Marcia Simmering on the Detection of Common Method Variance

[We’re pleased to welcome Marcia Simmering of Louisiana Tech University. Dr. Simmering recently published an article in Organizational Research Methods with Christie M. Fuller, Hettie A. Richardson, Yasemin Ocal, and Guclu M. Atinc entitled “Marker Variable Choice, Reporting, and Interpretation in the Detection of Common Method Variance: A Review and Demonstration.”]

  • What inspired you to be interested in this topic?

07ORM13_Covers.inddAfter the publication of my earlier piece on common method variance (Richardson, Simmering, Sturman, 2009 in ORM), where we found that marker variables could be potentially useful in detecting method variance, I kept getting questions from other researchers about what marker variables they should use in their own studies. I didn’t always have an answer, because the appropriateness of a marker variable depends on the study variables. So, I worked with a team of co-authors from different business disciplines on the current paper to find good marker variables in a variety of studies. As we all read articles using marker variables, we found so much variation in how they were used, and we learned that many had not been chosen or implemented properly. So, my coauthors and I decided to give an overview of how these techniques have been used (and misused). We took it a step further and tried to find out what these marker variables are really measuring and whether they’re measuring something different from presumed causes of common method variance (CMV), like social desirability and affectivity.

  • Were there findings that were surprising to you?

Yes! I would say that most of what we found in both studies surprised us. In Study 1 (the review of marker variable use), I didn’t expect so many authors to choose marker variables that really couldn’t properly capture CMV. And, I was surprised at how little journal space was given to tests of CMV. In Study 2, we didn’t know what we would find about what marker variables might detect in comparison to presumed causes of CMV, but we were still surprised to find that one added measure (either marker or presumed cause) is likely not enough to reasonably detect CMV and that multiple marker and CMV-cause variables in one study give much more information.

  • How do you see this study influencing future research and/or practice?

We hope that other researchers can find this article helpful in choosing appropriate marker variables and analyzing them in a way that can reasonably detect CMV. This is easier said than done, because a good marker variable is often chosen before data collection, and perhaps this article can influence more authors to do that. But, we hope, too, that reviewers gain some knowledge about how these techniques can be used to detect CMV. And, our ultimate goal is that this work can get us a little bit closer to understanding the large, complex, and still ambiguous phenomenon of CMV in social science research.

You can read “Marker Variable Choice, Reporting, and Interpretation in the Detection of Common Method Variance: A Review and Demonstration” from Organizational Research Methods for free for the next two weeks by clicking here. Want to know about all the latest research like this from Organizational Research Methods? Click here to sign up for e-alerts!

marcia_dickersonMarcia J. Simmering is the Francis R. Mangham Endowed professor of Management and assistant dean of Undergraduate Programs in the College of Business at Louisiana Tech University. Her current research focuses on the methods topics of common method variance and control variables. Additionally, she has published research on feedback, compensation, and training.

Christie M. Fuller is Thomas O’Kelly-Mitchener associate professor of Computer Information Systems at Louisiana Tech University. Her research in deception and decision support systems has been published in Decision Support Systems, Expert Systems with Applications, IEEE Transactions on Professional Communication, along with other journals and conference proceedings.

Richardson-Hettie for profileHettie A. Richardson is an associate professor and Chair of the Department of Management, Entrepreneurship, and Leadership in the Neeley School of Business at Texas Christian University. Her methodological research interests focus on common method variance and other measurement-related issues. She also studies employee involvement, empowerment, and strategic human resource management.

Yasemin Ocal is an assistant professor of Marketing at Texas A&M University-Commerce. Her research focuses on response rate and response bias in marketing research and has appeared in journals such as Journal of Leadership and Organizational Studies and numerous international conferences, including organization of a survey response rate issues session in World Marketing Congress of the Academy of Marketing Science.

atnicGuclu M. Atinc is an assistant professor of Management at Texas A&M University-Commerce. His current research addresses board composition, top management teams and ownership structures of young entrepreneurial firms, and research methods. Dr. Atinc’s research has appeared in journals such as Organizational Research Methods, Journal of Managerial Issues, and Journal of Leadership and Organizational Studies.

Are Consumers More Likely to Buy Green Products?

environment-1445492-mRecently, concern about the environment has become a crucial public issue. Increasing governmental regulations, intensifying consumer environmentalism and growing pressure from stakeholders have made firms decide to go green (Leonidou et al., 2011; Menon and Menon, 1997). There has been a rise in eco-friendly (EF) product preferences among consumers and firms are desperate to trap this new market opportunity. In turn, green marketing is becoming more important for firms (Chen et al., 2006). An article recently published in Global Business Review entitled “Linking Environmental Awareness and Perceived Brand Eco-friendliness to Brand Trust and Purchase Intention” analyzes the relationship among perceived brand ecofriendliness (PBE), Environmental Awareness (EA) and brand trust and the effect of brand trust on EF brand purchase intention.

The abstract:

The research examines the link among environmental awareness (EA), perceived brandhome_cover ecofriendliness (PBE) and brand trust and the subsequent effect on eco-friendly (EF) brand purchase intention. The article adopted structural equation modeling approach to test the hypotheses. Data were collected from 223 Indian consumers. The results show that there is a positive relationship between EA and PBE. Consumer’s EA and perception that a brand is eco-friendly, lead to trust in the brand. Findings support that higher brand trust leads to increasing purchase intention towards the EF brand. The article adds to the existing literature by dealing with consumer perception about brand ecofriendliness and its subsequent effect on purchase intention. Contribution of this study to the academic and practice is discussed.

Click here to read “Linking Environmental Awareness and Perceived Brand Eco-friendliness to Brand Trust and Purchase Intention” for free from Global Business Review! Make sure to sign up for e-alerts and be notified of all the latest research from Global Business Review!

Common Beliefs and Reality About PLS

[Editor’s Note: We’re pleased to welcome Dr. Jörg Henseler, who was the corresponding author on the article, “Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)” from Organizational Research Methods.]

The extent to which an issue is raised by successive generations of researchers and practitioners is a subtle indicator of its importance. The benefits and limitations of partial least squares path modeling (PLS) is such an issue that has been heatedly debated across a wide variety of disciplines. Tying in with this stream of research, Rönkkö and Evermann (2013), in their recent Organizational Research Methods article, sought to examine “statistical myths and urban legends surrounding the 07ORM13_Covers.inddoften-stated capabilities of the PLS method and its current use in management and organizational research.” Based on a series of arguments and simulations studies, Rönkkö and Evermann (2013) conclude that “PLS results can be used to validate a measurement model is a myth” (p. 438); “the PLS path estimates cannot be used in NHST [null hypothesis significance testing]” (p. 439); “the small-sample-size capabilities of PLS are a myth” (p. 442); “PLS does not have [the capability to] reveal patterns in the data” (p. 442); “PLS lacks diagnostic tools” (p. 442); “PLS cannot be used to test models” (p. 442); and “PLS is not an appropriate choice for early-stage theory development and testing” (p. 442). In light of these results, the authors conclude that the use of PLS is difficult to justify and that researchers should rather revert to regression with summed scales or factor scores.

Considering the increasing popularity of PLS in the strategic management (Hair et al. 2012a), marketing (Hair et al. 2012b) and management information systems disciplines (Ringle et al. 2012; Figure 1), these claims are certainly alarming. But how is it possible that Rönkkö and Evermann (2013) cannot find even a single positive attribute of PLS which stands against the research of great minds such as the founder of PLS, Hermann Wold, and key contributor’s such as Jan-Bernd Lohmöller and Theo Dijkstra? Does the criticism really hold what Rönkkö and Evermann (2013) promise or do these authors create myths by chasing myths?


The Organizational Research Methods article “Common Beliefs and Reality about Partial Least Squares: Comments on Rönkkö & Evermann (2013),” authored by Jörg Henseler, Theo K. Dijkstra, Marko Sarstedt, Christian M. Ringle, Adamantios Diamantopoulos, Detmar W. Straub, Dave J. Ketchen, Joe F. Hair, G. Tomas M. Hult, and Roger Calantone provides answers to these questions and shows that none of the alleged shortcomings of PLS stands up. More precisely, we show that Rönkkö and Evermann’s (2013) surprising findings are not inherent in the PLS method but are rather the result of several limitations in their study, which indisputably limit the validity of the authors’ findings.

The major shortcoming of Rönkkö and Evermann’s (2013) study is that they neglect that PLS estimates a composite factor model, not a common factor model. Although the composite factor model is often a good approximation to the common factor model, there are important differences. Rönkkö and Evermann (2013) regard PLS simply as a suboptimal estimator of common factor models. But like a hammer is a suboptimal tool to fix screws, PLS is a suboptimal tool to estimate common factor models. In contrast, PLS is a useful tool for estimating composite factor models.

Another fundamental limitation of Rönkkö and Evermann’s (2013) study relates to their simulation design. Research on PLS has generated a multitude of different simulation studies that compare the technique’s performance with that of other approaches to structural equation modeling. These studies vary considerably in terms of their model set-ups. In this context and despite the fact that most recent simulation studies use quite complex models with a multitude of constructs and path relationships, Rönkkö and Evermann (2013) chose to use a two-construct model with a single path as their basis for their simulation. This, however, inevitably raises the question whether this model can indeed be considered representative of published research from an applied standpoint. Bearing this in mind, we revisited review studies on the use of PLS in strategic management, marketing, and information systems research. Out of the 532 PLS models being estimated in 306 journal articles, there was exactly one model (0.2 percent) with two constructs. More precisely, the average number of constructs was 7.94 in marketing, 7.50 in strategic management, and 8.12 in information systems, respectively. There are several other aspects of Rönkkö and Evermann’s (2013) simulation design which cast doubt on their findings, suggesting that their simulation model set-up is not remotely representative of research studies using PLS. Further limitations relate to implicit assumptions in their interpretation of the PLS method, over-stretched generalization of their findings, misinterpretation of the literature and reporting errors in their simulation results. By disclosing these shortcomings, our study re-establishes a constructive discussion of the PLS method and its properties.

On a more general level, our article should also be read as a reminder that there is no such thing as an estimation method that is best for every model, every distribution, every set of parameter values and every sample size. For all methods, no matter how impressive their pedigree (maximum likelihood being no exception), one can find situations where they do not work as advertised. One can always construct a setup where a given method, any method, ‘fails’. A (very) small sample or parameter values close to critical boundaries or distributions that are very skewed or thick-tailed etc., or any combination thereof will do the trick. It is just a matter of perseverance to find something that it is universally ‘wrong.’

A constructive attitude, one that aims to ascertain when PLS work well, how it can be improved would seem to be more conducive to improving the quality of research: “We believe that such debates are fruitful as long as they do not develop a ritualistic adherence to dogma and do not advocate one technique’s use as generally advantageous in all situations. Any extreme position that (often systematically) neglects the beneficial features of the other technique and may result in prejudiced boycott calls [citations removed], is not good research practice and does not help to truly advance our understanding of methods (or any other subject)” (Hair et al. 2012c, p. 313).

Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012a). Applications of partial least squares path modeling in management journals: a review of past practices and recommendations for future applications. Long Range Planning, 45(5-6), 320-340.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012b). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012c). Partial least squares: The better approach to structural equation modeling? Long Range Planning, 45(5-6), 312-319.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, forthcoming.
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), iii-xiv.
Rönkko, M., & Evermann, J. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 16(3), 425-448.

Click here to read the paper “Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)” from Organizational Research Methods. Want to know about all the latest from Organizational Research Methods? Click here to sign up for e-alerts!

Jörg Henseler, Institute for Management Research, Radboud University Nijmegen, Nijmegen, the Netherlands and ISEGI, Universidade Nova de Lisboa, Lisbon, Portugal

Theo K. Dijkstra, Faculty of Economics and Business, University of Groningen, Groningen, the Netherlands

Marko Sarstedt, Otto-von-Guericke University Magdeburg, Magdeburg, Germany and University of Newcastle, Callaghan, Australia

Christian M. Ringle, University of Newcastle, Callaghan, Australia and Hamburg University of Technology, Hamburg, Germany

Adamantios Diamantopoulos, University of Vienna, Vienna, Austria

Detmar W. Straub, J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, USA

David J. Ketchen Jr., Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA

Joseph F. Hair, Coles College of Business, Kennesaw State University, Kennesaw, GA, USA

G. Tomas M. Hult, Broad College of Business, Michigan State University, East Lansing, MI, USA

Roger J. Calantone, Broad College of Business, Michigan State University, East Lansing, MI, USA

Do Celebrities Influence Our Vacation Destinations?

batterjob32 (cc)

batterjob32 (cc)

During the winter doldrums, it’s easy to find yourself daydreaming about where you’d like to escape for vacation. But as you start collecting brochures and flyers from your travel agent, ask yourself: do the celebrities endorsing your paradise have anything to do with your decision? Robert van der Veen and Haiyan Song explore this concept in their article “Impact of the Perceived Image of Celebrity Endorsers on Tourists’ Intentions to Visit,” published in the March edition of the Journal of Travel Research.

The abstract:

The purpose of this study is to empirically assess the mediating effects of the impact of the perceived JTR_72ppiRGB_powerpointimage of celebrity endorsers on tourists’ intentions to visit, using celebrity-endorsed print advertisements for travel destinations. The results indicate that celebrity endorsers have a significant impact on people’s attitudes and visit intentions, thus verifying the mediating effects of this variable. The study also provides clues to what extent celebrity-endorsed advertisements differ from nonendorsed advertisements and explores such differences in terms of destination match-up between native and nonnative celebrity-endorsed advertisements.

Not a Silver Bullet: PLS and Management Research

Editor’s note: We are pleased to welcome Mikko Rönkkö of Aalto University and Joerg Evermann of Memorial University of Newfoundland, whose paper “A Critical Examination of Common Beliefs About Partial Least Squares Path Modeling” is forthcoming in Organizational Research Methods and now available in the journal’s OnlineFirst section.

UntitledPartial Least Squares path modeling (PLS) is peculiar among statistical methods. At the same time, it is popular in some management and organizational research disciplines, but almost nonexistent in others. The method also stands out in the research methods literature. While other statistical methods are constantly analyzed in specialized journals, it is difficult to find any papers about PLS in the mainstream social sciences research methods journals.

Further, most of the introductory texts on statistical methods ignore the method. Instead its users rely on introductory articles in applied journals. Most of these present PLS as a structural equation modeling method and argue that it can provide advantages over earlier methods and other structural equation modeling methods. However, many of these papers lack any link to original methodological papers while others contradict each other and the original works that developed PLS, giving the reader an incomplete and possibly confusing picture of the method.

orm_200In our paper, we review how the PLS method has been applied in leading management journals. Based on this review, we identify six frequently repeated beliefs about PLS:

1. PLS has advantages over traditional methods because it is an SEM estimator
2. PLS reduces the effect of measurement error
3. PLS can be used to validate measurement models
4. PLS can be used for testing null hypotheses about path coefficients
5. PLS has minimal requirements on sample size
6. PLS is most appropriate for exploratory or early stage research

We trace the citations to the origins of these beliefs and present what evidence – if any – has been presented to support these beliefs. Our analysis suggests that many of the beliefs can be traced back to article small number of articles in the marketing discipline where they are presented mostly without evidence. We analyze each belief and discuss why contemporary understanding of statistics leads us to conclude that these beliefs are invalid. We use a simple example model to illustrate this. We conclude that the use of PLS for statistical inference is not justified. The method may be useful for statistical prediction, which it was initially intended for, but our review of the existing studies did not find any such applications.

This paper is not intended as the end to the discussion about PLS. Instead, we wish to pursue two goals: First, to raise the awareness of lack of evidence for the usefulness of the method among its users and reviewers encountering PLS-based studies. Second, to show that there is an urgent need for more attention toward the method in the mainstream social sciences research methods literature to better understand its strengths and weaknesses.

Read “A Critical Examination of Common Beliefs About Partial Least Squares Path Modeling” in Organizational Research Methods.

Mikko Rönkkö is a doctoral candidate at Aalto University, School of Science. His research interests are in statistics and research methods, with a focus on structural equation modeling.

Joerg Evermann is an associate professor of information systems. His research interests are in statistics and research methods, with a focus on structural equation modeling.

A Direct Comparison Approach for Testing Measurement Invariance

Gordon W. Cheung, The Chinese University of Hong Kong, and Rebecca S. Lau, The Open University of Hong Kong, published “A Direct Comparison Approach for Testing Measurement Invariance” on November 3rd, 2011 in Organizational Research Methods‘ OnlineFirst section. Other OnlineFirst articles can be found here.

The abstract:

Measurement equivalence/invariance (ME/I) is a condition that should be met before meaningful comparisons of survey results across groups can be made. As an alternative to the likelihood ratio test (LRT), the change in comparative fit index (DCFI) rules of thumb, and the modification index (MI), this teaching note demonstrates the procedures for establishing bias-corrected (BC) bootstrap confidence intervals for testing ME/I. Unlike the LRT and DCFI methods, which need a different model estimation per item, the BC bootstrap confidence intervals approach can examine itemlevel ME/I tests using a single model. This method greatly simplifies the search for an invariant item as the reference indicator in the factor-ratio test. Also demonstrated here is how the factor-ratio test and the list-and-delete method can be extended from the metric invariance test to the scalar invariance test. Finally, the BC bootstrap confidence interval procedures for comparing uniqueness variances, factor variances, factor covariances, and latent means across groups are shown.

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