Track Chairs 
Dr Chitharanjandas Chinnapaka, University of Brighton, UK 
Dr Jose Christian, University of Brighton, UK 
Dr Rageshree Sinha, University of Brighton, UK 
Dr Davit Marikyan, Newcastle University, UK 
 
Track description 
The convergence of AI, analytics, and marketing presents novel avenues for Information Systems (IS) research that extend beyond technical artefacts to socio-technical systems and user experience (Huang et al., 2022). The rise of generative AI has redefined content creation, offering both innovation and ethical dilemmas (Dwivedi et al., 2023). This track encourages interdisciplinary research that blends marketing, data science, and IS theory, contributing to a deeper understanding of value creation in digital ecosystems. 

This track addresses the increasing demand for IS research that integrates AI and data science with marketing innovation and social responsibility (Dwivedi et al., 2023; Huang & Rust, 2020). It builds on existing IS foundations while expanding interdisciplinary frontiers by incorporating theories from marketing, data privacy, and platform strategy. It aims to foster critical and reflective discussions on using AI to support human-centred, data-driven decision-making in digital ecosystems (Wedel & Kannan, 2016; Martin et al., 2017). 

Track areas include but are not limited to:

  • AI in business organisations 
  • Generative AI and large language models in customer engagement 
  • AI-powered personalisation and recommendation systems 
  • Big Data and predictive analytics in marketing strategies 
  • AI privacy, bias, and explainability in AI algorithms 
  • AI in customer service, Consumer behaviour (e.g., chatbots, virtual agents, recommendation systems) 
  • AI acceptance, adoption and implementation  

References
Dwivedi, Y. K., Hughes, D. L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., & Wamba, S. F. (2023). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 71, 102686.

Huang, M.-H., & Rust, R. T. (2022). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 50, 211–231.

Huang, M.-H., & Rust, R. T. 2020. Engaged to a Robot? The Role of AI in Service. Journal of Service Research, 24(1), 30-41. (Original work published 2021)

Martin, K. D., Borah, A., & Palmatier, R. W. 2017. Data privacy: Effects on customer and firm performance. Journal of Marketing, 81(1), 36–58.

Wedel, M. and Kannan, P.K., 2016. Marketing analytics for data-rich environments. Journal of marketing, 80(6), pp.97-121.