Sensor-Data-Driven Knowledge Creation Model: A Model and Empirical Test

Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

A new knowledge-creation model, called sensor-data-driven knowledge creation (SDD-KC), which utilizes sensor data for discovering tacit knowledge, is proposed and tested. The proposed model utilizes wearable sensors to digitize tacit activities such as location, motion, and social interaction of people. To derive practical knowledge, the obtained data is statistically analyzed and associated with performance outcome. An empirical test at a retail store demonstrated that the SDD-KC model was able to derive a rule that leads to customers’ behavioral change, which contributed to a sales increase. In contrast, the traditional knowledge-creation model, applied in the same setting, failed to identify effective ideas. The proposed SDD-KC model was thus shown to be effective for knowledge creation by overcoming cognitive limitations of people.

Keywords

Sensor Node Tacit Knowledge Knowledge Creation Situational Variable Average Sale 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We would like to thank members of the SocioInfo Project, led by Hitachi High-Technologies and Hitachi Central Research Laboratory, for their useful comments and technical support.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Central Research LaboratoryHitachi, LtdTokyoJapan
  2. 2.Department of Industrial Engineering and ManagementTokyo Institute of TechnologyTokyoJapan

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