| Autor: |
Daniel Viberg, Mohammad H. Eslami |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Technology Innovation Management Review, Vol 10, Iss 3, Pp 88-98 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1927-0321 |
| DOI: |
10.22215/timreview/1340 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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