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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.

People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches.

The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches.

Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach.

Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations.

This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

The purpose of this paper is to enhance consistent partial least squares (PLSc) to yield consistent parameter estimates for population models whose indicator blocks contain a subset of correlated measurement errors.

Correction for attenuation as originally applied by PLSc is modified to include a priori assumptions on the structure of the measurement error correlations within blocks of indicators. To assess the efficacy of the modification, a Monte Carlo simulation is conducted.

In the presence of population measurement error correlation, estimated parameter bias is generally small for original and modified PLSc, with the latter outperforming the former for large sample sizes. In terms of the root mean squared error, the results are virtually identical for both original and modified PLSc. Only for relatively large sample sizes, high population measurement error correlation, and low population composite reliability are the increased standard errors associated with the modification outweighed by a smaller bias. These findings are regarded as initial evidence that original PLSc is comparatively robust with respect to misspecification of the structure of measurement error correlations within blocks of indicators.

Introducing and investigating a new approach to address measurement error correlation within blocks of indicators in PLSc, this paper contributes to the ongoing development and assessment of recent advancements in partial least squares path modeling.

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.

People are increasingly responsible for making sound financial decisions to foster their financial satisfaction and well-being, which magnifies the importance of financial literacy, and this concept and measurement is still not yet crystallized in the literature, specifically capturing different behavior perceptions. Moreover, there is not a distinction based on different classifications of behavior, such as over or underconfidence, to understand the relation between literacy and decision process. To fill this gap, this paper aims to investigate whether the financial literacy conceptual model proposed applies similarly to every group independently of their previous self-confidence perception. For this purpose and quality control, OECD (2016) data were used with a final sample of 1,487 Brazilian citizens. Quantitative analysis technique using partial least squares structural equations path modeling and differences between groups using multi-group analysis was applied. In line with general studies, when analyzing the financial literacy usual model for the group as a whole, financial knowledge construct positively influences self-confidence, and both together positively affect financial behavior. However, for individuals with low financial knowledge and low self-confidence, as well as for those with too much or too little confidence, the model did not hold. Therefore, self-confidence perception influences the way financial knowledge is used for financial decisions and should be addressed in financial education and training to be more effective.

To operationalize the variables and test the paper’s hypotheses, the authors used the methodology developed in OECD (2016), based on the research instrument’s Brazilian application adapted from the questionnaire developed in OECD (2015), with data initially used and made available by Garber and Koyama (2016). Based on the recommendations of Hair Jr et al. (2017a, 2017b), the authors used partial least squares modeling PLS-PM (SmartPLS 3.2.6) to estimate the structural models.

Concerning structural relationships, the final model showed knowledge with a positive influence on self-confidence, self-confidence with a positive effect on behavior and knowledge with a positive influence on behavior, both directly and, through its relationship with self-confidence, indirectly. This underscores that, for the total sample, the greater people’s knowledge and self-confidence, the better their behavior. The unexpected absence of attitude in the final model, even allowing for potential measurement problems, brings up an important reflection on the mediating effect that the self-control variable may exert between attitude and behavior. A person may believe that saving for the future is important (attitude) but whether they actually save (behavior) may depend on self-control, which is needed to prevent immediate gains from being prioritized in practice.

The findings reported so far concern the study’s total sample. However, as expected from the literature review that provides the basis for the sixth and the most important hypothesis, respondents were found to be heterogeneous in terms of knowledge and self-confidence levels. These differences were evaluated by means of multi-group analyses that indicated that the model does not apply to respondents with low knowledge and low self-confidence and to those who are over- and underconfident. This implies inferring that financial education programs may be of little use if they only address technical knowledge development and fail to consider behavioral aspects such as those related to self-confidence, as this paper points out, and others. This signals the importance of diagnosing people’s profiles to enable developing solutions capable of minimizing the presence of behavioral biases. This need to be studied further.

The results imply inferring that financial education programs may be of little use if they only address technical knowledge development and fail to consider behavioral aspects such as those related to self-confidence, as this paper points out, and others. Models must be reviewed in light of natural diferences of cognition and lead to customized financial education.

This signals the importance of diagnosing people’s profiles to enable developing solutions capable of minimizing the presence of behavioral biases. Therefore, not only training topics in personal finance but also a deeper education program since the kindergarden must be considered.

Its practical contribution is to suggest the development of financial education programs that also take account of the potential presence of behavioral biases, which may prevent the misallocation of (scarce) public- and private-sector funds stemming from a limited focus on developing the population’s actual financial knowledge.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

As technology in tourism and hospitality (TTH) develops technical artifacts according to visitors’ demands, it must deal with both behavioral and design constructs in the context of structural equation modeling (SEM). While behavioral constructs are typically modeled as common factors, the study at hand introduces the composite into TTH to model artifacts. To deal with both kinds of constructs, this paper aims to exploit partial least squares path modeling (PLS-PM) as a confirmatory approach to estimate models containing common factors and composites.

The study at hand presents PLS-PM in its current form, i.e. as a full-fledged approach for confirmatory purposes. By introducing the composite to model artifacts, TTH scholars can use PLS-PM to answer research questions of the type “Is artifact xyz useful?”, contributing to a further understanding of TTH. To demonstrate the composite model, an empirical example is used.

PLS-PM is a promising approach when the model contains both common factors and composites. By applying the test for overall model fit, empirical evidence can be obtained for latent variables and artifacts. In doing so, researchers can statistically test whether a developed artifact is useful.

To the best of the authors’ knowledge, this is the first study to discuss the practical application of composite and common factor models in TTH research. Besides introducing the composite to model artifacts, the study at hand also guides scholars in the assessment of PLS-PM results.

因为旅游酒店科技（TTH）根据游客需求而定制科技产品, TTH必须在结构方程模型（SEM）下结合游客行为和设计等变量。一般行为变量在模型中是常见因子, 本研究将这些变量编入TTH结构成为模块。本研究采用PLS-PM方法来预估含有隐性变量和模块的模型。.

本研究设计PLS-PM模式, 即确定性全变量方法。TTH学者们通过引进结构形成模型模块, 使用PLS-PM研究方法, 以回答研究问题“模块xyz有用吗？”, 因此对TTH进一步理解。为了展示复合模型, 本论文采用实际验证。.

PLS-PM在面对模块内存在常见因子和复合模块的结构时是有力方法。实际验证结果通过整体最佳模型参数, 得到隐性变量和模块。为此, 研究者们能够在统计方法上测量是否开发的模型模块是否有用。.

据作者所知, 本论文是首个研究在TTH领域上应用模块和常见因子模型。本研究引进显性变量在模型模块中, 以指导学者评估PLS-PM结果报告。.

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.

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.