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Answered: - eBay is considering entering Country A. We have two very similar
eBay is considering entering Country A. We have two very similar countries, Country A and Country B. Read Waheeduzzaman (2008), use the following information and estimate market potential for eBay in Country A using Chain Ratio Method and Method of Analogy. Comment on the difference in estimation.
Number of households for Country A = 33 million.
Number of households for Country B = 27 million.
Percentage of households subscribing to Amazon.com in Country B = 15%
Percentage of households having Internet in Country A = 35%
Literacy rate in Country A = 95%
Percentage of households with substantial purchasing power in Country A = 65%
Market Potential Estimation in
International Markets:
A Comparison of Methods
Presentation Outline
Introduction
Objectives of the Study
Literature Review
Research Methodology
Findings of the study
Conclusion and Future direction
Objectives of the Study
Discuss various approaches to demand
or market potential estimation
Test the models/methods for demand
estimation
Compare/evaluate the methods
Provide direction for future research
Literature Review
Market Potential Estimation Approaches
? Time series growth models
? Stock adjustment models
? Diffusion models
? Consumer behavior studies
? Market potential estimations in
marketing
Evaluation of Estimation Methods
Estimation Methods
Method of Analogy
Proxy Indicators
Chain Ratio Method
Time Series Modeling
Multiple regression modeling
Criteria for evaluation
Precision
Prediction
Price
Pragmatism
A Research Methodology
Durables: Washing Machine
Emerging country markets: Argentina, Brazil, Chile,
China, Colombia, Egypt, Hungary, India, Indonesia,
Israel, Malaysia, Mexico, Peru, The Philippines,
Poland, Singapore, South Africa, Thailand, Turkey and
Venezuela. (20 countries)
Variables in the study: Income (INCOME), Per Capita
Energy Consumption (ENERGY), Life Expectancy
(LIFEXP), Female Labor (FEMALE), Urbanization
(URBAN)
Time period: 1977-2006 (30 years).
Data analysis: Correlation, Regression Analysis
Sources: The World Bank, Euromonitor, Freedom
House, Central Intelligence Agency, and Globaledge
of Michigan State University
Findings of the Study
Table 1: Evaluation of Estimation Methods
Table 2: Time Series Model Results
Table 3: Variables in Multiple Regression
Table 4A: Correlation Matrix
Table 4B: Regression Analysis
Table 5A: Basic Statistics for Thailand
Table 5B: Comparison of Different Methods
Table 6: Literature Review (Approaches in the
Study of Durables)
Table 1
Evaluation of Estimation Methods
Criteria/
Precision
Prediction
Price
Pragmatism
Method of
Analogy
Not so precise as it
depends on simple
analogy
Very robust
estimation
Inexpensive using
secondary data
Very convenient
and can be
estimated in a short
time
Proxy Indicators
Depends on choice
of variables
Very robust but can
be accepted
Reasonably
inexpensive
Convenient to
measure if data are
available
Chain Ratio
Method
Reasonably precise
if right variables
are use
Robust, yet can be
very close to real
data
Relatively
inexpensive
Convenient and not
very complex
Time Series
Modeling
Depends on the
quality of the series
data
Good as it uses
scientific
techniques
Usually expensive
since data need to
be purchased from
consultants
Relatively easy to
implement if
researcher has the
knowledge and
data are available
in the right format
Multiple
Regression
Modeling
Depends on the
model and data
quality
Good in situations
where causality is
preferred
Usually expensive
to buy data
Model building
requires specific
knowledge and skill
Country
Table 2
Time Series Model Results (Washing Machine)
F-Ratio
R-Square
Intercept
?1
?2
Argentina
4580.07**
0.9972
40.48**
0.51**
-0.007**
Brazil
1495.22**
0.9914
6.17**
1.09**
-0.015**
Chile
12828.3**
0.9990
9.96**
1.99**
-0.023**
China
3260.83**
0.9971
-1.37**
0.23**
-0.003**
Colombia
7230.70**
0.9982
10.80**
1.60**
-0.021**
Egypt
10847.50**
0.9989
-0.32**
0.16**
0.001**
Hungary
6468.58**
0.9980
20.55**
0.23**
0.009**
India
154.03**
0.9333
-1.56**
0.22**
0.001
Indonesia
420.49**
0.9745
-1.52**
0.28**
-0.001
Israel
2865.06**
0.9955
62.75**
2.22**
-0.040**
Malaysia
5696.94**
0.9977
67.17**
0.82**
-0.013**
Mexico
2098.1**
0.9938
15.91**
1.80**
-0.033**
Peru
12318.1**
0.9989
10.55**
0.43**
0.006**
Philippines
3450.36**
0.9962
4.61**
0.25**
-0.004**
Poland
541.62**
0.9766
11.52**
0.45*
0.045**
South Africa
16651.9**
0.9993
-4.38**
1.13**
-0.001
Singapore
357.44**
0.9649
67.47**
0.48**
0.021**
Thailand
716.13**
0.9822
3.99**
0.06**
-0.001**
Turkey
981.43**
0.9869
8.74**
0.93**
-0.008**
Table 3
Variables in Multiple Regression
Variables
Studies
INCOME: Income is the most influential variable
affecting our consumption. Higher income indicates
higher aspiration and better quality of life. Income
favorably affects the consumption of durables.
Alessie et al. 1997, Besley and Levenson 1996,
Freedman 1970, Grieves 1983, Mishkin 1976, and
Ruiter and Smant 1999.
ENERGY: Per capita energy consumption as an
indicator technological growth in a society.
Technology shapes the social, cultural, economic
behavior of the people. It indicates modernization
and is related to the consumption of durables.
Armour 2002, Conrad and Schroder 1991, Irwin 1975,
Waheeduzzaman 2006.
LIFEXP: Life expectancy is commonly perceived as an
indicator of quality of life. Higher life expectancy
means more savings and contribution to family
wealth. It favorably affects the consumption of
durables.
Ballew and Schnorbus 1994, Brandstatter and Guth
2000, Inglehart 2005, Inkeles and Smith 1974, Shaw
et al. 2005, Sirgy et al. 2006
URBAN: Urbanization indicates the industrial growth
of a society. Consumption pattern changes with
industrial growth and economic development. Fast
urban lifestyle demands various durables.
Bollen and Appold 1993, Bradshaw 1987, Rostow
1961, Schnaiberg 1970, and Timberlake and Kentor
1983.
FEMALE: Participation of women in the workforce
significantly changes the social and family
relationship The use of timesaving appliances like
microwave, dishwasher and washing machine
become a necessity. Also, dual income raises the
aspiration level of the family and contributes to the
acquisition of durables.
Bryant 1988, Chia et al. 2001, Freedman 1970,
Inkeles and Smith 1974, and Sumer 1998.
Table 4A
Correlation Matrix
INCOME
ENERGY
LIFEFX
URBAN
INCOME
1.00
ENERGY
0.39**
1.00
LIFEXP
0.53**
0.37**
1.00
URBAN
0.63**
0.51**
0.64**
1.00
FEMALE
0.10**
0.01
0.21**
-0.30**
FEMALE
1.00
Table 4B
Regression Results
F-ratio
R-square
Intercept
INCOME
ENERGY
LIFEFX
URBAN
FEMALE
Refrigerator
73.7**
0.51
-12.08
0.001**
0.001**
-0.50
0.76**
1.45**
Dishwasher
131.0**
0.65
9.31**
0.001**
0.0001
-0.41**
0.12**
0.28**
Washing
machine
128.8**
0.64
-124.27**
0.002**
0.0001
1.78**
0.32**
0.04
Microwave
oven
45.16**
0.38
-72.20**
0.001**
0.001**
1.29**
-0.27**
-0.05
Television
126.9**
0.64
-71.67**
0.001**
0.001**
0.83**
0.51**
1.09**
VCR
92.05**
0.56
-114.70**
0.002**
0.001**
1.25**
0.03
1.05**
Variable
Table 5A
Basic Statistics for Thailand
2006
2007
2008
2009
2010
GDP measured at PPP (Million
US $)
597380.0
640120.0
685621.0
733182.9
775523.9
Per capita income
9553.8
10138.6
10758.8
11402.5
11957.0
Population (?000)
62527.9
63136.7
63726.7
64300.3
64859.5
Family size
3.6
3.6
3.5
3.5
3.5
Total households (?000)
17514.8
17785.0
18052.9
18319.2
18584.4
Ownership of WSM per 100
households
43.1
46.2
48.4
50.8
52.7
Life expectancy at birth
71.3
71.7
72.0
72.3
72.5
Female labor at as percentage
of total (%)
45.9
45.9
45.9
45.9
46.0
Percentage of urban population
(%)
32.9
33.3
33.6
34.0
34.4
Per capita energy consumption
(KWH)
1526.0
1500.9
1469.9
1435.6
1400.8
Households with electricity (%),
3% growth
82.1
84.56
87.1
89.71
92.4
Households with resident
telephones (%)
42
42.47
42.29
42.14
41.87
Households with water supply
(%)
49.76
50.47
50.74
52.35
52.16
Table 5B
Comparison of Methods
Year/Method
2006
2007
2008
2009
2010
Method of Analogy
5523.87
5741.98
5994.23
6179.19
6332.85
Proxy Indicators
7356.22
7552.69
7634.94
7720.48
7780.49
Chain Ratio Method
7155.32
7590.18
7978.39
8602.68
8956.22
Time Series Analysis
4324.41
4469.37
4614.32
4757.49
4949.02
Multiple Regression
Modeling
5996.17
6439.20
6884.52
7339.03
7756.98
Average of all five
methods
6071.20
6358.68
6621.28
6919.77
7155.11
Yearly growth of avg.
market potential
?
287.48
262.60
298.49
235.34
8216.66
8737.60
9306.14
9793.97
Market potential as
7548.89
per Euromonitor data
Conclusion and Future Direction
Summary of results
Managerial Implications
Future Direction
Questions?
Table 6
Approaches in the Study of Durables
Time Series Growth and Stock
Adjustment Models
Studies
The primary goal of these studies was
to estimate the demand for various
consumer durables from a macro
perspective using various types of
stock adjustment models. Statistical
techniques including regression, logit
or probit analysis were used for the
purpose of estimation.
Alessie, Devereux and Weber 1995;
Anderson 1986; Barrett and Slovin
1988; Bayus, Hong and Labe 1989;
Bulow 1982; Clarida 1996; Fernandez
2000; Fine and Simister 1995;
Grieves 1983; Kugler and Bossard
1987; Madsen 2001; Nadenichek
1999; Orlov 1978; Ruiter and Smant
1999; Sadka and Yi 1996; Steffens
2001; Weder 1998
Table 6
Approaches in the Study of Durables
Diffusion Models
Studies
The cornerstone of these studies is the
Bass (1969) diffusion model. It assumes
that the current consumption can be
predicted on the basis of the number of
innovators and imitators (two-group
categorization) in a social system. The goal
of estimation is to determine the
coefficients of innovation and imitation and
to understand the nature, timing, peak and
decline in consumption. Various extensions
of the Bass model have been proposed and
used for different products in different
countries.
Bass 1969; Bass, Krishnan and Jain 1994; Ganesh,
Kumar and Subramaniam 1997; Gatignon,
Eliashberg and Robertson 1989; Gatignon and
Robertson 1985; Golder and Tellis 1997; Heeler and
Hustad 1980; Helsen, Jedidi and DeSarbo 1993;
Islam and Meade 2000; Jain and Rao 1990;
Kamakura and Balasubramanian 1988; Kalish,
Mahajan and Muller 1995; Kohli, Lehmann and Pae
1999; Kumar, Ganesh and Echambadi 1998;
Mahajan, Muller and Bass 1990; McMeekin and
Tomlinson 1998; Parker 1993; Parker 1994; Parker
and Gatignon 1992; Preble 2001; Putsis et al 1997;
Srinivasan and Mason1986; Sultan, Farley and
Lehmann 1990; Talukdar, Sudhir and Ainslie 2002;
Tigert and Farivar1981; Wilson and Norton 1989
Table 6
Approaches in the Study of Durables
Consumer Behavior Studies
Studies
The consumer behavior studies basically
investigated durables from the perspective
of the individual or household. The
popular consumer behavior models in
marketing can be applied to understand
the consumption of durables. It is difficult
to generalize the findings of this group as
the nature and characteristics of the
studies vary substantially. A good number
of demographic, social and economic
variables were suggested.
Bayus and Carlstrom 1990; Besley and
Levenson 1996; Brucks, Zeithaml and
Naylor 2000; Bryant 1988; Guillou 1991;
Hensher and Milthorpe 1986; Homberg and
Giering 2001; Jennings and McGrath 1994;
Johnson 1988; Kamakura and Gessner
1986; Lusch, Stafford and Kasulis 1978;
Medina, Beatty and Saegert 1996; Page
and Rosenbaum 1992; Paroush 1965;
Schultz and Rao 1986; Sultan 1999;
Throop 1992; Wells 1977; Winer 1985;
Zikmund-Fisher and Parker 1999
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