Search Results (3,629)

In recent years, greenwashing has been seen as a critical issue in the tourism and hospitality sector. This study is structured to systematically examine the literature on greenwashing in the tourism and hospitality industry and to establish a study identity. The study is based on the evaluation of 42 qualified articles from the WoS and Scopus databases using the SLR method, in harmony with the PRISMA protocol. As a result of the analyses, the research was classified into seven thematic headings: consumer perception and behavioral responses; employee behavior and internal effects; corporate communication and marketing strategies; strategic corporate social responsibility; critical approaches; greenhushing; and conceptual framework development. According to these findings, extensive study has been focused on consumer perceptions and behavioral responses, yet lacks information on environmentally friendly practices, employee behavior, and organizational structures. This study is important because it connects these different views, offering a practical model that works for both researchers and professionals. While the agricultural and retail dimensions have been well-documented, this study distinguishes itself by situating the analysis within the unique framework of tourism and hospitality. Full article

As an important industrial organizational form in the era of the digital economy, digital industry agglomeration exerts a profound impact on urban ecological resilience. Using panel data of 281 prefecture-level cities in China from 2011 to 2021, this study measures the level of digital industry agglomeration by means of the location entropy method, and constructs an urban ecological resilience evaluation system based on the “Pressure-State-Response (PSR)” model. It systematically examines the impact effects and action mechanisms of digital industry agglomeration on urban ecological resilience. The results show that: (1) The spatio-temporal evolution of the two presents a gradient pattern of “eastern leadership and central-western catch-up”, and their spatial correlation deepens over time, with the synergy maturity in the eastern region being significantly higher than that in the central and western regions. (2) Digital industry agglomeration significantly promotes the improvement in urban ecological resilience, and this conclusion remains valid after endogeneity treatment and robustness tests. (3) The promotional effect is more prominent in central cities, coastal cities, and key environmental protection cities, whose advantages stem from digital infrastructure and innovation endowments, industrial synergy and an open environment, and the adaptability of green technologies under strict environmental regulations, respectively. (4) Digital industry agglomeration empowers ecological resilience by driving green innovation and improving the efficiency of land resource allocation, while the construction of digital infrastructure plays a positive regulatory role. Full article

In an era increasingly defined by the imperative for sustainable development, the construction sector faces significant challenges, including resource limitations, environmental pressures, and high uncertainty. Within this context, the organizational capabilities of construction projects are widely recognized as a critical endogenous driver, closely linked to sustainable performance outcomes. Yet, empirical research to date has produced inconsistent conclusions, and a systematic understanding of how distinct dimensions of capability influence sustainability remains surprisingly fragmented. To address this gap, we employ a meta-analysis to synthesize 11,881 independent samples from 64 quantitative empirical studies. We systematically examined the overall relationship between organizational capability in construction projects and sustainable performance. It further compares the differential effects of project capabilities and dynamic capabilities across economic, social, and environmental performance. Additionally, the study investigated the moderating effects of key contextual and methodological factors. Our analysis yielded several important findings: (1) A significant, moderately positive correlation exists between organizational capability in construction projects and sustainable performance. (2) Project capability exerts a stronger association with economic and social performance, whereas dynamic capability demonstrates a more pronounced effect on environmental performance. This underscored distinct pathways through which different capability dimensions operate. (3) Moderation analysis revealed that the relationship between organizational capability and sustainable performance is stronger in emerging economies and collectivist cultural contexts. Methodologically, structural equation modeling tended to produce larger effect sizes compared to regression analysis. Although no significant moderation effect emerges across research time points, post-2015 studies generally showed slightly stronger effects. The findings enrich the application of the Resource-Based View and Dynamic Capability Theory within construction project contexts, emphasizing the multidimensional nature of organizational capabilities and their differentiated roles across triple-bottom-line performance. Consequently, this research offers valuable pathways for capability development and a strategic foundation for enhancing managerial practice in construction project management. Full article

This study presents a novel methodology that integrates Transformer-based textual analysis from professional networking platforms with traditional demographic variables within a survival analysis framework to predict turnover. Using a dataset comprising 4087 work events from Maimai (a leading professional networking platform in China) spanning 2020 to 2022, our approach combines sentiment analysis and deep learning semantic representations to enhance predictive accuracy and interpretability for HR decision-making. Methodologically, we adopt a hybrid feature-extraction strategy combining theory-driven methods (sentiment analysis and TF-IDF) with a data-driven Transformer-based technique. Survival analysis is then applied to model time-dependent turnover risks, and we compare multiple models to identify the most predictive feature sets. Results demonstrate that integrating textual and demographic features improves prediction performance, specifically increasing the C-index by 3.38% and the cumulative/dynamic AUC by 3.43%. The Transformer-based method outperformed traditional approaches in capturing nuanced employee sentiments. Survival analysis further boosts model adaptability by incorporating temporal dynamics and also provides interpretable risk factors for turnover, supporting data-driven HR strategy formulation. This research advances turnover prediction methodology by combining text analysis with survival modeling, offering small and medium-sized enterprises a practical, data-informed approach to workforce planning. The findings contribute to broader labor market insights and can inform both organizational talent retention strategies and related policy-making. Full article

Background: In the era of rapid technological advancement, digital transformation has emerged as a pivotal strategy for enhancing operational efficiency and competitiveness in logistics enterprises, particularly amid globalization and post pandemic recovery; this study aims to evaluate its multifaceted impact on business performance using a multi-criteria framework focused on Vietnamese firms. Methods: Employing structural equation modeling on primary survey data from 346 middle and senior level managers, alongside the Malmquist productivity index derived from data envelopment analysis on secondary financial indicators spanning 2020 to 2024, the research integrates latent variables such as organizational capability, technological innovation capability, institutional pressure, digital transformation, and business performance. Results: Key findings reveal a strong positive correlation between technological innovation capability and organizational capability (path coefficient 0.522), with organizational capability directly influencing business performance (0.359), while institutional pressure positively affects digital transformation (0.321) but negatively impacts business performance (−0.152); overall, digital transformation exhibits limited optimization, contributing to modest productivity gains and a potential 23% cost reduction through technologies like Internet of Things and artificial intelligence. Conclusions: These results underscore the necessity for logistics enterprises to strengthen organizational integration and training to maximize digital transformation benefits, thereby fostering sustainable competitiveness in global supply chains. Full article

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition. Full article

Construction industry in Afghanistan is crucial for economic and social advancement, particularly after years of instability. However, the construction industry has been already confronting huge time overruns, affecting all stakeholders. This research aims to identify the various risks associated with time overruns in construction projects within Afghanistan and to explore effective risk management strategies to mitigate these challenges. To address time overruns, this study employed Monte Carlo simulations using RiskPert to assess time overruns by combining expert judgment with historical data. This study assesses construction project historical data from 2002 to 2023, emphasizing the political and economic circumstances of that period using a literature review and an examination of 74 construction project reports, in addition to semi-structured interviews with industry experts to determine schedule-related risks and their frequent causes. This research found 29 distinct risk indicators classified into eight categories, facilitating a methodical integration of risks into the simulation model. The Monte Carlo Simulations conducted with @RISK software (version 8.0, Palisade Corporation, New York, NY, USA) assessed the influence of these risks on project performance over 10,000 iterations, demonstrating a robust association with actual project results and a standard deviation of ±15% in durations. Time overruns in projects are linked to socio-political, organizational, and financial risks. The findings emphasize the significance of these factors on project outcomes and recommend strategies for their mitigation to improve decision-making and ensure project management success. Full article

Unsafe behaviors remain a major contributor to workplace accidents within broader safety-management systems. Acknowledging the essential influence of organizational and leadership factors, this study focuses on systematically identifying and prioritizing individual-level determinants of unsafe behavior through an integrated qualitative–quantitative methodology to clarify their specific role within the wider safety framework. Grounded Theory analysis of semi-structured interviews with 40 industry professionals yielded a conceptual model encompassing demographic characteristics, general health, individual competencies, personality traits, and psychological factors. Subsequently, the Fuzzy Delphi Method, applied with 20 domain experts, validated and ranked these determinants. The analysis highlighted risk perception as the most influential factor, followed by work experience, skill level, knowledge, and risk-taking propensity, whereas variables such as family welfare, substance use, and self-display exhibited relatively minor effects. These findings reveal the multidimensional nature of unsafe behavior and underscore the importance of focusing on high-impact personal attributes to enhance workplace safety. By recognizing that many individual factors are shaped by organizational and psychosocial conditions, the study provides evidence-based insights for developing integrated safety management and targeted intervention strategies in industrial settings. Full article

In the era of digital transformation, organizations increasingly invest in Artificial Intelligence (AI) to enhance competitiveness, yet persistent evidence shows that AI investment does not automatically translate into superior firm performance. Drawing on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study aims to explain this paradox by examining how AI-enabled dynamic capability (AIDC) is converted into performance outcomes through organizational mechanisms. Specifically, the study investigates the mediating roles of organizational data-driven culture (DDC) and organizational learning (OL). Data were collected from 254 senior managers and executives in U.S. firms actively employing AI technologies and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that AIDC exerts a significant direct effect on firm performance as well as indirect effects through both DDC and OL. Serial mediation analysis reveals that AIDC enhances performance by first fostering a data-driven mindset and subsequently institutionalizing learning processes that translate AI-generated insights into actionable organizational routines. Moreover, DDC plays a contingent moderating role in the AIDC–performance relationship, revealing a nonlinear effect whereby excessive reliance on data weakens the marginal performance benefits of AIDC. Taken together, these findings demonstrate the dual role of data-driven culture: while DDC functions as an enabling mediator that facilitates AI value creation, beyond a threshold it constrains dynamic reconfiguration by limiting managerial discretion and strategic flexibility. This insight exposes the “dark side” of data-driven culture and extends the RBV and DCT by introducing a boundary condition to the performance effects of AI-enabled capabilities. From a managerial perspective, the study highlights the importance of balancing analytical discipline with adaptive learning to sustain digital efficiency and strategic agility. Full article

Human Resource Information Systems (HRIS) are often introduced as platforms expected to deliver strategic value through workforce analytics, decision support, and alignment with organizational goals. Yet evidence consistently shows that line managers’ use remains confined to administrative functions. This paper addresses this paradox by reframing it through the lens of the attitude-behavior gap (ABG), a concept established in consumer research to describe the disconnect between favorable attitudes and actual behaviors. Drawing on qualitative interviews with 25 line managers in five UK organizations, the study identifies three themes: HRIS as an Administrative Rather than Strategic Tool, Organizational Identity and Role Expectations, and Confidence Gaps and Habitual Routines. Together, these themes illustrate how supportive attitudes toward HRIS coexist with restricted behavioral engagement, sustained by cultural scripts, situational barriers, and ingrained routines. Theoretically, the study extends the ABG beyond consumer contexts into organizational technology use, challenging the linear assumptions of dominant adoption models such as TAM and UTAUT. Practically, it highlights the need for cultural reframing of HR’s role, user-centered system design, and sustained training and integration efforts to enable more strategic engagement. By framing HRIS adoption as a context-dependent practice shaped by organizational roles and behavioral patterns, the paper offers deeper insight into why favorable attitudes toward innovation frequently fall short of producing substantive engagement. Full article

In the context of Poland’s commitments under the European Union’s climate policy, including the European Green Deal and the Fit for 55 package, as well as the decision to ban imports of hard coal from Russia and Belarus, ensuring the stability of the domestic market for energy commodities is becoming a key challenge. The response to these needs is the Coal Platform concept developed by the KOMAG Institute of Mining Technology (KOMAG), which aims to integrate data on hard coal resources, production, and demand. The most important problem is not the just-in-time (JIT) strategy itself, but the lack of accurate, up-to-date data and the high technological and organizational inertia on the production side. The JIT strategy assumes an ability to predict future demand well in advance, which requires advanced analytical tools. Therefore, the Coal Platform project analyses the use of artificial intelligence algorithms to forecast demand and adjust production to actual market needs. The developed mathematical model (2024–2030) takes into account 12 variables, and the tested forecasting methods (including ARX and FLNN) exhibit high accuracy, which together make it possible to reduce overproduction, imports, and CO₂ emissions, supporting the country’s responsible energy transition. This article describes approaches to issues related to the development of the Coal Platform and, above all, describes the concept, preliminary architecture, and data model. As an additional element, a mathematical model and preliminary results of research on forecasting methods in the context of historical data on hard coal production and consumption are presented. The core innovation lies in integrating the just-in-time (JIT) philosophy with AI-driven forecasting and scenario-based planning within a cloud-ready Coal Platform architecture, enabling dynamic resource management and compliance with decarbonization targets. Full article

Background/Objectives: Medical device-related pressure injury (MDRPI) is a significant patient safety issue associated with increased morbidity, prolonged hospitalization, and healthcare costs. Although evidence-based guidelines for MDRPI prevention exist, nurses’ prevention performance remains suboptimal, and the mechanisms linking workload to preventive practice remain insufficiently elucidated. Within a patient safety and quality improvement framework, this study aimed to examine whether occupational coping self-efficacy (OCSE) is statistically consistent with an indirect association linking nurses’ workload and MDRPI prevention performance across the nursing practice continuum. Methods: This descriptive correlational study used a mediation model with data from 181 registered nurses working in intensive care units, general wards, and integrated nursing care wards in South Korea. Workload, OCSE, and MDRPI prevention performance were measured using validated instruments. Mediation was tested using hierarchical regression and bootstrapped analysis (PROCESS macro Model 4, 5000 resamples), controlling for demographic and work-related variables. Results: Higher workload was associated with lower OCSE, while higher OCSE was associated with better MDRPI prevention performance. When OCSE was included in the model, the direct association between workload and prevention performance was no longer significant. Bootstrapping confirmed a significant indirect association through OCSE, consistent with a full mediation pattern. Conclusions: Nurses’ workload appears to be indirectly associated with MDRPI prevention performance through OCSE. These findings suggest that strengthening nurses’ coping self-efficacy, alongside organizational strategies, may be essential for sustainable MDRPI prevention and patient safety improvement. Full article

This study introduces an Impact Thinking Approach (ITA) as a strategic framework to strengthen traceability implementation in the fashion and textile industry. The research examines how ESG impact dimensions shape sustainable strategy definition and how traceability can act as a strategic enabler rather than a mere compliance tool. A mixed-method design combining a narrative literature review, content analysis of 69 sustainability sources, and a two-round Delphi study with 19 experts was employed to identify, evaluate, and prioritize impact drivers related to traceability adoption. The resulting ITA framework connects regulatory requirements, impact materiality, and traceability demands into a unified structure that clarifies the strategic relevance of environmental, social, and governance dimensions. Findings reveal that governance-related factors—particularly data transparency, stakeholder engagement, innovation capacity, and cross-sector partnerships—are the strongest enablers for activating effective traceability schemes. The framework provides practitioners with structured guidance for integrating traceability into sustainable business strategies and for developing impact-aligned KPIs and decision-making mechanisms. The study contributes theoretical insights into the ESG–traceability nexus and offers a practical model to support regulatory alignment, organizational readiness, and long-term strategic planning. Full article

This study provides new evidence on how production digitalization investment affects firm financial performance across diverse European regions. A panel of 14,935 firm-year observations from 30 countries (2012–2022), including a focused Baltic subsample, is used alongside a refined digital capital intensity metric based on depreciated plant and machinery value. The results indicate a positive association between digital investment and operating revenue across Europe, with significantly stronger effects observed in the Baltic region. Interaction models reveal higher marginal returns for Baltic firms, suggesting that digital capital delivers amplified value in economies with lower digital saturation but greater absorptive urgency. Employee-related costs consistently predict revenue outcomes, underscoring their role in translating digital assets into performance. Intangible fixed assets exhibit a positive impact in Baltic labor-scale models but weaker effects elsewhere, indicating that institutional maturity mediates knowledge capital productivity. Implications: (1) digital investment yields asymmetric returns; (2) workforce investment enhances digital ROI; and (3) policy should prioritize organizational readiness alongside infrastructure. This study contributes by introducing a replicable proxy for production-level digitalization and by providing rare comparative evidence on digital returns in transitional versus mature European economies. Full article

The transformative potential of Artificial Intelligence (AI) in urban management is severely constrained by pervasive systemic fragmentation. While AI applications demonstrate high efficacy within isolated domains, they rarely achieve the cross-domain integration necessary for realizing systemic benefits. Our prior research identified this fragmentation paradox, revealing that 91.5% of urban AI implementations operate at the lowest levels of integration. While the Urban Systems Artificial Intelligence Framework (UAIF) offers a technical blueprint for integration, realizing this vision is contingent upon organizational readiness. This paper addresses this critical gap by introducing the Urban AI Governance Maturity Model (UAIG), developed using a Design Science Research methodology. Distinguished from generic maturity models, the UAIG operationalizes Socio-Technical Systems theory by establishing a direct Governance-Technology Interlock that specifically links organizational maturity levels to the engineering requirements of cross-domain AI. The model defines five maturity levels across five critical dimensions: Strategy and Investment; Organizational Structure and Culture; Data Governance and Policy; Technical Capacity and Interoperability; and Trust, Ethics, and Security. Through illustrative applications, we demonstrate how the UAIG serves as a diagnostic tool and a strategic roadmap, enabling policymakers to bridge the gap between technical possibility and organizational reality. Full article