IDEAS home Printed from https://ideas.repec.org/a/eee/japwor/v67y2023ics0922142523000294.html
   My bibliography  Save this article

A note on conglomerate mergers: The Google/Fitbit case

Author

Listed:
  • Nakagawa, Akihiko
  • Matsushima, Noriaki

Abstract

We consider conglomerate mergers using the Google/Fitbit case as an example. First, summarizing the importance of conglomerate merger control and the current enforcement standards for such mergers by competition laws, we briefly describe a review of the Google/Fitbit merger by the Japan Fair Trade Commission. Next, we explain the background of the merger and introduce Chen et al. (2022), who theoretically discuss a cross-market merger by considering the Google/Fitbit merger. Finally, we discuss the implications of Chen et al. (2022) for controlling conglomerate mergers. Specifically, personalized pricing based on data analytics can be a foreclosure device; in particular, merger-specific efficiencies can foster market foreclosure.

Suggested Citation

  • Nakagawa, Akihiko & Matsushima, Noriaki, 2023. "A note on conglomerate mergers: The Google/Fitbit case," Japan and the World Economy, Elsevier, vol. 67(C).
    • Handle: RePEc:eee:japwor:v:67:y:2023:i:c:s0922142523000294
    DOI: 10.1016/j.japwor.2023.101203
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0922142523000294
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.japwor.2023.101203?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. Zhijun Chen & Chongwoo Choe & Jiajia Cong & Noriaki Matsushima, 2022. "Data‐driven mergers and personalization," RAND Journal of Economics, RAND Corporation, vol. 53(1), pages 3-31, March.
    2. Biglaiser, Gary & Ma, Ching-to Albert, 2003. "Price and Quality Competition under Adverse Selection: Market Organization and Efficiency," RAND Journal of Economics, The RAND Corporation, vol. 34(2), pages 266-286, Summer.
    3. Garella, Paolo G. & Laussel, Didier & Resende, Joana, 2021. "Behavior based price personalization under vertical product differentiation," International Journal of Industrial Organization, Elsevier, vol. 76(C).
    4. Chongwoo Choe & Stephen King & Noriaki Matsushima, 2018. "Pricing with Cookies: Behavior-Based Price Discrimination and Spatial Competition," Management Science, INFORMS, vol. 64(12), pages 5669-5687, December.
    5. Zhijun Chen & Chongwoo Choe & Noriaki Matsushima, 2020. "Competitive Personalized Pricing," Management Science, INFORMS, vol. 66(9), pages 4003-4023, September.
    6. Krista J. Li & Sanjay Jain, 2016. "Behavior-Based Pricing: An Analysis of the Impact of Peer-Induced Fairness," Management Science, INFORMS, vol. 62(9), pages 2705-2721, September.
    7. Olivella, Pau & Vera-Hernandez, Marcos, 2007. "Competition among differentiated health plans under adverse selection," Journal of Health Economics, Elsevier, vol. 26(2), pages 233-250, March.
    8. Patrizio Bianchi & Sandrine Labory, 2022. "Industrial strategy in a transforming capitalism," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 46(6), pages 1481-1497.
    9. Katz Michael L, 2011. "Insurance, Consumer Choice, and the Equilibrium Price and Quality of Hospital Care," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 11(2), pages 1-44, January.
    10. Carl Shapiro, 2002. "Competition Policy and Innovation," OECD Science, Technology and Industry Working Papers 2002/11, OECD Publishing.
    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. Zhijun Chen & Chongwoo Choe & Jiajia Cong & Noriaki Matsushima, 2022. "Data‐driven mergers and personalization," RAND Journal of Economics, RAND Corporation, vol. 53(1), pages 3-31, March.
    2. Zhijun Chen & pch346 & Chongwoo Choe & Jiajia Cong & Noriaki Matsushima, 2020. "Data-Driven Mergers and Personalization," Monash Economics Working Papers 16-20, Monash University, Department of Economics.
    3. Choe, Chongwoo & Matsushima, Noriaki & Tremblay, Mark J., 2022. "Behavior-based personalized pricing: When firms can share customer information," International Journal of Industrial Organization, Elsevier, vol. 82(C).
    4. Wang, Yu & Li, Minqiang & Feng, Haiyang & Feng, Nan, 2023. "Which is better for competing firms with quality increasing: behavior-based price discrimination or uniform pricing?," Omega, Elsevier, vol. 118(C).
    5. Chongwoo Choe & Jiajia Cong & Chengsi Wang, 2024. "Softening Competition Through Unilateral Sharing of Customer Data," Management Science, INFORMS, vol. 70(1), pages 526-543, January.
    6. Michel Tolksdorf, 2023. "On Point Predictions and Reference Dependence in Behavior-Based Pricing Experiments," Journal of Economics and Behavioral Studies, AMH International, vol. 15(1), pages 1-14.
    7. Didier Laussel & Joana Resende, 2022. "When Is Product Personalization Profit-Enhancing? A Behavior-Based Discrimination Model," Management Science, INFORMS, vol. 68(12), pages 8872-8888, December.
    8. Qiuyu Lu & Noriaki Matsushima, 2024. "Personalized Pricing When Consumers Can Purchase Multiple Items," Journal of Industrial Economics, Wiley Blackwell, vol. 72(4), pages 1507-1524, December.
    9. Dubus, Antoine, 2024. "Behavior-based algorithmic pricing," Information Economics and Policy, Elsevier, vol. 66(C).
    10. Li, Jianpei & Zhang, Wanzhu, 2022. "Behavior-based price discrimination and signaling of product quality," MPRA Paper 111572, University Library of Munich, Germany.
    11. Flavio Pino, 2022. "The microeconomics of data – a survey," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 635-665, September.
    12. Didier Laussel, 2023. "Do firms always benefit from the presence of active customers?," Post-Print hal-03777069, HAL.
    13. Qiuyu Lu & Noriaki Matsushima, 2023. "Personalized pricing when consumers can purchase multiple items," ISER Discussion Paper 1192, Institute of Social and Economic Research, Osaka University.
    14. Didier Laussel & Joana Resende, 2022. "When Is Product Personalization Profit-Enhancing? A Behavior-Based Discrimination Model," Post-Print hal-03740642, HAL.
    15. Ding, Long & Liu, Peng & Hu, Sen, 2023. "Geo-Fencing or Geo-Conquesting? a strategic analysis of Location-Based coupon under different market structures," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    16. Kopalle, Praveen K. & Pauwels, Koen & Akella, Laxminarayana Yashaswy & Gangwar, Manish, 2023. "Dynamic pricing: Definition, implications for managers, and future research directions," Journal of Retailing, Elsevier, vol. 99(4), pages 580-593.
    17. Shi Bo & Chen Wen, 2018. "Individual Health Insurance Market with an Entrant – The ACA Health Insurance Exchange Observations," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 12(2), pages 1-27, July.
    18. Didier Laussel & Ngo Van Long & Joana Resende, 2023. "Profit Effects of Consumers’ Identity Management: A Dynamic Model," Management Science, INFORMS, vol. 69(6), pages 3602-3615, June.
    19. Pau Olivella & Marcos Vera-Hernandez, 2010. "How complex are the contracts offered by health plans?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 1(3), pages 305-323, July.
    20. Ratul Das Chaudhury & Chongwoo Choe, 2023. "Digital Privacy: GDPR and Its Lessons for Australia," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 56(2), pages 204-220, June.

    More about this item

    Keywords

    Conglomerate mergers; Google-Fitbit; Personalized pricing; Competition law enforcement;
    All these keywords.

    JEL classification:

    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L44 - Industrial Organization - - Antitrust Issues and Policies - - - Antitrust Policy and Public Enterprise, Nonprofit Institutions, and Professional Organizations

    Statistics

    Access and download statistics

    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:japwor:v:67:y:2023:i:c:s0922142523000294. 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.elsevier.com/locate/inca/505557 .

    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.