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This study aims to investigate the influence of external factors identified by the Political, Economic, Social, Technological, Environmental and Legal (PESTEL) framework on corporate social responsibility (CSR) performance in Vietnamese construction firms.

The snowball sampling method was employed to gather 182 validated responses. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the research analyzed how these factors correlate with CSR practices under institutional theory.

Results indicated that social, economic, environmental, legal and technological factors positively impacted CSR performance. Among these, social factors had the most significant effect, followed sequentially by economic, environmental, legal and technological influences. Intriguingly, political factors demonstrated no significant association with CSR performance.

The strong impact of social factors confirms that societal norms and cultural values are critical in shaping corporate behavior in Vietnam. Firms can leverage this insight by intensifying their community engagement and social investment. Additionally, the negligible role of political factors in shaping CSR suggests that firms might not need to focus heavily on political engagement in Vietnam. However, firms should remain aware of legal changes as legal factors influence CSR outcomes.

Despite CSR’s growing importance, there remains a notable research gap regarding how external macro-environmental factors influence CSR performance, particularly within the construction industry. The findings emphasize the importance of aligning business strategies with socioeconomic and environmental aspects.

Sustainable building materials (SBMs) have recently been promoted to foster sustainable construction. While previous studies successfully presented the broad picture of SBM adoption, the question ‘What is the current state of SBM adoption in construction firms?’ was unanswered. Moreover, there is a lack of studies that investigate the impact of firm size on the adoption of SBMs. Therefore, this study aims to examine the level of readiness for SBM adoption in construction firms of different sizes.

Drawing on theoretical lenses and a review of previous studies, the study identified 20 factors affecting the readiness for SBM adoption. These factors were then grouped into market, organization, and employee readiness. Using the questionnaire survey, 229 valid responses were collected. The multiple pairwise comparison test showed significant differences in readiness for SBM adoption in firms of different sizes. The Fuzzy Synthetic Evaluation (FSE) analysis was then used to assess the level of readiness for SBM adoption in construction firms of different sizes.

The analysis of FSE showed that large-sized companies demonstrated the highest level of readiness for SBM adoption, followed by medium-sized and small-sized firms, respectively. These findings imply that larger construction firms may have more resources and organizational capacity to adopt SBMs. Conversely, smaller firms may require additional support or incentives to enhance their readiness for SBM adoption. It also highlights the need for targeted interventions and policies to promote SBM adoption across medium-sized and small-sized firms. Moreover, while market readiness emerged as the least critical category, the findings highlight the pivotal role of employee readiness across all firm sizes.

These findings underscore the importance of prioritizing employee readiness initiatives to facilitate the successful adoption of SBMs. Policymakers and industry stakeholders may need to focus on developing training programs and awareness campaigns tailored to employees, ensuring they are equipped with the necessary knowledge and skills. Moreover, strategies to enhance market readiness should also be explored through regulatory measures and incentives to encourage the adoption of SBMs. Additionally, fostering collaboration and networking among stakeholders can further promote market readiness and accelerate the uptake of SBMs in the construction industry.

This study aimed to utilize Epistemic Network Analysis (ENA) for a thorough evaluation of policy documents concerning the digital transformation in Vietnam's higher education sector.

Adopting a quantitative ethnography approach, this research employed ENA to analyse a curated collection of 21 documents that specifically addressed higher education (HE) and digital transformation within Vietnam. The study also incorporated qualitative content analysis, utilizing the constant comparison method as outlined by Onwuegbuzie et al. (2009), for data coding. ENA facilitated the examination of connections among various policy aspects.

The study revealed a consistent overarching theme in Vietnam's digital transformation policies during and post-pandemic, focusing on key areas such as ADMINISTRATION, VISION, QUALITY, and INFRASTRUCTURE. However, a temporal shift in emphasis was observed: during the pandemic, policies were more focused on ADMINISTRATION and INFRASTRUCTURE, while post-pandemic, there was an increased emphasis on COLLAB, VISION, and TEACH_LEARN.

This research represents one of the initial efforts to showcase the utility and significance of ENA in analysing policy documents. It underscores ENA's potential in elucidating the complex interplay of policy elements in the context of digital transformation in higher education, particularly within a developing country setting.

We respond to the existing gaps regarding the drivers and outcomes of customer experience quality in the context of bank marketing by examining the interrelationships between distinct dimensions of VTM service quality, customer experience quality and customer loyalty.

This research follows the Stimulus-Organism-Response theory to examine the antecedents and behavioural outcomes of customer experience quality during their journeys with video teller machine (VTM) services in the banking industry (also known as LiveBank or SmartBank). First, we conducted in-depth interviews with 34 bank customers to develop distinct measurement scales for customer experience quality and VTM service quality. A structural equation model linking six dimensions of VTM service quality, including tangibles, interaction quality, empathy, reliability, user’s friendliness and efficiency with the affective-sensory and intellectual values of customer experience quality and customer loyalty to VTM service is tested using data obtained from 405 individual customers.

The findings reveal that tangibles, interaction quality, reliability, user-friendliness and efficiency contribute to customer experience quality, which, in turn, drives customers’ intention to use VTM again. This research provides crucial theoretical background and practical implications to accelerate the penetration of VTM among bank customers and hence, foster financial inclusion among societies.

This paper presents the first research that empirically employs the value-based approach to measure customer experience quality in the banking service industry and examine its linkages to service quality and customer loyalty. Moreover, given the emergence of VTM, this is also among the pioneering studies which validate measurement scales for VTM service quality. This could be either reused or revisited for further research about VTM. Overall, our study contributes to the literature about customer retention in the banking service industry from not only the customers’ backwards-looking evaluations of service performance (i.e. service quality) but also their forward-looking evaluations (i.e. their own experience).

The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks.

The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks.

The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies.

The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes.

Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.

The current study analyzes product review videos of influencers to determine why they are popular among customers. The study on electrical and electronic appliances covered by YouTubers is an entirely new field of research. Knowing why customers watch their product review videos before purchasing would be interesting.

We analyzed 172 product review videos from influencers on YouTube. Subsequently, the study employed negative binomial regression (NB2) to predict the explanatory power of the independent variables over the dependent variables.

This paper recommends two different models for viewer engagement in online review videos. One can be used for high and the other for low viewer engagement. Comparatively, viewers put more effort into commenting on a video than liking it. Yet both have their importance as per the requirement.

We only focused on video content in English and Hindi. The study data considered review videos from various electrical and electronic appliances. Future researchers may replicate this study on different product categories.

This study makes a remarkable contribution to how firms and their managers can optimize video content when designing marketing strategies, particularly for retailers and e-tailers.

The current paper takes the lead in contributing to the existing literature on marketing in two ways. First, focusing on product review videos from influencers and second, employing a video analysis approach. Furthermore, this study recommends two different viewer engagement models for marketing practices when employing quantitative and qualitative video content.

The ever-evolving landscape of financial technology (Fintech) has revolutionised payment methods and raised questions about what drives user behaviour in adopting these innovative solutions. This study, using narrative transportation theory as an underpinning theory, aims to investigate into the dynamics of green user behaviour in adopting Fintech payments.

This study used a deductive approach, and with data obtained from 635 respondents through the purposive sampling technique, partial least squares structural equation modelling was employed to yield significant insights.

The study found a positive association between green brand positioning and product differentiation. However, it unexpectedly didn't impact user attitudes towards Fintech payments. Green brand image and perceived performance positively influenced product differentiation. Perceived product differentiation fully mediated the association between green brand positioning and user attitudes. The study introduced fear of missing out's (FOMO) moderating role, enriching eco-conscious marketing insights and user behaviour understanding.

This study reveals crucial implications for marketers, policymakers and user experience (UX) designers operating within the Fintech industry. It emphasises green brand positioning's impact on product differentiation, user attitudes and its mediating role. It advocates for sustainability integration, innovation, strategic messaging and user-centric improvements to optimise user perceptions and competitiveness in the evolving Fintech landscape. The study's cross-sectional design may limit the ability to establish causal relationships over time and overlook temporal changes in green Fintech adoption dynamics; thus, longitudinal studies are warranted to better understand the evolving nature of user attitudes and behaviours towards green Fintech payments.

This study adds novelty to the existing body of literature by introducing the dimension of innovation appeal to green brand positioning and employing narrative transportation theory in the Fintech realm. The findings also add novelty by highlighting the moderating impact of fear of missing out in predicting the association between green brand positioning and product differentiation in the realm of green Fintech and green use behaviour.

With the rapid diffusion of the metaverse into all aspects of businesses and the education industry, scholars have predominantly focused on examining its projected benefits and harms, yet have overlooked to empirically explore its unpredictable nature, which offers an exciting realm of unexplored challenges and opportunities.

This research adopts a qualitative research design in the form of 24 interviews from a single EdTech to investigate the possibility of unexpected developments resulting from the integration of the metaverse into its solutions.

Three noteworthy observations have emerged from the analysis: technological obsolescence, resource allocation imbalance, and monoculturalism.

This research pioneers an empirical exploration of the latent outcomes stemming from metaverse adoption within EdTechs, while also introducing a novel theoretical framework termed “meta-governance,” which extends the Edu-Metaverse ecosystem.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.