@article {1340,
	title = {The Effect of Machine Learning on Knowledge-Intensive R\&D in the Technology Industry},
	journal = {Technology Innovation Management Review},
	volume = {10},
	year = {2020},
	month = {03/2020},
	pages = {88-98},
	publisher = {Talent First Network},
	chapter = {88},
	address = {Ottawa},
	abstract = {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.},
	keywords = {artificial intelligence, explicit knowledge, knowledge integration, ML, tacit knowledge, technological firm},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1340},
	url = {timreview.ca/article/1340},
	author = {Daniel Viberg and Mohammad H. Eslami}
}
