 The PLS agent: Predictive modeling with PLS-SEM and agent-based simulationSandra Schubring, Iris Lorscheid, Matthias Meyer and Christian Ringle Journal of Business Research, 2016, vol. 69, issue 10, 4604-4612 Abstract: 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 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:69:y:2016:i:10:p:4604-4612 DOI: 10.1016/j.jbusres.2016.03.052 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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