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Proximity or alienation? Can knowledge type influence the relationship between proximity and enterprise innovation performance?

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  • Zhao, Shuliang
  • Wang, Junchen
  • Ji, Jiaojiao
  • Vincent Ekow, Arkorful

Abstract

Knowledge, a key element in innovation networks that affects innovation performance, provides ideas for further improvement. However, the effect of knowledge types on innovation performance has not been fully studied. On the basis of social network theory and absorptive capacity theory, this study analyzes the effects of knowledge type and absorptive capacity type on proximity and innovation performance. This research uses the PLS path analysis method to examine the antecedent variables of corporate innovation performance and their influence paths from innovation network and absorptive capacity theories. The results show that the influence of organizational and cognitive proximity on enterprise innovation performance is significant. Analytical knowledge regulates the relationship between proximity and exploitation, while synthetic knowledge regulates the relationship between proximity and transformation. The mediating role of realized absorptive capacity exists under specific circumstances. Further analysis reveals that transformation, one of the dimensions of realized absorptive capacity, can fully mediate the relationship between cognitive proximity and firm innovation performance only when the degree of analyticity and synthesis is low. The conclusion of this study suggests that firms with different knowledge types need to take targeted measures to enhance transformation or exploitation to achieve higher innovation performance.

Suggested Citation

  • Zhao, Shuliang & Wang, Junchen & Ji, Jiaojiao & Vincent Ekow, Arkorful, 2024. "Proximity or alienation? Can knowledge type influence the relationship between proximity and enterprise innovation performance?," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:tefoso:v:202:y:2024:i:c:s0040162524001100
    DOI: 10.1016/j.techfore.2024.123314
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