Exploring the Relationship Between Extended 3Ps and Store Choice: Moderating Role of Consumer Demographics

This study is an attempt to bridge the link between factors representing stores’ decision boundaries and consumers’ decision-making criteria. It also explores the impact of consumer demographic variables on the relationship between the influence of extended 3Ps (process, people and physical evidence) and store choice. Around 290 responses were found suitable for further statistical refinements through a structured survey. After carrying out the reliability and validity properties of the data, structure equation modelling was applied. The findings of the study revealed that individual demographics (income, age and gender) moderated the relationship between the extended 3Ps and consumer store choice for shopping (in this study example of shopping smartphone). However, occupation and education were found to have no different influence by separate segments of population. Also, all demographics have an interpretation for marketers, which are discussed in the conclusion section. The framework used in this article opens up new vistas for research on exploring different consumer segments and service decision-making in retail marketing.

Spatial Shopping Behavior in a Multi-Channel Environment: A Discrete Choice Model Approach

Spatial impacts of online shopping are discussed frequently in retail geography. Here, online shopping is mostly regarded as a central driver of competition for physical retailing and its locations, such as town centers or malls. Due to its high popularity, cross-channel shopping is sometimes considered to be a support for physical retailing. However, traditional retail location theory does not consider shopping channels other than in-store shopping. Furthermore, although online shopping is far too important to be neglected in examining consumer spatial shopping behavior, there is an obvious lack in the previous literature towards incorporating multi- and cross-channel shopping into store choice models. The present study aims to identify the main drivers of store choice on the basis that both in-store and online shopping alternatives are available, as well as the opportunity for cross-channel shopping. Taking into account previous literature on both physical store choice and multi-channel shopping, hypotheses on the impact of different shopping transaction costs (such as travel time, delivery charges, or uncertainty with respect to the stores' assortment) were derived. Based on a representative consumer survey, real past shopping decisions in three retail sectors (groceries, consumer electronics [CE], and furniture) were collected. The econometric analysis of empirical store choices was performed using a nested logit model which includes both physical and online stores. The results confirm several assumptions of classical retail location theory as well as previous findings from single-firm studies and stated choice experiments on multi-channel shopping behavior. Travel time to physical stores reduces consumer utility and store choice probability, respectively. Consumer sensitivity towards travel time decreases with decreasing purchase frequency of the desired goods. Delivery charges also decrease the likelihood of choosing a store. The impact of cross-channel integration on store choice (assuming the reduction of consumer transaction costs) is considerably lower than expected and differs between retail sectors. While furniture retailers profit from enabling cross-channel shopping, there is no such competitive advantage found for grocery and CE retailers. The positive effect of assortment on condition of diminishing marginal utility is confirmed for grocery stores and CE stores, but not for furniture stores. From a theoretical perspective, this study shows that multi- and cross-channel shopping behavior does not contradict the main thoughts of classical retail location theory. From a practical perspective, the study is a contribution as store choice models play a significant role in both business location planning and governmental land use planning.

Examining the Influence of Store Environment in Hedonic and Utilitarian Shopping

Much of the literature on the attractiveness and pleasantness of retail stores has focused on the critical influence of store atmosphere or ambient attributes, which influence customer satisfaction and store choice. However, little is known about the environmental cues that influence customers’ satisfaction in different shopping contexts. In this context, the present research aims to answer the following questions: “Are the store atmospheric variables equally relevant in hedonic and utilitarian shopping?”; and further: “Does the influence of store environment on customer satisfaction vary depending on the type of shopping?”. For this purpose an empirical research is developed through PLS Structural Equation Modeling (PLS-SEM) based on data obtained from hedonic (n = 210) and utilitarian (n = 267) shopping contexts. Results indicate that customers perceive differently store atmospherics in utilitarian and in hedonic shopping. More precisely, findings report that customer satisfaction is driven by internal ambient and merchandise layout in hedonic shopping contexts; while the external ambient and the merchandise layout are major atmospheric cues in utilitarian shopping. Interestingly, store crowding does not influence customers’ satisfaction. This study provides a deeper understanding into the specific store attributes that influence customer satisfaction, which could be used by retailers to differentiate themselves from competitors.

Determinant Attributes of Store Choice in Organized Retail

Intense competition and price wars in the retail sector provide the impetus to examine consumer behavior in the retail industry. It has become increasingly important to scrutinize what drives consumer choice of retail stores. This chapter addresses this need and aims to establish the factors that drive consumer retail choice behavior. The empirical setting for the study was five organized Indian retail stores: Easy Day, Big Bazaar, More, Reliance Fresh, and Freshmart. Data were collected with the help of a questionnaire (n=700) in a store-intercept survey. Data were analyzed using multivariate data analysis techniques. A model identifying the variables that predicts retail store choice probabilities was developed and tested. Results establish that atmospherics, merchandise assortment, and customer satisfaction have a significant positive impact on retail store choice. On the other hand, price has a significant negative impact on retail store choice. Implications are discussed.