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The demand for and the supply of distribution services: A basis for the analysis of customer satisfaction in retailing

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Abstract

This paper brings together two bodies of literature. One of them is a literature on the special role of the consumer in retailing. The other one is the literature on customer satisfaction. This joining of literatures is accomplished by identifying distribution services as outputs of retail firms and fixed inputs into the production functions of consumers and relaxing the standard assumption that the demand for these services is always equal to the supply of these services. The result is a new conceptual framework for the analysis of customer satisfaction in retailing. This framework extends the basic ideas on customer satisfaction developed for manufacturing in a homogeneous single product setting to the heterogeneous multi-product setting relevant for many retailers. The paper illustrates one approach to the implementation of this framework with data for a set of supermarkets in Pamplona, Spain, that measure distribution services by asking consumers questions explicitly identifying these services. The five main categories of distribution services identified by the conceptual framework and measured in the data are economically important and statistically robust determinants of customer satisfaction with supermarkets. These results are obtained controlling for other variables typical of the customer satisfaction literature and measuring customer satisfaction in a manner consistent with that literature. The results are robust to corrections for sample selection and alternative estimation methods. Perhaps our most interesting novel result is that the effect of the determinants of customer satisfaction on future purchase intentions in the supermarket case is different when measured directly in a one stage process than when measured indirectly in a two stage process through the attributes/satisfaction/ purchase intentions chain.

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Notes

  1. For a discussion of how distribution services affect retail equilibrium configurations see, for example, Betancourt (2004, Chapter 2, Sections 4 and 5).

  2. A supermarket in Pamplona is defined as a self-service establishment, usually between 250 and 2,500 squared meters of surface area, with an assortment predominantly oriented toward food products. The sample of 11 supermarkets was selected from the 18 establishments classified as supermarkets in Pamplona by the 1997 Census. The universe consisted of 5 chains with 14 establishments and 4 single establishment firms. A small and a large establishment, in terms of square feet, was chosen from each of four chains and three of four establishments, also varying in size, were chosen from the four single establishment firms. Customers were interviewed as they exited the supermarket with purchases. Interviews were conducted by a team of five interviewers at each supermarket who conducted all the interviews spread throughout the day for a period of 6 days between March 23rd and April 4th of 1998. The response rates were between 40 and 50% at each supermarket. These rates are typical for supermarket surveys in Pamplona.

  3. Assortment has two dimensions: breadth and depth. The latter is normally associated with different varieties within a product line and the former with different product lines. Thus, breadth can in principle include product lines as different as food and banking services.

  4. For a more detailed description of all variables see the data Appendix.

  5. It turns out there are no observations for this variable that take on the value of 0 (and only one that takes on the value of 1), but there are 161 that take on the value of 10.

  6. When the observations are clustered, the assumption that the observations are independent of each other within the cluster is unlikely to be true. In the estimation of a mean, for example, this issue is incorporated into the analysis by modifying the standard formula for the sample variance of the mean, S 2/n, as follows [S 2/n] [1 + ρ(n c − 1)]. Here ρ is the intra-cluster correlation and n c is the sample size of each cluster. In this simple case ρ and n c have been assumed to be the same for each cluster. In more complex settings these assumptions are relaxed, but the basic idea for constructing variance estimates of regression coefficients to calculate robust t-ratios is the same. We implemented the correction with a widely used econometrics program, STATA, where it has become a standard feature in recent years.

  7. We also tried Tobit analysis in this context and the main results were the same as with OLS.

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Acknowledgement

We thank the participants at the seminars and conferences mentioned earlier for their comments and Ariel Benyishay for excellent research assistance. Special thanks are also due to two referees for very useful comments that considerably improved the paper.

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Correspondence to Roger R. Betancourt.

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Betancourt, R.R., Cortiñas, M., Elorz, M. et al. The demand for and the supply of distribution services: A basis for the analysis of customer satisfaction in retailing. Quant Market Econ 5, 293–312 (2007). https://doi.org/10.1007/s11129-007-9027-3

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