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

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?

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.

Outline

• Review and Hypotheses

• Data Description

• Model

• Analysis

• Comparative Forecasting Accuracy

• Conclusions and Implications

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

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

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

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

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?

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

• 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

Diffusion of an Interactive Technology

First Purchase Sales

Time

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

Network Externalities: Covariate Effects

• Covariates– Economic Regions

– Cultural variable

• Control Variables– Geographical Regions

– Lag Introduction Times

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

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

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.

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