%0 Journal Article %J Technology Innovation Management Review %D 2020 %T The Effect of Machine Learning on Knowledge-Intensive R&D in the Technology Industry %A Daniel Viberg %A Mohammad H. Eslami %K artificial intelligence %K explicit knowledge %K knowledge integration %K ML %K tacit knowledge %K technological firm %X The impact of such current state-of-the-art technology as machine learning (ML) on organizational knowledge integration is indisputable. This paper synergizes investigations of knowledge integration and ML in technologically advanced and innovative companies, in order to elucidate the value of these approaches to organizational performance. The analyses are based on the premise that, to fully benefit from the latest technological advances, entity interpretation is essential to fully define what has been learned. Findings yielded by a single case study involving one technological firm indicate that tacit and explicit knowledge integration can occur simultaneously using ML, when a data analysis method is applied to transcribe spoken words. Although the main contribution of this study stems from the greater understanding of the applicability of machine learning in organizational contexts, general recommendations for use of this analytical method to facilitate integration of tacit and explicit knowledge are also provided. %B Technology Innovation Management Review %I Talent First Network %C Ottawa %V 10 %P 88-98 %8 03/2020 %G eng %U timreview.ca/article/1340 %N 3 %1 Linköping University Daniel Viberg has a M.Sc in Industrial Engineering and Management and a B.Sc in Mechanical Engineering from Linköping University in Sweden. He has experience in computer science from various spare time projects connected to both commercial and research purposes. %2 Jönköping University Mohammad H. Eslami is an Assistant professor at Jönköping International Business School in Sweden. His research interests are in the field of innovation management and knowledge integration. His research has been published in Industrial Marketing management, Journal of engineering and technology management, international journal of innovation management and etc. %& 88 %R http://doi.org/10.22215/timreview/1340