Sandeep Kumar Dey

This study investigates the adoption of augmented reality (AR) as a training tool for housekeeping staff in star‐classified hotels in India, an emerging economy. Building on the Technological‐Organisational-Environment (T-O-E) framework, this study extends the theory by exploring the interplay of behavioural antecedents influencing AR adoption under conditions of cost sensitivity and economic constraint. Using a hybrid SEM–ANN modelling approach with data from 300 hoteliers, the study identifies external support systems, organisational flexibility, and perceived competitive advantage as key facilitators of adoption, while cost perceptions and technological anxiety act as inhibitors. Crucially, technological selfefficacy is introduced as a moderating factor, strengthening the influence of technological environments on adoption intention. From an economic perspective, the results highlight how AR can reduce training costs, improve efficiency, and enhance competitiveness in low‐margin service industries. In practice, the study recommends that hotels invest in “train‐the‐trainer” programmes to build managerial self‐efficacy, thereby mitigating technological anxiety and promoting sustainable digital transformation. The research offers novel insights by applying a hybrid inference model to AR adoption in the hospitality sector, demonstrating its theoretical and practical relevance in emerging economies. Future research should expand the analysis to include more regions and test experimental interventions to validate causal relationships.

Drawing from sensemaking theory, this study investigates how socially responsible human resource management (SRHRM) influences employee proactive work behaviour (EPWB) through underlying psychological processes and contextual factors. While prior research suggests SRHRM enhances positive employee outcomes, limited attention has been paid to the mechanisms through which employees interpret and internalize SRHRM practices, and how these interpretations drive proactive behaviours. Using a time-lagged survey of 756 employees in multinational companies in Southeast Vietnam, the study tests a serial mediation model where work meaningfulness and organizational pride sequentially transmit the effects of SRHRM to EPWB. Structural equation modelling results reveal that SRHRM does not directly predict EPWB but operates entirely through this psychological chain. Furthermore, multi-group analysis shows that leadership gender moderates the indirect pathway: female leaders amplify the positive effects of SRHRM on employees’ sensemaking and subsequent behaviours, while male leaders weaken them. These findings contribute to human resource management and organizational behaviour literature by advancing sensemaking theory in explaining how socially responsible practices translate into discretionary employee actions and identifying leadership gender as a critical contextual variable. Practical implications suggest organizations should design SRHRM interventions that foster meaningful work experiences and strategically leverage leadership characteristics to enhance employee proactivity.

Purpose: This study explores the role of artificial intelligence (AI) recommendations in portfolio optimization by extending the Black-Litterman (BL) model using consensus analyst opinions generated by ChatGPT. The aim is to assess if AI recommendations can improve portfolio diversification and risk-adjusted returns compared to traditional investment strategies. Design/methodology/approach: We conducted a quantitative analysis using weekly historical price data across equities, commodities, fixed-income securities and cryptocurrencies from January 2018 to May 2023. Portfolios constructed with the extended BL model were tested against standard benchmarks, including the S&P500 index and various mean-variance portfolios. Out-of-sample performance and robustness were evaluated through 100 random resampling procedures. Findings: Results indicate that integrating AI-generated analyst consensus significantly improves the BL portfolio’s risk-adjusted returns. The AI-enhanced model consistently outperformed traditional mean-variance portfolios, the unadjusted BL model and market benchmarks. Robustness tests confirmed the method’s stability and practical feasibility in real-world investing. Practical implications: Portfolio managers and individual investors can apply this enhanced BL model for more effective asset allocation decisions. Using AI-generated recommendations simplifies the integration of broad analyst perspectives, reduces reliance on subjective human judgments and leads to portfolios that deliver stronger and more consistent risk-adjusted performance. Originality/value: This research is the first to integrate ChatGPT-generated analyst recommendations directly into the BL framework. It addresses critical limitations of modern portfolio theory, particularly estimation errors, offering a practical solution leveraging AI advancements for portfolio optimization.

Small and medium‐sized hotels (SME hotels), which constitute the majority of the global hospitality sector, face mounting pressure to align with sustainability goals. However, their transition to circular economy (CE) practices remains under‐theorised and poorly implemented, particularly in emerging economies where institutional fragility and resource constraints prevail. Existing literature predominantly emphasises large firms and linear modelling approaches, overlooking the configurational, capability‐driven processes that enable SMEs to adopt CE under complexity. This study addresses this gap by developing a tri‐theoretical framework—grounded in the Resource‐Based View, Dynamic Capabilities Theory and Configurational Theory—to examine how internal resources and capabilities interact to shape CE adoption in SME hotels. Empirical insights were drawn from 300 executive respondents across Indian SME hotels. The study employs a hybrid analytical design integrating Partial Least Squares Structural Equation Modelling (PLS‐SEM) to capture net effects, and Fuzzy‐Set Qualitative Comparative Analysis (fsQCA) to explore causal asymmetries and equifinal pathways to CE implementation. Findings reveal Green Absorptive Capacity (GAC) as a consistent enabler and Green Dynamic Capabilities (GDC) as essential for adaptive CE strategies. While Green Human Capital (GHC) does not appear in dominant configurations, its role is found to be context‐dependent rather than absent. The study contributes theoretically by advancing an integrated model that explains CE adoption beyond linear assumptions and practically by offering strategic guidance for SME hoteliers navigating environmental transformation. It advocates for investment in organisational learning, dynamic capability‐building and targeted policy engagement to support sustainable transitions under constraint.

This paper aims to reveal the factors influencing investors' intention to accept AI advice in financial decision-making. By integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), it proposes a comprehensive model that elucidates the intricate relationships between social norms, attitude, perceived behavioral control, and the intention to accept AI advice, with a particular focus on examining risk tolerance as a moderating factor. A questionnaire survey was conducted with 569 Vietnamese investors to collect data in three different times. Partial least squares structural equation modeling (PLS-SEM) was utilized to analyze the measurement model and test the hypotheses. Results indicate that perceived usefulness, perceived ease of use, attitude, subjective norms, and perceived behavioral control positively influence the intention to accept AI advice. Furthermore, risk tolerance significantly moderates the link between attitude, subjective norms, perceived behavioral control, and intention to accept AI advice. This pioneering study introduces a comprehensive model unveiling the dynamics of AI advice acceptance in finance. It explores the novel concept of risk tolerance as a moderator, marking an important step in understanding human-AI interaction for financial decisions. Findings provide valuable insights into evolving AI adoption, especially in high-risk contexts.