When there are several possible segmentation variables, marketers must investigate the ramifications of their potential interactions. These include their mutual association, the identification of the best (the distinguished) segmentation variable and its predictability by a set of descriptor variables, and the structure of the multivariate system(s) obtained from the segmentation and descriptor variables. This procedure has been defined as segment congruence analysis (SCA). This study utilizes the information theoretic and the log-linear/logit approaches to address a variety of research questions in segment congruence analysis. It is shown that the information theoretic approach expands the scope of SCA and offers some advantages over traditional methods. Data obtained from a survey conducted by the Bonneville Power Administration (BPA) and Northwest utilities is used to demonstrate the information theoretic and the log-linear/logit approaches and compare these two methods. The survey was designed to obtain information on energy consumption habits, attitudes toward selected energy issues, and the conservation measures utilized by the residents in the Pacific Northwest. The analyses are performed in two distinct phases. Phase I includes assessment of mutual association among segmentation variables and four methods (based on different information theoretic functions) for identifying candidates for the distinguished variable. Phase II addresses the selection and analysis of the distinguished variable. This variable is selected either a priori or by assessment of its predictability from (segmentation or exogenous) descriptor variables. The relations between the distinguished variable and the descriptor variables are further analyzed by examining the predictability issue in greater detail and by evaluating structural models of the multivariate systems. The methodological conclusions of this study are that the information theoretic and log-linear methods have deep similarities. The analyses produced intuitively plausible results. In Phase I, energy related awareness, behavior, perceptions, attitudes, and electricity consumption were identified as candidate segmentation variables. In Phase II, using exogenous descriptor variables, electricity consumption was selected as the distinguished variable. The analysis of this variable indicated that the demographic factors, type of dwelling, and geoclimatic environment are among the most important determinants of electricity consumption.
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-1796 |
| Date | 01 January 1987 |
| Creators | Hosseini-Chaleshtari, Jamshid |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
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