-------- Forwarded Message -------- Subject: [AISWorld] CfP - ACIS (2020) - Track: DataAnalytics, Knowledge Management, and Strategic Decision Making Date: Mon, 29 Jun 2020 09:51:33 +1200 From: Nazim Taskin nazimtaskin@gmail.com To: aisworld@lists.aisnet.org

Dear colleagues,

Call for Papers

*ACIS (2020) - Track: Data Analytics, Knowledge Management, and Strategic Decision Making*

The goal of the Data Analytics, Knowledge Management and Strategic Decision-making track is to advance our knowledge of the complexity of the disruptive technologies of data analytics and their role in the advancement of KM for better strategic decision-making. Aligned with the conference theme, the emphasis of the track is on how individual and organizational knowledge can be distilled from and incorporated into data analytics for making more effective decisions and guidance to “navigate our digital future”. Therefore, we encourage submissions that offer significant theoretical and practical contributions on how organisations can utilise emerging and analytics technologies to create value and knowledge more effectively.

Data by itself is worthless unless business users analyse it and transform it into practical knowledge that delivers value to their organizations. That’s where data analytics and KM come into the picture. The success of business decisions and government policies relies heavily on the quality of the data and knowledge that underlie the decisions and policies. For this reason, gathering, analysing and considering reliable data and more importantly, transforming them into usable knowledge and expertise is increasingly 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 data analytics tools and technologies in KM has much to offer to help businesses and government agencies to succeed in their strategic decision-making (Li, Goh, & Jin, 2018). Nevertheless, the transformation of data into knowledge and the use of analytical tools in incorporating data and knowledge into strategic decisions is an underdeveloped field. While offering significant promises, data analytics poses new challenges for both practitioners and scholars. Therefore, it is important and timely that the fields of decision-making and 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.

This track invites submissions that address the challenges and opportunities associated with data analytics and KM, and their implication/incorporation into business processes and decision-making practices at the individual and organisational levels. The papers should address the conference theme.

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

Topics of interest include but are not limited to:

- Data analytics across decision-making levels: strategic, tactical, and operational - Data analytics and emerging research philosophies and methodologies - Methodologies, techniques, and analytical tools for individual and group decision-making - The integration of data analytics and KM for strategic decision-making: techniques, challenges and opportunities - Organizational barriers and enablers of the use of data analytics and KM in managerial and strategic decision-making - KM initiatives for managerial and strategic decision-making - Design, development, and use of KM technologies to support data-driven decisions and strategies - Organizational barriers and enablers of the use of data analytics for KM in managerial and strategic decision - Analytical tools and techniques, such as text analytics and sentiment analysis for analysing knowledge and disseminating expertise - Visualisation of structured and unstructured data and knowledge - Big data velocity and real-time analysis of data in decision-making at the individual and organisational levels - KM innovation, standards and challenges in managing data-driven decisions - Data Mining in explicit knowledge - Cultural challenges in organizations merging initiatives in analytics and KM - Individual, intra-, and inter-organizational data analytics and KM technologies

Conference website: https://www.acis2020.org/

Deadline (please check conference website for updates):

- August 10: Paper submission deadline - September 30: Notification of acceptance - December 1-4, 2020: Conference in Wellington, New Zealand

Track co-chairs:

Dr Ali INTEZARI, University of Queensland, a.intezari@uq.edu.au

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

Dr Tiong-thye GOH, Victoria University of Wellington, tiong.goh@vuw.ac.nz

Dr Hamed JAFARZADEH, Massey University, h.jafarzadeh@massey.ac.nz

References:

- Intezari, A., and Gressel, S. 2017. “Information and Reformation in KM Systems: Big Data and Strategic Decision-making,” *Journal of Knowledge Management* (21:1), pp. 71–91. - Li, L., Goh, T. T., and Jin, D. 2018. “How Textual Quality of Online Reviews Affect Classification Performance: A Case of Deep Learning Sentiment Analysis,” *Neural Computing and Applications* (doi:10.1007/s00521-018-3865-7). - McKenzie, J., van Winkelen, C., and Grewal, S. 2011. “Developing Organisational Decision‐making Capability: A Knowledge Manager’s Guide, *“ Journal of Knowledge Management* (15:3), pp. 403–421. - Nicolas, R. 2004. “Knowledge Management Impacts on Decision Making Process, “ *Journal of Knowledge Management* (8:1), pp. 20–31. - Pauleen, D. J., and Wang, W. Y. C. 2017. “Does Big Data Mean Big Knowledge? KM Perspectives on Big Data and Analytics,” *Journal of Knowledge Management* (21:1), pp. 1–6. _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org