The PLS Agent: Predictive Modeling with PLS-SEM and Agent-Based Simulation
Posted: 13 Jun 2017
Date Written: September 1, 2015
Partial 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.
Keywords: Partial least squares path modeling, PLS-SEM, Agent-based simulation, ABS, Predictive modeling, TAM
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