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The purpose of this paper is to assess the use of structural equation modeling in one specific field of marketing research, the image research.

A meta-analysis of a sample of image marketing works using structural equation modeling (SEM). The period of investigation is limited to the last five years to test for possible positive return of previous assessments of SEM use on the current SEM application.

Following this work, three major conclusions emerged: the study of homogenous samples of SEM models is required to get to accurate assessment of using the technique; SEM application is getting better probably due to learning from SEM reviews; and the reliance on a conjoint assessment of the various SEM issues is necessary to avoid parsimonious assessments. This study has provided a concise and refreshed view on the use of SEM in one marketing field, the image research.

47 SEM papers and 99 models along five years were examined through this research. Although the authors reviewed four of the most consulted databases in marketing, the authors might miss several interesting works not available in these databases during the investigation. It is interesting to add on the works reviewed in this study and to re-conduct the analysis. The objective is not to doubt the consistency of SEM image research but to provide writers and readers with tools that enable them to produce better quality SEM research. Moreover, the quantitative analysis could be larger. Future research can consider computing other statistics. Finally, in the standards of most of marketing journals, this paper is a bit long. But as suggested by Babin et al., journal editors should allow more space to SEM-based reviews as the nature of the discussion requires lengthening.

Mastering the statistical tool in marketing research is as important as mastering the conceptual tool. Statistical learning and/or cooperation with statisticians is recommended.

A multi-criteria review of works from one specific field in marketing research and across a recent period of time allowing for the test of possible positive return from previous reviews of SEM use on the quality of the current publications of SEM papers.

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.

Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

Partial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social sciences disciplines frequently use in their empirical analyses. The purpose of this paper is to shed light on several misconceptions that have emerged as a result of the proposed “new guidelines” for PLS-SEM. The authors discuss various aspects related to current debates on when or when not to use PLS-SEM, and which model evaluation metrics to apply. In addition, this paper summarizes several important methodological extensions of PLS-SEM researchers can use to improve the quality of their analyses, results and findings.

The paper merges literature from various disciplines, including marketing, strategic management, information systems, accounting and statistics, to present a state-of-the-art review of PLS-SEM. Based on these findings, the paper offers a point of orientation on how to consider and apply these latest developments when executing or assessing PLS-SEM-based research.

This paper offers guidance regarding situations that favor the use of PLS-SEM and discusses the need to consider certain model evaluation metrics. It also summarizes how to deal with endogeneity in PLS-SEM, and critically comments on the recent proposal to adjust PLS-SEM estimates to mimic common factor models that are the foundation of covariance-based SEM. Finally, this paper opposes characterizing common concepts and practices of PLS-SEM as “out-of-date” without providing well-substantiated alternatives and solutions.

The paper paves the way for future discussions and suggests a way forward to reach consensus regarding situations that favor PLS-SEM use and its application.

This paper offers guidance on how to consider the latest methodological developments when executing or assessing PLS-SEM-based research.

This paper complements recently proposed “new guidelines” with the aim of offering a counter perspective on some strong claims made in the latest literature on PLS-SEM. It also clarifies some misconceptions regarding the application of PLS-SEM.

This paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the industry stakeholders to measure the construction contract administration (CCA) performance and identify the strengths and weaknesses of the CCA system for running or completed projects.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. In the first phase, contract administration indicators were collected from relevant literature. In the second phase, an online questionnaire was prepared, and data were collected and analyzed using the crisp value of fuzzy membership function, and structural equation modeling (SEM). The fuzzy set was chosen for this study due to the presence of uncertainty and fuzziness associated with the importance of several key indicators affecting the CCA performance. Finally, SEM was used to test and analyze interrelationships among constructs of CCA performance.

The data collected from 336 construction professionals worldwide through an online survey was utilized to develop the fuzzy structural equation model. The goodness-of-fit and reliability tests validated the model. The study concluded a significant correlation between CCA performance, CCA operational indicators, and the process groups.

The contribution of this paper to the existing knowledge is the development of a fuzzy structural equation model that serves as a measurement tool for the contract administration performance. This is the first quantitative structural equation model to capture contract administration performance. The model consists of 93 Construction Contract Administration(CCA) performance indicators categorized into 11 project management process groups namely: project governance and start-up; team management; communication and relationship management; quality and acceptance management; performance monitoring and reporting management; document and record management; financial management; changes and control management; claims and dispute resolution management; contract risk management and contract closeout management.

Structural equation modeling (SEM) is a powerful multivariate statistical technique that requires careful application. The use of SEM in international business research has substantially increased recently, necessitating a critical evaluation of its use in the field. Through an analysis of 148 articles in the international business (IB) literature, we detail the state of current use of SEM in IB research and compare its use to the established best practices. In many instances, SEM's use in IB has been faulty, suggesting that authors may have drawn incorrect conclusions. To expand the IB field's knowledge base, methodological accuracy is essential. Based on our review of the technique's use in IB research coupled with the established practices in the social science literature, we provide practical suggestions for better applying SEM in the IB literature.

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.

The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields.

In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage.

PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity.

While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention.

This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. The cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.

This study aims to examine the current state of literature on structural equation modeling (SEM) studies in “cloud computing” domain with respect to study domains of research studies, theories and frameworks they use and SEM models they design.

Systematic literature review (SLR) protocol is followed. In total, 96 cloud computing studies from 2009 to June 2018 that used SEM obtained from four databases are selected, and relevant data are extracted to answer the research questions.

A trend of increasing SEM usage over years in cloud studies is observed, where technology adoption studies are found to be more common than the use studies. Articles appear under four main domains, namely, business, personal use, education and health care. Technology acceptance model (TAM) is found to be the most commonly used theory. Adoption, intention to use and actual usage are the most common selections for dependent variables in SEM models, whereas security and privacy concerns, costs, ease of use, risks and usefulness are the most common selections for causal factors.

Previous cloud computing SLR studies did not focus on statistical analysis method used in primary studies. This review will display the current state of SEM studies in cloud domain for all future academics and practical professionals.

Structural equation modeling (SEM) is a data analysis method of choice for many empirical supply chain management (SCM) researchers. As the statistical technique has evolved, so have its applications and reporting guidelines. Consequently, there has been a lack of standardized reporting across the SCM literature. The purpose of this paper is to provide a basis for standardizing reporting conventions.

This paper presents a review of recent SEM methods literature. In addition, the authors content analyze articles from International Journal of Logistics Management, International Journal of Physical Distribution and Logistics Management, and Journal of Business Logistics that contained either covariance-based or partial least square structural equation models. Using the aforementioned literature review and content analysis, the authors compare and contrast what is currently reported in extant supply chain literature with suggested reporting protocols in the methods literature.

Based on the review and analysis, the authors suggest standardized reporting conventions for the use of SEM in SCM research. In addition, the findings suggest that much more could and should be reported with respect to the decision as to which SEM technique to employ.

This paper can serve as a useful checklist and set of references for scholars publishing SEM research in supply chain journals.