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The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
The Value of Personal Information in the E-Commerce Market
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The Value of Personal Information in the E-Commerce Market

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Presented at the 2013 ITS European Regional Conference@ Florence, Italy.

Presented at the 2013 ITS European Regional Conference@ Florence, Italy.

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  • 1. Toshiya Jitsuzumi1 and Teppei Koguchi2 1 Faculty of Economics, Kyushu University, Japan 2 Faculty of Informatics, Shizuoka University, Japan
  • 2.  The purpose of this analysis is to clarify the effect of personal information on switching costs in the Internet shopping market.  We empirically demonstrate the extent to which personal information drive up switching costs.  We revealed that when users change Internet shopping sites, personal information registered on the site represent switching costs of the same magnitude as traditional switching costs.
  • 3. 1. Background 2. Analysis framework 3. Estimation 4. Conclusion
  • 4. Growth of the Japanese B2C e-commerce market 8.6% 9,000 7,788 8,000 6,696 7,000 6,000 6,089 other 2,321 2,222 2,838 3,000 3,867 2,000 1,000 2,567 2,535 5,544 5,000 4,000 8,459 4,375 2008 2009 5,253 5,892 Retail and service industries 2,706 0 2007 2010 2011 Source: Ministry of Economy, Trade and Industry
  • 5. Shares of Internet shopping sites in 2012 in Japan 28.8% Rakuten Amazon.co.jp Yahoo! Japan 52.6% 12.4% other 6.2% Source: Rakuten, Inc. fiscal 2012 Financial Results
  • 6.  In order to shop on Internet shopping sites, users must register and provide information. ◦ names, e-mail addresses, postal addresses, credit card numbers, etc.  In addition, many Internet shopping sites provide the user’s viewing and buying histories on the site to make shopping more convenient.  If a user changes to another Internet shopping site, ◦ The personal information that has been registered and stored on previously used site is not transferred to the new site.  The user must re-register his or her personal information.  The personal information on previously used site may be continuously used for the business of previously site. ◦ Viewing and buying histories on previously used site can’t watch in the new site.  The user can’t use wish list and recommendation function based on buying history.  This point may represent a switching cost for users.
  • 7.  Switching Cost; ◦ The psychological or economic costs incurred when customers switch from one good or service to another.  Traditional switching cost; familiarity, attachment, etc. to the service. ◦ Switching costs make consumer hard to switch different service.  If high switching cost exists, it is possible to prevent price competition.  Klemperer (1987) ◦ Analysis for competition between new entry brand and existing brand.  Shy(2002) ◦ A model analysis of switching costs in the financial industry.  Valletti and Cave (1998) ◦ Analysis the mobile phone market in the UK.  Brynjolfsson and Smith (2000) ◦ In the e-commerce market, consumer confidence in the service provider becomes the factor of switching costs and justifies price differences.
  • 8.  Hypothesis of scenario  “Rakuten and Amazon will be integrated, and one of them will close.”  Respondents recognize as switching costs for; ◦ Traditional switching cost  familiarity and attachment to the site that will close. ◦ Switching cost associated with personal information  the management of registered information (names, e-mail addresses, credit card numbers, etc.) on the site that will close.  the migration of the viewing histories at the site.  the migration of the buying histories at the site. Site Merger OR Personal information will be transferred to the merged site, but there are variations in the treatment of the information stored in the closed site. Cash compensation
  • 9. Attributes Levels Which site exists? Rakuten Amazon.co.jp What will become of information registered in the shuted down site ? Used in other business of shut down site operator Completely deleted What will become of buying history in the shuted down site ? Carry over to the surviving Completely deleted site What will become of browsing record in the shuted down site ? Carry over to the surviving Completely deleted site Compensation for the situation above 1,000 yen 5,000 yen 10,000 yen 20,000 yen
  • 10. Probability function   exp  i X Pik   g   d '  j exp  i X '   Utility function (Without shift parameter) U ij   Amazon,i  DAmazon   Rakuten,i  DRakuten   inf,i  Dinf   buy ,i  Dbuy   view,i  Dview   compensation,i  compensation   ij (With shift parameter) Amazon Amazon Amazon U ij  ( age  agei   freq  freqi   purchase  purchasei   Amazon,i )  DAmazon Rakuten Rakuten  ( age  agei   Rakuten  freqi   purchase  purchasei   Rakuten,i )  DRakuten freq inf  ( age  agei   inf  freqi   inf freq purchase  purchasei   inf,i )  Dinf buy  ( age  agei   buy  freqi   buy freq purchase  purchasei   buy ,i )  Dbuy  ( age  agei   freq  freqi   purchase  purchasei   view,i )  Dview Variables; D means dummy variable   compensation,i  compensation   ij If DAmazon = 1, merging into Amazon If DRakuten = 1, merging into Rakuten Shift parameter; If Dinf =1, deleting registered information age = age If Dbuy =1, carrying over buying history freq = purchase frequency during last year If Dview =1, carrying over viewing history purchase = average purchase price If Dcompensation =1, compensation for each situation (yen) view view view
  • 11. Without shift parameter Variable Amazon Rakuten Registered information Buying history Viewing history Compensation Coefficient Standard Error p-value 0.316 0.110 0.306 0.109 0.289 0.820 0.289 0.838 0.103 0.846 0.0000421 0.0000059 With shift parameter 0.004 0.005 0.004 0.006 0.222 0.000 Variable Amazon Shift Parameter age purchase frequency average purchase price Standard deviations Rakuten age purchase frequency average purchase price Standard deviations Registered information age purchase frequency average purchase price Standard deviations Buying history age purchase frequency average purchase price Standard deviations Viewing history age purchase frequency average purchase price Standard deviations Compensation Coefficient Standard Error p-value -2.431 1.101 0.016 0.008 0.566 0.105 0.060 0.112 0.163 0.489 -1.015 1.082 0.018 0.008 0.367 0.103 -0.056 0.112 0.393 0.601 0.272 0.949 0.010 0.007 -0.082 0.894 -0.027 0.097 0.014 0.342 1.416 0.987 -0.020 0.007 -0.013 0.093 -0.007 0.991 0.290 0.460 1.023 0.985 0.005 0.007 -0.053 0.094 -0.117 0.100 0.533 0.401 0.0000460 0.0000069 0.027 0.048 0.000 0.589 0.738 0.348 0.027 0.000 0.620 0.513 0.774 0.156 0.360 0.779 0.968 0.151 0.006 0.888 0.942 0.529 0.299 0.500 0.575 0.246 0.184 0.000
  • 12. Variable Amazon Rakuten Registered information Buying history Viewing history WTA (Yen) ¥7,506 ¥7,268 ¥6,865 ¥6,865 (Insignificant)
  • 13.  Purchase histories or registered personal information represent switching costs of the same magnitude as traditional switching costs such as brand attachment or familiarity with the site.  Viewing histories are not regarded as the factor of switching costs.  From the managerial perspective; ◦ It is effective to construct a system in which registered or stored personal information cannot be used at different sites. ◦ Especially, for young people, it is important to apply the services, for example reduced prices, to prevent from changing to different service providers.
  • 14.  From the perspective of government policies; ◦ It is necessary to analyze what types of personal information are registered or stored on these sites.  While some personal information become switching costs, others do not. ◦ If switching costs impede competition, we have to consider the policy that makes possible transportation of personal information.  The “midata” project (BIS in 2011).  The goal of the “midata” project is for consumers to be able to access and use their personal and company data.  This project would be able to solve the problem of switching costs associated with personal information and therefore promote more competition.
  • 15. Thank you for your attention

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