Journal of Information Technology

Generative Artificial Intelligence (GenAI) is rapidly entering academic work, including the peer review process. We examine the implications of GenAI for the peer review process – including its use by reviewers, editors, and authors (e.g. for pre-submission self-review or for operationalising review feedback during revision) – and articulates the position of the Journal of Information Technology (JIT). We advance two foundational premises: first, peer review should be seen as a creative process, sometimes even becoming a co-creation with the authors and editors, rather than mechanistic quality control; second, GenAI may augment peer review but should not replace or outsource scholarly judgement and insight. Drawing on recent state-of-the-art analyses of GenAI in peer reviewing, we identify four requirements for ethical use – confidentiality, accountability, bias mitigation, and transparency – and discuss how these principles apply across the reviewing process. We outline where GenAI can provide legitimate support, such as summarisation, language improvement, compliance checking, and workflow management, while emphasising that evaluative judgement must remain with human reviewers and editors. GenAI must not become a shortcut for efficiently producing good papers on average, while steering us away from papers that are more demanding to review but at the same time offering potentially much more impactful contributions. We articulate JIT’s editorial stance for AI-assisted peer review and propose role-specific guidance for authors, reviewers and editors. We also outline a research agenda for studying the impact of GenAI on review quality, timeliness, fairness and trust. Overall, we argue that peer review should evolve through responsible AI augmentation while preserving human-centred governance and the accountability that underpins scholarly evaluation.

Firms are increasingly adopting predictive artificial intelligence (AI) to address economic, social and environmental sustainability challenges. However, there remains limited understanding of how predictive AI contributes to the development of AI capabilities that underpin sensing, seizing and reconfiguring dynamic capabilities for business sustainability. Drawing on a multi-case study, this research identifies six AI capabilities: pattern recognition and emotional intelligence (linked to sensing), agile decision-making and interdisciplinary integration (linked to seizing), and resource orchestration and transformation mastery (linked to reconfiguring). These AI capabilities augment the potential of firms’ dynamic capabilities by enhancing their ability to sense opportunities, seize them strategically and reconfigure resources in response to sustainability imperatives. This study further reveals two distinct yet interrelated approaches through which this augmented potential is realised: a humanistic, stakeholder-focused approach and an instrumental, firm-focused approach. These approaches shape how AI capabilities are embedded in dynamic capability processes and lead to different sustainability outcomes. The humanistic approach prioritises social and environmental objectives, with economic benefits as secondary, whereas the instrumental approach foregrounds economic performance, with social and environmental sustainability as secondary. By clarifying how AI capabilities and organisational approaches interact to influence dynamic capability realisation, this study advances theoretical understanding of AI-enabled strategic change and its role in achieving business sustainability. The findings contribute to academic discourse and inform managerial and policy decisions.

This research adopts a configuration and business model perspective to enhance our understanding of the interactions between digital technologies and servitization business model elements across different contexts. An exploratory study of 726 manufacturing firms that offer different services was conducted using mixed content analysis and the set-theoretical analysis method. Our findings suggest that digital technology plays multifaceted roles in achieving high profits, relying on how it is configured with other business model components of servitization in different contexts. The findings also suggest that big data and analytics emerge as a consistent enabler of high performance, highlighting its critical role in servitization business model. The varied nature of IoT and cloud computing suggests their impact is contextualized, supporting the idea that companies need to adapt digital solutions to a firm’s situation. Our research theoretically broadens the explanatory framework of the mixed effects of servitization business models, expanding our understanding of how firms adapt their strategies to succeed in varied competitive environments.

Generative artificial intelligence (GenAI), based on large-language models (LLMs), such as ChatGPT, has taken organizations, academia, and the public by storm. In particular, impressive GenAI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation, carry profound implications for the conduct of literature reviews. This impacts science, organizations and the general public, as all can benefit from GenAI-supported literature reviews. Building on the technical foundations of GenAI and grounded in established methodological discourse, this work outlines approaches for conducting literature reviews using both general-purpose (e.g., ChatGPT, Gemini, Claude) and specialized GenAI tools (e.g., Consensus, Elicit). We provide illustrative examples of prompts and suggest methodologically-sound literature review strategies. Throughout this perspective paper, we adopt a balanced approach considering both the opportunities and the risks of relying on GenAI in the conduct of literature reviews. We conclude by discussing philosophical questions related to the effects of GenAI on long-term scientific progress, and also present fruitful opportunities for research on improving the core of GenAI’s technology—its architecture and training data—and suggest open issues in GenAI-based literature reviews methodology.

Information Systems (IS) research is well-positioned but under-equipped to study technological futures at a time when claims about artificial intelligence (AI) are reshaping investment, policy, and public discourse. This perspective advances three arguments. First, IS scholarship should engage more systematically with digital futures, drawing on approaches for reasoning under uncertainty, such as Bayesian methods and established Futures Studies techniques, to distinguish prediction, projection, possibility, and hype. Second, technology hype is itself a legitimate object of IS research, and widely used frameworks such as the Gartner Hype Cycle appear limited in their ability to inform practice. Third, AI serves as a critical test case, combining heavy supply-side investment with unproven demand-side impact and unresolved questions of value and consequence. We propose four analytically distinct lenses for studying AI, namely, capability, adoption, value, and consequence, and identify two underexamined blind spots: bad actors deploying AI at scale and structural over-dependence on imperfect AI. We invite contributions to the Journal of Information Technology that examine how claims about technological futures are produced, circulated, institutionalized, resisted, and realized.