[AISWorld] CfP - ACIS (2020) - Track: DataAnalytics, Knowledge Management, and Strategic Decision Making

[Nazim Taskin]

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 at uq.edu.au

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

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

Dr Hamed JAFARZADEH, Massey University, h.jafarzadeh at 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.