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The purpose of this paper is to describe potential flaws and pitfalls in the contemporary process of testing the theory of a research model in business research through the use of covariance-based structural equation modeling (CB-SEM).

This paper offers a foundation for discussion, debate and questioning regarding the contemporary process of testing the theory of a research model in business research through CB-SEM.

The contemporary process to test theory of a research model through CB-SEM in business research lacks to a large extent a stepwise and iterative process of an accumulation of knowledge to build sound and rigorous business theory that is both reliable and valid over time as well as across contexts.

This paper provides an awakening toward further debate and discussion on the relevance and suitability of the contemporary process to test the theory of a research model through CB-SEM in business research – is it science, quasi-science or just nonsense?

The primary implication of this paper is that its content will challenge most readers ' preconceptions of the topic and stimulate debate. Subsequently, it is the author’s hope that the content is thought-provoking and counterintuitive. Some scholars might reject the content, while others may find it valuable.

The paper intends to provide counterintuitive thoughts regarding the contemporary process of testing the theory of a research model in business research through the use of CB-SEM. CB-SEM offers potentially valuable merits in business research settings, if applied and performed properly.

Tourism research contains a large share of consumer behavior-orientated studies using multidimensional constructs (exogenous/endogenous). Accordingly, scholars have mainly made use of a two-step approach that can be referred to as PCA-MLR (principal component analysis and then ordinary least squares multiple linear regression analysis) to examine the relationships among exogenous and endogenous constructs in a statistical model. Although this two-step approach has contributed to the advancement of tourism research, it still suffers from a number of drawbacks which can readily be overcome by a so-called second-generation statistical tool, namely, partial least squares approach to structural equation modeling (PLS-SEM). The current chapter explains and illustrates (with an application to tourism data) the advantages (e.g., several layers of estimations, suiting small sample sizes, robustness to multicollinearity, model-based clustering, etc.) of PLS-SEM both from a statistical and practical point of view. Finally, an elucidation is also provided for suggesting PLS-SEM as an alternative to PCA-MLR instead of COV-SEM (covariance-based structural equation modeling). The chapter concludes by proposing that PLS-SEM is a reliable and flexible statistical approach that is of high value, in particular, for applied research.

Structural Equation Modeling (SEM) offers researchers additional flexibility and enhanced research conclusions, yet it is still underutilized in accounting. This may be in part because many researchers are not sufficiently familiar with SEM. SEM can be difficult to apply, especially if the research study was not appropriately planned to accommodate the necessary assumptions and data requirements. This article helps researchers overcome some barriers to using SEM by providing a simple guide to effectively planning a study suitable for an SEM analysis while also suggesting references and additional reading on the topic. To further encourage the use of SEM, the practical benefits of using SEM over the traditional regression approach for some research situations are also explained. Finally, a comparison of a regression and an SEM analysis of the same data testing the same theoretical model is included in the Appendices A and B in order to compare the differences in the research conclusions obtained by the two methods of analysis.

The purpose of this paper is to investigate as well as select various significant demand pull (DP) strategies affecting sustainable development (SD) in manufacturing organizations. The study deploys the structural equation modeling (SEM) technique to empirically validate the interrelationships between significant DP strategies and SD indicators in an SEM-DP Model.

Confirmatory factor analysis approach is applied to generate an effective SEM-DP model using the AMOS 21 (Analysis of Moment Structures) software. The data have been collected from different manufacturing organizations practicing DP strategies, using a well-framed DP questionnaire for the evolution of the SEM-DP model.

SEM of various DP strategies like stringent implementation of government regulations (SIGR), transforming capabilities, unionized labor (UL), and customer attributes (CA) toward achieving SD in manufacturing industries has been performed. The SEM-DP model has been planned and reports obtained before and after modification indices of the model are correlated, which further establishes improvements in the model’s effectiveness. The research concludes that significant DP strategies, namely, SIGR, UL, and CA support the manufacturing industries in accomplishing SD in terms of competitiveness, business performance enhancements, flexibility, customer satisfaction, and technological development.

In the present study, contributions of DP practices are determined to accomplish SD in Indian manufacturing organizations only. Hence, the results obtained may need some modifications before applying to other countries. Moreover, issue-wise independent modeling can also be performed to assess the importance of DP practices under specific orientations.

The results of various interrelationships among DP practices and SD indicators in the SEM-DP model portray the effectiveness of DP practices for achieving organizational goals and social commitments.

The outcomes of the study will help DP practitioners, organizational managers, and HR executives in the manufacturing industries to develop a clear understanding about the significant DP strategies to be followed holistically for accomplishing SD. The manufacturing enterprises will be able to frame and organize their policies, handle their UL issues and CA in a more appropriate way. Hence, the knowledge obtained from present study will help improve the overall performance of manufacturing industries involved in the present context.

Structural equation modelling (SEM) is a method that is very frequently applied by marketing and business researchers to assess empirically new theoretical proposals articulated by means of complex models. It is, therefore, a logical thought that the quality of the new advances in marketing and business theory depends, in part, on how well SEM is applied. This study aims to conduct an extensive review and empirical analysis of a broad variety of classic and recent controversies and issues related with the use of SEM, in order to identify problematic questions and prescribe a compendium of solutions for its suitable application.

The main analyses were conducted on a sample of 191 SEM‐based papers and 472 applications, i.e. all the SEM‐based studies published in four leading marketing journals during the period 1995‐2007.

Despite the maturity of SEM, its application in marketing research still has notable room for improvement. This is a general conclusion based on numerous problems detected and discussed here.

The study provides plausible solutions to the problems identified, a useful guide that is easy to follow and to apply adequately to present SEM issues in marketing and business studies.

The sample of SEM‐based papers and applications is limited to four publication outlets. A wider set or/and other journals different to those analyzed here may be preferred.

This is a valuable and timely study of the application of SEM in marketing and business research, and is also useful as a guiding framework for good practice. Likewise, as the problems discussed here presumably occur in other areas of social science, this paper should be welcome beyond the borders of the business disciplines.

Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM (CB-SEM) approach is dominant, the authors argue that the field’s dynamic nature and the sometimes early stage of theory development more often require a partial least squares SEM (PLS-SEM) approach. The purpose of this paper is to critically review the application of SEM techniques in the field.

The authors searched six journals with an international business (and marketing) focus (Management International Review, Journal of International Business Studies, Journal of International Management, International Marketing Review, Journal of World Business, International Business Review) from 1990 to 2013. The authors reviewed all articles that apply SEM, analyzed their research objectives and methodology choices, and assessed whether the PLS-SEM papers followed the best practices outlined in the past.

Of the articles, 379 utilized CB-SEM and 45 PLS-SEM. The reasons for using PLS-SEM referred largely to sampling and data measurement issues and did not sufficiently build on the procedure’s benefits that stem from its design for predictive and exploratory purposes. Thus, the procedure’s key benefits, which might be fruitful for the theorizing process, are not being fully exploited. Furthermore, authors need to better follow best practices to truly advance theory building.

The authors examined a subset of journals in the field and did not include general management journals that publish international business and marketing-related studies. Fur-thermore, the authors found only limited use of PLS-SEM in the journals the authors considered relevant to the study.

The study contributes to the literature by providing researchers seeking to adopt SEM as an analytical method with practical guidelines for making better choices concerning an appropriate SEM approach. Furthermore, based on a systematic review of current practices in the international business and marketing literature, the authors identify critical challenges in the selection and use of SEM procedures and offer concrete recommendations for better practice.

Structural equation modelling (SEM) has been defined as the combination of latent variables and structural relationships. The partial least squares SEM (PLS-SEM) is used to estimate complex cause-effect relationship models with latent variables as the most salient research methods across a variety of disciplines, including knowledge management (KM). Following the path initiated by different domains in business research, this paper aims to examine how PLS-SEM has been applied in KM research, also providing some new guidelines how to improve PLS-SEM report analysis.

To ensure an objective way to analyse relevant works in the field of KM, this study conducted a systematic literature review of 63 publications in three SSCI-indexed and specific KM journals between 2015 and 2017.

Our results show that over the past three years, a significant amount of KM works has empirically used PLS-SEM. The findings also suggest that in light of recent developments of PLS-SEM reporting, some common misconceptions among KM researchers occurred mainly related to the reasons for using PLS-SEM, the purposes of PLS-SEM analysis, data characteristics, model characteristics and the evaluation of the structural models.

This study contributes to that vast KM literature by documenting the PLS-SEM-related problems and misconceptions. Therefore, it will shed light for better reports in PLS-SEM studies in the KM field.

This article deals with how to test for and evaluate interdependence among control practices in a management control system using structural equation modeling. Empirical research on the levers of control (LOC) framework is used as an example. In LOC research, a path model approach to interdependence has been developed. The appropriateness of this approach is evaluated, developed, and compared with the correlation of residuals approach (seemingly unrelated regression) implemented in the wider complementarity literature. Empirical examples of the different models are shown and compared by using a data set on LOC of 120 SBUs in Sweden. The empirical results show that modeling interdependence among control practices in a management control system as non-recursive (bi-directional) paths or as residual correlations evidently affects the conclusions drawn about interdependence in terms of both presence and magnitude. The two models imply different views on how to conceptualize interdependence and are not statistically and empirically comparable. If using non-recursive path models, several model specification issues appear. To be able to identify such models, this needs to be carefully considered in the theory and research design prior to data collection.

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.

The purpose of this paper is to deploy structural equation modeling (SEM) technique to empirically validate the interrelationships amongst significant variables of 5S implementation and business excellence performance parameters (BEPP) in SEM_5S model.

The confirmatory factor analysis approach is utilized to generate the effective SEM_5S model by using AMOS 20.0 (analysis of moment structures) software. The data have been collected from different manufacturing organizations that have successfully deployed the 5S program by using well-designed questionnaire for the evaluation of SEM_5S model.

SEM of 5S various parameters has established that attributes like top management involvement initiatives, employee involvement initiatives, basic 5S initiatives (BFSI) and fifth S initiatives (Shitsuke) should be holistically emphasized during the implementation of 5S program, leading to attainment of high level of melioration in the BEPP. SEM has been deployed to evaluate the original and modification indices of the model, which further establishes the improvement in SEM’s effectiveness. The model establishes the significant impact of 5S implementation on business excellence of manufacturing organizations.

The outcomes of the study will help the organizational managers, HR executives and practitioners from manufacturing organizations to know about the significant factors which should be followed holistically to achieve overall organizational business excellence through strategic 5S initiatives.