Mohammad Soltani Delgosha, Associate Professor in Business Analytics University of Birmingham, UK

Nastaran Hajiheydari, Senior Lecturer in Digital Marketing & Analytics, Queen Mary University of London, UK

Track Call

In today’s hyper-competitive business landscape, leveraging vast data resources is no longer a luxury, but a necessity for business survival and growth. Big Data and Business Analytics are emerging as transformative strategic tools that empower organizations to make data-driven decisions with unprecedented accuracy and agility. The convergence of the digital world, big data, and new business analytics models and algorithms is challenging traditional decision-making methods. This shift influences how business problems are identified and formulated, as well as how solutions are explored, selected, and implemented.  This track invites submissions that apply, explore and develop Business Analytics theories, models and methods to address Data-Driven Decision-Making (DDDM) challenges and opportunities in the real-world. We welcome all types of research, including conceptual, theoretical, analytical, and/or empirical studies.

Track Areas

Suggested topics include, but are not limited to:

  • Data visualisation, dashboard design and business reporting
  • Time series analysis, business forecasting and big data technologies for predictive analytics
  • Optimisation, simulation, personalisation and action recommendations
  • Business Analytics for Innovation
  • Business Analytics for addressing societal challenges such as public health emergencies/epidemics/pandemics, sustainability and climate change
  • Democratising Business Analytics, collaborative DDDM and community engagement
  • Business value and monetizing of Business Analytics and big data
  • The Human Factor in Business Analytics and Human-machine collaboration in DDDM
  • data quality, data sharing, and trustworthiness of DDDM
  • Challenges and future directions of business analytics

References

Bar-Gill, S., Brynjolfsson, E., & Hak, N. (2024). Helping Small Businesses become more Data-Driven: A Field Experiment on eBay. Management Science. https://doi.org/10.1287/mnsc.2021.02026

Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025-1044.

Hindle, G., Kunc, M., Mortensen, M., Oztekin, A., & Vidgen, R. (2020). Business analytics: Defining the field and identifying a research agenda. European Journal of Operational Research, 281(3), 483-490.

 Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value?. Information Systems Journal, 27(3), 237-269.

 Soltani Delgosha, M., Hajiheydari, N., & Fahimi, S. M. (2021). Elucidation of big data analytics in banking: a four-stage Delphi study. Journal of Enterprise Information Management, 34(6), 1577-1596.

 Terekhov, M. A., Demirezen, E. M., & Aytug, H. (2024). Business analytics: Emerging practice and research issues in the health insurance industry. Production and Operations Management, 33(2), 432-455.