Spatial decision support for social hybrid organizations: siting new social supermarkets in Austria

Abstract

PurposeIncreasingly complex societal challenges call for new, innovative solutions that social hybrid business models can provide. Social supermarkets (SSMs) are one example offering access to affordable food to people living in poverty while reducing food waste of nearby retailers. Finding the “right” location is an essential part of this retail marketing strategy. However, limited research has attempted to investigate the specific conditions of locational planning for hybrid and nonprofit retail organizations. This paper illustrates the case of Austria where SSMs are well established.Design/methodology/approachA GIS-based white space analysis was carried out to identify potential neighborhoods or rural areas for new social supermarkets with sufficient nearby demand, supply and no existing SSMs. The empirical parameters for this spatial analysis can be transferred to European countries with similar ecosystems. The authors collected a unique data set of 79 (2014) and 88 SSMs (2019) and 4,665 (2014) and 4,211 (2019) food retailers as (potential) suppliers to SSMs. To determine demand, the authors relied on small-scale integrated wage and income tax data and unemployment rates (2011) from Statistics Austria.FindingsOverall, Austria has very good spatial access to grocery stores, including to SSMs. SSM access increased especially in the capital of Vienna between 2014 and 2019. The GIS-based white space analysis identified several other regions where residents have a high demand for affordable food with sufficient potential suppliers of surplus food but no SSM yet. Neighborhood-level findings are released as part of a publicly accessible spatial decision support system.Originality/valueThe methodology allowed a specific definition of the key areas of relevance by matching the demand for SSMs, calculated as the number of people with low incomes in the respective regions in Austria, with the supply of SSMs, calculated as the amount of potential food loss prevention by nearby retail stores. These parameters have proven to help in identifying the white spaces and therefore can be used in Austria and other European countries with similar ecosystems.