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Structural equation modeling (SEM) is a well-established and frequently applied method in various disciplines. New methods in the context of SEM are being introduced in an ongoing manner. Since formal proof of statistical properties is difficult or impossible, new methods are frequently justified using Monte Carlo simulations. For SEM with covariance-based estimators, several tools are available to perform Monte Carlo simulations. Moreover, several guidelines on how to conduct a Monte Carlo simulation for SEM with these tools have been introduced. In contrast, software to estimate structural equation models with variance-based estimators such as partial least squares path modeling (PLS-PM) is limited.

As a remedy, the R package cSEM which allows researchers to estimate structural equation models and to perform Monte Carlo simulations for SEM with variance-based estimators has been introduced. This manuscript provides guidelines on how to conduct a Monte Carlo simulation for SEM with variance-based estimators using the R packages cSEM and cSEM.DGP.

The author introduces and recommends a six-step procedure to be followed in conducting each Monte Carlo simulation.

For each of the steps, common design patterns are given. Moreover, these guidelines are illustrated by an example Monte Carlo simulation with ready-to-use R code showing that PLS-PM needs the constructs to be embedded in a nomological net to yield valuable results.

Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares (PLS) path modeling.

A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications.

The MICOM procedure appropriately identifies no, partial, and full measurement invariance.

The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account.

The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.

The research practice in management research is dominantly based on structural equation modeling (SEM), but almost exclusively, and often misguidedly, on covariance-based SEM. The purpose of this paper is to question the current research myopia in management research, because the paper adumbrates theoretical foundations and guidance for the two SEM streams: covariance-based and variance-based SEM; and improves the conceptual knowledge by comparing the most important procedures and elements in the SEM study, using different theoretical criteria.

The study thoroughly analyzes, reviews and presents two streams using common methodological background. The conceptual framework discusses the two streams by analysis of theory, measurement model specification, sample and goodness-of-fit.

The paper identifies and discusses the use and misuse of covariance-based and variance-based SEM utilizing common topics such as: first, theory (theory background, relation to theory and research orientation); second, measurement model specification (type of latent construct, type of study, reliability measures, etc.); third, sample (sample size and data distribution assumption); and fourth, goodness-of-fit (measurement of the model fit and residual co/variance).

The paper questions the usefulness of Cronbach's α research paradigm and discusses alternatives that are well established in social science, but not well known in the management research community. The author presents short research illustration in which analyzes the four recently published papers using common methodological background. The paper concludes with discussion of some open questions in management research practice that remain under-investigated and unutilized.

In recent years, the use of structural equation modeling (SEM) has become widespread in tourism and hospitality research. Because there are two different approaches to SEM (i.e., covariance-based SEM and variance-based, partial least squares SEM), this brings challenges for researchers about which SEM to use and what to report in each SEM approach. Therefore, the purpose of this chapter is to discuss the differences between CB-SEM and PLS-SEM and to provide comprehensive guidelines for researchers on how to apply each SEM. Within this context, the authors first briefly summarize the fundamentals and advantages of using SEM. Then, the authors explain in detail the major issues that should be considered when selecting between CB-SEM and PLS-SEM. Finally, to ensure rigorous research practices, the authors provide step-by-step guidelines for the application of both CB-SEM and PLS-SEM.

The purpose of this paper is to present a model of the relationships between personal resources (Psychological Capital (PsyCap)) and satisfaction with job resources, and their effect on work engagement and burnout.

Data were collected from a convenience sample of 407 full-time employees from various public and private sector organisations, using a questionnaire consisting of PsyCap (PCQ-24), the Utrecht Work Engagement Scales, the Maslach Burnout Inventory, and a questionnaire measuring job resources (Parker and Hyett, 2011). The data were analysed using variance-based structural equation modelling (SmartPLS 3).

The influence of employees’ satisfaction with job resources on both dimensions of burnout (emotional exhaustion and cynicism) was negative and statistically significant. Satisfaction with job resources had a statistically significant positive influence on both dimensions of work engagement (vigour and dedication). PsyCap had a statistically significant positive influence on satisfaction with job resources. Satisfaction with job resources partially mediated the influence of PsyCap on emotional exhaustion and cynicism, and partially on vigour and dedication.

As this was an exploratory study, it used a convenience sample and a variance-based approach to structural equation modelling (SmartPLS). It is suggested that future researchers replicate the model in different contexts to corroborate the proposed relationships using larger samples, probability-based sampling and a covariance-based approach to structural equation modelling.

Management must realise that employees’ satisfaction with job resources plays a central role in their work engagement and burnout levels. Workplace practices that reflect respect and care for the employee and the development of employees’ personal resources (i.e. PsyCap) will improve work engagement and reduce burnout.

This paper fills a gap in the literature by explaining how personal resources (PsyCap) and job resources (the organisation’s perceived respect for the employee and employer care) influence work engagement and burnout via mediation paths.

The purpose of this study is to review the use of partial least squares-structural equation modeling (PLS-SEM) in the field of hospitality and tourism and thereby to assess whether the PLS-SEM-based papers followed the recommended application guidelines and to investigate whether a comparison of journal types (hospitality vs tourism) and journal qualities (top-tier vs other leading) reveal significant differences in PLS-SEM use.

A total of 206 PLS-SEM based papers published between 2000 and April 2017 in the 19 SSCI-indexed hospitality and tourism journals were critically analyzed using a wide range of guidelines for the following aspects of PLS-SEM: the rationale of using the method, the data characteristics, the model characteristics, the model assessment and reporting the technical issues.

The results reveal that some aspects of PLS-SEM are correctly applied by researchers, but there are still some misapplications, especially regarding data characteristics, formative measurement model evaluation and structural model assessment. Furthermore, few significant differences were found on the use of PLS-SEM between the two fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).

To enhance the quality of research in hospitality and tourism, the present study provides recommendations for improving the future use of PLS-SEM.

The present study fills a sizeable gap in hospitality and tourism literature and extends the previous assessments on the use of PLS-SEM by providing a wider perspective on the issue (i.e. includes both hospitality and tourism journals rather than the previous reviews that focus on either tourism or hospitality), using a larger sample size of 206 empirical studies, investigating the issue over a longer time period (from 2000 to April, 2017, including the in-press articles), extending the scope of criteria (guidelines) used in the review and comparing the PLS-SEM use between the two allied fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).

Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues.

This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations.

PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible.

This paper provides updated guidelines of how to use PLS and how to report and interpret its results.

Many important constructs of business and social sciences are conceptualized as composites of common factors, i.e. as second-order constructs composed of reflectively measured first-order constructs. Current approaches to model this type of second-order construct provide inconsistent estimates and lack a model test that helps assess the existence and/or usefulness of a second-order construct. The purpose of this paper is to present a novel three-stage approach to model, estimate, and test second-order constructs composed of reflectively measured first-order constructs.

The authors compare the efficacy of the proposed three-stage approach with that of the dominant extant approaches, i.e. the repeated indicator approach, the two-stage approach, and the hybrid approach by means of simulated data whose underlying population model is known. Moreover, the authors apply the three-stage approach to a real research setting in business research.

The study based on simulated data illustrates that the three-stage approach is Fisher-consistent, whereas the dominant extant approaches are not. The study based on real data shows that the three-stage approach is meaningfully applicable in typical research settings of business research. Its results can differ substantially from those of the extant approaches.

Analysts aiming at modeling composites of common factors should apply the proposed procedure in order to test the existence and/or usefulness of a second-order construct and to obtain consistent estimates.

The three-stage approach is the only consistent approach for modeling, estimating, and testing composite second-order constructs made up of reflectively measured first-order constructs.

This paper aims to explain how Web 2.0, social connectedness online, has created incredible new business options. This research’s primary goal is to help businesses use these resources more effectively and perform better.

Variance-based structural equation modeling with the ADANCO program was used to examine the data. ADANCO software is used explicitly for variance-based structural equation modeling. To evaluate research models and test hypotheses, partial least square path modeling is used.

Theories encompassing social support and related approaches to “word of mouth” online, electronic purchasing and virtual communities mediated by technological platforms are the foundational frameworks for this research piece. It then produces a statistical model that enables users to predict how social commerce (s-commerce) building blocks, including forums, communities, ratings and reviews and recommendations, assist businesses in introducing innovative strategies to win in the digital markets. The results necessarily focus on trust, an essential component of e-commerce. Reciprocally, the study reverses engineer’s trust through the constructs of this moment mentioned.

The present study describes the scope of empirical testing and validation of this framework and assists practitioners in further strengthening s-commerce strategy, an emerging and essential platform in the e-commerce industry.

Research highlights the dearth of current analysis in such conceptual domains while generating novel research insights aimed at e-commerce and digital business. From the viewpoint of potential and recurring customers who interact with online communities and product offerings, the study captures the essence of human interactions, often known as trade relationships, online.

This study aims to investigate the relationship between prevention and panic of COVID-19 and distress tolerance, happiness and emotional intelligence (EI) in undergraduate and postgraduate accounting students in Iraq. In other words, this study seeks to answer whether or not different types of prevention and fear of COVID-19 can lead to improved distress tolerance, happiness and EI.

The study’s statistical population comprises 298 undergraduate and 138 postgraduate students in Iraq who were selected as the sample size using the Cochran sampling method. In this study, partial least squares regression (PLS) have been used to investigate the effect of independent variables on the dependent variable.

The results showed a positive and significant relationship between COVID-19 prevention and distress tolerance and happiness, but no significant relationship was observed between COVID-19 prevention and EI. Also, no significant relationship was observed between fear of COVID-19 and distress tolerance and happiness, but there was a positive and significant relationship between fear of COVID-19 and EI.

The present study’s results can provide valuable information to everyone and help the development of science and knowledge because so far, and to the best of the authors’ knowledge, no research has examined the impact of prevention and panic of COVID-19 on distress tolerance, happiness and EI in students.