1. Knowledge Transfer In Multi-Organizational Networks: Influence Of Causal and Outcome Ambiguities SEDSI Jennifer Lewis Priestley, Assistant Professor of Applied Mathematics February 24, 2005

2. Presentation Outline 1.Inter-Organizational Knowledge Transfer 2.The Role of Ambiguity in Knowledge Transfer a) Causal Ambiguity b) Outcome Ambiguity 3.Network Types 4.Research Model 5.Empirical Results 6.Conclusions and Implications

3. Inter-Organizational Knowledge Transfer Knowledge creates competitive advantage ( e.g., Hamel, Doz and Prahalad, 1989; Szulanski, 1996; Teece, 1998 ) Knowledge creates competitive advantage ( e.g., Hamel, Doz and Prahalad, 1989; Szulanski, 1996; Teece, 1998 ) Firms engage in networks, in part to access knowledge ( e.g., Madhavan, 1998; Gulati and Gargiulo, 1999 )… Firms engage in networks, in part to access knowledge ( e.g., Madhavan, 1998; Gulati and Gargiulo, 1999 )… …because research has demonstrated that network-membership is superior to independent operation for the purposes of knowledge access ( e.g., Argote, 1999; Dyer, 1997; Hamel, 1991 ) …because research has demonstrated that network-membership is superior to independent operation for the purposes of knowledge access ( e.g., Argote, 1999; Dyer, 1997; Hamel, 1991 )

4. Inter-Organizational Knowledge Transfer Specific examples of knowledge transfer studies within networks: Specific examples of knowledge transfer studies within networks: Powell et al., 1996 – study of biotech firms Powell et al., 1996 – study of biotech firms Darr et al., 1995 – study of pizza franchises Darr et al., 1995 – study of pizza franchises Ingram and Simons, 2002 – study of kibbutzim Ingram and Simons, 2002 – study of kibbutzim Postrel, 2002 – recounting of the 1999 Mars Climate Orbiter Postrel, 2002 – recounting of the 1999 Mars Climate Orbiter

5. Inter-Organizational Knowledge Transfer These very different networks experienced knowledge transfer very differently. These very different networks experienced knowledge transfer very differently. They accommodated different levels of competition, different types of governance structures and had different objectives. They accommodated different levels of competition, different types of governance structures and had different objectives. They also experienced “isolating mechanisms” ( Knott, 2004 ) of knowledge transfer – like ambiguity – differently. They also experienced “isolating mechanisms” ( Knott, 2004 ) of knowledge transfer – like ambiguity – differently.

6. Ambiguity and Knowledge Transfer Knowledge Transfer is “sticky” ( e.g., Von Hippel, 1994; Szulanski, 1996 ). Knowledge Transfer is “sticky” ( e.g., Von Hippel, 1994; Szulanski, 1996 ). A primary isolating mechanism is ambiguity ( e.g., Knott, 2004; Szulanski, 1996 ) A primary isolating mechanism is ambiguity ( e.g., Knott, 2004; Szulanski, 1996 ) Previous research has either addressed the well-established concept of causal ambiguity ( e.g., Mosakowski, 1997; Szulanski, 1996) or ambiguity in its most general form (e.g. Milliken, 1987 )… Previous research has either addressed the well-established concept of causal ambiguity ( e.g., Mosakowski, 1997; Szulanski, 1996) or ambiguity in its most general form (e.g. Milliken, 1987 )…

7. Ambiguity and Knowledge Transfer …however, no theoretical guidance exists that provides for an understanding of how ambiguity impedes (or enhances) inter-organizational knowledge transfer, and how different types of networks would be expected to experience ambiguities differently.

8. Causal Ambiguity Inputs X 1 X 2 X 3... X n Inputs X 1 X 2 X 3... X n Outcomes Outcome 1 Outcome 2 Outcome 3... Outcome n Outcomes Outcome 1 Outcome 2 Outcome 3... Outcome n Causal Factors Factor 1 Factor 2 Factor 3.. Factor n Causal Factors Factor 1 Factor 2 Factor 3.. Factor n

9. Causal Ambiguity Causal Ambiguity has been studied from two perspectives: Causal Ambiguity has been studied from two perspectives: Internal causal ambiguity ( e.g., Knott, 2005; Szulanski, 1996 ) Internal causal ambiguity ( e.g., Knott, 2005; Szulanski, 1996 ) External causal ambiguity ( e.g., Lippman and Rumelt, 1982; Wilcox-King and Zeithaml, 2001 ) External causal ambiguity ( e.g., Lippman and Rumelt, 1982; Wilcox-King and Zeithaml, 2001 ) Universally considered an isolating mechanism…however only studied in an intra-organizational or dyadic context. Universally considered an isolating mechanism…however only studied in an intra-organizational or dyadic context.

10. Causal Ambiguity H1: Causal Ambiguity will negatively affect knowledge transfer for firms operating within an inter-organizational network.

11. Outcome Ambiguity Outcome Ambiguity Other than “Causal”, the concept of ambiguity has been studied from a very generalized perspective Other than “Causal”, the concept of ambiguity has been studied from a very generalized perspective ( e.g., Milliken, 1987; Gerloff, et al., 1991 ). Organizations join large-scale multi- organizational networks, to cope with environmental uncertainties Organizations join large-scale multi- organizational networks, to cope with environmental uncertainties ( Gulati and Gargiulo,1999 )… …however, participation in a network may produce unintended consequences of increased uncertainty not captured in the extant literature …however, participation in a network may produce unintended consequences of increased uncertainty not captured in the extant literature.

12. Outcome Ambiguity Outcome Ambiguity …Specifically, there is ambiguity associated with the inability of the knowledge source to identify the possible outcome(s) associated with knowledge transfer. Particularly relevant in a multi-organizational environment. Two sources of this ambiguity:  “Unprovenness” of the knowledge in question (Szulanski, 1996)  Source/Recipient Relationship (e.g., Simonin, 1999; Hamel,1991)

13. Outcome Ambiguity - Typology Outcome Ambiguity - Typology Provenness of Knowledge High Low Knownness of Recipient’s Actions LowHigh Type 3 (Medium Outcome Ambiguity) Type 3 (Medium Outcome Ambiguity) Type 4 (High Outcome Ambiguity) Type 4 (High Outcome Ambiguity) Type 1 (Low Outcome Ambiguity) Type 1 (Low Outcome Ambiguity) Type 2 (Medium Outcome Ambiguity) Type 2 (Medium Outcome Ambiguity)

14. Outcome Ambiguity H2: Outcome Ambiguity will negatively affect knowledge transfer for firms operating within an inter-organizational network.

15. OutcomeAmbiguityOutcomeAmbiguity - -Inter-OrganizationalKnowledgeTransfer CausalAmbiguityCausalAmbiguity H1: H2: Partial Research Model

16. Network Types TCE – in a world of transaction costs, some governance forms are better than others (e.g. Williamson, 1973) TCE – in a world of transaction costs, some governance forms are better than others (e.g. Williamson, 1973) KBV – Firms organize to optimize deployment of resources…with knowledge recognized as a firm’s most important resource (e.g. Grant, 1997) KBV – Firms organize to optimize deployment of resources…with knowledge recognized as a firm’s most important resource (e.g. Grant, 1997) SNT – the “nodes” and “linkages” of networks should be understood to explain organizational behavior (e.g. Granovetter, 1985) SNT – the “nodes” and “linkages” of networks should be understood to explain organizational behavior (e.g. Granovetter, 1985)

17. Network Types Decentralized Centralized Low High Governance Structure Intensity of Competition Co-opetive Network Type Type Franchise Network Type Type

18. Network Types and Causal Ambiguity The Franchise Network The Franchise Network  Centralized governance structure expected to have authority to punish for non-compliance, establish branding, enforce image and quality controls, standardize customer experience, etc.  Common processes and experiences would be expected to lead to a common understanding of the inputs and factors needed to generate specific outcomes. The Co-opetive Network The Co-opetive Network  Although common operations, “external” causal ambiguity may actually be a goal of organizations engaged in an environment rife with the threat of opportunistic behavior and a decentralized governance lacking the authority to punish.

19. Network Type and Causal Ambiguity H3: Firms engaged in a co-opetive network will experience greater causal ambiguity than will firms engaged in a franchise network. H3a: Firms operating outside of a network will experience greater causal ambiguity than will firms engaged in a network.

20. Network Types and Outcome Ambiguity The Franchise Network The Franchise Network  The “shared destiny” (Kogut and Zander, 1996; Adler, 2001) within a franchise network would be expected to contribute to a high degree of “knownness” of the actions of the knowledge recipients.  The highly centralized governance structure would also be expected to contribute to a high degree of “knownness” of the actions of the knowledge recipients.  Common operational processes would be expected to contribute to a high degree of “knownness” of the application of the knowledge in question. The Co-opetive Network The Co-opetive Network  Because membership may be justified through shared risk/costs of research, knowledge in question may be proven or unproven.  Given the decentralization of authority and the potential for opportunistic behavior, the “knownness” of the actions of the knowledge recipients would be low.

21. Network Type and Outcome Ambiguity H4: Firms engaged in a co-opetive network will experience greater outcome ambiguity than will firms engaged in a franchise network. H4a: Firms operating outside of a network will experience greater outcome ambiguity than will firms engaged in a network.

22. H3a: M>C&F H4a: M>C&F H4: C>F H3: C>F Co-opetiveNetwork(C) FranchiseNetwork(F) OutcomeAmbiguityOutcomeAmbiguity - - Inter-OrganizationalKnowledgeTransfer CausalAmbiguityCausalAmbiguity Market-based Organizations (M) H1: H2: Comprehensive Research Model

23. “…there is a growing realization that the variables which are most theoretically Interesting are those which are least identifiable and measurable.” Spender and Grant (1996)

24. Research Summary Franchise Network selected = SunTrust Branch Network in Metropolitan Atlanta Area (n=165). Franchise Network selected = SunTrust Branch Network in Metropolitan Atlanta Area (n=165). Co-opetive Network selected = Credit Union National Association (CUNA) (n=~600 in research target). Surveys were pre-piloted and then piloted with some measurement items added or deleted as warranted. Total of 101 surveys received from CUNA (68 identifying as integrated members and 33 identifying as non- members) and 70 surveys received from SunTrust. Non-response bias testing performed. Common method bias testing performed. PLS used over SEM.

25. Empirical Results Network Ambiguity Factor(s) Path Coefficient (Model R2) t-value Hypothesis Supported? All (n=171) Causal Ambiguity Outcome Ambiguity.057.377 (.164) t=.814 t=4.13* Hypothesis 2 supported, but Hypothesis 1 not supported. Co- opetive (n=68) Causal Ambiguity Outcome Ambiguity.029.467 (.203) t=.713 t=2.78* Franchis e (n=70) Causal Ambiguity Outcome Ambiguity.147.563 (.395) t=.676 t=3.77*

26. Empirical Results Hypothesis Franchise Score Co-opetive Score Control Score Supported? Hypothesis 3 – Firms engaged in a co- opetive network will experience greater causal ambiguity than will firms engaged in a franchise network 3.553.95 N Hypothesis 3a – Firms operating outside of a network will experience greater causal ambiguity than will firms engaged in a network 4.48Y**/N Hypothesis 4 – Firms engaged in a co- opetive network will experience greater outcome ambiguity than will firms engaged in a franchise network 2.813.56 Y** Hypothesis 4a – Firms operating outside of a network will experience greater causal ambiguity than will firms engaged in a network 4.39Y***/Y* * significant at p<.1 ** significant at p<.05 *** significant at p<.01

27. Conclusions and Implications Study goal was to more specifically define and further develop the ambiguities that contribute to inter- organizational KT. Study goal was to more specifically define and further develop the ambiguities that contribute to inter- organizational KT. Because of the ambiguity gap between “general” discussions and causal ambiguity, we developed the concept of “outcome ambiguity”. Because of the ambiguity gap between “general” discussions and causal ambiguity, we developed the concept of “outcome ambiguity”. Although CA was found to be significant when tested as a single variable, it became insignificant to KT when OA was introduced, providing some proof for the need for its existence. There was no difference found to exist in CA between the two network types. OA was found to vary significantly between the two network types.

28. Questions? Comments? Discussion?