Sinh Duc Hoang

Purpose: This study aims to examine the influence of greedy and biased recommendation algorithms on consumers’ emotional arousal, impulsive buying behavior and post-purchase dissonance in artificial intelligence (AI)-driven short video platforms, such as TikTok, Instagram Reels and YouTube Shorts. It further investigates the moderating roles of promotion-proneness and inferences of manipulative intent. Design/methodology/approach: A quantitative survey was conducted with 766 Vietnamese consumers who had recently made purchases influenced by AI recommendations. Partial least squares structural equation modeling was used to test a conceptual framework grounded in cognitive dissonance theory and behavioral decision theory, incorporating mediation and moderation analyses. Findings: Greedy and biased algorithms significantly enhance emotional arousal, which mediates the pathway to impulsive buying. Impulsive buying, in turn, heightens post-purchase dissonance. Promotion-proneness strengthens the arousal to impulsive buying link, while inferences of manipulative intent amplify the impulsive buying to dissonance relationship. Serial mediation effects were fully supported, with no direct algorithmic impacts on dissonance. Research limitations/implications: This study relies on cross-sectional, self-reported data collected from Vietnamese consumers, which may limit causal interpretation and generalizability to other cultural and market contexts. Future research should use longitudinal, experimental or behavioral-tracking designs and examine other algorithmic characteristics, cultural settings and psychological factors influencing consumer responses to AI-driven recommendations. Practical implications: The findings suggest that platform designers and marketers should balance engagement-oriented personalization with consumer autonomy and well-being. Greater transparency regarding algorithmically selected or commercially prioritized content, meaningful user-control mechanisms and less manipulative promotional practices may help reduce impulsive purchasing and post-purchase dissonance. Firms may also use post-purchase reassurance, customer support and satisfaction guarantees to reduce consumers’ regret and maintain long-term trust. Originality/value: This research provides novel empirical evidence linking algorithmic design features to emotional and cognitive mechanisms in impulsive consumption. It advances understanding of AI’s ethical implications in digital commerce, highlighting psychological costs in emerging markets and informing strategies for balanced personalization.

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.

Scholarly attention to how informal coaching shapes job satisfaction has been limited, with the intervening role of innovative behavior and the boundary-setting function of delegative leadership especially neglected. Drawing on Self-Determination Theory, this study traces the mechanisms by which informal coaching practices influence employees’ work satisfaction. A two-wave survey of 895 academic staff across Vietnamese universities reveals that both supervisory and collegial informal coaching independently predict higher job satisfaction. Innovative behavior emerges as a significant conduit for these effects. Of note, high delegative leadership weakens the coaching–satisfaction association for collegial coaching, while no comparable moderation was detected for supervisory coaching on job satisfaction or innovative behavior, or for collegial coaching on innovative behavior. Innovative behavior partially mediates these effects, as the direct coaching–satisfaction associations remain significant alongside the significant indirect effects.

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.

This study analyzes the alignment of three distinct images—Overt Induced I, Overt Induced II, and Organic—pertaining to a tourism destination. Using a comprehensive content analysis of various sources, including strategic marketing materials from the Vietnam National Authority of Tourism, promotional brochures from leading tour operators, and user‐generated content from platforms such as Booking.com , TripAdvisor, Routard, and Lonely Planet, the data reveal significant congruence among these three images. Vietnam is represented as a destination that is rich in cultural, historical, and coastal attractions, which closely resonate with the perceptions of French tourists and influence their intentions to revisit or recommend the destination. This congruence supports the expectancy‐confirmation theory, underscoring that satisfaction is achieved when actual experiences align with or exceed expectations. Furthermore, the study identifies specific areas where the promotion of certain attributes could be enhanced, suggesting opportunities for effective destination branding.

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.

Purpose: This study aims to investigate pathways to enhancing sustainable organizational performance (SOP) by fostering green intellectual capital (GIC) through the integration of artificial intelligence (AI) and green human resource management (GHRM). Design/methodology/approach: This study uses a quantitative research design with survey data from 731 employees and 110 direct managers of information technology firms in Vietnam. Data were collected in two waves via LinkedIn to reduce common method bias and improve response quality. Analysis was conducted using multigroup structural equation modelling (SEM) in R with the lavaan package. Findings: The study shows that the relationship between GHRM and SOP is mediated by all three components of GIC: green human capital, green structural capital, and green relational capital. Furthermore, AI is found to negatively impact green structural capital, although this effect is reduced with higher levels of GHRM. Originality/value: The study contributes to existing literature on GHRM by considering a potential mediating role of GIC in the relationship between GHRM and SOP. The research further extends this view to include AI’s potential moderating effect on the relationship between GHRM and the individual components of GIC. The findings offer insight into why AI may fail to translate into more favourable green outcomes. While previous research has investigated the direct effects of AI, this is the first study to explore the moderating role of AI in the GHRM–GIC relationship.