ABSTRACT This study examines the strategic role of multiscreen utilization—defined as the concurrent or sequential use of multiple digital devices—in enhancing workplace interactive learning environments through improved knowledge processes. Drawing on adaptive structuration theory, we investigate how multiscreening behavior influences knowledge acquisition and dissemination and, subsequently, organizational responsiveness and performance. Using survey data from 245 professionals in Pakistan’s telecommunications sector, moderation and mediation analyses reveal that multiscreen utilization positively shapes knowledge flows and responsiveness, and that organizational culture significantly strengthens these effects. These findings demonstrate that multiscreening functions as a digitally enabled learning behavior that enhances information processing and organizational agility. The study underscores the need for organizations to foster supportive cultural and knowledge-sharing conditions to fully leverage multiscreening for sustained competitiveness in dynamic market environments.

ABSTRACT This study examines how generative artificial intelligence (GenAI) relates to job satisfaction among employees in the financial services sector, considering differences across types of work. Drawing on socio-technical systems theory, job satisfaction is conceptualized as an emergent outcome shaped by the alignment between technological change and organizational conditions. The analysis is based on a survey of 433 white-collar employees in Poland’s financial services industry and employs ordered logit models. The findings show that job satisfaction is positively associated with organizational support for AI and leadership effectiveness in managing AI-driven change, whereas individual enthusiasm toward GenAI is not positively related to satisfaction. Moreover, AI-related technostress is more strongly associated with lower job satisfaction in roles with higher exposure to AI-driven task transformation. Overall, the results highlight the contingent nature of GenAI’s impact on job satisfaction and emphasize the importance of socio-technical alignment in AI-enabled work environments.

ABSTRACT The rise of ChatGPT has sparked significant ethical concerns. Academia worries about issues such as originality and undetectable plagiarism, while ethicists and industry experts highlight problems such as biased outputs, data security, and privacy. Given its broad reach and accessibility and profound impact on productivity and efficiency across academic, workplace, and social contexts, there is a pressing need to examine the factors influencing users’ ethical judgments regarding ChatGPT. This study addresses this gap by exploring the factors influencing ethical judgments in ChatGPT use. Through qualitative inductive research, it captures users’ ethical considerations, analyses them through dominant ethical philosophies, and proposes an emergent theory elucidating ethical considerations of ChatGPT users contextualized within existing ethical theories and philosophies. The findings aim to assist organizations, academia, and policymakers in developing robust ethical guidelines to govern the use of generative AI tools like ChatGPT.

ABSTRACT Data scientists and users are experts in different domains, with the former possessing expertise in technology and the latter having experience in the problem domain. Current approaches to machine learning (ML) design enable data scientists and technical developers to drive the process. In contrast, this research presents an approach grounded in theory that enables the non-technical user to control ML design, at least initially. We discuss a study that applies a socio-technical systems thinking approach to enable non-technical users to drive machine learning design and ensure their actual needs are met. We extend socio-technical research by introducing a novel type of analysis- a social data system analysis, and provide empirical evidence on the feasibility of non-technical user-driven ML design. We provide practical guidelines for practitioners to enable non-technical user-driven ML design. The study also demonstrates the potential of a socio-technical approach in increasing fairness in training datasets.

ABSTRACT The growing use of algorithms to assess authenticity in online environments raises a critical question: can machines truly recognize deception? This study examines the limits of algorithmic trust by testing heuristic and unsupervised learning approaches on 850 genuine Starbucks customer reviews. Using sentiment analysis, TF-IDF, Word2Vec, principal component analysis and K-means clustering, the research explores whether linguistic and behavioral patterns can reveal fake or manipulated content without labeled data. While no deceptive clusters were identified, the results expose the structural weakness of unsupervised models when applied to subtle, human-generated deception. Rather than confirming failure, this outcome reframes fake-review detection as an epistemic boundary problem—where data alone cannot fully capture authenticity. The paper contributes a transparent, replicable framework for analyzing online trustworthiness and offers practical insights for designers of AI-driven moderation and content-verification systems in e-commerce and social platforms.

ABSTRACT As emerging technologies and capabilities dominate our economic landscape, many firms struggle to obtain positive performance outcomes from their digital transformation initiatives. Despite some nascent work on digital transformation, it is still unclear what conditions nurture and sustain digital transformation. Furthermore, the mechanisms through which digital transformation affects firm outcomes have not been adequately investigated. We posit that digital transformation enhances a firm’s outcomes by mediating the relationships between IT capability and firm performance, as well as organizational agility and firm performance. Using survey data collected from IT executives of 221 U.S. firms, we find that IT capability and agility are key enablers of digital transformation. The results reveal that IT capability enhances firm performance by mediating digital transformation. We find a direct negative link between agility and firm performance. However, through the mediation of digital transformation, organizational agility has a net positive effect on innovation and firm performance. The study advances our understanding of antecedents and performance outcomes of digital transformation initiatives by developing and validating the nomological relationships among IT capability, organizational agility, digital transformation, innovation, and firm performance.

ABSTRACT Although AI is rapidly spreading, research classifying user types and analyzing their characteristics remains limited. This study uses the Self-Organizing Map (SOM) algorithm to classify AI usage patterns among Korean adults and analyze characteristics, factors, and outcomes. Analyzing 4,324 adults aged 20–59, three types were identified: Diverse (13.5%), Selective (42.7%), and Limited AI Users (43.8%). Diverse users were primarily young, educated males in professional occupations with superior digital competence and self-efficacy, yet showing highest AI risk concerns. Selective users utilized AI for daily convenience, while Limited users showed minimal engagement. Both Diverse and Selective users found AI services beneficial, but only Diverse users showed significantly higher life satisfaction. Males demonstrated higher usage and competence. These findings reveal new digital divides in the AI era, confirming digital self-efficacy and competence as key factors. This study suggests customized policies, competence support, and solutions for an inclusive AI society.

ABSTRACT This study investigates how the use of artificial intelligence (AI) tools affects critical thinking and examines the moderating roles of trust calibration and AI literacy in a quantitative survey of 625 postgraduate students, teachers, and research scholars. Drawing on Cognitive Offloading Theory, we hypothesized that greater AI usage would undermine critical thinking, and that trust in AI would exacerbate this effect. Consistent with our predictions, results from moderated moderation analysis indicate that AI usage negatively predicts critical thinking, trust in AI amplifies this negative relationship, and high AI literacy significantly buffers the detrimental impact of trust. These findings demonstrate that fostering AI literacy is a key strategic approach for calibrating users’ trust and preserving critical engagement with AI-generated information. The study underscores the necessity of integrating AI literacy initiatives into educational curricula to mitigate cognitive risks posed by widespread AI adoption.

ABSTRACT Organizations face increasingly sophisticated cyber threats that traditional reactive cybersecurity approaches cannot adequately address. This research proposes an integrated framework combining Explainable Artificial Intelligence (XAI) with Ordinary Differential Deep Recurrent Unit Neural Network (OD-DRUNN) for proactive organizational threat mitigation. The methodology employs a Minimum Parameterized Muller Spanning Tree algorithm for comprehensive network traffic and user behavior analysis. The OD-DRUNN architecture overcomes traditional deep learning limitations through ordinary differential equation-based parameter isolation, while XAI provides transparent decision-making interpretability for security analysts. Threat severity assessment utilizes potential level scoring, with high-risk scenarios triggering Cycloid Curved Optimized Cryptography enhanced by Bernoulli Distribution-based Tuna Swarm Optimization. Experimental evaluation using the HIKARI-2021 dataset “for review, see ref. 21” demonstrates superior performance: 99.2% vulnerability detection accuracy, 97.8% packet delivery ratio, and 98.8% security level. The framework significantly outperforms existing approaches, providing organizations with comprehensive, interpretable, and proactive cybersecurity defense capabilities against evolving cyber attack vectors.

ABSTRACT This study examines the adoption of AI-powered audit tools in Indonesia, analyzing organizational antecedents, cognitive-perceptual mediators, and the moderating role of corporate governance in shaping audit efficiency, audit decision quality, and auditor professional reputation. Using data from 320 external auditors and partial least squares structural equation modeling, results show that top management support and innovation climate significantly promote AI adoption, while technology readiness and vendor ecosystem support do not. Auditor confidence mediates the link between AI adoption and audit efficiency, and perceived usefulness mediates the link with audit decision quality. Corporate governance strengthens the effect of AI adoption on auditor confidence, and both efficiency and decision quality enhance professional reputation. Findings underscore the need for strong leadership, innovation-friendly cultures, and robust governance to maximize the benefits of AI in emerging market auditing.