Urban sustainability has become a defining governance challenge as smart cities increasingly integrate artificial intelligence (AI) into public service delivery and decision-making. While AI-enabled systems promise efficiency and responsiveness, growing concerns regarding trust, legitimacy, and citizen engagement suggest that technological adoption alone does not guarantee sustainable urban outcomes. Existing studies have largely emphasized technological performance or individual adoption, paying limited attention to the governance mechanisms through which AI acceptance translates into sustainability co-creation. To address this gap, this study develops and empirically examines the AI–Urban Citizen Sustainability Co-Creation Framework (AI–CSCF) within the context of smart cities in Thailand. A quantitative survey was conducted with 1002 citizens across three smart city settings, and structural equation modeling (SEM) was employed to examine the relationships among AI acceptance, trust in AI, citizen adaptability, and sustainability co-creation. The results indicate that AI acceptance functions as a foundational condition shaping trust in AI and citizen adaptability, through which its influence on sustainability co-creation is indirectly transmitted. Trust in AI emerges as a key mediating mechanism linking AI-enabled governance to participatory sustainability outcomes. These findings underscore the importance of human-centered and trustworthy AI governance that strengthens citizen trust, enhances adaptive capacities, and positions citizens as active co-creators of sustainable urban development aligned with SDG 11.

Purpose: The purpose of this study is to concentrate on the moderating effect that ownership types, such as institutional ownership and minority shareholders, have on the link between environmental, social and governance (ESG) performance and the value of a firm.Design/methodology/approach: This study utilizes a dataset consisting of 422 firm-year observations from publicly traded Thai firms for the period from 2019 to 2023. This study employs pooled ordinary least squares (OLS) regression. Additional tests and a robustness check are performed to validate the findings.Findings: The results indicate a strong positive impact of ESG performance on a firm’s value, thus indicating that sustainable practices contribute to long-term economic success. The type of ownership is significant for minority shareholders having a positive moderating impact on the connection between ESG performance and firm value, whereas institutional ownership does not. The findings indicate that minority shareholders might enhance ESG benefits by aligning with their own interests.Practical implications: The findings have practical implications for investors, managers and policymakers. The findings demonstrate that firms with a substantial proportion of minority shareholders have to ensure that ESG activities are transparent, effective and in accordance with shareholder expectations. This alignment can mitigate the adverse impact of minority ownership on firm value. Policymakers in Thailand and comparable emerging countries might utilize this study to promote ESG transparency and standards, therefore mitigating information asymmetry. Regulations facilitating minority shareholders’ access to information may enhance the beneficial moderating impact of their participation on ESG performance.Originality/value: This study first demonstrated the moderating role of minority shareholders on the ESG-firm value relationship. This expands knowledge by focusing on ownership forms and their impact on ESG and firm value in emerging economies.

Accurate early-warning forecasting of corporate financial distress remains a critical challenge due to nonlinear financial relationships, severe data imbalance, and the high operational costs of false alarms in risk-monitoring systems. This study proposes an explainable voting ensemble framework for early-warning forecasting of corporate financial distress using lagged accounting-based financial information. The proposed framework integrates heterogeneous base learners, including Decision Tree, Neural Network, and k-Nearest Neighbors models, and is evaluated using financial statement data from 752 publicly listed firms in Thailand, comprising sixteen financial ratios across six dimensions: liquidity, operating efficiency, debt management, profitability, earnings quality, and solvency. To ensure robustness under imbalanced and rare-event conditions, the study employs feature selection, data normalization, stratified cross-validation, resampling techniques, and repeated validation procedures. Empirical results demonstrate that the proposed Voting Ensemble delivers a precision-oriented and decision-relevant forecasting profile, outperforming classical classifiers and maintaining greater early-warning reliability when benchmarked against advanced tree-based ensemble models. Probability-based evaluation further confirms the robustness and calibration stability of the proposed framework under repeated cross-validation. By adopting a forward-looking, early-warning perspective and integrating ensemble learning with explainable machine learning principles, this study offers a transparent and scalable approach to financial distress forecasting. The findings offer practical implications for auditors, investors, and regulators seeking reliable early-warning tools for corporate risk assessment, particularly in emerging market environments characterized by data imbalance and heightened uncertainty.

Residential buildings are a major source of urban carbon emissions, yet the uptake of smart home retrofitting remains far below the level required to meet decarbonization and sustainability targets. While technical solutions for energy-efficient renovation are well established, less is known about how behavioral, psychological, and institutional factors jointly shape household retrofit decisions and their broader sustainability implications. This study develops an integrated analytical framework that combines UTAUT2 with perceived risk, trust, innovativeness, and regulatory pressure, interpreted through a socio-technical systems perspective, to examine smart home retrofitting in Thailand and its contribution to Sustainable Community Development Goals (SCDG). Survey data were collected from 448 households in Bangkok and Chonburi and analyzed using structural equation modeling. The results show that traditional UTAUT2 predictors such as performance expectancy, effort expectancy, and social influence do not significantly influence adoption intention in this high-cost retrofit context. Instead, innovativeness, trust, price value, perceived risk, and regulatory pressure emerge as key behavioral and institutional drivers, while facilitating conditions and habits shape actual use behavior. Actual retrofit behavior is found to generate significant economic, environmental, socio-cultural, technological, and public-policy sustainability outcomes aligned with SCDG. These findings demonstrate the limitations of conventional technology acceptance models in infrastructure-based contexts and provide a mechanism-based explanation of how retrofit adoption is driven in high-cost sustainability contexts.

Artificial intelligence (AI) increasingly mediates how information is generated, ranked, and circulated in digital environments. However, it remains unclear under what conditions users explicitly articulate recognition of AI involvement in routine news-related discourse. This study examines how digital skepticism and AI-related discernment are expressed in naturally occurring social media discussions. Using an exploratory observational design, 6065 user-generated comments from 305 news-related Reddit threads were analyzed through a rule-based framework distinguishing general skepticism, structural suspicion, and explicit AI-related discernment. Within the sampled corpus, generalized digital skepticism is proportionally more visible than explicit attribution to AI-generated or synthetically produced content. Explicit AI-related attribution is unevenly distributed across discourse contexts, appearing more frequently in technology-oriented communities and remaining limited in mainstream news-related discussions. Differences across score-based visibility contexts do not correspond to a consistently higher representation of explicit AI attribution. These findings indicate a distributional difference between generalized skepticism and publicly articulated recognition of AI mediation. Rather than measuring levels of awareness, the results illuminate the contextual and linguistic conditions under which AI involvement becomes explicitly named in public interaction. By focusing on observable discourse rather than self-reported attitudes, the study provides a corpus-bound account of when AI mediation becomes discursively articulated in algorithmically mediated environments.