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Information Systems ResearchArticles in Advance, pp. 1–27ISSN 1047-7047 (print) � ISSN 1526-5536 (online) http://dx.doi.org/10.1287/isre.1110.0404

© 2012 INFORMS

Adoption and Impacts of InterorganizationalBusiness Process Standards:Role of Partnering Synergy

Viswanath VenkateshInformation Systems, Walton College of Business, University of Arkansas, Fayetteville, Arkansas 72701,

[email protected]

Hillol BalaOperations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405,

[email protected]

Notwithstanding potential benefits, such as quality of interorganizational relationships and operational andstrategic gains, adoption of information technology (IT)-enabled interorganizational business process stan-

dards (IBPS) is still limited. Given that these standards are designed for interorganizational business processes,we suggest that adoption of these standards depends not only on the factors pertinent to a focal firm butalso on factors that represent synergies between a focal firm and its trading partners. In this paper, buildingon the technological, organizational, and environmental (TOE) framework and interorganizational theories, wepropose a model that postulates that a set of TOE factors will have synergistic effects (i.e., interactions betweena focal firm’s and its partner’s factors) on IBPS adoption. We tested our model in a study of 248 firms (124dyads) in the high-tech industry implementing RosettaNet-based IBPS and found that three TOE factors (i.e.,process compatibility, standards uncertainty, and technology readiness) had synergistic effects and two fac-tors (i.e., expected benefits and relational trust) had direct effects on IBPS adoption. We also found that IBPSadoption led to greater relationship quality (i.e., partnering satisfaction) and operational efficiency (i.e., cycletime). Further, we found that IBPS adoption mediated the effect of TOE factors on partnering satisfaction andcycle time.

Key words : interorganizational relationships; business process; process standards; process compatibility;standards uncertainty; cycle time; relationship quality; partnering synergy; synergistic effects

History : Alain Pinsonneault, Senior Editor; Kevin Zhu, Associate Editor. This paper was received on December13, 2007, and was with the authors 24 months for 3 revisions. Published online in Articles in Advance.

IntroductionSuccessful interorganizational relationships (IORs) arecritical to firm performance, particularly in today’shypercompetitive business environment (Jap andAnderson 2007, Palmatier et al. 2007). Firms acrossthe world spend millions of dollars to create andsustain effective IORs (Hult et al. 2004, Rai et al.2009). For example, UPS spent $9 billion between 1986and the late 1990s to improve its interorganizationalprocesses and relationships with trading partners(Farhoomand and Ng 2000, Hult et al. 2004). Manyfirms invest in interorganizational systems (IOS)—information technology (IT)-based innovations, suchas electronic data interchange (EDI), electronic pro-curement innovations (EPIs), supply chain manage-ment (SCM), and customer relationship management(CRM) systems—that can help improve the qualityof IORs (e.g., Patnayakuni et al. 2006; Rai et al. 2006,2009; Straub et al. 2004). Although such IT innovations

can potentially boost organizational ability to lever-age IORs for strategic benefits, many firms fail torealize this potential because they lack IT-enabledinterorganizational process integration capabilities ormechanisms (Barua et al. 2004, Rai et al. 2006). Con-sequently, more than 60% of firms in the UnitedStates across different industries conduct business-to-business transactions through manual processes anddisconnected IT systems (Wailgum 2006). This poses aformidable challenge for firms related to how IT canbe leveraged to make IORs more successful (Malhotraet al. 2005, 2007; Rai et al. 2006). In recent years,many researchers have suggested IT-enabled interor-ganizational business process standards (IBPS)1—open

1 IBPS are designed and developed by consortia of firms in anindustry to automate, integrate, and facilitate value chain activi-ties that are interorganizational in nature—e.g., SCM, collaborativeforecasting, new product development, and inventory management

1

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Published online ahead of print April 18, 2012

Venkatesh and Bala: Adoption and Impacts of IBPS: Role of Partnering Synergy2 Information Systems Research, Articles in Advance, pp. 1–27, © 2012 INFORMS

specifications for integrating and automating interor-ganizational business processes using IT as a solutionto improve IORs (e.g., Chang and Shaw 2009, Gosainet al. 2003, Malhotra et al. 2005, Markus et al. 2006,Nelson et al. 2005, Zhao et al. 2005). IBPS providespecifications for interrelated, sequential tasks andbusiness documents that are agreed upon and sharedby trading partners to achieve a defined and commonbusiness objective (Bala and Venkatesh 2007). Articlespublished in academic and practitioner outlets havenoted that despite potential benefits, the adoption anddiffusion of IBPS are still limited (Bala and Venkatesh2007, Chang and Shaw 2009, Malhotra et al. 2005,Wailgum 2006).

Like other types of IOS (e.g., EDI systems, SCM sys-tems), adoption of IBPS requires a concerted approachby a focal firm and its trading partners (Gosain et al.2003, Malhotra et al. 2005). For example, in the con-text of EDI, Chwelos et al. (2001) noted (p. 306),“[A]doption of EDI requires coordination between atleast two firms, the relationship between the firmand its prospective trading partner(s) becomes salient.In the best-case scenario, both firms agree that adop-tion is in their best interest.” Several others have alsounderscored the importance of mutual and synergis-tic adoption characteristics for IOS (Grover and Saeed2007, Hart and Saunders 1997, Patnayakuni et al.2006). Recently, Rai et al. (2009) noted in the context ofEPI assimilation that “including suppliers and buyersand employing a dyadic research design should gen-erate additional insights about the role of their respec-tive beliefs” (p. 287). This suggests that diffusion ofIBPS will depend on the factors that are pertinent toboth a focal firm and its trading partner(s). In par-ticular, understanding factors that are only related toa focal firm will not provide a complete picture ofIBPS adoption, and understanding factors related toits trading partner(s) is also important.

Notwithstanding the importance of understandinga focal firm’s and its partner’s factors related to IOSadoption, our review of prior research has suggestedthat there has been little or no work that examined theinfluence of these factors simultaneously. Although

(Nelson et al. 2005). These are also known as vertical information sys-tems (VIS) standards and standard electronic business interfaces (SEBI)(Malhotra et al. 2007, Markus et al. 2006). For example, RosettaNet’sPartner Interface Processes (PIPs) is a VIS standard for the high-tech industry (e.g., IT, semiconductor, and electronic componentsindustry). The presence of business process elements (e.g., themessage exchange choreography—a sequence of steps required toexecute an atomic business process among trading partners) differ-entiates IBPS from other IOS standards (e.g., EDI standards) thatonly offer data and communication protocol standards (Malhotraet al. 2007).

prior research has provided rich insights on the fac-tors2 that determine IOS adoption, these factors wereprimarily considered from a focal firm’s point of view.For example, the determinants of EDI adoption havebeen studied from the perspective of one side of abuyer-seller dyad—i.e., buyer (Chwelos et al. 2001)or seller (Hart and Saunders 1997). Teo et al. (2003)examined the determinants of EDI adoption from afocal firm’s perspective. Zhu et al. (2006a, b) exam-ined e-business adoption and open-standards basedIOS adoption from a focal firm’s perspective. Raiet al. (2009) studied assimilation of EPIs from a buyerorganization’s perspective. In the context of IBPS,Bala and Venkatesh (2007) examined the determinantsof IBPS assimilation in dominant and nondominantfirms from a focal firm’s point of view. We arguethat employing a focal firm perspective has essentiallyprovided a one-sided understanding of what is clearlya dyadic phenomenon (Wang and Zajac 2007).

Against this backdrop, our objective is to under-stand IBPS adoption from a dyadic perspective.Specifically, we seek to understand the joint effectsof a focal firm’s and its partner’s factors on IBPSadoption. Building on the widely used technologi-cal, organizational, and environmental (TOE) framework(Tornatzky and Fleischer 1990), we develop and testa model of IBPS adoption and impacts. The modelis a holistic nomological network that incorporatesthe influence of synergistic factors—i.e., interactionsbetween a focal firm’s and its partner’s factors—onIBPS adoption, and the impact of IBPS adoption onrelationship quality (i.e., partnering satisfaction) andoperational efficiency (i.e., cycle time). In developingtheoretical arguments for the synergistic factors, weoffer three theoretical mechanisms—i.e., embeddedness,learning, and influence—from three major interorgani-zational theories: interorganizational networks theory,social information processing theory, and institu-tional theory. Integrating multiple theoretical perspec-tives has been suggested as important to developinsights on IORs because “none of the theories ofinterorganizational relationship formation is completeby itself 0 0 0 there is need for consideration of multi-ple perspectives as new theories are developed andtested” (Barringer and Harrison 2000, p. 395). Thisresearch thus deepens our understanding of IBPS dif-fusion by highlighting not only the factors that deter-mine IBPS adoption but also the mechanisms throughwhich these factors have synergistic effects on IBPSadoption.

2 These factors include trust, buyer and supplier power, trans-action-specific investments, reciprocal investments, informationprocessing needs, institutional pressures, network externalities,technology readiness, and instrumental benefits (Chwelos et al.2001, Mukhopadhyay et al. 1995, Premkumar et al. 1994, Teoet al. 2003).

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Venkatesh and Bala: Adoption and Impacts of IBPS: Role of Partnering SynergyInformation Systems Research, Articles in Advance, pp. 1–27, © 2012 INFORMS 3

Theoretical BackgroundTOE Framework and Drivers of IBPS AdoptionThe TOE framework is one of the most widelyused theoretical frameworks for studying the adop-tion of technology innovation (Iacovou et al. 1995;Zhu et al. 2003, 2004, 2006a). It posits that variouscontextual factors influence the organizational pro-cess of adoption and implementation of a techno-logical innovation (Zhu et al. 2006a). Tornatzky andFleischer (1990) suggested that these factors representthree important aspects of a firm’s context: techno-logical, organizational, and environmental. Technolog-ical factors represent the characteristics of currentand new technological innovations. Prior researchhas included various innovation attributes, such asthe five innovation characteristics from innovationdiffusion theory (i.e., relative advantage, complexity,compatibility, trialability, and observability; Rogers1995). Examples of organizational factors include sizeand managerial structure of a firm that is consid-ering the adoption of a new technological innova-tion (Zhu et al. 2006a). The TOE framework suggeststhat, in addition to these descriptive factors, organiza-tional resources and organizational innovativeness areimportant organizational factors (Depietro et al. 1990).Finally, environmental factors represent the character-istics of the environment in which a firm operates,such as industry, competitors, and relationships withpartners. Together, these three aspects of a firm’s con-text shape technology innovation adoption and imple-mentation in firms.

Given that IBPS are IT innovations to supportIORs, the TOE framework is an appropriate theoret-ical foundation to understand the contextual factorsthat influence IBPS adoption and outcomes (cf. Zhuet al. 2006a). Although the TOE framework allowsresearchers to examine a broad set of contextual fac-tors, we included a manageable, yet theoreticallyimportant, set of factors that is relevant to IBPSadoption in our research. Consistent with Zhu et al.(2006a), we undertook a two-step process to iden-tify these factors. These two steps complemented eachother and helped us identify a set of factors that istheoretically relevant to the context of IBPS adoption.The first step involved identification of factors fromprior IOS adoption research that employed the TOEframework as a theoretical foundation. We identifiedfour TOE factors that were consistently found to besignificant determinants of IOS and other types ofinterorganizational IT innovation adoption: expectedbenefits (Chwelos et al. 2001, Iacovou et al. 1995, Zhuet al. 2006b), technology readiness (Chwelos et al. 2001,Iacovou et al. 1995, Zhu et al. 2006a), organizationalinnovativeness (Hurley and Hunt 1998, Rogers 1995),and relational trust (Hart and Saunders 1997, Ring

and Van de Ven 1992). Among these factors, expectedbenefits and technology readiness are technology fac-tors because they represent instrumental benefits ofadopting an IT innovation and organizational techno-logical capabilities that facilitate the implementationof the innovation (Chwelos et al. 2001; Iacovou et al.1995; Rogers 1995; Zhu et al. 2006a, b). Organizationalinnovativeness is an organizational factor because itrepresents a firm’s culture and/or disposition to pur-sue and embrace innovations (Hurley and Hunt 1998).Relational trust is an environmental factor that cap-tures the extent to which a focal firm expects a cer-tain degree of stability and certainty in its relationshipwith trading partners who operate in the externalenvironment (Chwelos et al. 2001).

In the second step, we reviewed prior IBPS researchto understand characteristics of IBPS and assessed thetheoretical relevance of the TOE factors identified inthe first step. In addition, we identified factors thatwere not included in prior TOE-based studies butnevertheless are relevant to IBPS. Prior research hassuggested three key characteristics of IBPS (Bala andVenkatesh 2007). The first key characteristic is thatIBPS are interorganizational standards, and thereforea mutual and synergistic adoption by two or morepartnering firms is needed to benefit from these stan-dards (Markus et al. 2006). Further, these standardsfacilitate real-time integration of business processes ofa focal firm and its trading partners and, therefore,may potentially expose a focal firm’s internal infor-mation flow to its partners. Therefore, it is critical fortrading partners to have a high degree of relationaltrust between them before implementing IBPS in theirIORs to ensure that a firm is not subject to opportunis-tic behaviors by its trading partners, such as exploita-tion of vulnerabilities (Krishnan et al. 2006). Hence,we incorporated relational trust, which is an environ-mental factor, into our model as a key determinant ofIBPS adoption.

The second key characteristic of IBPS is that imple-mentation of these standards is resource intensiveand requires adopting firms to have substantial tech-nical and business process related expertise (Changand Shaw 2009). These standards are typicallybased on XML-based protocols and may requireservice-oriented architecture (SOA) for successfuldeployment. Most legacy applications and enterprisesystems are not readily compatible with these stan-dards (Cartwright et al. 2005). Therefore, firms aremore likely to adopt these standards if there are sig-nificant benefits of adoption, suggesting that expectedbenefits is a critical factor in this context. Further, fac-tors that capture technological capabilities and chal-lenges associated with IBPS implementation are alsorelevant to this particular characteristic of IBPS. Tech-nology readiness and standards uncertainty are twosuch technological factors that capture this aspect of

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IBPS adoption. Building on Bensaou and Anderson(1999), who underscored the importance of uncer-tainty in the context of IOR-specific investment, wesuggest that standards uncertainty is particularly per-tinent in the context of IBPS because a firm may bereluctant to adopt particular IBPS until the standardsbecome de facto standards in the industry (David andGreenstein 1990).

The third key characteristics of IBPS is that imple-mentation of these standards requires substantialchanges to a firm’s existing business processes tobe compliant with the standards specifications (Balaand Venkatesh 2007, Cartwright et al. 2005). Imple-mentation of IBPS requires not only process integra-tion with trading partners but also integration withinternal business processes. Such integration is pos-sible if IBPS specifications are compatible with inter-nal processes. For example, IBPS require seamless andreal-time information flow between trading partners.If a firm does not have the capability to supportthis requirement, it is more likely that it will findthat IBPS are not compatible with internal businessprocesses. Building on Rogers (1995), who proposedcompatibility as a key attribute of innovations, wesuggest process compatibility is a technological factorthat will inform the process change aspect of IBPSadoption. Process compatibility is a critical concernbecause many firms have high routine rigidity, i.e.,they are unable to change existing work processes andpractices that are deeply embedded in their value sys-tems or culture and that they consider a source ofsuccess (Gilbert 2005, Nelson and Winter 1982).

Table 1 Definition and Source of TOE Factors

Contexts Factors Definition Source Effects

Technological Expected benefits The degree to which a firm expectsoperational and strategic benefits fromadopting IBPS.

Chwelos et al. (2001), Iacovouet al. (1995), Zhu et al. (2006b)

Synergistic effect fromembeddedness mechanism

Process compatibility The degree to which IBPS are perceivedas being consistent with precursormethods for executinginterorganizational processes.

Premkumar et al. (1994),Ramamurthy et al. (1999),Rogers (1995)

Synergistic effect fromembeddedness andlearning mechanisms

Standards uncertainty Inability to forecast accurately whetherIBPS and associated technologies willbe stable over time and able to deliverthe intended outcomes.

Beckman et al. (2004), Bensaouand Anderson (1999)

Synergistic effect frominfluence and learningmechanisms

Technology readiness The degree to which a focal firm hasnecessary technology infrastructureand IT human resources to implementIBPS.

Chwelos et al. (2001), Iacovouet al. (1995), Zhu et al. (2006a)

Synergisitic effect fromlearning andembeddednessmechanisms

Organizational Organizationalinnovativeness

Innovativeness is the notion of opennessto new ideas as an aspect of a firm’sculture.

Chwelos et al. (2001), Hurley andHult (1998)

Synergistic effect frominfluence and learningmechanisms

Environmental Relational trust The expectation held by one firm thatanother will not exploit itsvulnerabilities when faced with theopportunity to do so.

Hart and Saunders (1997),Krishnan et al. (2006), Ringand Van de Ven (1992)

Synergistic effect fromembeddedness andinfluence mechanisms

Table 1 provides definitions of the six TOE fac-tors that we identified from our two-step process.We suggest that these factors will have synergisticeffects on IBPS adoption due to the different theoreti-cal mechanisms that we discuss in the theory develop-ment section. Although prior research has suggestedother organizational factors, such as firm size, andenvironmental factors, such as length of relationships,dependency, and institutional pressures (e.g., norma-tive, mimetic, and coercive), that influence IOS adop-tion, we did not include them as focal variables inour model because these variables have been theo-rized and found significant previously in the con-text of IBPS adoption (e.g., Bala and Venkatesh 2007).However, we do include them as control variables.

Theory DevelopmentFigure 1 presents our model of IBPS adoption andimpacts. The model posits that the six TOE factorsdiscussed in the previous section will influence IBPSadoption, which in turn will lead to organizationaloutcomes. The model also posits that IBPS adoptionwill partially mediate the influence of TOE factors onorganizational outcomes. The core tenet of this modelis partnering synergy conceptualized at a dyadic level,which suggests that IBPS adoption hinges not onlyon factors that directly influence each firm’s adoptiondecision but also on factors that are synergistic to trad-ing partners. In this section, we first discuss theoret-ical mechanisms for the synergistic effects. We thenhypothesize why and how the TOE factors will havesynergistic effects on IBPS adoption.

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Figure 1 A Synergistic Model of IBPS Adoption and Impacts

IBPS adoption

Control variables: Organizational factor: firm size Environmental factors: length of relationship;

dependency ; normative, mimetic, and coercive pressures

Operational efficiencyCycle time

Relationship qualityPartnering satisfaction

Impacts of IBPS adoption

Synergistic factors

Technological factorsExpected benefitsProcess compatibilityStandards uncertaintyTechnology readiness

Organizational factorsOrganizational innovativeness

Environmental factorsRelational trust

Note. The dotted line indicates that IBPS adoption partially mediates the influence of (a) technology readiness, process compatibility, and standards uncertaintyon cycle time and (b) expected benefits and relational trust on partnering satisfaction.

Theoretical Mechanisms for Synergistic EffectsConsistent with prior research and interorganizationaltheories, we argue that firms doing business withone another will form a dyadic relationship basedon interdependence and cooperation (Anderson et al.1994, Kim et al. 2006, Perrone et al. 2003). Research onIORs has suggested that firms are increasingly becom-ing deconstructed because they are relying on coordi-nated relationships with other firms to make valuechain activities (e.g., new product development, man-ufacturing, research and development, procurement,inventory management, forecasting, sales, and mar-keting) more effective and efficient (Palmatier et al.2007, Rai et al. 2006). Firms now work closely withother firms to manage the flow of goods and servicesalong the value chain. A dyadic relationship repre-sents a form of strong tie in which boundary spannersfrom a firm leverage social capital to access infor-mation and resources from their counterparts to gainvalue for the firm (Anderson et al. 1994). Firms in adyadic relationship form network inertia—“a persis-tent organizational resistance to changing interorgani-zational dyadic ties or difficulties that an organizationfaces when it attempts to dissolve old relationshipsand form new network ties”—that is a source of inter-dependence, cooperation, and synergy between theparticipating firms (Kim et al. 2006, p. 704). Thus,we expect that partnering synergy will have a signifi-cant influence on a focal firm’s decision to adopt anddeploy innovations that affect IORs.

We build on interorganizational theories to developthree theoretical mechanisms to justify why synergis-tic factors will influence IBPS adoption: embeddedness,learning, and influence mechanisms. The embedded-ness mechanism is based on interorganizational net-work theories that postulate the notion of relational

embeddedness; i.e., a firm with embedded ties toanother firm will form a strong tie based on famil-iarity, trust, and commitment (Hagedoorn 2006, Kimet al. 2006, Uzzi and Lancaster 2003). This will lead tosharing private information (e.g., unpublished infor-mation about a firm’s strategy, distinctive compe-tencies, undocumented product capabilities, insidemanagement conflicts or succession plan, critical sup-plier and customer dependencies, unpublished inno-vations, and underlying motives; Uzzi and Lancaster2003). A similar argument is made in the attachmentliterature, which suggests that boundary spanners inIORs tend to form strong exchange relationships andshare knowledge about organizational values, norms,and future plans (Seabright et al. 1992). Hagedoorn(2006) suggested that dyadic embeddedness is animportant form of relational embeddedness in whicha focal firm creates a partnering relationship withanother firm. It is likely that such a relationship willbe embedded (as opposed to arm’s length) and par-ticipating firms will share private information witheach other. Network inertia, a salient characteristic ofdyadic relationships that refers to a persistent orga-nizational resistance to changing IORs, also indicatesthe strength of embeddedness in dyadic relationships(Kim et al. 2006). Overall, the embeddedness mecha-nism will help partners understand each other’s posi-tion on matters of mutual interest.

The learning mechanism is based on social infor-mation processing (Salancik and Pfeffer 1978) andorganizational learning (Daft and Weick 1984) the-ories. Social information processing theory suggeststhat the social environment is the immediate sourceof information for individuals and it provides cuesthat individuals use to construct and interpret events(Salancik and Pfeffer 1978). In the context of a dyadic

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relationship, it is possible that boundary spannersfrom one firm will gather cues from their coun-terparts about the other firm’s culture, values, andgoals (Palmatier et al. 2007). Organizational learningtheory suggests that one of managers’ main tasks is togather, process, and act on information from the envi-ronment that is critical to organizational success (Hultet al. 2004). In sum, the learning mechanism suggeststhat a focal firm will have an understanding of itspartners’ positions and views on matters of commoninterest that in turn will influence its outcomes (e.g.,IBPS adoption).

Finally, the influence mechanism is based on insti-tutional theory (DiMaggio and Powell 1983) that sug-gests three sources of institutional isomorphism—i.e.,the process of gaining political and institutional legit-imacy and market positions: coercive, mimetic, and nor-mative influences. These sources force firms to conformto norms, traditions, and social expectations in aninstitutional environment and expedite the processof homogenization at the interorganizational level(Oliver 1997). Prior research theorized and found thatthese influences were key drivers of IOS and IBPSadoption in firms (Bala and Venkatesh 2007, Teo et al.2003). We extend the prior conceptualizations of influ-ence mechanisms by focusing on the influence processin dyadic relationships. Specifically, we suggest thatthe coercive and normative pressures will underliepartners’ influences on each other in a dyadic context.In such a context, mimetic influence is less pertinentbecause it represents the influence of successful com-petitors. A dominant firm can exert coercive pres-sure on its trading partner in a dyadic relationship byrequiring the adoption of IBPS (Bala and Venkatesh2007). If a firm is dependent on its partner for rev-enues and resources, it is more likely that the part-ner will be able to exert a coercive influence. In thecontext of IORs, normative pressure operates throughthe development of shared mental model. DiMaggioand Powell (1983) suggested that managers developa shared mental model in two ways: (1) because oftheir similar professional training, managers developa shared mental model of the institutional environ-ment and act accordingly and (2) managers interactwith each other through professional and trade asso-ciations and form perceptions of industry norms andexpectations. Boundary spanners in a dyadic relation-ship will develop a shared mental model throughtheir interactions and become aware of each other’splans and reactions toward IBPS adoption. Conse-quently, normative influence will be a key factor driv-ing IBPS adoption.

Role of Boundary Spanners in Synergistic EffectsThe three mechanisms that we discussed here sug-gest that boundary spanners in IORs play a crit-ical role in stimulating the synergistic effects on

IBPS adoption. Zaheer et al. (1998, p. 143) notedthat an IOR is not “faceless and monolithic, it isactively handled and managed by individual bound-ary spanners 0 0 0 [who] are more closely involved inthe interorganizational relationship than other mem-bers of the organization, and tend to interact withtheir counterparts to a greater extent 0 0 0 0” Bound-ary spanners develop a close approximation of posi-tions and views of their counterparts on differentmatters of interest. Further, because of the strengthand duration of relationship that are typically highin a dyadic relationship, boundary spanners developa sense of confidence in their counterparts’ behav-iors (Palmatier et al. 2007). The embeddedness mech-anism suggests that firms in a dyadic relationshipshare private information that they may not shareotherwise through boundary spanners. In practice,boundary spanners develop close interpersonal rela-tionships (e.g., attachment; Seabright et al. 1992) withtheir counterparts and discuss various issues rele-vant to their respective firms. The learning and influ-ence mechanisms suggest that boundary spannersdevelop a shared understanding of different mattersthat are of interest to both. Thus, boundary span-ners develop an understanding of how and what theircounterparts think of different factors pertinent toIBPS adoption.

We argue, based on the premise of embeddednessand learning mechanisms, that this understanding isan accurate reflection of what the boundary span-ners in the partnering firm truly feel about these fac-tors. Hence, we theorize that given that adoption ofIBPS is essentially a mutual and synergistic decision,decision makers in a focal firm adjust their assess-ment of the relative importance of a given factor inconcert with their partner’s assessment of the samefactor. This adjustment process is consistent withthe anchoring and adjustment heuristic that suggeststhat decision makers adjust their initial perceptionsbased on new information and signals they receivefrom different sources, including the environment(Kahneman and Tversky 1973). Overall, boundaryspanners in IORs help shape the nature of synergisticeffects of a focal firm and its partner’s factors onIBPS adoption.

Synergistic Effects ofTechnological FactorsExpected BenefitsExpected benefits represent the anticipated benefitsgained from adopting a new technology innovation—here, IBPS (Zhu et al. 2006b). The theoretical ratio-nale for expected benefits can be derived from theinnovation diffusion theory that posits that poten-tial adopters will conduct an explicit or implicit cost-benefit analysis and adopt an innovation that yields

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more benefits than the idea it supersedes (Rogers1995, Zhu et al. 2006b). Firms are more likely toadopt IBPS if decision makers perceive that IBPS willprovide operational and strategic benefits. Variousbenefits of adopting IBPS have been suggested: costreduction, operational efficiency, improved coordina-tion, knowledge creation and sharing, and partneringflexibility (Gosain et al. 2003, Malhotra et al. 2005).RosettaNet, for example, offers the following bene-fits of adopting its partner interface processes (PIPs):(1) reduction in cycle time; (2) reduction in inventorycosts; (3) improved productivity through automation;(4) standardized and simplified business processes;and (5) measurable supply chain return on investment(RosettaNet 2007).

As noted earlier, IBPS implementation is resourceintensive and requires substantial changes to businessprocesses. Decision makers incorporate these aspectsof IBPS implementation as they develop perceptionsabout expected benefits from IBPS. In addition totangible benefits, decision makers learn about intan-gible benefits of IBPS adoption that cannot be eas-ily measured (e.g., improved knowledge sharing andcoordination) from the trade press, promotions bystandards-development consortia, trading partners,and other boundary spanning activities (e.g., seminarsand workshops). We suggest that decision makers willdevelop a sense of their counterpart’s appraisal ofexpected benefits of IBPS through the embeddednessmechanism (Hagedoorn 2006, Uzzi and Lancaster2003). Boundary spanners from trading partners thathave a high degree of dyadic embeddedness arelikely to know each other’s assessment of variousexpected benefits of IBPS implementation. We sug-gest that this assessment will reinforce (i.e., adjust)the role of expected benefits in the process of IBPSadoption decision. In particular, if managers from afocal firm find that their counterparts view that IBPSimplementation is highly beneficial, it is more likelythat they will critically evaluate different facets ofexpected benefits to determine how their firm can alsoleverage these benefits by adopting IBPS. Thus, wehypothesize as follows.

Hypothesis 1 (H1). Expected benefits will have a syn-ergistic effect on IBPS adoption such that the positive effectof expected benefits on IBPS adoption will be stronger fora firm whose partner has high expected benefits.

Process CompatibilityCompatibility has been suggested as an impor-tant determinant of innovation adoption in firms(Fichman 2000, Rogers 1995). Research on standardshas also suggested that compatibility is a key con-sideration for firms before adopting a particular stan-dard (Chen and Forman 2006, David and Greenstein1990). Given that IBPS are developed externally by

industry consortia, the issue of compatibility is crit-ical because these standards may not be compatiblewith a firm’s internal business processes. We con-ceptualize the notion of process compatibility fromprior research and theories on organizational rou-tines that suggest that firms develop a stable patternof routines or processes based on historical successin the market (Feldman and Pentland 2003, Nelsonand Winter 1982). Successful routines are institution-alized over time and become a part of organizationalvalues and norms. Zollo et al. (2002) developed thenotion of interorganizational routines that are stablepatterns of interaction among two firms developedand refined in the course of repeated collaborations.Bala and Venkatesh (2007) argued that notwithstand-ing the benefits of changing routines by implementingIBPS (e.g., to reduce variance in routines), managersmay feel that changing to externally developed IBPSwill cause a major disruption in existing successfulroutines that are embedded in a firm’s values and cul-ture and may also result in a loss of control and powerover their interorganizational processes (Porter 2001).Therefore, process compatibility (or lack thereof) is acrucial factor in IBPS adoption decisions.

We suggest that decision makers in a focal firmwill not be willing to adopt IBPS if these standardsare not compatible with internal processes (i.e., ifbusiness logic, message flow, and message contentsorchestrated in the IBPS are not compatible with thoseof existing processes). Further, employees may resistIBPS implementation and may not execute the newprocesses appropriately if they feel that these pro-cesses are incompatible with their existing routines.We suggest that boundary spanners of a focal firmwill have an understanding of their trading partners’view toward process compatibility through embed-dedness and learning mechanisms. They will placevarying degrees of importance on their firm’s pro-cess compatibility depending on their partner’s viewof process compatibility. For instance, if a tradingpartner has a high degree of process compatibility, afocal firm may place less importance on its own pro-cess incompatibility because boundary spanners mayexpect that the trading partner will help the focal firmgo through the challenges associated with processchange (Bala and Venkatesh 2007). A trading partnerwith a high level of process compatibility will be ableto guide a focal firm as it tries to change businessprocesses to make them compatible with IBPS. Conse-quently, a trading partner’s high process compatibil-ity will buffer the relationship between a focal firm’sprocess compatibility and its IBPS adoption. Thus,we hypothesize as follows.

Hypothesis 2 (H2). Process compatibility will have asynergistic effect on IBPS adoption such that the posi-tive effect of process compatibility on IBPS adoption will

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be stronger for a firm whose partner has high processcompatibility.

Standards UncertaintyUncertainty, particularly technology-related uncer-tainty, has been shown to have a substantial impacton IOS implementations and IOR-specific investments(Bensaou and Anderson 1999, Grover and Saeed2007). Uncertainty has traditionally been treated as abroad environmental factor with several dimensions,such as market, product, demand, and technologyuncertainty. We conceptualize standards uncertaintyas a technology factor (as opposed to an external fac-tor) because it represents the perception of whetherthe process specifications and associated technolo-gies will be stable over time and able to deliver theintended benefits. As noted earlier, a firm may not bewilling to adopt a standard until it becomes a de factostandard in the industry (David and Greenstein 1990).IBPS are based on recently developed XML specifi-cations that are still evolving significantly. In addi-tion, the process specifications in IBPS may vary fromindustry to industry, which makes them difficult toadopt for firms that have cross-industry IORs (Zhaoet al. 2005). Further, decision makers may perceivethat technologies needed to implement IBPS and pro-cess choreography embedded in IBPS are not stableand are going to change in the future. Therefore, stan-dards uncertainty is expected to play a critical hinder-ing role in IBPS adoption.

Considering the costs associated with implement-ing IBPS (e.g., costs associated with technology infras-tructure, IT human capital, and change management)and difficulty in changing business processes, it ismore likely that decision makers will not make afavorable adoption decision if they perceive IBPSspecifications are not stable and may not becomea de facto industry standard in the long run. Deci-sion makers may develop such perceptions for severalreasons—e.g., there are competing standards in theindustry, the standard is not developed by industry-wide consortia, and the standard is not supported bymajor firms in the industry. The influence of stan-dards uncertainty on a focal firm’s IBPS adoptiondecision can be explained by the path dependencyhypothesis that suggests that a firm’s ability and will-ingness to adopt an innovation depends on its relatedexperience with prior innovations (Zhu et al. 2006b).If decision makers perceive that IBPS are not going tobe stable over time, it is more likely that they will stickto their current interorganizational processes to avoidpotentially high implementation costs and to main-tain existing relationship-specific assets. However, ifboundary spanners perceive that their counterpartshave a low degree of uncertainty related to IBPS, theywill reevaluate (i.e., adjust) their own positions with

respect to standards uncertainty. Boundary spannerswill receive this signal through influence and learningmechanisms. A low degree of standards uncertaintyin a trading partner signals stability and legitimacyof IBPS. During this reevaluation process, if boundaryspanners agree with their counterpart’s assessmentand lower their own level of standards uncertainty,it is more likely that there will be greater IBPS adop-tion between these trading partners. Hence, we expectthat the negative influence of standards uncertaintyon a focal firm’s IBPS adoption will strengthen in thepresence of low standards uncertainty in a tradingpartner. Thus, we hypothesize as follows.

Hypothesis 3 (H3). Standards uncertainty will havea synergistic effect on IBPS adoption such that the nega-tive effect of standards uncertainty on IBPS adoption willbe stronger for a firm whose partner has low standardsuncertainty.

Technology ReadinessTechnology readiness has been found to be an impor-tant determinant of IOS adoption in much priorwork. Our conceptualization of technology readinessis consistent with Zhu et al. (2006a), who emphasizedtwo aspects of technology readiness—i.e., technol-ogy infrastructure and IT human resources. We alsoinclude the willingness of a firm to invest in thesetwo aspects of technology readiness. From a tech-nology infrastructure point of view, implementationof IBPS requires an IT solution that helps integrateexternal and internal business processes and sys-tems. In other words, data and business documentsreceived from trading partners need to be integratedand processed seamlessly by internal systems. Con-sidering that about 60% of firms rely on legacyor spreadsheet-based applications for interorganiza-tional processes (Wailgum 2006), it is likely that thesefirms may not have the necessary technology infras-tructure in place for IBPS implementation. The roleof competent human resources is even more criti-cal because IBPS implementation requires personnelwho have an understanding of both technologicaland process aspects of these standards (Cartwrightet al. 2005).

As noted earlier, IBPS are based on recently devel-oped XML-based languages and protocols, and mosttraditional technology infrastructure and legacy sys-tems are not readily compatible with these languagesand protocols (Gosain et al. 2003). In some cases,a specialized middleware technology is required totranslate the XML messages and business documents.In addition to the translation, business process chore-ography needs to be interpreted and integrated withthe internal processes and systems. Hence, firms notonly need specialized technology infrastructure tosupport IBPS but also require expert IT personnel

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who are able to implement and support the tech-nology infrastructure and business process chore-ography. Overall, firms with a greater technologyreadiness are in a position conducive to adopt andimplement IBPS.

We suggest that technology readiness will have asynergistic effect on IBPS adoption primarily throughlearning and embeddedness mechanisms. These twomechanisms will help a focal firm understand the ITlandscape and IT-related human capital of its trad-ing partners. If a trading partner has high technologyreadiness, boundary spanners of a focal firm gatherfrom learning and embeddedness mechanisms thatthe focal firm will be able to adopt IBPS regardlessof its own technology readiness because the tradingpartner will be able to help the focal firm imple-ment IBPS. Consequently, boundary spanners of afocal firm will place less importance on the firm’sown technology readiness. If a partner has a low tech-nology readiness, a focal firm will carefully evalu-ate its own technology readiness and determine if itcan implement IBPS using its own technological capa-bilities and also help its trading partners implementIBPS. In this situation, if a focal firm also has low tech-nology readiness, it is more likely that it will not bewilling to adopt IBPS. However, if it has a high tech-nology readiness, it is more likely that it will be ableto help its trading partners to implement IBPS. It canalso exert normative and/or coercive pressures on itstrading partners to implement IBPS. Therefore, tech-nology readiness is important in this situation. Thus,we hypothesize as follows.

Hypothesis 4 (H4). Technology readiness will have asynergistic effect on IBPS adoption such that the posi-tive effect of technology readiness on IBPS adoption willbe stronger for a firm whose partner has low technologyreadiness.

Synergistic Effect of Organizational Factor:Organizational InnovativenessOrganizational innovativeness is the openness to newideas as an aspect of a firm’s culture (Hurley andHunt 1998). An innovative firm constantly seeks tofind ways to respond to customer needs and marketdemands (Hurley and Hunt 1998). We suggest thatif a firm has a culture of innovativeness, it is morelikely to consider adopting technological innovations,such as IBPS, to improve coordination with its trad-ing partners and increase the agility in its value chainactivities. Further, an innovative firm will be moreresponsive to the needs of its trading partners andthus is more likely to adopt innovations that can helpimprove the relationship with its partners. We arguethat an innovative firm will be in a better positionto adopt IBPS for two key reasons. First, an inno-vative firm is less likely to be vulnerable to organi-zational inertia (i.e., resource and routine rigidities),

a major barrier to innovation implementation, becauseof its experience and prior success with innovationimplementation. Decision makers in such a firm aremore likely to be willing to invest in innovations andchange routines or work processes that can potentiallylead to greater market success. Employees in such afirm are more likely to be favorable to changes in theirroutines or work processes because of new innova-tion implementation. Second, innovativeness has beenfound to influence organizational growth and prof-itability (Cho and Pucik 2005). These incentives willdrive an innovative firm to continue exploring newideas so that it can sustain the path of growth andprofitability.

Managers of a focal firm will develop a senseof their trading partners’ organizational innovative-ness through learning and influence mechanisms.Research on managerial cognitions has suggested thatmanagers constantly gather information from theirenvironment to make operational and strategic deci-sions (Daft and Weick 1984). Firms typically pro-vide signals and cues about their innovativeness (e.g.,product and/or service innovations, new technol-ogy implementations) through different communica-tion channels, such as press releases, news articles,and managers’ participation in professional meetingsand conferences. The learning mechanism, which wediscussed earlier, suggests that boundary spannerswill gather these signals and cues and develop asense of their trading partners’ level of innovative-ness (Palmatier et al. 2007). Influence mechanismswill also play a role here because boundary span-ners will be cognizant of their trading partners’ inno-vative activities and potentially adopt them becauseof normative pressure (DiMaggio and Powell 1983).We suggest that if managers of a focal firm get asignal that their trading partner has a strong cul-ture of innovativeness, it is more likely that theywill leverage their own innovativeness as a vehicle toimprove their relationship with this trading partner.Given that IBPS are a fruitful avenue to improve andsustain relationships with trading partners (Bala andVenkatesh 2007, Chang and Shaw 2009), managersof a focal firm will attempt to find ways to exploittheir firm’s innovativeness to implement IBPS. Thus,we hypothesize as follows.

Hypothesis 5 (H5). Organizational innovativenesswill have a synergistic effect on IBPS adoption such thatthe positive effect of organizational innovativeness on IBPSadoption will be stronger for a firm whose partner has highorganizational innovativeness.

Synergistic Effect of Environmental Factor:Relational TrustRelational trust has been found to be a key deter-minant of the quality and performance of IORs

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(Krishnan et al. 2006, Ring and Van de Ven 1992). Con-sistent with prior research that discussed the impor-tance of trust in the context of relationship-specificinvestments—e.g., IOS adoption (Hart and Saunders1997)—we expect that relational trust will be a sig-nificant predictor of IBPS adoption. Given that IBPSimplementation requires substantial resource commit-ment and willingness to change organizational rou-tines or processes, a firm will be willing to makeinvestments in IBPS to automate business processeswith a trading partner if there is a high degree oftrust in the relationship. Hart and Saunders (1997)provided two theoretical rationales for why relationaltrust will be a key determinant of IOS adoption anduse: (1) it will encourage firms to make investments ininformation sharing, and (2) it will discourage oppor-tunistic behavior. Further, transaction cost economics(TCE) theory suggests that opportunistic behaviorincreases uncertainty in IORs, which in turn hindersinvestments in relationship specific assets (Williamson1995). A high degree of relational trust will mitigateuncertainty by discouraging opportunistic behavior.Thus, a focal firm is more likely to adopt IBPS toautomate interorganizational processes with a trad-ing partner if there is a high level of relational trustbetween them.

Although we expect that all three theoretical mech-anisms will play a role in the synergistic effect ofrelational trust, we suggest that the embeddednessand influence mechanisms will be particularly salient.The influence of a focal firm’s relational trust onIBPS adoption for interorganizational processes witha partner will intensify if managers of the focal firmbelieve that the partner also has a similar level of rela-tional trust. Managers of a focal firm will develop thisbelief because of the embeddedness mechanism dis-cussed earlier that suggests that firms with embed-ded ties are more likely to form a strong tie basedon trust and commitment (Hagedoorn 2006). Further,if managers of a focal firm feel that a partner has ahigh degree of relational trust, they are more likelyto reciprocate (by increasing relational trust) becauseof the influence mechanism and adjustment processdiscussed earlier. This reciprocation will enhance theinfluence of relational trust on IBPS adoption of afocal firm. Thus, we hypothesize as follows.

Hypothesis 6 (H6). Relational trust will have a syn-ergistic effect on IBPS adoption such that the positive effectof relational trust on IBPS adoption will be stronger for afirm whose partner has high relational trust.

Outcomes of IBPS AdoptionFirms are more likely to adopt IBPS to achieve cer-tain outcomes, such as operational efficiency andIOR quality. Chang and Shaw (2009) suggested thatIBPS implementation can influence two levels of

business values: first order and second order. Ourfocus is on the first-order business values that rep-resent the immediate impacts of an IT innovationon important business processes. The second-orderbusiness values are typically broad firm performance,such as sales growth and market share. It may takeyears for a firm to achieve these benefits from IBPSimplementation. In addition, there are many otherorganizational and environmental factors that canpotentially influence these second-order business val-ues. Chang and Shaw (2009) noted that the first-orderbusiness values derived from IBPS adoption alsoinfluence the second-order business values. We focuson two first-order business values: operational effi-ciency and relationship quality. We conceptualizeoperational efficiency as the reduction in cycle time.Cycle time has been widely used as a key outcomeof interest to understand process performance (e.g.,Hult et al. 2004). Given that IBPS are process stan-dards, cycle time is an appropriate outcome to assessoperational efficiency gained from IBPS. There hasbeen limited scientific evidence of the impact of IBPSadoption on process level outcomes, such as cycletime. We expect that if a firm is able to successfullyimplement IBPS and integrate them into its valuechain, it is more likely see a reduction in cycle timebecause IBPS will standardize the message and doc-ument exchange between two trading partners andreduce the amount of time needed to complete a pro-cess cycle. Further, standardization through IBPS willreduce transaction errors and increase clarity in mes-sage and documentation. RosettaNet, for example,claims that adoption of RosettaNet-based IBPS in sup-ply chain processes can reduce manual transactionsby 80%, inventory level from four to two weeks, andplanning time from eight to four weeks (RosettaNet2007). Roos (2001) noted that RosettaNet-based IBPSreduced cycle time in order management processesfrom two weeks to two days for a major Intel supplier.Thus, we hypothesize as follows.

Hypothesis 7 (H7). IBPS adoption will be negativelyassociated with cycle time.

Another important outcome of interest is partner-ing satisfaction, which represents the relationship qual-ity between trading partners. Satisfaction is a positiveaffective state resulting from a favorable appraisal ofdifferent aspects of a working relationship (Smith andBarclay 1997). Partnering satisfaction is the degree towhich boundary spanners in a focal firm have a posi-tive appraisal of a working relationship with a tradingpartner. Given that IBPS provide seamless informa-tion flow between trading partners, improve coor-dination and synchronization in interorganizationalprocesses, and reduce process variations and errors

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(Gosain et al. 2003), a focal firm will have more vis-ibility into its partner’s processes and have betterunderstanding of the status of a transaction. Thiswill reduce uncertainty associated with a transactionand help trading partners set realistic expectations.Consequently, partners are more likely to be satisfiedwith each other following the implementation of IBPS.Thus, we hypothesize as follows.

Hypothesis 8 (H8). IBPS adoption will be positivelyassociated with partnering satisfaction.

Mediating Role of IBPS AdoptionConsistent with Kobelsky et al. (2008), who theorizedand found that TOE factors influence firm perfor-mance, we expect that the TOE factors will influ-ence IBPS outcomes. However, we argue that thiseffect will be partially mediated by IBPS adoption.In particular, we suggest that IBPS adoption will par-tially mediate the effects of (a) technology readiness,process compatibility, and standards uncertainty oncycle time and (b) expected benefits and relationaltrust on partnering satisfaction. There is no theo-retical basis to expect that the organizational fac-tor in our model, i.e., organizational innovativeness,will influence cycle time and partnering satisfactionbecause this factor represents an internal characteris-tic of a firm and thus is unlikely to affect outcomesof IBPS adoption that typically depend on techno-logical and/or relationship-specific factors. Further,organizational innovativeness represents a generalpropensity of a firm to explore new ideas or inno-vations. It does not necessarily influence the extentto which a firm is able to gain benefits from aspecific innovation once the innovation is adopted(Chwelos et al. 2001). Hence, although there maybe an association between organizational innovative-ness and IBPS outcomes, there is no theoretical rea-son for organizational innovativeness to influencethese outcomes.

We expect that IBPS adoption will partially mediatethe influence of three technology characteristics, i.e.,process compatibility, standards uncertainty, and tech-nology readiness, on cycle time because IBPS adop-tion takes place as a result of a firm’s appraisal ofthese characteristics as a vehicle to achieve successfulorganizational outcomes. Although expected benefitsand relational trust are likely to be associated withcycle time, there is no theoretical reason to expectthese two factors will have a causal influence oncycle time. We suggest that to achieve a reduced cycletime in an interorganizational business process (e.g.,order-to-cash process), the IBPS need to have a highdegree of compatibility with existing processes, theIBPS specifications should have a high degree of sta-bility, and a firm needs to have a high degree of tech-nology readiness to implement IBPS. However, these

factors will not be sufficient to achieve a reduced cycletime if a firm fails to adopt and implement IBPS in itsvalue chain. Therefore, we suggest that in addition tothe direct effect of these factors on cycle time, therewill be significant effects of these factors on cycletime that are likely to operate through IBPS adoption.Thus, we hypothesize as follows.

Hypothesis 9 (H9). IBPS adoption will partially medi-ate the effects of (a) process compatibility, (b) standardsuncertainty, and (c) technology readiness on cycle time.

We expect that IBPS adoption will partially medi-ate the effect of expected benefits and relational truston partnering satisfaction. We do not have a the-oretical reason to expect that technological factors,such as process compatibility, standards uncertainty,and technology readiness, will have an influence onpartnering satisfaction. Expected benefits are likely toinfluence partnering satisfaction because they repre-sent instrumental benefits of implementing IBPS witha particular trading partner. If managers of a focalfirm expect that IBPS will facilitate significant opera-tional and strategic benefits from the relationship witha trading partner, it is more likely that they will besatisfied with the relationship with the trading part-ner. However, we argue that if a focal firm and itstrading partners fail to implement IBPS, it is morelikely that expected benefits from IBPS will not beable to ensure partnering satisfaction between thesetwo firms. Therefore, the influence of expected ben-efits on partnering satisfaction will operate throughIBPS adoption. Along the same line, we argue thatrelational trust will influence partnering satisfactionmediated by IBPS adoption. If a focal firm expectsthat its trading partner will not exploit its vulnerabil-ities (i.e., a high level of relational trust), it is morelikely to be satisfied with its trading partner (Krishnanet al. 2006, Zaheer et al. 1998). However, we suggestthat the relationship between relational trust and part-nering satisfaction may not be salient in the long runif trading partners do not have an efficient and reli-able way of conducting business transactions. If trans-actions are not done efficiently and accurately, it ismore likely that trading partners will not be as satis-fied in their relationship. Given that IBPS adoption isexpected to increase transactional efficiency and accu-racy, and offers a mechanism for greater process inte-gration, we expect that the direct effect of relationaltrust on partnering satisfaction will be mediated byIBPS adoption. Thus, we hypothesize as follows.

Hypothesis 10 (H10). IBPS adoption will partiallymediate the effects of (a) expected benefits and (b) relationaltrust on partnering satisfaction.

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Control VariablesTo assess the unique contribution of the theoreti-cally derived constructs in explaining IBPS adoption,we control for organizational and environmental fac-tors considered to be important determinants of IOSadoption. We included six control variables: firm size;length of relationship; dependency on trading partner; andnormative, mimetic, and coercive pressures. Firm size, anorganizational factor, can potentially influence inno-vation adoption (Zhu et al. 2006a). It is more likelythat large firms are in a better position to adopt IBPSbecause of their ability to invest in resources neededfor IBPS implementation. Length of relationship anddependency on trading partners are also likely to influ-ence IBPS adoption. Prior research has suggested andfound that when partners have a long-term relation-ship with each other, they are likely to make decisions(e.g., investments in relationship-specific assets) thatwill provide mutual benefits and ensure continuationof the relationship (Patnayakuni et al. 2006). Similarly,dependency on trading partners (i.e., the degree to whicha focal firm needs business-related resources and/orservices that its trading partners can provide) hasbeen found to be a critical determinant of investmentsin relationship-specific assets (Chwelos et al. 2001,Dyer and Singh 1998, Klein and Rai 2009). Building oninstitutional theory, prior research has suggested andfound that normative (i.e., the extent to which a focalfirm is influenced by norms shared through relationalchannels by firms in its environment), mimetic (i.e.,the extent to which a focal firm changes over time tobecome more like other firms in its environment), andcoercive (i.e., formal or informal pressures exerted ona focal firm by other firms in its environment uponwhich the focal firm is dependent) pressures signifi-cantly influenced EDI and IBPS adoption (Bala andVenkatesh 2007, Teo et al. 2003).

MethodContext and SampleWe collected dyadic data from firms in the high-tech industry that were considering adoption ofRosettaNet-based IBPS. Given the nature of themodel and relationships proposed, collecting dyadic(matched pair) data was essential for this study.We collected data from 124 clients and their part-ners (248 total firms) of an IT solutions provider thatprovides an integrated solution for RosettaNet-basedIBPS among others. Founded in 1998, RosettaNetis a nonprofit consortium aimed at facilitatingbusiness-to-business exchanges in the high-techindustry—e.g., electronic components, semiconductormanufacturing, and telecommunications. RosettaNetwas an appropriate setting for this study because itis one of the few industry consortia that is dedicated

to collaborative development and rapid deploymentof process standards known as PIPs. RosettaNet PIPsdefine business processes between trading partnersby specifying the activities, decisions, and roles foreach partner involved in a particular business activ-ity. Each PIP includes a document with business logic,message flow, message content, and a business pro-cess choreography (e.g., sequence of activities). In thehigh-tech industry, RosettaNet is considered the lead-ing industry standard for interorganizational businessprocesses (Zhao et al. 2005). Likewise, there are dif-ferent process standards for other industries. Thereare also industry-independent process standards (seeZhao et al. 2005). By focusing on a single IBPS in asingle industry, we were thus able to control for cer-tain organizational (e.g., product, market) and envi-ronmental (e.g., regulatory environment) variables.It is important to note that IBPS, such as Roset-taNet, do not replace IOS, such as SCM systems.IBPS specify business documents and choreographyfor interorganizational business processes, and thesespecifications are typically integrated with SCM andother enterprise systems to develop a comprehen-sive technology and process solution for interorgani-zational transactions.

Data Collection ProcedureOur data collection spanned about two years andcomprised four phases. Our data collection procedureis consistent with prior research that collected dyadicdata (i.e., Perrone et al. 2003). Given that the basicunit of IORs is a dyad, prior research has emphasizedthe importance of dyadic research design to examineissues related to IORs (Anderson et al. 1994, Kleinand Rai 2009, Straub et al. 2004). However, becauseof practical limitations associated with such researchdesign, it is often difficult to collect dyadic data (Kleinand Rai 2009). Some studies are based on data fromonly one side of an IOR, and some other studies havedyadic data holding one side of the dyad to be con-stant (Hart and Saunders 1997, Malhotra et al. 2005,Klein and Rai 2009, Zaheer et al. 1998). The goal of ourresearch design was to conduct a true dyadic study.

Our source company gave us a list of 3,000 clientfirms in the high-tech industry that were aware of andwere considering adopting RosettaNet-based IBPS.In the first phase of our data collection, we sent asurvey to senior executives in these firms who wereresponsible for supply chain processes. We askedthem to forward the survey to a senior manager whohad a boundary spanning role (e.g., purchase, supplychain, and/or client manager). The boundary span-ners were asked to select the fourth-largest partner(in terms of dollar amount of transactions made inthe past year) and answer the survey questions withrespect to this partner. Given that there was the poten-tial for selection bias as the boundary spanners might

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Figure 2 Data Collection Procedure

Phase 13,000 surveys sent to

nonadopters

Phase 2611 surveys sent to thefourth largest partners

of Phase 1 firms

Phase 3(Follow up with respondents

from Phases 1 and 2)

Phase 4(six months after

RosettaNet adoption)

Measures:

Independent variablesCycle timePartneringsatisfactionControl variables

Measures:

Independent variablesCycle timePartneringsatisfactionControl variables

Measures:

Adoption ofRosettaNet (whethera firm deployed anyPIPs or not)

Measures:Number of PIPsimplementedExtent of PIPs useCycle timePartneringsatisfaction

select a key partner with favorable relationships, thisapproach, consistent with Perrone et al. (2003), ofselecting the fourth-largest firm helped us control forselection bias because boundary spanners were notable to select their preferred key partner. We askedthe boundary spanners to provide the contact infor-mation of this key trading partner (i.e., the fourth-largest partner firm) and not to discuss this surveywith the contact in the partner company to reduceperceptions of coercion by the partner or other biases.We also asked them to consult relevant senior man-agers to answer questions pertinent to technologyreadiness, standards uncertainty, and organizationalinnovativeness. For example, the boundary spannerswere asked to consult the senior IT executives toanswer questions related to technology readiness andstandards uncertainty. This approach of getting inputfrom relevant employees to answer survey questionsis consistent with prior research (e.g., Malhotra et al.2005). We also encouraged them to hold a meet-ing with appropriate individuals and fill in the sur-vey during the meeting. We received 611 (about 20%response rate) responses in three months (after threereminders). We compared the characteristics of earlyand late respondents on key control variables andfound no differences.

In the second phase, we sent the same survey tothe fourth-largest partners (611 firms) using the con-tact information that was provided by the bound-ary spanners in the first phase and asked them toanswer the questions with respect to either the focalfirms from phase 1 or a key trading partner of theirchoice. We followed the exact same procedure thatwe followed in phase 1, i.e., the boundary spannerswere asked to consult relevant senior managers toanswer questions pertinent to technology readiness,standards uncertainty, and organizational innovative-ness. Respondents in the second phase were allowedto freely choose a key partner (i.e., we did not forcethem to choose the partner who mentioned themas a key partner in the first phase of data collec-tion). We received 401 usable responses in the second

phase. However, boundary spanners from 167 firmsdid not select the focal firms from phase 1 as theirkey partners while answering the survey questions.We excluded these firms from our analysis. Overall,we received 234 matched pair responses (468 totalfirms) from the first and second phases.

In the third phase, we collected RosettaNet adop-tion data from these 234 matched pair firms andreceived 190 matched pair responses. This phasespanned about six months and required constant con-tact with the firms to learn whether they had adoptedRosettaNet. In the fourth and final phase, which wasa survey conducted six months after the implementa-tion of PIPs, we sent a survey to the boundary span-ners with questions about RosettaNet PIPs adoptionand pre- and post-implementation outcomes. Giventhat firms implemented different RosettaNet PIPs, theoutcome data for cycle time (pre and post) was asso-ciated with specific business processes identified bythe respondents. In the case of the firms that didnot adopt any PIPs, they provided pre- and post-implementation data for an end-to-end business pro-cess of their choice. Our final sample was 124 matchedpairs (248 firms). Of these 124 pairs, 71 reportedRosettaNet PIP implementation. Figure 2 shows ourdata collection procedure.

As can be expected, there was attrition becauseof the need for matched-pair and follow-up data.We compared the firm characteristics (i.e., size andrevenue) between the firms in the final sample andthe firms contacted in each of the phases and didnot find any statistically significant differences. Thissample size is comparable to prior IOR researchemploying dyadic data—e.g., Perrone et al. (2003) had238 usable responses and 119 dyads. We also com-pared the firm characteristics (i.e., size and revenue)between firms that adopted and firms that did notadopt RosettaNet PIPs and did not find any signif-icant differences. The boundary spanners were typ-ically senior managers responsible for purchasing,supply chain, customer relationships, and marketing

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(76%). The remaining 24% were senior IT and financemanagers who were responsible for supply chain rela-tionships and interorganizational transactions. Of the248 total boundary spanners, 21% were women, theaverage age was 41.42 (standard deviation 5.51), andthe average organizational tenure was 8.9 years (stan-dard deviation 4.32).

MeasuresMeasures were adapted from prior research wher-ever possible. Given that TOE factors were primar-ily based on prior research, we were able to usepreviously validated items. We modified these itemsto fit with the IBPS context. We solicited feedbackon the modified items from senior managers of oursource company and from scholars to ensure face andcontent validities. Minor modifications were madebased on their feedback. We pilot tested the itemswith executive MBA students and found acceptablepsychometric properties. Appendix A presents infor-mation about the measures. We used a seven-pointLikert scale to measure all the constructs except IBPSadoption.

Dependent Variables. IBPS adoption was measuredas the product of the number of PIPs adopted andextent of use of the PIPs. Extent of use of PIPs wasassessed as the percentage of transactions conductedthrough PIPs. Consistent with Rai et al. (2009), thepercentage was captured in seven groups (e.g., none;1% to 15%; 16% to 30%; 31% to 45%; 46% to 60%;61% to 75%; 76% to 100%). The use of the productterm to assess IBPS adoption ensures robustness ofour measure because it captures two essential charac-teristics of IBPS adoption—i.e., extent of implementa-tion and extent of utilization. Focusing on these twoaspects independently will not provide a true pic-ture of IBPS adoption in a firm. For example, if afirm implements six PIPs, but only 5% of its trans-actions are done using these PIPs, this firm will notreceive as high a score as a firm that only imple-ments three PIPs but conducts 50% of its transactionsusing these PIPs. The outcomes of IBPS adoption weremeasured using existing measures. Consistent withHult et al. (2004), cycle time, a measure of operationalefficiency, was assessed based on number of busi-ness days to execute a business process using Roset-taNet PIPs. For the pre-implementation cycle time,respondents provided cycle time for the processesfor which they were considering adopting IBPS. Part-nering satisfaction, a measure of relationship qual-ity, was assessed using three items from Smith andBarclay (1997).

Independent Variables. Relational trust was mea-sured as a second-order formative construct with11 items adapted from McKnight et al. (2002).These items were grouped into three dimensions

of trust: ability, benevolence, and integrity. Consis-tent with Chin et al. (2003) and Diamantopoulosand Winklhofer (2001), we used a principal compo-nents analysis to determine factor scores for rela-tional trust from these 11 items that we used in ourdata analysis. Expected benefits items were adaptedfrom Chwelos et al. (2001). We modified these itemsto make the benefits appropriate for IBPS. Giventhat expected benefits represent anticipated benefitsfrom adopting IBPS, we included various aspectsof potential benefits (e.g., coordination, productiv-ity, cost and error reduction, information sharing)while operationalizing expected benefits. However,with respect to the actual outcomes of IBPS adop-tion, we measured cycle time and partnering satisfac-tion because these two outcomes essentially capturethe various aspects of expected benefits. For example,a reduction in cycle time indicates improved coordi-nation, greater productivity, reduced cost, and fewererrors. Similarly, partnering satisfaction is a reflection ofimproved coordination and better information shar-ing. Process compatibility items were adapted fromVenkatesh et al. (2003), who reviewed various con-structs related to technology adoption at the individ-ual level. We modified these items to fit with the IBPScontext to capture whether IBPS specifications werecompatible with existing work processes. We adaptedstandards uncertainty items from the technology uncer-tainty items used by Bensaou and Anderson (1999).Organizational innovativeness items were adapted fromHurley and Hult (1998), who developed five items tocapture innovativeness as an aspect of organizationalculture. Technology readiness items were developedbased on the conceptualization of technology readi-ness from Zhu et al. (2006b), who suggested twoaspects of readiness—i.e., technology infrastructureand IT human capital. We incorporated both aspectsin our technology readiness items.

Control Variables. Firm size was measured basedon total number of employees. Length of relationshipwas measured as the number of years that the part-ners were in a business relationship. We resolvedinconsistencies in partner reports by contacting bothfirms for clarification. Dependency on trading part-ner was measured using two items from Hart andSaunders (1998) and Chwelos et al. (2001). We mea-sured normative, mimetic, and coercive pressures usingitems adapted from Teo et al. (2003). We also con-trolled for pre-implementation levels of cycle timeand partnering satisfaction to understand the extent ofchange that can be attributed to IBPS adoption.

Data Analysis ApproachAlthough the research model shown in Figure 1 isan integrated model of IBPS adoption and impacts,it was tested in two phases to accommodate both

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firm-level (i.e., single-level) and dyadic (i.e., mul-tilevel) data in our analysis. In phase 1, we con-ducted dyadic data analysis following the guidelinesof Kenny et al. (2006), who suggested a two-stepapproach to dyadic analysis. In the first step, anassessment of dyadic dependence is performed. Thisstep helps us assess the degree of interdependencebetween the partners with respect to the data thatwere collected from both partners. The second step ismodel testing using an appropriate data analytic tech-niques. Several data analytic techniques are appropri-ate for dyadic data analysis (see Kenny et al. 2006):ANOVA, PROC MIXED (an SAS procedure), struc-tural equation modeling (SEM), and hierarchical lin-ear modeling (HLM). Each of these techniques hasstrengths and weaknesses. Kenny et al. (2006) sug-gested that HLM is the most appropriate approach fordyadic data analysis because of its ability to handledifferent levels of variables within a single analyti-cal model. Therefore, considering the multilevel andinterdependent nature of the data that we collected(individual firms nested in dyadic relationships), weused HLM and followed the data analytic approachoutlined in Campbell and Kashy (2002) and used inCampbell et al. (2005). HLM explicitly accounts forthe nested nature of the data and can simultaneouslyestimate the impact of factors at different levels onlower-level outcomes while maintaining appropriatelevels of analysis for the predictors (Raudenbush andBryk 2002). In our case, level 1 represents the individ-ual firms (N = 248) and level 2 represents the dyads(N = 124). The cross-level model that we tested isshown below:

Yij4IBPS adoption5 = �00 +�104firm-specific factors5

+�204partner-specific factors5

+�014synergistic factors5

+u0j + rij 0

We used HLM 6.0 to test this cross-level model(Raudenbush et al. 2004). Following the guidelines ofCampbell and Kashy (2002), we created two data filesfor nondistinguishable (or interchangeable) dyads forthe HLM analysis.3 Given that firms in our samplehad multiple roles (e.g., supplier or buyer) in theirsupply chains, we felt that it would be misleading ifwe merely treat them as supplier or buyer—hence,we considered them nondistinguishable from a dataanalysis point of view. This is a typical characteristic

3 Nondistinguishable or interchangeable dyads are those for whichthe actors and partners have no distinguishable characteristics thatare of theoretical or empirical importance. For example, a dyad ofmale roommates is nondistinguishable and a dyad of husband andwife is distinguishable.

in the high-tech industry where a firm (e.g., Intel orCisco) can be a supplier of a certain electronic com-ponents and a buyer of certain other components.We used restricted maximum likelihood estimationfor the analyses. Following convention, all the inde-pendent variables were grand mean centered (e.g.,Campbell and Kashy 2002).

In phase 2, we conducted regression analysis usingHLM 6.0 in which IBPS adoption was the indepen-dent variable and IBPS outcomes (i.e., cycle time andpartnering satisfaction) were the dependent variables.Before conducting any substantive analysis in bothphases, we tested for outliers, heteroscedasticity, andmulticollinearity in our data set and did not find anysignificant problems in this regard. Following Cohenet al. (2003), we conducted the Levene’s test for homo-geneity of variance and found that the dyads haveequal variances (p-value > 0005 for all the variables).We also checked the variance inflation factors (VIFs)of independent variables and interaction terms toensure that there was no multicollinearity and foundthat the VIFs ranged from 1.07 to 1.22, suggestingthat there was no threat of multicollinearity in ouranalysis.

ResultsTo test for validity of the scales, we conductedan exploratory factor analysis (oblimin rotation).Appendix B presents the results from factor analy-sis. Factor loadings were consistent with Rai et al.(2006) and Chwelos et al. (2001). All items loadedon the respective factors, thus indicating that thescales exhibited internal consistency and discrimi-nant validity. Table 2 presents the descriptive statis-tics and correlations for the entire sample (N = 248).Bivariate correlations among the variables were all inthe expected direction. The table also shows that thescales were reliable (Cronbach alpha > 0083). The cor-relations were not greater than the square root of theaverage variance extracted (AVE) for each variable,thus supporting discriminant validity.

Assessment of Dyadic DependenceTo assess the degree of dependence because of thedyad, we computed for each variable the intraclasscorrelation (ICC) that expresses the degree of dyadicdependence in a variable (Kashy and Kenny 2000).In other words, the ICC represents the amount vari-ance in a variable explained by the group mem-bership. An ICC close to zero indicates the absenceof dyadic dependence, a positive ICC means pos-itive dependence or similarity between dyads, anda negative ICC means negative dependence or dis-similarity between dyads. ICCs are also shown inTable 2. As shown in the table, except for organi-zational innovation, all variables have a significant

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Tabl

e2

Desc

riptiv

ean

dCo

rrel

atio

nM

atrix

(N=

248)

Cons

truct

sM

ean

S.D.

ICC

AVE

12

34

56

78

910

1112

1314

1516

17

1Fi

rmsi

ze1,

222

587

0009

NANA

2Le

ngth

of60

2130

55—

NA00

13∗

NAre

latio

nshi

p3

Depe

nden

cy40

4410

3800

25∗∗∗

0071

0024

∗∗∗

0008

NA4

Norm

ativ

e40

8010

4000

28∗∗∗

0070

−00

20∗∗

0013

∗00

13∗

NApr

essu

re5

Mim

etic

4028

1022

0030

∗∗∗

0075

−00

0700

16∗∗

0031

∗∗∗

0028

∗∗∗

NApr

essu

re6

Coer

cive

4040

1020

0034

∗∗∗

0070

−00

15∗

0005

0038

∗∗∗

0024

∗∗∗

0017

∗∗

NApr

essu

re7

Rela

tiona

l30

6010

1700

31∗∗∗

0074

0008

0017

∗∗

−00

18∗∗

0014

∗00

17∗∗

0008

NAtru

st8

Expe

cted

3004

1028

0051

∗∗∗

0078

001

0015

∗00

17∗∗

0003

0016

∗00

0500

28∗∗∗

0092

bene

fits

9Pr

oces

s30

7110

2000

28∗∗∗

0079

0004

0014

∗00

0700

16∗

0017

∗∗

0008

0004

0032

∗∗∗

0089

com

patib

ility

10St

anda

rds

3072

0098

0027

∗∗∗

0070

0007

0002

0004

−00

15∗

−00

19∗∗

0004

−00

16∗

−00

13∗

−00

0100

91un

certa

inty

11Or

g.in

nova

t-40

2710

0400

1100

7400

15∗

0007

0005

0004

0003

0008

0014

∗00

1000

01−

0022

∗∗∗

0087

iven

ess

12Te

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logy

3020

1008

0012

†00

7300

14∗

0004

0003

0002

0004

0001

0011

0006

0004

−00

17∗∗

0025

∗∗∗

0083

read

ines

s13

IBPS

adop

tion

1909

470

7500

46∗∗∗

NA00

19∗∗

0017

∗∗

0016

∗∗

0024

∗∗∗

0020

∗∗∗

0021

∗∗∗

0035

∗∗∗

0036

∗∗∗

0040

∗∗∗−

0030

∗∗∗

0024

∗∗∗

0021

∗∗∗

NA14

Cycl

etim

e40

2320

21—

NA00

1000

14∗

0015

∗00

15∗

0013

∗00

1000

22∗∗∗

0017

∗∗

0019

∗∗

−00

0800

16∗

0008

0013

∗NA

(pre

)15

Partn

erin

g40

0110

80—

0080

0007

0016

∗00

14∗

0014

∗00

0800

16∗

0025

∗∗∗

0015

∗00

18∗∗

−00

0900

0800

1000

15∗

0033

∗∗∗

0084

sat.

(pre

)16

Cycl

etim

e30

5510

89—

NA00

24∗∗∗−

0013

∗−

0018

∗∗

0017

∗∗

0016

∗00

04−

0030

∗∗∗−

0028

∗∗∗−

0025

∗∗∗

0004

−00

17∗∗

−00

20∗∗∗−

0046

∗∗∗

0017

∗∗

−00

19∗∗

NA(p

ost)

17Pa

rtner

ing

4044

1060

—00

7500

22∗∗∗

0015

∗00

17∗∗

0019

∗∗

0002

0020

∗∗∗

0031

∗∗∗

0029

∗∗∗

0028

∗∗∗

0002

0020

∗∗∗

0023

∗∗∗

0046

∗∗∗

0015

∗00

15∗

−00

49∗∗∗

0086

sat.

(pos

t)

Note

s.Cr

onba

chal

pha

show

nin

diag

onal

s.NA

:Not

appl

icab

le,†p<

0010

,∗p<

0005

,∗∗p<

0001

,∗∗∗p<

0000

1.

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ICC, indicating that there are dyadic dependen-cies in the variables and a significant proportion ofvariance in the variables can be explained by thedyads. For example, IBPS adoption has an ICC of0.46 (p < 00001), which suggests that dyad mem-bers were similar with respect to their IBPS adoptionbehavior and 46% of the variance in IBPS adoptioncan be explained by dyads. Dyad membership wasnot able to explain significant variance in organiza-tional innovativeness, suggesting that partners in oursample were not very similar in terms of organiza-tional innovativeness as an aspect of organizationalculture. We believe that organizational innovativenessis a general aspect of a firm’s culture and is notassociated with any specific innovations that maybe pertinent to IORs. Further, it is an intraorganiza-tional characteristic (e.g., an internal capability) that

Table 3 Predicting IBPS Adoption

IBPS adoption

Independent variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9

Control variablesFirm size 0013∗ 0008 0002 0001 0007 0008 0002 0004 0002Length of relationship 0008 0004 0004 0004 0003 0006 0006 0005 0004Dependency 0017∗∗ 0009† 0005 0006 0006 0005 0009† 0007 0006Normative pressure 0016∗∗ 0013∗ 0014∗∗ 0015∗∗ 0013∗ 0014∗∗ 0013∗ 0012∗ 0012∗

Mimetic pressure 0008 0005 0000 0001 0001 0006 0002 0004 0000Coercive pressure 0016∗∗ 0011∗ 0011∗ 0009† 0012∗ 0013∗ 0009† 0011∗ 0008

Main effectsExpected benefits (firm) 0023∗∗∗ 0014∗∗ 0013∗

Process compatibility (firm) 0025∗∗∗ 0019∗∗ 0017∗∗

Standards uncertainty (firm) −0024∗∗∗ −0017∗∗ −0016∗∗

Technology readiness (firm) 0022∗∗∗ 0018∗∗ 0015∗∗

Organizational innovativeness (firm) 0014∗∗ 0005 0004Relational trust (firm) 0023∗∗∗ 0015∗∗ 0014∗∗

Expected benefits (partner) 0011∗ 0001 0001Process compatibility (partner) 0013∗ 0009† 0008†

Standards uncertainty (partner) −0018∗∗ −0011∗ −0011∗

Technology readiness (partner) 0014∗∗ 0001 0006Organizational innovativeness (partner) 0013∗ 0007 0004Relational trust (partner) 0014∗∗ 0006 0003

Synergistic effectsExpected benefits (firm) × Expected 0004 0001

benefits (partner)Process compatibility (firm) × Process 0016∗∗ 0014∗∗

compatibility (partner)Standards uncertainty (firm) × Standards 0024∗∗∗ 0019∗∗

uncertainty (partner)Technology readiness (firm) × Technology −0016∗∗ −0014∗∗

readiness (partner)Org. innovativeness (firm) × Org. 0004 0003

innovativeness (partner)Relational trust (firm) × Relational −0005 −0004

trust (partner)

R2 0012 0022 0026 0027 0025 0016 0021 0038 0052Adj-R2 0010 0019 0023 0024 0022 0013 0018 0036 0050ãR2 0010∗∗ 0014∗∗ 0015∗∗ 0013∗∗ 0004 0009∗ 0026∗∗∗ 0014∗∗

Note. N = 248 firms (124 dyads); †p < 0010. ∗p < 0005. ∗∗p < 0001. ∗∗∗p < 00001.

provides competitive advantage and growth. Firms,therefore, may not form IORs based on another firm’sinnovativeness.

Predicting IBPS AdoptionTable 3 provides the results related to IBPS adop-tion. As the table shows, four control variables—i.e.,firm size, dependency, normative pressure, and coercivepressure—were significant predictors of IBPS adoption,suggesting that firms are more likely to adopt IBPS ifthey are large and dependent on certain trading part-ners and if there are pressures from trading partnersto adopt IBPS. The control variables explained 12%of the variance in IBPS adoption (see Model 1 onTable 3). We theorized that six synergistic factors—i.e., expected benefits (H1), process compatibility (H2),standards uncertainty (H3), technology readiness (H4),

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organizational innovativeness (H5), and relational trust(H6)—would significantly influence IBPS adoption.

Table 3 shows that expected benefits did not have asynergistic effect on IBPS adoption. Thus, H1 was notsupported. However, expected benefits of a firm had asignificant direct effect on its IBPS adoption (b = 0013,p < 0005) in the final model (Model 9). As shown inTable 3, process compatibility, standards uncertainty, andtechnology readiness had significant synergistic (i.e.,interaction) effects on IBPS adoption (b = 0014, p < 0001for process compatibility; b = 0019, p < 0001 for stan-dards uncertainty; and b = −0014, p < 0001 for technologyreadiness), thus supporting H2, H3, and H4. Relationaltrust and organizational innovativeness did not have asignificant synergistic effect on IBPS adoption; thus,H5 and H6 were not supported. Relational trust had asignificant direct effect on its IBPS adoption (b = 0014,p < 0001) in the overall model (Model 9). Althoughfirm size, dependency, normative pressure, and coercivepressure had a significant influence on IBPS adoption inthe control variables-only model (Model 1), only nor-mative pressure (b = 0012, p < 0005) was significant inthe overall model (see Model 9). Overall, our modelexplained 52% of the variance in IBPS adoption.

We followed Cohen et al. (2003) to plot thesignificant interactions. Specifically, we plotted theHLM equation at conditional values of partner’s pro-cess compatibility, standards uncertainty, and technologyreadiness (one standard deviation above and below themean). As shown in Figure 3, at high levels of a part-ner’s process compatibility, the relationship between afocal firm’s process compatibility and IBPS adoption isstronger than at low levels of a partner’s process com-patibility. The figure also suggests that if a partner haslow process compatibility, a focal firm’s process compati-bility is unrelated to its IBPS adoption, suggesting thata trading partner’s high process compatibility providesan impetus for a focal firm to assess the critical role

Figure 3 Synergistic Effect of Process Compatibility on IBPS Adoption

4

5

6

Low process compatibility(firm)

High process compatibility(firm)

IBP

S a

dopt

ion

Low processcompatibility (partner)High processcompatibility (partner)

Figure 4 Synergistic Effect of Standards Uncertainty on IBPS Adoption

4

5

6

Low standards uncertainty(firm)

High standards uncertainty(firm)

IBP

S a

dopt

ion

Low standardsuncertainty (partner)

High standardsuncertainty (partner)

of process compatibility in making IBPS adoption deci-sion. Figure 4 shows the interaction plot for standardsuncertainty. Consistent with H3, the figure suggeststhat at low levels of a partner’s standards uncertainty,the relationship between a focal firm’s standards uncer-tainty and IBPS adoption is stronger. In particular, stan-dards uncertainty has a strong negative influence onIBPS adoption of a focal firm if its trading partnershave a low degree of standards uncertainty. If a partnerhas high standards uncertainty, a focal firm’s standardsuncertainty is unrelated to its IBPS adoption. Finally,Figure 5 shows the interaction plot for technology readi-ness. Consistent with H4, the figure shows that thepositive effect of technology readiness on IBPS adop-tion in a focal firm is stronger in the presence of lowtechnology readiness in its trading partners. If a trad-ing partner has high technology readiness, a focal firm’stechnology readiness is unrelated to IBPS adoption. Thisindicates that if a trading partner has high technologyreadiness, decision makers in a focal firm do not placemuch importance on technology readiness in determin-ing IBPS adoption because they expect that they willlikely to get technological support from the trading

Figure 5 Synergistic Effect of Technology Readiness on IBPS Adoption

4

5

6

Low technology readiness(firm)

High technology readiness(firm)

IBP

S a

dopt

ion

Low technologyreadiness (partner)High technologyreadiness (partner)

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Table 4 Impacts of IBPS Adoption

Outcomes of IBPS adoption

Cycle time Partnering satisfaction

Independent variables Model 1 Model 2 Model 1 Model 2

Firm size 0023∗∗∗ 0020∗∗∗ 0021∗∗∗ 0019∗∗∗

Length of relationship −0010 −0007 0013∗ 0010Dependency 0008 0005 0006 0004Cycle time (pre) 0017∗∗ 0014∗

Partnering satisfaction 0013∗ 0013∗

(pre)IBPS adoption −0038∗∗∗ 0040∗∗∗

R2 0010 0022 0011 0024ãR2 0012∗∗∗ 0013∗∗∗

Note. N = 248 firms; †p < 0010. ∗p < 0005. ∗∗p < 0001. ∗∗∗p < 00001.

partner to deploy IBPS. We further discuss these syn-ergistic effects in the discussion section.

Impacts of IBPS AdoptionTable 4 shows the association of IBPS adoption withtwo key outcomes—cycle time and partnering satisfac-tion. As expected, IBPS adoption had a significant neg-ative association with cycle time (b = −0038, p < 00001)and a positive association with partnering satisfaction(b = 0040, p < 00001), controlling for the control vari-ables and pre-implementation cycle time and partner-ing satisfaction, supporting H7 and H8. The negativeassociation with cycle time indicates that IBPS adop-tion reduced the number of business days neededto execute an interorganizational business process(e.g., order management) and the positive associationwith partnering satisfaction suggests that IBPS adop-tion improved the quality of relationship betweenthe partners. Firm size had a significant influence oncycle time (b = 0020, p < 00001) and partnering satisfac-tion (b = 0019, p < 00001), suggesting that large firmstypically have a long cycle time and these firmshave a greater level of satisfaction with their trad-ing partners.

Given that IBPS adoption is composed of number ofPIPs adopted and the extent of use, it is possible thatthese two variables have a differential impact on cycletime and partnering satisfaction. However, our analy-sis did not reveal any such difference. Particularly,we found that the association between both measuresof IBPS adoption and IBPS impacts remained virtu-ally identical. We further tested these relationshipsmaking IBPS adoption a formative construct and ran apartial least squares (PLS) structural model followingChin et al. (2003). The results remained unchanged(i.e., the PLS coefficients of first-order formative con-struct IBPS adoption were almost identical to the HLMcoefficients), suggesting that our measure of IBPSadoption is robust.

Table 5 Mediating Role of IBPS Adoption

DV: PartneringDV: Cycle time satisfaction

Model 1 Model 2 Model 3 Model 1 Model 2

Independent variablesProcess compatibility −0012∗

Standards uncertainty 0001Technology readiness −0012∗

Expected benefits 0008Relational trust 0012∗

MediatorIBPS adoption −0041∗∗∗ −0040∗∗∗ −0039∗∗∗ 0044∗∗∗ 0044∗∗∗

R2 0019 0019 0019 0019 0020

Notes. N = 248 firms; †p < 0010. ∗p < 0005. ∗∗p < 0001. ∗∗∗p < 00001.

Mediating Role of IBPS AdoptionWe followed the four-step approach proposed byBaron and Kenny (1986) to test for the mediationeffect of IBPS adoption on IBPS outcomes. The pur-pose of the first three steps is to establish that zero-order relationships among independent, dependent,and mediator variables exist. The correlation table(Table 2) and Tables 3 and 4 establish that signifi-cant zero-order relationships exist between TOE fac-tors and IBPS adoption, TOE factors and IBPS impacts,and IBPS adoption and IBPS impacts. Table 5 presentsthe fourth step of Barron and Kenny’s mediation test.As shown in Table 5, process compatibility and tech-nology readiness had significant effects on cycle timein the presence of IBPS adoption as a mediator. Stan-dards uncertainty did not have a significant effect.Thus, we found partial support for H9. Relational trusthad a significant effect on partnering satisfaction in thepresence of IBPS adoption. However, expected benefitsdid not have a significant effect. Thus, H10 was par-tially supported. Overall, these results suggest that inaddition to the direct influence on operational effi-ciency and relationship quality, IBPS adoption playsa key mediating role in the relationship between afocal firm’s technological capabilities, organizationalcharacteristics, and environmental factors and theseimportant outcomes.

DiscussionPrior research on IOS adoption has provided a richset of factors that firms consider while making adop-tion decisions. IBPS is a form of IOS that requiresa mutual adoption (e.g., a firm cannot use thesestandards unless one or more of its trading part-ner(s) implement the same IBPS). Our objective inthis research was to examine the joint influence of afocal firm’s and its partner’s factors on IBPS adop-tion. Building on the TOE framework, we developeda model of IBPS adoption and impact and theorized

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and tested the influence of synergistic (i.e., interac-tion between firm and partner factors) factors on IBPSadoption. We found that three synergistic factors, i.e.,interactions between a focal firm’s and its tradingpartner’s process compatibility, standards uncertainty,and technology readiness, had a significant influ-ence on IBPS adoption. We also found that expectedbenefits and relational trust had significant directeffects on IBPS adoption. IBPS adoption had a sig-nificant influence on two different organizational out-comes, i.e., operational efficiency measured as cycletime and relationship quality measured as partner-ing satisfaction. Further, IBPS adoption mediated theeffect of three TOE factors, i.e., process compatibil-ity, standards uncertainty, and technology readiness,on cycle time and the effect of expected benefitsand relational trust on partnering satisfaction. Thesefindings have important theoretical and practicalimplications.

Theoretical ContributionsWe drew on prior research on IOS adoption and usedthe popular TOE framework as a theoretical founda-tion to examine the concurrent impacts of a firm’sand its partner’s factors on IBPS adoption. Our keythesis was that given that IBPS adoption is a dyadicphenomenon, it will hinge not only on a focal firm’scharacteristics and inputs but also on its partner’scharacteristics and inputs (Wang and Zajac 2007). Ourresearch makes at least four key contributions to theIS literature. First, we theorized and empirically vali-dated the independent and joint influences of a focalfirm’s and its partner’s factors on IBPS adoption.Given that implementation of IBPS is a mutual andsynergistic activity, understanding the nature of thisinfluence is important for theory and practice (Gosainet al. 2003; Malhotra et al. 2005, 2007). To the bestof our knowledge, this is one of the first studies thatsimultaneously examined the influence of a firm’sand its partner’s factors on an IOS adoption. Sec-ond, we introduced two constructs that are relevantto IBPS adoption and implementation: process compat-ibility and standards uncertainty. Although we adaptedthese constructs from prior research (Fichman 2000,Rogers 1995), we conceptualized and operational-ized them from a process standards point of view.We found that these constructs had significant directand synergistic effects on IBPS adoption. Third, weidentified three different mechanisms, i.e., embedded-ness, learning, and influence mechanisms, for syn-ergistic effects and offered theoretical explanationsfor each of these mechanisms. Finally, we theorizedand tested the impact of IBPS adoption on two typesof outcomes: operational efficiency and relationshipquality. Although IBPS-developing consortia, such asRosettaNet, have been claiming positive impact of

process standards, this was one of first scientific vali-dations of the impact of IBPS implementation on theperformance and quality of IORs. We also theorizedand tested the mediating role of IBPS adoption in therelationships between TOE factors and organizationaloutcomes.

Building on interorganizational theories (i.e., inter-organizational networks theories, social informationprocessing theory, and institutional theory), we devel-oped a model to explain dyadic relationships inthe context of IBPS adoption and offered theo-retical mechanisms to explain why TOE factorswill independently and jointly influence IBPS adop-tion. We theorized that given the interdependentnature of relationship in interorganizational dyads(e.g., supplier-buyer or distributor-retailer), a focalfirm’s IOR-related decisions (e.g., IBPS adoption) willdepend not only on its characteristics and inputs butalso on its partner’s characteristics and inputs. Thethree theoretical mechanisms that we developed, i.e.,embeddedness, learning, and influence mechanisms,offer insights on how and why partner factors willinteract with a focal firm’s factors in predictingIBPS adoption. Although prior IOS adoption researchhas underscored the importance of partner factors(Chwelos et al. 2001, Hart and Saunders 1997), it doesnot offer theoretical justifications for and empiricalvalidation of synergistic effects between a focal firm’sand its partner’s factors in predicting and explainingIOS adoption. Our work thus extends prior researchthat primarily focuses on a focal firm’s factors byproviding theoretical mechanisms and empirical sup-port for the influence of partnering synergy on IBPSadoption.

Among the six TOE factors, we found that threetechnology factors were consistently significant acrossdifferent models (see Table 3): process compatibil-ity, standards uncertainty, and technology readiness.Compatibility has been found to be an importantdriver of innovation adoption (Rogers 1995), but ourfindings suggest that in the context of IBPS, a focalfirm’s adoption will depend not only its own pro-cess compatibility but also on the compatibility ofthese standards with its partner’s business processes.In particular, in the presence of high process compati-bility in a trading partner, a focal firm will place moreimportance on its own process compatibility whilemaking an IBPS adoption decision. If boundary span-ners of a trading partner perceive that their firm hashigh process compatibility, their counterparts from afocal firm will be aware of it through embeddednessand learning mechanisms. The focal firm will auditits own processes to assess the extent to which theseprocesses are compatible with IBPS. If the processesare compatible, decision makers will be confident ofmaking the IBPS adoption decision knowing that their

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trading partners also have high process compatibility.In contrast, if trading partners have low process com-patibility, a focal firm will deemphasize its own pro-cess compatibility and focus on other factors that aremore likely to ensure successful adoption and imple-mentation of IBPS.

We found a similar effect for standards uncertainty.If boundary spanners of a focal firm view that thefirm’s trading partners have a low standards uncer-tainly, it is more likely that the focal firm’s own viewof standards uncertainty will not play a substantivehindering role in making IBPS adoption. Further, lowlevels of standards uncertainty in a focal firm maynot lead to greater adoption of IBPS if trading part-ners have high levels of standards uncertainty. Priorresearch has suggested that a key reason for uncer-tainty is incomplete knowledge (Beckman et al. 2004).Firms attempt to cope with uncertainty with variousadaptation strategies and try to reduce uncertaintyin their environment (Bensaou and Anderson 1999).If a focal firm assesses that there is high standardsuncertainty associated with IBPS, one obvious adap-tation strategy is to not invest in IBPS. However, if itstrading partners view that IBPS are stable and thereis less uncertainty, a focal firm may attempt to getmore information from its trading partners and othersources to develop a more complete and accurateknowledge of uncertainty related to IBPS. In the end,if a focal firm adjusts its view of standards uncertaintyto a lower level, there is greater likelihood of adopt-ing IBPS because standards uncertainty is no longer ahindering force. In contrast, if a focal firm still viewsIBPS as having high levels of uncertainty, there isless of a likelihood of adopting them even if its trad-ing partners have low levels of standards uncertainty.These findings augment our current understandingof the role of uncertainty in relationship-specificinvestments by offering additional adaptation mech-anisms in which firms engage to cope withuncertainty.

The significance of technology readiness suggeststhat technological capabilities, such as technologyinfrastructure and human capital, are critical for afirm’s decision to adopt and deploy IBPS. This findingis consistent with prior research that found a signifi-cant influence of technology readiness on IOS adop-tion (Zhu et al. 2006a). However, we extend priorresearch by providing insights on the role of trad-ing partners’ technology readiness in IBPS adoptiondecisions at a focal firm. In particular, our findingssuggest that if trading partners have high technol-ogy readiness, a focal firm does not emphasize itsown technology readiness because it probably expectsthat the trading partners will offer technological assis-tance during IBPS implementation. In contrast, if trad-ing partners do not have high technology readiness,

a focal firm reevaluates its own technological capa-bilities and makes the IBPS adoption decision basedon this assessment. Our findings indicate that firmscomplement and support each other with respect totechnological capabilities during the process of IBPSadoption and implementation.

These findings and associated theoretical justifi-cations have important implications because muchprior research has studied the TOE factors from afocal firm’s perspective. We offer insights on howTOE factors have synergistic effects on IBPS adop-tion, i.e., how boundary spanners of a focal firmtake into consideration views of their counterpartswhile making IBPS adoption decisions. These findingsalso have implications for IOR theories that focus onresource similarity and resource complementarity asdrivers of relationship-specific investments and out-comes (Harrison et al. 2001, Wang and Zajac 2007).In particular, we found that the role of the factors thatare related to organizational routines and uncertaintyis consistent with the logic of resource similarity—i.e., firms that have a similar understanding of and/orposition in these factors are more likely to engage inrelationship-specific investments such as IBPS adop-tion (Wang and Zajac 2007). These factors capturebehaviors of a firm (e.g., what to do) that are boundby rules and customs and that do not change muchfrom one iteration to another. It is difficult to changethese factors because firms develop inertia and like tomaintain status quo. Process compatibility and stan-dards uncertainty are two such factors for whicha similarity in trading partners’ understanding andposition leads to greater IBPS adoption. In contrast,our findings indicate that the role of the factors thatare related to a firm’s competence and/or capabilitiesto perform a behavior (e.g., how to do) is consistentwith the logic of resource complementarity, suggest-ing that trading partners do not necessarily have tohave the same levels of competence and/or capabili-ties to invest in relationship-specific assets. It is morelikely that trading partners will help each other tomake relationship-specific innovations successful inthe event that a partner lacks in certain capabilities toimplement these innovations. These findings furtherour current understanding of resource similarity andcomplementarity in IORs.

Our findings extend prior research on IOS adop-tion that did not examine the influence of TOE factorson organizational outcomes. We found that exceptfor standards uncertainty, the other TOE factors,i.e., relational trust, expected benefits, process com-patibility, technology readiness, and organizationalinnovativeness, are associated with cycle time andpartnering satisfaction. We theorized and found sup-port for a partial mediation of IBPS in the relationshipbetween these factors and organizational outcomes.

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In particular, two technological factors, process com-patibility and technology readiness, influence cycletime directly and through IBPS adoption, suggestingthat having interorganizational processes compatiblewith industry standards and a high degree of tech-nological capabilities is not sufficient to reduce cycletime if a firm does not implement IBPS into valuechain activities. Our findings indicate that relationaltrust influences partnering satisfaction directly andthrough IBPS adoption. This suggests that althoughtrust with a trading partner is an important deter-minant of relationship quality, firms may be ableto develop a holistic understanding of relationshipquality if they implement relationship-specific inno-vations, such as IBPS, into their value chain. In otherwords, implementation of relationship-specific inno-vations operates as a mechanism through which rela-tional trust helps a focal firm improve relationshipquality with its trading partners.

This research contributes to the IOR literature intwo ways. First, it offers a set of factors and associatedmechanisms that lead to investments in assets thatare not relationship-specific (e.g., any firm can imple-ment IBPS because these are open standards) yet arepotentially beneficial for IORs. It suggests that eventhough firms may not consider IBPS a relationship-specific asset because these standards are typicallyopen standards developed by industry consortia, theystill consider partner characteristics and inputs a keyconsideration while making the adoption decision.Second, as noted earlier, this research is one of thefirst works to scientifically validate the impact of IBPSadoption on metrics related to processes and relation-ships. Specifically, we found that IBPS can lead toreduced cycle time and greater partnering satisfac-tion. Improvement in such areas will have strategicvalue in the long run.

Practical ImplicationsOur findings have important practical implications.First, boundary spanners in a firm considering theadoption of IBPS need to be aware of the factorsthat are important for IBPS adoption. They needto consider these factors not only from their ownperspectives but also from their partners’ perspec-tive. Understanding these factors from both perspec-tives may help them develop different strategies toinfluence their trading partners’ decision processeswith respect to IBPS adoption. Given the positiveimpacts of these standards on process and relation-ship performance (i.e., cycle time and partnering sat-isfaction), it is important that decision makers infirms that are considering the adoption collaboratestrategically with their trading partners to implementthese standards. As our findings suggest, the adop-tion depends on factors that are not only important

to a focal firm but also to its trading partners. Fur-ther, our findings indicate that the factors that deter-mine IBPS adoption also influence organizationaloutcomes directly and through IBPS adoption. Hence,boundary spanners should carefully assess these fac-tors and be aware of their trading partners’ positionsand views on the factors that we found significantin our study.

Our findings suggest that although certain factors(e.g., process compatibility and technology readiness)are important for IBPS adoption, it is possible thata focal firm with low levels of these factors canalso adopt IBPS under certain conditions. For exam-ple, if important trading partners have high processcompatibility, it is possible that a firm will adopt IBPSdiscounting its own levels of process compatibility.Similarly, boundary spanners reevaluate their percep-tions of standards uncertainty if important tradingpartners have low standards uncertainty. Overall, ourfindings suggest that boundary spanners in earlyadopters or firms that are actively considering imple-menting IBPS need to provide cues and signals totheir counterparts about their views about processcompatibility, standards uncertainty, and technologyreadiness. We believe that a focal firm will be recep-tive to these cues and signals because of three mecha-nisms we discussed earlier: embeddedness, learning,and influence mechanisms. Boundary spanners in afocal firm may perceive that their trading partnerswill be willing to offer support with respect to chal-lenges associated with process change and standardsuncertainty during IBPS implementation. This find-ing has important implications for IBPS implementa-tions and diffusion. Early adopters should be willingto offer technical and personnel support to their trad-ing partners to expedite the process of implementa-tion and diffusion.

We believe that standards-developing consortiashould focus on the factors that we found to be sig-nificant. Given that process compatibility, standardsuncertainty, and technology readiness were found tobe important, we suggest that standards-developingconsortia should focus on these factors in promo-tional events (e.g., seminars, workshops, and sym-posia) and training programs so that managers canhave a realistic understanding of how their exist-ing processes are different from or similar to IBPSspecifications, how the standards specifications maychange or evolve over time, and what technologicalinfrastructure and skills are necessary to implementthese standards. Vendors who offer IBPS solutionscan also leverage our findings by offering solutionsthat will help firms overcome issues related to pro-cess compatibility, standards uncertainty, and tech-nology readiness. For example, vendors can offerfree upgrade services if certain aspects of standards

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change over time. Standards developing consortiaand vendors can offer change management sup-port to help firms manage organizational changesfollowing IBPS implementation. We believe that ifa firm is able to implement IBPS by overcomingthe challenges associated with process compatibil-ity, standards uncertainty, and technology readinessbecause of the support from its partners, standards-developing consortia, and vendors, it is possible thatits trading partners will be willing to adopt IBPS assuggested in the synergistic effect model and gainfavorable outcomes from IBPS.

Limitations and Future ResearchOur findings should be interpreted in light of the lim-itations of this research. We believe that these limi-tations will open avenues for fruitful future research.Our first limitation is that we only included a fewfactors from the TOE framework. We included thesefactors based on their relevance in our context andon practical considerations (e.g., length of the survey).There are many other factors that can potentially beincluded in a model of IBPS adoption. Further, var-ious contingent and situational factors (e.g., indus-try and market characteristics, regulatory policies)can directly influence IBPS adoption or moderate theeffects of TOE factors on IBPS adoption. For example,firms that perceive their existing interorganizationalprocesses and systems to be inefficient and have lowsatisfaction with the current systems and IORs aremore likely to consider adopting IBPS. Further, firmdominance and power differences can play a criti-cal role in IBPS adoption (Bala and Venkatesh 2007).Although we controlled for dependency, it is possiblethat dominant and nondominant partners of a focalfirm will have a differential impact on a focal firm’sIBPS adoption. Future research can incorporate theseand other factors and helps us develop a comprehen-sive understanding of IBPS adoption. Also, we onlyincluded two outcomes of IBPS adoption (i.e., cycletime and partnering satisfaction). IBPS can poten-tially influence other important outcomes (e.g., eco-nomic performance) that can be examined in futureresearch. Related future work should employ othertheoretical models, e.g., TCE theory, resource-basedview (RBV) theory, agency theory, actor-network the-ory, and actor-partner interdependence model fromthe interpersonal relationship literature, and method-ological perspectives. For example, social networkanalysis can be conducted to understand whetherstructural characteristics of interorganizational net-works can influence IBPS adoption and impacts.

Our second limitation is that we only collected datafrom a single industry and in the context of onetype of process standards (i.e., RosettaNet). AlthoughRosettaNet is a leading IBPS in the high-tech industry,

there are many others types of IBPS for differentindustries (Zhao et al. 2005). The results may be differ-ent in other industries and for other standards. There-fore, a fruitful future research direction would be totest models of IBPS adoption and impacts in con-text of other industries and standards. Another lim-itation is that we collected data from one boundaryspanner from an organization who answered ques-tions about organizational characteristics based on hisor her perceptions. Although we encouraged inputsfrom other key organizational members, it is pos-sible that the respondent did not incorporate theseinputs and provided inaccurate or biased assessmentof organizational characteristics. Data can be collectedin future research from multiple senior executivesto get an accurate assessment of a firm’s charac-teristics. A final limitation of this research is thatwe treated each factor separately and did not theo-rize and test for interaction among the factors. Forexample, it is possible that in the presence of highstandards uncertainty (partner), relational trust (focalfirm) will have a stronger effect on IBPS adoption.Further, it is possible that IBPS adoption will moder-ate the relationship between TOE factors and organi-zational outcomes. We did not theorize and test forthese interaction effects to reduce complexity in ourmodel and to establish a theoretically strong base-line model that can be leveraged in future researchto examine these effects on IBPS adoption and subse-quent firm performance.

ConclusionsIBPS have created a new opportunity for firms toextend organizational boundaries and create effectiveIORs leveraging IT and open process specifications.However, a focal firm can only benefit from IBPS ifthey are simultaneously adopted by its trading part-ners. We suggested that IBPS adoption will hinge notonly on a focal firm’s characteristics and inputs butalso on its trading partners’ characteristics and inputs.However, current theories of IOS adoption do notallow for us to simultaneously examine factors from afocal firm and its trading partners’ perspectives. Also,there is little scientific evidence of the impact of IBPSadoption on organizational outcomes. We developeda model that incorporates factors that are synergisti-cally important to a focal firm and its partner whilemaking IBPS adoption decisions and the outcomesof IBPS adoption. We found that synergistic factorswere significant determinants of IBPS adoption overand above firm-level factors. We also found that IBPSadoption led to greater operational efficiency and rela-tionship quality. Our findings have implications forfirms that are increasingly relying on effective IORs tocreate unique value for their customers and maximizestakeholders’ benefits.

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Appendix A. Measures

Relational trustAbility

1. Our business partner is competent and effective in its interactions with our organization.2. Our business partner performs all of its roles very well.3. Overall, this business partner is capable and proficient.4. In general, this business partner is knowledgeable about its industry and business operations.

Benevolence1. Our organization believes that this business partner would act in our best interest.2. If our organization required help, this business partner would do its best to provide assistance.3. This business partner is interested in our organization’s well-being and not just its own.

Integrity1. This business partner is truthful in its dealings with our organization.2. Our organization would characterize this business partner as being honest.3. This business partner keeps its commitments.4. This business partner is sincere and genuine.

Expected benefits1. RosettaNet PIPs will improve coordination with our trading partners.2. RosettaNet PIPs will increase productivity in our value chain.3. RosettaNet PIPs will help us reduce cost in our value chain.4. RosettaNet PIPs will improve customer services.5. RosettaNet PIPs will reduce error rates in business-to-business transactions.6. RosettaNet PIPs will improve information sharing with trading partners.

Process compatibility1. RosettaNet PIPs are compatible with our current ways of doing things.2. RosettaNet PIPs will fit well with our existing work processes.3. We have to change our current processes to be compliant with RosettaNet. (R)4. RosettaNet PIPs do not contradict with our current business-to-business transactions.

Standards uncertainty1. RosettaNet PIPs are still going through frequent changes.2. We cannot predict the future of RosettaNet as the de facto standards in our industry.3. RosettaNet PIPS still require changes to be more efficient.4. I am not sure if RosettaNet will be the only standard in our industry.

Organizational innovativeness1. My organization readily accepts innovations based on research results.2. Management in my organization actively seeks innovative ideas.3. Innovation is readily accepted in this organization.4. People are penalized for new ideas that don’t work. (R)5. Innovation in this organization is perceived as too risky and is resisted. (R)

Technology readiness1. Our current systems will support PIPs that we need for our business.2. We have the IT infrastructure that we need to implement RosettaNet PIPs.3. We have in-house expertise to implement RosettaNet PIPs.

IBPS adoptionProduct of: Number of PIPs adopted and extent of use of PIPs.

Cycle timeNumber of business days to execute a supply chain business process (e.g., order management).

Partnering satisfaction1. Overall, my organization is very satisfied with our relationship with this partner.2. Compared to other relationships I’ve known or heard about, the relationship with this partner is quite good.3. We are happy with our working relationship with this partner.

Normative pressureWhat is the extent of RosettaNet PIP adoption by your firm’s partners currently?Scale: “none has adopted” to “all have adopted” (seven-point Likert scale)

Mimetic pressureWhat is the extent of RosettaNet PIP adoption by your firm’s competitors currently?Scale: “none has adopted” to “all have adopted” (seven-point Likert scale)

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Appendix A. Continued

Coercive pressureWhat is the extent of pressure from your key partners to adopt RosettaNet PIPs?Scale: “no pressure” to “strong pressure” (seven-point Likert scale)

Dependency1. What was the dollar value of your company’s sales (or purchase) to (from) this partner last year/what were your

company’s annual revenues (or cost of goods sold) last year?2. Rate the importance of this partner: (not at all important to very important)

∗Seven-point Likert type scale used for all constructs except dependency and IBPS adoption.(R) = Reverse scored.

Appendix B. Factor Analysis

Constructs Items 1 2 3 4 5 6 7 8 9 10 11 12 13

1. Normative 1 0.80 0.37 0.37pressure

2. Mimetic pressure 1 0.78 0.383. Coercive pressure 1 0.40 0.42 0.804. Dependency 1 0.38 0.71 0.38

2 0.40 0.69 0.395. Relational trust: 1 0.71 0.40 0.35

Ability 2 0.73 0.41 0.403 0.75 0.38 0.384 0.70 0.37 0.39

6. Relational trust: 1 0.41 0.69 0.38Benevolence 2 0.43 0.71 0.37 0.43

3 0.44 0.73 0.407. Relational trust: 1 0.40 0.41 0.70

Integrity 2 0.41 0.40 0.713 0.37 0.39 0.74 0.414 0.38 0.37 0.68

8. Expected 1 0.74benefits 2 0.77 0.38

3 0.43 0.804 0.44 0.69 0.395 0.39 0.67 0.396 0.73

9. Process 1 0.71compatibility 2 0.40 0.80 0.38

3 0.80 0.394 0.40 0.74

10. Standards 1 0.73uncertainty 2 0.82

3 0.704 0.70

11. Organizational 1 0.39 0.68 0.40innovativeness 2 0.70 0.42

3 0.37 0.71 0.434 0.735 0.72

12. Technology 1 0.38 0.71readiness 2 0.39 0.70

3 0.40 0.7013. Partnering 1 0.38 0.73

satisfaction 2 0.37 0.803 0.39 0.82

∗Cross-loadings below 0.35 are not shown.

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ReferencesAnderson, J. C., H. Håkansson, J. Johanson. 1994. Dyadic business

relationships within a business network context. J. Marketing58(4) 1–15.

Bala, H., V. Venkatesh. 2007. Assimilation of interorganizationalbusiness process standards. Inform. Systems Res. 18(3) 340–362.

Baron, R. M., D. A. Kenny. 1986. The moderator-mediator variabledistinction in social psychological research: Conceptual, strate-gic and statistical considerations. J. Personality Soc. Psych. 51(6)1173–1182.

Barringer, B. R., J. S. Harrison. 2000. Walking a tightrope: Creatingvalue through interorganizational relationships. J. Management26(3) 367–403.

Barua, A., P. Konana, A. B. Whinston, F. Yin. 2004. An empiricalinvestigation of net-enabled business value. MIS Quart. 28(4)585–620.

Beckman, C. M., P. R. Haunschild, D. J. Phillips. 2004. Friends orstrangers? Firm-specific uncertainty, market uncertainty, andnetwork partner selection. Organ. Sci. 15(3) 259–275.

Bensaou, M., E. Anderson. 1999. Buyer-supplier relations in indus-trial markets: When do buyers risk making idiosyncraticinvestments? Organ. Sci. 10(4) 460–481.

Campbell, L., D. A. Kashy. 2002. Estimating actor, partner, andinteraction effects for dyadic data using PROC MIXED andHLM: A guided tour. Personal Relationships 9(3) 327–342.

Campbell, L., J. A. Simpson, J. G. Boldry, D. A. Kashy. 2005. Percep-tions of conflict and support in romantic relationships: The roleof attachment anxiety. J. Personality Soc. Psych. 88(3) 510–531.

Cartwright, J., J. Hahn-Steichen, J. He, T. Miller. 2005. Roset-taNet for Intel’s trading entity automation. Intel Techn. J. 9(3)239–246.

Chang, H. M., J. Shaw. 2009. The business value of process sharingin supply chains: A study of RosettaNet. Internat. J. Electron.Commerce 14(1) 115–145.

Chen, P., C. Forman. 2006. Can vendors influence switching costsand compatibility in an environment with open standards?MIS Quart. 30(Special Issue) 541–562.

Chin, W. W., B. L. Marcolin, P. R. Newsted. 2003. A partial leastsquares latent variable modeling approach for measuring inter-action effects: Results from a Monte Carlo simulation studyand an electronic-mail emotion/adoption study. Inform. Sys-tems Res. 14(2) 189–217.

Cho, H., V. Pucik. 2005. Relationship among innovativeness, qual-ity, growth, profitability, and market value. Strategic Manage-ment J. 26(6) 555–575.

Chwelos, P., I. Benbasat, A. S. Dexter. 2001. Research report: Empir-ical test of an EDI adoption model. Inform. Systems Res. 12(3)305–321.

Cohen, J., P. Cohen, S. G. West, L. S. Aiken. 2003. Applied MultipleRegression/Correlation Analysis for the Behavioral Sciences, 3rd ed.Lawrence Erlbaum Associates, Mahwah, NJ.

Daft, R. L., K. E. Weick. 1984. Toward a model of organiza-tions as interpretation systems. Acad. Management Rev. 9(2)284–295.

David, P. A., S. Greenstein. 1990. The economics of compatibilitystandards: An introduction to recent research. Econom. Innova-tions New Techn. 1(1/2) 3–41.

Depietro, R., E. Wiarda, M. Fleischer. 1990. The context for change:Organization, technology and environment. L. G. Tornatzky,F. Mitchell, eds. The Processes of Technological Innovation. Lex-ington Books, Lexington, MA, 151–175.

Diamantopoulos, A., H. M. Winklhofer. 2001. Index constructionwith formative indicators: An alternative to scale development.J. Marketing Res. 38(2) 269–277.

DiMaggio, P., W. W. Powell. 1983. The iron cage revisited: Institu-tional isomorphism and collective rationality in organizationalfields. Amer. Sociol. Rev. 48(2) 147–160.

Dyer, J. H., H. Singh. 1998. The relational view: Cooperative strat-egy and sources of interorganizational competitive advantage.Acad. Management Rev. 23(4) 660–679.

Farhoomand, A. F., P. Ng. 2000. FedEx Corp. Structural trans-formation through e-business. Harvard Business School, CaseHKU098. Harvard Business School Press, Boston.

Feldman, M. S., B. T. Pentland. 2003. Reconceptualizing organiza-tional routines as a source of flexibility and change. Admin. Sci.Quart. 48(1) 94–118.

Fichman, R. G. 2000. The diffusion and assimilation of informa-tion technology innovations. R. W. Zmud, ed. Framing TheDomains of IT Management: Projecting the Future Through the Past.Pinnaflex Press, Cincinnati, OH, 105–128.

Gilbert, C. G. 2005. Unbundling the structure of inertia:Resource versus routine rigidity. Acad. Management J. 48(5)741–763.

Gosain, S., A. Malhotra, O. A. El Sawy, F. Chehade. 2003. Towardsfrictionless e-business: The impact of common electronic busi-ness interfaces. Comm. ACM. 46(12) 186–195.

Grover, V., K. Saeed. 2007. The impact of product, market, andrelationship characteristics on inter-organizational system inte-gration in manufacturer-supplier dyads. J. Management Inform.Systems 23(4) 185–217.

Hagedoorn, J. 2006. Understanding the cross-level embeddednessof interfirm partnership formation. Acad. Management Rev. 31(3)670–680.

Harrison, J. S., M. A. Hitt, R. E. Hoskisson, R. D. Ireland. 2001.Resource complementarity in business combinations: Extend-ing the logic to organizational alliances. J. Management 27(6)679–690.

Hart, P., C. Saunders. 1997. Power and trust: Critical factors in theadoption and use of electronic data interchange. Organ. Sci.8(1) 23–42.

Hart, P. J., C. S. Saunders. 1998. Emerging electronic partnerships:Antecedents and dimensions of EDI use from the supplier’sperspective. J. Management Inform. Systems 14(4) 87–111.

Hult, G. T. M., D. J. Ketchen, S. F. Slater. 2004. Information pro-cessing, knowledge development and strategic supply chainperformance. Acad. Management J. 47(2) 241–253.

Hurley, R. F., G. T. M. Hult. 1998. Innovation, market orienta-tion, and organizational learning: An integration and empiricalexamination. J. Marketing 62(3) 42–54.

Iacovou, C. L., I. Benbasat, A. S. Dexter. 1995. Electronic datainterchange and small organizations: Adoption and impact oftechnology. MIS Quart. 19(4) 465–485.

Jap, S. D., E. Anderson. 2007. Testing a life-cycle theory of coopera-tive interorganizational relationships: Movement across stagesand performance. Management Sci. 53(2) 260–275.

Kahneman, D., A. Tversky. 1973. On the psychology of prediction.Psych. Rev. 80(4) 237–251.

Kashy, D. A., D. A. Kenny. 2000. The analysis of data fromdyads and groups. H. T. Reis, C. M. Judd, eds. Handbook ofResearch Methods in Social Psychology. Cambridge UniversityPress, New York, 451–477.

Kenny, D. A., D. A. Kashy, W. Cook. 2006. The Analysis of DyadicData. Guilford Press, New York.

Kim, T., H. Oh, A. Swaminathan. 2006. Framing interorganizationalnetwork change: A network inertia perspective. Acad. Manage-ment Rev. 31(3) 704–720.

Klein, R. A. Rai. 2009. Interfirm strategic information flows in logis-tics supply chain relationships. MIS Quart. 33(4) 735–762.

Kobelsky, K. W., V. J. Richardson, R. E. Smith, R. W. Zmud. 2008.Determinants and consequences of firm information technol-ogy budgets. Accounting Rev. 83(4) 957–995.

Krishnan, R., X. Martin, N. G., Noorderhaven. 2006. When doestrust matter to alliance performance? Acad. Management J. 49(5)894–917.

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Malhotra, A., S. Gosain, O. A. El Sawy. 2005. Absorptive capacityconfigurations in supply chains: Gearing for partner-enabledmarket knowledge creation. MIS Quart. 29(1) 145–187.

Malhotra, A., S. Gosain, O. A. El Sawy. 2007. Leveraging standardelectronic business interfaces to enable adaptive supply chainpartnerships. Inform. Systems Res. 18(3) 260–279.

Markus, M. L., C. W. Steinfield, R. T. Wigand, R. T., G. Minton. 2006.Industry-wide information systems standardization as collec-tive action: The case of the U.S. residential mortgage industry.MIS Quart. 30(Special Issue) 439–465.

McKnight, D. H., V. Choudhury, C. Kacmar. 2002. Developingand validating trust measures for e-commerce: An integrativetypology. Inform. Systems Res. 3(3) 334–359.

Mukhopadhyay, T., S. Kekre, S. Kalathur. 1995. Business value ofinformation technology: A study of electronic data interchange.MIS Quart. 19(2) 137–157.

Nelson, R., S. Winter. 1982. An Evolutionary Theory of EconomicChange. Harvard University Press, Cambridge, MA.

Nelson, M., M. Shaw, W. Qualls. 2005. Interorganizational systemstandards development in vertical industries. Electronic Markets15(4) 378–392.

Oliver, C. 1997. Sustainable competitive advantage: Combininginstitutional and resource-based views. Strategic Management J.18(9) 697–713.

Palmatier, R. W., R. P. Dant, D. Grewal. 2007. A comparative lon-gitudinal analysis of theoretical perspectives of interorganiza-tional relationship performance. J. Marketing 71(4) 172–194.

Patnayakuni, R., A. Rai, N. Seth. 2006. Relational antecedents ofinformation flow integration for supply chain coordination.J. Management Inform. Systems 23(1) 13–49.

Perrone, V., A. Zaheer, B. McEvily. 2003. Free to be trusted? Organi-zational constraints on trust in boundary spanners. Organ. Sci.14(4) 422–439.

Premkumar, G., K. Ramamurthy, N. Sree. 1994. Implementa-tion of electronic data interchange: An innovation diffusionperspective. J. Management Inform. Systems 11(2) 157–186.

Porter, P. 2001. Deciphering RosettaNet. CIO Insight (July 1).Rai, A., P. Brown, X. Tang. 2009. Organizational assimilation of elec-

tronic procurement innovations. J. Management Inform. Systems26(1) 257–296.

Rai, A., R. Patnayakuni, N. Patnayakuni. 2006. Firm performanceimpacts of digitally enabled supply chain integration capabili-ties. MIS Quart. 30(2) 225–246.

Ramamurthy, K., G. Premkumar, M. R. Crum. 1999. Organiza-tional and interorganizational determinants of EDI diffusionand organizational performance: A causal model. J. Organ.Comput. Electronic Commerce 9(4) 253–285.

Raudenbush, S. W., A. S. Bryk. 2002. Hierarchical Linear Models:Applications and Data Analysis Methods. Sage, London.

Raudenbush, S. W., A. S. Bryk, R. Congdon. 2004. HLM 6 for Win-dows. [Computer software]. Scientific Software International,Inc., Lincolnwood, IL.

Ring, P. S., A. H. Van de Ven. 1992. Structuring cooperative rela-tionships between organizations. Strategic Management J. 13(7)483–498.

Rogers, E. 1995. Diffusion of Innovations. Free Press, New York.Roos, G. 2001. Time to check out benefits of RosettaNet. EE Times

(Sep 10).

RosettaNet. 2007. Accessed November 30, 2007, http://www.rosettanet.org.

Salancik, G. R., J. Pfeffer. 1978. A social information processingapproach to job attitudes and task design. Admin. Sci. Quart.23(2) 224–253.

Seabright, M. A., D. A. Levinthal, M. Fichman. 1992. Role of indi-vidual attachments in the dissolution of interorganizationalrelationships. Acad. Management J. 35(1) 122–160.

Smith, J. B., D. W. Barclay. 1997. The effects of organizationaldifferences and trust on the effectiveness of selling partnerrelationships. J. Marketing 61(1) 3–21.

Straub, D., A. Rai, R. Klein, 2004. Measuring firm performanceat the network level: A nomology of the impact of dig-ital supply networks. J. Management Inform. Systems 21(1)83–114.

Teo, H. H., K. K. Wei, I. Benbasat. 2003. Predicting inten-tion to adopt interorganizational linkages: An institutionalperspective. MIS Quart. 27(1) 19–49.

Tornatzky, L. G., M. Fleischer. 1990. The Processes of TechnologicalInnovation. Lexington Books, Lexington, MA.

Uzzi, B., R. Lancaster. 2003. Relational embeddedness and learning:The case of bank loan managers and their clients. ManagementSci. 49(4) 383–399.

Venkatesh, V., M. G. Morris, G. B. Davis, F. D. Davis. 2003. Useracceptance of information technology: Toward a unified view.MIS Quart. 27(3) 425–478.

Wailgum, T. 2006. Supply chain management: Integrationliberation. CIO Magazine (October 15).

Wang, L., E. J. Zajac. 2007. Alliance or acquisition? A dyadic per-spective on interfirm resource combinations. Strategic Manage-ment J. 28(13) 1291–1317.

Williamson, O. E. 1995. Transaction cost economics and organiza-tion theory. O. E. Williamson, ed. Organization Theory: FromChester Barnard to the Present and Beyond. Oxford UniversityPress, New York, 207–256.

Zaheer, A., B. McEvily, V. Perrone. 1998. Does trust matter? Explor-ing the effects of interorganizational and interpersonal trust onperformance. Organ. Sci. 9(2) 141–159.

Zhao, K. M. Xia, M. Shaw. 2005. Vertical e-business standards andstandards developing organizations: A conceptual framework.Electronic Markets 15(4) 289–300.

Zhu, K., K. L. Kraemer, S. Xu. 2003. E-business adoption by Euro-pean firms: A cross-country assessment of the facilitators andinhibitors. Eur. J. Inform. Systems 12(4) 251–268.

Zhu, K., K. L. Kraemer, S. Xu. 2006a. The process of innovationassimilation by firms in different countries: A technology dif-fusion perspective. Management Sci. 52(10) 1557–1576.

Zhu, K., K. L. Kraemer, V. Gurbaxani, S. Xu. 2006b. Migrationto open-standard interorganizational systems: Network effects,switching costs, and path dependency. MIS Quart. 30(SpecialIssue) 515–539.

Zhu, K., K. L. Kraemer, S. Xu, J. Dedrick. 2004. Information technol-ogy payoff in e-business environments: An international per-spective on value creation of e-business in the financial servicesindustry. J. Management Inform. Systems 21(1) 17–54.

Zollo, M., J. J. Reuer, H. Singh. 2002. Interorganizational rou-tines and performance in strategic alliances. Organ. Sci. 13(6)701–713.

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