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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.

This study aims to show the effectiveness and applicability of artificial intelligence applications in the measurement and evaluation of university services. Universities can gain competitive advantage through providing their students with quality services in various aspects, such as bookstores, dormitories, recreation centers as well as cafeterias. Among these facilities, university cafeterias are places where students spend a significant amount of time. Therefore, this study aims to integrate artificial intelligence application in the evaluation of university cafeteria services based on students' perceptions with two-stage structural equation modeling (SEM) and artificial neural network (ANN) approach.

An artificial intelligence based SEM-ANN hybrid approach was used to determine the factors that have significant influence on student satisfaction, sufficiency-of-services and likelihood-of-recommendation. Data were collected from 373 students through a face-to-face questionnaire. Initially, four service quality dimensions were attained through factor analysis. Then, hypotheses, which were determined via literature review, were tested through SEM-ANN hybrid approach.

Incorporating the results of SEM analysis into the ANN technique resulted in superior models with good prediction performance. Based on four ANN models created and ANN sensitivity analyses conducted, significant predictors of satisfaction, sufficiency, reliability and recommendation are determined and ranked.

Prior studies have assessed service quality using traditional techniques, whereas, this study integrates artificial intelligence in the assessment of higher-educational institutions' services quality. Also, as a distinction from previous studies, this study ranked importance levels of predictor variables through ANN sensitivity analysis.

This paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose.

This study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group.

The findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design.

Prior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity.

The research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology.

Cryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly.

Given the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.

This study aims to review relevant studies concerning consumer purchase of halal-certified products. A total of 35 studies related to the consumer purchasing behavior of halal-certified products in top-tier journals have been identified according to the recommended systematic literature review methodology.

A systematic literature review approach was implemented to examine, summarize and finally interpret the relevant research stream pertaining to consumer purchase of halal-certified products.

There are five research streams extracted from this systematic review, halal study context, theories adapted, covariance-based-structural equation modeling (SEM) vs partial least square-SEM, Muslim vs nonMuslim consumer and role of religiosity. Despite the growing interest in the quantitative approach in consumer purchase behavior in halal-certified products, scholars in halal consumer studies must have a greater extent of work. These include incorporating diverse theories in the framework, an advanced SEM approach, and relevant determinants to capture consumer purchasing of halal-certified products in the highly anticipated and profitable Muslim market.

Findings would help researchers in halal studies to consider and contemplate critical issues, according to the research stream presented in this review.

To the best of the authors’ knowledge, this study is the first review of quantitative studies on consumer purchases of halal-certified products.

Although user stickiness has been studied for several years in the field of live e-commerce, little attention has been paid to the effects of streamer attributes on user stickiness in this field. Rooted in the stimulus-organism-response (S-O-R) theory, this study investigated how streamer attributes influence user stickiness.

The authors obtained 496 valid samples from Chinese live e-commerce users and explored the formation of user stickiness using partial least squares-structural equation modeling (PLS-SEM). Artificial neural network (ANN) was used to capture linear and non-linear relationships and analyze the normalized importance ranking of significant variables, supplementing the PLS-SEM results.

The authors found that attractiveness and similarity positively impacted parasocial interaction (PSI). Expertise and trustworthiness positively impacted perceived information quality. Moreover, streamer-brand preference mediated the relationship between PSI and user stickiness, as well as the relationship between perceived information quality and user stickiness. Compared to PLS-SEM, the predictive ability of ANN was more robust. Further, the results of PLS-SEM and ANN both showed that attractiveness was the strongest predictor of user stickiness.

This study explained how streamer attributes affect user stickiness and provided a reference value for future research on user behavior in live e-commerce. The exploration of the linear and non-linear relationships between variables based on ANN supplements existing research. Moreover, the results of this study have implications for practitioners on how to improve user stickiness and contribute to the development of the livestreaming industry.

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.

Consumer expectations of quality have grown in recent years, forcing organisations, both service and manufacturing, to adopt total quality management (TQM) principles to satisfy customer demands efficiently. However, previous studies on the performance impacts of total quality management practices have mainly focused on the financial performance of firms in the manufacturing sector. This study focusses on the research questions: (1) What is the effect of TQM practices on operational performance? and (2) How do TQM practices combine to influence the operational performance of healthcare facilities?

Using a sample of 154 health facilities (i.e. private hospitals, pharmacies, maternity clinics, and diagnostic centres), the authors applied symmetric (PLS-SEM) and asymmetric (fsQCA) data analysis approaches to examine how TQM practices influence the operational performance of health facilities in the Ashanti Region of Ghana.

The PLS-SEM results revealed that five out of the seven TQM practices investigated influenced operational performance. However, the fsQCA results identify five different complex combinations of TQM practices that lead to operational performance.

Longitudinal studies can be conducted in the future to assess changes in the variables over time. A control variable, such as firm size, should be considered to assess the level of implementation of TQM practices based on firm size. A different performance measure, for instance, sustainability indicators or the balance score card, could be used to examine performance.

A proper and coordinated integration of the TQM practices is required for firms to be able to achieve operational performance. TQM practices vary in their sufficiency for operational outcomes; therefore, management needs to carefully consider their implementation as part of the organisation's strategy.

This research, by focussing on TQM practices from both symmetrical and asymmetrical perspectives, contributes to the understanding of the literature on TQM, thereby providing actionable insight on how to invest in the various TQM practices for improved operational performance.

This study aims to examine the key antecedents influencing the private firm’s intention to adopt big data analytics (BDA) in developing economies. To do so, the study follows the sequential explanatory approach.

To test the hypothesized model that draws on the technology–organization–environment (TOE) framework paired with the diffusion of innovation (DOI) theory, a purposive sampling technique was applied to gather data from 156 IT and management domain experts from the private firms that intend to adopt BDA and operate in Pakistan’s service industry, including telecommunication, information technology, agriculture, and e-commerce. The data were analysed using the partial least squares structural equations modelling (PLS-SEM) technique and complemented with qualitative analysis of 10 semi-structured interviews in NVIVO 12 based on grounded theory.

The empirical findings revealed that the two constructs – perceived benefits and top management support – are the powerful drivers of a firm’s intention to adopt BDA in the private sector, whereas IT infrastructure, data quality, technological complexity and financial readiness, along with the moderators, BDA adoption of competitors and government policy and regulation, do not significantly influence the intention. In addition, the qualitative analysis validates and further complements the SEM findings.

Unlike the previous studies on technology adoption, this study proposed a unique research model with contextualized indicators to measure the constructs relevant to private firms, based on the TOE framework and DOI theory, to investigate the causal relationship between drivers and intention. Furthermore, the findings of PLS-SEM were complemented by qualitative analysis to validate the causation. The findings of this study have both theoretical and practical implications.

Drawing on the adapted unified theory of acceptance and use of technology (UTAUT2) framework in the bottom of pyramid (BoP) context, this paper examines the number of causal recipes that foster m-health adoption in a developing country (Bangladesh). This paper aims to propose an extended UTAUT2 model along with identifying the necessary and sufficient factors affecting the m-health adoption intention in the BoP market.

The research model was empirically tested, combining two approaches: structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Data were collected from 221 housemaids and female security guards who earn around US$6 per day.

The SEM results revealed that while performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions, perceived cost (PC) and technology anxiety (TA) significantly influence the behavioural intention of BoP markets, hedonic motivation is the non-significant predictor. The fsQCA revealed that the two necessary conditions, PC and SI, can be combined with TA to increase the possibility of the success of m-health adoption in the BoP market.

For practitioners concerned with fostering the m-health adoption intention in BoP markets, the present study, which points out equifinality, recommends integrating the PC and SI in several combinations with PE, EE and TA.

To the best of the authors’ knowledge, no previous studies using the UTAUT2 theory examined the m-health services in the BoP market. This study contributes empirical data to the predominantly theoretical literature by offering a deeper understanding of the inclusion of TA and PC in several combinations with other UTUAT2 factors as predictors for explaining the m-health adoption intention of BoP markets.

This study aims to examine performance assessment of organizational units through psychological empowerment (PE) and employee engagement (EE) approach and whether this relationship differs among efficient and inefficient organization units.

This study drew on merging the principal component analysis (PCA), data envelopment analysis (DEA) and partial least square-multigroup analysis (PLS-MGA) to benchmark the performance of organizational units affiliated with Zagazig University in Egypt using PE dimensions as inputs and EE as output. Besides investigating whether PE inputs have the same effect among efficient and inefficient units.

Performance assessment based on independent data showed that all the investigated organizational units are not at the same efficiency level. The results revealed that there are eight efficient units versus seven inefficient ones. Moreover, PLS-MGA results demonstrated that no significant differences concerning the impact of PE inputs on EE between efficient and inefficient units groups. Nevertheless, the effect of these inputs was slightly higher in the former.

Studies on EE performance in the service sector are scarce in the literature, this study is a novel contribution of exploring EE efficiency in Egypt as a developing economy. Specifically, using the PCA-DEA-structural equation modeling approach.