Knowledge Management: The Value of Inter and Intra-firm Activities Towards Innovation Performance
Keywords:knowledge sharing, knowledge-based view, social network theory, channels of information, EU
Firms mainly depend on innovation to remain competitive in business. New knowledge is a major resource for firms on the path of achieving innovation performance. This paper seeks to examine how intra-firm and inter-firm activities promote knowledge sharing toward innovation performance. We used the variables channels of information to represent inter-firm activities and workplace organization methods to represent intra-firm knowledge-sharing activities. The proxies for innovation performance were product innovation and business process innovation. Cross-sectional country-level data from CIS 2018 for 17 countries within the EU region was used for our study. The OLS regression method was used for the analysis. While existing studies concentrate on inter-firm knowledge transfer and single country-level studies, our study focuses on a blend of intra-firm and intra-firm cross-country studies. We also emphasized the value of knowledge sharing and cognition in the knowledge transfer process toward achieving firm innovation performance. Our model was built on the knowledge-based view (KBV) and social network theory (SNT). We found that cross-functional workgroups, conferences, trade fairs and exhibitions have a positive significant impact on both product and business process innovation. Published patent had a significant impact on product innovation but was not statistically significant for business process innovation. In conclusion, we found that intra-firm knowledge-sharing activities impact more on innovation performance of firms more than inter-firm knowledge-sharing activities of firms in the EU. Our study is limited to using cross-sectional data and the number of countries within the EU involved in the study. We believe longitudinal data and the involvement of more EU countries in future studies will yield robust findings for more reliable inferences.
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