This practitioner paper examines how multinational organisations can scale digital transformation in highly regulated environments where compliance requirements vary across countries and functions. While many organisations successfully launch digital pilots, far fewer manage to scale them into enterprise-wide transformation. Drawing on 26 semi-structured interviews conducted within Roche, a global pharmaceutical company, we examine how two AI-enabled initiatives, AI-driven translation and robotic process automation for regulatory tasks, were scaled across complex regulatory environments. The findings identify three organisational development areas required for scalable digital transformation: building a mindset and capacity for scaling pilots, embedding standardisation and integration and managing information flows under regulatory complexity. We further identify two mechanisms that enable these development areas to translate into scalable outcomes: impact assessment as a stage-gate discipline and visioning around information flows. From this analysis, we distil five lessons for practice: (1) treat pilots as stepping stones rather than isolated experiments; (2) design digital pilots for reusability and interoperability; (3) embed regulatory adaptability into pilot design; (4) use impact assessment to govern scale-up decisions and (5) anchor digital initiatives in a shared vision of information flows. These findings offer practical guidance for organisations seeking to scale digital transformation while maintaining coherence, compliance and enterprise-wide impact in highly regulated industries.
ABSTRACT Although the rapid advancement of artificial intelligence (AI) has introduced significant ethical challenges, many studies have assumed a consensus on moral actions and outcomes, often overlooking the normative uncertainty inherent in AI development and use. Because the debate over which actions are morally appropriate in AI development and use, and in human–AI interaction, has intensified with AI's increasing autonomy, inscrutability, and learning capacity, this normative uncertainty warrants careful examination. Existing literature reviews have tended to reinforce the assumption of low normative uncertainty by emphasizing the similarity of the studies they examine. In this study, we conduct a problematizing review of empirical research on the ethics of AI published from 2010 to 2025. Drawing on two metaethical debates—the debate over the development of moral judgements (rationalist vs. non‐rationalist views) and the debate over their dynamics (absolutist vs. contextualist views)—we analyse how current research constructs moral judgements when studying AI and its interactions with humans. We identify two dominant field‐level ethical assumptions: that moral judgements about AI rely primarily on rational deliberation and that such judgements remain static across contexts. These assumptions are shared across three research domains: ethical AI development and governance, ethical evaluation of AI, and joint human–AI agency. Moreover, the assumptions shape the framing of research questions, the selection of research methods, and the conceptualization and measurement of constructs and relationships. By making these assumptions explicit, our study creates opportunities for theoretical inquiry into normative uncertainty. We propose five research approaches for examining rational, nonrational and context‐sensitive moral processes, offering scholars in the Information Systems discipline new pathways for theorizing ethical AI.
This study examines how issues across boundaries influence contention dynamics in a movement of movements on social media. Participants utilize social media to support or oppose goals and actions related to their visions regarding societal changes. As the boundaries of movements are fluid on social media, we investigate the actors' identities, issues, and activities and how they interact with each other. We extend information systems theories of boundary spanning and conflict dynamics in social media and social movements literature to further understand how issues within and across boundaries influence contention dynamics in a movement of movements. We employed iterative qualitative and quantitative analyses using the computational theory construction approach to analyse social media messages from Twitter (now X) during a climate movement of movements. We identified key themes related to contention dynamics to understand how social media participants may collaborate or dispute with each other in movement of movements. Our model reveals the evolution of social media-enabled association and contention dynamics of movement of movements and how the ideological differences arise when negotiation influences digital activism for climate-related goals and resolutions. This paper contributes to the understanding of how issues spanning specific social movements can lead to both conflicts and synergies in a movement of movements.
Referral strategies, whereby existing platform users recruit new users and thereby strengthen same-side network effects, have long been central to digital platforms' efforts to grow the installed user base. Current referral strategies often require the referee to complete a transaction as a prerequisite for the referrer to receive the reward, something which reduces the immediacy of positive reinforcement, thereby weakening the incentive of participation. Drawing on interview and platform-related data from users on Pinduoduo, the fastest-growing e-commerce platform in China, we study a new form of selfish referral strategy that is unbounded by dyadic relations and time delays. We develop a theoretical model of a multi-motivational selfish referral strategy that consists of three interrelated phases: the initiating phase, the continuing phase, and the aborting phase. Within each phase, we reveal the compositions and interrelationships of psychological, social, and technological motivational mechanisms. We contribute to research on platform user growth and referral strategies. We conclude by discussing the theoretical and practical implications of our model.
This study draws on Instagram-related data to examine how and why the equity and equality principles of fairness govern police communication with socially disadvantaged neighbourhoods on social media. Using the computationally intensive theory construction methodology and drawing on institutional theory and the framework for the interplay of digital technologies and social justice, we identify three forces that serve as sources of two tensions shaping equity-equality conflict in police social media communication with socially disadvantaged communities. These three forces include (1) an institutional pull toward equality, (2) police officers' push against equality, and (3) residents' push against received equality or equity treatments. The two central equity-equality tensions include (1) the conflict between organizational commitments to equality and officers' attempts to pursue equity in socially disadvantaged neighbourhoods, and (2) the plausible dissonance between institutional intent and residents' preferences for the treatment they receive. Our findings highlight the importance of aligning the broader institutional pursuit of fairness in social media communication with the specific needs and expectations of disadvantaged communities. This study contributes to theory by proposing a framework that conceptualizes equity-equality tensions in social media communication and to practice by offering insights for law enforcement and policymakers, among others.
To examine how information systems (IS) practitioners think in action, we rely on Donald A. Sch & ouml;n's general theory of the 'reflective practitioner'-a theory that Sch & ouml;n developed to explain how practitioners in architecture, psychotherapy, engineering, planning, and management think in action, and that we apply to IS practitioners. We go over Sch & ouml;n's explanation that a practitioner's thinking-in-action consists of what he calls five moments: knowing-in-action, surprise, reflection, criticism and on-the-spot experiment. Then, based on our interviews with a technical IS practitioner and a managerial IS practitioner, we show how their thinking in action demonstrates each of the five moments. Finally, based on the five moments, we offer recommendations to IS researchers on how to write papers to better support the work of IS practitioners.
Healthcare organisations are increasingly pursuing digital transformation (DT) but often struggle to achieve meaningful progress. Traditional IT operating models (ITOMs), defining how IT delivers value through structure, governance and technology, frequently lack the agility and maturity required for DT. Consultancy-led diagnostics and intervention plans also tend to fall short during execution and often overlook structural evaluations. This practitioner paper presents a scientifically grounded, practice-oriented approach to executing DT initiatives, based on a longitudinal case study at a leading Dutch radiotherapy clinic. In this case, the DT initiative focused on structural interventions within the ITOM, including its governance, sourcing, organisational structures and skills. The DT programme emphasised structural changes in day-to-day IT governance and decision-making process within the ITOM, while digital maturity, user satisfaction and IT cost indicators were used as outcome measures embedded in governance cycles to support continuous learning and prioritisation over time. A distinctive feature of this approach is its explicit integration of human factors through co-creation between IT and clinical leaders, multidisciplinary collaboration and adaptive governance. The methodology operationalises psychological and relational dynamics as early execution enablers rather than treating them as downstream effects of technical change. Ownership of digital initiatives gradually shifts from IT departments to care teams, while maintaining CIO accountability for coherence across the ITOM. These findings offer actionable guidance for healthcare leaders in similar contexts on how to integrate governance, ownership, capability development and measurement into day-to-day governance and prioritisation practices, thereby fostering a psychological shift towards engagement and shared responsibility in DT.
Sustainability has become a key priority for firms, and the increasing number of mandatory regulations only heightens its importance. The Corporate Sustainability Reporting Directive (CSRD) introduces stringent non-financial disclosure requirements, significantly expanding the scope and breadth of reporting, particularly for those operating in industries with global and complex supply chains. A major challenge companies face with sustainability reporting is that it requires collecting, processing, and interpreting thousands of data points, which have not been systematically collected or analysed previously. While many companies tackle the challenges in a reactive, regulation-by-regulation manner, CSRD provides a unique opportunity to rethink and elevate sustainability data management to support forward-looking sustainability initiatives and better decision-making. To guide companies in managing sustainability data proactively, we introduce the Data Excellence Model (DxM) for Sustainability as a comprehensive data management framework that links regulatory demands to concrete business goals, organisational and technical enablers, and measurable results. We illustrate the framework through an exemplary front-runner case of a leading European online fashion retailer and highlight key success factors and practices the firm undertook to holistically and systematically tackle the complex data requirements of CSRD. Building on the DxM for Sustainability, we outline a maturity assessment that companies can use as a diagnostic tool for proactive sustainability data management.
Artificial intelligence (AI)-enabled automation is often utilised in systems that perform human tasks. While useful, these technologies pose a risk of adverse consequences in the case of AI failures as they operate in open environments, learn and are subject to continuous updates. These factors create uncertainty about AI performance that users need to cope with. At the same time, trust in AI-enabled automation has become an elusive target that continuously evolves. In this study, we focused our investigation on the process through which users continuously form trust towards AI-enabled automation. Drawing from coping theory, we conducted a case study of Tesla's advanced driver assistance system and qualitatively analysed longitudinal data on real-life use experiences from a Tesla driver forum. Our findings suggest that users form and evaluate trust continuously as they encounter new experiences and gain new information about AI-enabled automation, which leads to varying levels of compartmentalised trust and diverging adaptation actions over time. Building on these findings, we elaborate on a process model and change mechanisms of users' trust to provide a better understanding of how trust in AI-enabled automation evolves over time.
Paradox is a powerful lens for theorising information systems (IS) phenomena. However, as scholars apply the term to fundamentally different phenomena, 'paradox' risks dilution. Much confusion stems from conflating two concepts under the same English label 'paradox': chaos-puzzles (seemingly impossible ideas, aligned with the Chinese term 'bei lun'); and spear-shields (contradictory pathways of action, aligned with the Chinese 'mao dun'). Through a hermeneutic analysis of 142 IS papers, we advance a framework of eight genres defined by variations in paradox grounding (chaos-puzzle vs. spear-shield) and paradox articulation (sociocentric vs. technocentric; sensing vs. responding). The framework offers an account of paradoxical IS theorising that is pluralistic by clarifying multiple meanings of paradox; directive by guiding authors, editors, reviewers and readers in articulating and evaluating genre-sensitive contributions; and generative in identifying new avenues for paradoxical IS theorising. By mapping out different ways in which IS scholars can engage with chaos-puzzles and spear-shields, we bring analytical clarity to paradoxical IS theorising and offer a foundation for its further development.
In today's dynamic environment, IT organisations face numerous challenges from disruptions such as natural disasters, cybersecurity threats, international conflicts, evolving regulations, pandemics and recent global incidents such as the CrowdStrike outage. To navigate these complexities and maintain operational continuity, companies must build IT organisational resilience, ensuring they can adapt, recover swiftly and continue delivering critical services. This study surveyed 157 IT leaders across various industries to explore how IT service quality and IT governance practices can bolster IT organisational resilience to face these turbulent events. Our findings indicate that both IT service quality and IT governance maturity are crucial building blocks of IT organisational resilience. The study shows that during environmental turbulence, leveraging these factors can help mitigate impacts and effectively address challenges. We also provide IT leadership with guidance on how these 'people' and 'process' approaches can be leveraged to build IT organisational resilience.
Government leaders across the globe are grappling with how to harness and integrate artificial intelligence (AI) to enhance public service delivery and efficiency. Yet, a key challenge faced is how to build and maintain the trust of stakeholders. Trust is critical for the acceptance and sustained adoption of AI technologies, as well as to gain the requisite funding, resourcing and authorization to implement AI solutions. However, inherent features of AI-its autonomous capabilities, dynamic learning, and inscrutable operating logic-create challenges for trust, particularly in public services that are subject to high expectations of accountability, transparency, and fairness. We present an in-depth case analysis of how an Australian government department was able to deploy a solution that was widely accepted, and identified as an exemplar of trustworthy AI use. We identify six trust-supporting approaches: benevolent customer-centricity, radical honesty, diverse input, rigorous development and testing, human discretion in decision-making, and aligning the authorising environment. For each approach, we explain how and why it supports trust, and then contrast that approach with a prominent, but widely distrusted application in the Australian government. We conclude with implications for public sector leaders seeking to engender trust in their use of AI.
Organisations should harness big data analytics to transform their supply chains and archive resilience. Despite the critical role of big data analytics in shaping supply chain resilience, existing literature on their relationship remains starkly fragmented and inconclusive. To address this critical paucity and reconcile the prevailing inconsistencies, we employed a multi-method research design, combining both qualitative and quantitative approaches to confirm our theoretical model. We first utilise deductive qualitative analysis across multiple cases to validate the underlying mechanisms and boundary conditions through which big data analytics capability influences supply chain resilience. Subsequently, our quantitative analysis unveils that the nexus between big data analytics capability and supply chain resilience operates through two mediated pathways (traditional and digital SC capabilities) under institutional environments (i.e., government intervention and guanxi). Specifically, government intervention amplifies both mediating effects, whereas guanxi weakens the mediation effect of digital supply chain capability. Furthermore, our results reveal contingency-dependent mediation pathways linking big data analytics capability and supply chain resilience. We find that weak institutional forces position digital supply chain capability as the primary pathway, whereas strong institutional forces shift the emphasis toward traditional supply chain capability as the central mechanism linking BDA capability to supply chain resilience. This study contributes to the emerging literature on Information Systems (IS) by theoretically exploring and empirically validating the mechanisms and boundary conditions through which big data analytics capability affects supply chain resilience.
The recent rise of Generative Artificial Intelligence (GenAI) is fundamentally changing the way businesses operate, with many now investing heavily in this technology. However, businesses are still exploring ways to extract value from GenAI and develop organisational capabilities. In this practitioner paper, we describe how Apollo Tyres Limited (ATL), a traditional tyre manufacturer in India, utilised GenAI to transform its IT service desk operations as a pilot project for enterprise-wide adoption and the tangible results it achieved from this integration. We identify the following four capabilities that ATL developed using GenAI: (i) enhanced management of customer interactions, such as overcoming delays due to multi-lingual conversations; (ii) proactive resolution of incidents, such as faster categorisation based on their textual descriptions; (iii) proactive management of IT assets, such as dynamic prediction of potential failures and system inefficiencies; and (iv) strategic analysis of operational data, such as a GenAI-driven frequently asked questions system. In addition, we illustrate ATL's actions to facilitate GenAI implementation in two broad categories: (i) building a foundation for GenAI implementation and (ii) realising GenAI-enabled capabilities. Seven lessons learnt are consequently generated to guide other enterprises in similar efforts to implement GenAI at IT service function as well as to scale GenAI initiatives across the organisation.
Most organisations are confronting a dilemma: the need to embark on digital transformation to be competitive and sustainable versus the inherently high costs and extremely high risks involved. Lack of executive support has long been identified as a leading cause of project failure, including digital transformation projects. What is not clear is what that support should entail. The prevailing practice-to strategise, delegate and monitor the transformation, but not get too involved-stems from a time when projects, organisations, and the context they operated in were less complex. Our research was motivated by the lack of guidance for Boards and C-suite despite the criticality of their role. We consulted over100 organisational leaders and followed an in-flight digital transformation, comparing transformations perceived to be successful with those that were not. We learnt that the governance and leadership traditions that serve stable organisations create blind spots to the forces at play when transforming. We term these forces transformation friction and identify a mindset shift and four practices the Board and C-suite can use to improve the pace and outcomes of digital transformation.
