@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}
}
@article {1025,
	title = {Boundary Objects as Part of Knowledge Integration for Networked Innovation},
	journal = {Technology Innovation Management Review},
	volume = {6},
	year = {2016},
	month = {10/2016},
	pages = {25-36},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {Networked innovation in co-creation networks is not possible without collaborative practices. Especially in complex projects, contextual knowledge is often spread among different stakeholders. To harness this dispersed knowledge for networked innovation, working knowledge management and collaborative practices are needed. This article addresses this need for better understanding and approaches to facilitate knowledge integration for networked innovation. We consider knowledge integration as the ability to put knowledge into action, and networked innovation as the co-created goal-driven output of selected partners. Our study focuses on describing and reporting a cross-learning type of expert knowledge-integration process with boundary objects, concrete or abstract {\textquotedblleft}bridges{\textquotedblright} for overcoming possible knowledge boundaries, in a co-creation network. This article adds knowledge on networked innovation through knowledge integration with boundary objects. The reported process will help managers to systematically approach problems requiring expert knowledge that does not exist within their own organization and to better integrate knowledge required for innovation within their project networks.},
	keywords = {boundary object, co-creation network, cross-learning, knowledge integration, networked innovation},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1025},
	url = {http://timreview.ca/article/1025},
	author = {Sari M{\"a}enp{\"a}{\"a} and Anu Helena Suominen and Rainer Breite}
}
@article {1022,
	title = {Editorial: Insights (October 2016)},
	journal = {Technology Innovation Management Review},
	volume = {6},
	year = {2016},
	month = {10/2016},
	pages = {3-4},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {boundary objects, ecosystems, entrepreneurial university, entrepreneurship, growth ambitions, innovation, knowledge integration, startups, strategy, tacit knowledge, teaching},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1022},
	url = {http://timreview.ca/article/1022},
	author = {Chris McPhee}
}
