Call for Papers - JAIS Special Issue: Challenges of GAI

Managing the Individual, Organizational, and Societal Challenges of Generative AI: Utopian, Dystopian, Neutropian Perspectives

Deadline for submission: 31st August 2024

The origins of generative AI (GAI) can be traced back to the 1950s, when Alan Turing (1950) proposed a test to determine whether a machine could be perceived as intelligent enough to generate responses to questions in a way indistinguishable from a human. Later, in the 1970s, researchers developed more advanced models capable of producing more realistic and coherent outcomes (Amari, 1972). Contemporary GAI models are based on state-of-the-art neural network architectures (e.g., Heising & Angelopoulos, 2023). They combine such architectures to develop large models (e.g., Large Language Models, or LLMs) that outperform existing benchmarked ones (Forbes, 2023; Kushwaha and Kar, 2021). Contemporary GAI solutions, such as ChatGPT (OpenAI, 2023a), DALL-E 2 (Ramesh et al., 2022), MidJourney (MD, 2023), and Stable Diffusion (Stable Diffusion, 2023), can therefore produce large amounts of contextual outputs on any specific topic. They are highly trained and sophisticated, enabling users to produce various types of AI- generated content (e.g., audio, texts, videos, and images).

Although GAI has been around for a while, recent developments have brought the potential of such solutions to the forefront. In particular, LLMs have the potential to transform the way we develop textual content and communicate with one another. In recent advances, transformer-based deep learning networks have been impressively effective for generating textual responses as outputs to cues from users based on LLMs (Dwivedi et al., 2023). Hence, in a way, the latest developments in GAI have increased its potential for use in a wide range of industries like healthcare, customer service, media, marketing, finance, biopharma among others.

The ongoing discourse on GAI seems to extol the promises of AI (utopian visions of a world free of drudgery and want) and the dangers (dystopian visions of machines taking over the world). Such discourse is well chronicled for many technologies in the Gartner hype cycle. New technologies spawn overenthusiasm, followed by disillusionment and eventually enlightenment, as we learn how to use them. In every case, the technology is heralded as incredible, and predictions, both constructive and destructive, run rampant. Our goal for this Special Issue is to offer a careful examination of the challenges faced in managing this powerful set of technologies for individuals, organizations, and society.

Many, but not all, of the challenges around GAI concern data. As per a Forbes report, over 90% of internet data will be produced by GAI models, triggering serious concerns about harmful and abusive content generation (IBM, 2023). To illustrate the challenge, most current GAI-triggered use involves chat-based digital assistants (Stokel-Walker and Noorden, 2023). While the outcome of GAI in these digital assistant-based applications is indeed remarkable, their effectiveness depends on the level of task specificity and the need for information synthesis. Also, both organizations and governments face challenges associated with policies of data protection and content moderation. Lately, the Italian government has temporarily limited the processing of personal data by OpenAI, thereby forcing OpenAI to block ChatGPT in that region (Italian Directive, 2023). OpenAI itself anticipated the potential applications by fraudsters, and released a feature that allows the users to apply a content filtering flag to potentially problematic content (Hacker et al., 2023; OpenAI 2023b). Organizations might have other challenges regarding data protection. For example, a computer manufacturer recently decided to adopt GAI and then transfer its knowledge base to GAI platforms to be able to provide users with actionable and custom responses. But in the process, these internal information assets of the firm became available in the data repository of the LLMs.

Call for Papers
Our special issue is focused on challenges for individuals, organizations, and society to manage the use of GAI and the threats to individuals and society. The list below is illustrative (but not exhaustive) of the themes we solicit for the special issue:

At the individual level, a number of challenges exist on how to effectively use GAI to augment individual productivity. For instance, how can GAI-based interactions positively or negatively affect customer experiences, how can GAI augment (vs. replace) human skills, how can individuals assess the veracity of GAI-based communications, how can poorly designed interactions (prompts) adversely affect outcomes and broader questions of how over-reliance on GAI systems may adversely impact the cognitive inability of users and learners.

At the organizational level, there are many challenges around governance. For instance, how can we govern the quality of content by GAI, which may have varying levels of diffusion among user communities, how can the adoption of GAI lead to disruption and even loss of internal data, knowledge, and trade secrets, how do we set up appropriate governance structures to manage GAI projects and ongoing GAI use, and how can we avoid unintended consequences of GAI adoption in firms?

At the societal (and “macro-economic’) level, there are extensive challenges around misinformation, bias, and privacy. For instance, how can GAI in social media fight misinformation, what is the impact of GAI models on societal ethics, socio-cultural bias, and socio- cultural well-being of users, how should GAI models be regulated to minimize cyber security risks (i.e., generate realistic fake data, commit fraud), how should society address privacy, given that GAI requires organizations to have access to huge volumes of high-quality data?

Our broad goal for the special issue is to attract papers that articulate the challenges theoretically and study them empirically, while making a strong contribution to the theory (Struijk et al., 2022) and practice (Davison, 2022) in the deployment of GAI. We are not interested in pure methodological contributions.

Methodologies
We welcome a variety of research approaches, including but not limited to the following:
• Conceptual/theoretical articles
• Theory testing, including quantitative studies based on primary data (surveys, experiments) and/or secondary data
• Theory building, including qualitative studies based on expert interviews, and case studies
• Design science research, including user research on GAI artifacts implemented in industrial applications.

We welcome studies that combine several different research methodologies (mixed methods) and submissions that include both theoretical model building and validation.

Key Dates
• Article Submission Deadline: 31st August, 2024
• First Review: 30th November, 2024
• Article Final Decision: 31st July, 2026

Articles will be screened for fit and quality by the SI Editors. Those that successfully go through the screening will be sent out for a full review.

Guidelines for authors
• Short Title for SI: Challenges of GAI
• Submissions must follow the author guidelines of the journal:
• SI submissions should be submitted through the journal’s review system.

Special Issue Editors
Varun Grover
George & Boyce Billingsley Endowed Chair and Distinguished Professor
Walton College of Business, University of Arkansas, USA.
vgrover@uark.edu

Arpan Kumar Kar (Coordinating SI Editor)
Professor and Chair Professor, Information Systems Group,
Department of Management Studies, Indian Institute of Technology Delhi, India. 
arpankar@iitd.ac.in , arpan_kar@yahoo.co.in
 
Rajiv Sabherwal
Edwin & Karlee Bradberry Endowed Chair and Distinguished Professor
Walton College of Business at University of Arkansas, USA.
rsabherw@uark.edu

Spyros Angelopoulos
Associate Professor
Durham University Business School, Durham University, UK
Spyros.Angelopoulos@durham.ac.uk

Hartmut Hoehle
Professor and Chair of Enterprise Systems
University of Mannheim, Mannheim, Germany
hoehle@uni-mannheim.de

Managing Editor: Anik Mukherjee
Indian Institute of Management, Calcutta, India.
anik.mukherjee@iimcal.ac.in

Review Board for the SI
Shahriar Akter, University of Wollongong, Australia
Hillol Bala, Indiana University, USA
Kevin Bauer, University of Mannheim, Germany
Roberta Bernardi, University of Bristol, UK
Michael Chau, The University of Hong Kong, Hong Kong
Alain Chong, University of Nottingham Ningbo China,
China Kieran Conboy, National University of Ireland Galway, Ireland
Yogesh Dwivedi, Swansea University, UK
Amany Elbanna, Royal Holloway University of London, UK
Weiguo (Patrick) Fan, University of Iowa, USA
Sumeet Gupta, Indian Institute of Management Raipur, India
Karlheinz Kautz, RMIT University, Australia
Stan Karaniosis, University of Queensland, Australia

Yeongin Kim, Virginia Commonwealth, USA
Ajay Kumar, EM Lyon, France.
Marijn Janssen, TU Delft, Netherlands
Agam Gupta, Indian Institute of Technology Delhi, India.
Shivam Gupta, NEOMA Business School, France
Taha Havakhor, McGill University, USA
Mary Lacity, University of Arkansas, USA
Xin (Robert) Luo, University of New Mexico, USA
Patrick Mikalef, Norwegian University of Science & Tech, Norway
Ilias O Pappas, University of Agder, Norway
Uthaysankar Sivarajah, University of Bradford, United Kingdom
Kai Spohrer, Frankfurt School of Finance and Management, Germany
Sujeet Sharma, Indian Institute of Management Nagpur, India.
Samuel Fosso Wamba, Toulouse Business School, France
Amber Young, University of Arkansas, USA
 
Special Issue Editors’ Bios
Varun Grover is the George & Boyce Billingsley Endowed Chair and Distinguished Professor at the Walton College of Business, University of Arkansas. His work, over a 30+ year career, has focused on IT value and impact, with recent work addressing digital strategy, impacts, and value. He has consistently been ranked among the top five researchers globally, based on his publications in top journals like MIS Quarterly, Journal of MIS, and Information Systems Research (over 400 publications), citations (over 50,000 citations) and an h-index (of 100). He has received numerous awards for teaching and research on IT/digitalization/AI business impacts, served multiple terms as Senior Editor of premier IS journals like MISQ, JAIS, and played major roles like Conference, Program, Doctoral Consortium, and MIS Camp Co-Chair at ICIS and AMCIS conferences. He is an AIS Fellow and a recipient of the AIS LEO award for lifetime achievement in the field of IS.

Arpan Kar is a Professor in Information Systems in Indian Institute of Technology Delhi, India and holds a Chair Professorship in Data Science. He works in the space of artificial intelligence, digital platforms and apps, social media and ICT-based public policy. He has authored over 150 publications of which over 60 publications are in ABDC A, ABS 3 or WoS Q1 journals of which 13 articles are in ABDC A* journals (H-Index 47). He is the Editor in Chief of International Journal of Information Management Data Insights, published by Elsevier. He has been a Guest Editor for journals like Decision Support Systems, Industrial Marketing Management, International Journal of Information Management, Information Systems Frontiers, Australasian Journal of Information Systems, etc. He has received over 20 national and international awards like the Research Excellence Award by Clarivate Analytics for highest citations from 2015-2020, Basant Kumar Birla Distinguished Researcher Award for the count of ABDC A*/ABS 4 level publications in India over 5 years and the Best Seller Award from Ivey / Harvard Cases in 2020 for his case on social media analytics.

Rajiv Sabherwal is a Distinguished Professor, Edwin & Karlee Bradberry Chair, in the Walton College of Business at University of Arkansas. He has published on the management, use, and impact of information technologies in top IS journals like Information Systems Research, MIS Quarterly, Journal of MIS, Journal of AIS, and Management Science. He has performed numerous editorial and conference leadership roles, including Editor-in-Chief for IEEE Transactions on Engineering Management, Conference Co-Chair for International Conference on Information Systems, Program Co-Chair for Americas Conference on Information Systems, and Senior Editor for MIS Quarterly. He currently serves as Senior Editor for Journal of AIS, and Journal of Strategic Information Systems, Department Editor for Decision Sciences, and a member of the editorial board for Journal of MIS. He is a recipient of the AIS LEO lifetime achievement award, a Fellow of IEEE, and a Fellow of the AIS.

Spyros Angelopoulos is an Associate Professor at Durham University Business School, in the UK. Before joining Durham University, he held academic appointments at Tilburg University in the Netherlands, the University of Lugano in Switzerland, and the University of Nottingham in the UK. His research focuses on user behavior on digital platforms, organizational adaptation during digital transformation, and on security and privacy issues of digital platforms. He has served as Guest Editor in special issues at the Journal of Operations Management, and the International Journal of Information Management. He is on the Editorial Board of the International Journal of Information Management, Senior Editor at the Information Systems Frontiers, and Associate Editor at the International Journal of Information Management Data Insights. He has served as Associate Editor at several tracks of AIS conferences, he is co-chairing the track on “AI is Information Systems Research and Practice” at ECIS 2024 and is co-chairing the UKAIS 2024 conference. He has been selected as ‘Faculty Expert’ by Google.

Hartmut Hoehle holds the chair of Enterprise Systems and is head of the Management Analytics Center at the Mannheim Business School. Prior to joining the University of Mannheim, he was an Associate Professor of Information Systems at the Sam Walton College of Business at the University of Arkansas. The main thrust of his research is to understand how firms can design, implement and use enterprise systems to support employees and service customers. In the past, he has particularly focused on the design of enterprise systems that can be used to deliver services and products through omni-channel environments. His work has appeared in MIS Quarterly, Journal of MIS , European Journal of Information Systems, Journal of the Association for Information Science and Technology, Decision Support Systems and others.

References
1.         Amari, S.I. (1972). Learning patterns and pattern sequences by self-organizing nets of threshold elements. IEEE Transactions on computers, 100(11), 1197-1206.
2.         Davison, R.M. (2022). Impact and implications for practise. Information Systems Journal, 1-5.
3.         Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (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, 102642.
4.         Forbes (2023). Is It True That Generative AI ChatGPT Will Flood The Internet With Infinite Content, Asks AI Ethics And AI Law. (Link)
5.         Hacker, P., Engel, A., & Mauer, M. (2023). Regulating chatgpt and other large generative AI models. arXiv preprint arXiv:2302.02337.
6.         Heising, L., & Angelopoulos, S. (2022). Operationalising fairness in medical AI adoption: detection of early Alzheimer’s disease with 2D CNN. BMJ Health & Care Informatics, 29(1).
7.         IBM (2023). What is generative AI, what are foundation models, and why do they matter? (Link)
8.         Italian  Directive (2023).  Artificial intelligence: stop  to ChatGPT by  the Italian SA. (link)
9.         Kushwaha, A. K., & Kar, A. K. (2021). MarkBot–a language model-driven chatbot for interactive marketing in post-modern world. Information Systems Frontiers, 1-8. (Link)
10.       MD (2023). About MidJourney. (Link)
11.       OpenAI. (2023a). ChatGPT. (Link)
12.       OpenAI. (2023b). Content Moderation. (Link) 
13.       Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125.
14.       Stable Diffusion (2023). Stable Diffusion Online. (Link)
15.       Stokel-Walker, C. and Noorden, R.V. (2023). What ChatGPT and generative AI mean for science. Nature, 614, 214-216.
16.       Struijk, M., Ou, C.X., Davison, R.M., & Angelopoulos, S. (2022). Putting the IS back into IS research. Information Systems Journal, 32(3), 1-4.
17.       Turing, A.M.     (1950). Computing machinery and intelligence. Mind, 59, 433–460. (Link)