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Given the growing use of virtual reality (VR) technology in marketing, our research focuses on the development trajectory of research in the marketing field from 2012 to 2022 to identify essential phases and sub-trends within this topic.

This study employs a main path analysis (MPA) methodology to analyze academic articles related to VR in marketing from the Web of Science database.

The research on VR in marketing has experienced significant growth over the past 10 years and is projected to continue thriving in the future. During the past decade, research in this field has transitioned from exploring VR affordances in marketing to realizing the potential of VR in marketing. From the information systems perspective, the three primary research trends that have garnered the most attention from researchers are VR technology as an artifact, marketers’ motivational approach and consumers’ motivational approach. With the continual advancement of VR technology, the research trend of Metaverse marketing will gradually displace VR in marketing.

To the best of our knowledge, this is the first research using MPA to explore the development trajectory of VR in marketing and provide a comprehensive picture of it under the Affordance-Actualization theory.

This study aims to combine two theories, the Theory of Planned Behaviour (TPB) and the Norm Activation Model (NAM), to investigate the relationship between the awareness of reducing single-use plastic waste's environmental cost and the behaviour to limit the use of single-use plastic products (SUPPs) by FPT university students.

Quantitative research methodologies were employed on a sample of 506 university students. The survey data was then examined using SPSS, SPSS AMOS and SmartPLS software.

The overarching conclusion of the study is that awareness of reducing single-use plastic waste's environmental cost has a positive impact on FPT university students' behaviour to reduce their use of single-use plastic products. Another intriguing discovery is how socialisation of responsibility affects pro-environmental behaviour through the interplay between personal norms, subjective norms and behavioural intention.

This study on the relationship between SUPP low-consumption awareness and behaviour and mediating factors is a necessary foundation for future studies related to changing the behaviour of students using SUPPs. That will also be a solid foundation for practical plans to change behaviour using SUPPs through communication campaigns to increase awareness.

This study aims to investigate the relationship between corporate tax avoidance and stock price crash risk and the moderating effects of corporate governance.

This study investigates the relationship between corporate tax avoidance and stock price crash risk using the sample consisting of listed firms in Vietnam for the period of 2011–2020 using panel regressions.

The authors find that there is a positive relationship between tax avoidance and stock price crash risk. Foreign ownership weakens the impacts of tax avoidance on stock price crash risk, while managerial ownership strengthens the impacts. Female Chief Executive Officers (CEOs) and female chairpersons weaken this relationship. Board gender diversity and state ownership have insignificant moderating impacts.

These findings could help the stock market build better internal monitoring mechanisms to reduce the impacts of tax avoidance on future stock price crash risk. Investors can recognize the characteristics of corporate governance, especially foreign ownership, managerial ownership, female CEOs and female chairpersons when making investment decisions. The policy makers should consider policies to attract foreign investment and support women entrepreneurship.

This paper contributes to the literature on the impacts of tax avoidance on stock price crash risk in emerging countries. This paper is the first to investigate the influence of corporate governance mechanisms including state ownership, foreign ownership, female CEOs and chairpersons and board gender diversity on this relationship.

This study analyses the direct and indirect impacts of risks on the service-oriented construction supply chain and its resilience during disruptions.

We utilised the service-dominant logic, contingency and information processing theories to identify service-oriented construction supply chain characteristics and risk behaviours during turbulent times.

Our analysis of 285 construction companies with a strong service orientation revealed that the proposed risk model explains a 33.6% variance in supplier performance, 46.4% operational performance, 47.1% customer satisfaction and 46.5% financial performance. Our findings highlight the importance of effectively monitoring risks in service-oriented construction supply chains and examining complex networks in which risk variables impact construction supply chain performance.

This study examines the influence mechanisms between risks and actors’ performance in construction supply chains, taking a service-oriented perspective.

Previous studies emphasise the risks that construction companies encounter from disruptions, such as maintaining operations and enhancing performance. Nevertheless, the research still needs to establish the transmission mechanism of the simultaneous impact (direct and indirect) of all forms of risk on supply chain performance.

In multi-label classification, selecting the most relevant features is crucial for enhancing predictive performance and reducing computational complexity. Real-world scenarios often involve significant costs in data acquisition, including time, financial and computational resources. However, most existing feature selection methods overlook the associated costs.

Multicriteria decision-making (MCDM) has emerged as a powerful tool for addressing complex problems involving multiple, often conflicting criteria. This study proposes a novel cost-sensitive multi-label feature selection method that fuses feature importance with feature cost within an MCDM framework. The proposed method transforms a cost-sensitive multi-label feature selection problem into an MCDM problem by leveraging mutual information. Furthermore, the data were converted into Fermatean fuzzy sets, and the Fermatean fuzzy simple weighted sum product (WISP) method was employed to rank features based on their relevance to labels and associated costs.

Extensive experiments conducted on ten benchmark datasets against five evaluation metrics demonstrated the superiority of the proposed method in selecting relevant features while minimizing costs and consistently outperforming existing methods.

Unlike existing methods that integrate costs through penalties and select features via a greedy search, the proposed approach adopts an MCDM-based strategy for feature ranking. This method aims to achieve globally optimal outcomes by balancing the trade-offs between conflicting objectives, marking a significant advancement over existing techniques.

The purpose of this study is to provide a comprehensive overview of sustainable fashion consumption from a consumer behavior perspective, combining scientometric analysis and the SPAR-4-SLR protocol to identify trends, key contributors and research gaps in the field.

The study analyzes 114 articles published between 2014 and 2024, sourced from the Scopus database. A hybrid approach is used, employing VOSviewer and Rstudio for quantitative analysis, along with the theory-context-characteristics-methodology framework to systematically review constructs, theories, contexts and methodologies in the selected articles.

The findings highlight critical insights into consumer behavior regarding sustainable fashion and identify gaps in the literature. The study also provides performance indicators, including publication trends and citation metrics, visualized through tables and maps. It offers practical guidance for businesses and policymakers to promote sustainable consumption practices.

This research contributes to the field by integrating scientometric and systematic review methods, providing a novel approach to understanding sustainable fashion consumption. It also suggests future research directions and explores how benchmarking techniques can enhance consumer engagement and sustainability strategies.

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.

This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.

The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R² and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.

This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.