Research background: Environmental, social, and governance (ESG) ratings have become increasingly important in investment decisions and corporate evaluation, yet they often rely heavily on publicly available firm disclosures. At the same time, artificial intelligence (AI)-related communication has expanded rapidly among listed firms, creating a new setting in Copyright (c) Instytut Bada & nacute; Gospodarczych / Institute of Economic Research (Poland) This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. which strategic narratives may shape external assessments before real innovation outcomes become fully observable. This raises an important question about whether AI-related disclosure is associated with ESG ratings beyond firms' verifiable technological progress. Purpose of the article: This article examines whether AI-related strategic disclosure is associated with higher ESG ratings through a gap between firms' AI-related narratives and their observable AI innovation. It also investigates whether any rating gains associated with such a gap reflect credible forward-looking information or instead capture rating sensitivity to soft disclosure. By linking AI-related disclosure to subsequent stock returns, future operating performance, long-run abnormal returns, and debt financing outcomes, the study seeks to distinguish between informational and distortionary interpretations. Methods: Using panel data for Chinese A-share listed firms from 2010 to 2023, the study constructs a residual-based AI talk-action gap measure that captures AI-related textual disclosure beyond the level predicted by verifiable AI patenting activity and firm characteristics. Firm fixed-effects models are employed to estimate the relationship between this gap and ESG ratings, followed by a set of forward-looking tests on market and operating outcomes. The empirical analysis is supplemented by alternative variable definitions, placebo tests, and cross-sectional analyses to assess robustness and heterogeneity. Findings & value added: The results show that a larger AI talk-action gap is associated with significantly higher ESG ratings, suggesting that ESG evaluators respond not only to observable AI innovation but also to AI-related strategic disclosure in excess of such innovation. The gap is further associated with more positive short-run stock market reactions, but it does not predict stronger future profitability, sustained long-run abnormal returns, or lower debt financing costs. These findings indicate that AI-related soft disclosure is associated with higher ESG ratings and temporarily more favorable equity market perceptions, but its informational content is only weakly validated by subsequent fundamentals. The article contributes to the literature on ESG rating efficiency, strategic corporate disclosure, and sustainable finance in the context of digital transformation.
Research background: Green financial instruments are increasingly promoted as mechanisms for mobilising private capital toward environmentally responsible production systems. However, investor-level adoption of such instruments remains insufficiently explained, particularly in emerging financial markets such as India, where sustainable finance frameworks, investor awareness, and green product credibility are still evolving. Purpose of the article: This study examines the behavioural drivers of green financial instrument adoption among individual investors in India. Drawing on an extended Value-Attitude-Behaviour Framework, it explains how value-based evaluative inputs and attitudes toward sustainable investment shape perceived impact, which then influences adoption propensity, actual adoption of green financial instruments, and responsible financial consumption aligned with SDG 12. Methods: The study uses cross-sectional survey data from 350 individual investors in major metropolitan regions of India. The model is tested using Partial Least Squares Structural Equation Modelling (PLS-SEM), with assessment of the measurement model, structural paths, explanatory power, and sequential indirect effects. Findings & value added: The results show that perceived environmental concern has the strongest effect on perceived impact, followed by perceived awareness, trust and credibility, attitude toward sustainable investment, perceived financial performance, and green knowledge. Perceived impact strongly increases adoption propensity, which then increases actual adoption of green financial instruments. Actual adoption positively influences responsible financial consumption aligned with SDG 12. The study adds value by showing that green financial instrument adoption is not only an investment decision but also a form of responsible financial consumption through which investors can direct capital toward environmentally responsible production systems.
Research background: Progress in artificial intelligence (AI) and robotics is reshaping the service landscape and driving technological innovation. Service robots are increasingly introduced to improve performance and support frontline employees in mitigating emotional and physical strains. However, evidence remains limited on whether service robots truly protect employees from stressors such as customer incivility. Drawing on the conservation of resources (COR) theory, this study addresses this gap by conceptualizing service robots as context-dependent technological resources and examines how their efficacy depends on organizational support and employee knowledge in robot-enabled environments. Purpose of the article: This study examined how customer incivility depletes frontline employees' resources and when these adverse effects can be mitigated in human-robot contexts. We assessed the mediating role of work engagement between customer incivility and in-role service performance and customer-helping behavior, and the moderating effects of service robot support and robot knowledge. Methods: We collected data from 200 frontline employees in South Korea, a region with rapid service robot integration. We utilized a two-wave survey design with a two-month interval to minimize common method variance. Hypotheses were tested via path analysis in Mplus using bootstrapping (N = 5,000). Findings & value added: Work engagement mediated the negative relationships between customer incivility and both in-role service performance and customer-helping behavior. We observed a significant three-way interaction among customer incivility, service robot support, and service robot knowledge on work engagement. When both service robot support and robot knowledge were high, the negative effects of customer incivility on work engagement and indirect outcomes were weakest. This study advances research on technology-enabled work by demonstrating that service robots function as context-dependent technological resources. Our findings highlight the importance of robot support and employee robot-related knowledge in buffering resource depletion and enhancing resilience in robot-enabled environments.
Research background: Achieving Sustainable Hotel Performance (SHP) has become a critical priority for the hospitality industry, particularly in emerging markets where operational risks, safety challenges, and supply chain complexities are pronounced. While prior studies have examined Occupational Health and Safety Management Practices (OHSMPs) and Food Supply Chain Quality Management (FSCQM) independently, limited attention has been given to their joint socio-technical effects and the motivational and digital mechanisms through which they influence sustainability outcomes. Purpose of the article: This study aims to develop and empirically test a socio-technical framework that integrates OHSMPs, FSCQM, Safety Motivation, and Supply Chain Digital Integration (SCDI) as key drivers of SHP. Drawing on the Job Demands-Resources (JD-R) model, Social Cognitive Theory (SCT), and Self-Determination Theory (SDT), the study investigates how safety-and quality-related practices interact to enhance sustainability in the hotel sector. Methods: A two-wave, time-lagged survey approach was utilized to gather data from 250 supervisory employees across 50 five-star hotels in Egypt. The intentional time gap between the measurement waves was implemented to reduce the likelihood of common method bias and to strengthen causal interpretations. The proposed structural relationships were evaluated using PLS-SEM using WarpPLS. Furthermore, the validity and reliability of the measurements, along with mediation effects, were thoroughly examined to ensure the robustness and credibility of the empirical findings. Findings & value added: The results demonstrate that OHSMPs significantly enhance both FSCQM and Safety Motivation. Safety Motivation plays a pivotal mediating role by positively influencing both SCDI and SHP. In contrast, the direct predictive effect of FSCQM on sustainable performance is relatively weak, indicating that safety-driven practices exert a more immediate and powerful influence on sustainability outcomes than quality management processes alone. This study contributes theoretically by extending the JD-R and SDT frameworks to link employee safety motivation with organizational sustainability through digital integration and supply chain mechanisms. In practice, the findings highlight the strategic importance of aligning occupational safety initiatives with digital integration strategies to foster safety-driven motivation and improve long-term, sustainable performance in hotels.
Research background: Manufacturing firms in emerging economies face simultaneous pressures to decarbonise and digitalise, yet how green human resource systems, responsible AI governance, and green innovation jointly translate into sustainability outcomes remains insufficiently understood. Purpose of the article: Grounded in the Natural Resource-Based View, this study tests a capability-conversion model linking green human resource management practices (GHRMPs), ethical AI (EA), green innovation performance (GIP), environmental performance (ENP), and operational economic performance (ECP) in Thai manufacturing. Methods: Cross-sectional online survey data were obtained from 422 key informants, with one supervisory, managerial, or executive respondent representing each medium-sized or large Thai manufacturing firm. The model was analysed using two-stage partial least squares structural equation modelling, a reflective-formative higher-order specification of GHRMPs, and 10,000-subsample bootstrapping. Findings & value added: GHRMPs were positively associated with EA ((3 = .846), EA with GIP ((3 = .733), and GIP with ENP ((3 = .810) and ECP ((3 = .765), whereas the direct GHRMP-ENP and GHRMP-ECP relationships were nonsignificant. The serial indirect associations through EA and GIP were significant for ENP ((3 = .502) and ECP ((3 = .475). These findings support an indirect capability-conversion pattern in which green HR architecture functions as a distal enabling capability, EA as an intermediate governance capability, and GIP as the proximal mechanism associated with environmental improvement and operational resource cost efficiency. The environmental coefficients were descriptively larger than the economic coefficients, but these differences were not formally tested and therefore represent tentative environmental-economic variation rather than confirmed asymmetry. The study contributes by integrating human, AI-governance, and innovation capabilities and by operationalising EA as an organisational governance construct in an emerging-economy manufacturing context.
