The information systems (IS) field has employed the term alignment to denote the need to functionally align IT strategies with business strategies. The field has treated the two strategies as separate spheres of activity where digital technologies are characterized as discrete technical resources to be coordinated with other organizational and managerial resources and capabilities to create business value. The alignment paradigm originated in the 1980s and has undergone significant evolution as digital technologies have advanced in a rapid pace providing new leverage points to create value. In this commentary we argue for a novel strategy paradigm, which frames the digital technology-strategy nexus through the ontological lens of digital objects and how firms in deeply pervasive digital environments create, curate, and recombine digital objects into service stacks and embed them operationally, virtually, and contextually for runtime performance to create value. In this paradigm digital technologies are not separate systems functionally aligned with a “given” strategy. Rather, digital objects arranged into service stacks form a central element in formulating and executing digital strategy because the stacks and supporting infrastructures mediate—not just support—strategy formulation and execution. Service stacks with their dynamic embeddings provide continuous opportunities to create and extract value through an expanding set of leverage points. The commentary calls for revived research on the digital technology-strategy nexus, which intentionally diverges from alignment paradigm opening vistas to engage meaningfully with the question: what is effective digital strategy?
Sistem alokasi sumber daya berbasis web menghadapi tantangan konkurensi kritis di mana permintaan simultan bervolume tinggi sering kali menghasilkan anomali data, khususnya double booking. Meskipun batasan teoretis dari kondisi perlombaan Time-of-Check to Time-of-Use (TOCTOU) telah didokumentasikan, efek majemuk latensi jaringan dalam arsitektur terdistribusi dan implikasi performa empiris dari pendelegasian kendali murni pada konstrain basis data masih jarang dieksplorasi. Penelitian ini mengukur kinerja dan integritas data untuk menentukan desain arsitektur mana yang mampu mempertahankan throughput tinggi sekaligus menjaga konsistensi data. Penelitian ini melakukan evaluasi load-testing komprehensif melalui studi ablasi terhadap tiga arsitektur: Application-Level Control (ALC), Database-Level Constraint (DLC) menggunakan EXCLUDE USING GIST pada PostgreSQL, dan arsitektur Hibrid. Di bawah tekanan pengguna virtual ekstrem, ALC terbukti rentan terhadap kebocoran data, di mana latensi jaringan terdistribusi memperburuk insiden double booking hingga 125% dibandingkan server monolitik. Sebaliknya, meskipun DLC menjamin integritas data absolut, pendekatan ini memicu degradasi throughput yang katastrofik anjlok hingga 18,1 RPS pada beban puncak yang disertai penalti latensi berat akibat penguncian eksklusif tingkat indeks dan kelaparan pangkalan koneksi (connection pool starvation). Arsitektur Hibrid memecahkan dikotomi ini dengan memadukan penyaring tingkat aplikasi asinkron sebagai penyerap kejut untuk mengurai kemacetan basis data, sukses mencapai integritas absolut sekaligus mempertahankan throughput tinggi (543,7 RPS). Penelitian ini mengkuantifikasi batas skalabilitas konstrain basis data dan memberikan kerangka kerja rekayasa defense-in-depth yang tangguh untuk sistem ketersediaan tinggi.
iPusnas adalah aplikasi perpustakaan digital yang dikembangkan oleh Perpustakaan Nasional Republik Indonesia (Perpusnas RI) yang memungkinkan pengguna meminjam dan membaca buku digital melalui ponsel pintar. Sebagai salah satu platform perpustakaan digital yang paling banyak digunakan di Indonesia, iPusnas telah menerima ribuan ulasan pengguna di Google Play Store yang mencerminkan berbagai sentimen publik terhadap performa dan fitur aplikasi. Penelitian ini bertujuan menganalisis sentimen ulasan pengguna iPusnas di Google Play Store menggunakan algoritma Random Forest. Data dikumpulkan dengan melakukan scraping ulasan pengguna dari Play Store, dilanjutkan dengan tahap preprocessing meliputi case folding, cleaning, normalisasi, tokenisasi, stopword removal, dan stemming. Pelabelan dilakukan menggunakan metode Lexicon-Based. Ekstraksi fitur menggunakan TF-IDF (Term Frequency-Inverse Document Frequency), dan ketidakseimbangan data diatasi menggunakan metode SMOTE (Synthetic Minority Over-sampling Technique). Hasil analisis menunjukkan bahwa model Random Forest mencapai akurasi 73,60%, presisi 72,60%, recall 72,10%, dan F1-score 72,30%, membuktikan efektivitasnya dalam mengklasifikasikan sentimen positif dan negatif pengguna iPusnas.
Perkembangan teknologi digital telah mengubah pola pikir manusia untuk berbagi informasi dalam setiap aktivitas dengan berbagi pengalaman pada setiap kegiatan, terutama di bidang pariwisata, serta penyampaian opini wisatawan melalui aplikasi perjalanan wisatawan seperti Google Maps review, TripAdvisor review, dan aplikasi media sosial. Ini merupakan informasi yang sangat penting untuk mengetahui persepsi terhadap kualitas, namun jumlah data yang banyak dan tidak terstruktur menjadi kendala utama dalam proses analisis secara manual.Tujuan penelitian ini adalah analisis sentimen wisatawan pada destinasi pariwisata di Kabupaten Sumba Barat Daya dengan metode Convolutional Neural Networks berbasis Deep Learning, Hasil pengumpulan data dilakukan proses pengolahan model data teks ke cleaning, tokenization, stopword removal, stemming untuk dipresentasikan ke numerik. Model Convolutional Neural Networks mengklasifikasikan kategori sentiment positif, negatif dan netral. Tahap pelatihan dan pengujian model untuk klasifikasi sentimen hasil analisis sentimen model confusion matrix dengan nilai accuracy 93%, precision 91%, recall 93%, F1-Score 92%. Hasil penelitian dapat membuktikan bahwa Convolutional Neural Network berbasis deep learning mampu ekstrak teks secara otomatis dari data manual dan mengenal pola ulasan data wisatawan dengan efektif dan menyediakan informasi bagi pemerintah daerah dan pengelolah tempat pariwisata sebagai rekomendasi pengembangan layananan serta merumuskan strategi yang berkelanjutan
TREVOIL Barbershop berdiri sebagai barbershop dengan target pasar kalangan pria, anak muda dan generasi masa kini, namun belum memiliki sistem informasi reservasi. Padahal sistem infromasi reservasi mempermudah pelanggan dan target pasar TREVOIL Barbershop. Oleh karena itu, perancangan sistem informasi reservasi dan manajemen pelanggan berbasis web menjadi sangat penting untuk meningkatkan efisiensi operasional dan kepuasan pelanggan. Tujuan penelitian ini adalah untuk mengetahui rancangan sistem informasi reservasi dan manajemen pelanggan berbasis web pada TREVOIL Barbershop yang terstruktur dan real-time. Penelitian ini menggunakan metode System Development Life Cycle (SDLC) dengan tahapan analisis kebutuhan, perancangan sistem menggunakan Unified Modeling Language (UML), perancangan basis data dengan Entity Relationship Diagram (ERD), pengembangan sistem berbasis web menggunakan framework CodeIgniter 3, serta pengujian menggunakan metode Black Box Testing. Setiap tahap dilakukan secara berurutan untuk memastikan sistem yang dibangun sesuai dengan kebutuhan pengguna dan tujuan penelitian. Hasil penelitian menunjukkan bahwa rancangan sistem informasi reservasi dan manajemen pelanggan berbasis web pada TREVOIL Barbershop dapat mempermudah proses reservasi, mengurangi kesalahan pencatatan reservasi, serta mempermudah pengelolaan data pelanggan secara real-time. Rancangan sistem informasi reservasi dan manajemen pelanggan berbasis web juga mendukung manajemen dalam pengambilan keputusan berbasis data. Selain itu hasil penelitian ini dapat dimanfaatkan oleh pihak barbershop, pelaku usaha jasa, dan pengembang sistem informasi sejenis sebagai referensi dalam digitalisasi layanan reservasi.
Penelitian ini bertujuan menyusun kerangka tata kelola TI bagi sistem keputusan berbasis AI di smart cities melalui sintesis literatur yang terintegrasi. Metode yang digunakan adalah integrative literature review terhadap 22 artikel jurnal internasional terindeks Scopus yang dipilih secara purposif berdasarkan relevansi dengan topik smart city, kecerdasan buatan, tata kelola, dan pengambilan keputusan. Proses penelitian dilakukan melalui identifikasi artikel awal, penerapan kriteria inklusi, pembacaan penuh, ekstraksi data, pengelompokan tematik, dan analisis tematik integratif. Hasil ekstraksi secara terukur berhasil memetakan 8 dimensi utama yang saling berhubungan dalam membentuk sistem keputusan berbasis AI di smart cities, yaitu: mediasi sosio-teknis, tata kelola kebijakan dan kelembagaan, tata kelola data, privasi dan keamanan, etika dan akuntabilitas, partisipasi warga, arsitektur sistem keputusan, serta orientasi hasil kebijakan. Penelitian ini juga menemukan bahwa kesenjangan utama dalam literatur terletak pada belum terintegrasinya kedelapan dimensi tersebut ke dalam satu kerangka IT governance yang utuh dan operasional. Secara strategis, penelitian ini memberikan dampak nyata melalui kerangka tata kelola TI yang dapat diimplementasikan sebagai panduan operasional bagi pemerintah kota dan pembuat kebijakan. Kerangka ini tidak hanya memitigasi risiko penggunaan AI, tetapi juga memastikan bahwa sistem keputusan di smart cities berjalan secara efektif, aman, transparan, dan selaras dengan kepentingan publik.
Monitoring hama lalat buah pada tanaman jambu umumnya masih dilakukan secara manual sehingga memerlukan waktu yang lama, kurang efisien, dan sering terlambat dalam mendeteksi serangan hama. Kondisi ini dapat menyebabkan peningkatan populasi lalat buah yang berdampak pada penurunan kualitas dan hasil panen. Oleh karena itu, dikembangkan sistem deteksi lalat buah berbasis Internet of Things (IoT) menggunakan algoritma YOLO untuk mendukung proses monitoring hama secara otomatis dan real-time. Dataset citra diperoleh langsung dari kebun jambu menggunakan kamera smartphone, kemudian dilakukan proses seleksi, anotasi bounding box, konversi ke format YOLO, serta pelatihan dan pengujian model. Evaluasi performa dilakukan menggunakan metrik precision, recall, accuracy, dan mean Average Precision (mAP). Hasil pengujian menunjukkan bahwa model mampu mencapai precision sebesar 0,86, recall sebesar 0,85, accuracy sekitar 86%, serta mAP@0.5 sebesar ±0,87. Model terbaik selanjutnya diimplementasikan pada Raspberry Pi 5 sebagai perangkat edge computing dan berhasil dijalankan dalam skema deteksi real-time. Sistem yang dikembangkan menunjukkan potensi sebagai solusi deteksi dini lalat buah untuk meningkatkan efisiensi pengendalian hama pada budidaya jambu.Kata Kunci— YOLO, deteksi objek, lalat buah, Internet of Things (IoT), mean Average Precision (mAP), Raspberry Pi.
How can incumbent firms reconfigure their legacy Enterprise Information Systems (EIS) for enterprise renewal? We address this question by developing an empirically grounded, mechanism-based theory of legacy EIS reconfiguration in incumbent firms that reconciles two opposing perspectives in IS research: legacy EIS as sources of technical debt that constrain agility, and legacy EIS as installed bases that can be leveraged for digital reinvention. Specifically, we draw on findings from a multiple-case study of five German incumbent manufacturing firms to identify four generative mechanisms through which incumbent firms leverage operational stability of their legacy EIS for digital pilots, activate historically accumulated data through analytics, augment legacy EIS with middleware interfaces, and retrofit legacy EIS for recurring revenue models. When enacted, these mechanisms reconfigure legacy EIS into ambidextrous EIS landscapes that preserve operational stability through contained and selectively reduced technical debt, while expanding potential options for future digital initiatives. We conclude by outlining actionable guidance for practitioners and a research agenda on how legacy EIS reconfiguration, rather than replacement, can serve as a foundation for enterprise renewal.
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.
Enterprise systems governance is undergoing a shift toward decentralization and federation, as platforms and tools increasingly empower business users to create and deploy applications outside professional IT. Low-Code Development Platforms (LCDPs) represent a prominent instance of this trend, enabling rapid application development while simultaneously introducing challenges for Enterprise Architecture Management (EAM). Through a multiple-case analysis of organizations from financial services, healthcare, and manufacturing, we identify three governance challenges organized around what is governed (architectural drift), who is governed (role ambiguity and shifting accountability), and how governance is enacted (tension between formal and informal control), and show that that organizations address these challenges through deliberately composed governance portfolios pairing formal instruments with informal enabling practices. Reasoning abductively across cases, we identify five mechanisms through which these portfolios produce durable EA outcomes: information symmetry, perceived legitimacy, developer capability, enacted decision-right alignment, and secure-by default conditions. Theoretically, we contribute by distinguishing the locus of control (centralized vs decentralized) from the mode of control (formal vs informal), and by offering a mechanism-based explanation of how governance portfolios produce EA outcomes under decentralized development conditions, moving beyond generic prescriptions for “balance” toward a concrete account of why specific governance compositions work. Practically, our analysis underscores the need to continuously recalibrate governance as decentralized technologies proliferate, requiring increasingly adaptive EAM strategies to balance innovation, flexibility, and architectural coherence.
Human emotions trigger physical reactions of the body that can be interpreted using artificial intelligence (AI) methods. AI-enabled detection mechanisms offer new opportunities to gain deeper understanding of human emotions. AI is already able to recognize basic emotions, such as joy, anger, or fear, but is challenged to detect complex emotions accurately, such as when someone is lying. The human voice conveys a wealth of subconscious information. This research utilizes natural language processing (NLP) to present a novel AI artifact that analyzes solely the human voice to detect whether a person is speaking to their true conviction. This is useful for use cases where it is important for decision makers to know whether a person's arguments are truthful. A suitable use case could be corporate recruitment, an area where people traditionally lie a lot. The artifact can support human resource (HR) personnel in making better decisions by overcoming their naturally weak ability to detect deception. To suit the sensitive context of recruitment talks, the artifact is designed to protect data privacy, and thus has no speech recognition capabilities and does not store information that identifies the speaker. The model detects deceptive statements with similar to 82% accuracy. Integrating the artifact into corporate business processes would enable managers to more accurately detect deceptive behavior of their interview partners. The increasing use of novel AI artifacts to compensate for humans innately weak capabilities to detect other people's complex emotions will challenge our theoretical conceptualization of organizational decision making in light of emerging human-AI hybrids.
Through a 2-year case study of a scaled agile transformation at a leading German automotive manufacturer, we examine how the role of IT architects changes during liminal stages of the transformation where the work setting in which they operate undergoes deep structure changes. Drawing on theories about roles as social positions and punctuated socio-technical change, we show how in the liminal stage of a scaled agile transformation, three deep structure changes are triggered: the introduction of new organizational structures, the re-definition of role expectations, and new software tool implementations. These changes introduce new role interdependency dynamics, namely, contested consensus expectations, increased conformity pressure, rising role conflicts, and adaptive role taking, which in turn prompt liminal role performance changes in terms of responsibility accumulation, communication intensification, technical competency increase, and intensified decision-making for architects. We contribute new insights about how deep structure changes that characterize the liminal stage of scaled agile transformations drive socio-technical role adaptations during the process of organizational transformation, which decrease rather than increase the stability of the work setting for architects.
Enterprise information systems (IS) research has long emphasized the value of deep alignment between IS and the business contexts they serve. Yet this premise becomes increasingly problematic as enterprises operate across heterogeneous contexts whose structures, demands, and operating logics differ not only in degree but in kind. Under such conditions, deeper embedding in one context may weaken transferability, reconfigurability, and coherence across others. To address this tension and theorize the future of IS in the enterprise, we develop a process model of adaptive digital transformation (DT) and, from the insights it generates, introduce context-transcendent IS as a new paradigm for enterprise IS under conditions of contextual heterogeneity. Empirically, the paper draws on an in-depth case study of Geely, a multinational automotive enterprise confronting growing heterogeneity across brands, markets, and operations. We show how Geely responded through three recursively linked IT governance practices enacted across three stages of adaptive DT: synchronizing in value anchoring, recombining in capability orchestration, and diversifying in asset platformization. Together, these practices enabled the emergence of context-transcending IS and, ultimately, a more durable context-transcendent IS form. The study makes two contributions. First, it problematizes the conventional embedding premise in enterprise IS research by identifying contextual heterogeneity as a distinct source of enterprise-level tension. Second, it develops context-transcendent IS as a theoretically distinct and increasingly necessary form of enterprise IS for the future enterprise under sustained conditions of contextual heterogeneity in the operating landscapes.
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.
In response to the rapid proliferation of artificial intelligence (AI), in particular generative AI, research on its application has mushroomed. However, problems arise when AI is taken to be just another IT artifact without fully appreciating what is new and different about it. We take two surprises about AI's peculiar behavior, found in the public domain, to problematize commonly held understandings of computing. We illustrate how the fact that AI systems exhibit inherent inaccuracies, and AI developers are unable to fully understand their own creations, challenges common expectations. Based on this insight, we put forward the provocative thesis that AI systems are not, in fact, information systems. We derive an ideal type understanding of information systems, as a rhetorical device, and analyze in detail the differences with AI systems. For doing so we focus on AI systems like ChatGPT that derive their core functionality from generative AI models. We show that they differ in principle in how they encode information parametrically rather than explicitly, function probabilistically rather than deterministically, remain static after training rather than maintain currency, are created in a trial-and-error process rather than engineered top down, and present practically as "black boxes" rather than auditable systems. Our analysis contributes a conceptual foundation for understanding AI systems on their own terms, avoiding category errors. This allows positing productive new research questions about AI system use, application, and design that will help the IS discipline maintain relevance, as AI reshapes computing at its core.
In order to tap the potential of digital technologies, two main concepts have been developed in IS Research: the currently much-discussed concept of 'digital transformation' and the concept of 'IS/IT-organizational transformation', which was developed many years ago. Both concepts refer to organizations that emerged in the pre-digital age. In addition, there are initial considerations as to what structures 'digital companies' have and whether companies from the pre-digital era can or should become digital companies. Until now, all three concepts are unconnected, yet they all address the same question, each within different contexts. This paper proposes a process-oriented meta-concept that relates the three established concepts. We reconstruct the process of digital-driven change in a very successful European media group over a period of 70 years and use the model of punctuated equilibrium as a theoretical lens. Rather than synthesizing the concepts into a unified new theory, our concept describes the alternation between relatively stable and open phases of a company in the context of the availability of new digital technologies, thereby providing an explanation of when and why each concept becomes salient. It also describes when an open phase starts and comes to closure. All three concepts, which were previously unconnected, can be positioned in this concept. In this way, we achieve significant progress in conceptual clarity and show in which context each of the three concepts is relevant. For practitioners, our integrative approach provides guidance for selecting and sequencing concepts in the specific situation of a company.
We explore how generative artificial intelligence (GenAI) affects leadership in knowledge-intensive environments, examining both short- and mid-term impacts. Drawing on findings of an in-depth case study within a large insurance company, we identify that short-term impacts mirror traditional technology adoption challenges while mid-term impacts require significant shifts in leaders' role, leadership style, and skills due to changing work content and organization. Leadership can no longer be understood solely through human-centered lenses. Instead, it must be extended to account for the interdependencies between humans and GenAI, requiring a shift in the nature of leadership to navigate and orchestrate teams rather than to manage and control them. Leaders must evolve from functional experts to orchestrators of human-AI collaboration, ensuring the best fit between human employees and GenAI and embodying employee-centric leadership. Our results provide empirical evidence to help managers proactively recalibrate their leadership practices to realize the promised efficiencies of GenAI sustainably. Based on this, we emphasize the need for future research exploring the GenAI-driven recalibration of leadership.
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.
While blockchain technologies are widely portrayed as a decentralising force, enterprise blockchains tend to reproduce centralised governance structures. To explain this paradox, we conducted a deductive, explanatory, multi-case study of four enterprise blockchains during their formative stage: Walmart DL Freight, Contour, Chronicled MediLedger, and Cardossier. We examine how variations in platform openness (the breadth of access to governance arenas) and participant inclusiveness (the depth of stakeholder influence on governance decisions) shape decentralisation trajectories as imprinting mechanisms: formative conditions that embed power asymmetries into sociotechnical infrastructures, constraining subsequent governance evolution. Empirically, we find that high openness and high inclusiveness supported decentralisation, low levels of both reinforced centralisation, and asymmetric configurations resulted in hybrid, semi-decentralised arrangements. Theoretically, we contribute a variance model that explains how early governance configurations shape decentralisation trajectories in enterprise blockchains. These contributions have practical implications for organisations designing blockchain governance: formative decisions around openness and inclusiveness can cast long institutional shadows, making early strategic alignment critical for realising blockchain's decentralisation potential.
This paper argues that artificial intelligence exposes the shortcomings of traditional regulatory paradigms, challenging Easterbrook’s ‘Law of the Horse’ view that general legal principles suffice. AI’s opacity, autonomy, and systemic risks demand risk-informed, technology-specific governance. We identify the pacing problem, where innovation outstrips regulatory capacity, and propose a tripartite framework distinguishing functional, structural, and relational risks. Comparative analysis of EU, US, UK, and Chinese approaches highlights divergent logics of precaution, market oversight, hybrid flexibility, and state control. Effective governance requires embedding risk into policy design through adaptive, proportionate, and harmonised mechanisms, balancing innovation with accountability. The paper underscores the urgency of global coordination and calls for interdisciplinary IS research to inform anticipatory, participatory, and ethically grounded regulation.
