IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v187y2023ics0040162522007399.html
   My bibliography  Save this article

Dynamic decision-making analysis of Netflix's decision to not provide ad-supported subscriptions

Author

Listed:
  • Zhou, Yufei
  • Wang, Sihan
  • Zhang, Nuo

Abstract

Netflix is a leading digital streaming platform that provides a variety of content to users. Netflix charges a fixed amount from its users for its services. Unlike other digital streaming platforms, Netflix does not support subscriptions supported by ads; consequently, Netflix's subscriptions are decreasing. However, Netflix does not offer ad-supported subscriptions. In this context, we aimed to explain why Netflix does not provide them by modelling a monopolist's decision-making using asymmetric information. We compared a shutdown policy with other strategies using the first-best contract with complete information as the benchmark. The results indicate a close relationship between the customer base and the efficiency of the shutdown policy. This outcome at least partly explains Netflix's choice of strategy, its past success, and its current dilemma.

Suggested Citation

  • Zhou, Yufei & Wang, Sihan & Zhang, Nuo, 2023. "Dynamic decision-making analysis of Netflix's decision to not provide ad-supported subscriptions," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
  • Handle: RePEc:eee:tefoso:v:187:y:2023:i:c:s0040162522007399
    DOI: 10.1016/j.techfore.2022.122218
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522007399
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2022.122218?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pierre‐André Chiappori & Bruno Jullien & Bernard Salanié & François Salanié, 2006. "Asymmetric information in insurance: general testable implications," RAND Journal of Economics, RAND Corporation, vol. 37(4), pages 783-798, December.
    2. Urmila Shrawankar & Chetan Ashokrao Dhule, 2022. "Resource-Efficient Pareto-Optimal Green Scheduler Architecture," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(2), pages 1-14, April.
    3. Yu Hao & Lingzhe Wang & Ying Liu & Jiulun Fan, 2022. "Information Entropy Augmented High Density Crowd Counting Network," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-15, January.
    4. Elisabetta Iossa & David Martimort, 2011. "The Theory of Incentives Applied to the Transport Sector," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 29, Edward Elgar Publishing.
    5. Kavita Sharma & B. B. Gupta, 2019. "Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches," International Journal of E-Services and Mobile Applications (IJESMA), IGI Global, vol. 11(2), pages 1-21, April.
    6. Armando Barbosa & Ig I. Bittencourt & Sean W. Siqueira & Diego Dermeval & Nicholas J. T. Cruz, 2022. "A Context-Independent Ontological Linked Data Alignment Approach to Instance Matching," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-29, January.
    7. Shahul Chettali Hameed, 2022. "Stock Market E-Assistance on Platform-as-a-Service (PaaS)," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(2), pages 1-11, April.
    8. Madhumala R. B. & Harshvardhan Tiwari & Devaraj Verma C., 2022. "Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(2), pages 1-12, April.
    9. Pierre‐André Chiappori & Bruno Jullien & Bernard Salanié & François Salanié, 2006. "Asymmetric information in insurance: general testable implications," RAND Journal of Economics, The RAND Corporation, vol. 37(4), pages 783-798, December.
    10. Saravanan Krishnan & Rajalakshmi N. R., 2022. "A Cost-Optimized Data Parallel Task Scheduling in Multi-Core Resources Under Deadline and Budget Constraints," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(2), pages 1-16, April.
    11. Zhang Ling & Zhang Jia Hao, 2022. "An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-25, January.
    Full references (including those not matched with items on IDEAS)

    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. Hyojoung Kim & Doyoung Kim & Subin Im & James W. Hardin, 2009. "Evidence of Asymmetric Information in the Automobile Insurance Market: Dichotomous Versus Multinomial Measurement of Insurance Coverage," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(2), pages 343-366, June.
    2. Cannon, Edmund & Tonks, Ian, 2016. "Cohort mortality risk or adverse selection in annuity markets?," Journal of Public Economics, Elsevier, vol. 141(C), pages 68-81.
    3. Mayock, Tom & Shi, Lan, 2022. "Adverse selection in the market for mortgage servicing rights," Journal of Housing Economics, Elsevier, vol. 58(PB).
    4. de Meza, David & Webb, David C., 2017. "False diagnoses: pitfalls of testing for asymmetric information in insurance markets," LSE Research Online Documents on Economics 65744, London School of Economics and Political Science, LSE Library.
    5. Coelho, Marta & de Meza, David, 2012. "Do bad risks know it? Experimental evidence on optimism and adverse selection," Economics Letters, Elsevier, vol. 114(2), pages 168-171.
    6. Daniel McFadden & Carlos Noton & Pau Olivella, "undated". "Remedies for Sick Insurance," Working Papers 620, Barcelona School of Economics.
    7. repec:mea:meawpa:12259 is not listed on IDEAS
    8. Herweg, Fabian & Müller, Daniel, 2008. "The Optimality of Simple Contracts: Moral Hazard and Loss Aversion," Bonn Econ Discussion Papers 17/2008, University of Bonn, Bonn Graduate School of Economics (BGSE).
    9. Liran Einav & Amy Finkelstein & Mark R. Cullen, 2010. "Estimating Welfare in Insurance Markets Using Variation in Prices," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 877-921.
    10. Peter Arcidiacono & Esteban M. Aucejo & Hanming Fang & Kenneth I. Spenner, 2011. "Does affirmative action lead to mismatch? A new test and evidence," Quantitative Economics, Econometric Society, vol. 2(3), pages 303-333, November.
    11. Hanming Fang & Michael P. Keane & Dan Silverman, 2008. "Sources of Advantageous Selection: Evidence from the Medigap Insurance Market," Journal of Political Economy, University of Chicago Press, vol. 116(2), pages 303-350, April.
    12. Raj Chetty & Amy Finkelstein, 2012. "Social Insurance: Connecting Theory to Data," NBER Working Papers 18433, National Bureau of Economic Research, Inc.
    13. Georges Dionne & Casey G. Rothschild, 2011. "Risk Classification in Insurance Contracting," Cahiers de recherche 1137, CIRPEE.
    14. Georges Dionne & Pierre-Carl Michaud & Maki Dahchour, 2013. "Separating Moral Hazard From Adverse Selection And Learning In Automobile Insurance: Longitudinal Evidence From France," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 897-917, August.
    15. Xi Wu & Li Gan, 2023. "Multiple dimensions of private information in life insurance markets," Empirical Economics, Springer, vol. 65(5), pages 2145-2180, November.
    16. Johannes Spinnewijn, 2017. "Heterogeneity, Demand for Insurance, and Adverse Selection," American Economic Journal: Economic Policy, American Economic Association, vol. 9(1), pages 308-343, February.
    17. Shi, Peng & Valdez, Emiliano A., 2011. "A copula approach to test asymmetric information with applications to predictive modeling," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 226-239, September.
    18. Michael Ludkovski & Virginia R. Young, 2010. "Ex Post Moral Hazard and Bayesian Learning in Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(4), pages 829-856, December.
    19. Dardanoni, V & Li Donni, P, 2008. "Testing For Asymmetric Information In Insurance Markets With Unobservable Types," Health, Econometrics and Data Group (HEDG) Working Papers 08/26, HEDG, c/o Department of Economics, University of York.
    20. Christian T. Litchepah & Issidor. Noumba & Mohammadou. Nourou, 2022. "Does reducing violence against women improve children’s health? The case of Cameroon," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 6(6), pages 187-194, June.
    21. Deryugina, Tatyana, 2012. "Does Selection in Insurance Markets Always Favor Buyers?," MPRA Paper 53583, University Library of Munich, Germany.

    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:eee:tefoso:v:187:y:2023:i:c:s0040162522007399. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.