A test for multigroup comparison using partial least squares path modeling

Klesel, M., Schuberth, F., Henseler, J., & Niehaves, B. (2019). A test for multigroup comparison using partial least squares path modeling. Internet Research, 29(3), 464-477. https://doi.org/10.1108/IntR-11-2017-0418Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Originality/value: 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.publishersversionpublishe

The moderator role of Gender in the Unified Theory of Acceptance and Use of Technology (UTAUT): A study on users of Electronic Document Management Systems

Venkatesh et al. [1] tried to integrate predictability capabilities from the different existing models of technology acceptance. This produced the Unified Theory of Acceptance and Use of Technology (UTAUT). This comprehensive model resulted in the identification of common aspects. It proposed several constructs with a greater explanatory power and analyzed moderating drivers, such as age, Gender, experience and voluntariness of use. By doing so, UTAUT identifies three major drivers of behavioral intention: performance expectancy, effort expectancy and social influence. On the other hand, facilitating conditions and behavioral intention were identified as determinant factors of actual use [1]. In addition to previous considerations about UTAUT, empirical research has scarcely analyzed the moderating role of Gender [2]. This is why this paper particularly aims to fill this gap. Hofstede [3] describes strength, competitiveness and guidance for material success as social roles linked to male values, whilst modesty, tenderness, sensitivity and concern for the quality of life are values associated with women. With respect to UTAUT, existing studies have shown that performance expectancy positively influences behavioral intention more strongly for men (cf. [4], [5], [6] and [7]). Moreover, it has been observed that effort expectancy positively influences behavioral intention more strongly for women (cf. [4], [5] and [6]), while social influence positively affects behavioral intention more strongly for women (cf. [5], [7] and [8]). In our research, with the aim of testing the moderating effects of Gender, a sample of 2,175 users of Electronic Document Management Systems (EDMS) in Portuguese municipalities was used. Taking into account that Gender is a categorical variable, we have adopted a multi-group or multi-sample analysis [9] -dividing the sample into two groups (male = 748; female = 1,427) and estimating each group of observations separately. Before comparing the groups, an analysis of the measurement invariance was carried out to make sure that the construct measures were invariant between both groups [10]. Once the metric invariance had been assessed, we carried out a set of multi-group analyses –interpreting statistically-significant differences in path coefficients as moderating effects. On the one hand, the parametric approach considering both equal variances and different variances has been used [11, 12]. On the other hand, we have applied non-parametric approaches exemplified by the permutation test [13], and Henseler’s PLS multi-group analysis [10, 12, 14]. This study notes slight differences in the results of the aforementioned methods. As a result, the moderating effect of Gender on the relation between performance expectancy and behavioral intention showed that this relationship is stronger among men than women. Finally, a discussion on the implications of Gender as a moderator for the UTAUT model is included

Multigroup analysis of more than two groups in PLS-SEM: A review, illustration, and recommendations

Multigroup analysis (MGA) in partial least squares structural equation modeling (PLS-SEM) has grown considerably in the past few years in many different research fields, particularly in the business area. However, a close examination of MGA in PLS-SEM articles revealed much less research that compared more than two groups. Furthermore, research applying MGA in PLS-SEM with more than two groups has several constraints. For instance, most researchers need clarification about using either the omnibus test of group differences (OTG) or non-parametric distance-based tests (NDT) for an overall difference across the groups. Moreover, they do not handle family-wise error when comparing more than two groups, nor do they check for measurement invariance. This article uses an empirical illustration to fully understand multigroup analysis with more than two groups, providing valuable guidelines and comprehensible recommendations for researchers applying PLS-MGA.info:eu-repo/semantics/publishedVersio

The influence of market heterogeneity on customer loyalty: A multigroup analysis.

Loyalty is configured as one of the main determinants of firm performance. Many works have proposed models that analyze the relationship between loyalty and its main determinants: the customer perceived value (PV), their level of satisfaction and their perceived switching costs (PSC). Thus, the aim of this study is to validate a model that gathers the relationships between these variables and analyze the influence of customer characteristics –propensity towards switching and customer involvement- on these relationships in the insurance industry. The results show that (a) for the whole sample, perceived value, satisfactions and switching costs are set as antecedents of loyalty; (b) however, for customers with high tendency to switch, the path to a loyalty behavior is only mediated by the influence of their perceived value in their satisfaction; and finally (c) for these individuals, the strength of the relationship between satisfaction and loyalty is lower than customers with low tendency to switch

Comparison of Mediation Effects on Interaction and Multigroup Approach in Structural Equation Modeling PLS in Case of Bank Mortgage

“Structural Equation Modeling is one of multivariate statistical method that used to explain multiple relationships between latent variables simultaneously to test a mediation model to conduct a formal test on mediation effects. Application PLS-SEM for exploratory research and theory development are increasing. Under certain conditions, the effect of exogenous variables on endogenous variable is also strengthened or weakened by moderating variable. In SEM, there are two approaches in analyzing moderation variables, namely the interaction method and the multigroup method. This article aims to compare the mediation effect on interaction approaches and multigroup approaches in Structural Equation Modeling. The data used is the case of timeliness of Bank X mortgage payments. In this article, statistical methods are evaluated to compare indirect effect between groups and examine indirect effect on each group. It was concluded that Collectability Status moderates the indirect relationship between Capital and the Timeliness of Payment through Willingness to Pay. Debtors with current collectability status more strongly effect the Timeliness of Payment than debtors with incurrect collectability status. Theresults of testing indirect effects on moderation with interaction and multigroup approaches are not much different. In the multigroup approach, the bootstrap interval bias is smaller than the bootstrap interval bias in the interaction approach. The Q-square Predictive Relevance value in both methods is quite high, indicating that the model is good. On the Current Collectibility Status group Q^2 is 89.3%, in the incurrect Collectibility Status Q^2 is 84.2%. While in the interaction approach, Q^2 is 70.4%. Researcher recommend a multigroup approach to data that has categorical moderation variables because differences between groups can be directly observed without adding interaction variables in the model.

Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments

Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assumption that data is collected from a single homogeneous population is often unrealistic. Sequential clustering techniques on the manifest variables level are ineffective to account for heterogeneity in path model estimates. Three PLS path model related statistical approaches have been developed as solutions for this problem. The purpose of this paper is to present a study on sets of simulated data with different characteristics that allows a primary assessment of these methodologies.Partial Least Squares; Path Modeling; Unobserved Heterogeneity

Evaluation of the online presence of family firms: A comparative analysis between Ibero-America and the US

Digital marketing strategies are an intermediary between marketing channels and communication of information. With the emergence of web 2.0, corporate websites’ have become the epicentre of digital marketing strategies. This study aims to fill a gap in the family business literature related to online presence and their differences between regions. Using structural equation modeling (SEM), the websites of the largest Ibero-American and American family businesses in the world (which were included in the Family Business Global Index (FBGI)) were examined by evaluating their content, form and function, as well as their presence in social networks. A multigroup analysis was used to compare the results in Ibero-America and America. One of the main results is that there is a negative relationship between website quality and a company’s turnover and a positive relationship between social networks and a company’s turnover. Regarding multigroup analysis, there are no significant differences among the family firms of the two regions with respect to the online presence. This study has relevant practical implications because it highlights the importance of a global strategy of online presence since it influences the company’s turnover

Analysis and evaluation of the largest 500 family firms’ websites through PLS-SEM technique

Dado que pocos estudios relacionan los aspectos técnicos de un sitio web corporativo con la facturación de una empresa, este documento tiene como objetivo examinar cómo la calidad de un sitio web corporativo influye en las redes sociales y la facturación de la empresa en grandes empresas familiares. También se prueba el efecto moderador y mediador de las redes sociales en las relaciones entre la calidad del sitio web y la rotación. Además, el documento realiza un análisis multigrupo para analizar las diferencias entre las empresas familiares con baja y alta concentración de propiedad familiar. La muestra utilizada en el estudio, los sitios web de las 500 empresas familiares más grandes de todo el mundo extraídos del Índice Global de Negocios Familiares compilados por la Universidad de St. Gallen, se analizaron utilizando modelos de ecuaciones estructurales de mínimos cuadrados parciales (PLS-SEM). Los resultados indican que tanto el efecto directo e indirecto de la calidad del sitio web en la rotación como el efecto moderador de las redes sociales en la relación entre la calidad del sitio web y la rotación fueron negativos y significativos. El análisis multigrupo revela algunas diferencias significativas entre ambos grupos. El estudio contribuye a la evaluación de la literatura del sitio web al explorar un nuevo sector de aplicación: las empresas familiares. Además, las empresas familiares más grandes deberían mejorar su presencia en las redes sociales para aumentar sus ventas.As few studies relate the technical aspects of a corporate website to a firm’s turnover, this paper aims to examine how the quality of a corporate website influences social networks and the company’s turnover in large family firms. The moderating and mediating effect of social networks on the relationships between website quality and turnover are also tested. In addition, the paper performs a multigroup analysis to analyze the differences between family businesses with low and high family ownership concentration. The sample used in the study, the largest 500 family firms’ websites around the globe extracted from The Global Family Business Index compiled by the University of St. Gallen, were analyzed using partial least squares–structural equation modeling (PLS-SEM). The results indicate that both the direct and indirect effect of website quality on turnover and the moderating effect of social networks in the relationship between website quality and turnover were negative and significant. The multigroup analysis reveals some significant differences between both groups. The study contributes to the evaluation of website literature by exploring a new sector of application: family businesses. Moreover, the largest family firms should improve their presence in social networks to increase their sales.• Junta de Extremadura y Fondos FEDER. AyudapeerReviewe

European management research using partial least squares structural equation modeling (PLS-SEM)

Hair, Sarstedt, Pieper, and Ringle's (2012) review study shows that partial least squares structural equation modeling (PLS-SEM) has become an increasingly applied multivariate analysis technique in management research. More recently, Richter, Sinkovics, Ringle, and Schlägel (2016) echo this result by showing that the number of PLS-SEM applications in (international) business research has increased substantially in the past few years. However, PLS-SEM is still new to many researchers who want to know: What exactly is PLS-SEM

plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares

We provide a package called plssem that fits partial least squares structural equation models, which is often considered an alternative to the commonly known covariance-based structural equation modeling. plssem is developed in line with the algorithm provided by Wold (1975) and Lohmöller (1989). To demonstrate its features, we present an empirical application on the relationship between perception of self-attractiveness and two specific types of motivations for working out using a real-life data set. In the paper we also show that, in line with other software performing structural equation modeling, plssem can be used for putting in relation single-item observed variables too and not only for latent variable modeling