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It is commonly observed in trainings that all trainees do not get satisfied with the performance of the trainer. The usual way to increase the satisfaction of trainees is a stress on improving task communication of the trainer. It is based on the assumption that effective task communication essentially fosters training effectiveness. This study aims to provide preliminary evidence that effective task communication can also obstruct training effectiveness besides promoting it. To achieve this objective, the authors hypothesized a dual-process model of training effectiveness based on uncertainty reduction theory.

This was a field study in which the authors collected time-lagged data from seven trainings. The trainings were designed to impart technical knowledge of multilevel analyses to professional social science researchers. Confirmatory factor analysis for ordinal indicators was used to test the measurement properties of the model and scales. Structural equation modeling for ordinal indicators was used to test hypotheses.

This study provided evidence of an overall positive effect of the trainer’s task communication on the trainee’s communication satisfaction. A complex mediation analysis also revealed the existence of two opposite psychological processes. While the first process transmitted the positive effect of task communication to communication satisfaction, the other process diminished this positive effect. Implications for the theory and practice of training are discussed.

Training scholars and practitioners universally believe that an effective task communication of trainers essentially promotes training effectiveness. This study has provided empirical evidence that this assumption is an incomplete picture of a complex reality that requires further investigation.

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.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

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.

In their paper titled “A Miracle of Measurement or Accidental Constructivism? How PLS Subverts the Realist Search for Truth,” Cadogan and Lee (2022) cast serious doubt on PLS’s suitability for scientific studies. The purpose of this commentary is to discuss the claims of Cadogan and Lee, correct some inaccuracies, and derive recommendations for researchers using structural equation models.

This paper uses scenario analysis to show which estimators are appropriate for reflective measurement models and composite models, and formulates the statistical model that underlies PLS Mode A. It also contrasts two different perspectives: PLS as an estimator for structural equation models vs. PLS-SEM as an overarching framework with a sui generis logic.

There are different variants of PLS, which include PLS, consistent PLS, PLSe1, PLSe2, proposed ordinal PLS and robust PLS, each of which serves a particular purpose. All of these are appropriate for scientific inquiry if applied properly. It is not PLS that subverts the realist search for truth, but some proponents of a framework called “PLS-SEM.” These proponents redefine the term “reflective measurement,” argue against the assessment of model fit and suggest that researchers could obtain “confirmation” for their model.

Researchers should be more conscious, open and respectful regarding different research paradigms.

Researchers should select a statistical model that adequately represents their theory, not necessarily a common factor model, and formulate their model explicitly. Particularly for instrumentalists, pragmatists and constructivists, the composite model appears promising. Researchers should be concerned about their estimator’s properties, not about whether it is called “PLS.” Further, researchers should critically evaluate their model, not seek confirmation or blindly believe in its value.

This paper critically appraises Cadogan and Lee (2022) and reminds researchers who wish to use structural equation modeling, particularly PLS, for their statistical analysis, of some important scientific principles.

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.

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.

This study aims to assess the status of current practices of accountability and its relationship with the practices of integrity system, internal control system and leadership qualities in the public sector of Malaysia.

This study collected primary data from 109 departments and agencies under 24 federal ministries in Malaysia. The data were analysed under descriptive statistics, ordinal regression and structural equation modelling (SEM). Several diagnostic tests were conducted to check the validity and reliability of data and models, such as Cronbach alpha test, Kaiser–Meyer–Olkin test, Shapiro–Wilk test, internal consistency reliability, indicator reliability, convergent validity and discriminant validity.

The regression and SEM results show that the practices of integrity system and leadership quality had statistically significant positive relationship, but the practice of internal control system showed mixed relationship with the practices of accountability.

The findings of the study will help the policymakers to ensure better accountability in the public sector in Malaysia and other countries.

This is an original study based on primary data to examine the current practices of accountability and its relationship with the practices of integrity system, internal control system and leadership qualities in the public sector of Malaysia.

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 performance management, as it applies to public sector organizations, has been addressed most often from a static perspective. A process-oriented view on performance is undertaken through use of perceived obstacles, garnered through two large surveys of U.S. government managers, to infer the adaptive paths federal agencies have followed. By applying a learning-based model founded on March’s framework of exploration and exploitation, the ideal-typical ways that public organizations adapt to a performance initiative can be distinguished, opening a window into the processes such learning entails. Structural equation modeling provides the statistical capacity to interpret exploration and exploitation as cohesive paths. Exploratory adaptation appears to have been largely counterproductive, as the obstacles associated with this dynamic indicated a roadblock rather than a path forward. But exploitation had the opposite effect, as its associated obstacles corresponded with greater use of performance measures for management activities and enhanced results orientation.