This special issue comprises seven articles exploring various facets of digital transformation and sustainability within entrepreneurial, corporate, and financial contexts. The articles showcase a variety of theoretical frameworks and empirical methodologies, including resource-based view (RBV), dynamic capabilities, complex adaptive systems, patent analytics, econometric modeling, and in-depth case studies. A unifying thread running through these studies is the recognition that digital capabilities–whether in the form of AI, fintech, data analytics, or broader transformation process and infrastructures–derive their transformative potential when aligned with sustainability imperatives. Their true value emerges when leveraged to support the strategic objectives of environmental and social impact, and when embedded within a broader cultural, organizational, and policy framework geared toward responsible innovation.

Users rarely take full advantage of the unprecedented possibilities offered by IT in the post-adoptive stage of use, creating a feature-use paradox. This paradox is particularly pronounced in the context of voice-activated devices (VADs), such as smart voice assistants, as VADs require users to rely entirely on memory when interacting with them. In this setting, we theorize that users' ability to recall which system features are available is crucial for effectively engaging with VADs and can thus shed light on the feature-use paradox. We define IT Feature Awareness in the context of VADs by adapting the brand awareness concept from marketing research, and we develop a conceptual model rooted in adaptive system use theory that captures the expected impact of IT feature awareness on innovative use behaviors. We conclude this work-in-progress by outlining our data collection plans and expected contribution to the post-adoptive IT use stream of the IS literature.

Wearable devices provide reliable biometric measurements in different contexts, and AI algorithms are increasingly being used to analyze this data. The objective of this review is to examine the use of wearable devices to collect biometric data combined with AI algorithms in an educational setting. A systematic review was conducted through the PRISMA methodology, by searching the Scopus database for works that included wearables, biometrics, and AI algorithms. A total of 43 studies were included and examined. The objectives, the type of collected data, and the methodologies of the included studies were investigated. Most articles utilized machine learning and deep learning algorithms for classification tasks, such as detecting stress or attention. Other applications included human activity recognition (HAR) for classroom orchestration and emotional or cognitive state detection. Many of the studies applied knowledge from previous works to the educational context, resembling exploratory research. Conversely, some authors developed tasks and methodologies tailored to the educational context. The strengths and weaknesses of the presented studies were discussed to propose future research directions. The main findings of this review highlight the advantages of the combination of multimodal sensing and predictive modeling in education with the eventual prospect of personalization. The absence of standardized acquisition and reporting remains the main barrier to replication, benchmarking, and synthesis across studies.

Digital transformation has garnered significant interest within the research and business communities and has become an umbrella concept to address the multiple technological, strategic, operational, and leadership dimensions involved in digitally enabled organizational renewal. Such transformation spans beyond mere automation of existing practices to a radical change of the business scope. Despite this increased attention, a shared understanding of exactly what digital transformation encompasses and how a digital transformation initiative can be defined and led is still underdeveloped. This article aims to contribute to this research gap by identifying the multifaceted conceptual and applied dimensions of digital transformation and integrating them into a single, unifying framework. Based on a synthesis of extensive yet fragmented literature and feedback from domain experts, we present a conceptual map (i.e., a Canvas) for successful digital transformation initiatives. In addition, we identify the key elements through which one can lead implementation (i.e., roles, competencies, behaviors, and enablers). The article contributes both to academia—by advancing the meaning and constituent factors of digital transformation—and practitioners, with the formulation of a conceptual tool for managers engaged in a systemic design of digital transformation initiatives.

Scale adaptation, where authors alter the wording of an already published scale, is a deeply rooted social practice in IS research. This paper argues that the time is ripe for questioning this activity as well as the beliefs that have progressively formed around it. We identify and challenge five fallacious scale adaptation beliefs that hinder the development of more robust measure development norms. Contributing to this area of research, this paper offers a conceptual definition of the cognitive validity concept, defined as the extent to which a scale is free of problematic item characteristics (PICs) that bias the survey response process and subsequent empirical results. Building on this conceptualization effort, a new methodological process for assessing the cognitive validity of adapted IS measures are introduced. Through a series of three programmatic studies, we find converging evidence that the method can benefit the IS field by making the scale adaptation process more robust, transparent, and consistent. Along with the method, we introduce a new index that IS scholars can use to benchmark the cognitive quality of their scales against venerable IS measures. We discuss the implications of our work for IS research (including detailed implementation guidelines) and provide directions for future research on measurement in IS.