Hamza Larosi , Pauline van den Berg (p), Theo Arentze

Graham Clarke

Staging experiences for consumers is considered crucial by retailers for the creation of economic value. Previous studies have indicated an important relationship between individual’s emotional responses to a retail setting and consumer behaviour, such as time- and money expenditures. Considering the important role of emotions in shopping behaviour, and the emotion-eliciting qualities of retail environments, it is hypothesized that a consumer’s experience of a sense of place might play an important role in consumer shopping behaviour. However, the relationship between sense of place and shopping behaviour within shopping areas has not yet been fully explored in current literature. The main objective of this research is therefore to examine the role of sense of place in mediating the relationship between shopping area appreciation and shopping behaviour. In this study, individual characteristics, situational characteristics, and personality traits of consumers are hypothesized to moderate the relationship between shopping area appreciation and sense of place. Additionally, these characteristics might also moderate the relationship between a consumer’s experience of a sense of place and shopping behaviour.

For this study survey data is collected among consumers at three shopping areas in the Amsterdam region. During a time-period of three weeks, 380 shoppers have completed the survey. Preliminary analyses of the data include bivariate analyses, mediation analysis and moderation analysis using multiple linear regression models. The first results indicate that shopping area characteristics indirectly influence consumer shopping behaviour by stimulating the experience of a sense of place. The extent to which consumers experience a sense of place positively stimulates time expenditures, money expenditures, store visits, use of food- & drink options, and social contacts. These findings regarding shopping behaviour hint towards an important role for the concept of sense of place in shopping behaviour.
The final paper will discuss findings of a path analysis in which these preliminary analyses will be integrated. The paper will discuss the implications of these findings and recommendations for shopping area management.

Ryan Urquhart , Andy Newing (p), Nick Hood

Pauline Van Den Berg

The grocery sector is at the forefront of applied spatial modelling – traditionally capturing predictable and habitual consumer behaviours/interactions (consumer travel to store). The nature of these interactions between demand and supply have changed following the introduction of e-commerce. Grocery sector e-commerce operations have resulted in supply side investment (e.g. dark stores, click and collect facilities, home delivery infrastructure etc.).

The impacts of these supply side investments have not been evenly distributed across the demand side, particularly the choice, availability and quality of groceries home deliveries. Retailers determine their own ‘service areas’ for grocery e-commerce home deliveries, with some neighbourhoods falling within the delivery area of a number of retailers (generating both choice and competition), whilst consumers in other neighbourhoods have a limited choice (or in some cases no availability) of grocery retailers offering e-commerce home deliveries.

Picking and packing arrangements (including store based vs ‘dark store’) and infrastructural capacity determine delivery slot availability, resulting in differences in the ‘availability’ and ‘quality’ of e-commerce home delivery services at a small-area level. This is akin to the ‘food deserts’ debate which was prominent in urban geography in the early 2000s – albeit ‘e-food deserts’ are based on the ‘accessibility’ of e-commerce delivery services, rather than physical stores.

We report on ongoing research which aims to capture the availability of e-commerce grocery home delivery services at a neighbourhood level. We link the supply side to the underlying demand side to assess the presence of and characteristics of ‘e-food deserts’ (areas which are not effectively served by groceries home delivery).

We also unpick some of the complexities in assessing the presence of ‘e-food deserts’, taking account of slot availability (the ability for customers to actually obtain a delivery slot) and cost of delivery, both of which are barriers to accessing e-commerce groceries home delivery services.

Helena Nilsson (p)

Andy Newing

see extended abstract

Graham Clarke (p), Tom Waddington, Andy Newing, Martin Clarke

Helena Nilsson

This paper develops and calibrates a spatial interaction model (SIM) incorporating additional temporal characteristics of consumer demand for the UK grocery market. SIMs have been routinely used by the retail sector for location modelling and revenue prediction and have a good record of success, especially in the supermarket/hypermarket sector. However, greater planning controls and a more competitive trading environment in the grocery market of many countries in recent years has forced retailers to look to new markets. This has meant a greater focus on the convenience market which creates new challenges for retail location models. In this paper we present a custom built SIM for the grocery market in West Yorkshire incorporating trading and consumer data provided by a major UK retailer. We show that this model works well for supermarkets and hypermarkets but poorly for convenience stores. We then build a series of new demand layers taking into account the spatial distributions of demand at the time of day that consumers are likely to use grocery stores. These new demand layers include workplace populations, university student populations and secondary school children. When these demand layers are added to the models we see a very promising increase in the accuracy of the revenue forecasts. This is especially true for convenience stores which are most susceptible to localised temporal demand fluctuations. We show the model is useful for what-if analysis, demonstrating how the new demand layers can be used to inform future store revenues and how the university holiday period impacts on store revenues and trading characteristics.