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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

计量经济分析 = Econometric analysis

Purpose: This paper discusses partial least squares path modeling (PLS), a powerful structural equation modeling technique for research on international marketing. While a significant body of research provides guidance for the use of covariance-based structural equation modeling (CBSEM) in international marketing, there are no subject-specific guidelines for the use of PLS so far.Methodology/approach: A literature review of the use of PLS in international marketing reveals the increasing application of this methodology.Findings: This paper reveals the strengths and weaknesses of PLS in the context of research on international marketing, and provides guidance for multi-group analysis.Originality/value of paper: The paper assists researchers in making well-grounded decisions regarding the application of PLS in certain research situations and provides specific implications for an appropriate application of the methodology.

The Use of Partial Least Squares Path Modeling in International Marketing

▶ Compact, hands-on and step-by-step introduction to quantitative market research techniques ▶ Presents the most important techniques and shows how to translate theoretical choices into SPSS and how to analyze the output ▶ Range of education elements such as learning objectives, keywords, self-assessment tests, case studies ▶ Innovative supplementary online concept, including mobile tags, sample datasets and additional cases

A Concise Guide to Market Research

This accessible, practice-oriented and compact text provides a hands-on introduction to the principles of market research. Using the market research process as a framework, the authors explain how to collect and describe the necessary data and present the most important and frequently used quantitative analysis techniques, such as ANOVA, regression analysis, factor analysis, and cluster analysis. An explanation is provided of the theoretical choices a market researcher has to make with regard to each technique, as well as how these are translated into actions in IBM SPSS Statistics. This includes a discussion of what the outputs mean and how they should be interpreted from a market research perspective. Each chapter concludes with a case study that illustrates the process based on real-world data. A comprehensive web appendix includes additional analysis techniques, datasets, video files and case studies. Several mobile tags in the text allow readers to quickly browse related web content using a mobile device.

A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the aggregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology’s effectiveness in evaluating PLS path modeling results.

Response-Based Segmentation Using Finite Mixture Partial Least Squares

Governance theories, such as transaction cost economics, argue that systematic deviations from an attribute–governance alignment should influence performance. This article investigates the performance implications of contract specificity for the procurement of information technology products. The authors argue that parties choose a level of contract specificity that economizes on both the ex ante contracting costs and the ex post transaction costs and that deviations between the observed and the predicted levels of contract specificity are an important determinant of these transaction costs. The authors test the hypotheses using a comprehensive archival data set of information technology transactions and employ a two-step estimation procedure. First, they estimate the “predicted” level of contract specificity, which accounts for key transactional attributes. Second, they study the consequences of deviating from this predicted level of contractual specificity. The results provide the first explicit demonstration of the trade-off between ex ante contracting costs and ex post transaction problems and suggest that parties need to economize jointly on these costs when choosing the governance form.

/pdf/contract-specificity-and-its-performance-implications-d4ck1ala2l.pdf

Contract Specificity and Its Performance Implications

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homoge- neous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in cop- ing with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the ag- gregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology's ef- fectiveness in evaluating PLS path modeling results.

https://www.springer.com/cda/content/document/cda_downloaddocument/9781441912794-c1.pdf

Response-Based Segmentation Using Finite Mixture Partial Least Squares Theoretical Foundations and an Application to American Customer Satisfaction Index Data

Erik Mooi

Papers

A Concise Guide to Market Research

A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics

Response-Based Segmentation Using Finite Mixture Partial Least Squares

Contract Specificity and Its Performance Implications

Response-Based Segmentation Using Finite Mixture Partial Least Squares Theoretical Foundations and an Application to American Customer Satisfaction Index Data