IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v65y2019i10p4712-4732.html
   My bibliography  Save this article

Pricing Ancillary Service Subscriptions

Author

Listed:
  • Ruxian Wang

    (Johns Hopkins Carey Business School, Baltimore, Maryland 21202)

  • Maqbool Dada

    (Johns Hopkins Carey Business School, Baltimore, Maryland 21202)

  • Ozge Sahin

    (Johns Hopkins Carey Business School, Baltimore, Maryland 21202)

Abstract

We investigate heterogeneous customer choice behavior in the presence of main products —ancillary services with options of pay-per-use and subscription— and outside option. The willingness to pay for a service subscription is derived as a closed-form expression, which enables us to characterize the optimal pricing strategy and the impact of service subscriptions on customer surplus. Analytical results and numerical experiments show that offering service subscriptions may result in “win-win,” “win-win-win,” “win-win-lose,” or “lose-lose-win” scenarios or in other situations for the firm, competitors, and customers in a variety of monopolistic and duopolistic scenarios. The advantages of service subscription still remain with heterogeneous customers differing on multiple dimensions including the nominal utility, uncertainty in the need of ancillary service, and purchase frequency. We find that if the product quality for both firms, measured by nominal utility, is not significantly different, more fierce price competition by offering a service subscription may result in a higher customer surplus, compared with that without a service subscription. Ancillary service subscription can help firms to better price-discriminate heterogeneous customers through different subscription decisions and subsequent purchase behavior.

Suggested Citation

  • Ruxian Wang & Maqbool Dada & Ozge Sahin, 2019. "Pricing Ancillary Service Subscriptions," Management Science, INFORMS, vol. 65(10), pages 4712-4732, October.
    • Handle: RePEc:inm:ormnsc:v:65:y:2019:i:10:p:4712-4732
    DOI: 10.1287/mnsc.2018.3168
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mnsc.2018.3168
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2018.3168?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ramandeep S. Randhawa & Sunil Kumar, 2008. "Usage Restriction and Subscription Services: Operational Benefits with Rational Users," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 429-447, December.
    2. Ward Hanson & R. Kipp Martin, 1990. "Optimal Bundle Pricing," Management Science, INFORMS, vol. 36(2), pages 155-174, February.
    3. Yao Cui & Izak Duenyas & Ozge Sahin, 2018. "Unbundling of Ancillary Service: How Does Price Discrimination of Main Service Matter?," Manufacturing & Service Operations Management, INFORMS, vol. 20(3), pages 455-466, July.
    4. Elena Belavina & Karan Girotra & Ashish Kabra, 2017. "Online Grocery Retail: Revenue Models and Environmental Impact," Management Science, INFORMS, vol. 63(6), pages 1781-1799, June.
    5. Richard Schmalensee, 1981. "Monopolistic Two-Part Pricing Arrangements," Bell Journal of Economics, The RAND Corporation, vol. 12(2), pages 445-466, Autumn.
    6. Dan Zhang & Daniel Adelman, 2009. "An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice," Transportation Science, INFORMS, vol. 43(3), pages 381-394, August.
    7. Ruxian Wang & Zizhuo Wang, 2017. "Consumer Choice Models with Endogenous Network Effects," Management Science, INFORMS, vol. 63(11), pages 3944-3960, November.
    8. Garrett van Ryzin & Siddharth Mahajan, 1999. "On the Relationship Between Inventory Costs and Variety Benefits in Retail Assortments," Management Science, INFORMS, vol. 45(11), pages 1496-1509, November.
    9. Hongmin Li & Woonghee Tim Huh, 2011. "Pricing Multiple Products with the Multinomial Logit and Nested Logit Models: Concavity and Implications," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 549-563, October.
    10. I. P. L. Png & Hao Wang, 2010. "Buyer Uncertainty and Two-Part Pricing: Theory and Applications," Management Science, INFORMS, vol. 56(2), pages 334-342, February.
    11. Walter Y. Oi, 1971. "A Disneyland Dilemma: Two-Part Tariffs for a Mickey Mouse Monopoly," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 85(1), pages 77-96.
    12. Sridhar Balasubramanian & Shantanu Bhattacharya & Vish V. Krishnan, 2015. "Pricing Information Goods: A Strategic Analysis of the Selling and Pay-per-Use Mechanisms," Marketing Science, INFORMS, vol. 34(2), pages 218-234, March.
    13. Guillermo Gallego & Özge Şahin, 2010. "Revenue Management with Partially Refundable Fares," Operations Research, INFORMS, vol. 58(4-part-1), pages 817-833, August.
    14. Marius F. Niculescu & Hyoduk Shin & Seungjin Whang, 2012. "Underlying Consumer Heterogeneity in Markets for Subscription-Based IT Services with Network Effects," Information Systems Research, INFORMS, vol. 23(4), pages 1322-1341, December.
    15. Vivek F. Farias & Srikanth Jagabathula & Devavrat Shah, 2013. "A Nonparametric Approach to Modeling Choice with Limited Data," Management Science, INFORMS, vol. 59(2), pages 305-322, December.
    16. Hongmin Li & Scott Webster & Nicholas Mason & Karl Kempf, 2019. "Product-Line Pricing Under Discrete Mixed Multinomial Logit Demand," Service Science, INFORMS, vol. 21(1), pages 14-28, January.
    17. Hayes, Beth, 1987. "Competition and Two-Part Tariffs," The Journal of Business, University of Chicago Press, vol. 60(1), pages 41-54, January.
    18. Gérard P. Cachon & Pnina Feldman, 2011. "Pricing Services Subject to Congestion: Charge Per-Use Fees or Sell Subscriptions?," Manufacturing & Service Operations Management, INFORMS, vol. 13(2), pages 244-260, June.
    19. Lingxiu Dong & Panos Kouvelis & Zhongjun Tian, 2009. "Dynamic Pricing and Inventory Control of Substitute Products," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 317-339, December.
    20. Ruxian Wang & Ozge Sahin, 2018. "The Impact of Consumer Search Cost on Assortment Planning and Pricing," Management Science, INFORMS, vol. 64(8), pages 3649-3666, August.
    21. Stephen Leider & Özge Şahin, 2014. "Contracts, Biases, and Consumption of Access Services," Management Science, INFORMS, vol. 60(9), pages 2198-2222, September.
    22. Jeffrey D. Shulman & Xianjun Geng, 2013. "Add-on Pricing by Asymmetric Firms," Management Science, INFORMS, vol. 59(4), pages 899-917, April.
    23. Guillermo Gallego & Ruxian Wang, 2014. "Multiproduct Price Optimization and Competition Under the Nested Logit Model with Product-Differentiated Price Sensitivities," Operations Research, INFORMS, vol. 62(2), pages 450-461, April.
    24. Berglas, Eitan, 1976. "On the Theory of Clubs," American Economic Review, American Economic Association, vol. 66(2), pages 116-121, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miao, Chun-Hui, 2022. "The pricing of ancillary goods when selling on a platform," International Journal of Industrial Organization, Elsevier, vol. 83(C).
    2. Xie, Lei & Guo, Guangtao & Chen, Jiao, 2024. "Bundling products and service on influencer channels," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    3. Luo, Sha & Fang, Shu-Cherng & Zhang, Jiahua & King, Russell E., 2023. "Price competition and cost efficiency facing buyer’s bounded rationality," International Journal of Production Economics, Elsevier, vol. 266(C).
    4. Zuidberg, Joost & de Wit, Jaap G., 2020. "The development of long-haul low-cost networks in the North Atlantic airline market: An exploratory data approach," Transport Policy, Elsevier, vol. 95(C), pages 103-113.
    5. Mohit Tyagi & Nomesh B. Bolia, 2024. "Optimal pricing of subscription services in the restaurant industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(3), pages 262-273, June.
    6. Pnina Feldman & Ella Segev, 2022. "The Important Role of Time Limits When Consumers Choose Their Time in Service," Management Science, INFORMS, vol. 68(9), pages 6666-6686, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Woonghee T. Huh & Hongmin Li, 2023. "Product‐line pricing with dual objective of profit and consumer surplus," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1223-1242, April.
    2. Guang Li & Paat Rusmevichientong & Huseyin Topaloglu, 2015. "The d -Level Nested Logit Model: Assortment and Price Optimization Problems," Operations Research, INFORMS, vol. 63(2), pages 325-342, April.
    3. Zhenzhen Yan & Karthik Natarajan & Chung Piaw Teo & Cong Cheng, 2022. "A Representative Consumer Model in Data-Driven Multiproduct Pricing Optimization," Management Science, INFORMS, vol. 68(8), pages 5798-5827, August.
    4. Aydın Alptekinoğlu & John H. Semple, 2021. "Heteroscedastic Exponomial Choice," Operations Research, INFORMS, vol. 69(3), pages 841-858, May.
    5. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    6. Guillermo Gallego & Huseyin Topaloglu, 2014. "Constrained Assortment Optimization for the Nested Logit Model," Management Science, INFORMS, vol. 60(10), pages 2583-2601, October.
    7. Chenhao Du & William L. Cooper & Zizhuo Wang, 2016. "Optimal Pricing for a Multinomial Logit Choice Model with Network Effects," Operations Research, INFORMS, vol. 64(2), pages 441-455, April.
    8. W. Zachary Rayfield & Paat Rusmevichientong & Huseyin Topaloglu, 2015. "Approximation Methods for Pricing Problems Under the Nested Logit Model with Price Bounds," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 335-357, May.
    9. Ruxian Wang, 2018. "When Prospect Theory Meets Consumer Choice Models: Assortment and Pricing Management with Reference Prices," Manufacturing & Service Operations Management, INFORMS, vol. 20(3), pages 583-600, July.
    10. James M. Davis & Guillermo Gallego & Huseyin Topaloglu, 2014. "Assortment Optimization Under Variants of the Nested Logit Model," Operations Research, INFORMS, vol. 62(2), pages 250-273, April.
    11. Ruxian Wang & Ozge Sahin, 2018. "The Impact of Consumer Search Cost on Assortment Planning and Pricing," Management Science, INFORMS, vol. 64(8), pages 3649-3666, August.
    12. Sentao Miao & Xiuli Chao, 2021. "Dynamic Joint Assortment and Pricing Optimization with Demand Learning," Manufacturing & Service Operations Management, INFORMS, vol. 23(2), pages 525-545, March.
    13. Hongmin Li & Scott Webster & Gwangjae Yu, 2020. "Product Design Under Multinomial Logit Choices: Optimization of Quality and Prices in an Evolving Product Line," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 1011-1025, September.
    14. Dan Zhang & Larry Weatherford, 2017. "Dynamic Pricing for Network Revenue Management: A New Approach and Application in the Hotel Industry," INFORMS Journal on Computing, INFORMS, vol. 29(1), pages 18-35, February.
    15. Xiaobo Li & Hailong Sun & Chung Piaw Teo, 2022. "Convex Optimization for Bundle Size Pricing Problem," Management Science, INFORMS, vol. 68(2), pages 1095-1106, February.
    16. Srikanth Jagabathula & Paat Rusmevichientong, 2017. "Nonparametric Joint Assortment and Price Choice Model," Management Science, INFORMS, vol. 63(9), pages 3128-3145, September.
    17. Xi Chen & Zachary Owen & Clark Pixton & David Simchi-Levi, 2022. "A Statistical Learning Approach to Personalization in Revenue Management," Management Science, INFORMS, vol. 68(3), pages 1923-1937, March.
    18. Ahmadi, Iman & Skiera, Bernd & Lambrecht, Anja & Heubrandner, Florian, 2017. "Time preferences and the pricing of complementary durables and consumables," International Journal of Research in Marketing, Elsevier, vol. 34(4), pages 813-828.
    19. Jalali, Hamed & Carmen, Raïsa & Van Nieuwenhuyse, Inneke & Boute, Robert, 2019. "Quality and pricing decisions in production/inventory systems," European Journal of Operational Research, Elsevier, vol. 272(1), pages 195-206.
    20. Aydın Alptekinoğlu & John H. Semple, 2016. "The Exponomial Choice Model: A New Alternative for Assortment and Price Optimization," Operations Research, INFORMS, vol. 64(1), pages 79-93, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:65:y:2019:i:10:p:4712-4732. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.