Spatial Shopping Behaviour Research Articles

Spatial shopping behavior during the Corona pandemic: insights from a micro-econometric store choice model for consumer electronics and furniture retailing in Germany

During the COVID-19 pandemic, e-commerce’s market share has increased dramatically, a phenomenon attributable to not only lockdowns but to voluntary changes in shopping behavior as well. The current study examines the main determinants driving shopping behavior in the context of both physical and online store availability, and investigates whether specific drivers have changed during the pandemic. The study aims to test whether fear of infection and mandatory wearing of face masks in shops have influenced consumer channel choice. The empirical analysis focuses on two product types (consumer electronics, furniture), with empirical data collected via a representative consumer survey in three German regions. The statistical analysis was performed using a hurdle model approach and the findings are compared to those of a study related to pre-pandemic shopping. The results show that the determinants of shopping behavior have largely not changed. Channel choice can be explained by shopping attitudes, age, and partially, by place of residence of consumers. Store choice is determined primarily by shopping transaction costs and store features. Fear of infection and the mandatory wearing of face masks exhibit minimal influence on channel choice, if any. The importance of cross-channel integration of stores/chains has decreased significantly, while average travel times for in-store purchases have declined.

A Micro‐Econometric Store Choice Model Incorporating Multi‐ and Omni‐Channel Shopping: The Case of Furniture Retailing in Germany

Online retailing and multi‐/omni‐channel shopping are gaining in importance. However, there is a significant lack of research focused on incorporating online shopping into models of spatial shopping behavior. The present study aims (1) to construct a store choice model which includes both physical and online stores as well as the opportunity for omni‐channel shopping, and (2) to identify the main drivers of spatial shopping behavior given the availability of both channels. Based on a representative survey, this study employs a revealed‐preference approach toward store choice and expenditures in furniture retailing. The statistical analysis is performed using a hurdle model approach, with the expenditures of individual consumers at (online or physical) furniture stores serving as the dependent variable. Results show that channel choice (online vs. offline) is mainly influenced by psychographic characteristics, place of residence, and age of the consumers. Store choice and expenditures are primarily explained by store features such as assortment size, omni‐channel integration, and accessibility. This study demonstrates that e‐shopping can be integrated into a store choice model and that both the modeling approach and the subsequent findings are of significance for retail companies and spatial planning.

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.

Auf dem Weg zur digitalen Nahversorgung? Determinanten des Einkaufsverhaltens im Multi- und Cross-Channel-Kontext am Fallbeispiel des Lebensmitteleinzelhandels

Multi- and cross-channel integration of German grocery retailing appears in terms of online-based delivery services and click-and-collect offers by established supermarkets. However, these trends raise the question whether digital services may complement established distribution channels, especially with respect to local supply. This paper describes the investigation of the main determinants of spatial shopping behaviour in the food retail sector. In particular, it was examined whether grocery stores can benefit from the clickand-collect option and which factors determine the choice of shopping location if both stationary and online providers are available. Individual store choices and the related expenditures at grocery retailers are investigated using a micro-econometric store choice model (hurdle model) based on a representative customer survey in two German regions (South Lower Saxony, Region Middle Upper Rhine). Pure grocery online retailing is found to have little relevance, whilst supermarkets gain little profit from offering the click-and-collect option. The likelihood of buying groceries online can be explained by psychographic characteristics of the consumers. In a situation where consumers may choose between offline and online retailers, channel-specific transaction costs (driving time, shipping charges) can be identified as important determinants of store choice.

Identifying the Determinants of Store Choice in a Multi-Channel Environment: A Hurdle Model Approach

Although the established perspective of retail geography is the consumers’ store choice, surprisingly, there is a lack in the investigation of spatial shopping behavior, given the opportunity of online shopping alternatively. The present study attempts to fill this gap by investigating store choice and expenditure flows incorporating both physical and online stores. Based on a representative survey in two German regions, the study employs a revealed-preference approach toward store choice and expenditures taking consumer electronics (CE) retailing as an example. For the analysis of individual shopping behavior, a micro-econometric model, the hurdle model, is applied. Results indicate that channel choice is mainly influenced by spatial, socio-demographic, and psychographic characteristics of the consumers, whilst channel- and store-specific transaction costs – including travel time, delivery time and delivery charges – are main drivers of store choice and expenditures. Furthermore, the cross-channel integration of multi-channel retailers increases the likelihood of such stores being chosen, although this cannot be confirmed for stores providing “click and collect”. It is shown that e-shopping can be integrated into a store choice model, which was originally designed for physical shopping locations only. Both the modeling approach and the results are of significance for retail companies and spatial planning.

A Hurdle Model Approach of Store Choice and Market Area Analysis in Grocery Retailing

Store choice models play an important role in applied geography, especially in location analysis and retail impact assessment. Econometric store choice models enable the analysis of variables influencing store choice based on observed shopping behavior. Using these models—such as the multiplicative competitive interaction model or the discrete choice model—faces several methodical and conceptual problems limiting their explanatory power and applicability. Addressing these problems, a new approach of store choice analysis is developed, based on a generalized linear model for truncated data, the hurdle model. This approach is applied to grocery stores in Karlsruhe, based on empirical data from a customer survey. Three main insights can be gained: (1) The explanatory performance of the model is superior to the common models as it allows decomposing spatial shopping behavior into a store choice and the intensity of customer–store interactions, such as shopping trips or expenditures. (2) The hurdle approach allows for dealing with a large number of empirical customer–store interactions equal to zero, which is an important statistical feature. (3) The model results confirm previous findings; for example, that spatial shopping behavior in grocery retailing is mainly affected by the accessibility and size of the stores, which proves the approach’s plausibility.

Assessment of model uncertainty in destinations and travel forecasts of models of complex spatial shopping behaviour

This paper reports the results of an analysis of model uncertainty in the context of shopping behaviour. This analysis is positioned in a discussion on the evolution of models of spatial shopping behaviour. For different generations of shopping models, the potential of uncertainty analysis is discussed. The actual analysis is based on an application of the Albatross model to the city of Rotterdam, The Netherlands. Characteristic of this model is that shopping trips are embedded in the prediction of daily more comprehensive activity-travel patterns, adding complexity and realism to traditional models of spatial shopping behaviour, such as the Huff and spatial interaction model. Results show that overall the uncertainty associated with the aggregate outcomes of the model is small. It is higher for the total number of visits to selected shopping areas.

Book Review: Muhammad Faishal Ibrahim and Peter J. Goldrick: Shopping Choices with Public Transport Options

This book has been motivated by the observation that by and large studies of spatial shopping behaviour have not taken into account transport mode, while most transport studies ignored the attributes of shopping centres. The study, described in this book represents an attempt to bridge this gap. To that effect, the authors try to identify and analyse the importance of different factors, which are taken into account by shoppers in their shopping centre choice in regions with a wide choice of public transport. In addition, they analyse shoppers' perceptions of transport modes in the context of shopping trips. They start however with a review of trends of retail decentralisation and transport planning.

Deriving performance indicators from models of multipurpose shopping behavior

Multinomial logit models have been widely used for predicting and analyzing spatial shopping behavior of consumers. The models typically used in applied settings do not account for impacts of multipurpose shopping behavior on retail system performance. This study focuses on a recent multipurpose shopping model that has favorable properties for use in applied settings. The purpose of the study is to derive indicators of center performance and travel demands from this model. An application shows how the multipurpose model can be estimated based on survey data and which information can be derived from it. Comparison with a conventional single-purpose shopping model indicates that spatial agglomeration impacts as predicted by the model can have substantial meaning for retail impact studies. Therefore, it is concluded that the more complicated models have the potential of improving information in retail planning and analysis.

Longitudinal study on consumer spatial shopping behaviour with special reference to out-of-town shopping: Experiences from Turku, Finland

Out-of-town shopping centres are seen as a threat both to city centre retailing and to small local shops. The gradual development of an edge-of-town retail park made it possible to carry out a longitudinal study of changes in consumers' shopping behaviour in a situation characterized by a considerable growth in the supply side. The study attempts to analyse the usage of different shopping areas and the changes in choice frequencies of different types of store location (city-centre shopping mall, edge-of-town retail park and local stores). The results show that the consumers changed their shopping habits less than expected.

Choice Experiments versus Revealed Choice Models: A Before-After Study of Consumer Spatial Shopping Behavior

The purpose of this article is to compare a set of multinomial logit models derived from revealed choice data and a decompositional choice model derived from experimental data in terms of predictive success in the context of consumer spatial shopping behavior. Data on consumer shopping choice behavior as collected before the opening of a new major clothing store in a shopping center were used to estimate the parameters of the various models. The estimated parameters were then used to predict market shares of the shopping centers after the opening of the new store. Predicted shares were then compared with data on actual behavior collected after the opening of the new store. Results indicate that the two modelling approaches perform almost equally well.

SPATIOTEMPORAL SEQUENCING PROCESSES OF PEDESTRIANS IN URBAN RETAIL ENVIRONMENTS

ABSTRACT In this article, the authors determine the extent to which several decision heuristics are present in pedestrians’spatial shopping behaviour in downtown retail environments. Two factors, length of the observed route and observed sequence in which destinations are visited, were used to define temporal heuristics. Further, two spatial heuristics were investigated: the tendency of some pedestrians to choose first the destination farthest away from the point they entered the city center and the tendency of others to choose first the destination closest to their entry point. Empirical analysis demonstrated that only a small proportion of the pedestrians reveals optimal choice behavior.

Spatiotemporal sequencing processes of pedestrians in urban retail environments

In this article, the authors determine the extent to which several decision heuristics are present in pedestrians' spatial shopping behavior in downtown retail environments. Two factors, length of the observed route and observed sequence in which destinations are visited, were used to define temporal heuristics. Further, two spatial heuristics were investigated: the tendency of some pedestrians to choose first the destination farthest away from the point they entered the city center and the tendency of others to choose first the destination closest to their entry point. Empirical analysis demonstrated that only a small proportion of the pedestrians reveals optimal choice behavior.

The Spatial Transferability of a Decompositional Multiattribute Preference Model

An empirical test is made of the spatial transferability of a decompositional multiattribute preference model, in the context of spatial shopping behaviour. The model which is estimated for the city of Maastricht is used to predict shopping patterns in a part of the city of Eindhoven. The results indicate that the goodness of fit of the model reduces only slightly. This supports the assumption that decompositional preference models may be used to uncover utility or preference functions which are independent of spatial structure.

Temporal influences upon spatial shopping behavior of consumers

Consumers invest time and money in their shopping activities and they derive pleasure as well as goods from shopping. Consumer selection of retail stores reflects their perceptions of prices, distances, the time required to complete the desired purchases and the extent to which they enjoy shopping itself. This paper develops a model which incorporates these phenomena. The model implies that stores selling the same product may offer shopping experiences which appeal to customer groups with differing time needs. As a result, lower order stores may successfully locate adjacent to higher order stores because consumers exhibit different attitudes to time.

Consumer Behavior and Spatial Cognition in Relation to the Extraversion-Introversion Dimension of Personality

Summary The present exploratory study examined whether the spatial cognitive levels and overt shopping behavior of different personality groups of consumers exhibited marked variations. Data were collected by questionnaire from 74 Ss, 46 from high- and 28 from low-income households. Statistics on the total number of retail centers used (spatial behavior) and personally known by consumers (spatial cognition) were obtained. Eysenck's short questionnaire for the assessment of personality was employed to classify consumers by degree of extraversion-introversion (E). In this study, however, the relationship hypothesized between E and consumer behavior-cognition was not substantiated and no statistically significant association was found to exist between spatial consumer behavior-cognition and degree of extraversion, either for the entire sample or for social divisions of it. The analysis serves, in fact, to reaffirm the suggestion that it is the social-class dimension that dominates variations in urban consume...

Decision-Making Experiments and Real-World Choice Behaviour

This article is concerned with the analysis of consumer spatial choice behaviour using conjoint measurements. The study's objectives are to assess the appropriateness of conjoint measurement to represent a consumer's spatial decision making process, to identify the nature of aggregate utility functions in the context of spatial shopping behaviour, to investigate whether systematic relationships can be found between the nature of the utility functions of the subjects and their socioeconomic background, and finally, to investigate whether utility values obtained under experimental conditions are systematically related to real world choice behaviour. The findings of the study suggest that conjoint measurement is a potentially useful approach to the study of consumer spatial decision-making. Conjoint measurement models provided a good description of individual decision making processes under experimental conditions and the inferred utility functions appeared to be systematically related to real world choice behaviour. The analysis also showed that subjects could be segmented on the basis of the nature of their utility function but no significant associations between the segmentations and a set of socio-economic background variables were found. The article ends with a discussion of some unresolved questions in the application of conjoint measurement in spatial analysis.

Behavioural Models and Spatial Planning: Some Methodological Considerations and Empirical Tests

This paper is concerned with the relevance of various geographic models of spatial shopping and residential choice behaviour to physical planning. It is argued that models relying on areal aggregation and overt spatial interaction patterns generally do not satisfy a number of methodological requirements considered extremely relevant in an applied context. In addition, it is argued that behavioural models provide a potentially more valuable approach for predicting consumer response to policy decisions with regard to spatial structure. Empirical evidence substantiating the claim that consumer evaluations bear some systematic relationship with objective attributes of spatial alternatives and overt choice behaviour is provided in the context of spatial shopping behaviour and residential choice behaviour.

Multiattribute Shopping Models and Ridge Regression Analysis

Policy decisions regarding retailing facilities essentially involve multiple attributes of shopping centres. If mathematical shopping models are to contribute to these decision processes, their structure should reflect the multiattribute character of retailing planning. Examination of existing models shows that most operational shopping models include only two policy variables. A serious problem in the calibration of the existing multiattribute shopping models is that of multicollinearity arising from the fact that strong linear relationships among policy variables frequently occur in real world situations. This paper points at the technique of ridge regression analysis to overcome the problem of multicollinearity in the development of multiattribute shopping models. The use of ridge regression analysis is illustrated in an application of the multiplicative competitive interaction model to spatial shopping behaviour.