In the face of the fast emerging modern organised retail industry, will the local kirana survive, evolve and reinvent itself? In this article, Paromita Goswami, Assistant Professor at Xavier Institute of Management Bhubaneswar, along with co-author Dr Mridula S Mishra of IIFT, Delhi, deliberates on the fate of India’s ‘mom & pop’ store – the local kiranas. Goswami presented this paper during the 2009 Asia-Pacific Conference of the Association for Consumer Research (ACR) , hosted at the ISB.
It is a matter of debate as to whether Indian Kirana stores would be able to survive in the face of competition from organised modern trade grocery retailers. Although traditional retail currently constitutes over 95 per cent of the total sales in the country, smaller kiranas that are unable to compete with new age retailers in terms of variety and scale have begun losing volume in several parts of the country (Vijayraghavan and Ramsurya, 2007). Internationally, while some studies suggest that large scale retailers like Wal-Mart are responsible for widespread closings of mom & pop stores (Wal-Mart Watch, 2005; Basker, 2005) and question whether cost to communities in terms of labour displacements and higher poverty is offset against benefits of lower prices and greater convenience (Goetz and Swaminathan, 2006), other studies suggest that the process of creative destruction unleashed by Wal-Mart has had no statistically significant long-run impact on the overall size and profitability of the small business sector in the United States (Sobel and Dean, 2006).
In India, modern trade or organised retailing already account for 30 to 40 percent of grocery sales in the top 6-7 cities of the country (Kakkar, 2008). The Prime Minister’s Office (PMO) of India has initiated a study on the impact of retail giants on small retailers and this move has been welcomed by the Confederation of All India Traders (CAIT).
As pointed out by Sanghavi (2007), so far retailers, who focussed on developing only supply-side efficiencies in terms of reaching retail productivity targets, need to think about demand-side efficiencies in terms of satisfaction of customers’ needs in order to optimise business performance. It is important to identify efficient levels of the various dimensions of satisfaction of customers’ needs that directly link to measures of specific firm outputs that firms intend to maximise in addition to supply side efficiencies (Blose et al, 2005).
In our research, we use two separate Data Envelopment Analyses (DEA) – first is the DEA using CCR method to examine the demand-side efficiency in terms of dimensions of satisfaction of customers’ needs (henceforth referred to as Customer DEA); and second, DEA using both CCR and BCC method (following Barros, 2006) to examine supply side efficiency in terms of retail productivity scores (henceforth called Retailer DEA). We have attempted to deduce the chances of survival of Kiranas by examining the two DEAs. The logic of using separate DEA is that even if an outlet is efficient on retailer productivity scores, if it is inefficient on consumer variables, it is bound to become inefficient on retailer productivity scores in the long-term. Thus, DEA on consumer variables may act as a diagnostic tool before it is too late and affects the efficiency of the outlet on retailer productivity scores as well.
In order to capture the demand-side efficiency, a consumer study was designed and to judge the supply-side efficiency, a study was done on the grocery retail outlet that the respondents of the consumer study patronised. The study was carried across four Indian cities- two major metros (Kolkata and Mumbai), and two smaller cities (Jamshedpur and Nagpur) with around 100 respondents from each city. Four grocery formats relevant for India are kirana and upgraded kirana stores, supermarkets and hypermarkets. From each city 25 customers of each of the four grocery formats were interviewed. Stratified systematic sampling design was followed. The strata were the four formats of grocery stores.
To maintain the systematic design of the sample, every fifth customer leaving the store was intercepted and interviewed with a structured questionnaire. The questionnaire had a set of statements on customer perception of grocery store attributes which had been used previously with tested validity and reliability (Goswami and Mishra, 2007). The retailer questionnaire was administered to the outlet manager and after rejecting incomplete questionnaires and accounting for non-responses we were left with 11 retail outlets (1 hypermarket, 3 supermarkets, 2 upgraded kiranas and 5 kiranas) and a consumer sample size of 237.
The Customer DEA was run on customer variables of cleanliness, offers, quality; helpful and trustworthy salespeople; in-store convenience, location; travel convenience; home shopping; store brands, family grocery shopping, parking facilities as inputs and customer patronage in terms of percentage of grocery shopping as output; and the Retailer DEA was run on retailer variables of store hours per week, number of full-time employees, selling area, average weekly sale, average customer transaction per week as inputs and gross profit, sales per square foot as outputs.
The Customer DEA revealed that of the 11 outlets, 1 supermarket, 1 upgraded kirana and 2 kiranas were efficient. The Retailer DEA revealed that with the exception of 1 supermarket and 1 kirana (this kirana was efficient on customer variables), the rest of the outlets were efficient. The results suggest that while 9 stores (out of 11) had efficient retail DEA scores, they had inefficient customer DEA scores, thus implying that corrective action needs to be taken for these stores for improving demand-side efficiencies, or else they might ultimately become inefficient in retailer scores in future. The store which has inefficient retailer score but efficient customer score needs to specifically look into reasons and take corrective measures as suggested by the DEA.
The stores which have Customer DEA Scores of less than one, should examine their operating procedures in identified areas of grocery store/shopping attributes to identify possible sources of inefficiency. Excess levels of inputs imply some stores are getting more in terms of efficient use of store/shopping attributes. Obviously no store would wish to purposely reduce the perceived levels of customer perception inputs, the adjustments prescribed by the DEA model serve as ordinal indicators of inefficiencies ripe for change (Blose et al, 2005).
We found one out of three supermarkets (0.33), one out of two upgraded kiranas (0.5), and one out of five kiranas (0.2) to be efficient. Upgraded Kiranas, in terms of probability suggested by this study, has maximum chances of survival (this may be attributed to modern trade-like ambience as well as greater customer insight); followed by supermarkets and lastly Kiranas. The results suggest kiranas would do best to try and upgrade in order to survive. Given that modern trade outlets have deeper pockets and can afford to make mistakes and get away with it in the short term, kiranas have to stay alert, try to upgrade and continue to serve customers well, while concentrating on innovating, evolving and remaining efficient on retailer productivity scores.