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Resource-Based Product and Process Innovation Model: Theory Development and Empirical Validation

Author

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  • Young Sik Cho

    (College of Business, Jackson State University, Jackson, MS 39217, USA)

  • Kevin Linderman

    (Carlson School of Management, University of Minnesota, Minneapolis, MN 55455, USA)

Abstract

Using a resource-based approach to innovation, this study proposes an alternative innovation model to the existing market-based Product Life Cycle (PLC) model. In particular, this study explores how the attributes of corporate strategic resources affect product and process innovation patterns. This study covers the development of new theory, as well as the empirical validation of the innovation model proposed in this study. Based on survey data from 257 chief operations officers in the United States, the ‘resource-based innovation model’ was tested and validated through various analytical methods, such as clustering, discriminant, and structural equation modeling analysis. The main findings are that (1) companies relying on both knowledge-based resources (KBR) and property-based resources (PBR) tend to focus on product and process innovation at the same time, (2) companies relying heavily on KBR tend to centralize their efforts for process innovation rather than product innovation, (3) companies with low reliance on KBR and PBR tend to minimize R&D efforts in product and process innovation, and (4) in a dynamic market condition, process innovations serves as order winners, while product innovations serves as order winners in a stable market. This research contributes to the operational management literature by proposing a new resource-based innovation algorithm that helps to understand innovation phenomena that are difficult to explain through the lens of the PLC-based innovation paradigm.

Suggested Citation

  • Young Sik Cho & Kevin Linderman, 2020. "Resource-Based Product and Process Innovation Model: Theory Development and Empirical Validation," Sustainability, MDPI, vol. 12(3), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:913-:d:313263
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