Incorporating Individual Differences in Conjoint Analysis: A Structural Equation Modeling Approach

• Published in: Structural equation modeling Vol. 32; no. 2; pp. 332 - 338
• Main Author: Cheng, Chung-Ping
• Format: Journal Article
• Language: English
• Published: Hove Routledge 04.03.2025; Psychology Press
• Subjects: Computer purchase intentions; Individual Characteristics; individual difference; Individual Differences; market segmentation; part-worth; random effect; Structural Equation Models
• ISSN: 1070-5511; 1532-8007
• DOI: 10.1080/10705511.2024.2346909

In the SEMWISE (Structural Equation Modeling for Within-Subject Experiments) framework, traditional conjoint analysis is treated as repeated measurements, which facilitates the incorporation of individual differences through structural equation modeling. This approach allows for the use of goodness-of-fit indices to assess data-model consistency and to test assumptions in conjoint analysis, thus extending the analysis to a broader framework. This paper advocates for the introduction of latent class variables within this framework to conduct market segmentation. An empirical dataset analysis, performed using lavaan and Mplus, demonstrates how to apply SEM to conjoint analysis, effectively leveraging individual differences for market segmentation and associating personal characteristics with market segments. This progression through increasingly complex models also illustrates a workflow for researchers using SEM in conjoint analysis studies.