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Work on construction sites involves individuals with diverse character, temperament,age, physical strength, culture, religion and experience level. A good number of these individuals are also alleged to involve themselves in substance and alcohol abuse due to the physically demanding nature of their work. These could promote the prevalence of violence on construction sites which could in turn affect safety on construction sites. However, there is a lack of empirical insight into the effect of violent behaviour and unsafe behaviour on construction sites. This study therefore pioneers an empirical inquiry into the relationship between violent behaviour and unsafe behaviour on construction sites.

Seventeen violent behaviours and 15 unsafe behaviours were measured on 12 construction sites among 305 respondents using a structured questionnaire. A total of 207 valid questionnaire responses were collected from site workers. Partial least square–structural equation modelling (PLS-SEM) technique was used to examine the relationship between violent behaviour and unsafe behaviour.

The results indicate that there is a significant positive relationship between violent behaviour and unsafe behaviour on construction sites.

The findings from this study provide valuable insight into a less investigated dimension of the problem of construction site safety management. A focus on attitudinal issues such as how workers relate toward others and toward self should be an important consideration in safety improvement interventions on construction sites.

The purpose of this study is to segment mobile wallet users using a finite mixture partial least squares (FIMIX-PLS) approach and evaluate the unobserved heterogeneity across segments.

Partial least square structural equation modeling (PLS-SEM) using a convenience sample of 744 responses was used to analyze the measurement, structural model and hypotheses testing. To examine unobserved heterogeneity and identify user segments, FIMIX-PLS technique was employed. To generate more precise recommendations, importance-performance map analysis (IPMA) was performed with attitude as the target variable.

A structural equation model revealed that except perceived ease of use (PEOU) all other dimensions, namely perceived usefulness (PU), lifestyle compatibility (LC), facilitating conditions (FC), trust and security significantly influences attitude which, in turn, determines intention. The FIMIX-PLS technique resulted in four segments – The Rationalist, Early Adopters, Late Adopters and The Innovators.

The paper provides segment specific and between segment differences to derive implications. Identification of relevant predictors and segments will help academicians, marketing researchers and practitioners in gaining further understanding of the mobile wallet adoption. The findings of the paper can guide mobile wallet providers to frame appropriate strategies and offerings pertaining to the obtained segments.

The paper builds upon Technology Acceptance Model (TAM) to propose an integrated model to explain adoption behaviors associated with mobile wallet. To the best of the authors' knowledge, this is one of the first empirical attempts using FIMIX-PLS technique to assess precursors of adoption and substantiates the perceived value-attitude-intention linkage to identify heterogeneity among mobile wallet users.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

The purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics.

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models using 100 wavelet coefficients. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with the LS-SVM model.

The correlation coefficient of prediction (r_p) and root mean square errors of prediction were 0.99 and 4.37 percent, respectively. The results suggest that NIR spectroscopy, combining with the LS-SVM method, has significant potential to quantitatively analyze cotton content in blend fabrics.

It may have commercial and regulatory potential to avoid time-consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

The present research study aims to explore the impact of renewable energy (RE) on investors willing to invest. This current study also investigates the mediation role of perceived benefit (PB) and living creature’s development (LCD) among RE and investors willing to invest.

Pakistani per capita income level is low; usually, the population lives hand to mouth. Only 10% to 15% of the population has been saving and is willing to invest in different sectors. To meet the aim of this study, data were collected from 300 individuals with a 40% response rate investors, equity fund managers and Pakistani stock exchanges using a nonprobability convenient sampling approach. The partial least square structural equation modeling technique and Smart partial least squares 3.0 were used to determine the primary and medicating effects of the variables.

The analysis shows that RE and investor willing to invest strongly linked each other directly and indirectly. PB and LCD significantly partial mediate the connection among RE and investor willing to invest. Hence, the results suggest that RE has more sustainable development goals with using and accessing affordable green and reliable energy.

The present study narrows the research gap by examining the effect of RE on investor willing to invest via PB and LCD. Also, it provides essential information for effective energy policies contributed to the sustainable development goals and gives valuable suggestions for policymaker and government.

The objective of this study is to investigate how country risk, different political actions from the government and bureaucratic behavior influence the activities in industry supply chains (SCs) in emerging markets. The main objective of this study is to investigate the influence of these external stakeholders’ elements to the demand-side and supply-side drivers and barriers for improving competitiveness of Ready-Made Garment (RMG) industry in the way of analyzing supply chain. Considering the phenomenon of recent change in the RMG business environment and the competitiveness issues this study uses the principles of stakeholder and resource dependence theory and aims to find out some factors which influence to make an efficient supply chain for improving competitiveness. The RMG industry of Bangladesh is the case application of this study. Following a positivist paradigm, this study adopts a two phase sequential mixed-method research design consisting of qualitative and quantitative approaches. A tentative research model is developed first based on extensive literature review. Qualitative field study is then carried out to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. A survey is carried out with sample of top and middle level executives of different garment companies of Dhaka city in Bangladesh and the collected quantitative data are analyzed by partial least square-based structural equation modeling. The findings support eight hypotheses. From the analysis the external stakeholders’ elements like bureaucratic behavior and country risk have significant influence to the barriers. From the internal stakeholders’ point of view the manufacturers’ and buyers’ drivers have significant influence on the competitiveness. Therefore, stakeholders need to take proper action to reduce the barriers and increase the drivers, as the drivers have positive influence to improve competitiveness.

This study has both theoretical and practical contributions. This study represents an important contribution to the theory by integrating two theoretical perceptions to identify factors of the RMG industry’s SC that affect the competitiveness of the RMG industry. This research study contributes to the understanding of both external and internal stakeholders of national and international perspectives in the RMG (textile and clothing) business. It combines the insights of stakeholder and resource dependence theories along with the concept of the SC in improving effectiveness. In a practical sense, this study certainly contributes to the Bangladeshi RMG industry. In accordance with the desire of the RMG manufacturers, the research has shown that some influential constructs of the RMG industry’s SC affect the competitiveness of the RMG industry. The outcome of the study is useful for various stakeholders of the Bangladeshi RMG industry sector ranging from the government to various private organizations. The applications of this study are extendable through further adaptation in other industries and various geographic contexts.

The purpose of this study is to use empirical data to examine the hierarchical impact of the Internet of things capability on supply chain integration (SCI), supply chain capability (SCC) and firm performance (FP) in the UK retail industry.

A deductive approach was employed to carry out this research. Structural equation modelling (SEM) was performed using the partial least square method (SmartPLS 3.3.3) to test theoretical predictions which underlie the relationships among Internet of things capability (IoTC), SCI, SCC and FP. Data are collected using an online survey completed by senior executives of 66 large, medium and small firms within the UK retail industry.

The empirical results of this research reveal that IoTC has a significant positive effect on the UK retail industry FP through the mediating role of SCI and SCC.

The research results from this study provide useful management insights for firms within the retail industry into the development of effective strategies for integrating their supply chain alongside the adoption of IoTC into SCI, consequently leading to improvements in FP.

Although previous studies have explored the impact of IoT on FP through the sequential mediating role of SCI and SCC, few have explored the impact of the IoT capability (IoTC) on FP through sequential mediators, i.e. SCI and SCC. This study examines the relationship between IoTC, SCI, SCC and FP in the UK retail industry supply chain to address this knowledge gap. Moreover, this study examines the effects of IoTC on FP by applying partial least square (PLS)-SEM techniques. Testing the sequential mediating role of SCI and SCI is undertaken, and the relationships among IoT-enabled SCI and SCC is analysed to improve FP. The robustness check's result through PLSpredict analysis also confirms the power of the model proposed in this study.

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.

A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.

The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.

This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

Integrating the social exchange and resource dependency theories, the study aims to comparatively examine the supplier relationship management (SRM) dimensions and organizational performance links of private and public hospitals in Ghana.

Comparative in nature; employing a quantitative approach; and using simple random and convenience sampling techniques, the study tested the proposed hypotheses using structural equation model-partial least square based on 205 usable questionnaires. Partial least square-multigroup analysis (PLS-MGA) was performed to test the significance of the difference in the parameters between the two samples: private and public hospitals in Ghana.

The dimensions of SRM (communication, cooperation, trust, atmosphere and adaptation) have a significant, positive impact on private hospitals’ performance in Ghana. Similarly, communication and trust were found to be positively and significantly correlated to public hospitals’ performance. In contrast, cooperation, atmosphere and adaptation dimensions showed no significant, positive effect on public hospitals’ performance. PLS-MGA disclosed that these observed differences in the findings between the private and public hospitals in Ghana are statistically significant.

The findings of the study, while limited to hospitals in Ghana, are likely to be relevant in other emerging economies for effective and enhanced supply chain relationship management.

The findings provide pragmatic insights for marketing practitioners and organizational leaders of hospitals about the significance of SRM dimensions in today’s globalized marketplace, and how to nurture them to enhance organizational performance.

The value of the study lies in the examination of the relationship between SRM and organizational performance in the health sector by comparing private and public hospitals in an emerging economy context.

The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness.

This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM.

Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses.

Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method.

In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.