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Introduction to the special section: Economic perspectives on retailing
Introduction to the Special Section: Economic Perspectives on Retailing BRIAN T. RATCHFORD State University of New York @ Buffalo
While the Journal of Retailing has always published articles employing the tools of economics and management science, the belief that these approaches to research still have considerable untapped potential for solving a wide variety of retailing problems led to the launching of this special section. It is hoped that the body of new work presented in this issue will generate fresh ideas into important retailing problems and stimulate further study. The articles published in this section clearly attain the objective of providing new insights. This great potential for stimulating both practical applications and further research provides a strong rationale for reading them carefully. The articles cover a wide range of topics, including shelf space, assortment and inventory management; the division of power between retailers and suppliers; provision of sales personnel; productivity comparisons between retail outlets. Each of these topics is an established research area, of importance to both researchers and practitioners, and each article adds something of value to all groups. That such a variety of important problems can be addressed with a methodology that stresses the mathematical modeling of economic variables attests to the value of this approach to researchers in retailing. To introduce them, I will briefly discuss each article below. In the first article, Urban (1998) begins by pointing out a stream of research on product assortment and shelf space allocation has developed in the marketing literature, while an independent stream of research on inventory management has evolved in the operations management literature. The problem is that assortment, shelf space and inventory decisions are interrelated, and that failure to consider this could create misleading results. Urban takes on the daunting task of developing a model that integrates these decisions. Key features of his approach are a distinction between backroom and display inventory, modeling demand as a function of displayed inventory, and considering the possibility of customers
Brian T. Ratchford,Departmentof Marketing,215EJacobsManagementCenter, Schoolof Management,State UniversityofNewYorkat Buffalo,Buffalo,NewYork 14260-4000,716-645-3211. Journal of Retailing, Volume 74(1), pp. 11-14, ISSN: 0022-4359 Copyright © 1998 by New York University. All rights of reproduction in any form reserved.
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Journal of Retailing Vol. 74, No. 1 1998
switching to another item when stockouts occur. In this context, Urban shows that a policy of keeping shelves fully stocked ("full shelf merchandising") can be quite far from optimal. Urban proposes two heuristics, a greedy algorithm and a genetic algorithm, for simultaneously solving the assortment, shelf space and inventory problems. Since test problems provided encouraging results, development of a real-world application of Urban's approach would seem to be a logical next step. The research itself can be considered a first pass at a difficult problem, and other researchers may be able to find ways to refine the approach. Given the increasing size of retailers, and the increased importance of store brands in many markets, the distribution of power between manufacturers and retailers has become an important topic. In the second paper, Betancourt and Gautschi (1998) study the distribution of power. They measure this construct as the ratio of retailer to manufacturer profit margins, between a manufacturer and a retailer who both have monopoly power. They consider a case in which the manufacturer exercises price leadership. The standard solution to this problem contains an implicit assumption that services provided by channel members, such as advertising, sales support, return policy, are invariant to the price charged by the manufacturer. Betancourt and Gautschi investigate what happens when this assumption is relaxed. If final demand is responsive to retail services, and if retailers can vary the level of their services, the authors show that the distribution of power shifts in favor of retailers. In effect, service provides the retailer with a weapon to counter the manufacturer's monopoly on the good. The manufacturer becomes willing to accept a lower price in return for the increased sales that are induced by a higher level of retail service. When the manufacturer rather than retailer provides the service, the authors show that a similar advantage does not accrue to the manufacturer. They further show that vertical integration still leads to higher channel profits when one party provides a service. However, they also show that more services may be forthcoming, and the consumer may be better off, when the channel members are independent. In effect the provider of the service (retailer or manufacturer) may compensate for the "too high" price created by double marginalization by offering "too much" service. These results have at least two testable implications. One is that retailers should have more power when the service elasticity of demand is high relative to the price elasticity and when the provision of services is more responsive to changes in wholesale price. For example, this might imply that specialty retailers should capture a higher share of profits than discount retailers. The other testable implication is that independent channel members should offer higher prices and higher levels of service than their integrated counterparts. A general implication of the results is that a retailer gains leverage over a manufacturer by successfully providing services that are in demand by consumers. A good topic for future research would be to extend the analysis of provision of services to the case of competing manufacturers and retailers using a framework similar to that employed by Choi (1996). In the third paper, Lam, Vandenbosch and Pearce (1998) point out that existing labor staffing models do not adequately account for the effect of the availability of sales clerks on sales. To remedy this, they develop a 2-equation model, with one equation representing sales as a function of store traffic and staffing, another representing traffic as a function of temporal patterns. Using traffic count data obtained from electronic counters, and the corresponding sales and staffing data, the authors estimate their equations on hourly data.
Introduction
13
They develop forecasts of traffic and, based on the forecasts and the estimated responsiveness of sales to traffic and staffing, they are able to solve for optimal (profit maximizing) staffing levels. It should be possible to apply the authors' basic approach in other settings, and to incorporate their model into computerized staffing programs. Their model might also be extended in a number of directions, including incorporating promotional activity into the model, developing improved methods for forecasting traffic, and developing dynamic optimization procedures to incorporate possible carry over effects of current staffing on future sales. In the final article of the special section, Donthu and Yoo (1998) provide an exposition of data envelopment analysis (DEA) as a tool for assessing store-level retail productivity. DEA is essentially a technique for estimating the efficiency of a unit, such as a store, relative to the most efficient unit. While DEA has been around for some time, the current application to retail stores appears to be unique (for an application to branch banks, which have many similarities to retail stores, see Kamakura, Lenartowicz, and Ratchford, 1996). What makes DEA particularly attractive for the purpose of comparing outlets is that it allows comparison of quantities of outputs to quantities of inputs, thus eliminating the effect of prices on comparisons of stores. Donthu and Yoo provide an example of the application of DEA to a chain of fast food restaurants. This provides a good example of the strengths and limitations of the technique and of how it may be applied, which should be of interest to those who might apply the technique in practice. While the authors argue for the choice of DEA over other techniques for comparing the productivity of outlets, other research suggests that the superiority of DEA is questionable (see, for example, Kamakura, Ratchford, and Lenartowicz). It would be helpful to have more research comparing alternative approaches to measuring the relative productivity of outlets. In closing, I would like to offer a word of appreciation to former editor, Charles Ingene for his efforts in developing the idea for the special publication and nurturing it through its early stages; to the reviewers, who provided some of the best reviews I have seen and were instrumental in markedly improving the articles; and to the authors of the articles in this issue for their efforts at responding to the criticisms of the reviewers. Special thanks go to the current editor who spearheaded a major effort to get this issue ready on time. I discovered that one of the benefits of being an editor is that one is forced to become thoroughly familiar with the articles being published under your guidance. I learned a great deal from the articles in this special issue and believe that readers who attend to them will do the same.
REFERENCES Betancourt, Roger and David Gautschi. (1998). "Distribution Services and Economic Power in the Channel," Journal of Retailing, 74(1):. Choi, S. Chan. (1996). "Price Competition in a Duopoly Common Retailer Channel," Journal of Retailing, 72(2): 117-134.
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Journal of Retailing Vol. 74, No. 1 1998
Donthu, Naveen and Boonghee Yoo. (1998). "Retail Productivity Assessment using Data Envelopment Analysis," Journal of Retailing, 74( 1):. Kamakura, Wagner, Thomas Lenartowicz, and Brian T. Ratchford. (1996). "Productivity Assessment of Multiple Retail Outlets," Journal of Retailing, 72(4): 333-356. Lam, Shunyin, Mark Vandenbosch, and Michael Pearce. (1998). "Retail Sales Force Scheduling Based on Store Traffic Forecasting," Journal of Retailing, 74(1):. Urban, Timothy L. (1998). "An Inventory-Theoretic Approach to Product Assortment and Shelf Space Allocation," Journal of Retailing, 74(1):.
While the Journal of Retailing has always published articles employing the tools of economics and management science, the belief that these approaches to research still have considerable untapped potential for solving a wide variety of retailing problems led to the launching of this special section. It is hoped that the body of new work presented in this issue will generate fresh ideas into important retailing problems and stimulate further study. The articles published in this section clearly attain the objective of providing new insights. This great potential for stimulating both practical applications and further research provides a strong rationale for reading them carefully. The articles cover a wide range of topics, including shelf space, assortment and inventory management; the division of power between retailers and suppliers; provision of sales personnel; productivity comparisons between retail outlets. Each of these topics is an established research area, of importance to both researchers and practitioners, and each article adds something of value to all groups. That such a variety of important problems can be addressed with a methodology that stresses the mathematical modeling of economic variables attests to the value of this approach to researchers in retailing. To introduce them, I will briefly discuss each article below. In the first article, Urban (1998) begins by pointing out a stream of research on product assortment and shelf space allocation has developed in the marketing literature, while an independent stream of research on inventory management has evolved in the operations management literature. The problem is that assortment, shelf space and inventory decisions are interrelated, and that failure to consider this could create misleading results. Urban takes on the daunting task of developing a model that integrates these decisions. Key features of his approach are a distinction between backroom and display inventory, modeling demand as a function of displayed inventory, and considering the possibility of customers
Brian T. Ratchford,Departmentof Marketing,215EJacobsManagementCenter, Schoolof Management,State UniversityofNewYorkat Buffalo,Buffalo,NewYork 14260-4000,716-645-3211
11
12
Journal of Retailing Vol. 74, No. 1 1998
switching to another item when stockouts occur. In this context, Urban shows that a policy of keeping shelves fully stocked ("full shelf merchandising") can be quite far from optimal. Urban proposes two heuristics, a greedy algorithm and a genetic algorithm, for simultaneously solving the assortment, shelf space and inventory problems. Since test problems provided encouraging results, development of a real-world application of Urban's approach would seem to be a logical next step. The research itself can be considered a first pass at a difficult problem, and other researchers may be able to find ways to refine the approach. Given the increasing size of retailers, and the increased importance of store brands in many markets, the distribution of power between manufacturers and retailers has become an important topic. In the second paper, Betancourt and Gautschi (1998) study the distribution of power. They measure this construct as the ratio of retailer to manufacturer profit margins, between a manufacturer and a retailer who both have monopoly power. They consider a case in which the manufacturer exercises price leadership. The standard solution to this problem contains an implicit assumption that services provided by channel members, such as advertising, sales support, return policy, are invariant to the price charged by the manufacturer. Betancourt and Gautschi investigate what happens when this assumption is relaxed. If final demand is responsive to retail services, and if retailers can vary the level of their services, the authors show that the distribution of power shifts in favor of retailers. In effect, service provides the retailer with a weapon to counter the manufacturer's monopoly on the good. The manufacturer becomes willing to accept a lower price in return for the increased sales that are induced by a higher level of retail service. When the manufacturer rather than retailer provides the service, the authors show that a similar advantage does not accrue to the manufacturer. They further show that vertical integration still leads to higher channel profits when one party provides a service. However, they also show that more services may be forthcoming, and the consumer may be better off, when the channel members are independent. In effect the provider of the service (retailer or manufacturer) may compensate for the "too high" price created by double marginalization by offering "too much" service. These results have at least two testable implications. One is that retailers should have more power when the service elasticity of demand is high relative to the price elasticity and when the provision of services is more responsive to changes in wholesale price. For example, this might imply that specialty retailers should capture a higher share of profits than discount retailers. The other testable implication is that independent channel members should offer higher prices and higher levels of service than their integrated counterparts. A general implication of the results is that a retailer gains leverage over a manufacturer by successfully providing services that are in demand by consumers. A good topic for future research would be to extend the analysis of provision of services to the case of competing manufacturers and retailers using a framework similar to that employed by Choi (1996). In the third paper, Lam, Vandenbosch and Pearce (1998) point out that existing labor staffing models do not adequately account for the effect of the availability of sales clerks on sales. To remedy this, they develop a 2-equation model, with one equation representing sales as a function of store traffic and staffing, another representing traffic as a function of temporal patterns. Using traffic count data obtained from electronic counters, and the corresponding sales and staffing data, the authors estimate their equations on hourly data.
Introduction
13
They develop forecasts of traffic and, based on the forecasts and the estimated responsiveness of sales to traffic and staffing, they are able to solve for optimal (profit maximizing) staffing levels. It should be possible to apply the authors' basic approach in other settings, and to incorporate their model into computerized staffing programs. Their model might also be extended in a number of directions, including incorporating promotional activity into the model, developing improved methods for forecasting traffic, and developing dynamic optimization procedures to incorporate possible carry over effects of current staffing on future sales. In the final article of the special section, Donthu and Yoo (1998) provide an exposition of data envelopment analysis (DEA) as a tool for assessing store-level retail productivity. DEA is essentially a technique for estimating the efficiency of a unit, such as a store, relative to the most efficient unit. While DEA has been around for some time, the current application to retail stores appears to be unique (for an application to branch banks, which have many similarities to retail stores, see Kamakura, Lenartowicz, and Ratchford, 1996). What makes DEA particularly attractive for the purpose of comparing outlets is that it allows comparison of quantities of outputs to quantities of inputs, thus eliminating the effect of prices on comparisons of stores. Donthu and Yoo provide an example of the application of DEA to a chain of fast food restaurants. This provides a good example of the strengths and limitations of the technique and of how it may be applied, which should be of interest to those who might apply the technique in practice. While the authors argue for the choice of DEA over other techniques for comparing the productivity of outlets, other research suggests that the superiority of DEA is questionable (see, for example, Kamakura, Ratchford, and Lenartowicz). It would be helpful to have more research comparing alternative approaches to measuring the relative productivity of outlets. In closing, I would like to offer a word of appreciation to former editor, Charles Ingene for his efforts in developing the idea for the special publication and nurturing it through its early stages; to the reviewers, who provided some of the best reviews I have seen and were instrumental in markedly improving the articles; and to the authors of the articles in this issue for their efforts at responding to the criticisms of the reviewers. Special thanks go to the current editor who spearheaded a major effort to get this issue ready on time. I discovered that one of the benefits of being an editor is that one is forced to become thoroughly familiar with the articles being published under your guidance. I learned a great deal from the articles in this special issue and believe that readers who attend to them will do the same.
REFERENCES Betancourt, Roger and David Gautschi. (1998). "Distribution Services and Economic Power in the Channel," Journal of Retailing, 74(1):. Choi, S. Chan. (1996). "Price Competition in a Duopoly Common Retailer Channel," Journal of Retailing, 72(2): 117-134.
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Journal of Retailing Vol. 74, No. 1 1998
Donthu, Naveen and Boonghee Yoo. (1998). "Retail Productivity Assessment using Data Envelopment Analysis," Journal of Retailing, 74( 1):. Kamakura, Wagner, Thomas Lenartowicz, and Brian T. Ratchford. (1996). "Productivity Assessment of Multiple Retail Outlets," Journal of Retailing, 72(4): 333-356. Lam, Shunyin, Mark Vandenbosch, and Michael Pearce. (1998). "Retail Sales Force Scheduling Based on Store Traffic Forecasting," Journal of Retailing, 74(1):. Urban, Timothy L. (1998). "An Inventory-Theoretic Approach to Product Assortment and Shelf Space Allocation," Journal of Retailing, 74(1):.