the choice of stiga table tennis blades —evidence - diva portal
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Dalarna University
Department of Economics and Social Sciences
C-Level Thesis for Bachelor Degree 2010
The Choice of STIGA Table Tennis Blades
—Evidence from China
Author: Lei Zhang & Xi You Supervisor: Sven-Olov Daunfeldt
ABSTRACT
The purpose of this paper is to investigate how individuals with different
characteristics make their choice-decisions when consuming STIGA table tennis blades,
which are combinations of various attributes, such as price, control, attack, etc. It is
expected that the general trend of choice behavior on this special commodity can be, at
least to some extent, revealed. Data were collected using questionnaires sent to registered
members of a table tennis club in China. The questionnaires included information and
questions about individuals’ monthly income levels, ages, technique styles, etc. A
multinomial logit model was then applied to analyze factors determining Chinese
consumers’ choice behavior on STIGA table tennis blades. The results indicated that the
main element influencing Chinese consumers’ choice of STIGA ping-pong blades was
the technique style and other variables did not seem to influence the choice of table tennis
blades. These results might be explained by the limited sample size as well as
unmeasured and immeasurable factors. Thus, a more extensive research is needed to be
conducted in the future.
KEY WORDS: Consumer Behavior, Individual Characteristics, Choice Experiment,
Multinomial Logit Model
CONTENTS
1. Introduction ……………………………………………………………...1
2. Theoretical Framework...………………………………………………...3
3. Empirical Analysis...……………………………………………………..8
3.1 Data and Variables...…………………………………………………8
3.2 Econometric Model………………………………………………....14
3.3 Hypothesis Testing of Regression Coefficients...…………………..18
4. Estimation Results and Interpretation…………………………………..20
5. Summary and Conclusion………………………………………………22
REFERENCES……………………………………………………………..25
APPENDIX………………………………………………………………...26
1
1. Introduction
In 1959, a Chinese player, Rong Guotuan, won the Men’s single on the 26th
Table
Tennis World Championship. That was the first world champion in the history of Chinese
sport. Since that victory, China started its domination on this area of sport. Until now,
Chinese players have won 169.5 champions on world-class events, including World
Championships, World Cups, and Olympic Games, and table tennis has also been
regarded as the “National Ball” of China.
Due to the special history and the brilliant achievements, table tennis has become the
most prevailing sport in China. Millions of people have devoted into this sport, including
both professionals and non-professionals. Professional equipments are used by a large
number of amateurs in China, creating a fantastic business potential. As a result, many
foreign manufacturers have entered Chinese market, such as STIGA from Sweden,
Butterfly from Japan, Donic from Germany, and so forth. The whole market is very
competitive, with many firms offering similar products.
In fact, an individual makes a choice-decision among different attributes when
consuming a certain good. This choice-decision is determined by his or her
characteristics, such as income, age, and so on. The purpose of this paper is to study what
determines Chinese players’ choice of table tennis blades1
and how those factors
influence the consumers’ choice behavior. In order to simplify, this study only focuses on
one brand—STIGA, which is a famous table tennis equipment manufacturer from
Sweden with a 60-year-world-leader history. The reason choosing STIGA as the
1 A blade is a bat without rubbers, usually used by professional players.
2
objective of the experiment is that STIGA is one of the most influential brands in China
enjoying quite a large market share. STIGA offers blades to many excellent players and
even owns the glory to cooperate with Chinese national team. This increases the
popularity and creditworthiness among the table-tennis-enthusiasts in China. Meanwhile,
STIGA provides various combinations of different attributes to consumers, satisfying
individuals’ necessity.
A large number of previous studies have investigated individuals’ choice behavior
involving various aspects of our life. Debra et al (2010), for example, analyzed young
German adults’ choice behavior on food, and Ben-Elia and Shiftan (2010) measured how
people make choice decisions regarding driving routes. However, no preceding study has,
as far as we know, investigated what determines the choice of table tennis blades.
Therefore, the purpose of this research is to explore how individuals with different
characteristics make their choice-decisions when consuming STIGA table tennis blades,
which are combinations of various attributes, such as price, control, attack, etc. Through
modeling the consumption on STIGA products, it is expected that the general trend of
choice behavior on this special commodity can be, at least to some degree, revealed.
To model the probability of choosing a specific alternative based on different
characteristics, including the age level, the income level, the technique style and the
specific training background, a multinomial logit model was used. The results indicated
that the technique style was the most influential elements impacting Chinese choosing
different STIGA table tennis blades. On the other hand, the estimation of other variables
failed to pass the hypothesis test, implying that they did not influence the choice of table
tennis blades. However, these results might be explained by the limited sample size as
3
well as unmeasured and immeasurable factors. A more extensive research is therefore
needed in the future to address this question based on a larger sample size and more
background information.
The remainder of this paper is organized as follows. Section 2 will set out the
theoretical framework of the study and Section 3 is going to present the design of the
survey used to collect the data and the econometric specifications. The results of the
experiment will be interpreted in Section 4 and the summary and conclusion will follow
in Section 5.
2. Theoretical Framework
Consumer behavior usually starts from “buying goods and services for their own
benefit and enjoyment” (Jewell, 2000, p.255). “Although we have boundless needs and
wants, the resources available to us are limited, so having more of one good thing usually
means having less of another” (Frank and Bernanke, 2007, p.4). Therefore, during the
consumption process, individuals must make a trade-off among the choice set, deciding
which alternative is preferred compared with other commodities and choosing a
combination of goods which can maximize their satisfaction.
Varian (2006, p.54) states that “utility is a way to describe the preference”. In a
mathematical approach, utility can be expressed as a functional form
,
4
where refer to the quantities of goods that might be chosen. It can also be illustrated
in a two-dimensional coordinate. “Utility is a measure of the relative satisfaction from, or
desirability of, consumption of various goods and services. Utility is usually applied by
economists in such constructs as the indifference curve, which plots the combination of
commodities that an individual or a society would accept to maintain a given level of
satisfaction (Wikipedia, 2010, [Internet]).”
Figure 1
Figure 1 shows a simplified two-good world. The indifference curve represents
combinations of alternatives. An individual makes a choice among these combinations,
along with the indifference curve, in order to satisfy his or her own necessity and
enjoyment.
The trade-off decision is constrained by personal income levels (personal budget lines).
In economics theory, a basic hypothesis is that an individual facing limited income makes
an economic choice to achieve as much utility as possible. That is, an individual
maximizes his or her utility subject to the income, which can be expressed in a
mathematical form
,
5
,
where are the prices of goods, and I represents the income. In a geometric figure, the
combination chosen which fulfills this condition is the point that the indifference curve
tangent to the budget line, illustrated in Figure 2.
Figure 2
In fact, a product is a combination of various attributes, and the levels of these
attributes are diverse. K. J. Lancaster (1966) emphasized “goods attributes” in his
research. “It is the attributes of goods that provide utility to individuals, and each specific
good contains a fixed set of attributes” (Nichloson and Snyder, 2009, p.192). Therefore,
when purchasing a certain good, people usually face a choice of being satisfied by
different combinations of attribute levels, that is, they must sacrifice some attributes so
that they can obtain others. Choice behavior is actually a process of weighing various
attributes and making a decision.
In the table tennis blade case, an enthusiast weighs attributes, and eventually chooses
one alternative product, which is a set of diverse attributes, based on his or her budget
constraint, as presented in Figure 3.
6
Figure 3
Figure 3 shows the consumer behavior of buying a table tennis blade. Suppose that
there are two attributes. The one is control, which refers to the intensity of spin of ball-
hitting; the other one is attack, which is concerned with the speed of ball-hitting. Each
product is a combination of these two property attributes. In such a case, the utility
function of an individual can be written as
.
An individual measures these attributes and makes a choice that he or she regards as
the “best” or the “fittest” among various alternatives, limited by the budget constraint.
Theoretically, consumers who have different budget constraints have different choice
spaces. They maximize their utility by making choices within their own affordable choice
sets.
The budget constraint is not the only determinant of an individual’s choice behavior.
“Individual consumers have preferences and/or an associated ordinal utility function that
7
characterizes their personal valuations of all bundles of commodities in the choice space”
(Mirowski and Hands, 2007, p.1-7). In other words, an individual will behave as if he or
she uses his or her purchasing power depending on the personality and preference.
Because utility refers to overall satisfaction, such a measure is clearly affected by a
variety of factors. “A person’s utility is affected not only by his or her consumption of
physical commodities, but also by psychological attitudes, personal experiences, etc”
(Nichloson and Snyder, 2009, p.88). Thus even people at the same income level also have
distinctive preferences and perform different buying behaviors, and these behaviors are
influenced by their own characteristics which concerned with the necessity to goods
attributes.
In this project, table tennis players have various characteristics. The most important
factors defined as determinants of this kind of choice behavior are the age, the technique
style, and the specific training background. Namely, an appropriate table tennis blade
which is suitable for a consumer’s own situation and needs should be regarded as the
basic thing for them to consider. In other words, a ping-pong player would, in terms of
economics, maximizes the utility determined by his or her own characteristics. However,
the economic reality, for example the income level, should still not be neglected for it is
the constraint of an individual’s purchasing power. The main task of our experiment is to
measure and reveal how these main points influence individuals’ behaviors when
consuming STIGA table tennis blades.
8
3. Empirical Analysis
In this section, the main purpose is to analyze to what extent the personal
characteristics could influence the decision-making of table tennis blade purchasing.
Firstly, the requisite data and variables used in the empirical analysis will be presented
and defined, followed by the introduction of the targeted econometric model and
estimation techniques. At last, a method of hypothesis testing—t-test will be shown and
discussed.
3.1 Data and Variables
To examine the relationship between the probability of choosing a certain table tennis
blade and personal characteristics, one hundred and eighty questionnaires were sent out
to registered members of a table tennis club, called Shengteng Sport in China. And 157
among the whole were received, corresponding to a response rate of 87%. Inside them,
101 respondents used STIGA blades. One respondent performed a rare technique style—
cutting defense and was thus excluded from the experiment. Hence, the empirical
analysis was based on 100 respondents. The original questionnaire form and the data are
presented in Appendix A1 and A2.
The original data collected contained the respondents’ choices, genders, ages, playing
years, monthly income levels, main playing technique styles, specific training
background, playing frequency, and so on. Four types of independent variables were used:
the age level, the monthly income level, the main playing technique style and the specific
9
training background. Among them, the main playing technique style and the specific
training background were dummy variables.
During the process of the experiment, other factors, such as the gender, the playing
frequency, the use of odd rubbers, were added into the analysis. However, the regression
result showed that they might be irrelevant variables and the reasons could be concluded
into three points: firstly, the overall fitness of the regression equations did not change
obviously; secondly, the standard errors of the estimated coefficients increased; thirdly,
the t-values of these variables were very small. Such a result implied that they might be
irrelevant to the choice of STIGA ping-pong blades, according to the principle of
econometrics of Studenmund (2006, p.172-173). Therefore, these factors were excluded
(the do-file is attached in Appendix A4).
The respondents’ choice of STIGA table tennis blades was chosen as a dummy
dependent variable. Definitions of these variables can be seen in Table 1(the table is
attached in Appendix A3).
Table 1 about here
There were six alternatives in the choice set: Clipper Wood, Offensive Classical,
Titanium 5.4, Carbon 5.4, Carbon 7.6, and Energy Wood. The market prices of these six
products were 580 SEK, 450 SEK, 1300 SEK, 920 SEK, 820 SEK, and 420 SEK,
respectively. In the experiment, the alternative Energy Wood was defined as the base of
the multinomial logit. Among the 100 respondents, 28 players chose alternative 1
(Clipper Wood), 23 players alternative 2 (Offensive Classical), 15 players alternative 3
(Titanium 5.4), 8 players alternative 4 (Carbon 5.4), 14 players alternative 5 (Carbon 7.6),
10
and 12 players alternative 6 (Energy Wood). The percentages were respectively 28%,
23%, 15%, 8%, 14%, and 12%. The percentages of the choices are illustrated in Figure 4.
Figure 4
Each alternative is a combination of the attributes control and attack. Control refers to
the intensity of spin of ball-hitting, whereas attack is concerned with the speed of ball-
hitting. Different consumers make choice-decisions among the alternatives according to
their own economic states, personal characteristics, and preferences to attributes. Figure 5
intuitively shows the attribute levels of control and attack in the map of indifference
curves.
0%
5%
10%
15%
20%
25%
30%
Clipper Wood Offensive Classical
Titanium 5.4 Carbon 5.4 Carbon 7.6 Energy Wood
Choice percentages
11
Figure 52
Age is an important element of sport capability for both professionals and non-
professionals. In this case, the mean value of the 100 respondents’ ages was 30.25 years
old, and the standard deviation was 9.992 years old. When becoming older, a player’s
strength declines. In table tennis, the decline in strength results in smaller speeds of ball-
hitting, reducing the threat in the game. Therefore, in order to keep the ball-speed, elder
enthusiasts might prefer alternatives with higher level of the attribute attack. However,
the sport ability does not decrease each year. For instance, an 18-year-old person and a
21-year-old player probably have no obvious distinction in strength. That is why the age
variable was defined as different levels with 5 years in each category. Meanwhile, in the
actual experiment, the continuous age was used in the regression analysis, but the result 2 In the figure, the abbreviations CL, OC, T54, C54, C76, EG respectively represent Clipper Wood,
Offensive Classical, Titanium 5.4, Carbon 5.4, Carbon 7.6, and Energy Wood.
12
suggested that the overall fitness of coefficients was lower than that of using the age
levels (the do file of this experiment is attached in Appendix 4). Therefore, the age
variable was defined as intervals rather than continuous numbers.
Figure 6
Figure 6 shows the distribution of age levels. There were 28 people belong to the age
level 1 (under 23 years old), 32 level 2 (24-28 years old), 14 level 3 (29-33 years old), 6
level 4 (34-38 years old), 10 level 5 (39-43 years old), and 10 level 6 (over 44 years old).
The percentages were respectively 28%, 32%, 14%, 6%, 10%, and 10%.
Income is a measure of individuals’ purchasing power, constraining their buying
behavior in an affordable choice space. Since the exact income referred to individuals’
privacy, in order to avoid such a sensitive question, this factor was loosened by using
income levels instead of precise numbers during the data collection. In addition, as the
0%
5%
10%
15%
20%
25%
30%
35%
under 23 24-28 29-33 34-38 39-43 over 44
Distribution of Age Levels
13
age variable, the income was also defined as levels because people with similar incomes
(but not the same) could perform within the same choice space.
Figure 7
As shown in Figure 7, the percentages of respondents in each monthly income level
illustrated were respectively 38% in level 1 (under 3000 SEK), 37% in level 2 (3000-
5000 SEK), and 35% in level 3 (over 5000 SEK).
A player’s technique style directly determines his or her preference to the attributes of
a blade. Generally, this variable was defined through two levels, speed-attack and loop-
attack (other styles like cutting-defense rarely exist among amateurs). As the terms
suggest, speed-attack players use large speeds of ball-hitting as their primary “weapon”,
whereas loop-attack players are better at making spins during the game. There were 52%
respondents’ technique style being loop-attack, whereas 48% speed-attack.
There are many enthusiasts who used to receive specific training, but did not become
professional players. However, they still keep the passion to table tennis and actively
under 300038%
3000-500037%
over 500035%
Percentgaes of Income Levels
14
enjoy the game. Due to the ever-specific training background, these people have better
skills of ball-hitting. They can better use their strength when hitting the ball, producing
much larger speed and threat than non-trainees during the game. Therefore, a player who
has specific background usually more emphasizes the control to the ball, so that they can
reduce their unforced faults in the game. Most enthusiasts consuming professional blades
were ever-specifically trained. Among the 100 respondents, 61% of them ever received
specific training, whereas 39% did not.
3.2 Econometric Model
In this research, there were six choices available at the same time and a decision
between multiple alternatives was made simultaneously. A model taking into account that
the respondent chose from more than two different alternatives was needed to be built
and estimated. And a multinomial logit model was chosen to analyze every table tennis
player’s choice-decision. “A multinomial logit model is an extension of the binomial
logit technique that allows several discrete alternatives to be considered at the same time.
If there are N different alternatives, we need dummy variables to describe the
choice, with each dummy equaling one only when that particular alternative is chosen”
(Studenmund, 2006, p.463).
Through explaining the binomial logit technique, it seems like that it is easier to
present the multinomial logit model clearer and more integrated. “The binomial logit is
an estimation technique for equations with dummy dependent variables that avoids the
unboundedness problem of linear probability model, whereas a linear probability model
15
is a linear-in-the-coefficient equation used to explain a dummy dependent variable”
(Studenmund, 2006, p.448-454):
,
where is a dummy variable that is equal to one if the individual i ( )
choose table tennis blade k ( ), and zero otherwise, while the X’s, and
are independent variables, regression coefficients, and an error term, respectively.
Assuming that an equation is estimated for a particular consumer, and measures the
probability that =1 for the ith observation, then
,
where indicates the probability that for the ith observation.
It is a major problem that is not bounded by 0 and 1, when OLS is used to estimate
the coefficients of an equation with a dummy dependent variable. The binomial logit
model manages to avoid this unboundedness problem. “The binomial logit is an
estimation technique for equations with dummy dependent variables by using a variant of
the cumulative logistic function” (Studenmund, 2006, p.454):
,
when → ,
;
when → ,
.
16
Thus, is bounded by one and zero. Therefore, the binomial logit model avoids the
unboundedness problem in dealing with dummy dependent variable when using the linear
probability model.
Since the original equation’s functional form is complicated, in order to simplify, taken
logarithm, the equation can be rewritten as
,
where is a dummy variable. If the logit functional form on the left side of the equation
is defined as
,
where L indicates that the equation is a logit of the functional form, the equation can be
written as
.
In a multinomial logit model, an alternative should be selected as the “base” alternative,
and other ones should be compared to the base with a logit equation. In the experiment,
six alternatives from the known brand “STIGA” would be compared at the same time and
they were labeled as (1) Clipper Wood; (2) Offensive Classical; (3) Titanium 5.4; (4)
Carbon 5.4; (5) Carbon 7.6 and (6) Energy Wood. So first, the alternative (6) Energy
Wood was selected as the “base” alternative.
Then the logit equation system was estimated as follow:
17
where:
= the probability of the ith respondent choosing the first alternative (1) Clipper Wood
= the probability of the ith respondent choosing the second alternative (2) Offensive
Classical
= the probability of the ith respondent choosing the third alternative (3) Titanium 5.4
= the probability of the ith respondent choosing the forth alternative (4) Carbon 5.4
= the probability of the ith respondent choosing the fifth alternative (5) Carbon 7.6
= the probability of the ith respondent choosing the “base” alternative (6) Energy
Wood
= the ith respondent’s age level, which was defined through 6 levels with 5 years in
each one; 1=under 23; 2=24 to 28, 3=29 to 33, 4=34 to 38; 5=39 to 43; 6=over 44
18
= the ith respondent’s monthly income level, which was defined through 3 levels
with 2000SEK in each one; 1=under 3000SEK; 2=3000 to 5000 SEK; 3=over
5000SEK
= 1 if the ith respondent chooses Loop-attack, 0 if chooses Speed-attack
= 1 if the ith respondent ever received trainings, 0 otherwise
= a classical error term
= coefficients to be estimated
3.3 Hypothesis Testing of Regression Coefficients
The hypotheses of regression coefficients can be seen in Table 2 (Table 2 is listed in
Appendix A3).
Table 2 about here
The t-test is a statistical tool that is used for hypothesis testing of regression
coefficients. The first step of the hypothesis test is to state the hypotheses to be tested.
The null hypothesis is a statement of the value that is not expected, whereas the
alternative hypothesis a statement that is expected. For example:
Null hypothesis: (the value not expected)
Alternative hypothesis: (the value expected)
19
To decide whether to reject or not to reject a null hypothesis based on a calculated t-
value, a critical t-value is used. The regression estimated t-value can be calculated
as
. A critical t-value, , is a value which is used
to distinguish the “acceptance” region from the rejection region. It is selected from a t-
table depending upon the type of test (a one-sided or two-sided test), the level of
significance and the degree of freedom. The degree of freedom is defined as the number
of observations minus the number of coefficients estimated (including the constant),
namely (where N is the number of observations, and K is the number of slope
coefficients). When a calculated t-value and a critical t-value have been obtained,
the null hypothesis can be rejected if and if the calculated t-value has the
sign implied by .
In this project, and it was a one sided test.
A 5% level of significance was selected as the standard. According to the t-table, the
critical t-value was 1.671.
Take Clipper Wood as an example:
Because the sign of the coefficient of Age was expected as positive, it was
hypothesized as
20
Here
. The critical t-value of a 5% level of significance
1.671 was taken as the standard. Since , and its sign was not the same as
that implied by , both of them indicated that could not be rejected.
4. Estimation Results and Interpretation3
The main regression results of the model are shown below and they are summarized,
based on the multinomial logit model, in Table 2 respectively. The complete table is
listed in Appendix A3.
Table 2 about here
Depending on the usage of the logit form of dependent variable, the logit coefficients
need to be divided by 4 to reach meaningful estimates of the effect of the independent
variables on the probability of choosing a product to the base alternative. For instance, if
of Clipper Wood was divided by 4, according to the estimation, the probability of a
loop-attack-player (Tech=1) choosing alternative 1 (Clipper Wood) to the base alternative
(Energy Wood) would be 60.35% (
) less than the probability of a
speed-attack-player (Tech=0), holding the other three independent variables constant.
The results presented in Table 2 indicated that the main technique style was an
influential element of the choice of table tennis blades. For all alternatives except
3 Statistic software STATA 11 was used in the estimation.
21
alternative 4 (Carbon 5.4), this variable passed the t-test. Holding the other three
independent variables constant, when players performed Loop-attack (Tech=1), the
probability of choosing alternative 2 and 3 (Offensive Classical and Titanium 5.4) to the
base alternative were 67.95% and 56.35% higher, while the probability of choosing
alternative 1 and 5 (Clipper Wood and Carbon 7.6) to the base alternative were 60.35%
and 50.30% lower than when player perform Speed-attack (Tech=0).
The monthly income level only seemed to influence the probability of alternative 3
(Titanium 5.4). For the other four alternatives, the monthly income level was
insignificantly determined, indicating that the choice of alternatives 1, 2, 4 and 5 (Clipper
Wood, Offensive Classical, Carbon 5.4 and Carbon 7.6) was not affected by the monthly
income level. The reason for such a result might be the feature of table tennis blades.
Generally, table tennis blades have two features, low price and durability. The mean price
of the choice set was 748 SEK. A blade can usually be used for many years if it is used
carefully, maybe causing insensitivity to a buyer’s income level. Thus a person with
relatively low income level might choose a blade with relatively high price. The only
exception in this case was the alternative Titanium 5.4, whose price was extraordinarily
high (1300 SEK), and the result also indicated that players with relatively higher income
levels were more probable to choose this alternative than those with lower income levels.
Finally, the age level and the training background were insignificantly determined to
all choices. This implied that Chinese consumers’ choice of STIGA table tennis blades
was not influenced by individuals’ age and training background.
22
5. Summary and Conclusion
In this paper, an experiment on Chinese consumers’ choice of STIGA table tennis
blades was performed. A multinomial logit model was used to study the relationship
between individuals’ choice behavior and their own characteristics, such as the age level,
the monthly income level, the technique style, and the training background. The results of
the choice experiment indicated that the technique style was important when choosing a
particular STIGA table tennis blade.
However, other things such as the monthly income level, the age level and the training
background did not seem to influence Chinese players’ choice of STIGA table tennis
blades. There exist many potential explanations for such results.
First of all, because table tennis blades are generally cheap and durable, it is not
sensitive to an individual’s income level. The mean price of the six alternatives was only
748 SEK. In the meantime, a blade can usually be used for several years, if it is normally
used without accidental damage. The low price and the durability might thus cause
insensitivity to buyers’ incomes. The alternative Titanium 5.4, whose price
extraordinarily reached to 1300 SEK, was the unique exception. The analysis also
showed that consumers with relatively higher income levels were more probable to
consume this alternative than those with lower income levels.
On the other hand, the complexity of table tennis means that many factors are
immeasurable and the limited sample size means that all important variables cannot be
modeled exactly. For example, the first one is the variation of individuals’ sport ability.
As it has been mentioned previously, an increase in age theoretically triggers the decline
23
in a person’s strength. It will affect the person’s speed and threat of ball-hitting. However,
because individuals’ physique is quite distinctive, such variation is not easy to be trialed.
Meanwhile, consumers’ psychological elements are also quite hard to judge. For the table
tennis case, a player’s psychological preference to the quality of ball-hitting (speed or
spin) is a typical example. An elder person whose hitting speed has declined might still
choose a blade with high control and low attack, because he hopes to defeat his rival by
better spins and less unforced faults. Because of the limited sample size (just 100
samples), such complex variables cannot be measured exactly.
Additionally, another variable unmeasured is the match of the blade and rubbers. This
project just focuses on the choice of blades, but in reality, the consumption on table
tennis equipment is more complex. A whole equipment of table tennis consists of a blade
and rubbers. Rubbers also have the same attributes defined as blades. Consumers usually
“produce” their equipments through balance attributes of blades and rubbers. Through
balancing the property of the blade and rubbers, an individual is also possible to choose
an alternative that is theoretically unexpected. For example, a loop-attack player
theoretically more prefers to the attribute control, more probably choosing a blade with
higher control level, such as Energy Wood rather than one with lower control level like
Carbon 5.4. However, in reality, he is still possible to choose Carbon 5.4, and by
matching rubbers with high control level he does not sacrifice the necessity to intensive
spin of ball-hitting. An elder player can also obtain the balance of properties by matching
a blade with lower attack, for instance, Energy Wood, to high attack rubbers. Thus an
individual’s behavior on choosing a table tennis blade might also be affected by this
unmeasured factor.
24
Therefore, to model this extremely complex consumer behavior more exactly, a deeper
and more extensive research is required in the future. To accomplish this, first, an
expansion of the sample size is necessary. Due to the complexity of table tennis, just 100
samples are far from enough to measure the extremely complicated choice behavior,
especially variables involving consumers’ sport ability and psychological preferences to
quality of ball-hitting. Second, information-collection about consumers’ match of blades
and rubbers should be noticed as well for the use of rubbers might change the theoretical
choice of a consumer’s blade.
25
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Studenmund, A. H., (2006), Using Econometrics: A Practical Guide, 5th
edition, Boston,
Mass: Addison Wesley
Varian, Hal R., (2006), Intermediate Microeconomics: A Modern Approach, 7th
edition,
New York: Norton
WEBSITES:
Wikipedia, (2010), Utility, http://en.wikipedia.org/wiki/Main_Page, Received: 2010-5-12
26
APPENDIX
A1. QUESTIONNAIRE
1. Respondents background
Age ：___________
Gender：___________
Education：__________
Occupation：__________
Years of playing table tennis：___________
Monthly income levels: A. under 3000 SEK
B. 3000-5000 SEK
C. over 5000 SEK
2. Which of the following brand would you like, if you buy a new blade?
A. STIGA
B. BUTTERFLY
C. DONIC
D. TIBHAR
E. Others ____
If “STIGA” of Q2, please go on
3. Which type of following would you buy?
A. Ebenholz NCT V
B. Offensive Wood NCT
C. Carbon 7.6
D. Carbon 5.4
E. Clipper Wood
F. Others________________
4. What the most important factor would you consider, when you choose a blade?
A. Prices
B. Properties and adaptation
C. Effects of famous player
27
D. Veneer
E. Others _____________
5. How often do you play table tennis? _____________Times/Week
6. Have you ever received special training at table tennis?
A. YES
B. NO
7. What is your technique-style?
A. Speed-attack
B. Loop-attack
8. Do you use “odd-rubber”? (Ex: long rubber, raw rubber, anti-loop……)
A. YES
B. NO
9. Who is your most favorite player? _________（Only 1 player）
10. Is your blade the same as any famous player’s?
A. YES (If yes, whose? __________)
B. NO
C. I don’t know
11. Would you consider other products with similar properties as a substitute, if the price
of you anticipated blade increased by 10 percent?
A. YES
B. NO
12. Would you buy a blade, if you did not know the property of that blade completely?
A. YES
B. NO
13. Please rank the following products by using 0-9, where 0 is “not at all” and 9 is very
much.
28
Offensive Classical
Under 600 SEK
5 Wood
Mid-soft
___________
Clipper Wood
Under 600 SEK
7 Wood
Hard
___________
Tube Carbon
Under 600 SEK
High-tech
Hard
___________
Offensive Wood NCT
600-1000 SEK
5 Wood
Mid-hard
___________
Optimum 7
600-1000 SEK
7 Wood
Mid-soft
___________
Carbon 5.4
600-1000 SEK
High-tech
Mid-hard
___________
Ebenholz NCT 5
Over 1000 SEK
5 Wood
Hard
___________
Rose 7
Over 1000 SEK
7 Wood
Mid-hard
___________
Titanium 5.4
Over 1000 SEK
High tech
Mid-soft
___________
29
A2. Data Base
Observation#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Choice
3
2
2
4
1
2
1
2
4
3
5
2
3
2
5
1
Age level
3
3
2
6
1
1
2
2
5
2
3
2
1
2
6
1
Income level
2
2
2
3
1
1
2
2
3
3
2
1
1
2
3
2
Technique
1
1
1
1
0
1
0
0
1
1
0
1
1
1
0
0
Training
1
1
1
0
1
1
0
1
0
1
0
1
1
1
0
1
30
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
4
3
1
5
1
2
1
3
2
1
6
6
5
3
3
1
6
2
5
2
6
5
5
1
2
6
1
1
2
2
3
1
1
1
6
3
2
1
5
2
4
3
2
5
2
1
2
3
1
1
2
3
2
1
1
1
2
2
3
1
3
1
2
3
1
3
1
1
0
0
0
1
0
1
1
0
1
0
0
1
1
0
1
1
0
1
1
1
0
1
1
0
1
1
0
1
1
1
1
0
0
1
1
1
0
1
0
1
1
0
31
Observation#
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
Choice
3
2
1
5
2
1
1
6
2
3
2
5
3
3
3
6
1
5
2
6
4
1
1
Age level
1
2
1
5
2
2
1
1
3
2
4
6
3
2
3
5
2
4
2
2
6
6
2
Income level
1
1
2
2
1
2
1
1
2
1
2
3
3
2
3
3
2
2
3
1
3
3
1
Technique
1
1
0
0
1
0
0
0
1
1
1
0
1
1
1
0
1
0
1
1
0
0
1
Training
1
1
0
1
1
1
1
1
0
1
1
0
0
0
1
0
1
0
0
1
0
0
1
32
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
2
2
1
6
2
1
1
4
5
5
2
4
1
5
3
6
1
1
1
6
1
5
2
5
1
5
1
2
2
1
2
1
3
3
3
5
6
2
4
1
1
4
2
3
1
2
1
3
1
2
1
1
2
1
3
3
2
3
3
1
2
1
1
2
2
2
1
1
0
1
1
0
0
0
0
1
1
1
0
0
1
1
0
0
0
0
1
0
1
0
1
1
1
1
1
1
0
1
1
0
1
0
0
0
0
1
1
0
0
0
33
Observation#
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Choice
2
6
4
1
2
1
6
2
1
1
4
5
1
3
1
3
2
Age level
2
2
6
4
6
1
5
1
2
1
1
1
3
2
1
2
1
Income level
1
2
2
3
2
1
3
1
2
1
1
1
2
3
2
1
1
Technique
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0
1
1
Training
0
1
0
0
0
1
1
1
1
1
0
0
1
1
1
1
1
34
A3. TABLES
Table 1: Definition and statistics of variables
Items Mean Standard deviation Percentage
Choices
CL
28%
OC 23%
T54 15%
C54 8%
C76 14%
EG 12%
Age (year)
Under 23
30.25 9.992
28%
24-28 32%
29-33 14%
34-38 6%
39-43 10%
Over 44 10%
Income level (SEK)
Under 3000
38%
3000-5000 37%
Over 5000 25%
Tech
Loop-attack
52%
Speed-attack 48%
Training
Yes
61%
No 39%
The abbreviations CL, OC, T54, C54, C76, EG respectively represent Clipper Wood, Offensive
Classical, Titanium 5.4, Carbon 5.4, Carbon 7.6, and Energy Wood.
35
Table 2: Regression Estimation of All Alternatives (The base alternative is Energy Wood)
Choice Estimated
coefficients
Standard
Errors
Hypothesized
signs
Calculated
t-value
5%
significance
level ,
(1)
Clipper
Wood
Intercept 1.680 1.470
Age -0.653 0.400 + -1.63 Insig.
Income 0.704 0.816 + 0.86 Insig.
Technique -2.414 0.875 - -2.76 Sig.
Training 0.366 0.947 - 0.39 Insig.
(2)
Offensive
Classical
Intercept -1.969 1.844
Age -0.178 0.397 - -0.45 Insig.
Income 0.220 0.734 + 0.30 Insig.
Technique 2.718 1.188 + 2.29 Sig.
Training 0.683 1.001 + 0.68 Insig.
(3)
Titanium
5.4
Intercept -3.330 2.022
Age -0.753 0.463 + -1.63 Insig.
Income 1.544 0.773 + 2.00 Sig.
Technique 2.254 1.236 - 1.82 Sig.
Training 1.000 1.114 - 0.90 Insig.
(4)
Carbon
5.4
Intercept 0.051 1.856
Age 0.159 0.418 + 0.38 Insig.
Income -0.121 0.957 + -0.13 Insig.
Technique -0.205 0.981 - -0.21 Insig.
Training -2.041 1.382 - -1.48 Insig.
(5)
Carbon
7.6
Intercept 1.328 1.664
Age 0.190 0.398 + 0.48 Insig.
Income -0.269 0.944 + -0.28 Insig.
Technique
Training
-2.012
-1.826
1.13
1.145
-
-
-1.99
-1.59
Sig.
Insig.
36
A4. DO-FILES
37
38