Presentation delivered at the EUI in Florence during the FSR C&M, CMPF and FCP Annual Scientific Seminar on 'Competition, Regulation and Pluralism in the Online World' (22-23 March 2018).

1. Estimating Consumer Inertia in
Repeated Choices of Smartphones
Lukasz Grzybowski Ambre Nicolle
IFSR C&M and CMPF Annual Scientific Seminar
Florence 22-23 March 2018

2. Motivation
According to market research, in December 2017 Android
loyalty was 91 percent, while iOS loyalty was 86 percent.
High switching costs and consumer inertia in smartphone OS
may pose antitrust concerns.
We try to assess how hard it is to switch smartphone OS and
brands?

3. European Union vs. Google
In April 2015 Google was accused by the EC of using Android
OS to promote its mobile services (apart from other issues).
The question is whether Google actions impede development
of the market of mobile devices and access of competitors by
demanding or encouraging smartphone manufacturers to
pre-install Google apps and services on their devices.
In understanding the competition between Android, iOS and
other OS, one of the key issues whether consumers are able to
switch between OS and smartphone brands.

4. Android-iOS Duopoly
Figure 1: Global smartphone OS market shares
Source: Kantar Worldwide Panel

5. Related literature
Switching costs in mobile markets:
Changing providers: Cullen and Shcherbakov (2010),
Weiergraber (2017).
Changing tariff: Grzybowski and Liang (2015).
Changing handsets: Park and Koo (2016) based on single
period survey data.
Impact on exclusive handsets offered by mobile operators on
consumer welfare: Sikinson (2014).
There is no paper which directly estimates consumer inertia and
switching costs between smartphone OS, which we can do thanks
to unique data.

6. Data: consumers information
A sample of 4,983 subscribers who switched handset at least
once between March 2012 to December 2014 (33 months).
All are users of SIM-only tariffs without handset subsidy, i.e.,
they have to purchase handset at full market price.
We observe 7,146 switchings of handsets between March 2012
to December 2014.
For each individual, we have information on tariff and handset
used every month.

7. Data: handset switching
Table 1: Switching per individual
Switchings Freq. Percent Cum.
1 3,637 72.99 72.99
2 902 18.1 91.09
3 267 5.36 96.45
4 98 1.97 98.41
5 32 0.64 99.06
6 24 0.48 99.54
7 8 0.16 99.70
8 2 0.04 99.74
9 5 0.10 99.84
10+ 8 0.16 100.00
Total 4,983 100.00 100.00

8. Shares of OS in our sample vs. market-level
Figure 2: Smartphone OS market shares in the sample and at the
country-level between March 2012 and December 2014

9. Data: tariffs and handsets
Characteristics of tariffs include prices, voice and data
allowances (information from the operator).
Characteristics of handsets include size of the screen,
thickness, weight, quality of the camera, OS (collected from
Internet).
Prices of handsets (published by the operator in the
catalogues available to consumers on quarterly basis).

10. Switching between OS
OS after switching
Android Blackberry Windows iOS Other No smartphone Total
Android 46.9 4.0 6.0 27.6 1.6 13.9 100
Blackberry 23.2 16.3 4.6 37.2 2.3 16.5 100
Windows 28.4 4.6 18.7 35.8 2.8 9.7 100
iOS 15.1 2.9 2.1 72.5 1.0 6.4 100
Other 32.4 4.7 7.8 28.5 4.7 21.9 100
No smartphone 30.0 4.5 4.6 16.0 4.0 41.0 100
Total 29.3 4.7 4.8 36.4 2.6 22.7 100
% of observations

11. Switching between brands
After switching
Apple BBerry HTC LG Nokia Samsung Sony Sony-E Other Total
Apple 72.5 2.9 1.0 0.9 3.5 15.8 1.9 0.7 0.9 100
BlackBerry 37.2 16.3 1.9 3.4 9.4 25.5 3.0 1.6 2.3 100
HTC 25.3 4.0 8.6 2.0 9.6 40.9 5.6 2.0 2.0 100
LG 17.8 5.5 3.9 4.7 13.8 42.3 3.6 2.0 6.3 100
Nokia 20.1 4.4 2.2 3.4 22.8 35.1 3.1 1.6 7.4 100
Samsung 22.7 3.7 1.9 4.0 10.8 46.8 4.0 1.3 5.4 100
Sony 27.7 2.1 1.1 1.1 10.6 27.7 26.6 1.1 2.1 100
Sony-Eric. 19.1 5.1 1.9 4.6 12.9 35.8 9.2 7.6 3.8 100
Other 20.2 6.6 1.4 5.7 13.9 34.6 4.1 1.9 12.1 100
Total 36.4 4.7 1.7 3.1 10.8 33.2 3.8 1.6 4.6 100
% of observations

12. Econometric model: choice set
The consumer chooses utility-maximizing combination of
handset and tariff.
In general, SIM-only subscribers do not switch to long-term
contracts with handset subsidies.
We create a choice set for each individual which consists of all
combinations SIM-only tariffs available on the market in a
given month & handsets offered in the catalogue at full price.
We lose observations on consumers who switched to older
handsets without price information.
Total choice set ranges between 474 and 1,224 alternatives,
where consumers choose between 3 to 10 unique tariffs and
between 71 to 194 unique handsets (16 brands) per month.

13. Econometric model: utility function
Uijkt = Xkβp − αppk
tariff
+ Xj βi
h − αhpjt
handset
+sijktγ + ijkt
pk denotes the price of tariff handset and pjt the price of the
handset.
Xk is the tariff characteristics vector and Xj is the handset
characteristics vector.
βp captures valuations of tariff attributes and βi
h of handset
attributes (we allow for consumer-specific unobserved
preferences for brands and OS).
sijkt denotes the vector of switching dummies.
γ coefficients represent the disutility from switching.
ijkt is the individual-specific valuation for handset j, tariff k
at time t.

14. Estimation results: switching dummies
(1) (2) (3)
Logit Logit Mixed logit
Main switching costs
from feature phone to smartphone -0.27** (0.10) -0.12 (0.14) -0.14 (0.15)
from smartphone to feature phone 0.02 (0.09) -0.34 (0.18) -0.38* (0.19)
for changing OS -1.21*** (0.04) -1.10*** (0.13) -0.97*** (0.14)
for changing brand -0.51*** (0.05) -0.46*** (0.05) -0.29*** (0.06)
for changing tariff -5.10*** (0.07) -5.10*** (0.07) -5.10*** (0.07)
Specific switching costs
from Android to iOS 0.43** (0.15) 0.40* (0.16)
from Android to Blackberry OS 0.19 (0.20) 0.18 (0.21)
from Android to other os -0.39 (0.28) -0.41 (0.28)
from Android to Windows OS 0.32 (0.19) 0.33 (0.20)
from iOs to Android -0.62*** (0.15) -0.74*** (0.16)
from iOs to Blackberry OS -0.34 (0.20) -0.41 (0.21)
from iOs to other os -1.12*** (0.30) -1.18*** (0.30)
from iOs to Windows OS -0.97*** (0.22) -1.03*** (0.23)
from Blackberry OS to Android -0.38* (0.19) -0.34 (0.20)
from Blackberry OS to iOs 0.42* (0.18) 0.39* (0.19)
from Blackberry OS to other -0.64 (0.38) -0.62 (0.38)
from Blackberry OS to Windows OS -0.33 (0.28) -0.28 (0.30)
from Windows OS to Android -0.12 (0.24) 0.00 (0.27)
from Windows OS to iOs 0.31 (0.24) 0.40 (0.26)
from Windows OS to Blackberry OS 0.00 (0.41) 0.15 (0.44)
from Windows OS to other -0.12 (0.51) -0.11 (0.51)
Observations 6,169,592 6,169,592 6,169,592
Log Likelihood -30,062 -29,996 -29,909
Standard errors in parentheses
* p < 0.05, ** p < 0.01, *** p < 0.001
Note: mixed logit includes unobserved individual-specific preferences for brands and OS.

15. Figure 3: Estimated switching costs between operating systems (in
terms of WTP)

16. OS market shares without switching costs
Figure 4: Simulation results for iOS and Android

17. OS market shares without switching costs
Figure 5: Simulation results for iOS in December 2014

18. Conclusions
There is significant state-dependency in consumer choices of
smartphone operating systems and brands, which reinforces
the dominant market positions of iOS and Android.
Switching costs between smartphone brands using the same
OS are of smaller magnitude than between OS.
In the absence of switching costs, the market share of Android
would further increase at the cost of iOS (in our sample by as
much as 34% as of December 2014).
Limitation: our sample is a specific segment of consumers
who are younger, more technology-orientated, more price
sensitive and who do not like commitment → higher share of
iOS and more switching between OS and brands.