The 10 most-cited articles in Journal of the Association for Information Systems, 2024

The discourse surrounding the societal impacts of generative artificial intelligence (GAI), exemplified by technologies like ChatGPT, often oscillates between extremes: utopian visions of unprecedented productivity and dystopian fears of humanity’s demise. This polarized perspective neglects the nuanced, pragmatic manifestation of GAI. In general, extreme views oversimplify the technology itself or its potential to address societal issues. The authors suggest a more balanced analysis, acknowledging that GAI’s impacts will unfold dynamically over time as diverse implementations interact with human stakeholders and contextual factors. While Big Tech firms dominate GAI’s supply, its demand is expected to evolve through experimentation and use cases. The authors argue that GAI’s societal impact depends on identifiable contingencies, emphasizing three broad factors: the balance between automation and augmentation, the congruence of physical and digital realities, and the retention of human bounded rationality. These contingencies represent trade-offs arising from GAI instantiations,…

The promise of AI for academic work is bewitching and easy to envisage, but the risks involved are often hard to detect and usually not readily exposed. In this opinion piece, we explore the feasibility, opportunities, and risks of using large language models (LLMs) for reviewing academic submissions, while keeping the human in the loop. We experiment with GPT-4 in the role of a reviewer to demonstrate the opportunities and the risks we experience and ways to mitigate them. The reviews are structured according to a conference review form with the dual purpose of evaluating submissions for editorial decisions and providing authors with constructive feedback according to predefined criteria, which include contribution, soundness, and presentation. We demonstrate feasibility by evaluating and comparing LLM reviews with human reviews, concluding that current AI-augmented reviewing is sufficiently accurate to alleviate the burden of reviewing but not completely and not for all cases. We then enumerate the opportunities of AI-augmented reviewing and present open questions. Next, we…

Rapid advances in artificial intelligence (AI), including recent generative forms, are significantly impacting our lives and work. A key aspect of our work as IS researchers is the publishing of research articles, for which peer review serves as the primary means of quality control. While there have been debates about whether and to what extent AI can replace researchers in various domains, including IS, we lack an in-depth understanding of how AI can impact the peer review process. Considering the high volume of submissions and limited reviewer resources, there is a pressing need to use AI to augment the review process. At the same time, advances in AI have been accompanied by concerns about biases introduced by AI tools and the ethics of using them, among other issues such as hallucinations. Thus, critical issues to understand are: how can AI augment and potentially automate the review process, what are the pitfalls in doing so, and what er the implications for IS research and peer review practice. I will offer my views on these issues in this opinion piece.

In this paper, we revisit the issue of collaboration with artificial intelligence (AI) to conduct literature reviews and discuss if this should be done and how it could be done. We also call for further reflection on the epistemic values at risk when using certain types of AI tools based on machine learning or generative AI at different stages of the review process, which often require the scope to be redefined and fundamentally follow an iterative process. Although AI tools accelerate search and screening tasks, particularly when there are vast amounts of literature involved, they may compromise quality, especially when it comes to transparency and explainability. Expert systems are less likely to have a negative impact on these tasks. In a broader context, any AI method should preserve researchers’ ability to critically select, analyze, and interpret the literature.

The use of conversational AI agents (CAs), such as Alexa and Siri, has steadily increased over the past several years. However, the functionality of these agents relies on the personal data obtained from their users. While evidence suggests that user disclosure can be increased through reciprocal self-disclosure (i.e., a process in which a CA discloses information about itself with the expectation that the user would reciprocate by disclosing similar information about themself), it is not clear whether and through which mechanism the process of reciprocal self-disclosure influences users’ post-interaction trust. We theorize that anthropomorphism (i.e., the extent to which a user attributes humanlike attributes to a nonhuman entity) serves as an inductive inference mechanism for understanding reciprocal self-disclosure, enabling users to build conceptually distinct cognitive and affective foundations upon which to form their post-interaction trust. We found strong support for our theory through two randomized experiments that used custom-developed text-based and voicebased CAs.…

The aim of this opinion piece is to examine the responsible use of artificial intelligence (AI) in relation to academic journal publishing. The work discusses approaches to AI with particular attention to recent developments with generative AI. Consensus is noted around eight normative themes for principles for responsible AI and their associated risks. A framework from Shneiderman (2022) for human-centered AI is employed to consider journal publishing practices that can address the principles of responsible AI at different levels. The resultant AI principled governance matrix (AI-PGM) for journal publishing shows how countermeasures for risks can be employed at the levels of the author-researcher team, the organization, the industry, and by government regulation. The AI-PGM allows a structured approach to responsible AI and may be modified as developments with AI unfold. It shows how the whole publishing ecosystem should be considered when looking at the responsible use of AI—not just journal policy itself.

The Journal of the Association for Information Systems has long had a reputation for promoting theory development. Yet theory development can be experienced as risky and frustrating because of a lack of divergence and convergence—both in terms of ideas and in the social dynamics among human theorists. These dichotomies can stymie progress and lead to unfinished works. Misconceptions about theory can also hamper advances. We examine the ways in which generative artificial intelligence (gAI) tools may be useful in developing theory in information systems (IS) through human-gAI collaboration, thus forging new frontiers in IS theorizing.

In the rapidly evolving landscape of academic research, artificial intelligence (AI) is poised to revolutionize traditional academic peer review processes and knowledge evaluation systems. We believe that the growing collaboration between humans and AI will disrupt how academics assess scholarly manuscripts and disseminate published works in a way that facilitates the closing of gaps among diverse scholars as well as competing scholarly traditions. Such human-AI collaboration is not a distant reality but is unfolding before us, in part, through the development, application, and actual use of AI, including language learning models (LLMs). This opinion piece focuses on the academic peer review process. It offers preliminary ideas on how human-AI collaboration will likely change the peer review process, highlights the benefits, identifies possible bottlenecks, and underscores the potential for democratizing academic culture worldwide.

Scientific journals, crucial components of our epistemic infrastructure, have continuously adapted to the changing technological landscape. Today, we stand at the precipice of a transformative phase brought about by generative AI, specifically large language models such as OpenAI’s GPT and Google’s Bard. In this opinion piece, I examine the implications of these models for the future of scientific journals and various stakeholders in the scientific community, including journals, scholars, and universities. To envisage the future trajectory of scientific journals, it’s imperative to comprehend the operational mechanisms of these models and the fundamentally recombinatorial nature of human knowledge creation. I suggest that one of the significant roles generative AI can play is facilitating “long jumps” in our knowledge exploration process. I further propose decentralization and deferred and temporary binding as two crucial characteristics of the evolving epistemic infrastructure that supports precarious knowledge production. I foresee a future where scientific journals extend beyond…

Digital workplace technologies lead to increasing levels of transparency. This, however, can invoke tensions: On the one hand, transparency facilitates employee empowerment, and on the other hand, transparency drives employee surveillance and privacy concerns. Furthermore, transparency has traditionally been one-sided, with control vested in managers, leading to a panoptical scenario. The contemporary workforce demands empowerment and bidirectional, inverse transparency, which challenges the assumptions of agency theory. These tensions are pertinent challenges that are even more salient today. Following a design science research process, we examine how a technical solution could be designed to mitigate these tensions and overcome the panopticon. Drawing on the knowledge base of stewardship theory and the concept of inverse transparency, we derive design requirements, design principles, and design features for an artifact that instantiates inverse transparency and drives the movement to stewardship behavior. We develop three theoretical conjectures on the artifact’s implications on…