- Title
- The PLS agent: predictive modeling with PLS-SEM and agent-based simulation
- Creator
- Schubring, Sandra; Lorscheid, Iris; Meyer, Matthias; Ringle, Christian M.
- Relation
- Journal of Business Research Vol. 69, p. 4604-4612
- Publisher Link
- http://dx.doi.org/10.1016/j.jbusres.2016.03.052
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2016
- Description
- 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.
- Subject
- partial least squares path modeling; PLS-SEM; agent-based simulation; ABS; predictive modeling; TAM
- Identifier
- http://hdl.handle.net/1959.13/1324311
- Identifier
- uon:25007
- Identifier
- ISSN:0148-2963
- Language
- eng
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