Organizational Design and Control across Multiple Markets: The Case of Franchising in the Convenience Store Industry

Dennis  Campbell; Srikant M. Datar; Tatiana  Sandino

Article | Accounting Review | November 2009

Organizational Design and Control across Multiple Markets: The Case of Franchising in the Convenience Store Industry

by Dennis Campbell, Srikant M. Datar and Tatiana Sandino

Abstract

Many companies operate units that are dispersed across different types of markets, and thus serve significantly diverging customer bases. Such market-type dispersion is likely to compromise the headquarter's ability to control its local managers' behavior and satisfy the divergent needs of different types of customers. In this paper we find evidence that market-type dispersion is an important determinant of delegation and the provision of incentives. Using a sample of convenience store chains, we show that market-type dispersion is related to the degree of franchising at the chain level as well as the probability of franchising a given store within a chain. Our results are robust to alternative definitions of market-type dispersion and to other determinants of franchising such as the stores' geographic distance from headquarters and geographic dispersion. Additional analyses also suggest that chains that do not franchise at all may cope with market-type dispersion by decentralizing operations from headquarters to their stores, and, to a weaker extent, by providing higher variable pay to their store managers.

Keywords: market dispersion; Decentralization; incentives; Business Headquarters; Geographic Location; Governance Controls; Distribution; Organizational Design; Franchise Ownership; Retail Industry;

Format: Print

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Citation:

Campbell, Dennis, Srikant M. Datar, and Tatiana Sandino. "Organizational Design and Control across Multiple Markets: The Case of Franchising in the Convenience Store Industry." Accounting Review 84, no. 6 (November 2009): 1749–1779.

This case follows Bill Booth, marketing manager of a regional wine distributor, as he applies unsupervised learning on data about his customers’ purchases to better understand their preferences. Specifically, he uses the K-means clustering technique to identify groups of customers who have purchased any number of 32 specific “deals” Booth offered over the year, differentiated by the wine varietal as well as its country of origin and a minimum number of bottles to purchase. Insights from this analysis may help him understand themes across the deals that can inform construction of new deals in the future.

Topics include unsupervised learning; similarity and proximity; K-means clustering, with measures of Euclidean distance and cosine similarity; Gaussian mixture models; and interpreting clusters.

This case builds directly on “Chateau Winery (A).” In this case, Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those customers closest to them.

Topics include supervised learning; collaborative filtering; K-nearest neighbor as a modeling technique, collaborative filtering with cosine similarity of customers, collaborative filtering with cosine similarity of products, and comparison of different prediction models.

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Organizational Design and Control across Multiple Markets: The Case of Franchising in the Convenience Store Industry

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