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.

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.

Background Argument evaluation, including recognizing argument structures and fallacies, is crucial for academic success, yet many university students struggle with these abilities. Drawing on the motivational model of integrative processing and expectancy-value theory, this study examined how distal motivational factors (topic involvement, need to evaluate, academic self-concept in argumentation) relate to proximal appraisals (expectancy of success, task value, emotional cost) and, in turn, to argument evaluation. Method Data from 675 preservice teachers were analyzed using structural equation modeling to test these assumptions within a path model. Results The recognition of argument structures was positively related to recognizing fallacies. Emotional cost was negatively related to both abilities, whereas task value was positively related to the recognition of fallacies. Proximal motivational factors were systematically related to distal motivational factors such as topic involvement, need to evaluate, and academic self-concept. Relations involving topic involvement varied depending on the presumed prior topic knowledge. Conclusions The findings highlight the importance of fostering argument evaluation and underscore the role of recognizing argument structures for the recognition of fallacies. In addition, the findings indicate the need to reduce emotional cost in instructional settings and to focus on topics that combine high topic involvement with sufficient prior knowledge.

Advancements in artificial intelligence (AI), particularly in generative AI and agentic AI, have intensified challenges related to transparency and explainability. While explainable AI (XAI) research has evolved to facilitate human interaction with such complex, black-box systems, research and practice lack clarity on the causal chain linking explanations, user perceptions, and real-world outcomes, a relationship that remains conceptually fragmented. To address this gap, we conducted a systematic literature review of 107 experimental user studies on XAI. We developed a conceptual framework guided by the stimulus-organism-response-consequences (S-O-R-C) model to systematize current human-XAI research and examine how users respond to explanations. Our study contributes to the literature by clarifying how explanations shape user interactions and downstream effects in real-world settings. We propose five research directions to help navigate the challenges of emerging AI systems (e.g., LLMs, AI agents) and evolving human-AI delegation.

The infusion of generative AI (GenAI) is already disrupting established services. This technology's generative and agentic nature challenges the design and management of service routines, which have been previously handled primarily by frontline service employees. Guided by organizational routines theory, our longitudinal study (2020-2024) examines how the infusion of GenAI changes routines in customer support services. We gathered interview data from 41 employees, managers, and AI experts in two phases, pre- and post-GenAI. Based on the analysis of the qualitative data, we revealed seven recurring micro-level augmentation patterns, illustrating how GenAI-infused service routines function. The results show that GenAI is primarily embedded in the backstage of knowledge-intensive services, from which it then permeates the frontstage. We contribute to the literature on hybrid human-AI service delivery by identifying augmentation patterns and conceptualizing service permeation via two mechanisms: (1) simultaneous service permeation, which unfolds as employees leverage GenAI in real-time and integrate GenAI's responses, recommendations, and adaptations into the frontstage; (2) sequential service permeation, which emerges as employees perform new routines of documentation and AI feeding to facilitate GenAI's adaptability in frontstage and backstage operations. The MAPs and service permeation mechanisms guide practitioners in integrating GenAI into service routines and managing novel employee-GenAI collaborations.

As artificial intelligence transforms customer interactions, enterprise conversational agents (ECAs) have become essential brand touchpoints. However, organizations often struggle to ensure that these agents consistently express their brand identity. While prior research highlights the importance of conversational design for brand identity, existing studies offer a fragmented understanding of how brand identity manifests in ECAs. This study presents a systematic review (n = 69) to consolidate current insights into the relationship between ECA design and brand identity. The review identifies key design cues that support alignment between conversational style and brand expression and explains how these cues, shaped by user and contextual factors, influence perceptions of both the ECA and the brand. The resulting framework links design elements to brand-related outcomes and provides a structured foundation for practice and research, enabling the development of ECAs that communicate brand identity more effectively within AI-mediated customer interactions.

Technological advancements and evolving value orientations reshape future value creation and pose new requirements for service innovation. While a variety of disciplines are developing new approaches to drive service innovation, this is primarily done in isolation and generates only fragmented solutions. Sociological theory has proposed “boundary objects” as an effective umbrella for communication and cooperation among communities. Therefore, we introduce continuous value shaping (CVS) as a boundary object describing service innovation approaches along five principles. We reflect on this concept through the different disciplinary lenses of researchers in service marketing, information systems, service engineering, sociology of work, and innovation management. These perspectives highlight how the CVS principles already connect to discourses within the individual disciplines. However, the CVS concept will not only provide an umbrella to embrace existing activities in different academic disciplines. It also assists to identify research themes that will benefit from uniting the power of these disciplines, and it can serve as an integrating framework to conceptualize complex service innovation approaches. Thus, the CVS concept should guide both researchers and practitioners to develop and implement novel innovation and transformation efforts—in and across organizations.

Although conversational agents are successfully applied in teaching, it is largely unclear which communication principles should be employed to optimise learning. We examine the influence of common ground (i.e. shared knowledge on which to build during conversation) on learning. In an in-class experiment, students studied with one of two pedagogical conversational agents. The control version provided information without emphasising grounding, whereas the common ground version emphasised grounding, for example, by encouraging students to monitor and repair common ground. After the learning unit, students evaluated their learning experience and the pedagogical conversational agent, after which they were tested on the studied material. Students in the common ground (vs. the control) condition performed better in a post-study knowledge test and engaged longer with the pedagogical conversational agent. Thus, the common ground emphasis facilitated learning with a conversational agent, indicating that grounding principles should be incorporated when designing conversational agents.

Given the critical role of data availability for growth and innovation in financial services, especially small and mid-sized banks lack the data volumes required to fully leverage AI advancements for enhancing fraud detection, operational efficiency, and risk management. With existing solutions facing challenges in scalability, inconsistent standards, and complex privacy regulations, we introduce a synthetic data sharing ecosystem (SynDEc) using generative AI. Employing design science research in collaboration with two banks, among them UnionBank of the Philippines, we developed and validated a synthetic data sharing ecosystem for financial institutions. The derived design principles highlight synthetic data setup, training configurations, and incentivization. Furthermore, our findings show that smaller banks benefit most from SynDEcs and our solution is viable even with limited participation. Thus, we advance data ecosystem design knowledge, show its viability for financial services, and offer practical guidance for privacy-resilient synthetic data sharing, laying groundwork for future applications of SynDEcs.

Emerging phenomena like the increasing volume and variety of available data, machine learning techniques, and, most recently, Generative AI are reshaping our research practices, affording new research methods for testing and developing theory. In this editorial, we discuss our two major observations from running the ‘Next-generation IS Research Methods’ Special Issue: (1) the need for research methods that enhance our understanding of complex and dynamic phenomena and (2) Generative AI as (potential) productivity enhancer. We compile these observations into an organizing framework and discuss possibilities for applying Generative AI in the fields of qualitative, quantitative, and engaged research. We highlight challenges that might occur when applying Generative AI in research and shed light on the changing role of researchers in such settings of human–AI collaboration.