Schubring, SandraSandraSchubringLorscheid, IrisIrisLorscheidMeyer, MatthiasMatthiasMeyerRingle, Christian M.Christian M.Ringle2019-12-122019-12-122016-10-01Journal of Business Research 69 (10): 4604-4612 (2016-10-01)http://hdl.handle.net/11420/4024Partial least squares structural equation modeling (PLS-SEM) is a widespread multivariate analysis method that is used to estimate variance-based structural equation models. However, the PLS-SEM results are to some extent static in that they usually build on cross-sectional data. The combination of two modeling methods ― agent-based simulation (ABS) and PLS-SEM ― makes PLS-SEM results dynamic and extends their predictive range. The dynamic ABS modeling method uses a static path model and PLS-SEM results to determine the ABS settings at the agent level. Besides presenting the conceptual underpinnings of the PLS agent, this research includes an empirical application of the well-known technology acceptance model. In this illustration, the ABS extends the PLS path model's predictive capability from the individual level to the population level by modeling the diffusion process in a consumer network. This study contributes to the recent research stream on predictive modeling by introducing the PLS agent and presenting dynamic PLS-SEM results.en0148-296320161046044612ABSAgent-based simulationPartial least squares path modelingPLS-SEMPredictive modelingTAMAllgemeines, WissenschaftJournal Article10.1016/j.jbusres.2016.03.052Other