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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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