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The purpose of this study is to use a lifetime income measure to evaluate the long-run housing affordability for an understudied cohort of households in the literature – the millennials. The authors do this in the context of Malaysia, measuring long-run affordability for four housing types across geographic locations and income distributions.

This study calculates a long-run housing affordability index (HAI) using data on house prices and household incomes. Essentially a ratio of predicted lifetime incomes to house prices, the HAI is computed for four common housing types in Malaysia from 2005 to 2016 and for six states in the country. The HAI is also compared across four income percentiles.

The analysis reveals varying patterns of housing affordability among different states in Malaysia. Housing affordability has declined since 2010, with most housing types being unaffordable for millennial-led households with the lowest income. Housing is most affordable for those in the highest income bracket, although even here, there are pockets of unaffordable housing as well.

Based on the findings, this study proposes three targeted interventions to improve housing affordability for Malaysian millennials.

This study fills a gap in the literature by examining the long-run housing affordability of Malaysian millennial-led households based on both geographic location and income distribution. The millennial population is understudied in the housing affordability literature, making this study a valuable contribution to the field.

The study aims to reflect on past research, uncover current trends and propose a future research agenda in the field of artificial intelligence (AI) for competency-based personalised learning.

The study followed the SPAR-4-SLR protocol to retrieve 855 articles related to the field indexed in the Scopus database. Performance analysis, network analysis and science mapping were then performed using VOSviewer and the Biblioshiny app.

The analysis identified nine clusters of intellectual structure (healthcare, competencies, learning systems, digital transformation, AI literacy, computer-aided education, AI ethics, e-learning and active learning) and twelve themes (including motor, basic, emerging and niche).

Following an extensive review of the literature, this would appear to be the first study to provide a panoramic view of AI for competency-based personalised learning based on the Scopus database. The core gaps in the current literature have been identified and the corresponding future agenda will be instrumental in shaping future research directions in the field.

Drawing on the reciprocity principle of social exchange theory situated within Service-dominant Logic, this study aims to examine how customers’ perception of knowledge sharing in co-production, their inherent scepticism and prosocial orientation relate to their willingness to co-create and provide feedback on services. The authors also explored the interplay between these factors to identify conditions in configurations comprising scepticism, which may help navigate its adverse effects.

The authors surveyed 556 online and offline mobile payment service users. They used a combination of partial least squares structural equation modelling (PLS-SEM) to assess the relationships among variables, and fuzzy-set qualitative comparative analysis (fsQCA) to identify configurations associated with feedback behaviour.

The study determined that customer perception of co-production knowledge sharing is positively associated with willingness to co-create and feedback behaviour. Additionally, prosocial orientation positively affects this relationship, while scepticism has an adverse effect. Willingness to co-create mediates the relationship between customer perception of co-production knowledge sharing and feedback behaviour. The fsQCA findings revealed configurations for potentially navigating doubts regarding feedback. To encourage valuable customer feedback, businesses may consider promoting a collaborative and supportive atmosphere, emphasising shared advantages or building trust even among hesitant and doubtful individuals.

This study uniquely examines how both prosocial tendencies and scepticism relate to customer feedback behaviour in co-creation by using a hybrid PLS-SEM/fsQCA approach to identify co-existing conditions in configurations comprising scepticism that may help navigate its adverse effects and leverage customer feedback for business improvement.

This chapter provides an exhaustive analysis on the development of cricket in China in order to advance existing theories of cricket's development and consider future implications for the international game. Adapted from two journal articles of He and Malcolm (2021) and He et al. (2023), it structures the development of cricket in China according to two key historical era: cricket as an ‘expatriate-only’ game and cricket as an ‘Asian Games sport’. The first era, cricket as an ‘expatriate-only’ game, is constructed according to three key phases: early development; post-war and the ‘opening-up’ era. The second era, cricket as an ‘Asian Games sport’, is constructed according to five periods: budding period (2003–2005), peak period (2006–2010), stable period (2011–2014), trough period (2015–2018) and revival period (2019–present). This paper offers a broadened examination of cricket's development in China, contending that cricket in the country (specifically the mainland) manifests itself in two distinct forms, that is, first, it survives as a grassroots sport, sustained by a resilient expatriate diaspora community. Second, it exists as a sport primarily directed by the state and bolstered by the Asian Games and deeply integrated into the Chinese educational system. It concludes that the degree to which the co-existed motives of multiple stakeholders aligned and misaligned, and the interdependence with the unstable ‘Asian Games sport status’ will serve as the cornerstone for cricket's future in China and contribute significantly to the international sport's global development.

This chapter delves into the crucial intersection of Destination Social Responsibility (DSR) and sustainable development within the context of coastal marine tourism destinations. The research objective is to offer a comprehensive analysis of how DSR practices influence the trajectory of sustainable development in these unique environments. Employing a qualitative interview with key stakeholders, content analysis of policy documents and quantitative surveys of tourists, this study endeavours to attain a holistic knowledge of the intricate dynamics at play. By triangulating these methods, we aim to establish a robust foundation for comprehending the multifaceted relationship between DSR initiatives and sustainable development outcomes. The findings reveal a nuanced interplay between DSR and sustainable development indicators. Notably, the integration of socially responsible practices positively correlates with enhanced environmental conservation efforts, increased community engagement and heightened visitor satisfaction levels. This research advances our comprehension of the interdependency between DSR and sustainable development. Hence, it is important to acknowledge its limitations. The practical implications of this study are manifold. Policymakers, destination managers, as well as industry stakeholders stand to benefit from a nuanced understanding of how DSR practices can be leveraged to drive sustainable development. This chapter is responsible for the current literature by offering an extensive analysis of the interplay between DSR as well as sustainable development in coastal marine tourism destinations. By synthesising empirical findings and theoretical frameworks, this research provides a valuable resource for scholars, policymakers and industry practitioners seeking to navigate the complex landscape of responsible tourism development.

This study aims to investigate the impact of geopolitical risk (GPR) on supply chain concentration (SCC) and the roles of operational capabilities and resources in this relationship.

Secondary longitudinal data from multiple sources is collected and combined to test for a direct impact of GPR on SCC. We further examine the moderating effects of firms’ operational capabilities and resources (i.e. firm resilience, operational slack and cash holding). Fixed-effect regression models are applied to test the hypotheses, followed by a series of robustness tests to check the consistency of the results.

Consistent with the tenets of resource dependence theory, our analysis reveals a significant negative impact of GPR on SCC. Moreover, we find that this adverse effect is attenuated for firms with higher levels of resilience, more operational slack and greater cash holdings. Further analysis suggests that maintaining a diversified supply chain base during heightened GPR is associated with a firm’s improved financial performance.

This study contributes to the supply chain management (SCM) literature by integrating GPR into the supply chain risk management framework. Additionally, it demonstrates the roles of diversification and operational resources in addressing GPR-induced challenges.

As technology increasingly integrates into the restaurant industry, avatar servers present a promising avenue for promoting healthier dining habits. Grounded in the halo effect theory and social comparison theory, this study aims to delve into the influence of avatars' appearance, humor and persuasion on healthier choices and customer satisfaction.

This paper comprises three experimental studies. Study 1 manipulates avatar appearance (supermodel-looking vs normal-looking) to examine its effects on perceived attractiveness, warmth and relatability. These factors influence customer satisfaction and healthy food choices through the psychological mechanisms of social comparison and aspirational appeal. Studies 2 and 3 further refine this theoretical model by assessing the interplay of appearance with humor (presence vs absence) and persuasion (health-oriented vs beauty-oriented), respectively.

Results suggest that avatars resembling supermodels evoke stronger aspirational appeal and positive social comparison due to their attractiveness, thus bolstering healthier choices and customer satisfaction. Moreover, humor moderates the relationship between appearance and attractiveness, while persuasion moderates the effects of appearance on social comparison and aspirational appeal.

This research bridges the halo effect theory and social comparison theory, offering insights enriching the academic discourse on technology’s role in hospitality.

The findings provide actionable insights for managers, tech developers and health advocates.

Despite its significance, avatar design research in the hospitality sector has been overlooked. This study addresses this gap, offering a guideline for crafting attractive and persuasive avatars.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.