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In search of understanding about knowledge and learning on innovation performance

Author

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  • Angélica Pigola

    (Nove de Julho University)

  • Priscila Rezende Costa

    (Nove de Julho University)

Abstract

This paper addresses an analysis of the literature about knowledge and learning providing theorical and empirical clarification to the constructs and their effects on innovation performance. We take a two-pronged approach: (1) conceptual substance of the knowledge, learning and innovation performance dimensions in the literature and (2) summary of findings as of a meta-analytical analysis of empirical studies. The results show that knowledge dimension is hugely used generating the highest mean effect size estimates. Among innovation performance outcomes—product innovation, overall innovation, processes innovation and patents—the total effects of knowledge dimension are negative. Furthermore, learning dimension mediate positively the effect of knowledge dimension on innovation performance. This paper offers significant basis, theoretically and empirically, to progress into knowledge and learning dimensional approach in scientific research by showing knowledge itself does not seem as relevant without learning on innovation performance in favor of more stringent research for combinative and cumulative development of these topics. We recognize that our findings may be interrelated with contexts among studies of our sample by our selection criteria and different statistical biases.

Suggested Citation

  • Angélica Pigola & Priscila Rezende Costa, 2022. "In search of understanding about knowledge and learning on innovation performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3995-4022, July.
  • Handle: RePEc:spr:scient:v:127:y:2022:i:7:d:10.1007_s11192-022-04417-3
    DOI: 10.1007/s11192-022-04417-3
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    More about this item

    Keywords

    Knowledge; Learning; Meta-analysis; Innovation performance;
    All these keywords.

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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