Yamada Hiroki

In Malaysia, the rapid proliferation of shopping malls has heightened consumer expectations, driving malls to diversify their scale, formats, and characteristics. This study employs block clustering to simultaneously classify consumers (rows) alongside area brand asset factors in Subang Jaya and store image factors at Sunway Pyramid (columns). Compared to traditional methods like K-means, this machine learning technique more efficiently processes complex data interrelationships. The analysis revealed minimal overlap between area-specific and mall-specific factors within the expectation clusters, suggesting a relatively weak correlation between the two. Furthermore, the identified consumer clusters reflect distinct trends in age, transportation, number of shopping companions, and the prioritization of the area versus the mall. These findings offer strategic insights into leveraging area brand assets and store image factors to enhance regional appeal and shopping mall satisfaction.

This study employs hierarchical Bayesian binomial logit models to examine the store image indicators that influence customer satisfaction at competing shopping malls in Malaysia, and whether area attractiveness and brand asset indicators affect shopping mall satisfaction. We construct proposed shopping mall satisfaction and area attractiveness ranking models for quantification with store image indicators and area attractiveness as explanatory variables, and the area attractiveness ranking model uses area brand asset indicators. The areas under assessment are Subang Jaya and Petaling Jaya. The shopping malls examined in this study are Sunway Pyramid, located in Subang Jaya, and 1Utama shopping center, located in Petaling Jaya. As the two areas are adjacent, the malls share a competitive relationship. We also estimate the two models simultaneously, providing measures to enhance shopping mall satisfaction and area attractiveness based on the estimation results of the models.

This study focuses on food supermarkets (hereinafter referred to as “supermarkets”). The objective is to use survey data to elucidate what perspectives (constituent concepts) consumers adopt when evaluating stores, depending on the set of stores under consideration (a single store versus two or more stores). Specifically, using survey data collected from consumers who shop in areas with intense supermarket competition, we will classify responses into those evaluating only one store and those evaluating two or more stores. We will then extract the perspectives (constituent concepts) that consumers prioritize when evaluating stores, and use confirmatory factor analysis to clarify the relationships between these perspectives and the observed variables, as well as the relationships among the constituent concepts themselves. Note that the stores consumers can evaluate are limited to those where they have made a purchase and plan to continue shopping in the future as part of their regular shopping routine. It is common for the set of products that consumers evaluate and consider to change before they reach the stage of purchasing a product. According to Moriguchi and Takemura (2018), the process leading to product selection involves a progression from the “accessible set”—the set of stores consumers can access—to the “known set”—stores consumers are aware of—to the “consideration set”—stores that could be potential choices—to the “selection set”—the final set of candidates—and finally to the “selection” itself. This study focuses specifically on the consideration set and aims to clarify the evaluation structures consumers possess based on differences in the consideration set.

This study aims to reveal the satisfaction structure of department stores located in specific urban regeneration urgent development areas and the degree of influence of the attractiveness of the areas. Specifically, we analyze the effects of store image factors and area attractiveness ranking on department store satisfaction ranking, and the effect of area brand asset factors on area attractiveness ranking simultaneously. We use a hierarchical Bayesian binomial logit model framework, and express the structure of department store satisfaction ranking and area attractiveness ranking for each consumer in the within-individual model. As a result of model estimation, we clarified store image factors that influence store satisfaction ranking, and area brand asset factors that influence area attractiveness ranking. Furthermore, we found that area attractiveness ranking also had a significant impact on store satisfaction ranking, and simultaneous analysis revealed area brand asset factors that can influence store satisfaction ranking. Based on these results, we were able to discuss perspectives on marketing strategies and public relations activities that department storeʼs management should take into account.

In this study, we focus on two competing urban drugstore chains in the Nagoya Station area—Matsumoto Kiyoshi (Matsudo City) and Amano Drug (Nagoya City)—to elucidate the structure of consumer satisfaction for each individual customer through modeling, and to derive insights regarding potential marketing strategies for these drugstores. We adopt a hierarchical Bayesian binary logit model, using the satisfaction ranking of the drugstores as the dependent variable and explaining it with store image components. Furthermore, assuming that the store image components of interest differ for each consumer, we incorporate the variable selection model used by Gilbride, Allenby, and Brazell (2006) in their brand choice model into the individual-level model. These models not only evaluate the magnitude of response parameters that differ by consumer but also capture the structural heterogeneity of the models across consumers. Furthermore, consumer attributes (gender, shopping frequency, and whether or not they had a pre-purchase intention) are used as explanatory variables in the hierarchical model. While Yamada (2023) evaluated the satisfaction structure using cross-sectional data, this study employs panel data to perform more reliable model estimation.