Chihiro Watanabe: Platform Ecosystems Impact on GDP - Increasing Dependency on Un-captured GDP and Its Consequences to Finland's Future

1
Chihiro Watanabe
Platform Ecosystems Impact on GDP
Research Professor, University of Jyvaskyla, Finland
Research Scholar, International Institute for Applied Systems Analysis (IIASA)
Professor Emeritus, Tokyo Institute of Technology
watanabe.c.pqr@gmail.com watanabe.foxcj@rhythm.ocn.ne.jp
Watanabe Foresight 20161123 Foresight Meeting
Helsinki 23 November 2016
WP 7 Platform Ecosystems Impact on GDP
Increasing Dependency on Un-captured GDP
and Its Consequences to Finland Future

Fig. 1. Growth Rate, Competitiveness and Happiness/Welfare in ICT Advanced Countries (2013).
1. New Stream of Innovation
- Spinoff to Un-captured GDP driven Co-evolutionary 3 Mega-trends
(1) Dilemma of World ICT Leaders
Figures in red and blue indicate top and lowest level in 12 countries. Sources: IMF, World Economic Forum (WEF), The Earth Institute, ILO, WHO.
2

Captured
GDP
Traditional
ICT
Un-captured
GDP
(2) Co-evolution of 3 Mega-trends – Shift from Traditional to New Co-evolution
Advancement of ICT
Paradigm
change
1. Internet has changed the computer-
initiated ICT world significantly.
2. Internet provides utility and happiness
to people but cannot be captured by
GDP.
5. Consequently, current ICT driven
economy depends on shifting two co-
evolutional mega-trends:
(1) Traditional co-evolution between ICT,
captured GDP and economic functionality,
(Singapore)
(2) New co-evolution between Internet,
un-captured GDP and supra-functionality
(Finland).
6. While Finland has shifted to new co-evolution,
Singapore has maintained traditional growth –
oriented co-evolution.
Fig. 2. Co-evolution between the Internet,
Un-captured GDP and Supra-functionality.
3. Un-captured GDP has become the
major source of consumers utility .
4. This corresponds to people’s preferences
shift and induces further Internet advancement.
Singapore
Finland
Internet
3
IoT

4
The 21st-century economy
How to measure prosperity
GDP is a bad gauge of material well-being.
Time for a fresh approach
April 30, 2016
第1章 ICTによるイノベーションと経済成長
第4節経済社会に対するICTの多面的な貢献
2．ICT化による経済社会の非貨幣的側面の変化
White Paper on Telecommunications
(Ministry of Internal Affairs and Communications)
August 1, 2016
Beyond GDP
IoT, Big data, AI
New values that network and data create

5
Fig. 3. Two-faced Nature of ICT.
T
T
Y
T
T
Y
X
X
Y
X
X
Y
Y
Y
TXFY























 ),(
Y
R
p
p
Y
R
T
Y
Y
T



decrease
Y
Y
decline
T
Y
decreasepT



Y: GDP, X: Traditional production factors
(Labor, Capital),
T: Technology stock ( ) , R: R&D investment
(Marginal productivity) (Relative prices)
RT 
Tyler Cowen
Professor of economics
at George Mason University
Tp
T
Internet dependency
ICT’s contribution to growth
(3) Un-captured GDP
1) Trap in ICT Advancement – Two-faced Nature of ICT
Prices
of
ICTprices
ICT advancement
Trend in ICT Prices
Prices increase by new functionality
Prices decline due to freebies,
easy copying, standardization
Growth
stagnation
Un-captured
GDP
Decline in marginal
productivity of
technology.
Increase in ICT stock
Search engine
Social networks
Online advertising
Socio-cultural value
Internet provides new utility and happiness
but cannot be captured by GDP data
（Un-captured GDP)

2) Bi-polarization of Digital Economy -Fatality of Logistic Growth
Logistic growth function as a function of time t can be depicted as follows:
This function can be developed to the following bi-polarization function:
at
be
N
Y
N
Y
aY
dt
dY



1
)1(
Inflection point
Fig. 4. Bi-polarization Fatal to Logistic Growth Function. 6
Excessive increase changes to a vicious cycle.

7
Fig. 5. Transformative Role of Co-evolutionary Acclimatization.
.
that harnesses the vigor of counterparts
enables both economies maintain
sustainable growth.
While the advancement of ICT contributes to
enhancing its prices by increasing new
functionality development, dramatic
advancement of the Internet tends to decrease
ICT prices due to freebies, easy copying and
mass standardization, among other things.
ICT advanced economies suffer a
vicious cycle between ICT advance-
ment and marginal productivity decline.
ICT growing economies expect growth
as ICT increases but they cannot afford
Co-evolutionary Acclimatization
3) Consequence of Two-faced nature of ICT

8
IGE: ICT
growing
economies
Fig. 6. Scheme of Optimal Dynamism for Balancing Captured and Un-captured GDP.
4) Optimal Balance between Captured GDP and Un-captured GDP
ICT advanced economies ICT growing economies

2. Measurement of Un-captured GDP
2.1 Framework of the Analysis
(1) Basic Understanding
Un-captured GDP can be traced from both sides as:
a. New functions of online intermediaries such as e-commerce,
online advertising and search engines, and
b. Consumer’s preferences shift from economic functionality
to supra-functionality beyond economic value.
(2) Both Sides of Un-captured GDP Emergence
1) New Services provided by Online Intermediaries
2) Consumers Preferences Shift
9
Locomotive of the Spin-off
Emerging Un-captured GDP.

10
Internet
Online Intermediaries (OI)
Core player
Provide platforms for the exchange of goods, services or information over the Internet
Substantially change the way that goods, services and information distribution
Social networks
Twitter, Facebook,
LinkedIN, YouTube
Search engine
Google, Yahoo,
Wikipedia
e-commerce platform
eBay, Amazon, Alibaba,
Rakuten, Priceminister
Cloud computing
Skype, Viber, Watspp,
Yahho Messenger
1. Direct GDP
contribution
2. Indirect GDP
contribution
by productivity increase
3. Beyond captured GDP
Services provided by OI by
(1) Private consumption
(2) Government consumption
(3) Investment involving OI*
(4) Exported or imported
Efficiency/cost improve by
(1) Search provider
(Reduce costs for information search)
(2) Social networks
(Find/exchange information efficiently)
(3) e-commerce
(Sell efficiently/purchase cheaper)
(4) Cloud computing
(Turning fixed costs into marginal costs)
Services not counted GDP as
(1) B2B platform by e-commerce
(not final cons. but input to others)
(2) Online advertising
(Similar to B2B)
(3) Consumer benefits of free services
as Google search
(4) Socio cultural value
induced by social networks *
220 bil. Euro (1.7% of GDP) 210 bil. Euro (1.65%) 640 bil. Euro (5.0%)
EU 27 in 2012 (GDP = 12,900 bil. Euro)
Original Source: The Impact of Online Intermediaries
on the EU Economy (Copenhagen Economics, 2013).
Substantial total values
including un-captured
GDP change current
2% p.a GDP growth
to 3-8% growth society.
Fig. 7. Beyond Captured GDP generated by Online Intermediaries.
Un-c.GDP measurement is critical
* Not included in the estimate.
In addition,
“Underground resources” (e.g.,
Un-licensed software, online piracy),
“Un-used potential” due to
organizational reform delay
cannot be overlooked.

11
2) Consumer’s Preferences Shift
(i) Measurement of Elasticity of Utility to Consumption
Un-captured GDP is non-reflection of utility to consumption (measured by captured GDP) and its magnitude can
be measured by elasticity of utility to consumption.
Utility is governed by ICT stock (I) and Internet dependency (J) in the digital economy, its elasticity to consumption
can be decomposed to elasticity of ICT to consumption and elasticity of the Internet to consumption as follows:
)()()(
),(
),(),,(),(
cjcicu
C
J
J
C
C
I
I
C
C
U
U
C
J
J
C
I
I
C
C
U
J
J
U
I
I
U
JIUU
JIQQJIVVQVUU
































U: Utility, C: Traditional consumption
V: Economic functionality, Q: Supra-functionality
J: Internet dependency, I: ICT stock
Thus, elasticity of utility to consumption can be estimated by a sum
of elasticity of I to C and J to C. Utility is particularly induced by J.
Fig. 8. Governing Factor of Utility in the Digital Economy.

1.76
0.49
1.27
0.30
0.23
0.37
0.68
0.15
0.40
0.10 0.10
0.47 0.48
0.53
1.05
0.55 0.57 0.58
Finland Singapore USA UK Germany Japan
Internet to Consumption
ICT to Consumption
12
Fig. 9. Elasticity of Utility to Consumption in 6 Countries (2013).
Finland Singapore USA UK Germany Japan
Table 1 Elasticity of Utility to Consumption in 6 Countries (2013)
2/1
(ii) Elasticity of Utility to Consumption in 6 Countries
0.23
0.30
0.39
0.49
1.27
1.76
0.37
0.68
0.15
0.40
0.10
0.47
0.10
0.48
Under the digital economy,
Elasticity of U to C
= Elasticity of I to C
+ Elasticity of J to C

CapturedGDPUn-capturedGDP
Fig. 10. Trends in Elasticity of Utility to Consumption in 6 Countries (1994-2013).
(iii) Trend in the Elasticity in 6 Countries
Singapore
USA
Japan
Germany
UK
Finland
13
Elasticityofutilitytoconsumption
1994 95 96 97 98 99 2000 01 02 03 04 2005 06 07 08 09 2010 11 12 2013
Singapore Conspicuously high U reflects to C High dependency on Captured GDP
Finland Extremely low U does not reflect to C High dependency on Un-captured GDP
Internet commercialization Net bubble bursting Lehman shock
cu
cu
cu

14
(3) Factual Observation
Captured
GDP
Traditional
ICT
Internet
Un-captured
GDP
Advancement of ICT
Paradigm
change
GDPatcurrent
prices(US$billions)
Stimulation from
ICT advancement
Inducement by people’s
preferences shift
Significance difference of consumption level between Finland and
Singapore notwithstanding the similar level of GDP can be attributed
to difference of un-captured GDP based consumption.
This difference can be attributed to the difference
of the state of spin-off in the shifting co-evolution
between 3 mega-trends.
Locomotive of such spin-off impacting on
un-captured GDP can be:
(i)Stimulation from ICT advancement, and
(ii)Inducement by people’s preferences shift.
Both maintain equilibrium leading to lifting power
which can be depicted as follows:
)1( nY
YXAeXAZ
n
 
Scale factor Primary impacts Secondary impacts
Singapore
Finland
300
200
100
Consumptionindex
Fig. 11. Utility of Consumption Measured by Un-captured GDP.
Fig. 12. The Locomotive for the Spin-off Impacting Un-captured GDP.

15
(4) Measurement of the Magnitude of Un-captured GDP
1) Consumption Function
Level of consumption
C = a’ + b’ Y
H = a + bY
Y Y + uY
Consumption measured by captured GDP
Estimated gross consumption measured by both captured and un-captured GDP
Gross income
level motivating
consumption
Additional consumption measured
by un-captured GDP (W)
Captured GDP un-captured GDP
aBase
consumption
2) Discrepancy between two consumption functions
(i) ICT advancement stimulation
(Attributed to the Internet (J) with secondary impacts of consumer’s preferences)).
: adjusting factor (= a - a’) (4)
[ see Note]
(ii) Consumer’s preferences inducement
(Represented by elasticity of utility to consumption (E) with secondary impacts of J)
(5)
  buYaaYbbaabYaYbaHCW  )'()'()'()(''


n
J
eEAbuY
Since (4) and (5) maintain equilibrium,
leading to lifting power.
(6)
(7)
(9)
Un-captured GDP ratio
(10)
nm
JE
eEAeJHbuY 

mn
m
n
EJ
E
J
eeJEA
eJ
eEAH 

 



1
nn
EJJEAH   lnlnlnln
b
eEe
uY
n
JA 


ln
Y
uY

 

m
E
eJHYb
(1)
(2)
(3)
3) Identification of un-captured GDP
a, a’: base consumption,
b, b’: marginal propensity to consume.
 







m
E
eJHHuY
H
Y
Y
H
Y
H
uY
Y
H
buY
H
eJeJ
Y
uY
H
Y
Y
H mm
EE 
 



 ,,
(8)
Note
(Y elasticity to H) 0
H



16
2.2 Empirical Result
Table 2 Governing Factors of Household Consumption in Finland and Singapore (1994-2013)
19158.1998.0.917.01003.1ln208.0ln185.1435.6ln 20.26.14
 
AICDWRadjEJJEH
19061.1998.0.887.01070.1ln214.0ln230.1520.6ln 22.25.14
 
AICDWRadjEJJEH
19060.1998.0.939.01003.1ln209.0ln158.1380.6ln 28.16.14
 
AICDWRadjEJJEH
12263.1982.0.575.11000.1ln198.0ln740.0721.3ln 222.000.12
 
AICDWRadjEJJEH
12163.1981.0.484.11070.1ln229.0ln735.0856.3ln 222.090.02
 
AICDWRadjEJJEH
12262.1982.0.658.11060.0ln172.0ln745.0593.3ln 222.010.12
 
AICDWRadjEJJEH
(7.24*) (2.23*2) (-3.10*) (1.60*4) (0.69*5)
(7.52*) (2.35*2) (-3.29*) (1.58*4) (0.64*5)
(6.69*) (2.10*2) (-3.63*) (1.61*4) (0.69*5)
(3.28*) (2.14*2) (-3.32*) (5.41*) (1.37*4)
(3.27*) (2.08*2) (-3.64*) (5.31*) (1.25*4)
(3.27*) (2.18*2) (-2.99*) (5.49*) (1.48*4)
Finland S
H
L
Singapore S
H
L
S: Standard estimate, H: higher possible estimate, L: lower possible estimate.
H: Household consumption (Index: 1994=100), E: Elasticity of utility to consumption, J: Internet dependency.
Singapore’s E for 1994 -1996 are adjusted taking backward trend between 1997-2000.
Figures in parenthesis indicate t-statistics (*, *2, *4, *5 means significant at the 1%, 5%, 20% and 50% level, respectively).

0.95 H
0.87 S
0.83 L
0.43 H
0.35 S
0.29 L
(1) Magnitude of Un-captured GDP – Un-captured GDP Ratio
Fig. 13. Trend in the Internet-driven Un-captured GDP Ratio in Finland and Singapore (1994-2012).
Un-captured GDP ratio = Un-captured GDP/ Captured GDP.
S: Standard estimate, H: Higher possible estimate, L: Lower possible estimate.
17

18
Finland
Singapore
Captured GDP
505
302
269
407
74
105
GDP at current prices (US$ billions)
GDP
1994 2000 2005 2010 2013
600
500
400
300
200
100
0
Fig. 14. Trends in Captured and Un-captured GDP in Finland and Singapore (1994-2013).
(2) Trends in Captured and Un-captured GDP

19
600
500
400
300
200
100
0
Singapore
302
407
Captured GDP74
1994 2000 2005 2010 2013
Fig. 15. Comparison of Captured and Un-captured GDP in Finland and Singapore (1994-2013).
While (captured) GDP is lower than Singapore,
Finland depends largely on un-captured GDP.
(3) Dependency on Un-captured GDP
GDP at current prices (US$ billions)
Captured GDP
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
300
250
200
150
100
50
Finland Singapore
Internet dependency 93 75
Experience ratio 48 30
Share of retail sales 9 2
Clothing/footwear purchase Popular 4
B2B Internet use 1 14(world rank)
Comparison of the Internet Use (2013) %
Sources: ITU, WEF, Statistics Finland, Singapore DOS.
Online
shopping
600
500
400
300
200
100
0
Finland
Un-captured GDP
269
505
Captured GDP105
1994 2000 2005 2010 2013
19
302
269

Finland (1996 – 2013)
1996
2013
Internet productivity of ICT
Singapore (1996 – 2013)
19962013
Fig. 16. Contrast of the Shift to New Co-evolution between Finland and Singapore (1996-2013).
3. Consequence of Increasing Dependency on Un-captured GDP
(1) Shift to New Mega Trend
2003
2000
20
Un-capturedGDPratio

21
(2) Correspondence to People’s Preferences Shift
Economic Functionality
Fig. 17. Shift from Economic Functionality to Supra-functionality beyond Economic Value.
People’s preferences shift in Japan
Source: Japan Cabinet Office.

1 FIN 2 SGP 3 SWE 4 NLD 5 NOR 6 CHE 7 USA 9 UK 12 DEU 13 DNK 15 ISR 16 JPN
Gaining profits
from abroad
GNI/GDPratio
Fig. 18. Comparison of Interactive Return Gain Structure
in 12 ICT Advanced Countries (2012, 2013 average).
Figures on the country indicates the ICT competitiveness rank in 2013.
Sources: World Economic Outlook Database (IMF 2013, 2014), World Health Statistics 2014 (WHO 2014),.
GNI / GDP Ratio
1 FIN 2 SGP 3 SWE 4 NLD 5 NOR 6 CHE 7 USA 9 UK 12 DEU 13 DNK 15 ISR 16 JPN
GDP Growth Rate
(2006-2013) % p.a. at fixed price
GDPgrowthrate(%p.a.)(3) Change in Interactive Return Gain Structure
Losing domestic
gains
Singapore’s Losing Structure
While Singapore enjoys growth, it loses
domestic gains. Finland gains profits
from abroad under the great stagnation.
22
GNI – GDP = Income balance + balance derived from favorable terms of trade
(higher exports prices with lower imports prices).
This irony can be attributed to the
consequences of the strategic option
in shifting to new co-evolution or
clinging to traditional co-evolution.

23
(4) Emergence of Disruptive Business Model
Fig. 19. Consumer Surplus of Music and Audio
- visual Services (Person per month: Aged 20s.).
Source: White Paper of Japan’s ICT (Min. of Internal affairs and
Communication, 2016).
Fig. 20. Un-captured GDP Emerged by Uber
(US$/trip, NYC).
Source： Uber's Ride-sharing Revolution (Watanabe et al., 2016).
Un-captured GDP
1) Music and Audiovisual Services 2) Ride Sharing Service: Uber
% of cumulative answer
Yen
Willing
to pay
Actual payment
(146 Yen/m)

24
IS LM
Principle of effective
demand
Consumption function
(a, b: coefficient）
Investment function
( r : interest rate)
Multiplier theory
GDP
VbaC 
)(rII 
Substitute C in V IGVbaIGCV  )(
I
b
G
bb
a
V 






1
1
1
1
1
Fiscal policy r Monetary
policy
MrI 
CV
(Money supply）
Price x Interest rate = Interest (Fixed)
(Bond yields)
Securing government fund
IS: Investment and Saving LM: Liquidity of Money
Tax revenue ＋ National bond
TaxV 
Income Tax ratio
r
IS-LM analysis
(Integration of effective demand and money supply and interest)
Growth
Private
consumption
Government
consumption
IGCExIGCV 
Invest
-ment
Net exports
(Exp – Imp)
(5) Reconstruction of Taxation System
Good economic condition
(Maximize synergy effects
as a consolidated system)
Lower r
I increase
V increase
Tax increase
C increase
V increase
G increase
Handled sole by European Central Bank (ECB) and each member country is allowed only Fiscal Policy initiative.
100% 61.1 20.6 21.1 - 2.8 (16.2-19.0) [Japan 2013]
68.5 18.2 16.4 -3.1(13.4 -16.5) [US 2014]
55.2 24.9 20.8 - 0.9 (38.2 -39.1) [Finland 2013]
35.0 9.8 29.1 26.1 (194.1-168.0) [S’pore 2013]
Fig. 20. Captured GDP Flow in EMU.
Imposed to ICT phase, Used for supra-functionality

25
(6) Optimal Dynamism Harnessing the Vigor of ICT Growing Economies
Past experiences Current resources
Future dream
Un-captured Captured
GDP GDP
Fig. 21. Optimal Dynamism Harnessing the Vigor of ICT Growing Economies.

National level
Elucidate
Trap in ICT advancement
Two-faced nature of ICT
Bi-polarization
Conceptualize
Operationalize
Business/Individual level
Un-captured GDP
Ride-sharing revolution:
Uber
Trust-based education
toward digitally-rich
learning environments
Commodification of
experiences
Digital music industry
Game industry
Printing/publishing ind.
Uber’s ride-sharing revolution
ICT-driven Disruptive Business Model (IDBM)
Uber’s worldwide
expansion
0. Overview 0.1 Journal Papers
Legal battles Co-evolution
IDBM without CCSD IDBM with CCSD
Co-evolution between Trust in Teachers and Higher Education
toward Digitally-rich Learning Environments
CCSD:
Consolidated
Challenge
to Social
Demand
Trust (Overdrawing past information)
Commodification of experiences 26

27
0. Overview
1. New Stream of Innovation
-Spinoff to Un-captured GDP-driven Co-evolutionary 3 Mega-trends
2. Uber’s Ridesharing Revolution
- ICT-driven Disruptive Business Model (IDBM)
3. Trust-based Education toward Digitally-rich Learning
Environments
4. Optimization through Commodification of Experiences
5. Harnessing the Vigor of Memory and Dream
6. Conclusion
Platform Ecosystems Impact on GDP
ICT-driven Disruptive Business Model with Consolidated Challenge to Social Demand

28
2. Uber’s Ridesharing Revolution
2.1 ICT-driven Disruptive Business Model
(1) Uber: Hero of Spin-off
Fig. 22. Scheme of Uber’s Spinoff Dynamism.
1. Customer: Convenient, cheep, time saving
2. Driver: No invest, use time, to be a boss
3. Uber: Market creation, optimal price, utilize
sleeping resources
4. Government: Accelerate disruptive innovation
People’s preferences shift
Advancement of ICT
Un-captured GDP emergence
Digital economy Uber

Fig. 23. Uber’s Astounding Rise in Trips and Continuous Decline in Prices in NYC.
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
Mar. 2009
Uber established
Jun 2013: Medallion
prices stagnate
May 2011: Uber
launched in NYC
2013/6 2015/1 2015/92014/52011/5
PT
PT
PU
UT
TT
MP
Un-captured
GDP per trip
31
2004/1 2009/3 2011/5 2013/6 2014/5 2015/9
Magnitude of
Un-captured
GDP
te
e
MP 02.0
36.61
2247



TT
TUTT
A
UT
UPTP
P



Trips
Prices
Estimated medallion
prices without Uber
Aggregated prices
(2) Uber’s Conspicuous Development Taxi price
Taxi price
Uber price
Uber trips
Taxi trips
Uber
Uber
Taxi
Taxi
NYC corporate medallion prices MP (1,000 US$)
1. Astounding rise in trips and prices decline.
2. Provide striking value to all stakeholders as
(1) Better services, with cost and time saving for passengers,
(2) High efficient operation without new investment for drivers,
(3) Optimal price-setting and market making for company
(4) Advancement of nation’s disruptive innovation for government.

Fig. 24. Dynamism of ICT-driven Disruptive Business Model (IDBM).
30
(2) Dynamism of ICT-driven Disruptive Business Model
Uber’s system success
1. Co-existing development trajectory with taxi corresponds to two-faced nature of ICT,
2. This can be attributed to a virtuous cycle between price decline and trips increase,
3. This virtuous cycle can be attributed to ICT’s self-propagating function, and
4. This function plays a vital role in spinoffs from traditional co-evolution to new co-evolution.
Computer initiated ICT
Taxi
Internet
Uber
Demand

31
2.2 Consequence of ICT-driven Disruptive Business Model
- Uber’s Expansion and Battles
(1) Rapid Expansion
Fig. 25. Uber’s Expansion in 479 Cities in the World
(as of June 2016).
Source: Uber.
Uber expanded rapidly: 479 cities in more than 75
countries by June of 2016.
(2) Emergence of Legal Battles
Proportional to such rapid expansion
legal battles emerged significantly.
Operating notwithstanding legality Ban/Partial ban
Fig. 26. Contrasting Features of Uber’s Global Expansion in 16 Countries (as of June 2016).
Sources: Authors classification based on, NY Times, HuffPo, Reuters, WSI, CNN and local news reports.

32
2.3 Specific Features of ICT
- Sources of Uber’s Expansion and Battles
(1) Specific Features
Since Uber is seen as the jewel of ICT, its system success (expansion) and failure (battles) can be
attributed to the following ICT’s indigenous function:

33
(2) ICT’s Indigenous Functions Derived from its Specific Features
1) Self-propagating Nature
Diffusion trajectory of innovative goods Y Simple Logistic Growth (SLG) with fixed carrying capacity (N)
Particular innovation which create new N during Logistic Growth within a Dynamic Carrying Capacity
the process of diffusion. (LGDCC)
Carrying capacity increases as Y increases.
Functionality spirally increases as Y increases.
Self-pr
)
)(
1)((
)(
N
tY
taY
dt
tdY
 at
be
N
tY 


1
)(
)
)(
)(
1)((
)(
tN
tY
taY
dt
tdY
 ta
aa
bat
k
k
k
k
ebe
N
Y 




/11









 
)(
)(1
1
1
)()(
tY
tY
a
tYtN
dt
tdY
tY
tY
a
tY
tY
tN
FD )(
)(
)(1
)(
1
1
)(
)(


 
Self-propagation
a. Spinoff
b. Institutional elasticity

Comparison of Spinoff State in Finland vs Singapore and Taxi vs Uber
Simple logistic growth Carrying capacity enhance Logistic growth within a dynamic carrying capacity
Self-propagating dynamism by spinning off to higher functionality level
Finland
Singapore
36
N a b ak bk c adj.R2
0.815
(31.73)
0.311
(8.50)
1.833
(9.28)
0.965
1.000
(8.47)
1.123
(2.35)
23.519
(1.19)**
0.149
(3.75)
2.734
(4.80)
0.047
(20.43)
0.985
0.344
(63.61)*
0.591
(10.57)*
16.58
(3.71)*
0.982
0.344
(63.46)*
0.591
(10.59)*
16.58
(3.71)*
1.00*10-9
(-)
1.00*10-9
(-) 0.982
(1994-2013)
SLC
LGDCC
SLC
LGDCC
Table 3 Comparison of Spin-off State between Finland vs Singapore and Taxi vs Uber
Self-
propagating
Self-
propagating

2) Bi-polarization Fatality of Logistic Growth
Logistic growth function as a function of time t can be depicted as follows:
This function can be developed to the following bi-polarization function:
at
be
N
Y
N
Y
aY
dt
dY



1
)1(
Inflection point
Fig. 27. Bi-polarization Fatal to Logistic Growth Function. 37

36Fig. 28. Transformative Role of Co-evolutionary Acclimatization
.
that harnesses the vigor of counterparts
enables both economies maintain
sustainable growth.
3) Two-faced nature of ICT
While the advancement of ICT contributes to
enhancing its prices by increasing new
functionality development, dramatic
advancement of the Internet tends to decrease
ICT prices due to freebies, easy copying and
mass standardization, among other things.
ICT advanced economies suffer a
vicious cycle between ICT advance-
ment and marginal productivity decline.
ICT growing economies expect growth
as ICT increases but they cannot afford

37
IGE: ICT
growing
economies
Fig. 29. Scheme of Optimal Dynamism for Balancing Captured and Un-captured GDP.
4) Optimal Balance between Captured GDP and Un-captured GDP
ICT advanced economies ICT growing economies
Uber Uber introducing countries

38
2.4 Structural Sources of Legal Battles
Given that Uber’s system success depends on the advancement of ICT, its considerable legal battles
proportional to its rapid expansion can be attributed to ICT’s indigenous functions characterized as
(1) Spin-off,
(2) Institutional elasticity, and
(3) Co-evolutionary acclimatization.

39
0.00
10.00
20.00
30.00
40.00
50.00
60.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Trips per day
2
1
T
T
U
U
(1) Optimal Growth Rate for Spin-off
Fig. 30. Comparison of Uber Trips Estimate (Jun. 2013 – Sep. 2015).
2013/6 2014/1 2014/5 2015/3 2015/9
Table 4 Comparison of Adaptability of Uber’s Development Trajectory
by Growth Rate to LGDCC (NYC, Jun. 2013-Sep. 2015)
Rapid growth 11% p.a (2015/3 – 2015/9)
Steady growth 9% p.a
Rapid growth Non self-propagation
Steady growth Self-propagation
Optimal velocity of growth would
be crucial to self-propagating
functionality development that
leads to spin-off.
While self-propagation can be
expected in steady growth by
fitting to LGDCC, it cannot be
expected to have rapid growth.
Figures in parenthesis indicate t-statistics: all significant at the 1% level except *3: 5%, *5: 15%, *6: 20%, x: non-significant.

40
Fig. 31. Correlation between Centralization of Wage Setting and Union and CBA Density
in 19 Countries in the Late 1990s.
CBA: Collective bargaining agreements. Union and CBA density = (Union density + CBA coverage)/2
Sources: Warner (2002), The Global Competitiveness Report 2015-2016 (World Economic Forum, 2015).
(2) Institutional Elasticity of the Host
Flexibility of wage setting (not centralized ratio)
UnionandCBAdensity
Ranking of flexibility of
wage determination

Functional Development level
Rapid growth Steady growth
Elastic nation/city
Non-elastic nation/city
Time
Level of
Functionality Development,
Institutional elasticity
Co-evolution
withinstitution
Disengagement
frominstitution
Germany
France
Singapore
Saudi Arabia
Tokyo
Finland
Non-adaptive level
Fig. 32. Scheme of Adaption of Uber in Institutional Systems .
)(
)(1
1
1
tY
tY
a
FD 


Uber Adaption in Countries/Cities depending on Growth Rate and Institutional Elasticity
43
Growth rate

42
1.Contrast between countries with and without legal battles can be attributed to with or without CCSD
(Consolidated Challenge to Social Demand).
2.While the former develops co-evolutionary dynamism, the latter results in disengagement.
Fig. 33. Co-evolution and Disengagement between Uber-driven IDBM and Institutional Systems.
(3) Co-evolution and Disengagement
Countries without legal battle Countries with legal battle
Uber induced CCSD leading to a co-evolution
between ride-sharing revolution and advancement
of the institutional systems.
Singapore: Induced incorporating users requirements into
the tripartism framework (company, employee, government) by
stimulating social demand (transport, job, productivity).
Saudi Arabia: Enabled women’s social participation by
providing the reliable transportation leading to a co-evolution.
Tokyo: Stimulated better service seeking competitive market,
broader stakeholders involvement for social demand solution.
Traditional quasi-monopolistic market protected by
non-innovative government impeded Uber’s revolution
resulting in disengagement from the institutional systems.
Germany: Government non-innovative policy urging
traditional legal requirements in response to taxi
companies’ requirement to preserve existing profit
impeded Uber’s disruptive innovation resulting in failing
CCSD construction.
France follows the similar results.
Finland: Suspicious to illegal, but connive to operate.

(4) Co-evolutionary Acclimatization
-ICT-driven Disruptive Business Model with Consolidated Challenge to Social Demand
Host: Uber introducing countries/cities
Harness the vigor of counterparts
(CCSD)
Co-evolutionary acclimatization
Bi-polarization between ICT
advanced and growing group
Fig. 34. Scheme for ICT-Driven Disruptive Business Model
with Consolidated Challenge to Social Demand (IDBM – CCSD).
Thus, ICT-Driven Disruptive Business Model with Consolidated Challenge to Social Demand
(IDBM – CCSD) by harnessing the vigor of counterparts would be decisive for resilient IDBM.
45
Computer initiated ICT
Taxi
Internet
Uber

44
2.5 Lessons from Success Model
2.5.1 IDBM with CCSD
(1) CCSD in Success
Table 5 Structure in CCSD in Success Countries/Cities
Consolidated challenge
by all stakeholders
Social demand Co-evolutionary
acclimatization
Singapore Tripartism framework
User involvement
Company, employee,
user, government
consolidation
Traffic service, Job
opportunity, Overall
productivity enhance,
Digital innovation
Tripartism framework,
Well developed
infrastructure,
Innovation seeking
spirit
Saudi Arabia Women (user, employee)
Company involvement
Government involvement
Women’s social
participation,
Education, Industrial
structure
Strong inertia to
women’s social
participation,
Affluent financial base
Japan User welcome
Company, employee
concern
Government
involvement
Traffic service, ICT
advancement,
e-commerce,
Depopulation,
Aging society
High potential demand,
Demanding nature,
Matured competitive
environment

45
NTUC this Week, 26 Jan. 2007 (NTUC: National Trades Union Congress).
(2) Lessons from Tripartism

2.5.2 Significance of Shift to IDBM with CCSD
(i) Nowadays, a key factor in obtaining business opportunities is the ability to solve social demand. [SD]
(ii) A company to gain a profit must consolidating all stakeholders: company, employee, user, and
government with respective heterogeneous objectives.
(iii) Developing systems that address all stakeholders’ demands in society as a whole can allow these
disparate groups to successfully function together. [CC]
(iv) The company that can attain such system success is required
following abilities:
(a) Penetrate the social demand that can be its business opportunity,
(b) Organize and orchestrate all stakeholders, and
(c) Attain the system success thereby gain profit.
(v) Development and utilization of ICT enable such endeavor.
[IDBM]
(vi) Thus, shifting to IDBM with CCSD has been becoming crucial.
(vii) Function of trust-based tripartism framework suggests the
significance of ICT and trust toward IDBM with CCSD in
the digitally-rich environment.
Company
Employee User
Government
Social
demand
Profit seeking
Job after retire
To be a boss
Utility,
eco, health,
comfort
Income, job, welfare, happiness,
eco, aging, health, safety
ICT advancement
(Internet, smartphone, big data）
48
Fig. 35. Consolidated Challenge to Social Demand.

47
3. Trust-based Education toward Digitally-rich Learning
Environments
3.1 Co-evolution with Trust as a Consequence of IDBM with CCSD
(1) Consequence of IDBM with CCSD
Fig. 36. Consequence of IDBM with CCSD.
Own experiences
+
Past information
Decrease risk and
uncertainty
Enhance trust

Fig. 37. Scheme of Spinoff Dynamism.
Uber
Ride sharing
evolution
DILE
(Digitally-rich Innovative
Learning Environments)
48
(2) Spinoff from Traditional Co-evolution to Un-captured GDP oriented New Co-evolution
Transforming
Infusing
Applying
Emerging
Digital economy
Shifting
from trust in personality
to trust in system of
augmented reality (AR)

49
3.2 Co-evolution between ICT, Trust and Higher Education
(1) Comparison in 20 Countries
Fig. 38-2. Higher Education (2013). Fig. 38-3. Trust in Teachers (2013).
Fig. 38-1. ICT Advancement (2012-2015 average).

50
(2) ICT-driven Education Development
Fig. 39. ICT-driven Education Development in 120 Countries (2013).
at
be
N
Y
N
Y
aY
dt
dY



1
)1(
IAC
ISC
IGC
ICT advancement (Z)
Highereducation(Y)

51
(3) Correlations
Table 6 Co-evolution and Disengagement between ICT, Educational Level, and Trust
in Teachers
Fig. 40-1. Correlation between Higher Education and Trust (2013). Fig. 40-2. Correlation between ICT and Trust (2013).
+: Co-evolution
- : Disengagement
Y
X
X
Z
Why trust decrease as
ICT advance?
(1)
(2)
(3)
( < 0)
IAC
ISC
IGC
IAC
ISC
IGC
872.0. 2
RadjIAC ISC IGC IAC ISC IGC 848.0. 2
Radj
D: Dummy (D1: IAC, D2: ISC, D3: IGC, D: Jpan, Kor, Isr, Chz =1, Others = 0). Figures in parenthesis: t-statistics (all significant at the 1 % level, except **: 3%)

52
3.3 Structural Sources of Trust Decrease as ICT Advance
(1) Bi-polarization
Fatality
(2) ICT Elasticity
to Trust
(3) Composition of ICT Elasticity to Trust (4) ICT Elasticity to Higher Education
IGC
ISC IAC
IAC: ICT advanced countries
ISC: ICT semi-advanced countries
IGC: ICT growing countries
1. Trust decrease
( ) )
depends on stage of
ICT advancement.
2. ISC suffers a vicious
cycle between Z and
Y.

53
(4) Blended Learning and Teacher’s Resistance - Transition to ICT Advanced Countries
Fig. 41. State of Hybrid Development in 20 Countries.
1. ICT’s contribution to higher education can be developed in a hybrid manner through traditional
teaching practice and blended learning (introduction of digital and online media in educational system).
2. While strong resistance by teachers impedes blended learning, once a certain higher education level has
been attained, increasing dependency on blended learning exceeds such resistance leading to co-
evolution between ICT and higher education.
3. While IAC has attained, ISC is in transition from traditional to blended learning overcoming resistance.

54
Fig. 42. Stages of ICT Integration in Education.
(5) Stages of ICT Integration in Education
1. ICT has been integrated to an education system
by 4 stages as
(i) Emerging, (ii) Applying, (iii) Infusing, and
(iv) Transforming.
2. While the 1st 3 stages are advancing toward
more digitally rich, the 4th stage transforms
learning environments into digitally-rich
new environments that can absorb and
apply ICT-driven new services to higher
education.
3. Transforming stage accelerates over-
drawing of past information by introducing
such ICTs as augmented reality (AR),
simulations and digital games thereby
increasing trust.
4. While IAC has shifted to transforming stage
and constructed a virtuous cycle between
ICT and trust by overdrawing past
information.
ISC and IGC have remained non-transforming
stage thereby suffered a vicious cycle between
ICT and trust.
Trust decrease depends on stage of ICT advancement

55
)
1
1(
FD
aY
dZ
dY

Overdrawing past
information
3.4 Co-evolutionary Acclimatization toward Digitally-rich
Learning Environments – Harness the vigor of Past Information
(1) Reconstruction of a Virtuous Cycle
ISC, IGC IAC
Shifting to higher productivity level by
harnessing the vigor of past information
Fig. 43. Co-evolutionary Acclimatization Harnessing the Vigor of Past Information.
dY
dZ

dY
dZ
2
,0
0)2(
)(
1
)1(
1
1
2
N
Yand
dZ
dY
when
dZ
dY
NY
YYN
aYdZ
d
dZ
d
dY
dZ
dZ
d
N
Y
dZ
dY

































Z
Enables absorption and
application of ICT-driven
new services for higher
education, which hitherto
could not be afforded
(Un-captured GDP)
Reconstruct a virtuous
cycle by overcoming trap
in ICT advancement
Transformative ICT

56
Current Past Future
National Economy
Uber Sleeping resources
Digital learning Time
Music/Game Memory Dream
(2) Transformative Direction of Co-evolutionary Acclimatization Target
Fig. 44. Transformative Direction of Co-evolutionary Acclimatization Target
in ICT-driven Disruptive Business Model.
Increasing significance of co-evolutionary acclimatization for ICT-driven disruptive business model.
Its target has been shifting from current economy (e.g., economic growth in growing economies) to sleeping
resources (e.g., Uber’s ride sharing revolution), past time (e.g., trust-based higher education), and to past
memory/future dream (e.g., digital music, game).

57Fig. 45. Optimal Dynamism Harnessing the Vigor of Time, Memory, Dream.
ICT advance T (Z)
)(
dY
dZ
dY
dT
(3) Optimal Dynamism Harnessing the Vigor of Time, Memory, Dream
Enables absorption and
application of ICT-
driven new services,
which hitherto could
not be afforded
(Un-captured GDP)
Past experiences Current resources
Future dream

58
4. Optimization through Commodification of Experiences
4.1 Commodification of Experiences
(1) Transfer the Anger into a Springboard for New Innovation
1. Shift of consumers preference from economic functionality to supra-functionality beyond economic
value emerges conflict in the transition leading to growing anger of consumers.
2. Innovation-consumption co-emergence has thus become crucial. Commodification of experiences may
provide significant solution to this problem.
Fig. 46. Dynamism in Transferring the Anger into a Springboard for New Innovation.

59
(2) From Invisible Hand of God to Voiceless Voice of Consumers
Fig. 47. Scheme in Conceptualizing Invisible Voice of Consumers.
Optimization

60
Fig. 48. Scheme of Facial Temperature Feedback Hypothesis.
(3) Facial Temperature Feedback Hypothesis
Confronting
unforgettable
memory

(1) Methodology
Monitor the consumers’ facial temperature by the thermography: novel
psychophisiologal measuring technique enables observation in the objective
circumstances without providing any cautions to examinees.
Record in a PC
Analyze the recorded data by the exclusive software
“FLIR Research IR” (able to identify a pin-point temperature)
33.4
4.2 Demonstration by Experiment
(a) With the measurement of the relationship between attractive goods and consumers’ facial
temperature elevations at the leading supermarkets in Japan and Finland.
(b) Demonstrate facial temperature feedback hypothesis.
63

Event wagon on which sweetened
ban is displayed (15 February)
Installation of PC for data recording
(15 February)
Hanging situation of the thermography
(15 February)
Shelf on which PC is stalled
(15 February)
Positions of event wagon and the
thermography (15 February)
Angle of the thermography
and target of monitoring
(21 February)
64
(2) Pilot Experiment at a Leading Supermarket 1) Tokyo
Fig. 49. Pilot Experiment at a Japanese Leading Supermarket in Tokyo (February 16-21, 2011).
Melon-bread
with reasonable
price, attractive
enough to empting
shoppers appetite

2) Finland
Cosmetics corner of Sokos Supermarket (7 March 2012)
Cosmetics corner Thermography
connected to PC
Shoppers accessing to
cosmetics corner
Examining anticipating
cosmetics
Inducement by
sales promoter Trial makeup
65
Fig. 50. Pilot Experiment at a Finish Leading Supermarket in Jyvaskyla (March 6, 7 2012).

31.0
31.5
32.0
32.5
33.0
33.5
34.0
1 2 3 4 5 6 7
Accessed to the
event corner
Perceived
Recognized
Decided
to purchase
31.4 31.7
31.7 31.6 31.9 31.7 33.3
Further perceived
Further recognizedAccessed to
the event corner
Perceived Further perceived Recognized
Further recognized
Resemble past learning Surprise
Remember
past learning
Gratification
Correspondence with
gratification experienced
Facetemperature
MCS SNS MCS SNS MCS
(3) Empirical Results – Tokyo
1) Facial temperature
31.4℃ 31.7 31.7 31.6 31.9 31.7 33.3
MCS: Metabolic Control System (elevate temperature), SNS: Sympathetic Nervous System (descend temperature)
Fig. 51. Standard Pattern of Facial Temperature Change in Purchased (Tokyo).
Decided to purchase
Facial temperatures elevate recalling gratification ever experienced.
66

2/19 15:00-16:00
2/19 16:00-17:00
2/19 17:00-18:00
2/20 15:00-16:00
2/20 16:00-17:00
2/20 17:00-18:00
2/20 18:00-19:00
0
2
4
6
8
10
12
14
16
18
20
0 100 200 300 400 500 600 700
S
X
Sales
volume:
S
2) Correlation between facial temperature and sales
Attractive goods elevate customer’s facial temperature.
Facial temperature index: X
67
Fig. 52. Correlation between Facial Temperature and Sales.

Roman holiday Sputnik No. 1 Tokyo Olympic Game Beatles Apollo
(1953 ） (1957) (1964) (1966) （1969)
4.3 Commodification of Experiences
(1) Unforgettable Impressive Memory Experienced in the 20s
68
Fig. 53. Major Impressive Memory Never Forget Experienced in their 20s.

69
(2) Sublimation of 20s Experiences into Supra-functionality beyond Economic Value
Social value
Cultural value
Aspirational value
Tribal value
Emotional value
Energy saving
Small is beautiful
Eco
Cool
Sensitivity
Private
brand
Voluntary social
service
Aesthetic sense
Quiet simplicity
Distance between firms and society, Creation of social value
Social welfare, Support of social disability
Creation of social communication, Contribution to social platform
Contribution to social needs (e.g. PV)
Shifting from tough agreement to loose agreement
Brand value, Private brand (PB)
Solicitation of saving mind
Aesthetic sense, Quiet simplicity, Cool, Cute, J-sense
Voluntary participation
Sensitivity of Japanese products (Steve Jobs aspired)
Authentic goods, Goods with high-grade sense
Professional, Rich
Sense of recognizing own position
Fellow feeling, Patriotism
Re-recognition of Made in Japan
Symbolic meaning
Five senses, Sensitivity
Only one
Casio-mini Cassette-tape Apple II Carry-compo Walkman PC Card calculator
(Casio) (Sony) (Apple) (Apple) (Sony) (NEC) (Sharp)
Cup noodle
Bulgaria yogurt
Chipstar
Surprise
chocolate

(3) Platform for Commodification of Experiences
for Innovation-Consumption Co-emergence
Utmost gratification of consumption ever experienced
Memorize in the brain
Similar innovative goods/services
Collate with memory of
utmost gratification
Facial temperature change
Elevate Descend
Awake sleeping experiences on utmost
gratification once experienced
Real Pseudo
Commodification of experiences corresponding
to utmost gratification of consumption ever experienced
Fig. 54. Platform for Commodification of Experiences.
Induce resonance between
innovative goods and consumers
Trigger innovation-consumption co-
emergence
70
This dynamism leads a way to
Optimal Dynamism Harnessing
the Vigor of Time, Memory,
Dream

69
5. Harness the Vigor of Memory and Dream
5.1 Digital Music as a Platform for Retrieving Music Information
– Bridging Digital Innovation to the World of Memory Retrieval
(1) Approach
1. The advancement of the Internet has changed the way of business and daily life dramatically.
Evolutional change in the music industry over the last decade can be one of the most striking example.
2. Fundamental source of this evolution can be attributed to the dramatic advancement of the Internet-
mediated technologies that enabled constructing a platform for retrieving music information thereby
customers can enjoy music ubiquitously by their own initiative.
3. This platform provides significant insight in bridging digital innovation to the world of memory
retrieval.
4. Given the increasing significance of memory retrieval in creating customer initiated business model
in the digital economy, dynamism of this platform should be analyzed.

Adoption of Digital music
Enable customers to enjoy music
ubiquitously by own initiatives
Music information retrieval (MIR)
MIR enabled applications
70
Fig. 55. Platform of Digital Music Industry.
Platform
Internet-mediated
technologies
Digital innovation
Musical Moment
Listen
Music
Cognitive itch
Stuck in head
Appreciate &
Memorize
Striking / intriguing
Hooked
Catchy partInvoke
Past memories
Emotionally, structurally,
perceptually
Catchy part of the song
Mediation tools
Description
Search music by Singing or humming, natural
language search, keyboard search, similarity
search, text search etc.
Music service providers, music search engines,
music generators etc.
Enable to categorize, manipulate and even create
music (Recommender systems, track separation,
instrument recognition, auto transcription,
categorization and music generation etc.)
Key features
Substitution for physical music
New style of music treat
(search, deep discovery, listening,
personalization and sharing)
Music intelligence enabled services
Transformation to music information
(2) Platform of Digital Music Industry

71
(3) Empirical Analyses
On the basis of the empirical analysis of the development trajectory of the music industry in the world
over the last 4 decades, unique dynamism of the music industry enabling to retrieve information
identical to music is elucidated.
Conceptualization of this dynamism applicable to business model construction in the digital economy
is also attempted.
1) Substitution Dynamism
1. Digital music for physical music (1974-2014) monthly
2. Within digital music (2004-2014) monthly
Logistic growth within a dynamic carrying capacity (self-propagation function)
Bi-logistic growth, tri-logistic growth (substitution dynamism)
2) Institutional Factors Contrasting Heterogeneous Dependency on Digital Music
1. 50 countries in 2007 – 2014 (comparison between 2007, 2010 and 2014?)
2. Music: Subscription vs streaming ?
Institutional factors: Global Competitiveness Report,
Global Information Technology Report
Principal Component Analysis
Factor Analysis
Note
Demand pattern of digital music
Almost all data are supply side (e.g., sales, revenues)

72
5.2 Social Gaming as a Platform for Harnessing the Vigor
of Dream

6. Conclusion
75
1. Spinoff to Un-captured GDP-driven Co-evolution of 3 Mega-trends
(1) Advancement of ICT incorporates a two-faced nature as ICT advancement has
resulted in price decrease due to freebies, easy copying and standardization.
(2) The Internet provides incredible services to people but they cannot be captured
by GDP.
(3) Un-captured GDP can largely be attributed to consumer utility which
corresponds to their preferences shift from economic functionality to supra-
functionality beyond economic value.
(4) This shift, in turn, induces further advancement of the Internet, leading to co-
evolution of the 3 mega-trends: advancement of ICT, a shift to un-captured GDP
and also a shift to people’s preferences.
(5) Consequently, today’s global digital innovation can be identified as spining-off
co-evolution toward un-captured GDP-driven new mega-trends.

6. Conclusion (2)
76
2. ICT-driven Disruptive Business Model - Uber’s Ridesharing Revolution
(1) Under such circumstances, we are in the midst of transformative shift in
business design.
(2) Business models move from pipes to platforms which allow external producers
and users to exchange value with each other, leading to users acting as
producers and creating value for other users.
(3) The ridesharing revolution initiated by Uber can be seen as the jewel of such an
ICT-driven platform ecosystems.
(4) It creates better services and higher value to all stakeholders: passengers,
drivers, company, and government.
(5) The success of Uber system suggests the significance of an ICT-driven disruptive
business model (IDBM).

6. Conclusion (3)
2.-2 Consolidated Challenge to Social Demand for Resilient Platforms
(1) While Uber has expanded rapidly worldwide, the contrast between co-
evolutionary success and legal battles with host countries/cities has become
distinct.
(2) This can be attributed to the bi-polar nature of ICT’s rapid advancement,
suggesting the significance of harnessing the vigor of counterparts.
(3) Lessons from successful co-evolution suggest the significance of IDBM with
CCSD (consolidated challenge to social demand).
(4) Thus, IDBM with CCSD suggest the significance of resilient platform ecosystems
that incorporate contingency.
77

6. Conclusion (4)
78
3. Trust-based Education toward Digitally-rich Learning Environments
(1) Lessons of IDBM with CCSD suggest the significance of ICT and trust toward the
digitally-rich learning environments.
(2) While ICT advanced countries have embarked on co-evolution between ICT, higher
education and trust, ICT growing countries have not been successful in this due to a
vicious cycle between ICT and trust.
(3) This co-evolution can be attributed to co-evolutionary acclimatization by harnessing
the vigor of past information thereby absorption and application of ICT-driven new
services for higher education, which hitherto could not be afforded, were enabled.
This is equivalent to un-captured GDP emergence.
(4) With the notion that trust depends on overdrawing of past information, such
business model as constructing co-evolutionary acclimatization by harnessing the
vigor of time should be envisioned.

6. Conclusion (5)
79
4. Optimization through Coomodification of Experiences
(1) Shift of consumers preferences to supra-functionality beyond economic value
emerges conflict in the transition leading to growing anger of consumers.
(2) This anger can be transformed into a springboard for new innovation.
(3) This highlights the significance of voiceless voice of consumers leading to the
increasing significance of commodification of experiences.
(4) This commodification enables to pave a way to identifying the optimal dynamism
harnessing the vigor of time, memory and dream.

78
References
1. C. Watanabe, K. Naveed, P. Neittaanmaki and B. Fox, “Consolidated Challenge to Social Demand for
Resilient Platforms: Lessons from Uber’s Global Expansion,” Technology in Society (2016) in print.
2. C. Watanabe, K. Naveed, P. Neittaanmaki, “Co-evolution of Three Mega-trends Natures Un-Captured
GDP: Uber’s Ride-sharing Revolution,” Technology in Society 46 (2016) 164-185.
3. C. Watanabe, K. Naveed, P. Neittaanmaki and Y. Tou, “Operationalization of Un-captured GDP:
Innovation Stream under New Global Mega-trends,” Technology in Society 45 (2016) 58-77.
4. C. Watanabe, K. Naveed and P. Neittaanmaki, “Dependency on Un-captured GDP as a Source of
Resilience Beyond Economic Value in Countries with Advanced ICT Infrastructure: Similarity and
Disparities between Finland and Singapore,” Technology in Society 42 (2015) 104-122.
5. C. Watanabe, K. Naveed and W. Zhao, “New Paradigm of ICT Productivity: Increasing Role of Un-
captured GDP and Growing Anger of Consumers,” Technology in Society 41 (2015) 21-44.
6. C. Watanabe, “Innovation-Consumption Co-emergence Leads a Resilience Business,” Innovation and
Supply Chain Management 7, No. 3 (2013) 92-104.
7. C. Watanabe, W. Zhao and M. Nasuno, “Resonance between Innovation and Consumers: Suggestions to
Emerging Market Customers,” Journal of Technology Management for Growing Economies 3, No. 1
(2012) 7-31.

Appendix I. Measurement of Un-captured GDP
81

(1) Spin-off State
Simple logistic growth Carrying capacity enhance Logistic growth within a dynamic carrying capacity
Self-propagating dynamism by spinning off to higher functionality level
Finland
Singapore
Finland
Singapore
Finland has spun-off to new co-evolution while
Singapore remains traditional co-evolution.
82
D: Dummy variable (2002-2005 = 1, other years= 0; During
the period of stagnation due to the bursting of the Net bubble.)
*, *** : Significant at the 1% and the 10% level, respectively.
0.815
(31.73)*
0.311
(8.50)*
1.833
(9.28)*
0.965
1.000
(8.47)*
1.123
(2.35)*
23.519
(1.19)***
0.149
(3.75)*
2.734
(4.80)*
0.047
(20.43)*
0.985
0.344
(63.61)*
0.591
(10.57)*
16.58
(3.71)*
0.982
0.344
(63.46)*
0.591
(10.59)*
16.58
(3.71)*
1.00*10-9
(-)
1.00*10-9
(-) 0.982
Finland
Singapore
1994 2000 2005 2010 2013
1.0
0.8
0.6
0.4
0.2
0.0
Un-captured
GDP ratio

81
Institutional factors Finland Singapore References
Small income disparity
Inequality (GINI index: 2010)
Capacity for innovation
Capacity for industry innovation
Association capacity and collaborative practices
University-industry collaboration in R&D
Absorptive capacity
Firm-level technology absorption
Trusting relationship
Willingness to delegate authority
Generosity (score)
Freedom to make life choices (score)
Women in working life
Women in labor force
Government ICT usage
Importance of ICT to government vision
Government online service
Government success in ICT promotion
Labor-employer relations
Cooperation in labor-employer relations
19
2
2
7
4
0.33
0.52
12
16
7
16
21
45
18
4
13
23
0.19
0.43
76
3
1
4
2
Distribution of Household Income by Source
(ILO, 2012)
The Global Information Technology Report
2014 (WEF, 2014)
The Global Competitiveness Report 2013-2014
(WEF, 2014)
The Global Information Technology Report
2014 (WEF, 2014)
(WEF, 2014)
World Happiness Report (The Earth Institute,
Columbia Univ. et al., 2013)
idem
(WEF, 2014)
The Global Information Technology Report 2014 (WEF, 2014)
idem
idem
The Global Competitiveness Report 2013-2014 (WEF, 2014)
Table A1.1 Engine and Brake in Finland and Singapore (2013) Figures indicate world rank otherwise indicated.
(2) Engine and Brake for Spin-off to the External Link
Finland’s advancement in shifting to new co-evolution can be attributed
to its powerful engine.
While Singapore’s strong government initiatives in accelerating its ICT
advancement has played a significant role as an engine for the nation to be a
world ICT leader, it resulted in the delay in transferring business initiative
which is essential in shifting from traditional co-evolution to new co-
evolution.

82
(3) Strategic Actions for Spin-off
Fig. A1.1. Strategic Actions for Spin-off.
By transferring strong engine obtained by government to industry, “muscular” economic environment should be created, which enables
high competitive home companies (HCs) to exploring global markets leading to increasing returns on foreign investment. This ultimately
contributes to increase “muscularity” of HCs which in turn develops “muscular” economic environment, thus constructing a virtuous
cycle.

83
Appendix II. Uber’s Ride-sharing Revolution

0
10
20
30
40
50
60
Jun-13
Aug-13
Oct-13
Dec-13
Feb-14
Apr-14
Jun-14
Aug-14
Oct-14
Dec-14
Feb-15
Apr-15
Jun-15
Aug-15
AII.1 Uber’s Conspicuous Launch
Fig. AII.1. Trends in Uber and Taxi Trips in the US (Jun. 2013 – Sep. 2015).
Sources: Taxi: Fig. 3-2; Uber: authors’ estimate based on ,
where UT: Uber trip, TT: Taxi trip, UD: Uber share (Fig. 2) (See
Appendix 1).
Trips per day
Uber
Taxi
TT
UD
UD
UT 


1
84
UT
TT
$10
$12
$14
$16
$18
$20
$22
$24
Jun-13
Aug-13
Oct-13
Dec-13
Feb-14
Apr-14
Jun-14
Aug-14
Oct-14
Dec-14
Feb-15
Apr-15
Jun-15
Aug-15
Taxi
Uber
Average prices per trip
PT
PU
Fig. AII.2. Trends in Uber and Taxi Prices in NYC (Jun. 2013 – Sep. 2015).
Sources: Taxi: Fig. 3-3; Uber – Jun. 2013 - Nov. 2014: Lunden (2014): other period:
authors ‘estimate based on TLC, Uber, Stone (2015) and Silverstein
(2014) (See Appendix 1).
1. Corresponding to its astounding success, Uber’s prices continued to decline and reached in May 2014 the same level as taxis, The
prices further declined with the introduction of UberPool in August 2014.
2. This decline in prices was reversed as a consequence of Uber’s surge pricing, which resulted in an “F” (flunk) rating from the
Better Business Bureau (BBB) in Oct. 2014. BBB cited complaints over unexpectedly high charges.
3. In response to such complaints and also to competition from competitors such as Lyft, Uber managed to decrease prices by introducing Uber Go in Nov. 2014.
4. This move, together with the establishment of the Uber Advanced Technology Center in Feb. 2015, led to lower prices again in 2015.
2014/5 14/8 14/10 14/11
2015/1

AII.2 Institutional Enablers Creating Platform Ecosystems
AII.2.1 Preference Shift to Sharing Economy
1. In line with people’s preferences shift from economic functionality to supra-functionality beyond economic
value, sharing economy in physical products (i.e., rooms and cars) has been gaining momentum.
2. The underlining paradigm of the original sharing economy is that users aim at increasing resource-use
efficiency. This is done either to lower costs or to create new value. The resources are offered to others at
times when the owners do not use them themselves.
3. Online trading platforms such as Napster and eMula were amongst the first to provide users with a shared
access to digital music and videos.
4. It was possible to download these digital products from lenders on the platform for free, and uploading and
downloading happened simultaneously. This constitutes the very essence of sharing economy (Winterhalter
et al., 2015).
5. People’s preference shift to supra-functionality has led to requests for a similar platform also for physical
products. People wish to use such products (which were provided passively, primarily for their economic
functionality) in a more sophisticated manner and by their own initiative (Adner, 2012).
6. Sharing economy for physical products (initiated by Uber and AirbnB: car transport and rooms, respectively)
is needed by the market with such underlining paradigm.
85

PU decrease
rate
Contribution by
Period
SP increase rate UT increase rate Miscellaneous
-2.58 -0.911 x 1.34 = -1.22 -0.173 x 11.28 = -1.95 0.59 2013/6 - 2014/9
-2.34 -0.737 x 0.61 = -0.45 -0.359 x 11.14 = -4.00 2.11 2014/10 - 2015/9
1. Uber’s prices have been governed by the
advancement of smartphones, learning and
economy of scale effects. The prices
continued to decline before serious
complaints over unexpectedly high
charges due to surge pricing in Oct. 2014.
2. While this upward shifting factor remains,
the price decline trend was maintained
by introducing Uber Go in Nov. 2014.
3. This demonstrated high elasticity of trips
to prices and compensated the
stagnation of smartphones’ share increase
in 2015.
PU is governed by SP advancement, learning and economy of scale effects.
SP: monthly trend in smartphone share of mobile subscriber market (comScore)
TU
TU
USPAP
USPAP
lnlnlnln 




Table AII.2 Contribution of PU decrease (Jun. 2013/6 – Sep. 2015) - % p.a
88
)58.4()97.10()88.3(32.2()71.2()33.5( )
01.1979.0.164.0ln359.0ln173.0ln737.0ln911.0105.7ln
*
2
2121

 DWRadjDUDUDSPDSPDP TTU
D1:2013.6 – 2014.9 = 1, rest = 0. D2:2014.10 – 2015.9 = 1, rest = 0, D:2014.10 = 1, rest = 0.
U
U
P
P
Table AII.1 Governing Factors of Uber Prices in the US (Jun. 2013 – Sep. 2015)
2014. 10
PU: Uber’s prices, SP: Smartphone subscriber market share (%), UT: Uber trips, and D1, D2 , D3: Dummy variables.
AII.2.2 ICT’s Self-propagating Virtuous Cycle
(1) Governing Factors of Uber Prices Decline
Uber was given an “F” (flunk) rating from the Better
Business Bureau (BBB) in response to complaints
over unexpectedly high charges due to surge pricing.

Fig. AII.4. Virtuous Cycle between Uber’s Trips and Its Prices (Jun. 2013– Sep. 2015).
Figures in parenthesis indicate t-statistics: all significant at the 1% level except #: 15% level.
)16.3()01.18()44.1()34.12()17.68(
#
2
2131211 08.1957.0.573.1ln289.0ln270.0ln398.0639.3ln

  DWRadjDUTDUTDUTDPU tttt
)63.4()45.16()52.3()27.20()09.24(
00.2991.0.887.33ln782.2ln275.9ln055.3979.10ln 2
111 2321

  DWRadjDPUDPUDPUDUT tttt
Fig. AII.3. Correlation between Uber’s Trips and Their Prices (Jun. 2013 – Sep. 2015).
Uber’s trips Uber’s
prices
D1:2013.6– 2014.6 = 1, rest = 0.
D2:2014.7 – 2014.11 = 1, rest = 0.
D3:2014.12 – 2015.9 = 1, rest = 0.
PU: Uber’s prices,
UT: Uber trips
D1, D2 , D3 Dummy variables.
D1:2013.6– 2014.6 = 1, rest = 0.
D2:2014.7 – 2014.11 = 1, rest = 0.
D3:2014.12 – 2015.9 = 1, rest = 0.
Source: Certify.
87
1. Uber’s prices demonstrated sharp
decline as smartphones advanced.
2. This decline induced more Uber
trips, which in turn further
accelerated the decline of Uber’s
prices.
3. Thus, a self-propagating virtuous
cycle has been created in Uber’s
development as demonstrated in
Fig. AII.4.
Jun. 2013
Sep. 2015
$10
$12
$14
$16
$18
$20
$22
$24
$26
0 10 20 30 40 50
Uber’sprices/trip
Uber’s trips
2014/7
2014/1
1
(2) Virtuous Cycle between Uber Trips Increase and Their Price Decline

$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
Jan-04
May-04
Sep-04
Jan-05
May-05
Sep-05
Jan-06
May-06
Sep-06
Jan-07
May-07
Sep-07
Jan-08
May-08
Sep-08
Jan-09
May-09
Sep-09
Jan-10
May-10
Sep-10
Jan-11
May-11
Sep-11
Jan-12
May-12
Sep-12
Jan-13
May-13
Sep-13
Jan-14
May-14
Sep-14
Jan-15
May-15
Sep-15
NYC Corporate Medallion Prices (1,000 US$)
Actual Logistic Growth
May 2011: Uber
launched in NYC
Mar. 2009
Uber established
May 2014
Jun 2013: Medallion
prices stagnate
Fig. AII.5. Trend in Corporate Medallion Prices and their Estimate without Uber in NYC (Jan. 2004 – Sep. 2015).
Source: NYC Taxi and Limousine Commision (TLC).
90
(1) Medallion Prices as a Proxy of the Trend in Taxi Demand
AII.3 Co-evolution of 3 Mega-trends Leading to a Spinoff to Sharing Economy
AII.3.1 Emergence of Un-captured GDP
* While advancement of ICT generally contributes to the enhancement of prices by increasing new functionality development, the dramatic
advancement of the Internet tends to decrease ICT prices due to its freebies, easy copying and mass standardization, among other things.
Estimated medallion prices without Uber
Decline
due to Uber
Magnitude of
un-captured
GDP
Fig. AII.6. Two-faced Nature of ICT*.
Uncaptured GDP

Fig. AII.7. Scheme of the Measurement of the Emergence of Uncaptured GDP in case of Uber in NYC.
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
Mar. 2009
Uber established
Jun 2013: Medallion
prices stagnate
May 2011: Uber
launched in NYC
2013/6 2015/1 2015/92014/52011/5
PT
PT
PU
UT
TT
MP
Uncaptured
GDP per trip*
91
2004/1 2009/5 2011/5 2013/6 2014/5 2015/9
Magnitude of
Un-captured
GDP
)(
1
1
1 1 UT
UT
T
TT
UTTT
TAT PP
PP
P
UT
PUPT
PPP 










Uncaptured GDP
where PT: Taxi prices, PU: Uber prices, PA: Aggregated prices,
TT: Taxi trip, UT: Uber trip, : UT/TT ratio.
te
e
MP 02.0
36.61
2247



TT
TUTT
A
UT
UPTP
P




(2) Magnitude of the Emergence of Un-captured GDP
The foregoing observation on the trend in medallion prices
inspires the following analogy with respect to discrepancy
of prices between taxis and Uber, as illustrated in Fig. 11:
Medallion prices Actual Medallion Un-captured GDP
without Uber MPe prices MP MPe - MP
Taxi prices PT Aggregated prices PA Un-captured GDP PT - PA
Based on this, the emergence of un-captured GDP was
estimated as follows:
Trips (Fig. 4)
Prices (Fig. 5)
Estimated medallion
prices without Uber
Aggregated prices
*PT vs Mpe and
PA vs MP
demonstrates
significant parallel
correlation.
(Appendix 3).

(3) Correlation between Uber’s Dependency and Medallion Prices
Fig. AII.9. Virtuous Cycle between Uber’s Dependency and Medallion Prices
(Jun. 2013 – Sep. 2015).
Figures in parenthesis indicate t-statistics: all significant at the 1% level except #: 30% level.
)07.5()31.3()22.14()06.1()50.11()43.77(
#
2
2131211 77.1948.0.131.0895.1ln420.0ln220.0ln554.0435.8ln

  DWRadjDDUDDUDDUDDMP tttt
)62.3()88.11()06.3()11.13()22.15(
46.1980.0.770.92ln910.1ln030.11ln051.2022.17ln 2
111 2321

  DWRadjDMPDMPDMPDUD tttt
Fig. AII.8. Correlation between Uber’s Dependency and Medallion Prices (Jun. 2013.6 – Sep. 2015).
Uber’s dependency
Medallion prices
D1:2013.6 – 2013.12 = 1, rest = 0.
D2:2014.1 – 2014.5 = 1, rest = 0.
D3:2014.6 – 2015.9 = 1, rest = 0.
D: 2013.7-8, 2015.9 = 1, other months = 0.
MP: Medallion prices,
UD: Uber dependency
D1, D2, D3, D: Dummy variables.
D1:2013.6 – 2013.12 = 1, rest = 0.
D2:2014.1 – 2014.5 = 1, rest = 0.
D3:2014.6 – 2015.9 = 1, rest = 0.
Medallion prices in NYC, while Uber dependency (% of Uber) is nationwide.
Sources: NYC Taxi and Limousine Commission (TLC) and Certify.
90
1. Uber’s astounding success brought its
charges below the taxi charges in
May 2014.
2. This resulted in a significant decrease
in medallion prices.
3. This in turn induced further
dependency on Uber, leading to a
virtuous cycle between medallion
price decline and increase in the
dependency, as demonstrated in Fig. .
4. In addition to the foregoing
significant parallel correlation
between PT vs Mpe and PA vs MP,
the correlation among Uber, taxi and
medallion demonstrates the
significance of the foregoing
analogy with respect to the
emergence of un-captured GDP and
endorses the view that the balance
between taxi prices and aggregated
prices represents the emergence of
un-captured GDP by Uber.
Jun. 2013 May. 2014
Sep. 2015
$600
$700
$800
$900
$1,000
$1,100
$1,200
$1,300
$1,400
$1,500
0 10 20 30 40 50 60 70
Medallionprices(1000US$)
Uber’s dependency %
Jan. 2014

TT
TUTT
A
UT
UPTP
P



13.5
14.0
14.5
15.0
15.5
16.0
16.5
17.0
17.5
18.0
Jun-13
July-13
Aug-13
Sep-13
Oct-13
Nov-13
Dec-13
Jan-14
Feb-14
Mar-14
Apr-14
May-14
Jun-14
July-14
Aug-14
Sep-14
Oct-14
Nov-14
Dec-14
Jan-15
Feb-15
Mar-15
Apr-15
May-15
Jun-15
July-15
Aug-15
Sep-15
PT
PA
Uncaptured
GDP
Average prices (US$/trip)
Fig. AII.10. Trends in Taxi Prices and Aggregated Prices in NYC (Jun. 2013 – Sep. 2015).
Aggregated prices PA are measured by the following equation:
93
AII.3.2 Emergence of Uber-Driven Un-captured GDP
(1) Concept of the Emergence of Un-captured GDP
High-quality services with lower cost and
shorter time. Increasing initiative of passangers
and the company’s systematic market strategy
of continuous reduction of costs and time in
search and matching, eliminating
information asymmetries and compiling a
massive database.
Supported by the foregoing endorsement, Fig.
demonstrates the magnitude of un-captured
GDP per trip by Uber.
The substance of this un-captured GDP
can be summed up as follows:
Taxi prices
Aggregated
prices
2014/6 2015/1
Fig. demonstrates that, while Uber nurtured
“negative un-captured GDP value” (its
services were unable to catch up with those of
taxi accumulated over the last 120 years) by
June 2014, it succeeded in nurturing
increasing un-captured GDP from the
beginning of 2015 corresponding to its
success in sustainable decline in prices from
the end of 2014.

-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Jun-13
July-13
Aug-13
Sep-13
Oct-13
Nov-13
Dec-13
Jan-14
Feb-14
Mar-14
Apr-14
May-14
Jun-14
July-14
Aug-14
Sep-14
Oct-14
Nov-14
Dec-14
Jan-15
Feb-15
Mar-15
Apr-15
May-15
Jun-15
July-15
Aug-15
Sep-15
Fig. AII.11. Trend in the Emergence of Uncaptured GDP by Uber in NYC ( Jun. 2013 – Sep. 2015).
ratio
T
U
T
T

94
(2) Increase in the Emergence of Uncaptured GDP
)(
1
1
1 1 UT
UT
T
TT
UTTT
TAT
PP
PP
P
UT
PUPT
PPP











Un-captured GDP (US$/trip)
Uncaptured GDP
where
On the basis of the foregoing review, the trend in
the value of un-captured GDP per trip by Uber in
NYC was measured as illustrated in Fig.
This figure demonstrates that Uber-induced un-
captured GDP has been increasing significantly
from the beginning of 2015.
As emulated in the following equation, this can
be attributed to a virtuous cycle between prices
(PU) decline and trips (UT) increase, as
demonstrated in Fig..
2015/1

AII.3.3 Spin-off to Sharing Economy
(1) Uber’s Self-propagating Function
93
Table AII.3 Estimates of Taxis’ and Uber’s Development Trajectories in NYC
From equation (3), dynamic carrying capacity can be expressed as follows:
(5)
This demonstrate that N(t) increases together with the increase of Y(t) and its growth rate as time goes by. This implies that the
LGDCC function demonstrates functionality development in the context of the self-propagating behavior (Watanabe et al.
(2004).








)(/1
1
)()( )(1
tY
tYtN
dt
tdY
b
Figures in parenthesis indicate t-statistics: all significant at the 1% level except *: 5 %, **: 15 %, #: non-significant.
Table demonstrates that
while taxis depend on SLG
Uber depends on LGDCC.
This demonstrates that Uber has
developed with the self-
propagating function.

AII.4 Conclusion
AII.4.1 Secret of the Success of Uber’s System
In light of the disruptive digital-technology-driven business model that Uber has used to trigger a ride-sharing
revolution, the institutional sources of the company’s platform ecosystem architecture were analyzed.
Aiming at elucidating institutional enablers creating Uber’s platform ecosystem, an empirical analysis of its co-
existing development trajectory with taxi was attempted.
Noteworthy findings include:
(i) This co-existing development trajectory corresponds to two-faced nature of ICT that is behind the emergence of uncaptured GDP,
(ii) This emergence can be attributed to a virtuous cycle between prices decline and trips increase,
(iii) This virtuous cycle can be attributed to its self-propagating function, and
(iv) This self-propagating function plays a vital role in spin-offs from traditional co-evolution to new co-evolution ICT advancement,
paradigm change and people’s preference shift.
AII.4.2 Noteworthy Elements Essential to Well-Functioning Platform Ecosystem Architecture
These findings form the base for the following suggestions supportive of constructing a well-functioning platform ecosystem:
(i) Penetrate the current demand and meet its challenge,
(sharing economy, saturation of taxi business, popularity of smartphone)
(ii) Fully utilize the advancement of ICT, particularly of the Internet,
(smartphone, digital payment, big data analysis)
(iii) Construct a co-evolution between sophisticated platform ecosystems and consolidation of stakeholders.
(mutual rating system among the company, its drivers and their passengers)
(iv) Take care of the platform orchestration for efficiency, development and innovation,
(Successive innovation for novel services as competitor like Lyft boosting and also as against movement emerging)
(v) Thereby, create a novel business model which has never been conceived before.
94

AII.4.3 Implications of Uncaptured GDP
The emergence of un-captured GDP in case of Uber can be attributed to
(i) People’s preference shift to sharing economy and advancement of ICT, particularly of the Internet and later on
smartphones,
(ii) Better services, with cost and time saving for passengers, high efficient operation without additional investment and
license fees for drivers, and optimal price-setting and market making beyond marginal cost for the company through a
massive database on driver and passenger behavior.
(iii) Paradigm shift from resources to ecosystem that corresponds to the shift from captured GDP to uncaptured GDP.
Thus, Uber’s un-captured GDP can be considered as a consequence of the co-evolution between people’s preference shift,
advancement of ICT and this paradigm shift.
This co-evolution has been leveraged by Uber to create new business, to create services through interactions between the
stakeholders: the company, drivers and passengers.
All this can be attributed to systems success: platform ecosystem architecture under the contemporary digital economy.
AII.4.4 Criticism to be Solved
However, as a consequence of transition to new dynamism, there remains the following areas of criticism:
(i) Discrimination (e.g., equivalence of services for remote areas with low population density),
(ii) Safety issues,
(iii) Treatment of privacy issues, and
(iv) Compliance with labor standards.
AII.4.5 Future Works
This analysis has explored a prototype of the analysis of co-evolution of three mega-trends that nurtures uncaptured GDP.
Further analyses applying this approach are expected to be undertaken for similar disruptive business models in (i) music
industry, (ii) game industry, (iii) printing and publishing industry, and (iv) education industry.
95

Source: NYC Taxi and Limousine Commission.
0
250
500
750
1,000
1,250
1,500
Jan-04
Oct-04
July-05
Apr-06
Jan-07
Oct-07
July-08
Apr-09
Jan-10
Oct-10
July-11
Apr-12
Jan-13
Oct-13
July-14
Apr-15
N
May 2011: Uber
launched in NYC Uncaptured
GDP
Medallion prices (1,000 US$)
Two-faced nature of ICT
Un-captured GDP
Fig. S2. Two-faced Nature of ICT
Fig. S3. Anticipating Un-captured GDP.
Supplement 1. Two-faced Nature of ICT and Uncaptured GDP
S1.1 Two-faced Nature of ICT and Subsequent Uncaptured GDP
Fig. S1. Trend in Corporate Medallion Prices in NYC
and Contributors (2004-2015).
The trend in medallion prices as a consequence of co-existing diffusion
trajectory of taxi with prices increase and that of Uber with prices
decrease suggests that this trajectory is subject to the two-faced nature
of ICT that is behind the emergence of un-captured GDP.
96
$0
$250
$500
$750
$1,000
$1,250
$1,500
Jan-04
May-04
Sep-04
Jan-05
May-05
Sep-05
Jan-06
May-06
Sep-06
Jan-07
May-07
Sep-07
Jan-08
May-08
Sep-08
Jan-09
May-09
Sep-09
Jan-10
May-10
Sep-10
Jan-11
May-11
Sep-11
Jan-12
May-12
Sep-12
Jan-13
May-13
Sep-13
Jan-14
May-14
Sep-14
Jan-15
May-15
Sep-15
2011/5 Uber
launched in NYC
2009/3
Uber established
2014/5
Start to
fall
2013/6
Peak
MP
Uber
Taxi
Contributors to medallion prices level
Phase 1 Phase 2
Uncaptured GDP
MP
MP without Uber

S1.2 ICT Prices Trajectory and Two-faced Nature
(1) Modified Bi-logistic Growth
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(A3)
ICT prices can be depicted by the following modified bi-logistic growth as illustrated in Fig. S4:
where I: ICT stock, J: dependency on the Internet, N: carrying capacity, :
diffusion velocity of I and J.
[1] Since the Internet has been playing a leading role in the whole ICT and providing significant impacts on the diffusion trajectory of ICT, the
carrying capacity of logistic growth in I and reverse logistic growth in J as well as their diffusion tempo ( ) were treated as behaving in
the similar way (a i I=a jJ).
Equation (A1) can be developed as follows:
(A1)
Fig. S4. Modified Bi-logistic Growth due to
Two-faced Nature of ICT.
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DWRadjDTU
MPN
N
TT 
 (-3.12) (-2.96) (8.54) (5.42)
Table S1 Co-existing Trajectory of Taxis and Uber in NYC (Jun. 2313 – Sep. 2015)
where N (carrying capacity) = 2247,(Table 5) MP: medallion prices, D: 2014. May., Aug., Sep. = 1.
This demonstrates that coexistence of taxis and Uber is subject to two-faced nature of ICT.
In case of a co-existing diffusion of taxis and Uber, J and I correspond to UT (Uber trips) and TT (taxi trips) and Eq.
(A2) can be represented as Table S1.
(2) Diffusion Coefficient
Coefficients governing modified bi-logistic growth in Eq. (A1) can be identified as follows (here J and I correspond to UT and TT ):
(A4)
(A5)

99
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TT TUI
ee
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2247
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

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
 (A6)*
* Demonstrating the state in Sep. 2015 when = 0.08.
This modified bi-logistic growth demonstrates contributors to medallion prices level illustrated in Fig.A2.

(3) Trip Elasticity to Prices
The marginal contribution of Uber and taxis dependency to medallion prices change can be depicted as follows:
Thus, the elasticity of Uber and taxi dependency to prices elasticity can be depicted as follows:
This demonstrates that, contrary to taxis’ prices increasing as their trips increase, Uber prices decrease as its trips increase leading to
a virtuous cycle for Uber. All this support the analysis of institutional sources being behind the emergence of un-captured GDP.
(A7)
(A8)

$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
Jan-04
May-04
Sep-04
Jan-05
May-05
Sep-05
Jan-06
May-06
Sep-06
Jan-07
May-07
Sep-07
Jan-08
May-08
Sep-08
Jan-09
May-09
Sep-09
Jan-10
May-10
Sep-10
Jan-11
May-11
Sep-11
Jan-12
May-12
Sep-12
Jan-13
May-13
Sep-13
Jan-14
May-14
Sep-14
Jan-15
May-15
Sep-15
NYC Corporate Medallion Prices (1000 US$ )
Actual Logistic Growth Parabola
May 2011: Uber
launched in NYC
Mar. 2009
Uber established
Uncaptured
GDP
May 2014
Jun 2013: Medallion
Prices stagnate
Logistic growth
Estimate t-value adj. R2
N 2247.11 7.23 0.976
a 0.02 14.21
b 6.36 7.21
Parabolic growth
Estimat
e
t-value adj. R2
a 288.30 25.80 0.977
b 5.31 11.91
c 0.02 5.42
at
be
N
Y 


1
S1.3 Prospect of Uncaptured GDP Nurtured by Uber
Fig. S5. Estimate of Uber’s Impact on Medallion Prices Decline (Jan. 2004 - Sep. 2015).
Table S2 Estimates of Medallion Prices (Jan. 2004 - Jun. 2013)
Y: Medallion prices,
N: Carrying
capacity,
t: Monthly trend,
a,b,c: Coefficients
0
500
1000
1500
2000
2500
Jan-04
Feb-05
Mar-06
Apr-07
May-08
Jun-09
July-10
Aug-11
Sep-12
Oct-13
Nov-14
Dec-15
Jan-17
Feb-18
Mar-19
Apr-20
May-21
Jun-22
July-23
Aug-24
Sep-25
Oct-26
Nov-27
Dec-28
Jan-30
Feb-31
Mar-32
NYC Corporate Medallion Prices (1000 US$)
Uncaptured
GDP
2247
Fig. S6. Estimate of un-captured GDP
anticipated by Uber (May. 2014 - May. 2032).
1. As reviewed in Fig. 9, the magnitude of un-captured
GDP can be measured by the balance between actual
medallion prices and medallion prices without Uber.
2. The former can be estimated by Eq. A6 and the
latter by Table A2.
3. Table S2 demonstrates how the trend in medallion
prices without Uber can be estimated both by logisti
growth and parabolic growth. The latter provides
higher estimate.
4. Fig. S6 demonstrates the prospect of un-captured
GDP emerging in case of Uber as estimated by the
foregoing approach.
100
2
ctbtaY 

Supplement 2 . Correlation between Medallion Prices and Taxi/Uber Prices
Figures in parenthesis indicate t-statistics: all significant at the 1% level
)81.5()24.5()81.4()38.5(
73.1931.0.247.19ln227.1ln607.53441.3ln 2
121

 DWRadjDPADPADMP
Fig. S7. Correlation between Taxi/Uber Aggregated Prices
(PA) and Medallion Prices (MP) (2014.5– 2015.9).
D1:2014.5 – 2014.11 = 1, rest = 0.
D2:2014.12 – 2015.9 = 1, rest = 0.
MP: Medallion prices,
PA: Aggregated prices per trip
D1, D2: Dummy variables.
D1:2014.5– 2014.8 = 1, rest = 0.
D2:2014.9– 2015.1 = 1, rest = 0.
D3:2015.2– 2015.9 = 1, rest = 0.
MPe: Estimated Medallion prices,
PT: Taxi prices per trip
D1, D2, D3 : Dummy variables.
101
May. 2014
Sep. 2014
Feb. 2015
Sep. 2015
$1,200
$1,250
$1,300
$1,350
$1,400
$1,450
$1,500
$15.6 $15.8 $16.0 $16.2 $16.4 $16.6 $16.8
MedallianpriceswithoutUber
(1000US$)
Taxi Prices (PT)
May. 2014
Nov. 2014
Sep. 2015
$600
$700
$800
$900
$1,000
$1,100
$1,200
$1,300
$1,400
$1,500
$13.0 $14.0 $15.0 $16.0 $17.0
Medallionprices(1000US$)
Aggregated price (US $)
)84.3()90.3()84.2()80.3()88.4(
26.1945.0.813.10ln144.1ln731.2ln127.1018.4ln 2
2321

 DWRadjDPTDPTDPTDMPe
Fig. S8. Correlation between Taxi Prices (PT) and Medallion
Prices without Uber (Mpe) (2014.5 – 2015.9) .
Table S3 Correlation between Taxi/Uber Prices and Medallion Prices
(2014.5 – 2015.9)
PT vs Mpe and PA vs MP
demonstrates significant parallel
correlation as far as 2015 is concerned.
This supports the significance of un-
captured GDP measurement depending on
the balance between PT and PA during the
above period.

102
0.00
10.00
20.00
30.00
40.00
50.00
60.00
1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728
UT
1
3.12 2.75
3.37 3.09
3.62 3.47
3.96 3.9
4.34 4.38
4.82 4.92
5.38 5.51
5.96 6.18
6.65 6.92
6.18 7.74
7.19 8.66
9.96 9.67
12.40 10.79
12.80 12.03
14.92 13.39
14.56 14.89
15.86 16.52
20.46 18.31
20.40 20.25
26.18 22.25
25.06 24.62
26.83 27.05
30.96 29.66
33.95 32.43
37.27 35.36
40.94 38.44
45.03 41.67
49.64 45.03
1 Jun-13
2 July-13
3 Aug-13
4 Sep-13
5 Oct-13
6 Nov-13
7 Dec-13
8 Jan-14
9 Feb-14
10 Mar-14
11 Apr-14
12 May-14
13 Jun-14
14 July-14
15 Aug-14
16 Sep-14
17 Oct-14
18 Nov-14
19 Dec-14
20 Jan-15
21 Feb-15
22 Mar-15
23 Apr-15
24 May-15
25 Jun-15
26 July-15
27 Aug-15
28 Sep-15
2TT UU
Period
Trips per day
Trips per day
2013/6 2014/1 2014/5 2015/3 2015/9
2T
T
U
U
Supplement 3. Sensitivity of Uber Trips Estimate
S3.1 Estimate without and with Spline Interpolation
Fig. S9. Comparison of Uber Trips Estimate (Jun. 2013 – Sep. 2015).
Table S4 Comparison of Uber Trips
Estimate (Jun. 2013 – Sep. 2015).
UT: Uber trips estimated by taxi trips and Uber dependency (Appendix 1)
UT2: Uber trips estimated with spline interpolation
In analyzing Uber’s diffusion trajectory (3.3 (1) and (2)), given the sensitive impacts of
fluctuation on the trajectory formation within the limited samples, comparative analysis
was attempted by comparing Uber trips estimate with and without spline interpolation, as
shown in Fig. S9 and Table S4. The function used for spline interpolation was based on the
logistic growth function .

13.5
14.0
14.5
15.0
15.5
16.0
16.5
17.0
17.5
18.0
Jun-13
Aug-13
Oct-13
Dec-13
Feb-14
Apr-14
Jun-14
Aug-14
Oct-14
Dec-14
Feb-15
Apr-15
Jun-15
Aug-15
TT
TUTT
A
UT
UPTP
P



Uncaptured
GDP
Average prices (US$/trip)
Fig. S10. Trends in Taxi Prices and Aggregated Prices in NYC (Jun. 2013 – Sep. 2015).
Aggregated prices PA are measured by the following equation:
105
S3.2 Effects of the Estimates of Uber-Driven Un-captured GDP
(1) Emergence of Un-captured GDP in case of Uber
Taxi prices
Aggregated
prices
2014/6 2015/1
PT
PA2
PA
With spline interpolation
Without spline interpolation
No substantial differences between the
interpolations with and without spline.

-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Jun-13
July-13
Aug-13
Sep-13
Oct-13
Nov-13
Dec-13
Jan-14
Feb-14
Mar-14
Apr-14
May-14
Jun-14
July-14
Aug-14
Sep-14
Oct-14
Nov-14
Dec-14
Jan-15
Feb-15
Mar-15
Apr-15
May-15
Jun-15
July-15
Aug-15
Sep-15
Fig. S11. Trend in the Emergence of Un-captured GDP in case of Uber in NYC ( Jun. 2013 – Sep. 2015).
ratio
T
U
T
T

106
(2) Increase in the Emergence of Uncaptured GDP in case of Uber
)(
1
1
1 1 UT
UT
T
TT
UTTT
TAT
PP
PP
P
UT
PUPT
PPP











Un-captured GDP (US$/trip)
Uncaptured GDP
where
Un-captured GDP
Un-captured GDP2
without spline interpolation
with spline interpolation
No substantial differences between
the interpolations with and without
spline.

S3.3 Effects of Uber’s Development Trajectory Estimate
105
Table S5 Estimates of Taxi and Uber’s Development Trajectories in NYC
Taxi: based on medallion prices (Fig. 9), Uber: based on trips (Fig. 4) with spline interpolation in case 2, without
spline interpolation in case 1.
Figures in parenthesis indicate t-statistics: all significant at the 1% level except *: 5 %, **: 15 %, ***: 20%, #: non-significant.
While Uber’s development trajectory estimate using the trips trend without spline interpolation demonstrates a slight
possibility of self-propagation by LGDCC, the estimate with spline interpolation demonstrates explicit self-
propagation and the significance of LGDCC.
This difference does not have any significant effects on aggregated prices and un-captured GDP estimates. The effects
on self-propagation can be attributed to a slightly higher pace (1-9%) of trips after March 2015. This suggests that an
optimal and not too rapid development pace seems essential for incorporating the self-propagating function.

106
Fig. S12. Correlation between Centralization of Wage Setting and Union and CBA Density
in 19 Countries in the Late 1990s.
CBA: Collective bargaining agreements. Union and CBA density = (Union density + CBA coverage)/2
Source: Warner (2002) [22]
Appendix III. Institutional Elasticity against Uber

107
1 Success
(1) Singapore [Legality is pending but operating actively]
1. Taxi drivers and passengers in Singapore are generally welcoming taxi app services.
2. This has led to a highly competitive taxi app market in Singapore, and existing taxi companies as
ComfortDelgro and Trans-Cab endeavoured to improved their services by introducing their own
mobile app services.
3. COE (Certificate of Entitlement) scheme based on the tripartism framework (consists of Ministry of
Manpower, National Trades Union Congress, and Singapore National Employers Federation) plays a decisive role
in Singaporean’s efficient utilization of ride-sharing.
4. Requirements and complaints can be solved through dialogues with the regulators, employers and
employees (drivers) under the tripartism framework.
5. Uber induced incorporating users (passengers) requirements into the tripartism framework by
stimulating better services, thereby consolidation of all stakeholders: company, employee, user and
government was constructed.
6. Government agile reaction to complains from incumbent through open dialogue with all
stakeholders by acknowledging new stream of innovation, not resisting played a key role.
7. The government is secretly* welcoming the taxi app services because (i) young people enjoy using
services like Uber, and the government must not resist innovation, (ii) they provide job opportunity
to Singapore citizen (toward aging society) and increase the overall productivity, (iii) the ride-
sharing can be an approach to tackle problems of traffic clog and achieve efficient road usage.
* Transport Minister urged that “we must always be fair to players, whether incumbent or insurgents, and strike a balanced approach.

108
(2) Tokyo [Seems illegal but operating]
1. Uber has had tremendous difficulties in making inroads into the Japanese market due to
“Byzantine” and complicated regulations.
2. Uber was ordered to suspend its pilot project in Fukuoka city in Feb. 2015 because it violate the laws.
Uber stopped the project in Mar. 2015
3. Tokyo has a rather tranquil market so far due to its qualified service seeking competitive market
with 50,000 taxi (20% of the total in Japan and 4 times the number in NYC).
Nov. 2013 Uber started in Tokyo (limited launch. Expanded whole Tokyo area from Aug. 2014).
Jan. 2014 Tokyo Hire-Taxi Association also introduced a mobile app service.
Jan. 2015 Japan’s largest taxi company, Nihon Kotsu launched a mobile app Line Taxi.
Mar. 2015 Japanese government stated Uber probably violates laws (unlicensed, safety).
Uber reacted continued to talk.
Mar. 2015 Japan’s e-commerce giant Rakuten entered the ride-sharing industry by purchasing 11.9% in Lyft.
Oct. 2015 Prime Minister Abe instructed relaxing regulation for ride-share in isolated areas.
4. Although the legal framework in Japan does not allow private cars or ordinary person to operate
as a paid taxi, taxi companies in Tokyo recognized Uber as a business competitor and worked
towards improving their services by developing new functions.
5. With CCSD (government, broader industries involvement for social demand (traffic, aging, isolated rural)) co-
evolution emerged between IDBM (existing taxi companies also improved their services by introducing
their own mobile app services) and advancement of institutional systems by solving social demand.

109
1. Black taxis have been the kings of the British capital's roads for over a century but now they are
battling a high-technology rival that threatens their dominance. Uber is active in three cities (London,
Manchester and Leeds) in the UK.
2. Uber has won a significant legal victory in the UK, with London's high court ruling that Uber’s app
does not constitute a taximeter.
3. The legal challenge was brought by London's transport agency Transport for London (TfL), following
pressure from the city's black cab and taxi drivers.
4. While taximeters devices which record distance travelled and are used to calculate fares are only allowed
for licensed taxis, the judge ruled that the legal definition of a taximeter doesn't include "smart phones
which rely on data from a server outside the vehicle."
5. Uber hailed the decision as a "victory for common sense," adding that the ruling means the company
won't have to change how its app works in London.
6. London Mayor reported that “The technological innovation should not banned unnecessarily that will
serve a good purpose to the Londoners”. This really showed the positive impact of the service in the
country. He also added that some solution needs to be sorted out that the growth of Uber services does
not impact the traditional black taxi drivers anyway.
7. London's Licensed Taxi Drivers Association, described the outcome as unbelievable. The transport
authority has also asked the court to determine if the service is in fact legal.
7. Notwithstanding the above victory, Uber still faces ongoing legal challenges in London, including
proposals to introduce compulsory five-minute wait times and the removal of car icons from the map
in the Uber app.
(3) London [Legality is pending but operating with expectation]

110
(4) USA [Generally Positive]
Uber is operating in 75% of US locations although banned in Nevada and Oregon, and there was
multiple on-going lawsuits.
State legislators in Ohio and Florida are moving ahead with regulations governing Uber and other ride
services that would designate all drivers as independent contractors, bolstering a critical but much-
disputed aspect of Uber's business model.

111
(5) Saudi Arabia
1. Saudi Arabia's discriminatory automotive policies against women have allowed Uber to achieve
great success, due to females having limited options in transportation.
2. Women are not allowed to drive as it is feared to damage their ovaries leading to children born with
clinical problems.
3. Since women cannot keep their jobs in Saudi Arabia because they have trouble in finding reliable
transportation to get to work, Uber triggered institutional revolution for women’s social
participation as demonstrated by the fact that women make up 70% of Uber's customers.
4. With such expectation, Uber operates in the holy Islamic cities of Mecca and Medina, as well as the
capital city of Riyadh, and the port cities of Jeddah and Dammam. The service is expected to be
available in several more cities in the near future.
5. While there remains the issue of compliance with traditional government regulations, negotiation
with the institutional regulators in Saudi Arabia have been extremely positive compared to other
countries reception towards the app business.
6. Thus, Uber is expected to grow 50-60 % in trips per months in Saudi Arabia in 2016, which in turn
accelerates social innovation in the country leading to a co-evolution between ICT-driven disruptive
innovation and change in institutional systems triggered by women’s social participation.
[Legality is pending but operating actively]

112
(6) Russia [No ban, but difficult to offer service]
1. Regulations in Russia are comparatively simple in comparison to other countries.
2. Moscow has already a culture of unlicensed taxis that makes Uber’s expansion there
difficult.
3. Citizens can often hail one by standing on the street corner or via a number of apps that
have existed for years before Uber arrived.
4. Since 2011, Russia’s main search engine company, Yandex, has been running an taxi-app
that most now simply known as Russia’s Uber.
5. Uber also trails Gett, known as the Uber of Israel, which operates 10,000 cars in Moscow.

113
(7) Canada [Changing to Support]
1. Uber drivers in Canada are required to register, collect and remit HST/GST from their fares to the
government, regardless of their income.
2. In December 2012, officials in the city of Toronto charged Uber with 25 municipal licensing
infractions. A Toronto city councilor has warned that passengers using UberX may be fined up to
$20,000.
3. Uber was made legal in the city of Edmonton by passing by-law. However, Uber ceased its operations
in Edmonton in March 2016 citing inability to obtain the necessary insurance. The City of Calgary,
Alberta has charged at least 17 drivers illegally driving for Uber.
4. These drivers were operating without legally mandated insurance. Uber continues to operate
illegally in the other regions of Canada.
5. Toronto Mayor expressed his support for Uber in 2014 and other cities are slowly beginning to look
at regulatory options.

114
(8) Philippines [Developed Nationwide Regulations Making Legal]
1. The Philippines became the first country to develop nationwide ride-hailing regulations, making it
legal for app-based transportation services like Uber to operate anywhere in the nation in Nov. 2015.
2. "We view technological innovation as a driver for progress, especially in transportation where it can
provide safer and more convenient commuting options to the public,” Jun Abaya, the Philippines’
Department of Transportation and Communications secretary, said. “App-based transport services
help address the increasing demand for mobility spurred by rapid urbanization.”
3. All the specifics of the regulations have yet to be finalized, but in general, the DOTC says cars that
operate on these services must have a GPS system; must be sedans, Asian Utility Vehicles (AUVs),
SUVs, or vans; and can’t be more than seven years old. Operators will also be required to obtain
certificates for each vehicle on the service, and drivers must be screened and accredited by Uber (or
other ride-hailing services) and registered with the local transportation regulatory board.
4. But Uber still faces challenges unique to the Phillipines. For one, Uber’s routing algorithm doesn’t
work as well in Manila, which has some of the world’s worst traffic. And as one writer in the
Philippines points out, Uber has been operating in the country a bit like a condo rental service;
operators are buying small fleets of brand new cars and hiring individual drivers - essentially
layering a new middleman on top of Uber itself. As the incentives Uber has put into place to spur
growth are being phased out, drivers’ salaries are apparently taking a hit so that these fleet owners
can break even.
5. Many locals do say that the service is often cheaper and more convenient than local cab services. But
Uber drivers, regulators and the company itself still have work to do to find the right fit if Uber
expects to keep growing in the Philippines.

115
(9) China [Generally positive]
Although there was some raids and fines against Uber in some of locations, there is no official and legal
banning to Uber services. Major challenge Uber facing in China is from the market rivals and
competitors.
Such lack of “legal banning” can be attributed to (i) lack of strict regulations in transportation market,
(ii) lack of legal protection to the taxi drivers, and (iii) current lousy services provided by taxi industry.

116
(1) France [Partial ban as illegal]
1. France government initially started to suppress the service with their policy and later started
allowing the Uber services in certain case, not all the services.
2. UberX is the low cost service that allows only the licensed drivers to operate the cabs and UberPop
is also the service but allows even the drivers without the driving license to operate the cab.
3. Government allowed the former but not the latter stating that it would severely affect the regular
taxi drivers drastically.
4. Uber did not accept the decision and filed against government which led to huge violent protests
by taxi drivers. Finally, Uber has suspended their UberPop services until hearing the final judicial
result.
5. Uber announced that it will re-launch its Pop services if the government considers Uber to be legal.
But that seems to be highly unlikely.
6. Currently, UberPop is banned from functioning in France. Uber was facing equal protests from
traditional Taxi drivers stating that it is not a fair competition as the taxi drivers are exempted
from the taxes paid by them.
2 Failure

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