Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity: Abstract

• Published in: Marketing science (Providence, R.I.) R.I.), 1997-01, Vol.16 (1), p.39
• Main Authors: Jedidi, Kamel, Jagpal, Harsharanjeet S, DeSarbo, Wayne S
• Format: Article
• Language: English
• Subjects: Cluster analysis; Consumer behavior; Consumers; Data analysis; Expected values; Market segmentation; Market segments; Marketing; Researchers; Salespeople; Structural equation modeling
• ISSN: 0732-2399; 1526-548X

Two endemic problems face researchers in the social sciences (e.g., Marketing, Economics, Psychology, and Finance): unobserved heterogeneity and measurement error in data. Structural equation modeling is a powerful tool for dealing with these difficulties using a simultaneous equation framework with unobserved constructs and manifest indicators which are error-prone. When estimating structural equation models, however, researchers frequently treat the data as if they were collected from a single population (Muthen 1989). This assumption of homogeneity is often unrealistic. For example, in multidimensional expectancy value models, consumers from different market segments can have different belief structures (Bagozzi 1982). Research in satisfaction suggests that consumer decision processes vary across segments (Day l977). This paper shows that aggregate analysis which ignores heterogeneity in structural equation models produces misleading results and that traditional fit statistics are not useful for detecting unobserved heterogeneity in the data. Furthermore, sequential analyses that first form groups using cluster analysis and then apply mulfigroup structural equation modeling are not satisfactory. We develop a general finite mixture structural equation model that simultaneously treats heterogeneity and forms market segments in the context of a specified model structure where all the observed variables are measured with error. The model is considerably more general than cluster analysis, multigroup confirmatory factor analysis, and multigroup structural equation modeling. In particular, the model subsumes several specialized models including finite mixture simultaneous equation models, finite mixture confirmatory factor analysis, and finite mixture second-order factor analysis. The finite mixture structural equation model should be of interest to academics in a wide range of disciplines (e.g., Consumer Behavior, Marketing, Economics, Finance, Psychology, and Sociology) where unobserved heterogeneity and measurement error are problematic. In addition, the model should be of interest to market researchers and product managers for two reasons. First, the model allows the manager to perform response-based segmentation using a consumer decision process model, while explicitly allowing for both measurement and structural error. Second, the model allows managers to detect unobserved moderating factors which account for heterogeneity. Once managers have identified