Projects per year
The complexity technologies require that companies have in-depth knowledge of the nature and effect of knowledge - its depth and breadth. Companies need to master expanding technological knowledge bases creating tensions for MOT. We examine how big data in patent landscaping creates insights into MOT. Using big data to manage Competitive Technical Intelligence, companies can foster new forms of adaptive learning processes in MOT. This however requires that managers augment human judgment with machine-learning tools, prompting challenges to management traditions. We demonstrate how unsupervised learning creates insight into MOT by identifying topical knowledge foci and showing the dynamics of knowledge domains among companies. Using unsupervised learning and network analysis; we show how a semantic analysis leads to the identification of opportunities in complex environments. We illustrate this using a case in globally operating telecommunication companies using a full-text copy of USPTO-database with approximately 6 million patents data. Our results show the landscape of the companies and the underlying knowledge embedded in the companies. We discuss how managers can evaluate their technological knowledge against competitors, balancing current needs with the adoption of new knowledge. We further discuss how a semantic analysis can lead to the discovery of latent patterns and identification of opportunities.
|Title of host publication||Management of Engineering and Technology (PICMET), 2015 Portland International Conference on|
|Publisher||IEEE Institute of Electrical and Electronic Engineers|
|ISBN (Electronic)||978-1-8908-4331-1, 978-1-8908-4332-8|
|Publication status||Published - 24 Sep 2015|
|MoE publication type||A4 Article in a conference publication|
|Event||Portland International Conference on Management of Engineering and Technology, PICMET 2015 - Portland, United States|
Duration: 2 Aug 2015 → 6 Aug 2015
|Conference||Portland International Conference on Management of Engineering and Technology, PICMET 2015|
|Abbreviated title||PICMET 2015|
|Period||2/08/15 → 6/08/15|
- big data
- data mining
- unsupervised learning
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- 1 Finished
Suominen, A. & Toivanen, H.
1/01/15 → 31/12/16
Project: Business Finland project