the effects of innovation characteristics, country characteristics and context on international...
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The Effects of Innovation Characteristics, Country Characteristics and Context on International Diffusion of Multi-generation Cellular Mobile Phone Adoption
Towhidul Islam
University of Guelph, Canada
and
Nigel Meade
Imperial College, University of London, UK
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International Diffusion of Multi-generation Cellular Mobile Phone Adoption
• Diffusion of technological innovation occurs at different rates in different countries – For cellular telephones– Austrians took 12 years to achieve 5% penetration – Australians took 8 years for to achieve the same penetration
• Insights into the causes of the differences in national diffusion patterns are valuable – relevant questions:– What is the market potential for this innovation in this country? – How long will it take to reach 50% (say) of the market potential?– If my company enters the market in several countries, what is
the optimal sequencing of its product launches?
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International Diffusion of Multi-generation Cellular Mobile Phone Adoption
Our modeling approach answers these questions for the cellular telephone market by combining:
• Diffusion model Bass (1969)
• Multi – Generation Norton & Bass (1982)– exploitation of the generational nature of this technology – launch of the first analog telephones was in 1980 – launch of digital was in 1992
• International eg Gatignon et al (1989)– The information set for model estimation includes
• country specific data, economic, cultural, geographical • countries’ adoption of the preceding generation of technology.
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Outline
• Review and Hypotheses
• Data Description
• Model
• Analysis
• Comparative Forecasting Accuracy
• Conclusions and Implications
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0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
1987
1990
1993
1996
1999
2002
Cumulative Non- Cumulative
Sample Data: Australia
-1,500
-1,000
-500
0
500
1,000
1,500
2,000
2,500
3,000
3,500
1987 1990 1993 1996 1999 2002
Analog
Digital
Analog
Digital
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Review and Hypotheses - 1
• Generalised Bass model Bass et al (1994):– Probability density of an adopter adopting at time t
– Coefficient of Innovation– Coefficient of Imitation– Marketing
factor
– There are m potential adopters (m = Market potential)
1f t p qF t F t x t
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Review and Hypotheses - 2
• Hypotheses related to cellular telephones– Connection charge has negative effect on
• Market Potential• Marketing Factor
– Main telephones/capita has negative effect on • Coefficients of imitation for analog and digital
– Coefficient of innovation is constant over generations
Price Effect
Measured as % of GDP
Substitution Effect
Why get a mobile if I have a fixed line?
Early generations suffer from bugs => p decreases
Later generation has more goodies => p increases
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Review and Hypotheses - 3
• Hypotheses related to the Country– Wealth has positive effect on
• Market Potential• Coefficients of innovation and imitation
– Media Density has positive effect on • Coefficients of innovation for analog and digital
– Mobility has positive effect on • Coefficient of imitation
– Economic and population heterogeneity has negative effect on
• Coefficients of imitation and market potential
Measured by GDP/capita
& growth in GDP
Measured by newspapers
& TVs per capita
Mobility fosters imitation
Differences impede diffusion
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Review and Hypotheses - 4
• More hypotheses related to the Country– % of women in work force has positive effect on
• Coefficients of imitation
– Innovativeness has positive effect on • Coefficients of innovation for analog and digital
– Geography affects• Coefficient of innovation and imitation
Increased social interaction
fosters diffusion
Measured by patents and
% of R&D workers
Regional learning effect?
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Review and Hypotheses - 5
• Hypotheses related to the Country Context– Individualism (+ve); Power-Distance (-ve) Uncertainty
Avoidance (-ve) Political Rights (-ve) affect• Coefficients of innovation
– High Context culture has positive effect on • Coefficients of imitation for analog and digital
– Lag in introduction of technology positively affects• Coefficient of imitation
Deference & risk averseness
decrease diffusion
Japan more imitative than UK/US
Countries learn from earlier adopters
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• Network Externalities• Direct and Indirect
• Network Externalities: Measurement Issues and Models
• Do cellular telephones exhibit a direct network effect?• Mahler and Rogers (1999) – do not face externality problem• We expect to see evidence of critical mass or externality in the
diffusion of analog cellular phones in developing countries
• Does a model’s forecasting accuracy improve, if direct network effects are taken into consideration?
Review and Hypotheses - 6
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Diffusion of an Interactive Technology
First Purchase Sales
Time
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Data Description 1
• Countries categorised by – Economic sector
• High, Upper Middle, Lower Middle, Low Income
– Geographical Sector• Asia and Pacific;
• Europe, North America and Oceania;
• Eastern Europe and Central Asia;
• Middle East and North Africa;
• Sub-Saharan Africa;
• Latin America and Caribbean
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Data Description 2
• Wealth Related• Access to and availability of Information• Mobility• Economic Heterogeneity • Population Heterogeneity• Demographics• Innovativeness • Culture• Political / Country Risk• Fixed Telephone Infrastructure• Price
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Model 1
• Parameterization
01
01
01
exp
exp
m
p
q
k
gct m g m ig m ici
k
gct p g p i p ici
k
gct q g q ig q ici
m Z t
p Z t
q Z t
g – generation
c – country
t - time Constrained to
be Positive
Could be negative
Negative word of mouth effects
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Model 2
• Expected cumulative adoption of analog cellular telephones
• Expected cumulative adoption of digital cellular telephones
1 1 1 2 2( ) ( ) 1 ( ) for 0tE Y t F t m F t t
2 2 2 2 1 1 2( ) ( ) ( ) fort tE Y t F t m F t m t
Erosion due to introduction of Digital
Increased potential due to analog
owners changing to Digital
Analog market potential
Incremental digital market potential
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Model 3
• Taking expected adoptions per period:– New analog adoptions at time t
– New digital adoptions at time t
1 1 1 1 1 1 1 1 12 2 1ct c ct ct ct ct ctc t c ty m F F F F F F
2 2 1 1 1 1 22 2 1 2 2 1ct c c ct ct ctc t c t c t c ty m F F m F F F F
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Model 4
• Error variance assumptions– Generalisation of binomial assumption– Example for first generation (analog)
– We found λ = 2 (λ = 1 for binomial)
• Objective function (maximum likelihood)
21 1 11 11 12 121 1ct ct ct ct ct ctm P P P P
ln gct
g c t gct
Normal density function
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Analysis 1
Coefficient of Innovation• Positive effects
– No of researchers in R&D, no of patents– Uncertainty Avoidance
• No effect– Newspaper readership – Individualism
• Negative effect– Power distance, political rights– TV ownership
Contrary to expectations
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Analysis 2 - Coefficient of Innovation
0
5
10
15
20
25
30
0.0
02
0.0
04
0.0
06
0.0
08
0.0
10
0.0
12
0.0
14
0.0
16
0.0
18
p (an a lo g )
Fre
qu
en
cy
Mean of 0.006 is low compared with
mean of 0.03 found by Sultan, Farley
and Lehman (1990) in their
meta-analysis of 213 applications.
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Analysis 3
Coefficient of Imitation• Positive effects
– Wealth, women in the labour force, mobility– Lagged introduction– Gini Index
• No effect– High context culture – Population heterogeneity
• Negative effect– No of fixed lines
Contrary to expectations
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Analysis 4 - Coefficient of Imitation
High Income Upper Middle Income
Lower Middle Income
Low Income
0.100
0.200
0.300
0.400
0.500
0.600
0.700
q_A
nalo
g
48
High IncomeUpper Middle Income
Lower Middle IncomeLow Income
0.500
0.600
0.700
0.800
q_D
igit
al
40
Box plots by Economic Sector
Analog Digital
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Analysis 5
Market potential• Positive effects
– Wealth, (economic sector membership)– Gini Index
• No effect– High context culture – Population heterogeneity
• Negative effect– Connection charge– Economic Risk
Contrary to expectations
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Analysis 6 - Total Market potential
Market potential (per 1000 people)
for lower middle income sector
Is 35% greater than for
low income sector
High Income Upper Middle Income
Lower Middle Income
Low Income
0
200
400
600
800
1000
1200
mar
ket p
oten
tial 65
46
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Comparative Forecasting Accuracy 1
-5
0
5
10
15
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
Ad
opti
on
Tanzania
-10
0
10
20
30
40
50
60
1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
Year
Ad
opti
on
China
-50
0
50
100
150
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
Year
Ad
opti
on
United States
-50
0
50
100
150
200
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
Year
Ad
opti
on
Hong Kong
Examples of
Model Fit
Data: solid line
Forecast: dashed
Analog: lower lines
Digital: upper lines
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Comparative Forecasting Accuracy 2
• Benchmark – Univariate Bass model fitted by country
HorizonGeometric mean(rmse
model/rmse Bass)
1 1.162 0.973 0.864 0.775 0.67
No better than univariate
Comparative accuracy
improves with horizon
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Network Externalities: Measurement Issues and Models
• Sales Takeoff Times
• (Tellis, Stremersch and Yin 2005: Marketing Science)
• Logistic Hazard Model
0
150
300
450
600
0.11 0.39 0.77 1.38 2.27 3.78
Household Penetration (%)
Gro
wth
rat
es (
%)
Threshold line
Takeoff growth
USA
• Gamma-Shifted Gompertz Model
• Bemmaor and Lee 2002: Marketing Science
)t)qp(exp(p
q1
)t)qp(exp(1)t(F
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Analysis: Sales Takeoff Times
0
10
20
30
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Take-off time (years)
Fre
qu
ency
Analog
Digital
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Network Externalities: Covariate Effects
• Covariates– Economic Regions
– Cultural variable
• Control Variables– Geographical Regions
– Lag Introduction Times
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Analysis: Sales Takeoff Times
0
0.2
0.4
0.6
0.8
1 3 5 7 9 11 13 15 17 19Years
Haz
ard
Rate
s
Analog: Asia and Pacific
High Income
Low Income
0.0
1.0
2.0
3.0
4.0
1 3 5 7 9 11 13 15 17 19
Years
Haz
ard
Rat
es
Digital: High Income Cetegory
Europe & N America
Sub-Saharan Africa
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Analysis: Skew Coefficient
• Estimation of skew coefficients (α )
High Upper Middle
Lower Middle
Low
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Skew
(Ana
log)
484
70
30
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Forecasting Performance
Horizon 1 2 3 4 5
Country Skew/Bass 1.16 0.99 0.86 0.83 0.63
Covariate Skew/ Bass 1.10 0.99 0.90 0.92 0.81
• Geometric mean of the ratio of root mean square errors for the country skew models and the covariate skew models with the root mean square of the Bass model for combined analog and digital adoption.
• Forecasts were computed for one to five year horizons using data up to 1997 to 2001.
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Conclusions and Implications
• Implications for firms– Model can answer:
• What is the market potential in country A?
• What is the likely path to this market potential?
• In what sequence should these markets be entered?
– Model could be used normatively• Eg introduce digital in 1, 2, 3 etc years time
• Implications for Research – Findings are replicable, realistic and the model’s
predictive validity has been established– Explicit modelling of variance