期刊
JOURNAL OF BUSINESS RESEARCH
卷 69, 期 10, 页码 4604-4612出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jbusres.2016.03.052
关键词
Partial least squares path modeling; PLS-SEM; Agent-based simulation; ABS; Predictive modeling; TAM
类别
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. (C) 2016 Elsevier Inc. All rights reserved.
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