Track: Knowledge Management and Business Intelligence

Track Co-chairs:

Ali INTEZARI, Massey University, a.intezari@massey.ac.nz

David J. PAULEEN, Massey University, d.pauleen@massey.ac.nz

Wei HE, Texas Tech University, wei.he@ttu.edu

Gee-Woo BOCK, gwbock@gmail.com, Sungkyunkwan University

Description and Topics of Interest:

The goal of this track is to advance our knowledge of the complexity of the integration of analytics into KM for better strategic decision-making. The emphasis of the track is on how individual and organizational knowledge can be distilled from and incorporated into Big Data using effective tools and techniques.

The success of business decisions and government policies relies heavily on the quality of the knowledge that underlies the decisions and policies. For this reason, gathering, analysing and considering reliable data and information, and more importantly, transforming them into usable knowledge and expertise is increasingly becoming critical in strategic decision-making (Intezari & Gressel, 2017; Nicolas, 2004). Due to the extensive uncertainty, ambiguity and risk associated with strategic decisions (McKenzie et al., 2011), the role of analytics tools and technologies in KM has much to offer to help businesses and government agencies to succeed in their challenging endeavors. Nevertheless, the transformation of data into knowledge and the use of analytical tools in incorporating knowledge into strategic decisions is an underdeveloped field. Therefore, it is important and timely that the field of KM respond to the profound changes that Big Data and Analytics are bringing to organizations (Pauleen & Wang, 2017). We are particularly interested in the incorporation of KM systems into strategic decisions and policies, as well as the use of analytic tools and techniques in fully supporting and successfully operationalizing people’s knowledge.

The track accepts empirical papers, as well as the conceptual papers that offer deep theoretical insights into the areas of interest. The track is open to papers employing various research methods.

Areas of interest include, but are not limited to:

  • KM initiatives for managerial and strategic decision-making and policy-making
  • KM and the use of analytics for competitive strategies
  • Design, development, and use of KM technologies to support data-driven decisions and strategies
  • Organizational barriers and enablers of the use of technology for KM in managerial and strategic decision- and policy-making
  • Analytical tools and techniques, such as text analytics and sentiment analysis for analysing knowledge and disseminating expertise
  • Visualisation of knowledge
  • Real time analysis of knowledge
  • Data-knowledge transformation in strategic decisions
  • KM innovation and standards
  • KM challenges in managing data-driven operational, managerial, and strategic decision-making
  • Data Mining in the tacit and explicit knowledge
  • Cultural challenges in KM at the organizational, national and international levels
  • Individual, Intra-, and inter-organizational KM technologies

References

  • Intezari, A., & Gressel, S. (2017). Information and reformation in KM systems: big data and strategic decision-making. Journal of Knowledge Management, 21(1).
  • McKenzie, J., van Winkelen, C. and Grewal, S. (2011), “Developing organisational decision-making capability: a knowledge manager’s guide”, Journal of Knowledge Management, Vol. 15 No. 3, pp. 403-421.
  • Nicolas, R. (2004), “Knowledge management impacts on decision making process”, Journal of Knowledge Management, Vol. 8 No. 1, pp. 20-31.
  • Pauleen, D. J., & Wang, W. Y. C. (2017). Does big data mean big knowledge? KM perspectives on big data and analytics. Journal of Knowledge Management, 21(1), 1-6.