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MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
Vol. 10, No. 3, Summer 2008, pp. 429-447
DOI: 10.1287/msom.1070.0180
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Usage Restriction and Subscription Services: Operational Benefits with Rational Users

Ramandeep S. Randhawa, Sunil Kumar

McCombs School of Business, University of Texas at Austin, Austin, Texas 78712
Graduate School of Business, Stanford University, Stanford, California 94305

rsr{at}mail.utexas.edu
skumar{at}stanford.edu

This paper studies a rental firm that offers reusable products to price- and quality-of-service–sensitive customers—Netflix or Blockbuster can be thought of as the canonical example. Customers' perception of quality is determined by their likelihood of obtaining the product or service immediately upon request. We study the alternatives of offering either a subscription option that limits the number of concurrent rentals in return for a flat fee per-unit time, or a pay-per-use option with no such restriction. Customers are assumed to desire a nominal usage rate of the product, which they meet by adjusting their request rate in either option. Thus, they have a higher request rate in the subscription option. We propose a Markov chain model for customer behavior under the subscription option equivalent to the standard Poisson model under the pay-per-use option. In a large market setting, assuming exponential demand, we show that using the subscription option is more profitable for the firm. Further, via a numerical study, we show that this assumption is not essential for the result to hold. However, we show that the subscription option does not necessarily dominate the pay-per-use option in quality of service. The firm manages the trade-off between price and quality of service better in the subscription option. Moreover, we show that social welfare and the consumer surplus can also be higher in the subscription option, indicating that both the firm and the consumers can benefit from the subscription option.

Key Words: subscription services; pay-per-use services; operational benefits; pricing; capacity sizing; finite customer population; loss system; on-off model; diffusion approximation
History: Received: June 19, 2005; accepted: March 23, 2007.




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