Modular systems theory and the dynamic capabilities perspective suggest that modularity in the IT function, both architectural and organizational, shapes a firm’s digital transformation capabilities (DTCs), yet the joint effect of these two organizational-design choices has not been tested. Drawing on survey data from 188 U.S. firms and partial least squares structural equation modeling, this study examines how IT architecture flexibility and IT governance decentralization, separately and jointly, influence digital sensing, digital seizing, and digital transforming. Findings suggest that IT architecture flexibility has strong positive effects on all three dimensions, while IT governance decentralization positively affects sensing and seizing but not transforming. Crucially, the interaction between flexibility and decentralization negatively moderates the relationships with sensing and seizing, indicating that technical and organizational modularity can substitute rather than complement. The study contributes a nine-micro-foundation operationalization of DTCs and challenges the prevailing complementarity assumption between architectural and governance modularity.
Green information systems (IS) has been rising to be one of the most critical aspects of research in the field of IS. The field broadly refers to the use of information systems and technology to support sustainability. One of the upcoming technologies that has the capability to impact the way we understand the technology and the world is quantum technology and, in this study, we analyze the potential impact of quantum technology in supporting green IS research in field of green hydrogen, an environmentally sustainable fuel source. Organizations and economies are considering quantum computing technologies to decarbonize their businesses and have high-impact sustainability applications. We base our research in the theoretical context of Organizing Vision Theory (OVT) and Stakeholder Theory (ST). Together, these theories address gaps in the Information Systems literature on managing disruptive technologies within sustainability contexts and offer a framework for strategic visioning and collaboration. Using qualitative methods, including interviews and focus-group discussions, this study captures diverse experiences and perspectives, identifying key challenges, opportunities, and strategies for advancing the National Green Hydrogen agenda. The findings highlight the green hydrogen ecosystem as a critical clean-energy vector in four key sectors (agricultural practices, industrial operations, transportation and electricity), while quantum computing emerges as an enabling technology that improves catalyst discovery, electrolysis efficiency, storage optimization, and system-level decision making for the success of this mission.
Quick Response code-based verification technologies are revolutionizing the food and beverages supply chain by offering product traceability and authentication, enabling consumers to access information about their consumables. These systems function via quick response codes, supporting either blockchain-based distributed traceability (farm-to-fork) or company-managed centralized authentication databases. Although manufacturers adopt Quick Response code-based verification to foster trust, consumer resistance remains prevalent. This study compares India and France across contrasting national uncertainty-avoidance profiles and examines active and passive resistance barriers using innovation resistance theory and status quo bias theory. It focuses on two verification mechanisms—traceability and authentication—designed to combat counterfeiting and misinformation, examining how user and system characteristics contribute to resistance. Based on data from 581 retail shoppers, the study applies fuzzy set Qualitative Comparative Analysis to identify key combinations of functional, psychological, system-related, and individual barriers. The findings offer practical insights for selecting and deploying effective traceability/authentication strategies to mitigate consumer resistance.
As visitors increasingly rely on AI-enabled apps and recommender systems, AI is becoming an important mediator of tourism choice. Tourism decision-making is cognitively demanding, and visitors rarely evaluate all available destinations, attractions, travel periods, and services equally. Consistent with bounded rationality, they simplify complex choice environments through limited information, heuristics, and platform-mediated evaluation. This creates opportunities for AI systems not only to personalise experiences, but also to influence how attention and demand are distributed across attractions and seasons. This research develops and computationally examines a mechanism-based conceptualisation of how AI-enabled information systems mediate visitor choice and how micro-level decision processes accumulate into tourism-system outcomes. Drawing on digital choice architecture, information filtering, platform ecosystem perspectives, and bounded rationality, the research conceptualises AI as a decision-mediating infrastructure. Using an agent-based model of a tourism platform ecosystem, it compares three visitor-facing AI intervention conditions: predictive nudging, a recommender-system filtering, and fairness-oriented visibility enhancement. The model examines how differences in attention, salience, choice-set formation, evaluation, satisfaction, and subsequent behaviour are associated with system-level outcomes. The findings show that AI-mediated interventions can increase visitor activity and spending, but their effects differ across intervention conditions and do not automatically reduce seasonality. The research contributes to Information Systems and smart-tourism research by showing how AI-enabled decision environments can structure attention, consideration, and visibility, while highlighting the limits of digital interventions when destination outcomes are also shaped by wider structural conditions.
Generative AI (GenAI) is increasingly deployed as support infrastructure, yet its capacity to accommodate heterogeneous and fluctuating user conditions through interactional design remains underdeveloped and unevenly realized. This study asks how GenAI can be designed to sustain usable, appropriately bounded support amid such variability. This question is addressed through an echeloned design science research (DSR) process organized into a four-stage theory-development pipeline, using career support for professionals with ADHD as a critical case in which inclusive design requirements become empirically tractable. The evidence base comprises one naturalistic online subcommunity corpus and four participant samples spanning controlled and ecological evaluation. Across four stages, we specify the problem space, derive empirically grounded design requirements, operationalize them as interaction policies within a configured large language model (LLM), and evaluate the resulting policies through controlled and situated evaluation. These stages culminate in a design theory of inclusive intelligence, instantiated through MATRIX, a set of six interaction-level design principles (momentary regulation, ability amplification, trust and stability, resonant attunement, intentional flourishing, and experiential integration) that govern how usable, contextual, and bounded support is enacted in practice. Inclusive intelligence is thus advanced as a mid-range design theory that specifies how GenAI support is calibrated and bounded as user conditions change, locating value in interactional governance rather than model capability. Its transferability is bounded to episodic, text-mediated support characterized by cognitive and affective variability, where guidance must be structured, paced, and delimited under shifting demand.
