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

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

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

Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity Research limitations/implications – While rese

Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research

The objective of this paper is to provide a basic framework for researchers interested in reporting the results of their PLS analyses. Since the dominant paradigm in reporting Structural Equation Modeling results is covariance based, this paper begins by providing a discussion of key differences and rationale that researchers can use to support their use of PLS. This is followed by two examples from the discipline of Information Systems. The first consists of constructs with reflective indicators (mode A). This is followed up with a model that includes a construct with formative indicators (mode B).

How to Write Up and Report PLS Analyses

http://files.ferraz8.webnode.com/200000065-e27a6e46de/Artigo2.pdf

Lean manufacturing: context, practice bundles, and performance

https://isiarticles.com/bundles/Article/pre/pdf/8941.pdf

Defining and developing measures of lean production

Use of structural equation modeling in operations management research: Looking back and forward ☆

A typical approach to studying capabilities in the operations management literature is to assess the intended or realized competitive operational performance and their contribution to business and organizational objectives. While it is crucial to identify the operational performance that helps create competitive advantage, it is equally important to understand the means for delivering the needed performance at the operational level. Drawing on the resource-based view (RBV), we argue that routines are a critical source of operations capabilities and subsequently investigate operations capabilities by means of their underlying routines. Because a common problem to studying capabilities is the ambiguous and confusing definitions, we conduct an extensive literature review to address the semantic confusion among various definitions of capabilities and delineate it from other related terms. We identify improvement and innovation as two critical plant level capabilities, each consisting of a bundle of interrelated yet distinct routines. We then empirically measure the two capabilities as second-order latent variables and estimate their effects on a set of operational performance measures. The results suggest that routines form internally consistent bundles which are significantly related to operational performance. This supports our notion of “capabilities as routine bundles” that are difficult to imitate and thus a source of competitive advantage.

Rachna Shah

Papers

Lean manufacturing: context, practice bundles, and performance

Defining and developing measures of lean production

Use of structural equation modeling in operations management research: Looking back and forward ☆

Linking Routines to Operations Capabilities: A New Perspective

Linking routines to operations capabilities: A new perspective