[37371] |
Title: The PLS Agent: Predictive Modeling with Partial Least Squares and Agent-Based Simulation. |
Written by: Schubring, Sandra and Lorscheid, Iris and Meyer, Matthias and Ringle, Christian M. |
in: June 2015 (2015). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: 2nd International Symposium on Partial Least Squares Path Modeling: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: Conference Paper |
DOI: |
URL: |
ARXIVID: |
PMID: |
Note:
Abstract: Partial least squares structural equation modeling (PLS-SEM) is a widespread multivariate analysis method that is used to measure variance-based structural equation models. However, the results generated from PLS-SEM are static and cannot provide any information as to what might happen if one influential factor changed over time. The combination of two modeling methods, agent-based simulation (ABS) and PLS-SEM, allows us to make PLS-SEM results dynamic. We contribute to existing research by introducing the PLS Agent, which uses the static path model and PLS-SEM results to determine the settings of the dynamic ABS model-ing method. Besides presenting the conceptual underpinnings of the PLS Agent, this research includes an empirical application of the new approach to the well-known technology acceptance model.