Search Results (1,311)

Higher-education institutions increasingly use AI-enabled chatbots, personalised communication, recommendation systems, and predictive information services in academic marketing. Adoption of these systems depends not only on technical availability, but also on institutional trust, emotional engagement, and skepticism regarding the reliability, transparency, and autonomy implications of AI. This study examines the Trust-Tech Nexus framework using stakeholder survey data collected at MIT Art, Design and Technology University, Pune, India (N = 300). The analysis combines psychometric validation, WLSMV confirmatory factor analysis for ordered indicators, and cross-validated predictive modelling. Four three-item constructs were measured with five-point Likert indicators, as follows: AI Adoption, Institutional Trust, Emotional Engagement, and AI Skepticism. Reliability and convergent validity were acceptable, and the WLSMV CFA showed strong practical fit (CFI = 0.991, TLI = 0.988, RMSEA = 0.040, SRMR = 0.039). Discriminant validity was supported by HTMT and Fornell–Larcker evidence, while Harman’s single-factor result was treated only as an initial diagnostic. Construct-only ridge regression produced positive out-of-sample predictive evidence (CV R-squared = 0.352; RMSE = 0.642; MAE = 0.501). Exploratory classification results were moderate and are interpreted only as supplementary segmentation evidence because the binary targets were derived from the AI Adoption composite. The study supports a validated four-construct measurement structure and moderate predictive association in one institutional context, while avoiding causal claims. Full article

This study examines the relationship between Environmental, Social, and Governance (ESG) performance and financing and pricing outcomes in green and conventional bond markets in China over the period of 2017–2024. Drawing on signaling theory, information asymmetry theory, and market segmentation theory, the study argues that the role of ESG performance differs across bond types because green and conventional bonds operate within different institutional and informational environments. Using a comparative analysis of green and conventional bonds, the findings show that ESG performance is more strongly and consistently associated with conventional bond characteristics, particularly in relation to issuance amount, yield to maturity, and credit spreads. In contrast, ESG effects in green bonds are weaker and less consistent, suggesting that investors place greater emphasis on certification mechanisms, environmental project objectives, and sustainability-related bond characteristics than on broader issuer-level ESG disclosures. The findings also suggest that ESG information does not affect all debt instruments in the same way or always functions as a purely risk-reducing signal. In the Chinese market, stronger ESG exposure may also be associated with transition risks, regulatory pressures, and sector-specific sustainability challenges, particularly in conventional bond markets. Overall, the results indicate that the financial relevance of ESG performance depends not only on firm characteristics but also on the institutional and informational environment of the financial instrument itself. The findings remain robust across alternative model specifications and sensitivity analyses, providing additional confidence in the reported differences between green and conventional bond markets. The study contributes to the sustainable finance literature by showing that the pricing relevance of ESG information is instrument-specific rather than uniform across debt markets. It also provides practical implications for regulators, investors, and issuers by highlighting the importance of disclosure quality, transparency standards, and external verification mechanisms in strengthening investor confidence and reducing potential greenwashing risks in sustainable finance markets. Full article

Saudi Arabia’s audit market faces three governance challenges that existing frameworks may not fully address. These challenges concern a potential regulatory gap around autonomous AI accountability, a trust dimension that standard technology-adoption models may not fully capture, and limited mechanisms for independently verified ESG assurance under Vision 2030. This study adopts a conceptual design approach within the design science research tradition and proposes the CMA Agentic AI Platform as a practical response to these challenges. The platform comprises two segments. Segment 1 deploys autonomous drone swarms to verify corporate assets across four audit tasks—asset valuation, ESG compliance, anomaly detection and construction progress—using deep learning, thermal imaging and social-media cross-referencing. Segment 2 continuously monitors discretionary accruals and uses objective earnings-management data to inform auditor assignment and rotation decisions. This approach replaces subjective reputational assessments with transparent, quantifiable governance criteria. The platform is governed through the Triadic Agentic Framework, which extends classical agency theory by distributing authority across the Principal, the Human Agent and the AI Agent. The framework also operationalises Trust Expectancy as the primary adoption condition. The evidence base draws on two complementary streams: a PRISMA-guided systematic review and bibliometric analysis of thirty-nine peer-reviewed studies, and a documentary analysis of four national agentic-AI regulatory frameworks (SDAIA, MDDI/IMDA, NIST and ICO). The study contributes the concept of Algorithmic Accountability as a distinct governance domain, the Triadic Agentic Framework as an operational architecture for autonomous regulatory monitoring, and a reframing of the UTAUT trust construct for agentic-AI adoption in mature professional contexts. The platform converts theoretical governance into a regulatory architecture with direct implications for concentrated capital market regulators. Full article

The second-hand Battery Electric Vehicle (BEV) market in Italy is affected by substantial information asymmetry, particularly with regard to battery State of Health (SOH), residual value, and expected maintenance costs. This lack of transparency limits consumer confidence and reduces the potential of used BEVs to support a broader and more inclusive electric mobility transition. In this study, a data-driven decision-support framework is developed to improve the evaluation of second-hand BEVs in the Italian market. The proposed approach combines market data collected from major online platforms with historical price reconstruction and an assessment of the information asymmetries that limit user confidence in the second-hand BEV market. It also incorporates a semi-empirical SOH estimation model based on observable vehicle characteristics. The results reveal a consistent depreciation gap between BEVs and comparable internal combustion engine vehicles across different market segments and indicate that battery-related uncertainty appears to be one of the factors associated with consumer hesitation. The framework shows that combining non-invasive battery-health estimation with maintenance-related information can support a more objective assessment of used electric vehicles. Overall, the study demonstrates the potential of integrated digital and engineering-based tools to reduce uncertainty and enhance transparency in the second-hand BEV market. Full article

Shopping mobility accounts for a significant share of total travel, while the growth of e-commerce is reshaping consumer purchasing behaviour and retail dynamics. Comprehending how territorial and sociodemographic factors shape the choice between physical and digital retail channels is therefore a key issue for transport planning and sustainable urban mobility. In this context, it is important to understand how accessibility to different classes of retailers is configured and how it can impact purchasing choices. Through a discrete choice analysis, this study examines the sociodemographic and territorial determinants of purchasing behaviour, focusing on the clothing market. Four purchase alternatives are considered: medium-sized and small urban retail stores, shopping malls, online purchasing, and no purchase. This multi-alternative framework enables the direct estimation of substitution patterns not only between physical and digital retail, but also between distinct forms of physical retail. Data were collected through a survey conducted in Southern Italy, providing empirical evidence from a territorial setting that is structurally underrepresented in the existing literature. A multinomial logit model and a two-level hierarchical logit model incorporating pedestrian accessibility—measured as walking time from residence to the nearest clothing store—alongside sociodemographic and territorial attributes were calibrated to analyse alternative choice behaviour. The calibrated models show interesting results, highlighting the role of pedestrian accessibility in the choice of clothing stores in city centres. Age, income, and territorial variables further differentiate channel preferences across population segments. The findings offer relevant implications for policymakers, governance managers, urban planners, and researchers concerned with retail location, sustainable accessibility, and consumer behaviour. These insights are highly valuable for developing planning that addresses the United Nations 2030 Agenda, particularly Sustainable Development Goal 11. Full article

This paper presents a comparative analysis of selected economic indicators within the Italian railway passenger transport sector during the 2010–2024 period. Characterized by high-speed rail (HSR) saturation and advanced market liberalization, the Italian railway system serves as a reference model for investigating structural shifts within mature transport networks. The study aims to quantify the dynamics of transport performance through a synthesis of multiple analytical dimensions: passenger volume, transport performance (passenger-kilometers), modal split, average transport distances, and indicators of general and dynamic population mobility. The methodological framework is based on the application of chain and base indices, enabling the precise identification of cyclical fluctuations, exogenous disruptions (primarily the impact of the COVID-19 pandemic), and the subsequent degree of systemic resilience. The analysis suggests a significant shift in demand composition after 2014, characterized by an expansion of short- and medium-distance segments alongside a transformation in travel behavior. The research findings determine the correlation between infrastructure investment and the actual positioning of rail transport within a multimodal system. This work provides an analytical foundation for strategic planning in transport policy and sustainable mobility within the context of European transport integration. Moreover, these insights are practically applicable for transport operators and planners in forecasting demand, optimizing network capacity, and enhancing infrastructure resilience against future exogenous shocks. Full article

The conventional “resources–energy–environment–economy” growth paradigm has imposed severe environmental pressures on China, including land desertification and smog pollution. In the context of carbon peaking and carbon neutrality, the synergistic advancement of pollution reduction, carbon mitigation, ecological expansion, and economic growth (PCEG) has become a critical development pathway. Drawing on Pareto improvement theory, this study applies a super-efficient slack-based measure (SBM) model to evaluate PCEG synergistic efficiency across 30 Chinese provinces from 2003 to 2020. We further investigate its temporal evolution, regional heterogeneity, and convergence characteristics. The empirical results reveal that (1) PCEG synergistic efficiency follows a U-shaped trajectory; (2) both technological change and efficiency change contribute positively to post-2018 recovery; (3) substantial regional heterogeneity and cross-regional overlap are observed, with intra-regional disparities playing an equally important role in shaping overall inequality as inter-regional differences; and (4) no σ-convergence is observed at the national or regional level; β-convergence is significant in the non-spatial setting but drops sharply once spatial dependence is incorporated, indicating that administrative barriers, market segmentation, and frictions in factor mobility hinder the convergence process. These results inform a policy mix that addresses within-region heterogeneity, sustains the post-2018 momentum of technological progress, and dismantles spatial barriers to factor mobility. Full article

The 2008 financial crisis pushed policymakers around the world to rethink how banks could manage risk, leading to the implementation of stricter regulations, including capital buffers and bail-in mechanisms, aimed at making the financial system more resilient. This study examines how three key regulations under Basel III, namely, the Countercyclical Capital Buffer (CCyB), the Capital Conservation Buffer (CCB), and the Capital Surcharge (CS), shape lending patterns in Indonesian banks. The effectiveness of the bail-in policy in helping banks strengthen their capital base is also examined. This study uses difference-in-differences analysis on panel data from 97 banks between 2010 and 2024 to examine the impact of stricter capital regulations on banks’ ability to channel credit to the public and business sectors. Basel III aims to strengthen the resilience of banks; however, this policy could impact credit access and banking stability in Indonesia. This study found a positive impact on LDR of large banks after the treatment, which indicates the banks’ efforts to use the funds collected through credit distribution. This study empirically examines the impact of capital buffer regulation and the bail-in instrument in Indonesia as an emerging-market country with a segmented banking sector and banks’ classification by ownership and core capital value. Full article

This study investigates the development and current status of agritourism in Croatia, with a specific focus on Istria County, a region characterized by favorable Mediterranean climatic conditions and a long-standing tourism culture. The research aims to assess the structure and success factors of agritourism enterprises within the broader Croatian tourism market. An empirical field survey was conducted on a sample of 58 agritourism businesses operating in Istria County. The collected data were analyzed using descriptive statistical methods, and enterprises were segmented into three groups according to their length of business operation. The results reveal significant differences among the identified groups in terms of demographic and professional characteristics, including age, educational attainment, prior tourism experience, years in business, and annual tourist visits. The analysis further identifies three key dimensions influencing agritourism: future development (1), consisting of economic and social variables enhancing the business; limitations (2) of land, capital, and laws that can hinder their future; and state interventions (3), such as incentives and taxes created by state authorities. The findings suggest that the sustainable development of agritourism in Istria depends on coordinated policy support, effective utilization of farm, local nature, and heritage resources, as well as continuous improvement in service provision. This study contributes to a better understanding of agritourism dynamics in emerging rural tourism markets and provides a basis for future research and policy development. Full article

This study examined differences in self-justification and symbolic consumption tendency according to levels of impulse-buying tendency among South Korean adult members of Generation Z sports consumers. Drawing on Cognitive Dissonance Theory (CDT) and Symbolic Self-Completion Theory (SSCT), the study aimed to clarify whether impulse-buying tendency functions as a meaningful basis for segmentation in sports product consumption. Data were collected from South Korean adults aged 20 years or older who had purchased sports products within the previous 12 months. For group-based comparison, participants were classified into low (n = 128) and high (n = 106) impulse-buying tendency groups using a mean-split procedure. A 29-item questionnaire assessed impulse-buying tendency (9 items), self-justification (4 items), and symbolic consumption tendency across five subdimensions (16 items). Confirmatory factor analysis supported the adequacy of the measurement model (χ²/df = 2.285, p < 0.001, IFI = 0.918, TLI = 0.906, CFI = 0.918, SRMR = 0.044, RMSEA = 0.074), with satisfactory reliability and convergent validity. MANOVA results showed that the high impulse-buying tendency group reported significantly higher levels of self-justification, self-development and reinforcement, conformity and belonging, and communication and exchange than the low group. These findings suggest that impulse-buying tendency differentiates consumers in terms of post-purchase cognitive responses and identity-related consumption patterns among adult Generation Z sports consumers. The results highlight the heterogeneity of South Korean Gen Z sports consumers and suggest that sports product marketing may benefit from more segmented strategies based on post-purchase reassurance and symbolic value. However, causal interpretations are limited by the cross-sectional design. Full article

H3N2 subtype avian influenza virus (AIV) is prevalent in poultry and wild birds and typically causes asymptomatic or mild respiratory infections. However, genetic reassortment between H3N2 and other AIV subtypes generates novel strains capable of crossing the species barrier, posing a threat to both poultry and public health. In this study, nine H3N2 AIVs were isolated from ducks in live poultry markets (LPMs) in Guangxi, southern China, during 2022–2024. Phylogenetic analysis revealed that all eight gene segments of the nine isolates were clustered within the Eurasian lineage, with internal genes derived from multiple subtypes, including H1, H2, H3, H4, H5, H6, H7, and H9. These findings indicate complex gene reassortment of H3N2 AIVs in Guangxi. Importantly, the PB2 genes of certain isolates were closely related to those of highly pathogenic H5 subtype viruses, suggesting that H3N2 AIVs may contribute internal genes to H5 viruses. Three representative isolates (LZD44, NND98, and NND100) were assessed for pathogenicity in SPF chickens and mice. All three strains successfully replicated in the respiratory tissues of both species. Notably, the LZD44 virus, which harbored the mammalian-adaptive mutations PB2-MVV and NP-I353V, presented significantly higher virulence in chickens and mice than the other two strains. These results demonstrate that H3N2 subtype AIVs are capable of replicating in certain tissues of chickens and mice without prior adaptation, underscoring a potential risk for cross-species transmission. Consequently, sustained surveillance of H3N2 subtype AIVs is essential to prevent the spillover of novel recombinants into the human population. Full article

With growing interest in health and leisure, walking tourism has emerged as a significant segment of the tourism market. Coastal trails have gained prominence as attractive tourist attractions offering unique experiences that combine coastal and forest environments. Understanding the experiences of tourists using these trails is essential to their sustainability and the revitalization of nearby regions and tourist destinations. However, the sustainable management of coastal trails and the understanding of the perceptions and evaluations of tourists using them remain limited. Therefore, this study aims to analyze walking tourism experiences on coastal trails using online review data to identify tourists’ perceptions and evaluations. Three representative coastal trails in South Korea were selected as the study sites, and 21,289 reviews (including course information, titles, review content, and posting dates) were collected from Durunubi, a walking tourism application operated by the Korea Tourism Organization. The research methodology employed text mining and sentiment analysis in Python 3.12.13 and spatial analysis using GeoDa 1.22.0.20 and QGIS 3.40.11. This study explores the emotional geography of walking tourism experiences along Korean coastal trails by integrating the analysis of online review data using text mining, sentiment analysis, and spatial analysis. The analysis revealed that positive sentiments were associated with natural landscapes, while negative sentiments were associated with trail management. These emotional experiences exhibit distinct spatial clustering patterns. This finding has important implications for establishing sustainable trail management strategies. Full article

In this study, we examine the dynamics of housing prices across 35 Chinese cities from 2010 to 2024 using an error correction model (ECM). Using two-stage least squares (TSLS), we address endogeneity in housing stock. The results show a stable long-run relation between housing prices, income, user cost, and employment. The findings indicate that user cost exerts a significant negative pressure on housing valuations. While housing stock exhibits a positive, long-run correlation with prices due to rapid urbanization, its expansion effectively dampens price growth in the short term. We also find differences across market segments. The newly built housing market returns to equilibrium in about 33 months, while the second-hand market requires about 60 months. These results underscore the necessity of considering segment-specific adjustment speeds and fundamental drivers when formulating urban housing policies in China. Full article

Since launching the Saudi Vision 2030, it has witnessed a reflective sustainability action (SA) transformation. However, robust theoretical models investigating the multifaceted catalysts and consequences of SA in this less-developed country are still lacking in investigation. This lag prompted us to advance and validate a composite framework integrating multiple theories (e.g., institutional theory, the resource-based view (RBV), stakeholder theory, dynamic capabilities theory, and contingency theory) elucidating how policy direction (PD), market incentives (MIs), and knowledge collaboration (KC) stimulate SA adoption encompassing its environmental practices (EPs), social practices (SPs), and circular economy practices (CEPs). The investigation also probes how SA thereafter drives sustainable performance outcomes. A machine-learning approach using the PLS-SEM facility was applied based on 400 questionnaires targeted at managerial positions working in the tourism and hospitality segment based in Saudi Arabia. The findings reveal that all the proposed relationships were supported, providing strong empirical support for the proposed sustainability framework in the Saudi tourism and hospitality context. Institutional pressure and the governance/regulatory environment also showed a significant impact on environmental practices, sustainable performance, and circular economy practices, whereas cost efficiency, competitive advantage, customer demand for sustainability, and knowledge collaboration also demonstrated a positive impact on sustainability actions and outcomes. Furthermore, robust analysis shows that larger firms respond more strongly to MI in terms of cost efficiency, competitive advantage, and customer demand, while CEP produces a modest improvement in hotels compared with restaurants. Our model develops a theoretical synthesis beyond fragmented views. It also provides tangible guidance for industry leaders and regulators in driving strategic alignment with the SDGs and in developing a resilient, situational model that promotes regenerative tourism in high-growth, vulnerable destinations. Full article

This study examines the determinants of regulated electricity contract prices in Colombia during the period 2009–2021, with a particular focus on the role of electricity-market fundamentals and macroeconomic conditions. Although regulated contracts are designed to reduce exposure to short-term volatility, limited evidence exists on how their price formation behaves across different segments of the distribution. To address this issue, the analysis combines quantile regression with autoencoder-based dimensionality reduction, allowing the incorporation of a large set of macroeconomic variables without overparameterizing the model. The results show that regulated contract prices are more consistently associated with electricity-system factors than with broad macroeconomic conditions. In particular, the spot price becomes significant only in the upper quantiles, where it appears to operate as an indicator of operational stress, while hydropower and thermal generation exhibit localized effects across the distribution. By contrast, most macroeconomic factors display weak, uneven, or non-significant effects, with only the exchange-rate-related component becoming clearly relevant at relatively high price levels. A robustness analysis based on principal component analysis broadly supports these patterns. Overall, the evidence suggests that the Colombian regulated market behaves as a relatively stable contractual system, in which price formation is shaped mainly by electricity-sector conditions, indexation rules, and long-term risk-management mechanisms, while macroeconomic influences appear more limited and non-uniform across quantiles. Full article