Calls for Papers

Special Issue: The Transformative Power of Generative AI for Digital Platorms

Guest Editors

Ajay Kumar
EMLYON Business School, France

Yuanzhu Zhan
University of Birmingham Business School, UK

Thanos Papadopoulos
University of Kent Business School, UK

Kim-Kwang Raymond Choo
University of Texas at San Antonio, USA


Recent advancements in Generative artificial intelligence (AI) have received significant interest from both scholars and practitioners (Epstein et al., 2023; Deloitte, 2023; Gartner, 2023). Such AI systems are proficient in autonomously generating extensive textual content, code, simulations, images, and videos based on input prompts provided by users (Hacker et al., 2023). Among the most prominent applications of Generative AI is ChatGPT, developed by OpenAI. This sophisticated chatbot can produce text of a quality comparable to human output. Notably, within just two months of its release, ChatGPT amassed an unprecedented user base, with 100 million users recorded in January 2023 alone, setting a new record for the fastest user growth rate in consumer application history (Koc et al., 2023). Gartner (2023) further suggests that by the year 2025, 30% of outbound messages will be synthetically produced by Generative AI.

This special issue pays particular attention to the opportunities and challenges that Generative AI poses for digital platforms. Existing literature has well documented how platform-based business models can upend firms’ value propositions in traditional business sectors and reshape entire industries (Parker et al., 2017; Fehrer et al., 2018; Cutolo and Kenney, 2021). For example, social media platforms like YouTube, Amazon, Netflix, Uber, and Meta have been transformative, disrupting industries by enabling unprecedented connections and exchanges of goods, services, and information across diverse user groups that would have been difficult or impossible to connect without the platform. In such a context, digital platforms serve different stakeholders—end users, organizational team members, company executives, and ecosystem partners—all with varying interests. As a disruptive technology, Generative AI has the great potential to revolutionize these stakeholder dynamics, enhancing interactions and value exchange across the entire network. According to Forbes (2019), the integration of AI can lead to profound shifts in digital platforms, potentially disrupting established markets while catalyzing new avenues for value creation. For instance, stakeholders may leverage Generative AI to automate processes, significantly reducing labor hours and elevating the platform experience (Hacker et al., 2023). Besides, Generative AI’s capability for hyper-personalization could empower complementors to instantaneously adapt their services, aligning closely with each user’s unique preferences (Hendriksen, 2023; Wamba et al., 2023).

Moreover, Generative AI can revolutionize the way people understand, manage, and use digital platforms, offering a wide range of opportunities for individuals, communities, organizations, as well as society. It catalyzed innovations and operational efficiencies, capturing scholarly attention focused on its impact within the digital ecosystem (Gartner, 2023). However, the rapid evolution of Generative AI also presents adaptation challenges for digital platforms, introducing complexities such as potential misuse, data privacy concerns, reliance on AI-generated content, verification difficulties, and systemic biases (Wamba et al., 2023). Furthermore, the wider societal implications, such as job displacement risks and the widening digital divide, are pressing concerns (Dwivedi et al., 2023). As a result, theorizing these technology-related opportunities and tensions for digital platforms is at the very core of scholars in varies disciplines, as it enables a comprehensive understanding of the multifaceted effects of Generative AI on business performance, customer engagement, governance, and digital infrastructure development.

Although previous research within the information systems field has provided valuable knowledge concerning AI’s business implications and its management (Berente et al., 2021; Lysyakov and Viswanathan, 2023), the relationship between the Generative AI’s inherent potential and its transformative power for digital platforms remains underexplored, requiring more in-depth investigations (Alsharhan et al., 2023; Ooi et al., 2023; Yu et al., 2023). This special issue seeks to gather state-of-the-art research that addresses the engineering and management challenges posed by Generative AI within digital platforms. Particularly, we encourage submissions that explore the development, deployment, and governance of Generative AI technologies and their transformative values on digital platform ecosystems with clear theoretical and practical contributions. This may include new developments, theories, models, methods, and frameworks. Potential research questions that may be addressed include (but are not limited to):

Generative AI Development

  • How can operations and innovation management principles streamline the development process of Generative AI applications for digital platforms?
  • What are the best practices for managing R&D teams in the design and development of Generative AI systems?
  • How do the principles of systems engineering apply to the lifecycle of Generative AI development?

Generative AI Deployment

  • What are the key challenges and strategies in deploying Generative AI systems across different digital platforms?
  • How can organizations assess the readiness of their digital infrastructure for the integration of Generative AI?
  • What role does technology management play in the successful deployment of Generative AI on digital platforms?
  • How can Generative AI contribute to the resilience and adaptability of digital platforms in the face of evolving market demands?

Generative AI Governance

  • How should regulatory frameworks evolve to address the deployment and scaling of Generative AI on digital platforms?
  • What are the implications of Generative AI on data governance and intellectual property rights within digital platforms?
  • How can managers ensure ethical considerations are embedded in Generative AI governance policies?
  • What are the risk management strategies for Generative AI in the context of critical digital platform operations?

Social and Economic Impact of Generative AI

  • What are the economic impacts of Generative AI on digital platform ecosystems?
  • How will Generative AI affect the labor market and job roles associated with digital platform management and operations?
  • In what ways can Generative AI potentially exacerbate or mitigate socio-economic disparities through digital platforms?
  • What are the implications of Generative AI for the scalability and sustainability of digital platform architectures?

We invite comprehensive studies of Generative AI’s disruptive potential across digital platforms from a variety of perspectives, such as ethical, organizational, behavioral, economic, technical, operational and supply chain. Submissions may span a broad methodological spectrum, from theoretical frameworks to analytical models, as well as empirical investigations leveraging secondary data, surveys, experiments, simulations, in-depth case studies, and data mining. In alignment with IEEE-TEM’s standards, contributions should deliver substantive, original insights to the discourse on technology and engineering management.

Notes for Prospective Authors:

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Conference papers are only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper. Manuscripts should be submitted through the publisher’s online system. Submissions will be reviewed according to the journal’s rigorous standards and procedures through double-blind peer review by at least two qualified reviewers.

We welcome informal enquiries relating to the special issue, proposed topics and potential fit with the Special Issue objectives. Please direct your inquiries to Ajay Kumar ( ) or Yuanzhu Zhan (

Please note that systematic literature review (SLR) papers will not be considered for this special issue.

Submission Process

Please prepare the manuscript according to IEEE-TEM’s guidelines ( and submit to the journal’s Manuscript Central site ( Please upload the paper on the IEEE TEM Editorial Manager clearly indicating it is submission for the IEEE TEM Special Issue on Transformative Power of Generative AI for Digital Platforms.


  • Submissions Open:  1 April 2024
  • Submission Deadline: 30 October 2024
  • Review process: On a rolling basis from 1 April – 30 October 2024
  • Expected Publication: Autumn 2025

Guest Editor Bios

Ajay Kumar is an Associate Professor of Business Analytics at EMLYON Business School, France. His research and teaching expertise lie in data and text mining, decision support systems, business intelligence, and enterprise modelling. He has held postdoctoral fellowships at the Massachusetts Institute of Technology and Harvard University. He has published several research papers in reputed journals, including IEEE Transactions on Engineering Management, Production and Operations Management, Harvard Business Review, INFORMS Journal on Computing, Decision Support Systems, European Journal of Operational Research, European Journal of Information Systems, British Journal of ManagementInternational Journal of Operations & Production Management, and Journal of Business Research, etc.

Yuanzhu Zhan is an Associate Professor in Operations and Supply Chain Management at the University of Birmingham. His research focuses on examining the impact of emerging technologies on practices in operations, innovation management and sustainable supply chain management. Yuanzhu’s research has been published in various journals including IEEE Transactions on Engineering Management, International Journal of Operations and Production Management, European Journal of Operational Research, Journal of Service Research, and International Journal of Production Economics. Yuanzhu serves as the Editorial Board Member for International Journal of Production Economics, International Journal of Operations and Production Management, and Industrial Management & Data Systems. He has served as co-editor for three special issues addressing emerging topics for journals such as International Journal of Production Economics and Journal of Business Research.

Thanos Papadopoulos is a Professor of Management and the Head of the Department of Analytics, Operations & Systems at Kent Business School, UK. Thanos is a Department Editor of IEEE Transactions on Engineering Management, an Associate Editor for British Journal of Management, and International Journal of Operations and Production Management journals.  He also serves as an Editorial Board Member for International Journal of Information Management, International Journal of Information Management Insights, and Industrial Management and Data Systems. Thanos has published over 140 articles in peer reviewed journals and conferences including IEEE Transactions on Engineering Management, European Journal of Information Systems, British Journal of Management, Decision Sciences, European Journal of Operational Research, International Journal of Operations and Production Management, International Journal of Production Research, International Journal of Production Economics, Technological Forecasting and Social Change, and Production Planning and Control.

Kim-Kwang Raymond Choo is a Professor and holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio, U.S. Raymond is a Department Editor of IEEE Transactions on Engineering Management, the Associate Editor of IEEE Transactions on Dependable and Secure Computing, and IEEE Transactions on Big Data, the founding co-Editor-in-Chief of ACM Distributed Ledger Technologies: Research & Practice, and the founding Chair of IEEE TEMS Technical Committee on Blockchain and Distributed Ledger Technologies. He currently serves as an Advisory Council Member of the Anti-Human Trafficking Intelligence Initiative (a 501(c)3 US-based non-profit, aims to disrupt the market of human trafficking, child exploitation, and child sexual abuse material), and is an Adjunct Professor (Courtesy Appointment) at the University of South Australia and Singapore University of Technology and Design.


Alsharhan, A., Al-Emran, M. and Shaalan, K. (2023). Chatbot Adoption: A Multiperspective Systematic Review and Future Research Agenda. IEEE Transactions on Engineering Management, DOI:

Berente, N., Gu, B., Recker, J. and Santhanam, R., (2021). Managing artificial intelligence. MIS Quarterly, 45(3).

Cutolo, D. and Kenney, M., 2021. Platform-dependent entrepreneurs: Power asymmetries, risks, and strategies in the platform economy. Academy of Management Perspectives, 35(4), pp.584-605.

Deloitte (2023). Generative AI in operations management: How fulfillment operations will be transformed.

Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M. and Albanna, H., 2023. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of Generative conversational AI for research, practice and policy. International Journal of Information Management, 71, p.102642.

Epstein, Z., Hertzmann, A., Investigators of Human Creativity, Akten, M., Farid, H., Fjeld, J., Frank, M.R., Groh, M., Herman, L., Leach, N. and Mahari, R., (2023). Art and the science of Generative AI. Science, 380(6650), pp.1110-1111.

Fehrer, J.A., Woratschek, H. and Brodie, R.J. (2018), A systemic logic for platform business models, Journal of Service Management, Vol. 29 No. 4, pp. 546-568.

Forbes (2019). Artificial Intelligence: Why it is Essential for Digital Platforms.

Gartner (2023). Beyond ChatGPT: The Future of Generative AI for Enterprises.

Hacker, P., Engel, A. and Mauer, M., (2023). Regulating ChatGPT and other large Generative AI models. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 1112-1123.

Hendriksen, C. (2023). Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption? Journal of Supply Chain Management,

Koc, E., Hatipoglu, S., Kivrak, O., Celik, C. and Koc, K., 2023. Houston, we have a problem!: The use of ChatGPT in responding to customer complaints. Technology in Society, 74, p.102333.

Lysyakov, M. and Viswanathan, S., (2023). Threatened by AI: Analyzing Users’ Responses to the Introduction of AI in a Crowd-sourcing Platform. Information Systems Research, 34(3), pp.1191-1210.

Parker, G., Van Alstyne, M., and Jiang, X. (2017). Platform Ecosystems. MIS Quarterly, 41, 1, 255-266.

Wamba, S.F., Queiroz, M.M., Jabbour, C.J.C. and Shi, C.V., 2023. Are both Generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence?. International Journal of Production Economics, 265, p.109015.

Yu, Y., Lakemond, N. and Holmberg, G. (2023). AI in the Context of Complex Intelligent Systems: Engineering Management Consequences. IEEE Transactions on Engineering Management,  

IEEE Transactions on Engineering Management is journal of the Technology and Engineering Management Society of IEEE, published quarterly since 1954. It is dedicated to the publication of peer-reviewed original contributions, by researchers and practitioners, regarding the theory and practice of engineering, technology, and innovation management.

Editor in Chief

Tugrul U Daim, PhD
Professor and Director
Technology Management Doctoral Program
Department of Engineering and Technology Management
Maseeh College of Engineering and Computer Science
Portland State University, Portland OR
United States

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