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

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