Search Results (5,040)

Green hydrogen—hydrogen produced from renewable electricity—is central to global decarbonization strategies. However, despite their fragile governance, damaged infrastructure, water scarcity, and limited investment security, conflict-affected developing economies remain largely absent from hydrogen research. This study addresses that gap by developing and validating a multi-evidence strategic framework for green-hydrogen (GH₂) adoption in fragile institutional environments, using Palestine as a challenging test case. Methodologically speaking, the framework integrates four evidence streams—barrier prioritization by 45 Palestinian experts using the Analytic Hierarchy Process (AHP); structural modeling of barrier–adoption–sustainability relationships using partial least squares structural equation modeling (PLS-SEM); strategic-pathway ranking using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS); and an original Sustainable Development Goal (SDG) Contribution Index—externally validated by an independent panel of 120 energy experts across 18 Middle East and North Africa (MENA) countries. Three findings stand out. Firstly, expert perception and structural evidence diverge: technical barriers receive the highest expert weight (56.2%) yet show the weakest structural effect on adoption (β = −0.230), whereas social barriers, weighted lowest by experts (4.8%), rank second in predictive power (β = −0.310). Secondly, Small-Scale Community Production is the most robust deployment pathway, ranked first under every weighting scenario tested. Thirdly, government policy quality acts as a governance multiplier, raising the sustainability returns of adoption by 20.2%, with benefits concentrated in SDGs 7, 13, 8, and 9. Practically speaking, the framework yields seven strategic goals and a phased 2026–2040 roadmap for fragile developing economies. Full article

This study examines the role of artificial intelligence (AI) in marine insurance within the Moroccan maritime sector. Drawing on Dynamic Capabilities Theory, the study investigates the relationships among AI Adoption, Risk Assessment Accuracy, Fraud Detection Capability, Claim Processing Efficiency, and Customer Trust, while also examining the mediating role of these operational capabilities. A quantitative survey was conducted among maritime and insurance professionals operating within the Tangier Med and Casablanca port ecosystems, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that AI Adoption is positively associated with Risk Assessment Accuracy, Fraud Detection Capability, and Claim Processing Efficiency. These operational capabilities are also positively associated with Customer Trust and function as significant mediating pathways between AI Adoption and stakeholder confidence. The study contributes to the emerging literature on AI applications in marine insurance by providing empirical evidence from an emerging maritime economy and offers theoretical and practical implications for insurers, maritime operators, and policymakers. 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

Controversy persists on a global scale regarding the trade-offs between greenhouse gas (GHG) emissions, yield, the global warming potential (GWP), and GHG intensity (GHGI) following organic fertilizer substitution within vegetable cropping systems. This study aimed to quantify these effects under diverse conditions and elucidate the direct and indirect drivers governing these outcomes through a meta-analysis and structural equation modeling (SEM). We synthesized 655 paired observations from 69 published studies using random-effects meta-analysis, finding that organic fertilizer substitution significantly increased CH₄ emissions and GWP compared to inorganic fertilizer controls. Although this was the general trend, organic fertilizer could reduce GWP under specific climatic and soil conditions by reducing N₂O emissions, such as mean annual precipitation <400 mm or soil total nitrogen ≥3 g kg⁻¹. These conditions were also associated with substantially higher yield and lower GHGI. Furthermore, SEM demonstrated that field management practices exerted significant direct effects on N₂O emissions, GWP, and GHGI. Reductions in N₂O emissions, GWP, and GHGI could be achieved with fertilizer application duration ≥10 years, total N application rate ≥300 kg ha⁻¹, and field cultivation or plowing. GHGI was also reduced through yield enhancement under a moderate organic substitution rate (33–66%) or irrigation ≥300 mm. Our study provides a scientific basis for moving beyond universal recommendations towards precision organic management, which is essential for optimizing fertilization strategies to mitigate agricultural GHG emissions. 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 study explored the associations among business intelligence (BI) capabilities and innovative performance (IP) in four- and five-star luxury hotels, while also examining the moderating key role of absorptive capacity (ACAP). Based on the Resource-Based View (RBV), the study conceptualised BI as a multidimensional construct comprising six key capabilities. Data were collected from a sample of 470 hotel managers, and the model was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results revealed that four BI dimensions (analytical decision-making culture, use of information in business processes, information access quality, and information content quality) have a significant positive association with IP. On the contrary, analytical capability and data integration did not exhibit a direct significant association with IP. The moderation analysis offered further insights, illustrating that ACAP can selectively strengthen the association between information content quality and IP, as well as between data integration and IP. These findings highlighted that, in the luxury hotel context, the value of BI depends not only on technological infrastructure but also on the firm’s ability to transform high-quality, integrated data into actionable knowledge. The study contributed to the literature by indicating the moderating role of absorptive capacity in the BI–IP relationship and by providing nuanced insights into how distinctive BI capabilities can drive innovation in a service-intensive setting. From a practical perspective, the results suggested that hotel managers should prioritise promoting a data-driven culture, improving data quality, and designing organisational learning capabilities to leverage BI for IP fully. Full article

Circular economy principles are increasingly influencing tourism development strategies, particularly in rural destinations characterized by strong linkages between agriculture, gastronomy, and local economic systems. This study develops and empirically examines a conceptual model investigating the relationship between circular economy practices, gastronomy integration, perceived authenticity, and destination competitiveness within rural tourism economies. The research focuses on the role of gastronomy as a circular tourism resource capable of connecting local sourcing, sustainability, and experiential value creation. Data were collected through a stakeholder-based survey targeting tourism enterprises, local producers, destination management organizations, and hospitality providers operating in rural tourism destinations. The proposed relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) implemented in SmartPLS 4 and bootstrapped mediation analysis. The findings indicate that circular economy practices positively influence gastronomy integration, while gastronomy integration significantly enhances perceived authenticity. Furthermore, authenticity demonstrates a strong positive effect on destination competitiveness. The mediation analysis confirms that gastronomy integration and perceived authenticity function as intermediary mechanisms through which circular economy practices contribute to competitiveness outcomes. The study contributes to tourism economics and destination competitiveness literature by developing and empirically testing a mediation-based framework linking circular gastronomy, authenticity, and rural tourism competitiveness. The findings provide theoretical and practical implications for destination managers and policymakers seeking to strengthen sustainability, local value creation, and competitiveness through circular gastronomy strategies. Full article

This study investigates the residual web crippling behavior of pultruded glass fiber-reinforced polymer (P-GFRP) hollow sections after exposure to elevated temperatures. The primary objective is to evaluate the combined influence of temperature and loading configuration on web crippling capacity, failure mechanisms, and structural performance, and to develop practical prediction models for engineering applications. A total of twenty pultruded GFRP hollow section specimens were exposed to temperatures of 24 °C, 200 °C, 250 °C, 300 °C, and 350 °C and tested under four loading configurations: End Ground (EG), Interior Ground (IG), End Two Flange (ETF), and Interior Two Flange (ITF). In addition to web crippling tests, tensile, SEM-EDS, TGA-DSC, DMA, and FT-IR analyses were conducted to investigate the mechanical, thermal, and microstructural degradation mechanisms. The results showed that elevated temperatures significantly reduced the web crippling capacity, with strength losses reaching up to 80% at 350 °C due to matrix degradation, fiber–matrix debonding, and loss of structural integrity. Among the investigated loading configurations, IG exhibited the highest load-carrying performance, whereas ETF experienced the greatest capacity reduction. A temperature-dependent reduction factor and unified empirical prediction equations were developed and demonstrated good agreement with the experimental results, with experimental-to-predicted ratios ranging from 0.97 to 1.15. The findings provide valuable insight into the post-fire behavior of pultruded GFRP hollow sections and offer practical guidance for the design, assessment, and fire safety evaluation of GFRP structural members exposed to elevated-temperature environments. Full article

Sustainability has become a key strategic priority in resource-intensive industries such as food processing, yet limited research has examined how specific internal sustainability governance and management components influence firm financial performance. Drawing on the institutional theory and Resource-Based View, this study investigates the direct effects of sustainability-oriented vision and business policy, sustainability-oriented organizational culture, and sustainability strategies on financial performance in the food processing industry. The empirical analysis is based on survey data from 247 firms in Slovenia and employs multiple regression analysis and structural equation modeling (SEM) to test the proposed relationships. The results indicate that sustainability strategies exhibit the strongest positive and statistically significant effect on firm financial performance, followed by sustainability-oriented organizational culture. In contrast, sustainability-oriented vision and business policy show a statistically significant negative direct association, suggesting that formal sustainability commitments alone do not translate into financial benefits without effective strategic integration and organizational support. These findings demonstrate that sustainability does not influence financial performance uniformly, but through distinct organizational mechanisms. The study contributes to the literature by distinguishing between normative, cultural, and strategic dimensions of sustainability and demonstrating their different direct implications for financial performance. It also provides practical insights for managers by highlighting the importance of embedding sustainability into organizational culture and core strategic processes in order to support long-term financial value creation. Full article

Renovations aimed at improving energy conservation in older urban residential areas are essential for sustainable urban development; however, they encounter obstacles such as energy inefficiency and issues in sustaining long-term sustainability following renovation. Based on resource-based theory and collaborative governance theory, this study investigates how organisational resilience affects the efficacy of energy-saving renovations and confirms the mediating role of resource allocation efficiency. A mixed-methods approach was used in this investigation. Grounded theory was first used to establish the components of organisational resilience. A questionnaire survey was then used to gather information from those participating in energy-efficient renovation of old urban residential complexes. System dynamics (SD) was applied for empirical validation and simulation analysis across many intervention scenarios after structural equation modelling (SEM) was used to develop and evaluate study hypotheses. The results show that rather than the support of any particular strategy, the crucial elements in improving the efficacy of energy-saving renovations are efficient interdepartmental coordination and rational budget allocation. Notably, all energy-saving renovation outcome measures in this study are based primarily on stakeholder perceptions and survey responses rather than objectively measured energy consumption data. Full article

Public trust is essential for the effectiveness and long-term sustainability of open government data (OGD) initiatives, particularly in small island developing states (SIDS), where digital governance systems often operate under infrastructural and institutional constraints. Despite growing global research on OGD trust, limited research has examined how the quality dimensions of information systems’ success models shape citizens’ trust in OGD platforms within Caribbean SIDS. This study examines the hypothesised relationships between service quality, system quality, information quality, data quality, and public trust in OGD using an extended information systems success model (ISSM). Data were collected through an online survey of 904 respondents across Caribbean SIDS and analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that all proposed relationships were statistically significant. Data quality showed the strongest statistical association with public trust, followed by system quality. Service quality was also significantly associated with system, information, and data quality. In addition, system, information, and data quality showed significant indirect statistical relationships in the association between service quality and public trust in OGD. This study extends the ISSM framework by conceptualising data quality as a distinct construct within OGD environments. The findings provide practical insights for governments seeking to strengthen transparency, citizen engagement, and sustainable digital governance through higher-quality OGD systems and datasets. The results further highlight the role of open government platforms in improving public service delivery by providing citizens with complete, accurate, and accessible data, interactive feedback mechanisms, and effective data visualisation tools that support informed decision-making and public participation. Full article

This study examines the associations among circular economy (CE) awareness, CE maturity, and circular material flows in the construction industry using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis is based on cross-sectional self-reported survey data collected from 265 firms within the Sustainable Habitat Cluster of Medellín, Colombia. The proposed model examines relationships among cognitive, organizational, and operational dimensions of circularity across the construction supply chain. Results indicate that CE awareness is positively associated with both CE maturity and circular material flows, suggesting that firms reporting higher levels of CE knowledge, communication, training, and access to information systems also report stronger organizational engagement with circular economy initiatives and better circularity outcomes. In contrast, the association between CE maturity and circular material flows was not statistically significant, and no significant mediation effect was observed. These findings indicate that the CE implementation capabilities captured by the maturity construct were not significantly associated with stronger operational circularity outcomes within the sampled firms. The study provides empirical evidence from an emerging-economy construction cluster and highlights the importance of complementing awareness-building initiatives with organizational and operational mechanisms that facilitate the implementation of circular practices across construction ecosystems. Full article

Grain for Green, as an important ecological restoration method, profoundly affects soil nitrogen (N) cycling by altering the soil physicochemical properties and microbial community. Soil nitrogen mineralization is a key process in the terrestrial N cycle. However, the dynamics and underlying driving mechanisms of soil N mineralization rate (Rmin) that respond to afforestation remain unclear. In this study, we selected a typical afforestation sequence in Northeast China, including farmland (F), 21-year-old larch plantation (L21), 42-year-old larch plantation (L42), and natural larch forest (NL). The soil Rmin, associated soil physicochemical properties, and microbial community characteristics were determined to explore the effects of afforestation on soil Rmin and its potential mechanisms of action. The results suggested that soil Rmin was ranked in the order of L42 (0.41 mg kg⁻¹ d⁻¹) > F (0.39 mg kg⁻¹ d⁻¹) > L21 (0.23 mg kg⁻¹ d⁻¹) (p < 0.05) along the afforestation sequence, with no significant difference between L42 and F. Compared to the L42, the NL exhibited significantly lower soil Rmin (0.23 mg kg⁻¹ d⁻¹) (p < 0.05). The changes in soil Rmin during the afforestation were significantly positively related to soil total N (TN) and organic carbon (SOC) concentrations, but significantly negatively related to pH (p < 0.05). Furthermore, the abundances of Proteobacteria and Acidobacteria (bacteria) and Ascomycota (fungi) were also closely correlated with soil Rmin. Structural equation modeling (SEM) analysis further indicated that the afforestation mainly regulated soil Rmin by altering soil temperature (ST) and NH₄⁺-N content. Meanwhile, soil NH₄⁺-N content could also exert a significantly positive effect on soil Rmin by influencing the microbial community. In conclusion, afforestation effectively altered soil Rmin, which was even higher in the plantation than in natural forests. This finding further enhances our understanding of forest restoration and land management practices on soil N cycling in temperate regions. Full article

Agricultural intensification has simplified European agroecosystems, leading to biodiversity loss and altered ecosystem functioning. Within these landscapes, (semi)natural elements such as riparian zones may enhance biodiversity, yet the mechanisms linking habitat structure to functional diversity remain insufficiently understood. In this study, we investigated how landscape attributes and community-level processes jointly shape avian functional diversity in an intensively managed Mediterranean agricultural landscape in central Greece. Bird communities were surveyed at 60 sampling stations across riparian zones, green elements, and croplands. We quantified taxonomic and functional diversity using generalized linear models and examined direct and indirect relationships using piecewise structural equation modelling (SEM), complemented by variation partitioning and redundancy analysis. Riparian habitats supported significantly higher species richness and functional richness compared to croplands, with green elements showing intermediate values. However, functional dispersion (FDis) showed limited variation among habitat types. SEM results revealed that landscape variables influenced functional diversity primarily through indirect pathways, mediated by species richness, Shannon diversity, and functional richness. Variation partitioning further showed that functional dispersion was overwhelmingly explained by community-level attributes, with negligible independent contribution of landscape variables. These findings suggest that higher-order functional structure in bird communities is not directly associated with habitat configuration but is more strongly related to internal community organization. Our results highlight the importance of considering both taxonomic and functional dimensions, as well as indirect ecological pathways, when designing habitat management strategies aimed at sustaining biodiversity and ecosystem functioning in agricultural landscapes. Full article

Background: Caregivers of long-term care (LTC) residents often face decisional conflict during unplanned hospitalization decisions. This study aimed to adapt and psychometrically validate a modified Decisional Conflict Scale (DCS) for primary family caregivers. Methods: A cross-sectional survey was conducted among 205 caregivers in 20 LTC facilities. Exploratory and confirmatory factor analyses (EFA/CFA) were performed on randomly split subsamples, and structural equation modeling (SEM) examined associations among Decision Antecedents, Decision-Making Process, and Decisional Conflict. Bollen–Stine bootstrap procedures were applied to provide robust estimates under slight deviations from multivariate normality. Given the cross-sectional design and single-sample nature of this study, the SEM findings should be interpreted as theory-informed associations rather than causal effects. Longitudinal or intervention-based studies are needed to establish temporal ordering and determine whether improvements in caregiver readiness and decision-making processes lead to subsequent reductions in decisional conflict. Results: The modified DCS demonstrated strong internal consistency and a single-factor structure (α = 0.98, factor loadings 0.83–0.90). SEM indicated that Decision Antecedents and Decision-Making Process were significantly associated with Decisional Conflict (R² = 0.68). The mediation analysis suggested that the Decision-Making Process partially mediated the relationship between Decision Antecedents and Decisional Conflict. Conclusions: The modified DCS appears to be a reliable and valid instrument for assessing decisional conflict among LTC caregivers. Findings highlight the importance of caregiver readiness, support, and communication in shaping decisional experiences. Given the cross-sectional design and single sample, results should be interpreted as associations rather than causal effects. Future research should replicate these findings in larger, diverse samples and explore short-form versions of the scale. Full article