City University, Malaysia

While pricing policy has emerged as a critical demand-side lever for decarbonizing mobility, its bidirectional effects on modal shift remain unexplored. Dynamic pricing in high-speed rail (HSR) creates a double-edged environmental outcome: advance discounts attract passengers from aviation, yet last-minute premiums may reverse these gains. Using 2.4 million price observations from Madrid-Barcelona (2019), we introduce a carbon leakage framework that quantifies this phenomenon within a multi-source validated framework. Our analysis reveals a structural tension: while early-bird pricing attracts 274,431 annual passengers from aviation-saving 23,650 tonnes CO2/year-last-minute scarcity premiums systematically drive passengers back to air travel. Multi-source calibrated elasticity (epsilon=-0.95, validated through triangulation across CNMC corridor data, meta-analytic evidence, and recent empirical studies within the range [-1.91,-0.75]) shows that 22.3% of last-minute tickets exceed the EUR 120 aviation threshold, creating 1511 tonnes CO2 leakage annually (6.4% offset of gross savings). Critically, this leakage ratio is shown to be structurally independent of elasticity specification, being determined by the price distribution shape rather than demand parameters. Scenario analysis suggests that under static assumptions, price caps at EUR 110-120 would eliminate leakage while preserving an estimated 94% of operator revenue, though general equilibrium effects remain unmodeled. These findings identify illustrative scenario thresholds for carbon-aware revenue management, demonstrating that demand-side decarbonization requires not only attracting passengers to sustainable modes but also preventing their reversal to high-carbon alternatives.

Artificial intelligence (AI) is reshaping learning in higher education, particularly within the global shift towards sustainable education and human-centric visions. However, as traditional human mentoring faces challenges such as limited availability and inconsistent support, the potential of AI to function as an academic mentor remains underexplored. This study aims to investigate the alternative motivations for university students to accept AI as an academic mentor. Based on cognitive appraisal theory (CAT) and the artificially intelligent device use acceptance (AIDUA) model, we employed a mixed-method approach combining structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to analyse the influencing paths and antecedent configurations. SEM results reveal that students' AI acceptance decisions exhibit a distinct three-stage process: in the cognitive appraisal stage, perceived humanness and novelty value are the primary drivers; in the affective appraisal stage, performance expectancy is a stronger trigger for emotion than effort expectancy; and in the decision-making stage, AI literacy emerges as the key determinant of final acceptance. fsQCA further identifies three typical configurations: an efficiency-prioritized type driven by instrumental rationality, a social interaction type centred on emotional experience, and an exploration-driven type characterized by the pursuit of innovation. These findings confirm that students' acceptance of AI academic mentors is not solely dependent on technical performance but is shaped by the complex interplay of cognitive, affective, and competency factors. The study provides important implications for higher education institutions seeking to integrate AI tools effectively and ethically.

This study aims to provide a systematic review of agrotourism in Malaysia, including its development, socio-economic impacts, challenges, and potential to assist in post-COVID-19 recovery, all while aligning with the United Nations’ Sustainable Development Goals. Utilising a systematic review approach based on the PRISMA guidelines, literature from key academic databases was scrutinised, yielding 20 relevant articles from 2011 to 2024. The review highlights that Malaysia’s agrotourism, strengthened by government policies and entrepreneurial efforts, has potential benefits such as rural income diversification and cultural heritage preservation. However, it also faces challenges, including skill gaps and infrastructure limitations. The study emphasises the need for improved skills training, infrastructure upgrades, sustainability promotion, and targeted marketing efforts. This review contributes to the theoretical understanding of agrotourism’s role in sustainable rural development and the integration of agrotourism within Malaysia’s post-pandemic recovery strategy. The study suggests practical measures for incorporating agrotourism into Malaysia’s recovery plans and identifies avenues for future research, including exploring tourist motivations, assessing community and environmental impacts, examining technological innovations, and evaluating its integration with broader rural development strategies. Overall, this review underscores agrotourism’s potential as a key driver of sustainable rural growth, economic resilience, and the preservation of Malaysia’s cultural and natural heritage.

This study aims to examine the relationship among promotion, job security and work engagement on employee retention in the pharmaceutical industry of Bangladesh. Drawing on the social exchange theory, the research examines how work engagement mediates the relationships among promotion, job security and employee retention. Data were collected using a quantitative survey questionnaire approach from 309 medical representatives, which was analysed using PLS-SEM. The study results revealed that promotion and job security have a significant relationship with work engagement and work engagement has a significant relationship with employee retention. Hence, this study emphasizes that clear policies to promote and discuss job security may lead to work engagement, which is associated with employee retention. Practical implications highlight human resource management and how they should support sufficient promotion opportunities, guarantee job security and implement active work programmes to boost employee loyalty. This study concludes that specific HR practices influence employee retention through work engagement, highlighting their value in strengthening organizational performance. Although based on a single industry, the findings provide a foundation for broader investigation. Future research should test these relationships across diverse contexts to enhance generalizability.

In response to the challenges of using traditional multimodal human–computer interaction models to understand complex user intentions in dynamic scenarios and traditional multimodal human–computer interaction models' heavy dependency of multimodal data on strong supervision signals, processing of high-dimensional heterogeneous modal data captures slow operating speed with poor robustness, this paper develops an artificial intelligence (AI)-powered multimodal human–computer interaction algorithm framework. In the first step, it builds a unified latent space by cross-modal self-supervised contrastive learning to develop implicit multimodal alignment and mitigate semantic alignment issues. Secondly, applied Spatio-Temporal Graph Neural Network (ST-GNN) to improve user intent recognition ability in dynamic scenarios, employing graph attention mechanisms to exploit spatiotemporal dependencies of multimodal behaviors. Thirdly, the warm-starting a human–computer interaction model, merged two different action value estimation techniques, Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) in a hybrid environment, thereby optimizing action selection and agent policy optimization. Finally, implemented model-agnostic meta-learning (MAML) to enable a quick process of user personalized learning under low sample conditions and for joint multi-sourced signal optimization through multimodal loss function above a single point of execution for user intent recognition accuracy. Experimental results show that ST-GNN achieves an average accuracy of 96.22