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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.
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