knowledge - its depth and breadth. Companies need to master expanding technological knowledge b ases creating tensions for MOT. We examine how big data in patent landscaping creates insights into MOT. Using big

TL;DR: It is demonstrated how unsupervised learning creates insight into MOT by identify ing topical kno wledge foci and showing the dynamics of knowledge domai ns among companies using a full-tex t copy of USPTO-da tabase with approximately 6 million patents data.

Abstract: that managers augment human judgment with machine-learning tools, promptin g challenges to management traditio ns. We demonstrate how unsupervised learning creates insight into MOT by identify ing topical kno wledge foci and showing the dynamics of knowledge domai ns among companies. Using unsupervised learning and network analysis; we show how a semantic analysis leads to the identifi cation of opportun ities in complex environm ents. We illustra te this using a case in global ly operatin g telecommunicatio n compa nies using a full-tex t copy of USPTO-da tabase with approximately 6 million patents data. Our results show the landscape of the companies and the underlyi ng knowledge embedded in the companies. We discuss how managers can evaluate their technological knowledge against competitors, balancing c urrent needs with the adoption of new knowledge. We further discuss how a semantic analysis can lead to the discover y of latent patterns and identifi cation of opportun ities.