We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we'll assume that you are happy to receive all cookies on the ESCP website. However, if you would like to, you can change your cookie settings at any time.


Prof. Michael Haenlein and his co-authors won this year's Jan-Benedict Steenkamp Award for Long-Term Impact.

Professor Michael Haenlein added a new prize to his growing list of achievements: He received the prestigious 2021 Jan-Benedict E.M. Steenkamp Award for Long-Term Impact from the International Journal of Research in Marketing (IJRM) and the European Marketing Academy (EMAC), which recognizes “exceptional contributions in academic marketing research , published in IJRM, that have demonstrated long-term impact”, for his work with Werner Reinartz and Jörg Henseler on structural equation modeling published in 2009.


Here is the statement from the Award Committee, which speaks for itself:

During the past two decades, there has been a renewed interest among researchers to use partial least squares analysis (PLS). However, prior research has not evaluated the relative performance of covariance- based structural equation modeling (CBSEM) and PLS approaches per a set of key characteristics such as sample size, number of indicators per construct, distributional assumptions etc. An interesting and important question is whether we can arrive at a set of rules that researchers may use in their choice of CBSEM versus PLS.
The paper by Reinartz, Haenlein, and Henseler explores this issue and examines whether each of the approaches converge to a proper solution, the degree of parameter accuracy between the approaches, the relative importance of the different design factors on parameter accuracy, statistical power etc. The authors conduct a set of Monte Carlo simulations based on 240 scenarios defined by a full factorial design of four design factors. The results suggest that the statistical power of PLS is always larger than or equal to that of maximum likelihood-based CBSEM. Further, their simulations show that PLS can be a very good methodological choice if sample size is low. Finally, their results indicate that CBSEM is actually extremely robust to violations of its underlying distributional assumptions.

This paper led by a clear margin by receiving the most votes of the IJRM Editorial Board and was approved unanimously by the award committee. In addition, the evidence of its impact is highlighted by 1039 Web of Science and 2320 Google Scholar citations, which makes it the best cited paper among all the initial round award nominees and shows its consistent use by scholars to decide which method to use.