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The PLS agent: Predictive modeling with PLS-SEM and agent-based simulation
Publikationstyp
Journal Article
Date Issued
2016-10-01
Sprache
English
TORE-URI
Journal
Volume
69
Issue
10
Start Page
4604
End Page
4612
Citation
Journal of Business Research 69 (10): 4604-4612 (2016-10-01)
Publisher DOI
Scopus ID
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.
Subjects
ABS
Agent-based simulation
Partial least squares path modeling
PLS-SEM
Predictive modeling
TAM
DDC Class
000: Allgemeines, Wissenschaft