Moreover, these standardsare picked up voluntarily (Damsgaard & Lyytinen, 2001b).Consequently, IOISs are highly complex, because theirdesign and use demands coordinated action and they aresubject to multiple social constructions and networkeffects. At the same time, these standards based IOIS offerflexibility, lower switching costs and create economies ofscale. Multiple IOIS standards have been developed overthe last three decades through a multilateral process inUnited Nations, American National Standards Institute(ANSI) or Organization for the Advancement of Struc-tured Information Standards (OASIS), including UN/Electronic Data Interchange For Administration, Com-merce and Transport (EDIFACT), ANSI X.12 and a host ofXML standards (Graham et al., 2003).

Configuration analysisA configuration can broadly be described as a constella-tion of conceptually distinct elements or traits thatcommonly occur together and form an integrative,meaningful whole (Miller, 1986; Meyer et al., 1993;Miller, 1996). In the social domain industries, organiza-tions, cultures and technology uses are prone to formconstellations that also are often called clusters, arche-types, configurations or gestalts. A large number of differentelements can be selected to form constellations and theseelements can, in turn, theoretically generate an infinitenumber of structural variations. The condition for cogentconfigurational analysis is that investigators can theore-tically justify what types of elements will count increating stable configurations. These elements must beinterdependent in essential ways as to cluster system-atically and emerge as meaningful holistic units (Miller,1986; Meyer et al., 1993). The elements, when puttogether, will therefore not vary endlessly, but align intoa limited set of coherent patterns called configurations.

Definition of adopter configurationBy definition each IOIS adoption involves at minimumtwo adopters and no adoption takes place outside aconfiguration. The unit of diffusion for IOIS is, therefore,not a set of IOIS functionalities being adopted by anindividual organization but rather an adopter configura-tion and related functions. Hence, the configurationconcept forms the fundamental observation and analysisunit for the analysis of IOIS adoption. For useful analysisit is therefore important to define what constitutes thecritical elements that make an adopter configuration?

These elements will consequently guide IOIS adoptioninvestigation to be theoretically sensitive to the holisticnature of the configuration that consists of actors,technology and institutional elements. By carefullyselecting theoretically critical elements the investigatorcan open to the dynamism of the configuration (whichis, in fact, the diffusion analysis) and become more sen-sitive to the differences among configurations as deter-mined by variation in the elements and their essentialrelationships. To wit, a key assumption in the analysis isthat each IOIS will be adopted through a series of varyingconfigurations of adopters and each such separate con-figuration creates a distinct adoption system with differ-ent organizing principles and logic.

We define an adopter configuration as a set of interrelatedIOIS adopters united by an organizing vision and asso-ciated key functionality, which determine the structure,mode of interaction and appropriation available for theparticipating organizations. Accordingly, the elementsthat define an adopter configuration are: (1) organizingvision, (2) functionality, (3) structure, (4) mode of inter-action and (5) mode of interaction as constitutiveelements of an adopter configuration (see Table 1).

The design and deployment of IOIS exhibit high levelof complexity generating a variety of interpretations ofthe functionality and goals of IOIS. Due to the net-work effects, the IOIS deployment also demands mutualcoordination with respect to these features (Lyytinen &Damsgaard, 2001, Markus et al., 2006). Therefore, theaims and functions of the IOIS must be agreed uponthrough creating and sharing organizing visions and thenpropagated in chorus by coordinating adopter behaviors.This vision conveys a persuasive cognitive model of howthe IOIS will improve inter-organizational structures andprocesses (Swanson & Ramiller, 1997). Though Swanson

Table 1 Key elements of an adoption configuration

Adopter configuration

element

Definition

Organizing vision Conveys a persuasive cognitive model of

how the IOIS helps organize better

inter-organizational structures and

processes.

Key functionality Defines, in turn, the scope and content of

data exchanges and related business

functionality in terms of the contents

of messages, their choreography and

coverage

Structure Defines the scope and volume of structural

relationships among participating

organizations.

Mode of interaction Nature of relationships between the

participating organizations as defined by

the IOIS

Mode of appropriation The scope and intensity of potential effects

of adopting the IOIS for the participating

organization.

Inter-organizational information systems adoption Kalle Lyytinen and Jan Damsgaard498

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& Ramiller (1997) originally applied the organizing visionconcept to families of technologies like Enterpriseresource planning (ERP) systems, we apply this idea to aninstance of a technology. Due to the malleability, complex-ity and ambiguity of the IOIS, the necessity for forming anorganizing vision holds. The key functionality defines thescope and content of data exchanges and related businessfunctionality in terms of the contents of messages, theirchoreography and coverage, for example, to what extentthe selected IOIS automates the whole order-fulfillmentcycle. The structure can vary from simple dyadic relation-ships into complex industry-wide hubs or distributednetworks. The mode of interaction can range from voluntaryand equal relationships (match mode) to obligatory andhierarchical interactions (conflict mode). Finally, the modeof appropriation can differ in terms of intensity, strategicemphasis and level of process integration.

Another key assumption underlying the configurationanalysis is the dominance of ‘downward causation’(Andersen et al., 2000). Elements of an adopter config-uration are affected more by the whole than vice versa. Aconfiguration is not a simple aggregation of behaviors ofits independent elements; rather, it forms a set of emer-gent behaviors that differ from the sum of the parts.Accordingly, an IOIS adopter’s behaviors neither forman independent observation unit (as most statisticalanalyses assume) nor can they be analyzed in isolation.As elements of any adopter configuration cannot varyindependently; that is, a chosen organizing vision obli-gates a creation of a certain structure (Miller, 1986; Miller,1996), each configuration entails a dedicated way oflinking would-be-adopters in their use of the IOIS (Miller,1986). These conditions furthermore alternate signifi-cantly between a finite and relatively small number ofpossible, viable configurations.

Definition of adopter populationWe define an adopter population as the set of allorganizations that have participated (or could have parti-cipated) in at least one adopter configuration. This isexclusively a theoretical concept like the one of a perfectmarket or an ideal gas. Empirically, we normally deal withan active adopter population that involves contempo-rary organizations participating in at least one adopterconfiguration. We can also consider an adopter configura-tion population that includes all the organizations parti-cipating in a selected adopter configuration. Finally,we call the set of all configurations present in an activeadopter population a configuration ensemble. Thus, at anypoint in time we can expect to observe several adoptionconfigurations in a diffusion arena.

Each organization can participate at any time in multi-ple adopter configurations and an organization’s overallIOIS adoption ‘profile’, thus involves implementingmultiple IOIS technologies in different pockets of anorganization. Thus, organizations in the active adopterpopulation typically participate simultaneously withinseveral configurations. For example, an organization

may first use a specific IOIS implementation to highlyintegrate its business process with one of its businesspartners (a dyadic relationship configuration), whilelater becoming a ‘spoke’ using the same IOIS system toconnect to a powerful customer base (hub and spokeconfiguration). It may, at the same time, adopt a standardIOIS service connecting the whole industry for its lessintense and critical trades of common goods (industryconfiguration). Finally, it may have been mandatedto adopt a national IOIS system for customs clearance(community wide configuration). It is important to notethat many times the underlying technology may be quitesimilar and even based on the same set of standards,but their adoptions and related processes can vary signifi-cantly, given that these configurations involve differentvisions, structure, or modes of interaction. It is thusparamount to specify which sort of configuration is inbeing examined when probing an organization’s percep-tion of an IOIS and its utility.

Figure 1 illustrates this nested feature of IOIS diffusionand the necessary multi-level constructs to characterizedifferent ‘units’ of analyzing adoption. On the left, wedepict a common situation where a company (the leastshaded) has adopted IOIS for communicating with itssupplier (dyadic relationship, labeled x). At the sametime, the same company has adopted another system forinteracting with a powerful customer (hub and spokeconfiguration, labeled y), while also having adopted acommon industry level service (industry configuration,labeled z). All three configurations labeled x, y and z formseparate analysis units and need to be investigatedusing an alternative theory and methodology as will bediscussed in subsection 3.4.

A critical assumption of the configuration analysis isthat explanations centered on singular adopters do nothelp understand why the very unit of adoption – theadopter configuration – emerged and made the IOISdiffusion possible, and what role it plays in influencingadopter’s behaviors and the overall diffusion. This

(z)(y)(x)

Figure 1 The nested nature of IOIS adoption (left) and three

configurations (right) each with their separate organizing vision

and functionality.

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requires a careful analysis of the context, and the historyof each identified configuration, including its organizingvision, mode of interaction, key functionality, numberof participants, etc. In fact, there are also interactionsbetween adopter behaviors participating in several adop-ter configurations at the same time. Some adoptionsmay exclude possibilities of participating with otherconfigurations, while others may come with options forparticipating with others. Due to these interdependenciesand the coevolution among configurations, organiza-tions may also shift between configurations over time,or may drop out voluntarily or involuntarily. The set ofconfigurations an organization has participated in at anygiven time can thus be viewed as a critical antecedent forthe organization to behave in certain ways, including theoption to revert to its earlier adoptions. This analysis thusrecognizes that within any point of time some actorsin the adopter population will be more prone to adoptnew IOIS, while others may not, depending on whattypes of configurations they participate in. Understand-ing why this occurs remains an important, yet unre-solved, question for diffusion accounts that focus onsingle organizations as the main unit of analysis.

These concepts help one determine an adequate scope– the real population – for a given IOIS adoption study(Pouloudi & Whitley, 1997; Boonstra & de Vries, 2008).Accordingly, we have to give up the concept of a fixedand homogeneous population. In contrast, the scope andsize of the adopter population depends on the techno-logical, temporal and institutional elements embeddedin the set of adopter configurations available to would beadopters, the constant shaping of the technology by theadopters and promoters, and structural properties of thenetworks created by adopter configurations (includingmodalities of interactions, trust, history, culture, globa-lization etc.). Accordingly, both the adopted technologyand set of potential adopters can and will change overtime (Boonstra & de Vries, 2008).

Analyzing the adoption of IOISThe difference between traditional single organizationbased analysis and configuration based analysis of IOIS

can now be illustrated as shown in Figure 2. The exhibiton the left of Figure 2(I) shows the analysis based onindependent singular organizations (adopters), whilethe exhibit on the right Figure 2(II) illuminates stepsusing the configuration analysis. The history and num-ber of IOIS adoptions by organizations remain thesame in these two illustrations. Next, we elaborate howthe two alternative modes of analysis will guide IOISinvestigators.

Traditional analysis Traditional analysis approaches thediffusion of IOIS as an instance of ‘viral’ behavior withina population. It seeks to explain a single organization’spropensity to adopt the IOIS through for example,drawing on the viral ‘paradigm’ outlined in Rogers’(2003) Diffusion of Innovation (DOI) model. Othermodels of adoption like the Technology AcceptanceModel (TAM) and Unified Theory of Acceptance, andUse of Technology model (UTAUT) (Davis, 1989;Venkatesh et al., 2003) make similar assumptions. Thesefamilies of models focus on internal cognitive states of asingle person or adopter (organizational unit), and usethem as salient predictors for an organizational adoptiondecision. They assume that adopters are independent andmake voluntary decisions to accept or reject an innova-tion, based on perceived benefits (Larsen, 2001; Lyytinen& Damsgaard, 2001). Such models approach the unit ofanalysis most often by probing a selected member fromthe organization – typically the CEO, CTO or the CIO.Instead of asking each organizational member to deter-mine a statistical average, it is assumed that the whole isembodied in the part, that is, it is sufficient to ask a(carefully selected) member to learn about the views ofthe whole organization.

The virtue of DOI, TAM and UTAUT models is that theyhelp to predict the adopter’s behavior over a generic set oftechnologies, which transcend the population of organi-zations. Each small square in Figure 2(I) represents anindependent organization as a potential adopter. Thoseturned black at any given point of time are adopters,while those remaining ‘white’ portray current non-adopters. The whole circle – in turn – represents theboundary of the adopter population. The proportion ofthe blacks in relation to whites represents the cumulativecontagion at any given time point in the population.Saturation occurs when all dots have turned black, orwhen no more adoptions – conversions – take place overa period of time. The model assumes that adoptions(squares changing color) depend on the properties ofthe singular adoption unit, including its position in thepopulation (e.g., communication networks) and theinnovation itself. Yet, the decision is independent andconstitutes a singular atomic act. By identifying thesefeatures, one can analyze generic adoption behaviors ofthe whole population (such as diffusion levels) byaggregating observations of individual adopter’s beha-viors in relation to the nature of technology, propertiesof the adopters, and the structural features of the system.

(II) Configuration analysis(I) Traditional analysis

Figure 2 Analyzing the adoption of IOIS.

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As such, these models can be used effectively to makebets on how likely it is for an organization to adopt aninnovation in the next ‘round’.

Configuration analysis The exhibit 2(II) on the rightdepicts (by means of different types of graphs) threeadopter configurations that might be identified as a resultof a configuration analysis in the same populationand at the same time as in exhibit 2(I). In other words,we can see a configuration ensemble consisting of threeadopter configurations belonging to different classes.This configuration ensemble determines the diffusionecology for a given set of organizations. The lines showingthe configuration ‘boundaries’ within this ensemblein Figure 2(II) depict a natural diffusion scope for eachconfiguration as defined by its current structural condi-tions. Adopter configuration (a) in Figure 2(II) is limitedto one industry sector and involves a dyadic relationshipbetween two adopting organizations. This is a typicalexample of a set of bilateral IOISs observed often in theretail industry (Damsgaard & Lyytinen, 2001b). Adopterconfiguration (b) shows a centralized hub and spokeconfiguration, which is defined by a strongly centered,hierarchical mode of interaction (conflict), and an orga-nizing vision that supports a specific mode of hierarchicalinteraction and key functionality for order fulfillmentcycle (Webster, 1995; Boonstra & de Vries, 2008). Finally,graph (c) reaches across the whole population andinvolves many organizations. This could be an exampleof an electronic market in the car industry (MacKay,1993; Howard et al., 2006). Together these three adopterconfigurations form the active adoption population inFigure 2(II). The potential adopter population is larger,but none of the conditions for engaging with themanifested configurations are met as perceived by theremaining organizations. Each of these three configura-tions form separate units of analysis. One must, therefore,first determine the type of configuration an individualadopter belongs to in order to understand the adopter’sbehavior. Comparing two otherwise similar organizationsthat belong to different types of adopter configurationsdo not necessarily make sense (Pavitt, 1984; Miller, 1986).Therefore, each configuration embeds its own logic andeach is only one of the triggers that can tease actors toappropriate IOIS. For example, the organizing vision of acommunity wide IOIS is very different from that of adyadic configuration, and if the organizational actors areinvolved in each, their IOIS setup needs to be probedregarding their motivations and experiences that theywill (most probably) not easily reconcile.

A typology of IOIS configurationsOne task of configurational researchers is to identifygeneric classes of configurations with similar propertiesas the three different classes identified above during theconfiguration analysis. These properties accrue from theholistic composition of the configuration elements. Again,these categories provide then theoretical conditions

to formulate more accurate theoretical models andexplanations of adoption behaviors and/or evolutionprinciples specific to that class of configurations.

Two alternative schools of how to derive such classifi-cations exist (although not exclusively): (1) concept-ually / deductively derived typologies, and (2) empiricallyderived taxonomies through cluster or pattern analysisbased on field data (Meyer et al., 1993; Miller, 1996).Cluster analysis as a means of identifying commoncontexts has been more common in the study of IOISadoption (Meier & Suhl, 1995; Jimenez-Martinez & Polo-Redondo, 2001; Choe, 2008). In most cases such analyseshave been the end results of the cluster analysis with theintention to show that collected IOIS adoption data doesindeed form distinct clusters, which can be meaningfullylabeled. However, these clusters are never investigated inmore detail for more encompassing theory formulation.To our knowledge, no IOIS adoption study has used anyidentified clusters as their theoretical units of analysis.

For the purposes of theory development, we presentnext a tentative typology of IOIS configurations. It is sup-ported with ample observations from the literature andour own experiences in analyzing IOIS adoption overthe last two decades. This typology is founded on keyelements of adopter configurations from Table 1: organiz-ing vision, key functionality, structure, mode of inter-action and mode of appropriation. The typology issynthesized in Table 2. Overall, we distinguish betweenthe following adopter configuration classes: (1) dyadicrelationships, (2) hub and spoke configurations,(3) industry spanning configurations and (4) commu-nity configurations. Each of these implies a differentway the elements are organized, and what theoreticalaccounts are brought to bear in accounting for the IOISadoption and its effects.

Dyadic relationships emerge when two autonomousorganizations adopt an IOIS at the same time and dependon each other’s actions in exploiting it (Ali et al., 2008).The organizing vision originates from contingencies ofthis relationship. Either it involves creating an electronicpartnership (Lee & Lim, 2005) for virtual businessintegration (match mode), or seeking a dominant posi-tion in a conflict mode where a powerful company reapsadditional benefits. Hence, existing power structures areusually replicated in the way that the dyadic IOIS isorganized (Oliver, 1990; Nagy, 2006; Ali et al., 2008). Byparticipating in several dyadic relationships operating inmatch mode, an organization can speed up learningabout the technology. However, participation in severaldyadic configurations can also yield the opposite experi-ence. For example, in Hong Kong a container terminalwas forced into multiple dyadic relationships thatoperated in conflict mode, as driven by powerful custo-mers (shipping lines) (Damsgaard & Lyytinen, 1997).Characteristics to consider in dyadic analysis include:the complexity of key functionality, the embeddedcommunication structures (Bouchard, 1993; Premkumar &Ramamurthy, 1996), differences among users, characteristics

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of the innovation (like trialability, observability) (Rogers,2003) and types of innovation decision (Tornatzky &Klein, 1982).

To understand dyadic relationships and their evolution,organizational level analysis is applicable, as it highlightscharacteristics of organizations seeking to appropriatethe IOIS. Concepts adopted from organization studies(e.g., dependency and power), economics and innovationtheory can be drawn upon to examine adoption behaviors(Williamson, 1979; Mintzberg, 1983; Perrow, 1986; Rogers,2003).

Hub and spoke configurations span a single industry andinvolve at least three adopters. In this class of configura-tions the organizations’ decisions need to be coordinated,and they depend on some ‘central’ hub endowed withkey technological capabilities and power. Thus, in thepower based hub and spoke setup, the hub initiates theIOIS and controls the related business relations (Webster,1995; Manabe et al., 2005). The organizing vision isnormally that of a ‘middleman’ or a ‘clearing house’(Webster, 1995; Kumar & van Dissel, 1996; Nagy, 2006).Similar to dyadic relationships, a hub and spoke config-uration can evolve either in conflict mode or in matchingmode. Iacovou et al. (1995) distinguish between recom-mendations, promises and threats, as submission strate-gies employed by hubs to persuade the smaller companiesto align with the IOIS. The hub thus provides the spokeswith little choice in rejecting the IOIS (Kumar & vanDissel, 1996; Nagy, 2006). Trust is also a critical factorin adoption decisions (Hart & Saunders, 1997). In thematching mode, a hub offers IOIS services to businesspartners as an additional benefit when doing business(MacKay, 1993), or to improve virtual business integra-tion (Kambil & Short, 1994). Appropriate theoreticallenses to study hub and spoke configurations are analysesof supply chains (Christopher, 1998) and power depen-dency analysis (Emerson, 1962; Porter, 1985).

Industry configurations span is part of an industry orbusiness process. These relationships are typically shorterin duration and less idiosyncratic than in the previousconfigurations. Industry configurations depend on in-stitutional involvement, where the organizing vision isdriven by the concept of a ‘common good’. The initiatingactor is often an association (Oliver, 1990; O’Callaghan &Eistert, 1995; Damsgaard & Lyytinen, 2001b) that pro-motes the organizing vision and convenes the modeof interaction. Whenever no central association can orwants to get involved more strategic industry con-figurations emerge. Often strategic IOIS networks operateexclusively, only inviting organizations with comple-mentary interests to participate (Nygaard-Andersen &Bjørn-Andersen, 1994). Organizations that are notinvited thereby inevitably become (non-voluntary) non-adopters. This illustrates vividly why two similar organi-zations can become either a non-adopter or an adopter,depending on the nature of the adopter configurations.Analysis of industry configurations follows industrialanalysis, which concentrates on networks of interacting

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actors. It expands the adoption investigation from thelocal adopter to identifying multiple relevant actors, theirroles and types of relationships (Miller, 1986). It caters forthe networked nature of IOIS adoptions, related adopterdependencies and resource and power relationshipsamong actors as to narrate emerging interactions bet-ween technologies, organizations and external institu-tions (Kambil & Short, 1994; Delhaye & Lobet-Maris,1995; Cox & Ghoneim, 1996; Damsgaard & Lyytinen,2001b; Bakos & Katsamakas, 2008). Industrial analysislocates the organizing vision within a broader industrialfield by using strategy analysis (Porter, 1985), powerand resource dependency analysis (Emerson, 1962), andsupply chain management (Christopher, 1998).

Community wide configurations are networks of multipleconfigurations often belonging to different classes (theadopter ensemble). In community wide configurations,several configurations coexist. These often become ele-ments of regional or national IOIS initiatives that engagepublic institutions or governments (Damsgaard &Lyytinen, 2001a). National IOIS plans are often imple-mented to spur a wide general adoption of IOIS,especially where it is envisioned to lead to a competitiveadvantage over other nations or regions (King et al.,1994). Institutional analysis with such initiatives drawsboundaries within the potential diffusion population byrecognizing those regulatory regimes that will act as focalpoints in constraining, or enabling IOIS diffusion.Institutional regimes cover either the boundaries of eachconfiguration or the whole area. Such regimes can beidentified using concepts from institutional theory andpolitical theory (King et al., 1994; Damsgaard & Lyytinen,1998; Damsgaard & Lyytinen, 2001b). Although there aresome comparisons of IOIS adoption at the national level(King & Konsynski, 1990; King et al., 1994; Wrigley et al.,1994; Surmon & Huff, 1995; Damsgaard & Lyytinen,1997), the varying influence of regulatory regimes duringthe diffusion of these types of IOIS is not well understood(Kurnia & Johnston, 2000; Lyytinen & Damsgaard, 2001).The potential predictors include incentives, symbolicvalue given to technologies or mobilization of bias(DiMaggio & Powell, 1983; King et al., 1994).

Dynamics of adopter configurationsEach adopter configuration evolves and its elements arethus time dependent that is, its organizing vision, keyfunctionality and mode of appropriation will change asthe configuration ‘matures’. Changes include adding newtypes of functionality, or reorganizing the structure, orthe mode of appropriation. Hence, each configuration isdynamic in the sense that it can cease to exist, or itcan transform into an alternative configuration. This issimilar to Mintzberg’s (1983) discussion of how organiza-tions transform from one organizational form to another,as the organization matures or changes when the environ-ment coerces it to do so. For example, a dyadic configura-tion can be elevated into hub and spoke strategic network.In other cases an intense dyadic IOIS relationships can

spin off from a community system (Damsgaard, 1996).Thus, the life spans of adopter configurations in eachclass need to be distinguished through identifying theirstart and end times, as well as tracking changes in theiressential characteristics. Finally, these changes may alterthe adopter’s behavior within the configuration, but itmay also influence how other configurations will evolve.

Overall, change in adopter configurations is inherentlycomplex and difficult to coordinate. For example, thedecision to adopt a new standard, or to rally around a neworganizing vision is not a unilateral decision, but requirescoordination and persuasion. In addition, it has rippleeffects among the members of the configuration. Orches-trating a coordinated switch to a new standard is often aninsurmountable obstacle, and many configurations nevermanage to evolve beyond their original vision andstructure. At the same time, change happens oftenthrough the evolutionary formation of new configurationsand abandoning of existing ones. Frequently, as ina Darwinian process, a large number of ‘false starts’ areneeded before a viable composition of a configurationemerges victorious. For example, in Hong Kong it tookalmost a decade to establish a viable communitywide IOISafter several initiatives were left stranded because the earlytrials included ‘wrong compositions’ of organizing visions,structure, actors and incentives (Surmon & Huff, 1995;Damsgaard, 1996). Similar stories abound around electro-nic markets for car parts in the electronics industry.

Still, comparisons of adoption behaviors across thesame type of adopter configurations must be approachedwith caution, because their initial conditions and evolu-tionary paths can differ significantly. Highly varying IOISadoption trajectories often emerge due to inadvertentevents. For example, adding a small number of newstructural elements to an adopter configuration cangenerate non-linear effects and these ensembles alsoexhibit significant path dependency. Changes in con-figurations are thus not seen as outcomes of a-historicexternal forces; in contrast, they portray emergent,history dependent features of the evolution in theprevailing adoption context (Miller, 1986; Meyer et al.,1993). For example, to understand the adoption of XMLbased IOIS, it is necessary to consider how previousEDIFACT based IOIS manifested themselves organization-ally and institutionally (Damsgaard & Truex, 2000).

Sometimes a shift to an alternative configuration cantake place quickly and disruptively – in a punctuatedmanner (Miller, 1986; Lyytinen & Newman, 2008). Thesepunctuations are triggered by abrupt changes in theunderlying technology or in the strategic or regulatoryenvironment. At the same time, configurations oftenactively resist these changes in more stubborn ways thanwhat an individual adopter can exhibit (Meyer et al.,1993) because within adopter configurations, organiza-tions need to move in concert often without sharing acommon interest. Yet, if they fail to do so, this canultimately result in the configuration break-up and decay(Mintzberg, 1983; Miller, 1986). Scholars subscribing to

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a configuration perspective, therefore, need to approachevolutionary processes within configurations as beingdriven by punctuated equilibria, where periods of equili-brium are followed by bursts of disequilibria (Miller, 1986;Lyytinen & Newman, 2008). Hence, change across con-figurations is episodic (Lyytinen & Newman, 2008).

Due to inherent dynamism and path dependency,configuration analysis needs to heed the evolution ofconfigurations (and associated adopter populations) andtheir interactions with potential adopters and otherconfigurations. At the same time, adoption configura-tions exhibit considerable autonomy in their behaviors aseach adoption configuration can make relatively inde-pendently decisions about its exploitation of the IOIS.Yet, these decisions are often influenced and can bepartially explained by what adopters in other adopterconfiguration know about such deeds (e.g., networkexternalities, herd effects or strategic preempting). Finally,though a stable set of configurations for a given diffusionarena is often reached at early stages of technologydiffusion, and it typically prevails over an extendedperiod of time, this cannot be assumed ex ante, and is animportant task of inquiry. Overall, tracing the dynamicsof such configuration ensembles is thus critical inunderstanding the overall patterns of diffusion relatedto IOIS. This dynamic analysis searches for drivers andpatterns underlying change in adopter configuration andthus reveals how different ecologies evolve and adaptthrough the actions of multiple actors as they respond tonew technological and business opportunities.

Discussion: a research agenda for configurationanalysis in the study of IOIS diffusionThe above discussion invites to explore a number ofresearch avenues in the study of IOIS diffusion. These relatefirst to advancing new theoretical angles in interpretingdata sets, and second crafting novel research methods andapproaches that are up the job of carrying out rigorousconfiguration analyses. Next, we address each in turn.

Theoretical views and research questions that driveconfiguration analysisThe expected benefits of configuration analysis are:(1) more accurate accounts of a multitude of adoptioncontexts and their dynamics, (2) recognition of theshifting boundary conditions for IOIS diffusion and(3) integrating multiple theoretical views to account forthe IOIS diffusion. The new vistas opened by theconfigurational approach for theory building and relatedresearch are accordingly summarized in Table 3. Theserelate to the five specific domains and levels of analysis:(1) differences among adopters within adopter config-urations, (2) differences among adopters across adopterconfigurations, (3) differences among adopter configura-tions, (4) dynamics of adopter configurations and(5) dynamics of adopter ensembles and ecologies. Foreach area, we list a set of germane issues that call for moreattention, list typical research questions, and offer

plausible theoretical models that can be harnessed toaddress the issues. Due to space limitations, each topicwill be discussed briefly and our main intention is solelyto clarify the main implications of the proposed approachfor future studies on IOIS.

The topic ‘differences among adopters within adopterconfigurations’ addresses the need to change the modeof individually focused adoption studies, if they hope toexplain singular independent adopter’s behavior vis a visadopter configurations so as to improve their ecologicalvalidity. We propose that studies need to better controlfor the type of adopter configuration being studied byusing configurations as controls or moderators. Anotherway is to conduct meta-analyses of existing IOIS stu-dies by coding the functionality and organizing visionof IOIS being studied. We also purport that researchersneed to carefully report whether they analyze an adop-tion of a single IOIS across all adopters, or differentIOIS across the same set of adopters in order to warnabout potential confounds in their findings concerningadoption outcomes.

The topic ‘differences among individual adopters acrossadopter configurations’ focus on the difference in behaviors– if any – of the same individual adopters across differentadopter configurations. As noted, organizations oftenparticipate in multiple adopter configurations each withits separate logic and organizing vision. The end productof such configuration analysis can be labeled as a set of‘dominant designs’, that is, a set of configurations readilyavailable to align an organization’s technology, strategy,and structure. A dominant design of configurations canbe ‘favorable’, because it encapsulates a set of proven andeffective ways to apply IOIS. Although the understandingof these processes is central to the concerns of manyresearchers and practitioners, the IS literature containsvery few analyses of this type. We posit, therefore, that itis as important to understand differences in adopterbehaviors across different available adopter configura-tions as it is to understand what is common acrossall adoption situations and what the adopter’s effectivechoices are. In particular, we need to examine thedifferences in evolutionary processes within configura-tions and their outcomes. We should also seek plausibleexplanations of these differences. In short, we need tocontrol for variance in the adoption situations to findcommonalities in the adopter behaviors across all adop-tion contexts (if any). Finally, we consider the antece-dents to these behaviors to be alternative mixes ofadopter configurations to which different organizationsparticipate. To carry out these studies, we need to developuniform and better measures of critical elements ofadopter configurations, including organizing vision,structure or mode of interaction (see Table 1).

The topic ‘differences among adopter configurations’ focuson the critical differences in adopter configurations andwhat antecedents explain the emergence and diffusion ofeach adopter configurations. When the underlying IOIStechnology is networked, complex and flexible, and it

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depends on the coordinated actions of many would-be-adopters, technology providers, standards and institu-tional actors, it invites deployment of adopter configura-tion as the unit of analysis. In this situation theinvestigator needs to determine the type of configurationof the studied IOIS. It is necessary to determine if similaror analogous configurations have been observed in thepast, and what were their adoption patterns and impacts.The researcher must reflect upon rigorous ways to classifyobserved configurations regarding adopter configurationsand their features. For example to identify the effectivecauses for the emergence of some adopter configurations.

In addition, when the unit of analysis is the adopterconfiguration, the investigator needs to approach adop-tions as series of interrelated events in contrast toassuming unrelated solo adoptions.

The topic ‘dynamics of adopter configurations’ focuses onthe change and evolution of adopter configurations, andon explanations of such change. Here, the investigatorneeds to take seriously the path dependent and oftenchaotic nature of the change, and the drivers of change.At this level, configuration analysis by definition impliesprocess oriented analysis and theorizing over diffusionprocesses in specific contexts (Langley, 1999; Pentland,

Table 3 Research questions and implications for theory building

Level of analysis Issues Critical research questions Theoretical viewpoints applicable

Differences among

individual

adopters withinadopter

configurations

Lack of attention

to the role of

adopterconfiguration in

explaining

individual

adoptionbehaviors

What are the impacts of adopter

configurations on diffusion dynamics among

adopters? Under what conditions does theadopter configuration not have any impact

on adoption behaviors? How can we

compare or validate findings concerning

individual adopters across different adopterconfigurations?

Integration of adopter configuration

measures as controls or moderators in

traditional DOI diffusion models. Meta-analyses of diffusion studies with new

controls or antecedents.

Differences among

individual

adopters across

adopterconfigurations

Lack of attention

of the presence

of multiple

adoptions andadopter

configurations

for individual

adopters

Do organizations behave differently in

different adopter configurations? Are the

antecedents to adoption similar or different

across different adopter configurations?To what extent do previous adoptions

impede or promote future adoption

behaviors? What are the different mixes of

adoption behaviors across different adopterconfigurations? What elements in organizing

vision, structure, etc., explain variance in the

configuration?

Carry out studies that focus on the adoption

of multiple IOIS by individual adopters over

time and differences in the adoption rates

and antecedents.Control for the evolution and change in the

adopter configurations. Analyses of mixes of

adoption patterns across different adoption

configurations (cluster analysis).Develop measures and control for the

impact of variance in the vision, structure or

mode of appropriation on adoption rates orimpacts.

Differences amongadopter

configurations

Lack of attentionto the diffusion

of adopter

configurations

What are fruitful and appropriate ways toclassify and analyze and measure adopter

configurations? What are the antecedents to

adoption? What are the different classes of

adopter configurations? What adopterconfiguration classes are typical at early or

late stages of technology diffusion? To what

extent does technology evolution,regulatory or strategic factors explain the

emergence or extinction of different adopter

configurations?

Develop better theoretical models of IOISrole and functionality to improve typologies.

Develop methods to yield taxonomies and

how to integrate them with data analyses.

Develop diffusion theories and dynamicmodels for the change and evolution in

adopter configurations.

Integrate institutional and strategic theoriesto account for changes classes of adopter

configurations.

Dynamics of

adopter

configurations

Lack of attention

to the inherent

dynamics in theevolution of

adopter

configurations

What are the key drivers in the evolution of

adopter configurations? What are the

patterns of change in the adopterconfigurations? How do incremental and

disruptive changes relate in evolution of

configurations?

Develop event based theoretical models to

adopter configuration change e.g., applying

complexity theories.Use computational theories to analyze and

trace dynamics of adopter configuration

change.

Dynamics of

adopterensembles

Lack of attention

to the structureand dynamics of

diffusion

ecologies

How do diffusion ecologies emerge, evolve

and change? What explains that change andhow do technology, regulatory and

industrial factors explain that change? Are

some ecologies more prone to fast or slow

change and growth?

Develop ecological models of the diffusion

of IOIS and their change. Integrateregulatory, economic and technological

models to explain IOIS diffusion and change.

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1999; Van de Ven et al., 1999). This is defined by eachadoption configuration (primary context), and adopterensembles (secondary context). It invites simultaneousexamination of actor’s engagements with IOIS over anumber of contexts (adopter configurations), and anexploration of how IOIS become embedded into adopterpopulations. Thus, the use of adopter configurationsleads to theoretical accounts that are accurate andoperate with a few simple concepts. Here, an investigatorneeds to investigate in motion the interactions betweenorganizations and configurations, and between config-urations; and observe changes at multiple levels: singularadopters, within adopter configurations, and acrossadopter configurations. Critical in these analyses isobserving both quantitative and qualitative changes:differences in singular adopters’ adoption rationale,evolution and change in character and volume of adopterconfigurations, and stabilization of adopter configura-tions, etc. We need also to reckon how, due to increasedmaturity, configurations can transform due to changes inother configurations or to changes in technology.

Finally, the topic ‘dynamics of adopter ensembles’addresses the evolution of adopter ensembles and theirpatterns or principles of their composition and transfor-mation. In order to explain such complex and multi-layered changes, the investigator needs to mobilizeseveral theoretical frames: organizational, industrial andinstitutional at different levels of analysis. The necessityto use several frames emerges from the layered structureof the dynamics associated with the ecology. The breadthand depth of deployed frames may vary, dependingon the set of configurations examined. In order to tapinto the key functionality and structure of the adopterconfiguration, the investigator needs to also carry out anevolutionary technological analysis that focuses on theevolving traits of the IOIS technology within the ecology,including: (1) the changing nature of application func-tionality, (2) its changing dependence on standards and(3) the openness of the technology to different inter-pretation (e.g., enterprise portals and ERP systems arevery different in this respect). Unfortunately (and quitepuzzling), this type of analysis has not received muchattention in the IOIS literature, although a few studies onERP systems have addressed similar issues (Ciborra,2000), as well as a few more recent studies on socio-materiality (Orlikowski, 2007; Orlikowski & Scott, 2008).

Research methods in configuration analysisA typical way to carry out configurational analysis couldproceed as follows. One can start by using the typologyas depicted in Table 2 as a seeding category to catego-rize observed phenomena into a set of potential config-urations. One then proceeds to identify specific instancesof the configurations implicated by the collected empi-rical data. These can include demands for coordi-nating the infrastructure, standardization of technology,process coordination, etc. Actors’ behavior within theconfigurations is then analyzed in more detail as a series

of adoption processes (trajectory based analysis) or as asocial movement (configuration level and ensemble levelanalysis). The number and types of adopter configura-tions are not decided a priori in configuration analysis.

There is no single way of identifying instances ofadopter configurations: some instances may only bediscovered through detailed analysis of field data. Adetailed structural analysis is required at least in the earlystages of the research (Yin, 2010) until a stable set ofadopter configurations has been identified and theirsystematic elements have been identified to developnomological explanations. As noted, adopter configura-tions transform and crisscross with one another, callingfor dynamic analysis. For example, alternative IOISarchitectures may imply varying adoption scopes, andaffect the evolution of adopter configurations. Accord-ingly, actual adopting scope and organizing visions canbecome extremely complex, contested and multi-faceted.

To meet with all requirements when the unit ofanalysis is adopter configurations, their evolution datamust be collected longitudinally. This also invites the useof multiple data collection and analysis methods, inclu-ding pattern matching (Ragin, 1987; Mingers, 2001). Italso invites triangulation of data (Yin, 2010) and theoryto as to identify adoption configurations. Finally, evolu-tion calls for event based or process studies (Langley,1999), which can be combined with computational(simulation) models offering a deeper understanding ofthe change dynamics and the impacts of adopterconfiguration changes on the diffusion processes.

ConclusionsIn this article we have proposed a new complementaryapproach – called configuration analysis – to study theadoption of IOIS. One motivation for advocating thisapproach is our observation that often the same technologywithin the same population is adopted in different waysdue to variation in institutional, industrial, organizationaland technological contexts (Kurnia & Johnston, 2000;Damsgaard & Lyytinen, 2001b). Although extant IOISadoption literature has noticed the interdependenciesamong contexts, IOIS structure and adoption behaviors, ithas not integrated these elements into its analysis models.Therefore, as a complementary approach, we have pro-posed configuration analysis, which seeks to bring multi-level and dynamic investigations into the study of IOIS.Configuration analysis is a flexible, yet systematic way ofprobing the IOIS adoption phenomenon as a complexevolutionary system consisting of multiple levels. Accord-ingly, in configuration analysis the invariant is not a set ofindependent and dependent factors and a model thatexplains their relationship with a significant variance, but israther a set of viable configurations around which IOISadoption processes occur.

We have formulated the key concepts of configurationanalysis – adopter configuration – that help narratetheoretically the diffusion of IOIS. The concept of anadopter configuration carries the idea of a difference in

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the structure, mode of interaction, organizing vision, keyfunctionality and mode of appropriation in each adoptedIOIS that has an impact on the scope and processof adoption. The social elements of the configurationinclude actor relationships – power, resource dependencyand capability – and the significance attributed tothe technology by each actor (organizing vision). Thetechnical elements include specific capabilities of IOIS asdefined by its key functionality. Each identified config-uration weaves together both technical and socialelements in a holistic fashion that can lead to deeperand broader embedding of IOIS diffusion into organiza-tions. The adoption process, in addition, unfolds in adynamic and often seemingly chaotic way and has pathdependent characteristics (i.e., history counts) (Arthur,1989; Van de Ven et al., 1999).

We note two limitations in engaging with configura-tion analysis: (1) complexity of theoretical models, and(2) complexity and diversity of data. Configurationanalysis calls for a theoretical attitude where one seeksto orchestrate multi-theoretical inquiries of the evolutionof each configuration. We have learned that this is not aneasy task, as it requires careful and disciplined conceptualrefinement that is often painful and ongoing (Ragin,1987; Klein & Kozlowski, 2000). Moreover, for eachconfiguration the scope and the nature of theoreticalframes vary. This can easily lead to escalating theoreticalcommitments where investigators simultaneously needto deploy multiple frameworks located at different levels.This is challenging, because moving between levels ofanalysis can set up tantamount challenges. Configurationanalysis also calls for longitudinal investigations where

data sets are complex, unstructured, sparse and un-bounded. Without longitudinal data, our understandingof the variety and diversity of adoption processes doesbecome (too) limited. Unfortunately, this methodologi-cal stance is a tall order. It requires intensive fieldwork,long time spans to collect data, and access to multipleorganizations and contexts. All this adds to the investi-gator’s effort, and makes it risky. In spite of all heroicefforts, collected data sets still remain incomplete andsparse. In this sense, the approach to data equals the useof historical research (Mason et al., 1997).

We note several research opportunities for the future. First,there is an ample opportunity to develop valid measures totap into the key elements of adopter configurations so as toexplain the success rates in adoption, adoption speed orparticipant satisfaction (Davis et al., 2007). Second, weobserve significant opportunities to carry out rigorousprocess analyses within each adopter configuration andacross adopter configurations (Van de Ven et al., 1999).Third, in contrast to analyzing adoption events of withinconfigurations, it would look promising to observe how theevolution of configurations explains the adoption, refine-ment and expansion of a number of IOIS functionalities.

AcknowledgementsWe are thankful to John King for comments on the earlier

drafts of the manuscript. We are also grateful for the AE and

two reviewers for constructive and helpful comments. All theremaining weaknesses remain the responsibility of the

authors. This research was in part supported by The Danish

Council for Independent Research, Social Sciences (FSE),grant number # 275–08–0342.

About the authors

Kalle Lyytinen is a Professor of Information Systems atthe Weatherhead School of Management at Case WesternReserve University and an adjunct Professor at theUniversity of Jyvaskyla. He has published eight books,over 50 journal articles and over 80 conference presenta-tions and book chapters.

Jan Damsgaard is the Professor and director of Center forApplied Information and Communication Technology atCopenhagen Business School. His research focuses on thediffusion and implementation of networked IT such asIntranets, Inter-organizational information systems andmobile and wireless technologies.

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