The unprecedented increase in the amount of data available for processing has created novel innovative opportunities for individuals, organizations, and society. This is creating a huge impact across industries (e.g. healthcare, finance, energy, retail and sports) when engaging in complex analytical tasks. The ability to manage big data and generate insights is also leading towards significant organizational transformation. At a higher level, big data and analytics applications are driving positive impact in society in areas, such as health and well-being (e.g. in the fight against Covid19), poverty mitigation, food security, energy, and sustainability. Organizations are allocating greater resources to enhance and develop new innovative applications of advanced analytics capabilities. As organizations transform into data and analytics centric enterprises (e.g. health insurance companies, automobile companies), more research is needed on the technical, behavioral, and organizational aspects of this progress. On one hand, research focused on the creation and application of new data science approaches, like deep learning and cognitive computing, can inform different ways to enhance decision making and improve outcomes. On the other hand, research on organizational issues in the analytics context can inform industry leaders on handling various organizational and technical opportunities along with various challenges associated with building and executing big data driven organization. Examples include data and process governance and ethics and integrity issues, management and leadership, and driving innovation and entrepreneurship.
The track “Data Science and Analytics for Decision Support” seeks original research that promotes technical, theoretical, design science, pedagogical, and behavioral research as well as emerging applications in analytics and big data. Topics include (but are not limited to) data analytics and visualization from varied data sources (e.g. sensors or IoT data, text, multimedia, clickstreams, user-generated content) involving issues dealing with curation; management and infrastructure for (big) data; standards, semantics, privacy, security, legal and ethical issues in big data, analytics and KM (knowledge management); intelligence and scientific discovery using big data; analytics applications in various domains such as smart cities, smart grids, financial fraud detection, digital learning, healthcare, criminal justice, energy, environmental and scientific domains, sustainability; business process management applications such as process discovery, performance analysis, process conformance and mining using analytics and KM, cost-sensitive, value-oriented, and data-driven decision analysis, and optimization. Visionary research on new and emerging topics that make innovative contributions to the field are also welcome.
Ciara Heavin, University College Cork, firstname.lastname@example.org
Aleš Popovič, NEOMA Business School, email@example.com
Vic Matta, Ohio University, firstname.lastname@example.org
Computational Social Science Research through Analytics
Computational social science research has garnered much interest from multiple disciplines through the use of massive, multi-faceted, and authentic data. The analysis of huge amount of trace data, which are event-based records of activities of transactions, to unveil insights on how to address larger societal issues. A recent trend in understanding social phenomena using computational social science research, especially through the use of analytics has led to many discoveries, and confirmation of hypotheses and theories interdisplinarily.
This minitrack encourages research on using trace data from human digital footprint to investigate human activities and relationships, and potentially come up with innovative and theory-grounded models of the social phenomena. Submissions may focus on descriptive research process, novel algorithm designs, questions forming, new and interesting directions in computational social science. The formulation of nascent theories through a bottom up approach using data is especially encouraged. Research in any domains are welcome.
Ace Vo, email@example.com
Yan Li, firstname.lastname@example.org
Anitha Chennamaneni, email@example.com
Smart Tourism and Tourism Analytics
Smart tourism generates a sheer volume of rich and comprehensive real-time data, including tourists’ psychographics, behaviors, and travel patterns. Such data can be analyzed to extract information necessary for contextual marketing and personalized services for tourists, improving destination management and tourists’ experiences and satisfaction.
This minitrack aims to bring together leading academic scientists and researchers to share state-of-the-art studies on the topic of smart tourism. We thus invite papers that provide insights into all aspects of smart tourism and tourism analytics. Topics of interest include, but are not limited to:
Tourism information management and advanced analytics
Analytics for tourism planning, management, and marketing
ICT-driven innovation and challenges in tourism
Business intelligence for destination management
Smart tourism and sustainable development
Tourism analytics, tourism design, and smart tourism
Social and visual media data analytics for tourism management and marketing
Big data analytics in the tourism management and marketing
Soyoung Park, firstname.lastname@example.org
Jahyun Goo, email@example.com
C. Derrick Huang, firstname.lastname@example.org
Chul Woo Yoo, email@example.com
Chulmo Koo, firstname.lastname@example.org
Applied Use of GIS for Data Science, Decision-making, and Innovation
Today, the growing availability of data analytic tools, along with ever increasing data sources, are allowing the extraction of knowledge from data in a manner previously unseen. This activity is often described as data science. At the same time, there is a growing awareness that GIS data, and related analytic techniques, can enhance this activity by providing a “spatial lens” with which to extract further knowledge. This mini-track provides a research forum to examine the applied use, and practice, of GIS for location-based analytics and decision-making, GIS data organization and processing, and innovative GIS application development.
As such, papers are solicited across topics such as, but not limited to:
Python and R for GIS Machine Learning and Deep Learning
GIS for Data Science
GeoAI: Advanced GIS Data Science
Big GIS Data Management and Analytics
Big GIS Data Mining and Knowledge Discovery
Cloud-based GIS Applications
Decision-making Using GIS
Brian Hilton, email@example.com
Daniel Farkas, firstname.lastname@example.org
James Pick, email@example.com
Avijit Sarkar, firstname.lastname@example.org
Namchul Shin, email@example.com
Data-Driven Process Mining and Innovation
One of the main aspects of business analytics is process innovation driven by the use of data generated from the day-to-day business operations of an organization. Process innovation involves workflow re-design and resource re-configuration for higher efficiency, better quality, and effectiveness, improving decision-making processes for better information flow and decision-enablement. Process mining plays a significant role in enabling such innovations.
The objective of Process Mining is to discover, monitor, and improve actual business processes by extracting knowledge from existing data generated as a result of the execution of those processes.
The aim of this mini-track is to promote theoretical and empirical research addressing the aspects mentioned above.
Example topics may include, but are not limited to – data-driven modeling, analysis, and improvement of organizational processes; design of data-driven decision-making processes; case studies and empirical evaluation of data-driven process innovation; multi-perspective approaches for process mining.
Arti Mann, firstname.lastname@example.org
Sagnika Sen, email@example.com
Behavioral Research in Data Science and Analytics
The ability to take advantage of data analytics (DA) and artificial intelligence (AI) has become an important factor for firm success. With the availability of data with high velocity, volume, and variety, many firms have invested in DA and AI technologies to improve the quality of their decisions. However, firms also recognize the critical role of human factors in analytics-based decision-making.
The focus of this minitrack is to explore and enhance understanding of the behavioral aspects of implementing and using DA and AI technologies. Particularly, this minitrack focuses on perceptions, attitudes, intentions, and behaviors related to analytics and their impacts on decision-making processes and outcomes in organizational and social settings.
*Explainable AI and DA
*Ethical and privacy aspects of AI and DA
*Trust in AI and DA
*DA and decision-making quality
*DA and technostress
*DA and discrimination
*DA and cognitive biases
Nima Kordzadeh, firstname.lastname@example.org
Maryam Ghasemaghaei, email@example.com
Big Data Analytics for Business and Societal Transformation
The minitrack aims to explore the business and societal transformations BDA entail, and how they enable innovative ways to support improved decision making that can contribute towards data-driven development and the SDGs . To understand how BDA can be of value requires an examination of the interplay between various factors (e.g., social, technical, economical, environmental), as well as interrelations among different actors in a BDA ecosystem (i.e., academia, private and public organisations, civil society, and individuals).
Emphasis will be placed on interdisciplinary papers that bridge the domains of organizational science, information systems management, information science, marketing, and computer science. This mini track aims to further explore the business and societal benefits of BDA and therefore welcomes quantitative, qualitative, and mixed methods papers, as well as reviews, conceptual papers, and theory development papers.
Ilias Pappas, firstname.lastname@example.org
Patrick Mikalef, email@example.com
Paul Pavlou, firstname.lastname@example.org
Network Analytics for Big Data
The network science has been used as a theory to understand an emergent phenomenon and as a methodology to model the relationships. More recently, we are starting to see network analytics techniques getting used in conjunction with other approaches such as deep learning or natural language processing. Such innovative and hybrid use of methodologies in newer application domain such as COVID-19 spread, infodemic, etc. can push the frontiers of scientific discovery. The focus of this mini track, therefore, is to solicit manuscripts that utilize network science as core methodology to model interactions in the large datasets in any business or scientific domain. This minitrack invites manuscripts adapting network analysis as a descriptive, a predictive or a prescriptive tool. Either theory building or theory testing applications of network analytics are encouraged as well.
Pankush Kalgotra, email@example.com
Ashish Gupta, firstname.lastname@example.org
Ramesh Sharda, email@example.com