Search results
1 – 10 of 796Jörg Henseler and Florian Schuberth
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
Researchers should be more conscious, open and respectful regarding different research paradigms.
Practical implications
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.
Originality/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.
Details
Keywords
Francesca Magno, Fabio Cassia and Christian M. Ringle
Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also…
Abstract
Purpose
Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also increasingly using PLS-SEM, this growing interest calls for guidance.
Design/methodology/approach
Based on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals.
Findings
The use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method.
Research limitations/implications
This research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals.
Practical implications
The results of this analysis guide researchers who use the PLS-SEM method for their quality management studies.
Originality/value
This is the first article to systematically review the use of PLS-SEM in the quality management discipline.
Details
Keywords
Joseph F. Hair, Pratyush N. Sharma, Marko Sarstedt, Christian M. Ringle and Benjamin D. Liengaard
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…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.
Originality/value
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.
Details
Keywords
Nicole Franziska Richter, Rudolf R. Sinkovics, Christian M. Ringle and Christopher Schlägel
Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Originality/value
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.
Details
Keywords
Gabriel Cepeda-Carrión, Joseph F. Hair, Christian M. Ringle, José Luis Roldán and Jerónimo García-Fernández
S. Mostafa Rasoolimanesh, Siamak Seyfi, Raouf Ahmad Rather and Colin Michael Hall
This paper aims to investigate the interplay of memorable tourism experiences (MTE) dimensions in driving behavioral intentions of heritage tourists through the mediating role of…
Abstract
Purpose
This paper aims to investigate the interplay of memorable tourism experiences (MTE) dimensions in driving behavioral intentions of heritage tourists through the mediating role of satisfaction.
Design/methodology/approach
Empirical data were collected from tourists in the heritage city of Kashan, Iran. Partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were applied to analyze the data.
Findings
The results of PLS-SEM showed that three dimensions of MTE as follows: local culture, involvement and knowledge, significantly directly or indirectly influence tourists’ behavioral intention toward a destination. However, the results of fsQCA identified greater heterogeneity among the respondents by highlighting the positive effects of hedonism and novelty on satisfaction and revisit and word-of-mouth intentions.
Originality/value
This study enriches the empirical evidence on MTE by constructing a composite picture of the memorability of tourists’ experiences within a heritage tourism context. This study is one of the first to investigate the effects of dimensions of MTE on behavioral intentions using both symmetric (PLS-SEM) and asymmetric approaches to identify the more significant dimensions of MTE, as well as sufficient combinations of dimensions to predict behavioral intentions.
研究目的
本文旨在研究难忘旅游体验 (MTE) 各维度通过满意度这个中介变量来驱动遗产旅游游客行为意图的机制
研究设计/方法论/研究方法
实证数据是从伊朗遗产城市卡尚的游客那里收集的。本研究采用偏最小二乘结构方程模型(PLS-SEM)和模糊集定性比较分析(fsQCA)对数据进行分析
研究发现
偏最小二乘结构方程模型的研究结果表明,难忘旅游体验(MTE)的三个维度:当地文化、参与度和熟悉程度,显著地直接或间接地影响游客对目的地的行为意向。然而,模糊集定性比较分析的研究结果表明受访者间存在更大的异质性,其结果凸显了享乐主义和新鲜感对满意度、重游意向和口碑(WOM)意向的正效应
独创性/价值
本研究通过构建遗产旅游背景下游客体验难忘性的相互影响机制,丰富了关于难忘旅游体验(MTE)的实证研究证据。本研究是第一个同时使用对称方法(PLS-SEM)和非对称方法(fsQCA)来探究MTE各维度对行为意向的影响的研究之一,通过这种方式可以识别出MTE各维度中更为重要的维度以及维度组合,以此来预测行为意向
Propósito
Este artículo investiga la influencia de las dimensiones de las experiencias turísticas memorables (ETM) en el fomento de las intenciones de comportamiento de los turistas del patrimonio a través del papel mediador de la satisfacción.
Diseño/metodología/enfoque
Se recogieron datos empíricos de turistas en la ciudad patrimonial de Kashan, Irán. Para analizar los datos se aplicaron las técnicas partial least squares – structural equation modeling (PLS-SEM) y fuzzy-set qualitative comparative analysis (fsQCA).
Conclusiones
Los resultados que proporcionó el análisis PLS-SEM mostraron que tres dimensiones de las ETM: cultura local, implicación y conocimiento, influyen significativamente, de forma directa o indirecta, en la intención de comportamiento de los turistas hacia un destino. Sin embargo, los resultados del enfoque fsQCA identificaron una mayor heterogeneidad entre los encuestados al destacar los efectos positivos del hedonismo y la novedad sobre la satisfacción y las intenciones tanto de volver a visitar el destino como de realizar una comunicación de boca a boca (WOM).
Originalidad/valor
Este estudio enriquece la evidencia empírica sobre las ETM al construir una imagen combinada del carácter memorable de las experiencias de los turistas dentro de un contexto de turismo patrimonial. Este estudio es uno de los primeros en investigar los efectos de las dimensiones las ETM en las intenciones de comportamiento utilizando enfoques simétricos (PLS-SEM) y asimétricos para identificar las dimensiones más significativas las ETM, así como para determinar las combinaciones necesarias de dimensiones para predecir las intenciones de comportamiento.
Details
Keywords
Florian Schuberth, Manuel E. Rademaker and Jörg Henseler
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Originality/value
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.
Details