Search Results (1,724)

Background/Objectives: Artificial Intelligence (AI) integration in healthcare promises improved diagnostic accuracy, patient safety, and operational efficiency. However, AI acceptance among healthcare workers remains limited due to knowledge gaps, risk concerns, and governance challenges, particularly in developing countries like Saudi Arabia, where rapid healthcare modernization faces unique infrastructure, organizational, and cultural challenges. This research investigates the factors influencing AI acceptance among medical practitioners, nurses, administrators, and students in Saudi Arabian hospitals to identify key determinants and barriers to adoption. Methods: This cross-sectional study employed an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework integrated with ethical considerations from the Model for Ethical Assessment and Analysis of AI in Medicine (MEAAM). A structured bilingual questionnaire was administered to 119 healthcare professionals and students across Saudi Arabia, measuring constructs including Awareness and Knowledge, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Trust, Perceived Risk, Ethical Governance, and Price Value. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for quantitative analysis, supplemented by thematic analysis of open-ended qualitative responses. Results: The PLS-SEM analysis explained 59.8% of variance in behavioral intention to adopt AI (R² = 0.598). Awareness and Knowledge emerged as the strongest predictor (β = +0.505, p < 0.001), followed by Performance Expectancy (β = +0.229, p < 0.05) and Social Influence (β = +0.123). Perceived Risk functioned as the primary barrier (β = −0.185, p < 0.05). Qualitative findings identified infrastructure gaps, regulatory ambiguities, and training deficiencies as major implementation barriers, while emphasizing opportunities in diagnostic accuracy and remote monitoring. Conclusions: AI acceptance in Saudi healthcare is primarily driven by knowledge, with perceived usefulness and peer support as secondary facilitators, while safety and accountability concerns remain substantial obstacles. Successful AI integration requires coordinated efforts in education, transparent governance frameworks, and institutional support. This study contributes theoretically by validating extended UTAUT in a non-Western healthcare context and practically by providing evidence-based strategies for sustainable AI adoption that enhance healthcare quality while respecting professional roles and ethical principles. Full article

Drawing on diffusion of innovation theory, this cross-national study examines the association between cultural dimensions and artificial intelligence (AI) capability on a 78-country sample. This cross-country, worldwide approach enables a more comprehensive understanding of differences in cross-national AI capability, providing cultural explanations for a new perspective on the diffusion of novel technologies. Our main findings reveal that individualism demonstrates the most stable positive association across model specifications. Uncertainty avoidance and motivation towards achievement and success are significant in the baseline SEM, but the results become sensitive after adding country-level control variables. Long-term orientation is significant in some OLS models but not in the baseline SEM. Power distance and indulgence are not supported in the baseline SEM. Results suggest that cultural values should be considered alongside economic, infrastructural, and regional conditions when analyzing cross-national differences in AI capability. Our findings provide a contextual perspective for policymakers and managers that are developing strategies for achieving competitive advantage. Considering the turbulence of the business and social environments, we argue that cultural adaptive capabilities are essential for global competitiveness. Full article

Purpose: This study investigates the mediating role of internal marketing (IM) in the relationship between artificial intelligence (AI) applications and quality of work life (QWL). Methodology: A quantitative cross-sectional research design was employed. Data were collected via self-administered questionnaires from a sample of 418 employees across service ministries in Saudi Arabia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) as the analytical instrument. Findings: The results reveal that the direct association between AI applications and QWL was not statistically significant. However, a significant indirect relationship was established, indicating that the effect operates entirely through IM. Specifically, AI applications are positively associated with IM practices, which in turn strongly predict higher QWL in the tested model. Originality/Contributions: The study advances current literature by empirically validating IM as the critical organizational mechanism required to translate AI deployment into employee well-being within public-sector institutions. Practical Implications: Decision-makers must couple AI adoption with targeted IM strategies—such as continuous training, job empowerment, and effective internal communication—to ensure a sustainable, human-centered digital transformation. Full article

Low health literacy is associated with risky alcohol use among young people, particularly those exposed to social and environmental factors that normalize drinking. In digital contexts, innovative and engaging interventions are needed to strengthen alcohol-preventive competencies among hazardous drinkers. This study evaluated the effectiveness of an online health literacy promotion program integrating artificial intelligence (AI) and gamification in improving health literacy and alcohol-preventive behaviors among hazardous-drinking vocational students. A quasi-experimental two-group pre-test–post-test design with a 1-month follow-up was conducted among 114 first-year Higher Vocational Certificate students aged 18–20 years in Bangkok, Thailand. Participants were assigned to an intervention group (n = 57) or a comparison group (n = 57). The intervention group received the ALC Literacy Program, while the comparison group received standard educational materials on alcohol prevention. Data were analyzed using descriptive statistics, chi-square tests, independent t-tests, and two-way mixed-design repeated-measures ANOVA with Bonferroni post hoc comparisons. At baseline, no significant between-group differences were observed. After the intervention and at 1-month follow-up, the intervention group showed significantly greater improvements in both health literacy and alcohol-preventive behaviors than the comparison group (p < 0.001). Large interaction effect sizes were observed for health literacy (partial η² = 0.623) and alcohol-preventive behaviors (partial η² = 0.622). These findings indicate that the ALC Literacy Program was effective in enhancing health literacy and strengthening alcohol-preventive behaviors among hazardous-drinking vocational students. This intervention may represent a potentially useful digital health promotion approach for alcohol prevention in educational settings. Full article

Sustainable management of sugar agro-industrial value chains requires a multidimensional approach that integrates economic, environmental, and social criteria. Current literature addresses risk management, circularity, and artificial intelligence in isolation, without an integrated framework that generates synergistic value. The objective of this research is to validate an integrated framework for the sustainable management of sugar agro-industrial value chains. A mixed-methods, qualitative-quantitative, descriptive-retrospective study was conducted on the Cuban sugar agro-industry during 2023–2025. The procedure was structured into five phases and 10 stages; Petri net simulation was used to validate its logical consistency. Material, economic-financial, and knowledge flows were mapped; 16 stakeholder groups and their influence–dependence relationships were analyzed; 41 risks were identified, of which six were classified as critical. Simulation-based scenario modeling, which integrates risk, circularity, and AI interventions, projects an average potential reduction of 33.4% in total chain lead time, pending empirical validation. Petri nets confirmed the absence of connectivity errors, free-choice violations, and flow noise, formally validating the logical consistency of the procedure. The research supports the hypothesis that an integrated framework combining risk management, circularity, and AI, validated using Petri nets for logical consistency, projects improvements in the efficiency and sustainability of the value chain. Full article

Efficient and financially sustainable social protection systems are essential under conditions of economic instability and increasing social demand. However, traditional administrative models are often characterized by high operational costs, procedural complexity, and delayed benefit delivery. This study examines the role of digitalization, process automation, and AI-driven administrative solutions in reducing administrative expenses while enhancing the sustainability and resilience of social protection systems. An integrated Automation Index is developed using standardized proxy indicators that reflect reductions in operational and transaction costs associated with digital and automated technologies. To assess future trajectories of administrative expenses, scenario-based modelling is applied under three digital transformation paths—baseline, moderate, and intensive. Administrative efficiency is estimated using a translog Stochastic Frontier Analysis (SFA) framework. The results indicate that digitalization and automation significantly reduce administrative costs only when supported by favorable institutional conditions, including decentralized governance, effective inter-agency coordination, and clearly regulated administrative procedures. Under the intensive digital transformation scenario, administrative expenses decline substantially relative to the baseline, while system responsiveness and beneficiary coverage improve. In contrast, weak institutional environments limit the efficiency gains of technological solutions. The study concludes that AI agents and automated systems should be viewed not as substitutes for human decision-making but as tools for optimizing administrative architectures. This transition from resource-intensive to technology-intensive models is particularly important for developing countries seeking sustainable social protection under constrained fiscal conditions. Full article

The growing use of artificial intelligence (AI) in social media marketing has accelerated the emergence of AI-generated virtual influencers. While these influencers offer brands advantages such as scalability and message control, they also raise concerns regarding manipulation and ethical persuasion. Grounded in the Persuasion Knowledge Model (PKM), this study examines how different AI disclosure conditions influence perceived manipulation, perceived ethics, and purchase intention in AI influencer marketing. A three-condition between-subjects experimental design was employed to compare a human influencer, a disclosed AI influencer, and an undisclosed AI influencer using identical Instagram stimuli. Data were collected from 762 Generation Z female consumers in Türkiye. Structural equation modeling (SEM) was used to test the proposed relationships. The findings revealed that both disclosed and undisclosed AI influencer conditions significantly increased perceived manipulation. Perceived manipulation negatively affected perceived ethics, whereas perceived ethics positively influenced purchase intention. In addition, AI literacy positively affected perceived manipulation and perceived ethics while negatively affecting purchase intention. The findings further demonstrated that disclosure conditions indirectly influenced purchase intention through sequential cognitive and ethical evaluation processes. The study contributes to the AI influencer and digital persuasion literature by demonstrating that disclosure cues shape consumer responses through interconnected psychological mechanisms. Full article

The existing literature highlights that artificial intelligence (AI) creates both hope and threat perceptions among managers and workers, particularly due to concerns about potential job losses and the negative effect on continued professional development. Employee trust in AI-based systems varies depending on their features and performance. Furthermore, regardless of the performance of such systems, some individuals are inherently opposed to AI, a phenomenon known as AI aversion. In this study, an Integrative AI Adoption Framework is developed, drawing upon principles from established theories, including the technology acceptance model, behavioral decision theory, and emotion-based frameworks, to assess how perceived usefulness and perceived ease of use, along with perceived threat, trust, and AI aversion, influence human resources (HR) professionals’ attitudes and behavioral intentions to use AI-based recruitment systems. In doing so, the study conceptualizes AI-based recruitment as a socio-technical system in which a technical subsystem (the system’s instrumental and AI-specific properties) and a social subsystem (the affective and trust-related responses of HR professionals) must be jointly considered to explain adoption. The model was tested using the partial least squares structural equation modeling (PLS-SEM) approach through survey-based data collected from 242 HR professionals. The study’s findings indicate that attitude plays an important role in shaping behavioral intention, and perceived usefulness is a key driver of attitude. AI aversion negatively influences attitudes, while trust has a twofold effect of reducing AI aversion and positively influencing attitude. Additionally, perceived threat significantly increases AI aversion, which is driven by concerns over job replacement and personal development. Full article

This contribution examines the role of artificial intelligence technologies in the co-construction of social reality, with specific attention to AI-generated data as emergent agents of knowledge production. Building on perspectives from science and technology studies and recent debates on algomorphic sociology, the contribution conceptualizes generative AI systems not as research instruments, but as active participants in epistemic processes. The analysis argues that AI-generated data exhibit a performative character: they do not simply represent social phenomena but actively contribute to their stabilization, classification, and circulation. This performativity fosters a shift from researcher-centered interpretation toward hybrid configurations in which meaning emerges through human–machine assemblages. Through a theoretical synthesis of recent methodological and epistemological reflections, the contribution highlights a transition from anthropocentric models of knowledge production to post-anthropocentric, relational frameworks in which agency, cognition, and sense-making are distributed across sociotechnical networks. The contribution concludes by outlining the implications of this shift for the future of digital social research and also for reflexivity, methodological design, and the ethics of social research, advocating a critical and adaptive stance toward AI as a co-producer of knowledge rather than a subordinate analytical tool. Full article

In the current context, where sustainability has become a global imperative, emerging markets have increasingly incorporated green finance as a strategic pillar to foster long-term growth and stability. This study examines the evolution, trends, and key challenges of sustainable investment in emerging economies, with a particular focus on the integration of environmental, social, and governance (ESG) criteria. A systematic literature review was conducted using Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, based on a sample of 399 articles published over the past decade. The findings reveal a significant expansion in academic output on ESG investments in emerging markets, with an average annual growth rate of 14.06% and an international co-authorship rate of 37.34%. China, the United Kingdom, South Africa, and the United States emerge as leading contributors, particularly since 2020. However, critical gaps persist, including inconsistencies in ESG ratings and the limited adaptation of ESG frameworks to local socioeconomic and institutional conditions. Future research should focus on strengthening public policy frameworks, designing effective fiscal incentives, assessing the distributive implications of green finance, and leveraging technologies such as fintech, blockchain, and artificial intelligence to enhance ESG rating consistency, transparency, risk measurement, and the overall efficiency of sustainable investments. Full article

AI technologies are changing the world of work in ways that are hard to predict, and this uncertainty is felt particularly strongly by young people who are just beginning to think about their futures. This study explores how high school students in Turkey perceive AI’s potential impact on their career choices, using Social Cognitive Career Theory (SCCT) and Uncertainty Management Theory (UMT) as interpretive lenses rather than formally tested models. SCCT helps frame AI as an environmental force that shapes how students think about their career options, while UMT helps explain how students emotionally and cognitively respond to uncertainty that cannot easily be resolved. Using a cross-sectional survey of 354 12th-grade students, we developed and validated the AI-Related Career Perception Questionnaire (AICP-Q), which yielded four factors: AI Anxiety and Career Precarity, AI Literacy and Technological Awareness, Proactive Career Adaptation, and Socio-Technical Uncertainty. Students showed moderate AI awareness but relatively high levels of socio-technical uncertainty. Academic track emerged as an exploratory statistical correlate of AI Anxiety, a descriptive association suggesting that students’ sense of threat from AI may relate more to the specific skill demands of their chosen field than to the prestige of their school, though no causal inference can be drawn from these cross-sectional data. A key finding is “the planning gap”: students recognized the potential career disruptions associated with AI but did not consistently respond with adaptive behaviors. Drawing on UMT, we advance the tentative hypothesis, to be tested in future research, that this pattern may relate to a lack of the appraisal resources needed to translate awareness into action; because these constructs were not directly measured, this remains an interpretive suggestion rather than an empirical finding. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

Autonomous artificial intelligence (AI) systems increasingly participate in decision-making processes that affect individuals, markets, and public administration. Their growing autonomy complicates the attribution of legal responsibility, particularly within regulatory frameworks that were designed around identifiable human actors and relatively stable products. Although European Union instruments such as the GDPR, the AI Act, and the revised Product Liability Directive address specific dimensions of risk and compliance, they do not fully resolve how responsibility should be allocated across the lifecycle of complex AI systems. The difficulty does not lie so much in the absence of legal rules. Rather, it reflects the structural tension between traditional liability models and the distributed architecture of contemporary AI development and deployment. By examining how existing EU regulatory instruments interact, the paper identifies fragmentation in responsibility allocation that may weaken institutional accountability. It then proposes a tiered model of legal responsibility based on meaningful control at different stages of system design, deployment, and operational oversight. Rather than introducing new forms of legal personhood, the model seeks to clarify how existing doctrines can be interpreted and coordinated in order to maintain regulatory coherence and socially intelligible accountability in digitally mediated environments. The model allocates responsibility according to meaningful control within distributed systems, offering a structurally coherent alternative for EU governance. Full article

Person–Environment Fit Theory explains organizational match in beliefs and values influences employee satisfaction and motivation in the workplace. Automated systems [e.g., artificial intelligence (AI)] and advanced technology have been integrated into business operations to compete in the digital era. However, how employee technology orientation and individual differences influence workplace preferences is underexplored. This study advances how organizations can strategically attract talent aligned with their technological infrastructure and work design. Parallel mediation path analysis was conducted on a surveyed U.S. convenience sample (SPSS PROCESS Model 4; N = 912). Technology adaptability was positively associated with preference for a job role highlighting working with automated systems relative to emphasizing supportive coworkers. Technology adaptability related to a greater need to belong and job satisfaction (as parallel mediators) and thereby less preference for a role working with automated systems (i.e., preference for a supportive coworkers job ad). The findings reveal that job ads promoting automated systems do not unilaterally attract tech-adaptive employees. Belonging needs and job satisfaction can function as psychological factors that redirect tech-savvy workers towards socially enriched roles. Proactively advertising social belonging and job satisfaction cues alongside advanced technology use could more comprehensively appeal to tech-adaptive job seekers. This can signal a better value congruence between an organization and these job seekers. Full article

As artificial intelligence (AI) is increasingly integrated into the workplace, employee–AI collaboration is evolving from a personal productivity tool to a social cue that coworkers can observe and interpret. Existing research has largely emphasised the performance and well-being effects of employee–AI collaboration; however, few studies have revealed, from the observer’s perspective, its potential negative spillover mechanisms on coworkers’ helping behaviour. Based on attribution theory, this study constructs a theoretical model of ‘employee–AI collaboration–coworker attributions–coworker helping behaviour’, distinguishing two mechanisms—laziness attribution and responsibility-avoidance attribution—and examines the boundary role of human–AI task interdependence. Study 1, based on 375 two-wave coworker survey responses, tested the hypotheses using hierarchical regression and bootstrapping methods. Study 2 employed a 2 × 2 scenario experiment to further test the effects of employee–AI collaboration and human–AI task interdependence on coworker attributions and willingness to help. The results indicate that higher perceived employee–AI collaboration is associated with lower coworker helping behaviour; laziness attribution and responsibility-avoidance attribution play a mediating role between perceived employee–AI collaboration and coworker helping behaviour. The higher the human–AI task interdependence, the more likely coworkers are to interpret employee–AI collaboration as laziness or responsibility-avoidance, thereby reinforcing the aforementioned negative effects. Full article