The Effect of Machine Learning on Knowledge-Intensive R&D in the Technology Industry

Abstract

The rapid pace of innovation in the context of new technology development has attracted significant attention of technology firms, as this offers potential for using these tools for knowledge integration as a means of creating and sustaining competitive advantage (Grant & Baden-Fuller, 2004). Previous research has shown that knowledge integration has great potential to accelerate innovation, since identifying and combining distributed knowledge can enhance the competitive advantage of firms, distinguishing them from their competitors (Carlile & Rebentisch, 2003; Yang, 2005). However, while firms acknowledge the advantages and necessity of knowledge integration, they typically face different difficulties in accessing distributed knowledge (Enberg et al., 2006; Schmickl & Kieser, 2008). In other words, integrating distributed knowledge is challenging, especially tacit knowledge, as this is knowledge gained through personal experience, making it difficult to transfer or codify.