This study was designed to research factors which consumers consider when choosing a shopping center and to develop a suggestion model for shopping center choice. First a questionnaire about choosing a shopping center was formed. Then the questionnaire was given to 300 randomly chosen consumers and collected on the next day. Two hundred and sixty-two fully filled out questionnaires were analyzed. Besides demographic questions, effective factors determining people's shopping center choice were asked for 17 items. Four models related to latent variables assumed to affect choice of a shopping center were tested by using the LISREL computer program with Structural Equation Modeling (SEM). Features of materials sold and geographical location of shopping center play very important roles in choice of a shopping center.
Keywords: shopping center choice, structural equation modeling, latent variables.
Structural Equation Modeling (SEM) is a comprehensive statistical method used in testing hypotheses about causal relationships among observed and unobserved (latent) variables and has proved useful in solving problems in formulating theoretical constructions (Reisinger & Turner, 1999). Its function has been found to be better than those of other multivariate statistics techniques including multiple regression, path analysis and factor analysis. Other statistics techniques could not take into consideration the interaction effects among dependent and independent variables. Therefore, a method that can examine a series of dependence relationships simultaneously is helpful in addressing complicated managerial and behavioral issues. SEM also can expand the explanatory ability and statistical efficiency for model testing with a single comprehensive method (Pang, 1996).
Steenkamp and Baumgartner (2000) reflect on the role of SEM in marketing modelling and managerial decision making, and discuss some of its benefits. They say that although SEM has potential for decision support modeling, it is probably most useful for theory testing, which is a key phase in developing marketing models (For SEM and LISREL see Byrne, 1998; Cheng, 2001; Cudeck, Toit, & Sörbom, 2000; Hayduk, 1987; Jöreskog & Sörbom, 2001).
Applied to data on attitudes, perceptions, stated behavioral intentions, and actual behavior, SEM can be used to specify and test alternative causal hypotheses. It has been found that, as might be expected, causality is often mutual. The assumption that behavior is influenced by attitudes, perceptions, and behavioral intentions without feedback does not hold up when tested using SEM. These results challenge the assumption, held by some, that stated - preference choices can be directly scaled into revealed-preference choice models. Path analysis was used to demonstrate empirical evidence that the causal link from choice behavior to attitudes is stronger than the link from attitudes to choice behavior. Subsequent studies using different forms of simultaneous equation modeling showed consistently that attitudes, especially perceptions, are conditioned by choices, while at the same time, attitudes affect choices (Golob, 2001b). Gärung, Fujii, and Boe (2001) explored decision making involving driving choices by using an SEM with latent variables to test links among attitude towards driving, frequency of choice of driving, and revealed presence of a certain type of decision process known as script-based. Golob (2001a) tested a series of joint models of attitude and behavior to explain how both mode choice and attitudes regarding a combined household variables and toll facility (hot lanes) differ across the population. Applying Weighted Least Squares (WLS) estimation to a data set from San Diego, California, the author demonstrates that choices appear to influence some opinions and perceptions, but other opinions and perceptions are independent of behavior and dependent only upon exogenous personal and household variables. …