Track Co-Chairs
Aneesh Krishna, Curtin University,
Madhushi Bandara, University of Technology Sydney,
Sajib Mistry, Curtin University
Track Description
This track seeks to advance understanding of data analytics and Artificial Intelligence (AI) technologies and their potential to generate positive societal impact from both economic and humanistic perspectives. It focuses on how individual and organisational knowledge can be derived from data and embedded within business and social contexts to support effective decision-making, as well as the broader implications of these technologies for organisations and communities. We welcome submissions that offer meaningful theoretical and practical contributions to these areas.
The track invites conceptual and empirical manuscripts, as well as teaching cases. It is open to a broad range of research methods and welcomes both completed research papers and research-in-progress submissions.
Topics of interest include, but are not limited to:
- Design, development, and application of analytics and AI to support innovative data-driven decision-making and strategy
- Human factors in the development of data-driven systems, including methodological advancements
- Analytical tools and techniques, such as text analytics, sentiment analysis, and generative AI, and their application across relevant domains
- Advances in the visualisation of structured and unstructured data and knowledge
- Real-time and streaming analytics that enable timely decision-making at the individual, organisational, and societal levels
- Integration of data analytics and AI for strategic decision-making that promotes economic prosperity and a sustainable future
- Adoption and implementation of data analytics and AI tools in the workplace, and their implications for workforce development
- Governance of data analytics and AI technologies, including implications for policy and practice
- Responsible data analytics and AI, including fairness, transparency, explainability, trustworthiness, accountability, and human-centred design
- Privacy-preserving analytics and AI, including federated learning and secure data sharing
- Hybrid human-AI collaboration and AI-enabled decision support in organisational contexts
- Data quality, data governance, and organisational readiness for analytics and AI-driven transformation
- Applications of analytics and AI for sustainability, social good, and community resilience