Track Chairs
Dr Chitharanjandas Chinnapaka, University of Brighton
Dr Rageshree Sinha, University of Brighton
Dr Davit Marikyan, Newcastle University
Dr Efstathios Papanikolaou, Durham University
Track call
The theme “AI and Big Data for Next-Generation Digital Transformation in Information Systems” captures a fundamental shift from periodic, technology-led change towards continuous, intelligent, and data-driven organisational transformation. Within the Information Systems (IS) discipline, digital transformation is increasingly understood as a socio-technical process that integrates technological innovation with organisational structures, human capabilities, and institutional contexts (Vial, 2019).
Artificial Intelligence (AI) and Big Data play a central role in enabling this next generation of transformation by embedding real-time analytics, predictive capabilities, and agentic and autonomous decision-making into organisational processes. This shift allows organisations to move beyond process automation towards dynamic capability building and sustained competitive advantage (Mikalef et al., 2020). At the same time, it raises critical IS challenges related to governance, transparency, and ethical accountability, particularly as algorithmic systems influence strategic and operational decisions (Rai et al., 2019).
This theme, therefore, encourages research examining how AI and data-intensive systems reshape business models, digital infrastructures, and human–technology interactions, while also addressing issues of trust, explainability, and responsible innovation. By bridging technical, organisational, and societal perspectives, it positions IS scholarship at the forefront of shaping next-generation digital transformation that is not only intelligent, but also inclusive and sustainable (Verhoef et al., 2021).
Key Topics Include (but are not limited to):
- How AI reshapes business models and value creation
- Data-driven decision-making at executive and operational levels
- Agentic AI capabilities required for AI-enabled transformation
- Human-AI collaboration and augmentation
- Trust, transparency, and explainability in intelligent systems
- Responsible use of data in large-scale systems
- Algorithmic bias, fairness, and accountability
- AI architectures, cloud platforms, and data ecosystems
- Real-time data processing and intelligent automation
- Integration of AI with IoT, blockchain, and digital platforms
- AI-enabled innovation processes
- New forms of digital products and services
References:
Mikalef, P., Krogstie, J., Pappas, I.O. and Pavlou, P., 2020. Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), p. 103169.
Rai, A., Constantinides, P. and Sarker, S., 2019. Editor’s comments: Next-generation digital platforms: Toward human–AI hybrids. MIS quarterly, 43(1), pp. iii-ix.
Verhoef, P.C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J.Q., Fabian, N. and Haenlein, M., 2021. Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, pp.889-901.
Vial, G., 2021. Understanding digital transformation: A review and a research agenda. Managing digital transformation, pp.13-66.