The purpose of this paper is to provide a systematic overview with guidelines how to use partial least squares (PLS) path modeling in longitudinal studies. Practical examples from a study of the acceptance of battery electric vehicles (BEVs) in corporate fleets are used for demonstration purposes.
In this study, data at three points in time were collected: before the initial use of a BEV, after three and after six months of extensive usage of BEVs.
Three different models are identified depending on the research objective and on the data basis. Multigroup analyses are suggested to test the difference between the path coefficients of latent variables at different points in time. Limitations for the use of repeated cross-sectional data have to be observed.
Academics and practitioners will benefit from this paper by receiving an overview of the different PLS path models in longitudinal studies. A decision-tree enables them to make a choice regarding the most appropriate model and suggests a sequence of complementary analyses. So far, there is a lack of a tutorial type paper delivering such guidance.
The research was funded by the German Federal Ministry of Transport and Digital Infrastructure.
The author would like to thank Wynne Chin, Elina Petersone, Lukas Burs, as well as the participants of the 2nd UsePLS Conference 2015, Seville, for insightful and constructive comments on earlier versions of the paper.
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