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Marketing researchers are increasingly taking advantage of the instrumental variable (IV)-free Gaussian copula approach. They use this method to identify and correct endogeneity when estimating regression models with non-experimental data. The Gaussian copula approach’s original presentation and performance demonstration via a series of simulation studies focused primarily on regression models without intercept. However, marketing and other disciplines’ researchers mainly use regression models with intercept. This research expands our knowledge of the Gaussian copula approach to regression models with intercept and to multilevel models. The results of our simulation studies reveal a fundamental bias and concerns about statistical power at smaller sample sizes and when the approach’s primary assumptions are not fully met. This key finding opposes the method’s potential advantages and raises concerns about its appropriate use in prior studies. As a remedy, we derive boundary conditions and guidelines that contribute to the Gaussian copula approach’s proper use. Thereby, this research contributes to ensuring the validity of results and conclusions of empirical research applying the Gaussian copula approach.

There are enduring misconceptions in the marketing and management literature about the potential biasing effects of Common Method Variance (CMV). One belief is that the biasing effect of CMV is of greater theoretical than practical importance; another belief is that if CMV is a potential problem, it can be easily identified with the Harman one-factor test. In this article, we show that both beliefs are ill founded and need correction. To demonstrate our key points with greater generality, we use analytical derivations rather than empirical simulations. First, we examine the effects of CMV on correlations between observed variables as a function of measure unreliability and the sign and size of the “true” trait correlation. We demonstrate that, for negative trait correlations, CMV leads to a substantial upward bias in observed correlations (i.e., observed correlations are less negative than the trait correlation), and under certain conditions observed correlations may even have the wrong sign (assuming that the method loadings are both positive or both negative). We also show that, for positive trait correlations, the downward bias due to measurement unreliability does not always mitigate the upward bias due to CMV (again assuming that the method loadings are either both positive or both negative). Importantly, our results indicate that the inflationary effect of CMV is larger at lower levels of (positive) trait correlations, whereas the deflationary effect of unreliability is larger at higher levels of trait correlations. Second, we demonstrate analytically the serious deficiencies of the popular Harman one-factor test for detecting common method variance and strongly recommend against its use in future research.

Survey research methodology is widely used in marketing, and it is important for both the field and individual researchers to follow stringent guidelines to ensure that meaningful insights are attained. To assess the extent to which marketing researchers are utilizing best practices in designing, administering, and analyzing surveys, we review the prevalence of published empirical survey work during the 2006–2015 period in three top marketing journals—Journal of the Academy of Marketing Science (JAMS), Journal of Marketing (JM), and Journal of Marketing Research (JMR)—and then conduct an in-depth analysis of 202 survey-based studies published in JAMS. We focus on key issues in two broad areas of survey research (issues related to the choice of the object of measurement and selection of raters, and issues related to the measurement of the constructs of interest), and we describe conceptual considerations related to each specific issue, review how marketing researchers have attended to these issues in their published work, and identify appropriate best practices.

The results of this research suggest a new mandate for discriminant validity testing in marketing. Specifically, the authors demonstrate that the AVE-SV comparison (Fornell and Larcker 1981) and HTMT ratio (Henseler et al. 2015) with 0.85 cutoff provide the best assessment of discriminant validity and should be the standard for publication in marketing. These conclusions are based on a thorough assessment of the literature and the results of a Monte Carlo simulation. First, based on a content analysis of articles published in seven leading marketing journals from 1996 to 2012, the authors demonstrate that three tests—the constrained phi (Jöreskog 1971), AVE-SV (Fornell and Larcker 1981), and overlapping confidence intervals (Anderson and Gerbing 1988)—are by far most common. Further review reveals that (1) more than 20% of survey-based and over 80% of non-survey-based marketing studies fail to document tests for discriminant validity, (2) there is wide variance across journals and research streams in terms of whether discriminant validity tests are performed, (3) conclusions have already been drawn about the relative stringency of the three most common methods, and (4) the method that is generally perceived to be most generous is being consistently misapplied in a way that erodes its stringency. Second, a Monte Carlo simulation is conducted to assess the relative rigor of the three most common tests, as well as an emerging technique (HTMT). Results reveal that (1) on average, the four discriminant validity testing methods detect violations approximately 50% of the time, (2) the constrained phi and overlapping confidence interval approaches perform very poorly in detecting violations whereas the AVE-SV test and HTMT (with a ratio cutoff of 0.85) methods perform well, and (3) the HTMT .85 method offers the best balance between high detection and low arbitrary violation (i.e., false positive) rates.

Purpose This article presents a model of consumer engagement in co-production. Method A theoretical paper which develops a five-stage dynamic model of consumer involvement in co-production. Results and Conclusions The article discusses the basic linkages between co-production and customization and presents co-production as a dynamic process which is composed of five distinct stages. It also specifies five distinct phases of the production activity chain where consumers can become involved in co-production. The model offers researchers an analytical framework conducive for more advanced studies of the phenomenon from both descriptive and analytical points of view. Managers can use it to segment consumers according to their tendencies to engage in co-production and suggests bases for developing corresponding offers of co-production possibilities which focus on diverse consumer benefits.

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.

Endogeneity in regression models is a key marketing research concern. The Gaussian copula approach offers an instrumental variable (IV)-free technique to mitigate endogeneity bias in regression models. Previous research revealed substantial finite sample bias when applying this method to regression models with an intercept. This is particularly problematic as models in marketing studies almost always require an intercept. To resolve this limitation, our research determines the bias's sources, making several methodological advances in the process. First , we show that the cumulative distribution function estimation's quality strongly affects the Gaussian copula approach's performance. Second , we use this insight to develop an adjusted estimator that improves the Gaussian copula approach's finite sample performance in regression models with (and without) an intercept. Third , as a broader contribution, we extend the framework for copula estimation to models with multiple endogenous variables on continuous scales and exogenous variables on discrete and continuous scales, and non-linearities such as interaction terms. Fourth , simulation studies confirm that the new adjusted estimator outperforms the established ones. Further simulations also underscore that our extended framework allows researchers to validly deal with multiple endogenous and exogenous regressors, and the interactions between them. Fifth , we demonstrate the adjusted estimator and the general framework's systematic application, using an empirical marketing example with real-world data. These contributions enable researchers in marketing and other disciplines to effectively address endogeneity problems in their models by using the improved Gaussian copula approach.

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.

Researchers often struggle when applying ‘golden rules of thumb’ to evaluate structural equation models. This paper questions the notion of universal thresholds and calls for adjusted orientation points that account for sample size, factor loadings, the number of latent variables and indicators, as well as data (non-)normality. This research explores the need for flexible cutoffs and their accuracy in single- and two-index strategies. Study 1 reveals that many indices are biased; thus, rigid cutoffs can become imprecise. Flexible cutoff values are shown to compensate for the unique distorting patterns and prove to be particularly beneficial for moderate misspecification. Study 2 sheds further light on this ‘gray’ area of misspecification and disentangles the different sources of misspecification. Study 3 finally investigates the performance of flexible cutoffs for non-normal data. Having substantiated higher performance for flexible reference values, this paper provides to managers an easy-to-use tool that facilitates the determination of adequate cutoffs.

Event studies examine stock price movements around corporate events. These events can be voluntary firm announcements (e.g., new product introduction, alliance formation, channel restructuring) or announcements made by other entities such as regulatory bodies (e.g., FDA approval) or competitors (e.g., new market entry). The event study methodology was developed by finance researchers but has been widely adopted in other fields, including marketing. We review the manner in which event studies have been used in the marketing literature and summarize the current state of knowledge about the design and interpretation of event studies. We provide guidelines for researchers who use this methodology and for readers who draw inferences from results obtained from event studies, and we highlight a few areas where the methodology can be leveraged to help us better understand the financial value of marketing actions.