Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.

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A new criterion for assessing discriminant validity in variance-based structural equation modeling

Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.

PLS-SEM: Indeed a Silver Bullet

The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness.,This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM.,Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses.,Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method.,In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.

https://www.emerald.com/insight/content/doi/10.1108/EBR-11-2018-0203/full/pdf

When to use and how to report the results of PLS-SEM

Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research—despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.

An assessment of the use of partial least squares structural equation modeling in marketing research

This study provided a comprehensive examination of the full range of transformational, transactional, and laissez-faire leadership. Results (based on 626 correlations from 87 sources) revealed an overall validity of .44 for transformational leadership, and this validity generalized over longitudinal and multisource designs. Contingent reward (.39) and laissez-faire (-.37) leadership had the next highest overall relations; management by exception (active and passive) was inconsistently related to the criteria. Surprisingly, there were several criteria for which contingent reward leadership had stronger relations than did transformational leadership. Furthermore, transformational leadership was strongly correlated with contingent reward (.80) and laissez-faire (-.65) leadership. Transformational and contingent reward leadership generally predicted criteria controlling for the other leadership dimensions, although transformational leadership failed to predict leader job performance.

http://m.timothy-judge.com/TA-TF%20Paper--JAP%20published.pdf

Transformational and transactional leadership: a meta-analytic test of their relative validity.

In this article we review literature to build a broad understanding of what constitutes e-leadership in organizations. We propose a framework based on Adaptive Structuration Theory that could be used to study how Advanced Information Technology could influence and is influenced by leadership. According to our framework, the effects of Advanced Information Technology emerge from their interaction with organizational structures of which leadership is a part. Furthermore, organizational structures, including leadership, may themselves be transformed as a result of interactions with Advanced Information Technology. We use our Adaptive Structuration Theory–based framework to pool relevant results and suggestions from a diverse array of literature to provide recommendations for developing a research agenda on e-leadership.

E-leadership: Implications for theory, research, and practice

A longitudinal laboratory experiment was conducted to evaluate the effects of leadership style (transactional vs. transformational) and anonymity level (identified vs. anonymous) on group potency and effectiveness of 36 undergraduate student work groups performing a creativity task using a Group Decision Support System (GDSS). GDSS are interactive networks of computers for generating solutions to unstructured problems. Results indicated that GDSS anonymity amplified the positive effect of transformational leadership on group potency relative to transactional leadership in the group writing session of the task. GDSS anonymity also increased the effect of transformational leadership relative to transactional leadership on group effectiveness. Implications for practice and future research are discussed.

Effects of leadership style and anonymity on group potency and effectiveness in a group decision support system environment.

Much of group and organization research is constrained by either limited sample sizes and/or nascent theoretical development. Wold developed the partial least squares (PLS) data analytical techniqu...

http://journals.sagepub.com/doi/pdf/10.1177/1059601108329198

Silver Bullet or Voodoo Statistics? A Primer for Using the Partial Least Squares Data Analytic Technique in Group and Organization Research

Employing media richness theory, a model is developed to open the black box surrounding the impact of computer-mediated communication systems on decision quality. The effects on decision quality of two important communication system factors, cue multiplicity and feedback immediacy, are examined in light of three important mediating constructs: social perceptions, message clarity, and ability to evaluate others. A laboratory experiment examining two tasks and employing face-to-face, electronic meeting, electronic conferencing, and electronic mail communication systems is used to assess the model's validity. Results provide consistent support for the research model as well as media richness theory.

Richer media facilitate social perceptions (total socio-emotional communication and positive socio-emotional climate) and perceived ability to evaluate others' deception and expertise. Leaner media (electronic mail and electronic conferencing) facilitate communication clarity when participants have less task-relevant knowledge. The impacts of these mediating constructs on decision quality were found to depend on the levels of participant expertise and deception. In general, it was found that richer media can have significantly positive impacts on decision quality when participants' task-relevant knowledge is high. Moreover, effects of participant deception can be mitigated by employing richer media.

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Exploring the Core Concepts of Media Richness Theory: The Impact of Cue Multiplicity and Feedback Immediacy on Decision Quality

Prior research suggests that both transformational leadership and anonymity may affect creativity in groups using group decision support systems to generate ideas A laboratory study examined effects of high and low levels of transformational leadership on group creativity under anonymous and identified electronic brainstorming conditions Four measures of group creativity were used: fluency, flexibility, originality, and elaboration (Torrance, 1965) Results indicated that groups working under higher levels of transformational leadership generated more idea elaborations and original solutions than groups working under lower levels of transformational leadership Anonymous groups were more flexible in generating ideas than identified groups Results also indicated a significant Leadership Style x Anonymity interaction for flexibility

Surinder S. Kahai

Papers

E-leadership: Implications for theory, research, and practice

Effects of leadership style and anonymity on group potency and effectiveness in a group decision support system environment.

Silver Bullet or Voodoo Statistics? A Primer for Using the Partial Least Squares Data Analytic Technique in Group and Organization Research

Exploring the Core Concepts of Media Richness Theory: The Impact of Cue Multiplicity and Feedback Immediacy on Decision Quality

Transformational Leadership and Dimensions of Creativity: Motivating Idea Generation in Computer-Mediated Groups