Associate Professor - Department of Marketing
His research has been published in several premier academic journals, including Information Systems Research, MIS Quarterly, Psychometrika, Nature Human Behavior, Multivariate Behavioral Research, Long Range Planning, Journal of Business Research, and Marketing Letters.
He is a co-developer and co-founder of SmartPLS (www.smartpls.com) which offers software applications for modeling structural equation models with Partial Least Squares (PLS) path modeling.
His research interest and expertise focus on the digital transformation of marketing and consumer behavior as well as marketing analytics, behavioral research methods and computational statistics.
For more information on publications and current research activities please visit:
Google Scholar: https://scholar.google.de/citations?user=6-gGk0UAAAAJ
Becker, Jan-Michael; Proksch, Dorian & Ringle, Christian M. (2022)
Revisiting Gaussian copulas to handle endogenous regressors
Journal of the Academy of Marketing Science, 50, s. 46- 66. Doi: 10.1007/s11747-021-00805-y
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.
Karagür, Zeynep; Becker, Jan-Michael, Klein, Kristina & Edeling, Alexander (2021)
How, Why, and When Disclosure Type Matters for Influencer Marketing
International Journal of Research in Marketing Doi: 10.1016/j.ijresmar.2021.09.006
Consumers’ changing media consumption behaviors and skepticism toward traditional forms of advertising have prompted the growth of influencer marketing. Even as regulatory authorities call on brands and influencers to disclose the posts as advertising, no consistent guidelines exist. The distinct effects of self-generated versus platform-initiated disclosures also remain unclear, nor has research addressed the interplay of key influencer characteristics and marketing disclosures. This article reports on findings from the first academic field study of influencer marketing disclosures, as well as three experimental studies, which indicate that disclosure is a double-edged sword. When provided through a platform-initiated branded content tool, disclosure consistently exerts the strongest effect on perceptions of advertising, negatively relating to influencer trustworthiness and consumer engagement. The effects of disclosure type also depend on the number of followers and number of previously endorsed products (i.e., influencer characteristics). Yet consumers also express appreciation for transparency when influencers disclose posts as advertising, which increases perceived trustworthiness of the influencer and engagement with the post. The implications of these findings should inform choices by public policy makers, brand managers, and influencers.
Rigdon, Edward E.; Sarstedt, Marko & Becker, Jan-Michael (2020)
Quantify Uncertainty in Behavioral Research
Nature Human Behaviour, 4(April), s. 329- 331. Doi: 10.1038/s41562-019-0806-0
The behavioral sciences underestimate the uncertainty of research fndings and thus overestimate replicability. Metrologists in the physical sciences quantify all material components of uncertainty, even if some components must be quantifed using non-statistical means. Behavioral science should follow suit.
Rigdon, Edward E.; Becker, Jan-Michael & Sarstedt, Marko (2019)
Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note
Psychometrika, 84(3), s. 772- 780. Doi: 10.1007/s11336-019-09677-2
Parceling—using composites of observed variables as indicators for a common factor—strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy. In special cases where the ratio of loading to residual variance is the same for all items included in each parcel, factor indeterminacy is unaffected by parceling. Otherwise, parceling worsens factor indeterminacy. While factor indeterminacy does not affect the parameter estimates, standard errors, or fit indices associated with a factor model, it does create uncertainty, which endangers valid inference.
Rigdon, Edward E.; Becker, Jan-Michael & Sarstedt, Marko (2019)
Factor Indeterminacy as Metrological Uncertainty: Implications for Advancing Psychological Measurement
Multivariate Behavioral Research, 54(3), s. 429- 443. Doi: 10.1080/00273171.2018.1535420
Researchers have long been aware of the mathematics of factor indeterminacy. Yet, while occasionally discussed, the phenomenon is mostly ignored. In metrology, the measurement discipline of the physical sciences, uncertainty – distinct from both random error (but encompassing it) and systematic error – is a crucial characteristic of any measurement. This research argues that factor indeterminacy is uncertainty. Factor indeterminacy fundamentally threatens the validity of psychometric measurement, because it blurs the linkage between a common factor and the conceptual variable that the factor represents. Acknowledging and quantifying factor indeterminacy is important for progress in reducing this component of uncertainty in measurement, and thus improving psychological measurement over time. Based on our elaborations, we offer a range of recommendations toward achieving this goal.
|2011||University of Cologne||Dr.rer.pol|
|2020 - Present||BI Norwegian Business School||Associate professor|
|2011 - 2020||University of Cologne||Assistant professor in the german habilitation system|
|2007 - 2011||University of Cologne||Doctoral student, research and teaching assistant|