"The Influence of Knowledge Management Strategies on Product Innovation and the Moderating Role of Big Data Analytics Use"
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2024-07-03
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en
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This study was designed to answer the research question What is the impact of knowledge management strategies and the use of big data analytics on product innovation and how do SMEs in the manufacturing industry organize knowledge management strategies and big data analytics for product innovation?
In the literature, different relationships are found between a particular KM strategy and product innovation. A KM personalization strategy shares knowledge via personal communication, which mainly involves tacit knowledge, and is a means to create new knowledge (Cavusgil et al., 2003; Greiner et al., 2007; Hansen et al., 1999). New knowledge is necessary for radical PI (Darroch & McNaughton, 2002), since radical PI concerns innovation that is completely new – substantially different from the firm’s current product offerings. It is therefore expected that a KM personalization strategy has a positive effect on radical PI. Although this research does not confirm this relationship quantitatively, there seems to be a joint option amongst the interview respondents that for radical innovation, new knowledge is necessary, which is often retrieved from sources external to the firm, or that tacit knowledge is necessary, which exists in the firm’s knowledge workers. From the interviews it appears that a KM personalization strategy fosters product innovation. A KM personalization strategy at firms is much organized by knowledge workers who are put together, but there are more specific ways of practicing a KM personalization strategy, such as positioning switches and the master-apprentice principle.
With respect of Big Data Analytics (BDA), it is reasoned that firms using BDA show to have a stronger positive effect of a KM codification strategy on incremental PI. Firms with a focus on a KM codification strategy have the computerized system in place to store, distribute and access codified knowledge. Since BDA insights are in codified form, it allows it to be distributed via the firm’s codified ways. The insights from BDA can be the grounds for incremental PI, as is confirmed by interviewees who show to use the BDA insights for improvements to machines, that is, incremental PI. However, it does not follow that the interaction of BDA use with a KM codification strategy fosters incremental PI.
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Faculteit der Managementwetenschappen