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The urgency to address climate change and its devastating consequences has never been more pressing. As societies become increasingly aware of the detrimental impact of traditional housing on the planet, there is a growing demand for eco-friendly housing solutions that prioritize energy efficiency, resource conservation and reduced carbon emissions. Therefore, this study aims to investigate the factors that influence customers’ priority toward eco-friendly house purchasing intention.

This study collected 386 data using a quantitative research strategy and purposive sampling method. This study uses a hybrid analysis technique using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approaches to identify the influencing factors.

The PLS-SEM analysis found that attitude toward the eco-friendly house, subjective norms, performance expectancy, environmental knowledge and environmental sensitivity have a positive influence on eco-friendly house purchasing intention. However, perceived behavioral control and willingness to pay were found to have insignificant effect on customers’ intention to purchase eco-friendly houses. The fsQCA results further revealed complex causal relationships between the influencing factors.

This research will not only contribute to academic knowledge but also provide practical guidance to real estate developers, policymakers and individuals looking to make environmentally responsible choices. By understanding the factors that influence consumers’ intentions to purchase eco-friendly houses, we can pave the way for a more sustainable and resilient future.

This study has used a hybrid analysis technique, combining PLS-SEM and fsQCA, to enhance the predictive accuracy of eco-friendly house purchase intentions among individuals residing in densely populated and highly polluted developing countries, such as Bangladesh.

This study aims to investigate the factors affecting technological innovation intentions at banks listed in Vietnam, a developing country, to develop business activities and accounting benefits according to the trend of the 4th Industrial Revolution.

To collect and analyze the data for this study, qualitative and quantitative methods were used. Specifically, 20 finance and banking experts and 45 managers in the field of information technology were interviewed in qualitative research over a period of three months. Then, 1,000 questionnaires were sent to banks within six months, with the final sample for quantitative research being 324 respondents. Finally, the structural equation modeling (SEM) was used to check the hypotheses. Regarding the tools used, the qualitative study used a semistructured questionnaire to collect information. Meanwhile, SPSS software was used to analyze quantitative research information, including checking common method bias, nonresponse bias, evaluating scale quality and checking SEM.

The findings show that the usefulness, ease of application, credibility, innovation and efficiency of technology have certain impacts on technological innovation intentions at banks listed in Vietnam. Using the SEM analysis, the results showed that the five factors had a favorable influence on the technological innovation intentions. More specifically, this study proposed adding an efficiency factor, and the results showed that it has the greatest impact on technological innovation intentions.

This study would be considered a continuation of prior studies because it provides empirical evidence for business models at banks listed in developing countries (for example, Vietnam) and so provides useful advice for bank management not only in Vietnam but across Asia. In fact, bank managers should consider introducing new technology as appropriate to make their reports more clear and up-to-date, therefore improving their performance. Banking managers, in particular, should focus on enhancing the bank’s application technology indicators to obtain a competitive edge.

This is a pioneering study that uses a combination of the reasoned action theory, planned behavior theory, transaction cost theory and unified theory of acceptance and use of technology to expand knowledge about technological innovation intentions at listed banks in the context of a developing country. The study also discovered and added the efficiency factor as a key factor affecting the intention to innovate technology at listed banks. These contribute to improving the literature of technological innovation intentions.

This study aims to investigate the relationship of employee motivation to team support, financial incentives and public sector motivation in various agencies under the Binh Dinh People's Committee in Vietnam. These agencies fulfil state management functions in many fields, such as investment, finance, construction, sports, culture and tourism.

This study applies the quantitative method to test team support, financial incentives and public service motivation (PSM) in relation to employee motivation in the public sector. The data are analysed using covariance-based structural equation modelling (SEM), with a sample size of 263 employees who work at provincial government agencies.

The study results show that team support, financial incentives and PSM have a positive influence on employee motivation in the public sector.

The findings provide theoretical evidence that team support, financial incentives and PSM are key predictors of employee motivation in the public sector in the context of an emerging economy. Consequently, the authors propose that managers in the public sector should motivate employees by communicating with employees about the employees' roles in improving the local people's lives to stimulate the PSM of employees. In addition, managers should always provide constructive feedback that recognises employees' achievements and pay bonuses based on job performance and successful projects to improve public service.

Although various booking platforms have been contributing to the dramatic growth of hotel industry, little research has been conducted to understand consumer psychological processes and behaviors in online hotel booking. To fill this gap, the current study examines the effect of switching barriers (switching cost and alternative attractiveness) on consumers' decision postponement and repurchase intention. Additionally, the moderating effect of time pressure in different phases of booking decision is investigated.

A total of 352 samples was collected through an online platform. Data analysis was conducted via Amos 23 (structural equation modeling) and SPSS 24 (descriptive analysis and PROCESS macro).

Results show that switching cost and alternative attractiveness are two significant drivers of decision postponement and repurchase intention. Meanwhile, time pressure only has a significant moderating effect on the relationship between switching cost and decision postponement.

The findings of this research reveal that hotel operations need to implement strategies to prevent customers' delayed booking decisions and overcome the influence of time pressure on customer decision-making.

These findings stress the importance of consumer perceptions of switching barriers and time span when making hotel reservations online. Hotel practitioners are encouraged to provide multiple human–computer interaction applications to attract novice consumers and increase their familiarity with booking process.

This study aims to examine the role of religion on Indonesia’ Muslim consumers’ emotional bonding and loyalty.

The sample consists of 505 and structural equation modelling was used to confirm research hypotheses.

The results indicated that religion has a positive and significant effect on Muslim consumers’ emotional bonding; furthermore, emotional bonding play an important role in mediating the relationship between consumers’ religiosity and consumers’ loyalty.

Future research is required to confirm the validity of this study throughout the sector and among Muslim banking consumers.

Bank managers also promote their consumers as change agents to recommend their companies to others. It is also essential in strengthening the relationship between consumers and the companies.

This study provided the Muslim consumers’ loyalty standpoint, the study enlightened bank managers about consumers’ loyalty through religiosity and emotional bonding.

This study aims to evaluate the usage of partial least squares structural equation modeling (PLS-SEM) in journals related to logistics and supply chain management (LSCM).

Based on a structured literature review approach, the authors reviewed 401 articles in the field of LSCM applying PLS-SEM published in 15 major journals between 2014 and 2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.

LSCM researchers sometimes did not clarify the reasons for using PLS-SEM, such as sample size, complex models and non-normal distributions. Additionally, most articles exhibit limited use of measurement models and structural model evaluation techniques, leading to inappropriate use of assessment criteria. Furthermore, progress in the practical implementation of advanced analysis techniques is slow, and there is a need for improved transparency in reporting analysis algorithms.

This study contributes to the field of LSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.

Rigorous applications of analytical tools in information systems (IS) research are important for developing new knowledge and innovations in the field. Emerging tools provide building blocks for future inquiry, practice and innovation. This article summarizes the findings of an analysis of the adoption and reporting of partial least squares structural equation modeling (PLS-SEM) analytical tools by Industrial Management & Data Systems authors in the most recent five-year period.

Selected emerging advanced PLS-SEM analytical tools that have experienced limited adoption are highlighted to broaden awareness of their value to IS researchers.

PLS-SEM analytical tools that facilitate understanding increasingly complex theoretical models and deliver improved prediction assessment are now available. IS researchers should explore the opportunities to apply these new tools to more fully describe the contributions of their research.

Findings demonstrate the increasing acceptance of PLS-SEM as a useful alternative research methodology within IS. PLS-SEM is a preferred structural equation modeling (SEM) method in many research settings and will become even more widely applied when IS researchers are aware of and apply the new analytical tools.

Emerging PLS-SEM methodological developments will help IS researchers examine new theoretical concepts and relationships and publish their work. Researchers are encouraged to engage in more complete analyses by applying the applicable emerging tools.

Applications of PLS-SEM for prediction, theory testing and confirmation have increased in recent years. Information system scholars should continue to exercise sound practice by applying these new analytical tools where applicable. Recommended guidelines following Hair et al. (2019; 2022) are included.

The partial least squares structural equation modeling (PLS-SEM) approach for construction management (CM) scholars has become the preferred approach for its capability of assessing the complex relationship and relaxed normality and sample size assumptions. This paper systematically maps the structure of knowledge about PLS-SEM in CM using bibliometric analysis. Also, the study employs meta-analysis to explore how data and model characteristics, model evaluation and advanced modeling techniques have been utilized in the CM domain.

This study integrated two methods: bibliometric analysis on a sample of 211 articles identified using the PRISMA framework and meta-analysis on 163 articles identified based on the availability of full-length articles and relevant information.

The results revealed the leading knowledge formation entities (countries, institutions, authors, sources and documents). Also, the study employs full content analysis to identify six research themes, and meta-analysis is used to explore the use of PLS-SEM based on the following criteria: (1) reasons for using PLS-SEM in CM, (2) data characteristics, (3) model characteristics and evaluation and (4) use of advanced modeling and analysis techniques. Further, the study uses regression analysis and identifies “advanced modeling and analysis techniques” as the critical feature responsible for the publication in a journal with high scientific prestige. Finally, the study presented the comprehensive guidelines to be used by construction management scholars who wish to use PLS-SEM in their research work.

To the author’s knowledge, it is the first study of this kind to use PLS-SEM in CM research. This study provides an extensive analysis of the Scopus database and an in-depth review of the data characteristics, model characteristics and use of advanced modeling techniques in CM research.

Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.

This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.

The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.

This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.

The purpose of this study is to discuss the construction of a structural measurement model utilizing structural equation modelling (SEM) to confirm the link between Industry 4.0 technologies, sustainable manufacturing practices and organizational sustainable performance. Relationship among the paradigm has yet to be fully investigated, necessitating a more conceptual and empirical examination on what impact they have on organizational sustainable performance when used together.

Industry 4.0 and sustainable production practices aim to progress a company's business competitiveness, forming sustainable development that benefits manufacturing companies. The aim of the study is to analyze the relationship between constructs that lead to operational excellence in firms that use Industry 4.0 technologies and sustainable manufacturing techniques. Experts from diverse automotive industries, who are applying both Industry 4.0 and sustainable manufacturing practices, provided data for the study.

Statistical estimations (hypotheses) are created to substantiate the measurement model that has been developed. The structural model was analysed, and the findings were discussed. The statistical estimate is either approved or rejected based on the findings. According to the conclusions of this study, strong link exists between Industry 4.0 technologies and sustainable manufacturing practices that affect organizational sustainable performance environmentally, economically and socially.

The research was conducted in the framework of automobile component manufacturing companies in India. The outcomes of the study are practically feasible.

The authors' novel contribution is the construction of a structural model with Industry 4.0 technologies and sustainable manufacturing practices into account.