Australia's highly concentrated food retail environment is examined in the context of competitive entry in a small city. Based on a conceptual framework that draws on existing literature, food stores' customers' share of wallet (SOW) is measured in a survey (n=379) which brackets the opening of a new supermarket. A number of variables are recorded in the survey that are not available from other data collection methods. The drivers of SOW are determined using a 2-limit Tobit model which incorporates the direct and interactive aspects of the pathways identified in the Conceptual Framework. At one of the stores (Woolworths), the influence of loyalty schemes is found to vary with customers' perceptions of stores, with implications for enhanced customer targeting by food retail managers. The impact of loyalty programs is found to be mitigated by the entry of a competitor, particularly in the case of price-conscious customers. Senior citizens are found to allocate higher SOW to small rather than large stores, and there are small effects due to the sex of the customer. There are few indications of a bespoke small city model of the drivers of SOW, but a number of interactions are identified for future research.

Empirical studies of customers' response to their favored brands' being out of stock (OOS) have focused on customers' characteristics and have been almost exclusively conducted in metropolitan areas of Europe and North America. Less is known about the effect of new retail market entry on customers' OOS response and the associated implications for retailers and manufacturers in the context of small regional centers and cities. This paper investigates customers' response to OOS in the context of a small Australian city experiencing market entry by a new supermarket. A Multinomial Logit model is used to analyze the primary survey data from 378 food shoppers. Prices for a selection of food items were also tracked for the relevant period. Our results show that changing competitive environment is likely to be a driver of customers’ switching store in response to OOS. Overall, most of the influential variables are positively associated with switching stores rather than switching brands, and this indicates that OOS is more costly for retailers than for manufacturers.