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Scandinavian Journal of Information Systems

Editorial

Welcome to the Winter issue of the 37th volume of the Scandinavian Journal of Information Systems (SJIS)!

In keeping with the journal’s tradition, we announce two transitions on our editorial board. We warmly welcome Ilias Pappas as the new Norwegian editor for the period 2026-2029. This issue also marks the end of the four-year term of Elena Parmiggiani as the Norwegian editor of SJIS. Olgerta Tona from Sweden will be taking the role of editor-in-chief starting in January 2026.

As many readers might recall, the editorial board of SJIS organized an open debate during the SCIS/IRIS 2025 conference in Oscarsborg, Norway, to engage the IRIS community in a discussion of the role of Generative AI (GenAI) in future scholarly practice. The editorial board has been quite active since then and is happy to announce two tangible results! First, we authored an analytical summary of the themes and possible future avenues that emerged during the debate. This report is the first article published in this issue, titled Are We Ready for The New Reality of Scholarly Publishing? A Nordic IS community perspective on GenAI, by Parmiggiani, Tona, Fischer, and Järveläinen.

This article also provides an important backdrop for introducing and motivating the second result of the debate session: a new GenAI policy for SJIS, now available at https://aisel.aisnet.org/sjis/policies.html and effective immediately for all new submissions to SJIS. The new policy is developed with the foundational values of trust, participation, and transparency in mind and is designed as a living policy. We consider it a starting point for the journal’s conversation with the community that is open to evolving as GenAI-related technologies and concerns emerge.

A huge thank you from us to all community members who have taken part in the conversation and will continue to do so!

Let us now present the articles and themes in this rich issue, comprising four regular articles, a a reflection note, and a special issue on data work in healthcare.

The four regular articles present a timely and critical exploration of the ongoing digital transformation, focusing on two interconnected themes in contemporary practice: the scaffolding role of negotiation in data governance and the foundational challenges of implementing Artificial Intelligence (AI) systems ethically. As public and private organizations in the Nordic countries and beyond continue to explore data-driven operations, these articles shed light on the often-overlooked sociotechnical labour required to translate grand visions into responsible reality. This issue brings together a critical analysis of these challenges, arguing that digital transformation hinges on taking data governance seriously, overcoming fundamental conceptual fragmentation, and engaging in resource-intensive organizational preparation to promote an ethical approach to data-driven work with AI.

We start by investigating the unwritten rules of data governance. Even strong organizational visions of digital transformation often encounter unpredictable tensions in practice, requiring relational skills to maintain functionality. Data governance frameworks, traditionally conceived as formal policies and predefined roles, are usually insufficient when confronted with the daily realities of multi-actor complexity. The article Modalities of Data Diplomacy: How Negotiations Shape Data Governance in Practice by Benfeldt, Zambach, and Gierlich-Joas proposes the notion of data diplomacy as a governance practice rooted in negotiation and interpersonal engagement. The study identifies four specific modalities—charter, at-the-table, doorway, and backchannel diplomacy—that explain how professionals employ relational strategies, varying in their timing (proactive/reactive) and approach (formal/informal), to manage tensions around data ownership, access, and control. This work repositions data governance not as top-down rule enforcement, but as ongoing, context-specific labour aimed at mediating access, resolving tensions, and achieving consensus in everyday work.

The continuous evolution and inherent complexity (or ‘in-the-making’ nature’) of sociotechnical systems involved in digital transformation often results in conceptual ambiguity—both in theory and in governance practice. This is particularly evident in the case of AI, where the opacity of AI systems directly contributes to a lack of clarity and consistency in defining the concepts used to govern them.

The immediate consequence of this trend is conceptual fragmentation regarding what responsible digital transformation means and entails. This theme is further explored in two compelling reviews that dissect the state of ethics in AI and Data-Driven Decision-Making (DDDM). The article Building Ethics into AI: A Cross-Disciplinary Systematic Review, by Flatås, Nordsteien, Eide, Lysdahl, Sanchez, Stendal, Turk, and Eide provides a systematic review of efforts to embed moral responsibility directly into AI systems, finding a significant gap between global policy ambitions and the current technical state of the art. The review concludes that the debate on AI ethics suffers from a predominance of a ‘thin’ conception of ethics, often reduced to the technical operationalization of a single moral principle, primarily fairness or the mitigation of data bias. The authors highlight a critical lack of cross-disciplinary collaboration, with computer scientists and engineers dominating the field, thus limiting the integration of ethical pluralism found in philosophical and social science discourse.

This finding is reinforced by the article Understanding Ethics and Agency in Data-Driven Decision-Making—a literature review by Lehto, Järvinen, and Alastalo, which, through a scoping review, confirms a wide range of loosely defined, fragmented, and problem-oriented ethics-related concepts in data-driven decision-making (DDDM) literature. The challenge of ethics extends directly into the question of agency and responsibility in our increasingly automated workplaces. Lehto et al.’s article introduces a sociomaterial perspective to analyse how the capacity to act is distributed among human, technological, and distributed forms of agency within DDDM. The study cautions that detaching ethics from agency risks creating a state of organized irresponsibility, in which accountability is diffused, and ethical issues are ignored.

This concern is particularly relevant for the Nordic public sector, as detailed in our final featured article. In Navigating the Future: Planning for AI in Public Services, Grøder, Parmiggiani, and Williams investigate the foundational preparatory work for AI deployment both within and across public organizations. They lift the veil on how the often-overlooked early-stage, pre-implementation phase of AI shapes the future performance and use of (responsible) AI. The authors take AI’s nature as a phenomenon-in-the-making as a starting point, where organizing visions for its use—such as demanding clarity in data and laws, or establishing a need for standardized terms—emerge not from top-down mandate, but from the bottom-up practices of wrestling with messy data, existing regulations, and the fundamental question of legal scope of action.

Collectively, these four regular articles urge IS researchers and practitioners to shed further light on the nuanced practices of cross-disciplinary collaboration and diplomatic engagement to ensure that data and our AI systems are governed responsibly.

Finally, SJIS is always interested in methodological reflections. Lanamäki contributes a provocative reflection note, Rethinking Observability Beyond Critical Realism, which critiques the field’s scant attention to observability—a prominent concept in Critical Realism’s stratified ontology. Taking this as a point of departure and challenging the premise of unobservability, Lanamäki argues that IS research should favour observation grounded in instrumentation, experimentation, and testimony.

Following the regular issue, we invite you to enjoy the special issue on data work and information systems in healthcare curated by the guest editors Pernille Bertelsen, Claus Bossen, Yunan Chen, and Katheen Pine in their introductory article Constructing healthy data: Data work in healthcare. In closing, we send a warm wish of a joyful 2026 to all the reviewers, authors, guest editors, and readers of this journal!

Elena Parmiggiani, Olgerta Tona, Louise Harder Fischer, and Jonna Järveläinen