Search Results (451)

Industry 5.0, deploying artificial intelligence (AI) at its core, reframes industrial evolution from a predominantly technology- and efficiency-driven innovation model toward a virtuously human-centric, sustainable, and resilient model of value creation by organizations. This review paper, based on an interdisciplinary literature review, explores how AI, within the Industry 5.0 paradigm, reshapes economic logics, the understanding of information asymmetry, and sustainability trajectories, and the implications for entrepreneurial strategy and business model innovation, which demand the development of a new form of organizational intelligence. While the literature suggests that AI, when deployed within a mature Industry 5.0 framework, could generate synergistic economic and sustainability values through circular, human-centered, and digitally augmented systems, human–AI co-intelligence gains are contingent on insights that address systems quality, reskilling, ethics, and reorienting resources from overly short-term profit maximization toward wisdom for long-term socio-ecological, climate resilience, and ESG performance. This study introduces a framework for tackling organizational sustainability dynamics, anticipating the emergence of new industries and the retransformation of enduring ones amid creative destruction in the AI era. Future studies to fill knowledge gaps and implications for human competencies that will enhance organizational intelligence are articulated. Full article

Artificial intelligence is reshaping warehouse work through algorithmic task allocation, scanner-based monitoring, KPI feedback, dynamic scheduling, and real-time performance control. Although these systems can improve coordination and operational visibility, they also create governance risks related to fairness, transparency, autonomy, privacy, workload pressure, trust, and employee resistance. This study develops a human-centered decision framework for prioritizing algorithmic management governance packages in third-party logistics (3PL) warehousing. The main contribution is to translate employee-level governance concerns into a scenario-sensitive decision model that helps managers select appropriate governance packages under different operational pressures. The study uses survey data from 380 warehouse employees to examine key psychological and behavioral mechanisms, including procedural fairness, transparency, system/information quality, autonomy, privacy concern, workload, trust, acceptance, and resistance/disengagement. These survey-supported constructs are then converted into six governance criteria: procedural fairness, transparency and contestability clarity, system and information quality, autonomy support, privacy boundary governance, and workload protection. A seven-expert panel evaluates five governance packages under three scenarios: peak season surge, labor shortage/high turnover, and audit pressure/compliance scrutiny. Methodologically, the framework combines Dynamic Multi-Facet Fuzzy Sets to capture membership, non-membership, hesitancy, engagement, and resistance; Bayesian Network weighting to reflect dependencies among governance criteria; and PCA-based ranking optimization to generate scenario-specific and robust rankings. Comparative validation with SAW and TOPSIS is also used to assess ranking consistency. The findings show that effective algorithmic management governance is not a fixed compliance solution. Transparency, workload protection, autonomy support, privacy boundary governance, and procedural fairness become more or less important depending on the operational scenario. A2, which combines transparency, workload protection, and autonomy support, emerges as the strongest robust package. A1 performs best under labor shortage/high turnover, while A3 performs best under audit pressure/compliance scrutiny. These results suggest that 3PL warehouses should adopt adaptive governance routines that combine explainability, contestability, workload safeguards, privacy boundaries, and employee voice mechanisms. The study contributes to the literature on AI in socio-technical systems by showing how human, organizational, and ethical concerns can be embedded into an interpretable decision framework for responsible algorithmic management in logistics work environments. Full article

The integration of Generative Artificial Intelligence (GenAI) in higher education has opened new possibilities for optimizing administrative and academic services, particularly in contexts characterized by high-demand communication processes. Within the framework of service science, this study addresses the challenge of efficiently managing high volumes of email inquiries in a university master’s program, aiming to improve service quality and operational efficiency. The study examines the implementation of GenAI-based assistants, specifically NotebookLM and custom Gem AI assistants, trained in regulatory, curricular, and historical data from the University Master’s in Teacher Training at Rey Juan Carlos University. A mixed analytical approach is adopted, combining elements of data science to quantify efficiency gains and service science to analyze organizational and service-related transformations. The implementation of GenAI assistants contributes to improved response times, enhanced accuracy of information provided, and a reduction in administrative workload. The results suggest that GenAI can support the scalability and quality of academic administrative services when integrated within a structured service framework. However, its effective adoption requires careful consideration of ethical, organizational, and governance dimensions to ensure sustainable and responsible implementation. Full article

This study critically investigates the scientific literature on the intersection of management accounting and intellectual capital using a bibliometric performance analysis and science-mapping approach. Drawing on a sample of 59 publications from the Scopus and Web of Science databases, the paper maps the intellectual structure, key contributors, and thematic evolution of the field. This study conceptualizes management accounting not merely as a neutral technical system but as a socio-political mechanism that shapes how intellectual capital is rendered visible, measurable, and governable within organizations. The findings identify five dominant research clusters (intellectual capital and corporate strategy, management accounting and performance, green intellectual capital, digitalization and value creation, and management control and intangibles), revealing how accounting practices actively participate in constructing organizational realities and legitimizing particular forms of value and knowledge. The analysis highlights that measurement and reporting practices privilege certain dimensions of intellectual capital while potentially obscuring others, raising critical questions about power, visibility, and accountability in knowledge-based economies. In particular, the growing emphasis on digitalization and sustainability reflects shifting governance regimes in which accounting systems extend their influence over organizational conduct and strategic decision-making. By integrating bibliometric techniques with a critical interpretive lens, this study contributes to the literature by reframing management accounting as a key site where knowledge, control, and organizational value are negotiated. It also identifies gaps for future research, particularly regarding the ethical and political implications of accounting for intangible resources in increasingly digital and transparency-driven environments. Full article

The rapid digitalization of airport operations has transformed airports into complex data-driven ecosystems, where effective data governance has become a critical challenge. While prior studies have explored big data applications in aviation, limited attention has been given to the interdependent structure of data governance challenges at the airport level. This study proposes a decision-oriented analytical framework integrating the entropy and DEMATEL methods in two sequential stages systematically identify, prioritize, and model the causal interactions among key big data challenges in airport ecosystems. Using Istanbul Airport (IGA) as a case study, an initial, expert-based assessment was conducted to assess nine critical challenges, including data privacy, integration, organizational culture, and regulatory compliance. The results revealed that data privacy and security is not only the most critical factor but also a primary causal driver, influencing multiple downstream challenges such as ethical considerations and regulatory compliance. The findings further demonstrate that technical and organizational barriers are strongly interconnected, requiring sequenced, system-level interventions rather than isolated solutions. By combining objective weighting with causal analysis, this study contributes to the literature by providing a holistic and actionable decision support framework for airport data governance. The proposed approach offers practical insights for airport authorities and policymakers to design more resilient, secure, and data-driven operational environments. Full article

This article develops a conceptual framework to explain how theological convictions of sanctity within the Catholic Church can become institutional risk factors for unethical behavior. Existing analyses of clerical sexual abuse emphasize governance failures, clerical culture, or individual misconduct, but pay limited attention to how sacred identity shapes institutional reasoning. Integrating organizational ethics, social identity theory, and moral psychology, the article adapts the NGO halo effect to propose a three-stage model distinguishing between intrinsic sanctity, institutional sacralization (the Church halo), and the activation of moral mechanisms (the halo effect). The analysis shows how mission, moral teaching, and ordained ministry—while theologically coherent—can become amplified within institutional life in ways that alter how ethical dilemmas are weighted. It further identifies moral justification, moral superiority, and moral naivety as mechanisms through which such amplification may contribute to safeguarding failures. By analytically separating theological meaning from institutional amplification, the article advances scholarship on religious organizations and reframes clerical abuse as partly linked to the dynamics of sacralized identity. The model offers a transferable framework for examining how moral purpose and moral failure can coexist in faith-based institutions. Full article

The study aimed to identify the factors influencing the adoption of social media analytics (SMA) for enhanced organizational intellectual capital. It also intended to reveal the challenges linked to the effective incorporation of SMA in organizations for the attainment of enhanced intellectual capital. A systematic literature review (SLR) methodology was applied to address the study’s objectives. The required studies were retrieved from twelve major academic databases (Web of Science, Scopus, ScienceDirect, SpringerLink, Emerald, Wiley Online Library, Taylor & Francis, Sage, INFORMS, SSRN, Dimensions, and Business Source Complete) along with Google Scholar to ensure comprehensive coverage. A total of 40 peer-reviewed journal articles published between 1 January 2012 and 31 December 2025 were selected based on predefined inclusion and exclusion criteria. The findings manifested that factors of human capital, technological infrastructure, social networks, knowledge management, and big data analytics positively influence the adoption of social media analytics (SMA) in organizations for enhanced intellectual capital. It was also identified that data complexity, skills constraints, integration barriers, and ethical concerns negatively affected the incorporation of SMA in organizations. On the basis of the study’s findings, a framework has been developed to efficiently adopt SMA in organizations for enhanced intellectual capital. The framework is universally applicable across all disciplines providing a robust foundation for future empirical validation. The study has provided pertinent theoretical, practical, methodological, and social implications. Full article

Background: Digital health technologies are increasingly integral to healthcare delivery worldwide; however, successful adoption depends on more than technological availability. In nursing, readiness is particularly important because digital systems increasingly shape documentation, communication, decision support, and care delivery. Within the context of Saudi Arabia’s healthcare transformation, doctoral nursing students are positioned as future educators, clinicians, and leaders whose perceptions can provide insight into digital health readiness and preparation. Aim: This study aimed to explore doctoral nursing students’ perceptions of their readiness to adopt digital health technologies in Saudi Arabia, guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Methods: This exploratory, qualitative, descriptive study recruited 9 doctoral nursing students from a public university in Saudi Arabia using purposive sampling based on predefined eligibility criteria. Individual semi-structured interviews were conducted online and audio-recorded. Data were analyzed using a hybrid inductive–deductive thematic approach. UTAUT2 informed the deductive component of the analysis, while inductive coding and cross-case comparison supported theme generation. Results: Four interrelated themes were identified. First, readiness was positive but conditional, shaped by movement from openness to professional necessity, familiarity, workflow fit, and caution about the possible weakening of foundational or manual competence. Second, adoption depended on practical value and system credibility, including access, convenience, efficiency, safety, documentation integrity, accuracy, privacy, and reliability. Third, adoption was organizationally mediated through leadership, peer culture, infrastructure, implementation conditions, training, follow-up, and academic preparation. Fourth, digital health was understood as supporting, not substituting for, nursing work by reducing avoidable burden and creating more space for direct care while preserving human presence, communication, and clinical judgment. Conclusions: In this sample of doctoral nursing students, digital health readiness was positive but conditional. The findings suggest that readiness reflects a context-sensitive professional judgment shaped by educational preparation, organizational support, system credibility, workflow compatibility, and the perceived ability of digital technologies to enhance nursing work rather than replace it. Implications: The findings suggest that nursing education and practice should strengthen applied digital health competencies through simulation-based preparation, electronic documentation training, privacy and ethics education, workflow-aligned implementation, and sustained organizational support. Full article

In a time of pandemic interruptions, work arrangements and flexible work environments are becoming more and more crucial in service firms. While this issue is central to the ethics and effectiveness of human–AI interaction, it has received limited focused attention in both research and practice. As businesses increasingly deploy AI to enhance productivity and efficiency, concerns are emerging about its potential impact on employee well-being resulting specifically in work–life conflict. This study investigates how AI implementation can simultaneously drive performance and contribute to burnout, drawing on an empirical framework. Using a quantitative research design, data will be collected from employees at a university in Kuwait actively integrating AI technologies into their workflows. Guided by the IMPACT model and grounded in the Conservation of Resources (COR) theory and the Social Cognitive Theory (SCT), this study explores how organizational investment in AI influences employees’ experiences of work–life conflict. The findings will highlight AI’s dual role as a productivity enhancer and a potential stressor within a Kuwaiti institution. The study underscores the importance of balanced digital strategies—aligning technological advancement with leadership empathy, robust support systems, and employee well-being initiatives. By contextualizing global research within Kuwait’s evolving digital landscape, this study contributes region-specific insights and practical recommendations for fostering human-centered, sustainable AI integration. Ultimately, it aims to guide organizations in designing AI policies that enhance productivity without compromising employee health, advancing the responsible and ethical management of AI in the workplace. Full article

Artificial intelligence (AI) is increasingly influencing healthcare administration and clinical informatics by supporting disease diagnosis, clinical decision-making, treatment personalization, drug discovery, remote monitoring, public health surveillance, and hospital operations. However, the successful adoption of AI in healthcare depends not only on algorithmic performance, but also on its safe integration into clinical information systems, organizational workflows, and governance structures. This article presents a narrative critical review of recent advances in AI-driven healthcare, with a focus on four major domains: AI-enabled disease diagnosis, treatment personalization and clinical decision support, drug discovery and biomedical knowledge generation, and healthcare administration. Evidence from radiology, pathology, ophthalmology, dermatology, and cardiology shows that AI systems can achieve strong diagnostic performance in selected settings, while applications in electronic health records, natural language processing, telemedicine, and predictive analytics are increasingly used to support healthcare delivery and operational decision-making. At the same time, important barriers continue to limit real-world implementation, including fragmented data infrastructures, limited interoperability, poor data quality, algorithmic bias, lack of explainability, privacy and cybersecurity risks, unclear accountability, and insufficient external validation. This review critically examines these challenges and proposes a governance-oriented roadmap for responsible AI integration in healthcare administration and clinical informatics. The proposed roadmap emphasizes data readiness, model validation, workflow integration, institutional accountability, post-deployment monitoring, and workforce readiness. The findings suggest that AI can contribute to more efficient, accessible, and patient-centered healthcare only when it is implemented within trustworthy medical informatics ecosystems supported by ethical governance, human oversight, and continuous evaluation. Full article

Despite sustained interest in sustainable tourism, the gap between environmental concern and travel behavior is still often explained as an individual failing. This article develops an integrative conceptual review and synthesis of behavioral, discursive, organizational, household decision-making, environmental ethics, and political-economic studies to reframe this gap as a multilevel condition of environmental incoherence. The proposed model explains how values, household negotiations, platform-mediated choices, institutional incentives, and mobility regimes interact to produce and stabilize misalignment between environmental commitments and travel practices. Its main contribution is a diagnostic framework that distinguishes temporary inconsistency from structurally reproduced incoherence, identifies the mechanisms through which incoherence circulates across micro-, meso-, and macro-levels, and clarifies which institutional levers may strengthen alignment between sustainability discourse and material mobility practices. Full article

Generative artificial intelligence (GenAI) is rapidly reshaping higher education, yet barriers to its adoption across different disciplines and institutional roles remain underexplored. The existing literature frequently attributes adoption barriers to individual-level factors such as perceived usefulness and ease of use. This study instead investigates how such barriers are associated with structural conditions. Drawing on a multi-method survey analysis of 272 academic and professional service (PS) staff at Russell Group university, we examine how disciplinary contexts and institutional roles influence perceived barriers. By integrating multinomial logistic regression (MLR), structural equation modelling (SEM), and semantic clustering of open-ended responses, we move beyond descriptive accounts to develop a multi-level account of GenAI adoption. Our findings reveal patterned differences: non-STEM academics primarily report ethical and cultural barriers related to academic integrity, whereas STEM and PS staff disproportionately emphasize institutional, governance, and infrastructure constraints. We conclude that GenAI adoption barriers are deeply embedded in organizational ecosystems and epistemic norms, while also reflecting individual experiences and other unmeasured factors, suggesting that universities must move beyond generalized training to develop role-specific governance and support frameworks. Full article

The use of artificial intelligence (AI) in the radiological field has been extensively investigated from the radiologists’ perspective. Existing studies have primarily focused on AI’s contribution to diagnostic processes and on how its introduction has transformed—and continues to transform—radiologists’ professional practice. The perspectives of radiographers remain underrepresented in the literature, despite their central role in image acquisition and their position as the primary “on-the-ground” operators and managers of imaging technologies. The objective of this study was to analyze the perceptions, attitudes, and expectations of Italian radiographers regarding the introduction of AI, and to provide insights to inform professional training and organizational strategies within healthcare systems. A cross-sectional survey study with qualitative enhancement was adopted as the study design. A survey was administered to a convenience sample, comprising 222 respondents. The findings reveal a high level of familiarity with AI in everyday life, accompanied by an almost complete absence of cultural resistance, suggesting a workforce that is both receptive and ready to evolve. Nevertheless, this individual readiness is contrasted with a substantial institutional and operational gap, characterized by the lack of standardized protocols, regulatory uncertainty, and an uneven distribution of technological resources. The effective integration of AI therefore requires a comprehensive and coordinated approach. Educational reform is necessary to integrate AI and radiomics into university curricula and continuing professional development programs, encompassing not only technical competencies but also ethical, deontological and communication skills. Finally, national and European regulatory frameworks must evolve to clearly define radiographers’ responsibilities within AI-assisted workflows, to establish robust guidelines for data governance and the management of algorithmic outputs. Full article

Artificial intelligence (AI) increasingly permeates organizational work, yet research on AI–human collaboration remains fragmented and lacks a unified structure. This study provides a comprehensive bibliometric mapping of AI–human collaboration by examining its intellectual foundations and emerging research fronts across multiple disciplines. Using document co-citation and bibliographic coupling analysis, the study examines how research on AI–human collaboration has evolved and where it is heading. Data were collected from the Scopus database. A total of 2178 primary documents and 15,078 secondary documents were retrieved and analyzed using VOSviewer (1.6.20) software to visualize the thematic interconnectedness. Results from document co-citation revealed five significant research clusters underlying AI–human collaboration research, including psychological and social foundations of AI; organizational applications of AI in higher education; ethical–cognitive foundations of generative AI; AI literacy and educational transformation; and behavioral foundations of AI adoption. The bibliometric coupling results identified four active research fronts: AI governance, ethics, and humanization; AI–customer relationship management (CRM) adoption, capabilities, and organizational performance; anthropomorphic AI and consumer emotional response; and AI conversational agents and consumer experience dynamics. These findings suggest a thematic shift from technology-centered automation toward collaborative and human-centered integration. The study contributes theoretically by synthesizing insights across organizational behavior, psychology, and information systems to clarify the intellectual structure of this emerging domain. It also outlines implications for leaders designing AI-enabled workplaces that prioritize collaboration, ethical alignment, and adaptive capacity. Full article

This study examines the relationship between individuals’ perceptions of corporate social responsibility (CSR) and their job-seeking intentions, with a particular focus on the mediating role of personal values and attitudes toward social responsibility. The research was conducted in Romania’s south-west region between June and September 2025, using a stratified sample of 453 respondents. Data were analyzed using SMART-PLS 3.0 through structural equation modeling. The results indicate a positive association between perceived CSR and job-seeking intention, with personal values and attitudes toward CSR significantly mediating this relationship. The findings suggest that participants in this study who perceive organizations as socially responsible also report higher levels of organizational attractiveness, particularly when there is alignment between personal and organizational values. At the same time, the results highlight that consistent CSR practices are associated with stronger perceptions of employer attractiveness. Overall, the study suggests that CSR is closely linked to employment-related attitudes and intentions, supporting the view that alignment between individual values and organizational ethical principles represents an important dimension of contemporary human resource strategies. Full article