Abstract
Open innovation ecosystems rely upon inter-organisational knowledge transfer to support co-creation. Despite the significance of this process, and an abundance of open innovation research, empirical investigation and discussion of diverse knowledge transfer conditions across open innovation ecosystems remains unaddressed within existing literature. Using a mixed-method approach, this study investigates how knowledge, firm, and partner-relationship characteristics affect the successful exchange of knowledge between ecosystem partners. Interpretive Structural Modelling was employed to ascertain expert opinions regarding the interrelations between the transfer conditions. The combinatory nature of these conditions, and their integration into solutions for success, was further explored utilizing fuzzy-set Qualitative Comparative Analysis. Results indicate that conditions for knowledge transfer success are highly interrelated and co-dependent. Limitations and implications are discussed.
Highlights
The rapid evolution of today’s business environment necessitates the acquisition and integration of diverse, novel capabilities, generated through ecosystem engagement [1]
Innovation-related outcomes have been analyzed by other scholars utilizing fuzzy-set Qualitative Comparative Analysis (fsQCA) [5] as well as additional novel techniques such as Interpretive Structural Modelling (ISM) [6]
In response to the first research question of this study, the application of ISM within the first phase of analysis highlighted that no condition possessed greater prominence
Summary
The rapid evolution of today’s business environment necessitates the acquisition and integration of diverse, novel capabilities, generated through ecosystem engagement [1]. Williams and Davies [4] utilize fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze knowledge transfer conditions for open innovation but fail to concretely ascertain their importance. Innovation-related outcomes have been analyzed by other scholars utilizing fsQCA [5] as well as additional novel techniques such as Interpretive Structural Modelling (ISM) [6]. Existing literature reveals that individual-level analyses remain unexplored [7]: Ritala, Kraus and Bouncken [8] call for the application of more novel techniques for ecosystem analyses. Against this backdrop, this research aims to assess ecosystem partner perceptions of the extent to which combinations of conditions are responsible for knowledge transfer success, in the context of innovation ecosystems. Are there multiple solutions for knowledge transfer success? Using the principles of complexity theory, this will be investigated through fsQCA
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