Consumer Decision-making Styles: Comparison Between

Consumer Decision-making Styles:Comparison Between

Shanghai and Hong KongUniversity Consumers

A Consumer Styles Inventory Approach

BY

Chan Hoi Yee, Bertha02005174

China Business Studies Option

An Honours Degree Project Submitted to theSchool of Business in Partial Fulfillment

of the Graduation Requirement for the Degree ofBachelor of Business Administration (Honours)

Hong Kong Baptist UniversityHong KongApril 2005

ii

Acknowledgements

I would like to give my heartiest thanks to my supervisor Dr. Shi Yi Zheng who has

sacrificed a lot of his valuable time for guiding me in doing this honor project,

suggesting precious advice, pointing out and correcting my mistakes. He is very

patient in answering and explaining my questions all the time. I really have learnt a lot

from him.

In addition, I would like to express my sincere thanks to my dearest friends, Mr. Peter

Wong and Miss Susanna Wong, for squeezing lots of time for helping me in

conducting survey.

Also, I would like to thank my family and friends who always gave me support and

encouraged me when I feel depressed in doing the project.

Last but not least, I would like to thank all the teachers in the Hong Kong Baptist

University who teach me a lot about marketing knowledge in the past three years.

_____________________

Chan Hoi Yee, Bertha

26th April, 2005

iii

Abstract

Consumers use a variety of decision-making styles. This study investigates

decision-making styles of consumers in Shanghai and Hong Kong by analyzing the

Consumer Style Inventory (CSI), which is administered to 150 Shanghai and Hong

Kong university consumers respectively. Factor analysis is adopted to develop the

CSI inventories.

Findings indicate that six types of decision-making styles and fifteen statements are

valid and reliable in Shanghai, whereas five types of decision-making styles and

twenty statements are valid and reliable in Hong Kong. Significant differences can be

found in the dimension of quality conscious, brand conscious, fashion conscious and

shopping carefulness. Business implications, which address the above findings, are

provided for marketers in the following section. Limitations of this paper are the final

chapter.

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Table of Contents

Content Page

Acknowledgements ii

Abstract iii

Chapter 1. Introduction1.1 Background Information 11.2 Research Problem Development 1.2.1 Why Shanghai vs. Hong Kong? 1 1.2.2 Why University Students? 21.3 Research Objectives 3

Chapter 2. Literature Review2.1 Historical Researches on Decision-making Styles 42.2 The Consumer Style Inventory (CSI) 42.3 Application of CSI Across Cultures 6

Chapter 3. Research Methodology 3.1 The Sample 7

3.2 Instrument 7 3.3 Data Collection Method 8 3.4 Data Analysis Method 8

Chapter 4. Hypothesis Development 4.1 Differences in Brand Consciousness and Price Consciousness 10 4.2 Differences in Fashion Consciousness and Confusion by Overchoice

11

Chapter 5. Research Findings and Analysis 5.1 Personal Information of the 300 Samples from Shanghai and

Hong Kong 5.1.1 Shanghai 13 5.1.2 Hong Kong 13 5.1.3 Comparison 14 5.2 Decision-making Styles of Shanghai University Consumers 15 5.3 Decision-making Styles of Hong Kong University Consumers 16 5.4 Comparison of Decision-making Styles Between Shanghai and

Hong Kong University Consumers 5.4.1 Number of Dimensions 18 5.4.1 Item Loadings 20 5.4.1 T-test: Test of Hypotheses 21

Chapter 6. Business Implications 6.1 For Shanghai 26 6.2 For Hong Kong 27 6.3 For both Shanghai and Hong Kong 27

v

Chapter 7. Limitations 7.1 Generality of Consumer Characteristics 28 7.2 Limitation of the Sample 28 7.3 Limitation of Culture and Economic Background 29

Chapter 8. Conclusion 30

Chapter 9. References 31

Chapter 10. Appendix 3510.1 Explanation of the eight factors loading by Sproles and Kendall 36

10.2 Tables 38 10.3 Questionnaires 47 10.4 SPSS Outputs 58

1

Chapter 1. Introduction

1.1 Background Information

Decision-making is more complex and even more important for consumers today than

in the past. Consumers are besieged by advertising, news articles, and direct mailings

that provide an abundance of information, much of it with mixed messages. In

addition, increases in the number and variety of goods, stores, and shopping malls,

and the availability of multi-component products and electronic purchasing

capabilities have broadened the sphere for the consumer choice and have complicated

decision making [Hafstrom, Chae, and Chung, 1992].

Profiling consumers’ decision-making styles focuses on studies of the majority of

consumer interest (eg, Bettman, 1979; Sproles, 1985; Thorelli, Becker, and Engeldow

1975; WestBrook and Black, 1985). Consumer affairs specialists use such profiles to

understand consumers’ shopping behaviour, while advertisers and marketing

researchers use them to segment the consumers into various niches for product

positioning [Srinivas and Andrews, 1993].

1.1 Research Problem Development

1.2.1 Why Shanghai vs. Hong Kong?

Shanghai is the most metropolitan province in China, and Hong Kong is also a very

prosperous city in the world. Hong Kong and Shanghai are relevant cities in China for

comparative studies. They have several similarities. Geographically, both cities are

located at the coast of China. Historically, both cities had experienced western

2

colonization for a long time. Culturally, both cities have shared modern and traditional

characterizations. They both are international metropolises that have much

international links. However, there are something different. For example, number of

brothers and sisters, source of income, source of information and culture.

Comparing between these two cities can help companies formulating marketing

strategies. For those companies who have only invested in Hong Kong and have

interest to enter into the Shanghai market, they can study the difference and

similarities between these two cities and then formulate an entering strategy for

Shanghai based on the existing marketing strategy for Hong Kong, and vice versa.

1.2.2 Why University Students?

The university students market is quite large. According to the statistics, there are

189,400 university students in Hong Kong in 2004, amounting about 11.5% of the

educational population [Hong Kong Census and Statistics Department, 2004]. And

there are 378,500 university students in Shanghai in 2004, amounting about 10.8% of

the educational population [Shanghai Statistical Yearbook, 2004]. It is a significant

market in both Shanghai and Hong Kong.

The role of the young especially in consumer decision making should be defined and

examined for several reasons. Young people are eager to consume, are conscious of

their experience [Sproles and Kendall, 1986]. Young consumers are recognized as a

specialized market segment for a variety of goods and services [Moschis and Moore,

1979]. The young within the family often influence family purchasing decisions [Turk

and Bell, 1972]. Consumer socialization is defined as “process by which young

3

people acquire skills, knowledge, and attitudes relevant to their functioning as

consumers in the marketplace” [Ward, 1972]. Socialization usually takes place within

the family and may shape consumer patterns. In this way, it may affect not only

present but also future consumer well-being.

1.2 Research Objectives

Although the CSI research is widely conducted in different nations, few of it is related

to Chinese society, related to the comparison between Hong Kong and Shanghai, and

focused on universities students.

There are three main objectives in this paper:

1. To investigate the decision-making style of Shanghai universities consumers by

purifying the items of CSI.

2. To investigate the decision-making style of Hong Kong universities consumers

by purifying the items of CSI.

3. Comparison of decision-making styles between Shanghai and Hong Kong

universities consumers.

4

Chapter 2. Literature Review

2.1 Historical Researches on Decision-making Styles

Consumer-interest researchers have long been interested in identifying the underlying

decision styles of shoppers. For example, consumers are identified as economic

shoppers, personalizing shoppers, ethical shoppers, apathetic shoppers [Bellenger and

korgaonkar, 1980; Darden and Reynolds, 1971; Stone, 1954], store-loyal shoppers

[Moschis and Gorge, 1976; Stephenson and Willett, 1969], recreational shoppers

[Bellenger and Korgaonkar, 1980; Stephenson and Willett, 1969], convenience

shoppers [Korgaonkar, 1984; Stephenson and Willett, 1969; Williams et al., 1978],

price-oriented shoppers [Korgaonkar, 1984; Stephenson and Willett, 1969; Williams

et al. 1978], brand-loyal shoppers [Jocoby and Chestnut, 1978; Moschis and Gorge,

1976], name-conscious shoppers [Darden and Ashton, 1974-75], fashion shoppers

[Lumpkin, 1985], brand conscious shoppers [Korgaonkar, 1984] and impulse

shoppers [Gehrt and Cater, 1992]. These classifications have provided a number of

measuring methods for the marketers to segment the general public in the consumer

markets [Alice and Noel, 2001].

2.2 The Consumer Style Inventory (CSI)

To further consolidate the above various approaches, Sproles and Kendall [1986]

designed a new model to measure decision-making styles of consumers.

According to Sproles and Kendall [1986], a consumer decision-making style is

defined as a mental orientation characterizing a consumer's approach to make

5

consumer choices. Broadly speaking, there are three types of approaches in studying

consumer decision-making styles: the psychographic/lifestyle approach, which

identifies hundreds of characteristics related to consumer behavior; the consumer

typology approach, which classifies consumers into several types; and the consumer

characteristics approach, which focuses on different cognitive dimensions of

consumer decision-making. For a review of these different approaches, see Sproles

and Kendall [1986].

Building on the literature related to consumer decision-making in the field of

marketing and consumer studies [Maynes, 1976; Miller, 1981; Sproles, 1979; Thorelli,

Becker and Engledow, 1975], Sproles [1985] identified nine decision-making style

traits and developed a 50-item instrument using the consumer characteristics approach.

Using data collected from 111 undergraduate women in two classes at the University

of Arizona and employing a factor analysis technique, Sproles [1985] found that six

out of the nine traits were confirmed to be present.

In a later study, Sproles and Kendall [1986] used a similar approach with a slightly

revised model of consumer decision-making with eight dimensions. An instrument of

48 items was developed. Each dimension of consumer decision-making was

represented by six questions. The questionnaire was administered to 482 students in

29 home economics classes in five high schools in the Tucson, Arizona area. The

eight-factor model was confirmed by a factor analysis using the survey data, although

not all questions were deemed to be useful in representing intended dimensions of a

consumer styles inventory [CSI]. The eight dimensions included in the CSI were:

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1. Perfectionistic and high-quality conscious consumer,

2. Brand conscious and price equals quality consumer,

3. Novelty and fashion-conscious consumer,

4. Recreational and hedonistic consumer,

5. Price conscious and value for money consumer,

6. Impulsive and careless consumer,

7. Confused by over-choice consumer, and

8. Habitual and brand-loyal consumer.

Appendix 10.1 (page 35) shows the explanations of the eight factors loading by

Sproles and Kendall. It is a pretty good benchmark for us to explain our data analysis

result.

2.3 Application of CSI Across Cultures

The applicability of the CSI has been investigated across several cultures [Alice and

Noel, 2001; Durvasula et al., 1993; Fan and Xiao, 1998; Hafstrom et al., 1992:

Lysonski et al., 1996; Shim and Gehrt, 1996]. These cross-cultural studies have

shown that four consumer styles are relatively more applicable to different countries

as suggested by the factor structure and reliability estimates of the factors. They are

namely quality conscious, brand conscious, fashion conscious and recreational

conscious [Alice and Noel, 2001].

7

Chapter 3. Research Methodology

3.1 The Sample

The sample size is 300, 150 of Shanghai undergraduate students and 150 for Hong

Kong undergraduate students.

3.2 Instrument

A questionnaire based on the exploratory studies of Sproles [1985] and Sproles and

Kendall [1986] was used to measure consumer decision-making styles in Hong Kong

and Shanghai. The questionnaire was translated into Chinese. Some mainland Chinese

and Hong Kong students and professors reviewed the translations. This ensured that

idiomatic or colloquialistic wording was minimized [Douglas and Craig, 1983;

Parameswaran and Yaprak, 1987].

The questionnaire is divided into two parts. The first part contains the forty

instruments. This instrument will have the following five-point Likert scale: “strongly

disagree (1), somewhat disagree, neither agree nor disagree, somewhat agree, strongly

agree (5).” The second part is the personal information, which includes sex, number

of siblings, income source, monthly cost of living and information source, which are

used to verify the difference between Shanghai and Hong Kong university students

noted before.

8

3.3 Data Collection Method

A non-probability sampling survey method is conducted in the universities in Hong

Kong and Shanghai during March 2005. I did the survey in Hong Kong by myself.

The survey in Shanghai universities were done by my relatives who live in Shanghai,

as it is prohibited for the non-Chinese residents to conduct survey without

authorization by the local government and due to the huge transportation fee occurred.

3.4 Data Analysis Method

SPSS was used to analyze the data collected.

Firstly, frequency was used to display the distribution of consumers’ demographic

background and personal information.

Secondary, CSI for Hong Kong and Shanghai will be developed in two steps

following the method used by Sproles [1985] and Sproles and Kendell [1986].

In the first step, factor analysis, the principal components method with varimax

rotation of factors, was performed to identify characteristics of consumer decision-

making. Factor analysis is designed to identify a set of variables in terms of a smaller

number of hypothetical variables or to explore underlying dimensions [Kim and

Mueller, 1978].

In the second step, Cronbach's alpha, a conservative technique for assessing

reliabilities for each factor [Carmines and Zeller, 1979] was used. For consistency, it

was decided that reliabilities should not be below 0.4, the same level used by Sproles

9

and Kendall [1986].

Thirdly, comparison between Shanghai and Hong Kong was done by comparing the

CSI and by calculating the T-Test (by taking the mean score for each of the factor of

CSI).

The negatively worded items had been reversed before the data analysis proceeded, in

order to analyze the data easily. The scores of question 5, 7, 20, 22, 24, 31, 32 and 40

had been reversed.

10

Chapter 4. Hypothesis Development

We expect that Shanghai and Hong Kong university consumers will differ in terms of

brand consciousness, fashion consciousness, price consciousness and confusion by

overchoice, based on the explanations as follows.

4.1 Differences in Brand Consciousness and Price Consciousness

Since the late 1970s, one-child-per-couple campaign was taken to curtail the

population explosion. As Chinese per capita income has risen and fertility declined,

Chinese parents' love and money have focused on a single child, resulting in unique

social and economic implications such as the perilous 4-2-1 indulgence: four

grandparents and two parents indulging one child. Many of these children are

self-centered and demand material luxuries from their parents [Baker 1987]. While in

Hong Kong, government did not practice “One Child Policy”. Many families had two

to four children in the 1980s [The International Encyclopedia of Sexuality: Hong

Kong].

On the other hand, many Shanghai universities students depend on their parents as

their only income source, parents must pay for what they want. While in Hong Kong,

students have multiple income sources, especially part time jobs, they treasure what

they earn [Francis, 2004].

Based on the above differences, we expect that university consumers in Shanghai are

more brand conscious and less price conscious than Hong Kong university consumers.

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H1: Shanghai university consumers are more brand consciousness than Hong

Kong university consumers.

H2: Hong Kong university consumers are more price consciousness than

Shanghai university consumers.

4.2 Differences in Fashion Consciousness and Confusion by Overchoice

Hong Kong was a British colony for over 150 years (1842-1997). Citizens were

educated to apprehend Western values. Hong Kong people have long been exposed to,

and fast to learn from, Western culture [Alex, Guijun, Fuan, Nan, 2003]. Nowadays,

Hong Kong people are accustomed to, and want to continue, this lifestyle: Their

aversion to the return of sovereignty to China reflected a fear of lifestyle discontinuity

[Lau and Kuan, 1989]

China adopted an open door policy in 1979; however, the country is not fully open to

Western culture. Nowadays, the Chinese government viewed, and still views, the

inflow of the Western lifestyle as a double-edged sword. Western products improve

people’s material well-being, but at the same time they foster capitalistic consumption

values and Western political ideologies, which corrupt Chinese’s people spiritual life

and threaten communist rule. The Chinese government has launched a number of

movements to counteract the inflow of Western thoughts, including the 1983

Anti-Spiritual Pollution movement and the 1989 Anti-Liberalization of the

Bourgeoisie Class movement [Alex, Guijun, Fuan and Nan, 2003]. The government

also keeps a close eye on electronic media and filters “sensitive” Western materials

such as the websites of CNN, Washington Post, Playboy, and Penthouse [Edupage,

12

1996]. When the movie “Titanic” broke the box-office records across Chinese cities in

1997, Chinese officials expressed their concerned that Western movies could be a

“Trojan horse” aimed at speeding up the American cultural invasion of China [Platt,

1998].

As Hong Kong universities consumers always and easily come into contact with

information than Shanghai, and Hong Kong has a longer history involvement of

Western values, we expect that university consumers in Hong Kong are more fashion

conscious and more confused by overchoice than Shanghai university consumers.

H3: Hong Kong university consumers are more fashion consciousness than


H4: Hong Kong university consumers are more confused by over choice than


13

Chapter 5. Research Findings and Analysis

5.1 Personal Information of the 300 samples from Shanghai and Hong Kong

5.1.1 Shanghai

Among the 150 university student respondents in Shanghai, 44% (66) were male and

56% (84) were female. Most of the respondents have no sibling (125, 83.3%), few

respondents have two to three siblings (25, 16.7%), while no respondents have more

than three siblings. A majority of them viewed parents as their only income source

(111, 74%), while few of them had multiple income sources (39, 26%). Over one-third

of them paid ￥1001-￥1500 as their cost of living (52, 34.7%); then “￥501-￥

1000” (48, 32%); “≦￥500” (26, 17.3%); and “＞￥1501” (24, 16%). Finally,

overwhelming of them viewed television (125, 83.3%), Internet (119, 79.3%),

magazine (113, 75.3%) and family and friends (96, 64%) as their information source.

5.1.2 Hong Kong

Among the 150 university student respondents in Hong Kong, 37.3% (56) were male

and 62.7% (94) were female. Most of the respondents have two (52, 34.7%) or three

(52, 34.7%) siblings. A number of them have three siblings (30, 20%), while only few

respondents have no sibling (16, 10.7%). A majority of them had multiple income

sources (109, 72.7%), while few of them viewed parents as their only income source

(41, 27.3%). Most of them paid $1501-$2000 as their cost of living (45, 30%); then

“≦$1500” (42, 28%); “＞$2501” (32, 21.3%); and “$2001-$2500” (31, 20.7%).

Finally, overwhelming of them viewed television (127, 84.7%), family and friends

14

(114, 76%), Internet (103, 68.7%), magazine (102, 68%) and newspaper (96, 64%) as

their information source.

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Table 1: Personal Information of the 300 samplesfrom Shanghai and Hong Kong (Page 39)

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5.1.3 Comparison

Comparing the characteristics of the two sets of respondents in Hong Kong and

Shanghai, there were some similarities and differences identified.

Similarities

1. The cost of living in Hong Kong and Shanghai are very similar.

2. The information source in Hong Kong and Shanghai are very similar.

Differences

1. Most of the respondents in Hong Kong had siblings, while most of those in

Shanghai had not.

2. Most of the respondents in Hong Kong had multiple income sources, while most

of them in Shanghai viewed parents as their only income source.

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5.2 Decision-making styles of Shanghai university consumers

The 40 items of the consumer decision-making scales of Shanghai were subjected to

principal components analysis (PCA) using SPSS. Prior to performing PCA the

suitability of data for factor analysis was assessed. Inspection of the correlation matrix

revealed the presence of many coefficients of 0.3 and above. The Kaiser-Meyer-Oklin

value was 0.608 [Kaiser, 1970, 1974] and the Barlett’s Test of Sphericity [Bartlett,

1954] reached statistical significance, supporting the factorability of the correlation

matrix.

Principal components analysis revealed the presence of 12 components with

eigenvalues exceeding 1, explaining 15.113%, 12.663%, 8.073%, 6.216%, 5.901%,

5.401%, 4.747%, 3.783%, 3.310%, 3.055%, 2.853% and 2.686% of the variance

respectively. An inspection of the screeplot revealed a clear break after the six

components. Using Catell’s [1996] scree test, it was decided to retain six components,

Varimax rotation was performed. The cross-loading items and items that had a factor

loading value less than 0.4 were removed. The rotated solution (presented in

Appendix page 84) revealed the presence of simple structure [Thurstone, 1947], with

all components showing a number of strong loadings, and all variables loading

substantially on only one component. The eight factor solution explained a total of

68.887% of the variance, with the six components contributing 14.194%, 13.467%,

12.586%, 11.910%, 9.709% and 7.021% respectively (more details are presented in

Appendix 10.4.2, page 65).

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The interpretation of the six components was consistent with previous research on the

CSI, with Novelty-fashion consciousness items loading strongly on Component 1,

Perfectionistic and high-quality consciousness items loading strongly on Component

2, Habitual and brand-loyal consumer orientation items loading strongly on

Component 3, Impulsive and careless consumer orientation items loading strongly

on Component 4, Price consciousness and “value for money” orientation items

loading strongly on Component 5 and Brand consciousness and “price equals

quality” items loading strongly on Component 6. The results of this analysis support

the use of CSI as separate scales.

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Table 2: Factor Loadings and Construct Reliability of Shanghai CSI (Page 41)

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5.3 Decision-marking styles of Hong Kong university consumers

The 40 items of the consumer decision-making scales of Hong Kong were subjected

to principal components analysis (PCA) using SPSS. Prior to performing PCA the

suitability of data for factor analysis was assessed. Inspection of the correlation matrix

revealed the presence of many coefficients of 0.3 and above. The Kaiser-Meyer-Oklin

value was 0.649 [Kaiser, 1970, 1974] and the Barlett’s Test of Sphericity [Bartlett,

1954] reached statistical significance, supporting the factorability of the correlation

matrix.

Principal components analysis revealed the presence of 14 components with

17

eigenvalues exceeding 1, explaining 4.902%, 3.565%, 2.931%, 2.367%, 1.967%,

1.568%, 1.491%, 1.332%, 1.281%, 1.241%, 1.141%, 1.130%, 1.064% and 1.015% of

the variance respectively. An inspection of the screeplot revealed a clear break after

the five components. Using Catell’s [1996] scree test, it was decided to retain five

components, Varimax rotation was performed. The cross-loading items and items that

had a factor loading value less than 0.4 were removed. The rotated solution (presented

in Appendix page 107) revealed the presence of simple structure [Thurstone, 1947],

with all components showing a number of strong loadings, and all variables loading

substantially on only one component. The five factor solution explained a total of

53.140% of the variance, with the five components contributing 13.82%, 10.98%,

10.22%, 10.10% and 7.99% respectively.

The interpretation of the five components was consistent with previous research on

the CSI, with Brand consciousness and “price equals quality” items loading strongly

on Component 1, Perfectionistic and high-quality consciousness items loading

strongly on Component 2, Novelty-fashion consciousness items loading strongly on

Component 3, Habitual and brand-loyal consumer orientation items loading

strongly on Component 4 and Price consciousness and “value for money”

orientation items loading strongly on Component 5. The results of this analysis

support the use of CSI as separate scales (more details are presented in Appendix

10.4.4, page 90).

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Table 3: Factor Loadings and Construct Reliabilityof Hong Kong CSI about here (Page 42)

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5.4 Comparison of decision-making styles between Shanghai and Hong Kong

universities consumers

5.4.1 Number of Dimensions

The identified dimensions of CSI are very similar for university consumers in

Shanghai and Hong Kong. Shanghai has six and Hong Kong has five dimensions.

With the same dimensions: (1) fashion conscious, (2) high-quality conscious, (3)

brand-loyal, (4) price conscious, and (5) brand conscious. The dimension of

“Impulsive and careless” was found only in Shanghai CSI.

There is no cross-loading item between Shanghai and Hong Kong CSI. So, the results

support the use of CSI as separate scales.

“Impulsiveness” is not identified as a dimension of consumer decision-making styles

for the Hong Kong university consumers. The reasons are as follows.

Impulsive shopping is opposite to habitual shopping [Fan and Xiao, 1998], in order to

find out why Shanghai has the dimension of “impulsiveness” while Hong Kong does

not, we take a look into the “habitual” dimension.

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Table 4: Comparison of “Habitual and brand-loyal consumer”dimension of Shanghai and Hong Kong (Page 43)

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Question 37 and 39 loaded on both Shanghai and Hong Kong in the “habitual”

dimension. Question 33 “There are so many brands to choose from that often I feel

confused” loaded positively on the “habitual” dimension for the Shanghai sample, but

19

did not load significantly on any factor for the Hong Kong sample. This may be

caused by differences in the interpretation of the question asked in different languages.

However, it is also possible that Shanghai university consumers are loyal to some

brands but at the same time, they are still facing confusion because there are still

many new brands invading into their minds every day. As noted earlier, as more and

more consumer products are becoming available in Shanghai, Shanghai university

students may feel confused and have to “try” these new brands in a certain extent.

While in Hong Kong, many brands are already in the consumers’ minds, they do not

have to “try”, so Hong Kong university consumers are less impulsive.

There is still one reason of why Shanghai has the dimension of “impulsiveness” while

Hong Kong does not. “Impulsive purchases” may be interpreted as “I have not

gathered enough information for this product before I purchase” in Chinese [Fan and

Xiao, 1998]. China has many counterfeit products. How to differentiate and avoid

buying counterfeit products is one of the most salient consumer issues in China. Many

famous brands, both domestic and foreign, are being counterfeited and sold in the

market, and these counterfeit products are usually of poor quality yet have high prices.

Thus, the consequences of buying the wrong products for Chinese consumers may be

different from those for Hong Kong consumers when they make careless purchases.

The careless purchases by Hong Kong consumers may result in a waste of money. For

Chinese consumers, the products bought carelessly may not only be counterfeit and

expensive, but also unable to perform basic functions, and may sometimes be unsafe

and even fatal (examples are some food and electronic products) [Fan and Xiao, 1998].

So, customers in Shanghai may always find themselves impulsive in shopping.

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5.4.2 Item Loadings

The items loading on each dimension are quite similar, although not exactly the same.

Now, let’s take a look of the dimensions while includes more differentiation between

Shanghai and Hong Kong. They are “brand conscious” and “fashion conscious”.

Firstly, let’s take a look in the “brand conscious” dimension.

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Table 5: Comparison of “Brand conscious and price equals quality consumer” dimension of Shanghai and Hong Kong (Page 44)

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Only Question 14 loaded the same in both places, while Question 11, 12, 13 and 35

only loaded on Hong Kong but did not load significantly on any factor for the

Shanghai sample. As suggested by Fan and Xiao [1998], national brands may be

treated as a quality product, and the newly imported brands will be treated as

brand-named product by Chinese consumers. We did not consider this concept when

items were constructed. So, this may be a reason why the items loaded differed from

Shanghai to Hong Kong in the dimension of “brand conscious”.

Secondly, let’s take a look in the “fashion conscious” dimension.

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Table 6: Comparison of “Novelty and fashion-conscious consumer”dimension of Shanghai and Hong Kong (Page 45)

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21

Only Question 15 loaded the same in both places, while Question 16 and 21 only

loaded on Shanghai but not on Hong Kong, and Question 18, 20 and 22 loaded on

Hong Kong but not in Shanghai. It seems very different, however, it is not. Items 20,

21 and 22 have loaded on the “recreational and hedonistic conscious” dimension in

Sproles and Kendall’s research [1986]. Sproles and Kendall also found their

fashion-consciousness factor was significantly correlated with recreational

consciousness factor. This correlation is quite intuitive because for most consumers to

be fashion conscious, they have to spend time paying attention to changing fashions

[Fan and Xiao, 1998]. To conclude, although the items loaded in Shanghai are

different from Hong Kong, the nature of the items are similar.

5.4.3 T-test: Test of Hypotheses

Independent-sample t-test was conducted to compare the CSI scores for Shanghai and

Hong Kong university consumers, six t-tests instead of only four mentioned in the

“Hypothesis Development” were performed in order to discover a full picture of

difference. We first look at if there is any difference, then look at the effect size, it

provide an indication of the magnitude of the differences between groups. The

guidelines [Cohen, 1988] for interpreting these values are: 0.01 =small effect, 0.06

=moderate effect, 0.14 =large effect.

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Table 7: Comparison of decision-making styles betweenShanghai and Hong Kong universities consumers (Page 46)

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T-Test 1: Brand conscious and price equals quality consumer

There was significant difference in scores for Shanghai (M =2.3933, SD =0.75881)

and Hong Kong (M =2.8813, SD =0.63799; t(289.46) =-6.029, p =0.00) university

consumers. The magnitude of the differences in the means was large (eta squared

=0.11).

Hong Kong university consumers are more brand conscious than Shanghai. It is

different from what we expected (H1: Shanghai university consumers are more

brand consciousness than Hong Kong university consumers). One possible reason is

the different exposure to brand names. As noted before, Hong Kong is more open to

foreign cultures and brands. The more brands they know the more chance they would

become brand conscious. Furthermore, although the Shanghai university consumers

are indulged by their parents, it is not necessary that they will become brand

conscious.

T-Test 2: Perfectionistic and high-quality conscious consumer


and Hong Kong (M =3.7973, SD =0.49480; t(272.76) =6.203, p =0.00) university

consumers. The magnitude of the differences in the means was large (eta squared

=0.11).

Shanghai university consumers are more quality conscious than Hong Kong. We did

not expect this. But this is consistent to the result that Shanghai university consumers

are not as brand conscious as Hong Kong. When you are quality conscious, you

23

would not consider too much about brands. In addition, according to Oliver [1994],

consumers in China always focus on durability when shopping, so Shanghai

university consumers focus on quality in their shopping.

T-Test 3: Novelty and fashion-conscious consumer


and Hong Kong (M =3.4333, SD =0.65517; t(273.03) =-4.612, p =0.00) university

consumers. The magnitude of the differences in the means was moderate (eta squared

=0.07).

Hong Kong university consumers are more fashion conscious than the Shanghai. This

result is the same as we expected (H3: Hong Kong university consumers are more

fashion consciousness than Shanghai university consumers).

T-Test 4: Habitual and brand-loyal consumer

There was no significant difference in scores for Shanghai (M =2.9222, SD =0.82143)

and Hong Kong (M =3.0422, SD =0.78890; t(298) =-1.290, p =0.198) university

consumers. The magnitude of the differences in the means was small (eta squared

=0.01).

This result is the same as we expected. According to the mean, we can see that both

places are not very focus on brand-loyalty.

24

T-Test 5: Price conscious and value for money consumer

There was no significant difference in scores for Shanghai (M =3.6000, SD =0.81306)

and Hong Kong (M =3.5689, SD =0.71476; t(298) =-1.290, p =0.725) university

consumers. The magnitude of the differences in the means was small (eta squared

=0.00).

We expect that Hong Kong university consumers are more price consciousness than

Shanghai university consumers (H2), but this is not the case, there are no differences

between them, and both of them are quite price conscious. According to Oliver [1994],

consumers in China are still encouraging frugality, many of them still have the mind

that “To practice thrift is a virtue” (節儉是美德). This may be one of the reasons that

Shanghai university consumers are as price conscious as the Hong Kong students.

T-Test 6: Impulsive and careless consumer


and Hong Kong (M =0, SD =0; t(149.00) =61.380, p =0.00) university consumers.

The magnitude of the differences in the means was very large (eta squared =0.93).

Shanghai university consumers are more impulsive than the Hong Kong. We did not

expect this. The same as the result of the above factor analysis, we have found that the

“impulsive” dimension appear in the Shanghai sample but not in Hong Kong. The

main reasons are noted above in the part of 5.4.1.

25

We also expect that Hong Kong university consumers are more confused by over

choice than Shanghai university consumers (H4), however, from the result of factor

analysis, the “confused” dimension is even not appear in both places. It shows that

university consumers in Shanghai and Hong Kong can take advantage of the available

information and make better choices [Fan and Xiao, 1998]. It may be because both of

them are highly educated and have certain judgment of the markets, so they can utilize

the information, regardless of the information received.

26

Chapter 6. Business Implications

6.1 For Shanghai

Shanghai university consumers are perfectionistic and impulsive. They always make

special effort to obtain the best quality and perfect choice; however, there are too

many counterfeit products that make them feel regretted after the purchase. Marketers

should stress on improving the overall attributes of the products so that the quality of

product could match the requirement of consumers. Overall quality of product can be

divided into two items: extrinsic and intrinsic [Olson and Jacoby, 1972; Jonansson,

1989; Gabbot, 1991]. Extrinsic attributes refer to the brand, country of origin,

advertising, independent consumer, price, after sell services, and distribution channel.

Intrinsic attributes refer to physical product attributes such as shape, type of surface,

color, weight, material used, taste and performance. Using “good quality” as an

outstanding and clear image would catch the attention of the consumers. Better

customer services should also be provided. As the consumers are still in the stage of

impulsive purchasing, they are still trying each product, offering them a good product

and service can keep them as long term customers.

27

6.2 For Hong Kong

Hong Kong university consumers are brand and fashion conscious. Therefore,

companies should try to do deep marketing researches to and build their brand once

they enter Hong Kong market. In addition, the content and style of marketing and

promotion programs should be fun, trendy and fashionable.

6.3 For both Shanghai and Hong Kong

Both Shanghai and Hong Kong university students are price conscious. Marketers

should promote their products by offering benefits to consumers, in order to make

them feel that their purchases are “value for money”.

28

Chapter 7. Limitations

There are several limitations that warrant future research.

7.1 Generality of Consumer Characteristics

Consumers have different perceptions on different types of products. For example,

their value for a luxury and durable product, which is totally different from an inferior

and non-durable product [Kaynak,E. & Cavusgil, S.T., 1983]. We cannot assume that

a consumer with high brand consciousness would consider “name” products on every

decision. Other characteristics may lack perfect generality as well [Sproles and

Kendall, 1986]. Indeed, a consumer may have different consumer styles for each

product category. Therefore, future research should look at consumer decision-making

in various product categories for details.

7.2 Limitation of the Sample

The sample may not represent the true population we want to obtain. Hong Kong

(Shanghai) university students may not be real Hong Kong (Shanghai) university

students, some of them maybe the exchange students who live here for only a short

period and may leave very soon. So, their answer may not represent the true

population.

Last but not least, due to time and coast constraints, the sample size was limited to

150 for each place. This small sample size may not completely representative of all

29

university consumers in Shanghai and Hong Kong.

7.3 Limitation of Culture and Economic Background

The Shanghai and Hong Kong student sample may not exhibit certain consumer

decision-making characteristics due to the cultural reasons, for example the Man-to

nature orientation, Man-to-himself orientation, Relational orientation, Time

orientation and Personal-activity orientation [Oliver, 1994]. And the economic

reasons, for example, the income of the families, should also be take into account also.

However, the CSI used in this study provides a good starting point for further

development of the CSI inventory in Shanghai and Hog Kong consumer context.

More items and dimensions that are idiosyncratic to Shanghai and Hong Kong culture

need to be developed in future studies. It would be helpful to develop more items to

improve the psychometric properties of three dimensions; they are quality and price

conscious.

30

Chapter 8. Conclusion

The objectives of this study were fulfilled. Decision-making styles of university

consumers in Shanghai and Hong Kong are classified, and several similarities and

differences in decision-making styles were identified. The most important findings are

that Shanghai university consumers are perfectionistic and impulsive, whereas Hong

Kong university consumers are brand conscious and fashion conscious, and they both

have the characteristic of price conscious. This paper provides a good starting point

for marketers who want to enter Shanghai or Hong Kong market. Marketers should

pay more attention in these aspects as to win consumers’ hearts. They should also take

into account of the culture issues that do not cover in this paper.

31

Chapter 9. References

Journals

1. Alex S. L. Tsang, Guijun Zhuang, Fuan Li, Nan Zhou (2003), “A Comparison of

Shopping Behavior in Xi’an and Hong Kong Malls: Utilitarian versus

Non-Utilitarian Shoppers”, Journal of International Cousumer Marketing, Vol.

16(1) 2003

2. Alice S.Y.HIU, Noel Y.M. Siu and Charlie C. L. Wang, and Ludwig, M.K. Chang,

(2001) “An Investigation of Decision-Making Styles of Consumers in China”, The

Journal of Consumer Affairs: (Winter); 35,2; 326-345

3. Baker, Rod. (1987). "Little Emperors' born of a one-child policy." Far Eastern

Economic Review, 137 (July 16): 43-44

4. Bartlett M. S. (1954) A note on the multiplying Faciors for various chi square

approximations. Journal of the Royal Statistical Society, 16(Series B), 296-298

5. Bellenger, Danny N. and Pradeep K. Korgaonkar.(1980). “Profiling Recreational

Shopper”. Journal of Retailing 56 (fall) 77-91

6. Bettman, Jams R. (1979), “An Information Processing Theory of Consumer

Choice, Reading”, MA: Addison-Wesley Publishing Company

7. Carmins, Edward G. and Richard A. Zeller (1979), Realiablility and Validity

Assessment, Beverly Hills, CA:Stage Publications.

8. Catell’s, R.B. (1996) The scree test for number of factors. Multivariate Behavioral

Research, 1245-276

9. Darden, William R. and Dub Ashton. (1974-75) “Psychographic Profiles of

Patronage Preference Groups”, Journal of Retailing, 50(Winter): 99-112

10. Douglas,Susan and C. Sammel craig.(1983). International Marketing Research,

Englewood Cliffs. NJ Prentice-Hall, Inc.

11. Durvasula. Sribivas. Steven Lysonski, and J. Craig Andrews. (1993). Corss culture

generlizability of a Scale for Profiling Consumers’ Decision making Styles. The

Journal pf Consumer Affairs, 27,1:55-65

12. Fan, Jessie X. and Jing J. Xiao. (1998). “Consumer Decision-making Styles of

Young-adult Chinese.” The Journal of Consumer Affairs, 32:275-294

13. Francis Wing-lin Lee (2004), Life values of young people: a comparative study

between Hong Kong and Shanghai [Hong Kong], Dept. of Social Work and

32

Social Administration, University of Hong Kong.

14. Gabbot, Mark (1991), “The Role of Product Cues in Assessing Zrisk in

Secondhand Markets,” European Journal of Marketing, 25,9, 38-50

15. Gehrt, Kenneth C. and Kent Carter. (1992). “An Explortory Assessment Catalog

Shopping Orientations”. Journal of Direct Marketing, 6:29-39

16. Hafstrom, Jeanne L., Chae, Jung Sook, Chung, Young Sook. (1992), “Consumer

Decision-Making Styles: Comparsion Between United States and Korean Young

Consumers”, The Journal of Consumer Affairs. Madison: 26, Iss 1, (Summer);

P146-159

17. Hong Kong Trade Development Council (2001), The two cities: Shanghai, Hong

Kong / Research Department, Hong Kong: The Council.

18. Jacoby, Jacob and Robert W. Chestnut. (1978) “Brand Loyalty: Measurement and

Management”, New York: John Wiley and Sons.

19. Johansson, Johny K. (1989), “Determinants andEfects of the Use of “Made in

Labels”, International Marketing Review, 6,1, pp 47-58

20. Kaiser (1970), A second generation Little Jiffy. Psychometrika, 35, 401-415

21. Kaynak,E. & Cavusgil, S.T. (1983).“Consumer attitudes towards products of

foreign origin: do they vary across product classes?” International Journal of

Advertising, 2, pp.147-157

22. Kim, Jae-On and Charles W.Mueller (1978), “Introduction to Factor Analysis:

What it is and How to Do it, Bevely Hills”, CA: Sage Publications.

23. Korgaonkar, Pradeep K. (1984). “Consumer Shopping Orientations, Non-store

Retailers, and Consumers’ Patronage Intentions: A Multivariate

Investigation”.Journal of the Academy of Marketing Science, 12:11-22

24. Lau, A and Kuan, H. (1989) The Ethos of the Hong Kong Chinese. Hong Kong:

The Chinese University Press, P.19

25. Lumpkin, James R. (1985) “Shopping Orientation Segmentation of the Elderly

Consumer”, Journal of Academic of Marketing Science, 13:271-289

26. Lysonski, Steven, Srini Durvasula, and Yiorgos Zotos. (1996). “Consumer

Decision-making Styles: A Multi-country Investigation”. European Journal of

Marketing, 30,12:10-21

27. Maynes,E. Scott (1976), “Decision-making for Consumers: An Introduction to

Consumer Economics”, New York: Macmillan.

28. Miller, Roger LeRoy (1981) Economic Issuse for Consumers, Third Edition, and

33

New York: West Publishing Company.

29. Moschis, George P. (1976), “Shopping orientations and Consumer Uses of

Information”. Journal of Retailing, 52(Summer): 61-70,93

30. Moschis, George P. (1976), “Shopping orientations and Consumer Uses of

Information”. Journal of Retailing, 52(Summer): 61-70,93

31. Moschis, George P. and Roy L, Moore (1979), “Decision making amoung the

Youth: A Socialization perspective,” Journal of Consumer Research, 6(setember):

101-102

32. Oliver H. M. Yau (1994) Consumer Behaviour in China Customer satisfaction and

cultural valves Routledge Lodon and New York

33. Olson, Jerry C. and Jacob Jacoby: (1972), “ Cue Utilization in the Quality

Perception Processes, in M. Venkatesan (ed), The Journal of Consumer Affairs:

(Winter); 12,2; pp.167-179

34. Parameswaran, Ravi and Attila Yaprak (1987), “A Cross-National Comparison of

Consumer Research Measures,” Journal of International Business Studies, 18

(Spring): 35-49.

35. Shanghai Statistical Yearbook, (2004)

36. Shim, Soyeon and Kenneth C. Gehrt. (1996), “Hispanic and American

Adolescents: An Exploratory Study of Their Approach to Shopping”, Journal of

Retailing, 72,3:307-324

37. Sproles, George B (1985), From Perfectionism to Fadism: Measuring Consumers’

Decision Making styles,” Proceedings, American Council on Consumer interests:

79-85

38. Sproles, George B. (1979), Fasion: Consumer Behaviour Toward Dress,

Minneapolis: Burgess Publishing Company

39. Sproles, George B. and Elizabeth L. Kendall (1986), “A Methodology for

Profiling Consumers’ Decision-Making Styles,” The Journal of Consumer Affairs.

20(Winter): 267-279

40. Srinivas Durvasula, Steven Lysonski, and J. Craig Andrews (1993), “Cross-

Cultural Generalizability of a Scale for Profiling Counsumers’ Decision-Making

Styles,” The Journal of Consumer Affairs, 27 (Summer): P.55-65

41. Stephenson, R.P. and R.P. Willett. (1969), “Analysis of Consumers’ Retail

Patronage Strategies. In Marketing Involvement in Society and The Economy”,

edited by P.R. Mcdonald, Chicage: American MarketingAssociation, 316-322

34

42. Stone, Gregory. P. (1954) “City Shoppers and Urban Identification: Obervations

on the Social Psychology of City Life.”, American Jounral of Sociology, 60:36-45

43. Thorelli, Hans B., Helmut Becker, and Jack Engeldow (1975), “The In formation

Seekers; An International Study of Consumer Information and Advertising Image”,

Cambridgek,MA: Ballinger.

44. Thorelli, Hans B., Helmut Becker, and Jack Engeldow (1975), “The In formation

Seekers; An International Study of Consumer Information and Advertising Image”,

Cambridgek,MA: Ballinger.

45. Thurstone, L. L. (1947) Multiple factor analysis. Chicago: University of Chicago

Press.

46. Turk James L. and Norman W. Bell, (1972) “measuring Power in the Family,”

Journal of Marriage and the Family, 34(May): 215-222

47. Ward, Scott (1974) “ Journal of Consumer Research, 1(September): 1-14

48. Westbrook, Robert A. and William C. Black (1985), “A Motivation-Based

Shopper Typology,” The Journal of Retailing, 61(Spring): 78-103

49. Williams, Robert H., Jon J. Painter, and Herbert R. Nicholas (1978), “A

Policyoriented Typology of Grocery Shoppers”, Jounral of Retailing, 54 (Spring):

27-34.

Websites

1. Platt, K (1998). “Titanic” Cultural Invasion Hits China. The Christian Science

Monitor.

http://www.csmnitor.com/durable/1998/04/20/pls3.htm (April20)

2. The International Encyclopedia of Sexuality: Hong Kong,

http://www2.hu-berlin.de/sexology/IES/hongkong.html

3. Edupage. (1996). China Screens Out “Spiritual Pollution” on Net. WWW URT:

http://www.edupage.edu/pub/ edupage/archives/96/ edupage-0905.htm

(September 5)

4. Hong Kong Census and Statistics Department, 2004

http://www.info.gov.hk/censtatd/home.html

35

Chapter 10. Appendix

Appendix Page

10.1Explanation of the eight factors loading by Sproles and Kendall 36

10.2Tables 38

10.3Questionnaires 47

10.4SPSS Outputs 58

36

10.1 Explanation of the eight factors loading by Sproles and Kendall

Factor 1: Perfectionistic and high-quality consciousness

Items loading on this factor measure a consumer’s search for the best quality in

products. Those consumers who have higher perfectionism could also be expected to

shop more carefully and systematically. They are not satisfied with the “good enough”

product.

Factor 2: Brand consciousness and “price equals quality”

It measures consumers’ orientations toward buying the more expensive, well-known

national brands. High scorers are likely to believe that a higher price means better

quality. They appear to have positive attitudes toward department and specialty stores,

where brand names and higher prices are prevalent. They also appear to prefer best

selling, advertised brands.

Factor 3: Novelty-fashion consciousness

High scorers on this characteristic are fashion conscious and apparently novelty

conscious as well. They are likely to gain excitement and pleasure from seeking out

new things. They keep up-to-date with styles, and being in trendy is important to them.

Variety-seeking also appears to be an important aspect of this characteristic.

Factor 4: Recreational and hedonistic shopping consciousness

Those scoring high on it find shopping pleasant. They shop just for fun of it. In

previous research, this was a “shopping avoider” or time-saver factor, and thus several

37

items load negatively on it. However, the loadings show that this factor measures

shopping for recreation and entertainment.

Factor 5: Price consciousness and “value for money” orientation

Those scoring high look for sale prices and appear conscious of lower prices in

general. Importantly, they are also concerned with getting the best value for their

money. They are likely to be comparison shoppers.

Factor 6: Impulsive and careless consumer orientation

High scorers on this characteristic do not plan their shopping. Furthermore, they

appear unconcerned about how much they spend or about the “best buys”.

Factor 7: Confused by over choice characteristic

High scorers on this characteristic perceive many brands and stores from which to

choose and have difficulty in making choices. Furthermore, they experience

information overload, as several items in this factor imply.

Factor 8: Habitual and brand-loyal consumer orientation

High scorers on this characteristic are likely to have favourite brands and stores and to

have formed habits in choosing these. Habitual behaviour is a well-known aspect of

consumer decision-making, and this factor reinforces its existence as a general

characteristic.

38

10.2 Tables

Table Page

Table 1Personal Information of the 300 samples from Shanghai and Hong Kong 39

Table 2Factor Loadings and Construct Reliability of Shanghai CSI 41

Table 3Factor Loadings and Construct Reliability of Hong Kong CSI 42

Table 4Comparison of “Habitual and brand-loyal consumer” dimensionof Shanghai and Hong Kong

43

Table 5Comparison of “Brand conscious and price equals quality consumer” dimensionof Shanghai and Hong Kong

44

Table 6Comparison of “Novelty and fashion-conscious consumer” dimensionof Shanghai and Hong Kong

45

Table 7Comparison of decision-making styles between Shanghai and Hong Kong universities consumers

46

39

Table 1Personal Information of the 300 samples from Shanghai and Hong Kong

Shanghai Hong Kong

Frequency

Percentage

Frequency

Percentage

Male 66 44.0 56 37.3Female 84 56.0 94 62.7

Gender

Total 150 100.0 150 100.01 125 83.3 16 10.72 13 8.7 52 34.73 12 8.0 52 34.7More than 3 0 0 30 20.0

NumberofBlood Siblings

Total 150 100.0 150 100.0Parents 111 74.0 41 27.3Scholarship/Grant/Loan 8 5.3 6 4.0Part-time 3 2.0 30 20.0Partly Parents, partly Part-time

15 10.0 41 27.3

Partly Parents, partly Scholarship/Grant/Loan

7 4.7 9 6.0

Partly Scholarship/Grant/Loan, partly Part-time

3 2.0 13 8.7

Partly Parents, Scholarship/Grant/Loan, and Part-time

3 2.0 10 6.7

Income Source

Total 150 100.0 150 100.0

40

￥500 26 17.3

$1500 42 28.0￥501-￥1000 48 32.0$1501-$2000 45 30.0

￥1001-￥1500 52 34.7

$2001-$2500 31 20.7＞￥1501 24 16.0

＞$2501 32 21.3

CostofLiving

Total 150 100.0 150 100.0Television 125 83.3 127 84.7Radio 26 17.3 44 29.3Newspaper 86 57.3 96 64Magazine 113 75.3 102 68Internet 119 79.3 103 68.7Transportation Advertisement

65 43.3 64 42.7

Exhibition 26 17.3 20 13.3Family and friends 96 64 114 76Others 0 0 9 6

Inform-ationSource

Total 656 437.1 679 452.7

41

Table 2Factor Loadings and Construct Reliability of Shanghai CSI

Shanghai CSI Construct Reliability

Factor Loading

Novelty and fashion-conscious consumer 0.7647shcsi15 I usually have one or more outfits of the very newest style. .848shcsi16 I keep my wardrobe up-to-date with the changing fashions. .884shcsi21 Going shopping is one of the enjoyable activities of my life. .702Perfectionistic and high-quality conscious consumer 0.7283shcsi01 Getting very good quality is very important to me. .893shcsi02 When it comes to purchasing products, I try to get the very best or perfect choice.

.690

shcsi04 I make special effort to choose the very best quality products. .799Habitual and brand-loyal consumer 0.6791shcsi33 There are so many brands to choose from that often I feel confused. .774shcsi37 I have favorite brands I buy over and over. .708shcsi39 I go to the same stores each time I shop. .831Impulsive and careless consumer 0.6189shcsi30 Often I make careless purchases I later wish I had not. .640shcsi31 I take the time to shop carefully for best buys. .802*shcsi32 I carefully watch how much I spend. .640*Price conscious and value for money consumer 0.4742shcsi05 I really don’t give my purchases much thought or care. .803shcsi07 I shop quickly, buying the first product or brand I find that seems good enough.

.763*

Brand conscious and price equals quality consumer -shcsi14 The most advertised brands are usually very good choices. .93

*Scores had been reversed

42

Table 3Factor Loadings and Construct Reliability of Hong Kong CSI

Hong Kong CSI Construct Reliability

Factor Loading

Brand conscious and price equals quality consumer 0.7501hkcsi11 The higher the price of a product, the better its quality. .666hkcsi12 Nice department and specialty stores offer me the best products. .734hkcsi13 I prefer buying the best-selling brands. .786hkcsi14 The most advertised brands are usually very good choices. .764hkcsi35 The more I learn about products, the harder it seems to choose the best.

.53

Perfectionistic and high-quality conscious consumer 0.6006hkcsi01 Getting very good quality is very important to me. .582hkcsi02 When it comes to purchasing products, I try to get the very best or perfect choice.

.692

hkcsi03 In general, I usually try to buy the best overall quality. .582hkcsi04 I make special effort to choose the very best quality products. .573hkcsi08 A product doesn’t have to be perfect, or the best, to satisfy me. .50Novelty and fashion-conscious consumer 0.6491hkcsi15 I usually have one or more outfits of the very newest style. .675hkcsi18 To get variety, I shop different stores and choose different brands. .553hkcsi20 Shopping is not a pleasant activity to me. .786*hkcsi22 Shopping other stores wastes my time. .729*Habitual and brand-loyal consumer 0.7339hkcsi37 I have favorite brands I buy over and over. .797hkcsi38 Once I find a product or brand I like, I stick with it. .827hkcsi39 I go to the same stores each time I shop. .752Price conscious and value for money consumer 0.5055hkcsi05 I really don’t give my purchases much thought or care. .706*hkcsi07 I shop quickly, buying the first product or brand I find that seems good enough.

.770

hkcsi25 I buy as much as possible at sale price. .59

* Scores had been reversed

43

Table 4Comparison of “Habitual and brand-loyal consumer” dimension of Shanghai and Hong Kong

Habitual and brand-loyal consumerShanghai Hong Kongshcsi33There are so many brands to choose from that often I feel confused.

shcsi37 + hkcsi37I have favorite brands I buy over and over.

shcsi39 + hkcsi39I go to the same stores each time I shop.

hkcsi38Once I find a product or brand I like, I stick with it.

44

Table 5Comparison of “Brand conscious and price equals quality consumer” dimension of Shanghai and Hong Kong

Brand conscious and price equals quality consumerShanghai Hong Kong

hkcsi11The higher the price of a product, the better its quality.hkcsi12Nice department and specialty stores offer me the best products.hkcsi13I prefer buying the best-selling brands.

shcsi14 + hkcsi14The most advertised brands are usually very good choices.

hkcsi35The more I learn about products, the harder it seems to choose the best.

45

Table 6Comparison of “Novelty and fashion-conscious consumer” dimension of Shanghai and Hong Kong

Novelty and fashion-conscious consumerShanghai Hong Kong

shcsi15 + hkcsi15I usually have one or more outfits of the very newest style.

shcsi16I keep my wardrobe up-to-date with the changing fashions.

hkcsi18To get variety, I shop different stores and choose different brands.hkcsi20Shopping is not a pleasant activity to me.

shcsi21Going shopping is one of the enjoyable activities of my life.

hkcsi22Shopping other stores wastes my time.

46

Table 7Comparison of decision-making styles between Shanghai and Hong Kong universities consumers

Mean Std. Deviation

Sig. (2-tailed)

Significance Difference?

eta squared

Effect size

T-Test 1: Brand conscious and price equals quality consumerSH 2.3933 .75881HK 2.8813 .63799

0.00 0.11 Large

T-Test 2: Perfectionistic and high-quality conscious consumerSH 4.2222 .67739HK 3.7973 .49480

0.00 0.11 Large

T-Test 3: Novelty and fashion-conscious consumerSH 3.0156 .89521HK 3.4333 .65517

0.00 0.07 Moderate

T-Test 4: Habitual and brand-loyal consumerSH 2.9222 .82143HK 3.0422 .78890

0.198 0.01 Small

T-Test 5: Price conscious and value for money consumerSH 3.6000 .81306HK 3.5689 .71476

0.725 0.00 Small

T-Test 6: Impulsive and careless consumerSH 2.6778 .53431HK .0000 .00000

0.00 0.93 Very large

47

10.3 Questionnaires

Questionnaires Page

Shanghai Version 48

Hong Kong Version 53

48

编号︰________

上海大学生购物决定的问卷调查

你好！本人为香港浸会大学学生，现正进行一项有关于上海大学生购物决定的问

卷调查。这份问卷调查只需约数分钟便可完成，搜集的资料只供学术研究分析。

多谢你的合作！

你是大学生吗？

是：请看甲部。

否：问卷已完成，谢谢！

<甲部>

首先，我们想知道你一般购物时考虑的因素。若你非常同意该句子，请选择「5」，若你非常不同意该句子，请选择「1」。如此类推。

1. 好质量的货物对于我来说是相当重要的。

非常不同意 1 2 3 4 5 非常同意

2. 每次购物，我要得到最好/完美的选择。


3. 通常而言，我会购买那些质素最好的货物。


4. 我尽量会选择最好质素的货物。


5. 每次购物，我都不会特别留意和思索。


6. 我对货物的期望和标准是相当高。


49

7. 我的购物过程很快，购买第一次接触的货物和品牌不需经过太多考虑。


8. 一件不完美/不是最好的货物是不能满足我的要求。


9. 全球最知名品牌的货品对我来说是最好的。


10. 越贵的货物我越会选择。


11. 货物的价钱越高，质量越好。


12. 出色/好的连锁店能为我提供最好的货物。


13. 我较为喜欢购买最好销量的货物。


14. 广告越多的货物通常是最好的。


15. 我通常拥有多过一件最时款的服装。


16. 我会转换时装，令到我衣柜里的衣服追上潮流。


17. 时髦的/吸引人的款式对我来说是非常重要的。


50

18. 逛不同店铺和选择不同品牌能令我得到更多种类的选择。


19. 购买新奇和特别的货物是有趣的。


20. 购物对我来说不是愉快的活动。


21. 逛街购物是我生活其中一个最享受的节目。


22. 到不同地方购物很浪费我的时间。


23. 我喜欢购物只因为它是有趣的。


24. 我很快完成我每次购物的旅程。


25. 我尽量在减价时购物。


26. 越平价的货物我越会选择。


27. 我小心地花钱，并且花得最有价值。


28. 我应该更好地计划我的购物情况。


51

29. 我在购物时显得冲动。


30. 我经常乱购物而且感到后悔。


31. 我会花时间来选购最好的货物。


32. 我会好好计划花多少金钱于购物上。


33. 我会为很多品牌而感到困惑。


34. 有时我会很难决定到那些商店购物。


35. 对所有的货物认识越深，我越难从中选择最好的。


36. 取得多种货物的信息会令我更困惑。


37. 我有一个固定而喜欢的品牌，并且买了很长时间。


38. 如果发现一个十分喜欢的品牌和货物，我会忠于它。


39. 每次我都会到同一商店购物。


52

40. 我定期转换货物的品牌。


<乙部> 最后，我们想知道你一些简单的个人资料，搜集的资料只供学术研究分析，内容绝对保密。

性别

1. 男

2. 女

家庭(亲)兄弟姊妹数目 (包括你自己)1. 12. 23. 34. 多于 3

生活费来源

1. 父母

2. 奖学金/助学金/贷款3. 本人兼职

4. 部分由父母提供，部分靠本人兼职

5. 部分由父母提供，部分依赖奖学金/助学金/贷款6. 部分依赖奖学金/助学金/贷款，部分靠本人兼职7. 部分由父母提供，部分依赖奖学金/助学金/贷款，部分靠本人兼职

每月生活费(人民币)数额 (包括食宿交通及零用钱，但不包括学费)1. 500或以下2. 501 – 10003. 1001 – 15004. 1501或以上

接收信息的主要来源 (可选多项)1. 电视

2. 电台

3. 报章

4. 杂志

5. 互联网

6. 车厢/地铁广告7. 展览

8. 家人朋友

9. 其它 (请注明) _____________________________

问卷已经完结，谢谢你热心的合作！

53

編號︰________

香港大學生購物決定的問卷調查

你好！本人為香港浸會大學學生，現正進行一項有關於香港大學生購物決定的問

卷調查。這份問卷調查只需約數分鐘便可完成，搜集的資料只供學術研究分析。

多謝你的合作！

你是大學生嗎？

是：請看甲部。

否：問卷已完成，謝謝！

<甲部>

首先，我們想知道你一般購物時考慮的因素。若你非常同意該句子，請選擇「5」，若你非常不同意該句子，請選擇「1」。如此類推。

1. 好質量的貨物對於我來說是相當重要的。


2. 每次購物，我要得到最好/完美的選擇。


3. 通常而言，我會購買那些質素最好的貨物。


4. 我盡量會選擇最好質素的貨物。


5. 每次購物，我都不會特別留意和思索。


6. 我對貨物的期望和標準是相當高。


54

7. 我的購物過程很快，購買第一次接觸的貨物和品牌不需經過太多考慮。


8. 一件不完美/不是最好的貨物是不能滿足我的要求。


9. 全球最知名品牌的貨品對我來說是最好的。


10. 越貴的貨物我越會選擇。


11. 貨物的價錢越高，質量越好。


12. 出色/好的連鎖店能為我提供最好的貨物。


13. 我較為喜歡購買最好銷量的貨物。


14. 廣告越多的貨物通常是最好的。


15. 我通常擁有多過一件最時款的服裝。


16. 我會轉換時裝，令到我衣櫃裏的衣服追上潮流。


17. 時髦的/吸引人的款式對我來說是非常重要的。


55

18. 逛不同店鋪和選擇不同品牌能令我得到更多種類的選擇。


19. 購買新奇和特別的貨物是有趣的。


20. 購物對我來說不是愉快的活動。


21. 逛街購物是我生活其中一個最享受的節目。


22. 到不同地方購物很浪費我的時間。


23. 我喜歡購物只因為它是有趣的。


24. 我很快完成我每次購物的旅程。


25. 我盡量在減價時購物。


26. 越平價的貨物我越會選擇。


27. 我小心地花錢，並且花得最有價值。


28. 我應該更好地計劃我的購物情況。


56

29. 我在購物時顯得衝動。


30. 我經常亂購物而且感到後悔。


31. 我會花時間來選購最好的貨物。


32. 我會好好計劃花多少金錢於購物上。


33. 我會為很多品牌而感到困惑。


34. 有時我會很難決定到那些商店購物。


35. 對所有的貨物認識越深，我越難從中選擇最好的。


36. 取得多種貨物的資訊會令我更困惑。


37. 我有一個固定而喜歡的品牌，並且買了很長時間。


38. 如果發現一個十分喜歡的品牌和貨物，我會忠於它。


39. 每次我都會到同一商店購物。


57

40. 我定期轉換貨物的品牌。


<乙部> 最後，我們想知道你一些簡單的個人資料，搜集的資料只供學術研究分析，內容絕對保密。

性別

1. 男

2. 女

家庭(親)兄弟姊妹數目 (包括你自己) 1. 12. 23. 34. 多於 3

生活費來源

1. 父母

2. 獎學金/助學金/貸款3. 本人兼職

4. 部分由父母提供，部分靠本人兼職

5. 部分由父母提供，部分依賴獎學金/助學金/貸款6. 部分依賴獎學金/助學金/貸款，部分靠本人兼職7. 部分由父母提供，部分依賴獎學金/助學金/貸款，部分靠本人兼職

每月生活費(港幣)數額 (包括食宿交通及零用錢，但不包括學費)1. 1500或以下2. 1501 – 20003. 2001 – 25004. 2501或以上

接收資訊的主要來源 (可選多項)1. 電視

2. 電臺

3. 報章

4. 雜誌

5. 互聯網

6. 車廂/地鐵廣告7. 展覽

8. 家人朋友

9. 其他 (請注明) ____________________________

問卷已經完結，謝謝你熱心的合作！

58

10.4 SPSS Outputs

SPSS Outputs Page

10.4.1Personal Information of the 300 samples from Shanghai and Hong Kong 59

10.4.2Decision-making styles of Shanghai university consumers 65

10.4.3Cronbach’s alpha Reliability method: Shanghai CSI 85

10.4.4Decision-making styles of Hong Kong university consumers 90

10.4.5Cronbach’s alpha Reliability method: Hong Kong CSI 108

10.4.6Comparison of decision-making styles between Shanghai and Hong Kong universities consumers

113

59

10.4.1 Personal Information of the 300 samples from Shanghai and Hong Kong

Shanghai

Sex (SH)

66 22.0 44.0 44.084 28.0 56.0 100.0150 50.0 100.0150 50.0300 100.0

MaleFemaleTotal

Valid

SystemMissingTotal

Frequency Percent Valid PercentCumulativePercent

Number of Blood Siblings (SH)

125 41.7 83.3 83.313 4.3 8.7 92.012 4.0 8.0 100.0150 50.0 100.0150 50.0300 100.0

123Total

Valid

SystemMissingTotal


Source of Income (SH)

111 37.0 74.0 74.08 2.7 5.3 79.33 1.0 2.0 81.3

15 5.0 10.0 91.3

7 2.3 4.7 96.0

3 1.0 2.0 98.0

3 1.0 2.0 100.0

150 50.0 100.0150 50.0300 100.0

ParentsScholarship/Grant/LoanPart-timePartly Parents, partlyPart-timePartly Parents, partlyScholarship/Grant/LoanPartlyScholarship/Grant/Loan,partly Part-timePartly Parents,Scholarship/Grant/Loan,and Part-timeTotal

Valid

SystemMissingTotal


60

Cost of Living (SH)

26 8.7 17.3 17.348 16.0 32.0 49.352 17.3 34.7 84.024 8.0 16.0 100.0150 50.0 100.0150 50.0300 100.0

</=$500$501-$1000$1001-$1500>$1501Total

Valid

SystemMissingTotal


Information Source (SH)

300 100.0SystemMissingFrequency Percent

Television (SH)

25 8.3 16.7 16.7125 41.7 83.3 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Radio (SH)

124 41.3 82.7 82.726 8.7 17.3 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Newspaper (SH)

64 21.3 42.7 42.786 28.7 57.3 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Magazine (SH)

37 12.3 24.7 24.7113 37.7 75.3 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


61

Internet (SH)

31 10.3 20.7 20.7119 39.7 79.3 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Transportation Advertisment (SH)

85 28.3 56.7 56.765 21.7 43.3 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Exhibition (SH)

124 41.3 82.7 82.726 8.7 17.3 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Family and friends (SH)

54 18.0 36.0 36.096 32.0 64.0 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Others (SH)

150 50.0 100.0 100.0150 50.0300 100.0

NoValidSystemMissing

Total


62

Hong Kong

Sex (HK)

56 18.7 37.3 37.394 31.3 62.7 100.0150 50.0 100.0150 50.0300 100.0

MaleFemaleTotal

Valid

SystemMissingTotal


Number of Blood Siblings (HK)

16 5.3 10.7 10.752 17.3 34.7 45.352 17.3 34.7 80.030 10.0 20.0 100.0150 50.0 100.0150 50.0300 100.0

123>3Total

Valid

SystemMissingTotal


Source of Income (HK)

41 13.7 27.3 27.36 2.0 4.0 31.330 10.0 20.0 51.3

41 13.7 27.3 78.7

9 3.0 6.0 84.7

13 4.3 8.7 93.3

10 3.3 6.7 100.0

150 50.0 100.0150 50.0300 100.0

ParentsScholarship/Grant/LoanPart-timePartly Parents, partlyPart-timePartly Parents, partlyScholarship/Grant/LoanPartlyScholarship/Grant/Loan,partly Part-timePartly Parents,Scholarship/Grant/Loan,and Part-timeTotal

Valid

SystemMissingTotal


Cost of Living (HK)

42 14.0 28.0 28.045 15.0 30.0 58.031 10.3 20.7 78.732 10.7 21.3 100.0150 50.0 100.0150 50.0300 100.0

</=$1500$1501-$2000$2001-$2500>$2501Total

Valid

SystemMissingTotal


63

Information Source (HK)

300 100.0SystemMissingFrequency Percent

Television (HK)

23 7.7 15.3 15.3127 42.3 84.7 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Radio (HK)

106 35.3 70.7 70.744 14.7 29.3 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Newspaper (HK)

54 18.0 36.0 36.096 32.0 64.0 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Magazine (HK)

48 16.0 32.0 32.0102 34.0 68.0 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Internet (HK)

47 15.7 31.3 31.3103 34.3 68.7 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


64

Transportation Advertisment (HK)

86 28.7 57.3 57.364 21.3 42.7 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Exhibition (HK)

130 43.3 86.7 86.720 6.7 13.3 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Family and friends (HK)

36 12.0 24.0 24.0114 38.0 76.0 100.0150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


Others (HK)

141 47.0 94.0 94.09 3.0 6.0 100.0

150 50.0 100.0150 50.0300 100.0

NoYesTotal

Valid

SystemMissingTotal


65

10.4.2 Decision-making styles of Shanghai university consumers

Shanghai CSI – Factor Analysis

66

Correlation Matrix SH CSI 01 SH CSI 02 SH CSI 03 SH CSI 04 SH CSI 05 SH CSI 06

SH CSI 01 1 0.468476 0.566205 0.619297 -0.06757 0.415655

SH CSI 02 0.468476 1 0.468217 0.361046 0.116942 0.526069

SH CSI 03 0.566205 0.468217 1 0.603896 -0.20735 0.476839

SH CSI 04 0.619297 0.361046 0.603896 1 -0.03195 0.490067

SH CSI 05 -0.06757 0.116942 -0.20735 -0.03195 1 0.004879

SH CSI 06 0.415655 0.526069 0.476839 0.490067 0.004879 1

SH CSI 07 0.04714 0.021945 -0.22114 0.025578 0.311903 0.14484

SH CSI 08 0.336379 0.456334 0.297271 0.350266 0.063793 0.358551

SH CSI 09 0.296712 0.266531 0.321982 0.382919 -0.32896 0.368282

SH CSI 10 0.098645 0.311492 0.286536 0.170389 -0.22654 0.330286

SH CSI 11 0.107465 0.159822 0.34144 0.242674 -0.24135 0.235988

SH CSI 12 0.166504 0.130526 0.249611 0.254599 -0.12626 0.329876

SH CSI 13 0.115883 0.275283 0.227922 0.180403 0.010653 0.248206

SH CSI 14 -0.11262 -0.02891 -0.07222 -0.18696 -0.02553 -0.035

SH CSI 15 0.184559 0.240006 0.08335 0.202646 -0.04431 0.166949

SH CSI 16 0.052591 0.160901 0.03193 0.081044 -0.03375 0.034904

SH CSI 17 0.091405 0.278279 -0.00845 0.120837 -0.0954 0.062353

SH CSI 18 0.290323 0.428142 0.119435 0.36165 0.224544 0.234709

SH CSI 19 0.172789 0.236736 0.238647 0.365102 -0.06007 0.191606

SH CSI 20 0.114939 0.000614 -0.04404 0.183556 0.193714 0.043689

SH CSI 21 0.063491 0.084534 -0.03089 0.127326 0.179243 0.111281

SH CSI 22 0.031155 -0.16607 -0.18548 0.07138 0.275827 0.031092

SH CSI 23 -0.02519 0.168893 -0.13455 -0.03377 -0.00609 0.102139

SH CSI 24 -0.10137 -0.07199 -0.40816 -0.15303 0.384698 -0.34038

SH CSI 25 -0.07456 0.090315 -0.10635 -0.06155 0.168385 -0.08957

SH CSI 26 -0.17149 -0.05438 0.007934 -0.22402 0.070522 -0.05587

SH CSI 27 0.159842 0.111958 0.140431 0.05066 0.030338 0.159087

SH CSI 28 -0.12027 -0.15977 -0.19381 -0.13081 0.086874 -0.02127

SH CSI 29 -0.14442 -0.07272 0.012693 0.012588 0.014687 0.092807

SH CSI 30 -0.11565 -0.02445 -0.01161 0.022937 -0.04382 0.088939

SH CSI 31 -0.04734 -0.09935 -0.03373 0.029641 0.04515 0.061392

SH CSI 32 -0.01788 -0.06863 -0.00674 0.017185 0.198426 -0.03032

SH CSI 33 0.002357 0.047232 0.324585 0.080661 -0.08777 0.23385

SH CSI 34 -0.05893 -0.06103 0.137522 -0.03445 -0.02897 0.029917

SH CSI 35 0.186406 0.041669 0.101024 0.089405 -0.09579 0.224157

SH CSI 36 0.163018 -0.01553 0.211936 0.131073 0.062321 0.250116

SH CSI 37 0.100394 0.151943 0.330955 0.311958 -0.07669 0.232156

SH CSI 38 0.24923 0.1214 0.194888 0.251114 -0.12407 0.189153

SH CSI 39 0.102622 0.171759 0.376568 0.210017 -0.14946 0.397655

Correlation

SH CSI 40 0.01186 0.087681 0.018698 0.080959 0.202147 -0.06718

67


SH CSI 01 0.04714 0.336379 0.296712 0.098645 0.107465 0.166504

SH CSI 02 0.021945 0.456334 0.266531 0.311492 0.159822 0.130526

SH CSI 03 -0.22114 0.297271 0.321982 0.286536 0.34144 0.249611

SH CSI 04 0.025578 0.350266 0.382919 0.170389 0.242674 0.254599

SH CSI 05 0.311903 0.063793 -0.32896 -0.22654 -0.24135 -0.12626

SH CSI 06 0.14484 0.358551 0.368282 0.330286 0.235988 0.329876

SH CSI 07 1 -0.19982 0.217684 -0.01742 -0.04106 0.043123

SH CSI 08 -0.19982 1 0.34122 0.376158 0.315378 0.135987

SH CSI 09 0.217684 0.34122 1 0.618969 0.510331 0.592811

SH CSI 10 -0.01742 0.376158 0.618969 1 0.667219 0.469828

SH CSI 11 -0.04106 0.315378 0.510331 0.667219 1 0.495489

SH CSI 12 0.043123 0.135987 0.592811 0.469828 0.495489 1

SH CSI 13 -0.05652 0.311848 0.150963 0.089085 0.248123 0.223821

SH CSI 14 -0.08476 0.109365 0.106239 0.166047 0.213947 0.279788

SH CSI 15 -0.09763 0.144413 0.158689 0.112215 -0.00038 0.090177

SH CSI 16 -0.12978 0.096432 0.046423 0.090299 -0.07101 -0.00178

SH CSI 17 0.073631 0.081187 0.286752 0.181063 0.026743 0.266663

SH CSI 18 0.080854 0.273392 0.154152 0.213067 -0.02955 0.127527

SH CSI 19 -0.21438 0.086988 0.093844 0.21999 0.055726 -0.02365

SH CSI 20 0.130962 -0.08556 -0.04257 -0.20139 -0.18227 0.053875

SH CSI 21 0.117302 0.132127 -0.06244 -0.03551 -0.1154 -0.00979

SH CSI 22 0.227257 -0.10491 -0.16738 -0.25896 -0.22048 -0.0087

SH CSI 23 -0.07567 0.17075 -0.14382 0.052334 -0.08367 -0.18249

SH CSI 24 0.385143 -0.16932 -0.19552 -0.19291 -0.23399 -0.01622

SH CSI 25 0.214487 0.086416 0.022627 0.025939 0.080108 0.024743

SH CSI 26 0.019374 -0.15726 -0.29577 -0.25053 -0.23299 -0.33964

SH CSI 27 0.288911 0.021897 0.104228 0.048302 0.123542 0.07965

SH CSI 28 0.103636 -0.10145 -0.28281 -0.21699 -0.01937 -0.20937

SH CSI 29 -0.02228 0.077831 -0.14313 -0.08942 -0.08565 -0.0873

SH CSI 30 -0.03977 0.02501 -0.02684 -0.02764 -0.01302 -0.0009

SH CSI 31 0.050979 -0.11593 -0.08953 -0.15588 -0.12319 -0.03898

SH CSI 32 0.089642 -0.03119 -0.10897 -0.10186 -0.03409 -0.0622

SH CSI 33 -0.07506 0.098755 0.230508 0.276255 0.187547 -0.0077

SH CSI 34 0.005538 -0.11702 -0.04927 -0.05971 -0.06277 -0.16503

SH CSI 35 -0.11455 0.235598 0.193045 0.118792 0.086134 0.102519

SH CSI 36 -0.00709 0.241797 0.150678 0.056683 0.117848 -0.00274

SH CSI 37 0.018949 0.3238 0.363952 0.141651 0.200306 0.250098

SH CSI 38 0.25548 0.128561 0.527268 0.228221 0.281485 0.588595

SH CSI 39 -0.06936 0.295995 0.401304 0.23785 0.252226 0.244111

Correlation

SH CSI 40 -0.13938 0.193496 -0.21592 -0.22883 -0.21056 -0.30351

68


SH CSI 01 0.115883 -0.11262 0.184559 0.052591 0.091405 0.290323

SH CSI 02 0.275283 -0.02891 0.240006 0.160901 0.278279 0.428142

SH CSI 03 0.227922 -0.07222 0.08335 0.03193 -0.00845 0.119435

SH CSI 04 0.180403 -0.18696 0.202646 0.081044 0.120837 0.36165

SH CSI 05 0.010653 -0.02553 -0.04431 -0.03375 -0.0954 0.224544

SH CSI 06 0.248206 -0.035 0.166949 0.034904 0.062353 0.234709

SH CSI 07 -0.05652 -0.08476 -0.09763 -0.12978 0.073631 0.080854

SH CSI 08 0.311848 0.109365 0.144413 0.096432 0.081187 0.273392

SH CSI 09 0.150963 0.106239 0.158689 0.046423 0.286752 0.154152

SH CSI 10 0.089085 0.166047 0.112215 0.090299 0.181063 0.213067

SH CSI 11 0.248123 0.213947 -0.00038 -0.07101 0.026743 -0.02955

SH CSI 12 0.223821 0.279788 0.090177 -0.00178 0.266663 0.127527

SH CSI 13 1 0.418649 0.287159 0.309919 0.154354 0.049854

SH CSI 14 0.418649 1 0.129874 0.052354 0.211116 -0.19813

SH CSI 15 0.287159 0.129874 1 0.689501 0.425182 0.191463

SH CSI 16 0.309919 0.052354 0.689501 1 0.45665 0.312971

SH CSI 17 0.154354 0.211116 0.425182 0.45665 1 0.343528

SH CSI 18 0.049854 -0.19813 0.191463 0.312971 0.343528 1

SH CSI 19 -0.1313 -0.25004 0.204408 0.255884 0.276206 0.613846

SH CSI 20 0.218704 -0.04276 0.301506 0.3644 0.383572 0.309045

SH CSI 21 0.204634 0.217297 0.450454 0.44442 0.298588 0.302609

SH CSI 22 0.204973 0.053929 0.392409 0.492136 0.13009 0.16353

SH CSI 23 0.311207 0.153723 0.431493 0.429013 0.063272 0.152282

SH CSI 24 -0.04929 0.159589 0.15576 0.230321 0.325447 0.229426

SH CSI 25 -0.07488 0.016548 -0.20264 -0.33997 -0.1275 -0.11964

SH CSI 26 0.239134 0.069945 -0.06079 0.094308 -0.23782 -0.17081

SH CSI 27 -0.11432 -0.01428 -0.1761 -0.26357 0.006785 -0.14792

SH CSI 28 -0.09216 -0.1168 -0.15295 -0.10237 -0.20252 -0.09319

SH CSI 29 -0.03755 -0.11681 -0.12298 -0.04737 -0.09237 -0.00538

SH CSI 30 0.07942 0.051632 0.039347 0.075177 -0.00011 -0.1414

SH CSI 31 -0.0261 0.04364 0.028546 -0.04042 -0.18369 -0.14398

SH CSI 32 -0.00615 0.099563 -0.00657 -0.01308 -0.05435 -0.03285

SH CSI 33 -0.01435 -0.13793 -0.06545 0.008675 -0.20978 -0.0746

SH CSI 34 -0.13287 -0.32596 -0.237 -0.07618 -0.32963 -0.046

SH CSI 35 0.112613 0.007051 -0.03542 0.045735 -0.23392 -0.08214

SH CSI 36 0.068868 -0.00624 -0.12303 -0.05865 -0.25888 -0.20032

SH CSI 37 0.229355 -0.08177 0.146172 0.137662 0.034302 0.092869

SH CSI 38 0.074189 0.145494 0.078793 0.030218 0.297275 0.119205

SH CSI 39 0.184583 -0.12923 -0.06907 0.036125 -0.2465 -0.01003

Correlation

SH CSI 40 0.28503 0.098963 0.093642 -0.03466 -0.02727 -0.02646

69


SH CSI 01 0.172789 0.114939 0.063491 0.031155 -0.02519 -0.10137

SH CSI 02 0.236736 0.000614 0.084534 -0.16607 0.168893 -0.07199

SH CSI 03 0.238647 -0.04404 -0.03089 -0.18548 -0.13455 -0.40816

SH CSI 04 0.365102 0.183556 0.127326 0.07138 -0.03377 -0.15303

SH CSI 05 -0.06007 0.193714 0.179243 0.275827 -0.00609 0.384698

SH CSI 06 0.191606 0.043689 0.111281 0.031092 0.102139 -0.34038

SH CSI 07 -0.21438 0.130962 0.117302 0.227257 -0.07567 0.385143

SH CSI 08 0.086988 -0.08556 0.132127 -0.10491 0.17075 -0.16932

SH CSI 09 0.093844 -0.04257 -0.06244 -0.16738 -0.14382 -0.19552

SH CSI 10 0.21999 -0.20139 -0.03551 -0.25896 0.052334 -0.19291

SH CSI 11 0.055726 -0.18227 -0.1154 -0.22048 -0.08367 -0.23399

SH CSI 12 -0.02365 0.053875 -0.00979 -0.0087 -0.18249 -0.01622

SH CSI 13 -0.1313 0.218704 0.204634 0.204973 0.311207 -0.04929

SH CSI 14 -0.25004 -0.04276 0.217297 0.053929 0.153723 0.159589

SH CSI 15 0.204408 0.301506 0.450454 0.392409 0.431493 0.15576

SH CSI 16 0.255884 0.3644 0.44442 0.492136 0.429013 0.230321

SH CSI 17 0.276206 0.383572 0.298588 0.13009 0.063272 0.325447

SH CSI 18 0.613846 0.309045 0.302609 0.16353 0.152282 0.229426

SH CSI 19 1 0.163781 0.186896 0.063998 0.164519 -0.04705

SH CSI 20 0.163781 1 0.501429 0.636987 0.179964 0.463182

SH CSI 21 0.186896 0.501429 1 0.621332 0.502195 0.430473

SH CSI 22 0.063998 0.636987 0.621332 1 0.328238 0.492539

SH CSI 23 0.164519 0.179964 0.502195 0.328238 1 0.216493

SH CSI 24 -0.04705 0.463182 0.430473 0.492539 0.216493 1

SH CSI 25 -0.17609 -0.29965 -0.05585 -0.18114 -0.00083 0.082917

SH CSI 26 -0.26623 -0.00959 0.16801 -0.00217 0.202167 -0.01863

SH CSI 27 -0.17821 -0.1628 -0.1753 -0.27951 -0.18733 -0.02181

SH CSI 28 -0.06916 -0.04137 0.028492 0.054004 0.045738 0.02352

SH CSI 29 0.000745 0.017154 -0.00369 0.037179 0.004073 0.031297

SH CSI 30 -0.03889 -0.06869 0.109098 -0.02337 0.137421 -0.06713

SH CSI 31 -0.04057 -0.04742 0.074948 0.031018 0.102984 -0.09753

SH CSI 32 0.062217 0.045102 0.176629 0.100556 0.051896 0.069455

SH CSI 33 0.087074 -0.28358 -0.25043 -0.21302 -0.15777 -0.43729

SH CSI 34 -0.07105 -0.24256 -0.17726 -0.16898 -0.14364 -0.29116

SH CSI 35 -0.1949 -0.07053 -0.00184 0.055175 0.02842 -0.22625

SH CSI 36 -0.19169 -0.28134 -0.1111 -0.11607 -0.08442 -0.40713

SH CSI 37 0.085598 -0.09436 0.064906 -0.04794 0.005501 -0.21173

SH CSI 38 -0.01783 -0.04399 0.028196 -0.06865 -0.32063 0.072377

SH CSI 39 -0.02546 -0.14685 -0.12211 -0.19658 -0.07201 -0.49965

Correlation

SH CSI 40 -0.06896 0.118116 0.135859 0.058149 0.026669 0.045132

70


SH CSI 01 -0.07456 -0.17149 0.159842 -0.12027 -0.14442 -0.11565

SH CSI 02 0.090315 -0.05438 0.111958 -0.15977 -0.07272 -0.02445

SH CSI 03 -0.10635 0.007934 0.140431 -0.19381 0.012693 -0.01161

SH CSI 04 -0.06155 -0.22402 0.05066 -0.13081 0.012588 0.022937

SH CSI 05 0.168385 0.070522 0.030338 0.086874 0.014687 -0.04382

SH CSI 06 -0.08957 -0.05587 0.159087 -0.02127 0.092807 0.088939

SH CSI 07 0.214487 0.019374 0.288911 0.103636 -0.02228 -0.03977

SH CSI 08 0.086416 -0.15726 0.021897 -0.10145 0.077831 0.02501

SH CSI 09 0.022627 -0.29577 0.104228 -0.28281 -0.14313 -0.02684

SH CSI 10 0.025939 -0.25053 0.048302 -0.21699 -0.08942 -0.02764

SH CSI 11 0.080108 -0.23299 0.123542 -0.01937 -0.08565 -0.01302

SH CSI 12 0.024743 -0.33964 0.07965 -0.20937 -0.0873 -0.0009

SH CSI 13 -0.07488 0.239134 -0.11432 -0.09216 -0.03755 0.07942

SH CSI 14 0.016548 0.069945 -0.01428 -0.1168 -0.11681 0.051632

SH CSI 15 -0.20264 -0.06079 -0.1761 -0.15295 -0.12298 0.039347

SH CSI 16 -0.33997 0.094308 -0.26357 -0.10237 -0.04737 0.075177

SH CSI 17 -0.1275 -0.23782 0.006785 -0.20252 -0.09237 -0.00011

SH CSI 18 -0.11964 -0.17081 -0.14792 -0.09319 -0.00538 -0.1414

SH CSI 19 -0.17609 -0.26623 -0.17821 -0.06916 0.000745 -0.03889

SH CSI 20 -0.29965 -0.00959 -0.1628 -0.04137 0.017154 -0.06869

SH CSI 21 -0.05585 0.16801 -0.1753 0.028492 -0.00369 0.109098

SH CSI 22 -0.18114 -0.00217 -0.27951 0.054004 0.037179 -0.02337

SH CSI 23 -0.00083 0.202167 -0.18733 0.045738 0.004073 0.137421

SH CSI 24 0.082917 -0.01863 -0.02181 0.02352 0.031297 -0.06713

SH CSI 25 1 0.07485 0.312088 0.166662 0.023446 0.148611

SH CSI 26 0.07485 1 0.08148 0.134348 0.138943 0.161239

SH CSI 27 0.312088 0.08148 1 0.095973 0.047732 -0.00105

SH CSI 28 0.166662 0.134348 0.095973 1 0.409196 0.38436

SH CSI 29 0.023446 0.138943 0.047732 0.409196 1 0.269808

SH CSI 30 0.148611 0.161239 -0.00105 0.38436 0.269808 1

SH CSI 31 0.036258 0.158963 -0.06588 0.285262 0.094412 0.235161

SH CSI 32 0.058524 0.126821 0.04505 0.250734 0.129467 0.303691

SH CSI 33 -0.11585 0.229366 0.173602 -0.00775 0.036308 -0.08865

SH CSI 34 -0.03866 0.374066 -0.07267 0.19942 0.09705 0.02168

SH CSI 35 -0.13239 0.075263 -0.06784 0.036418 -0.02934 -0.0116

SH CSI 36 -0.10513 0.215792 0.257581 -0.02061 0.004677 -0.01873

SH CSI 37 0.069303 0.09702 0.135907 -0.23075 0.00028 -0.0063

SH CSI 38 0.107976 -0.19871 0.337204 -0.25368 -0.16082 -0.14699

SH CSI 39 -0.06913 0.174095 0.084299 0.05051 0.092559 0.118007

Correlation

SH CSI 40 -0.09133 0.199637 -0.18495 -0.14726 0.066304 0.094883

71


SH CSI 01 -0.04734 -0.01788 0.002357 -0.05893 0.186406 0.163018

SH CSI 02 -0.09935 -0.06863 0.047232 -0.06103 0.041669 -0.01553

SH CSI 03 -0.03373 -0.00674 0.324585 0.137522 0.101024 0.211936

SH CSI 04 0.029641 0.017185 0.080661 -0.03445 0.089405 0.131073

SH CSI 05 0.04515 0.198426 -0.08777 -0.02897 -0.09579 0.062321

SH CSI 06 0.061392 -0.03032 0.23385 0.029917 0.224157 0.250116

SH CSI 07 0.050979 0.089642 -0.07506 0.005538 -0.11455 -0.00709

SH CSI 08 -0.11593 -0.03119 0.098755 -0.11702 0.235598 0.241797

SH CSI 09 -0.08953 -0.10897 0.230508 -0.04927 0.193045 0.150678

SH CSI 10 -0.15588 -0.10186 0.276255 -0.05971 0.118792 0.056683

SH CSI 11 -0.12319 -0.03409 0.187547 -0.06277 0.086134 0.117848

SH CSI 12 -0.03898 -0.0622 -0.0077 -0.16503 0.102519 -0.00274

SH CSI 13 -0.0261 -0.00615 -0.01435 -0.13287 0.112613 0.068868

SH CSI 14 0.04364 0.099563 -0.13793 -0.32596 0.007051 -0.00624

SH CSI 15 0.028546 -0.00657 -0.06545 -0.237 -0.03542 -0.12303

SH CSI 16 -0.04042 -0.01308 0.008675 -0.07618 0.045735 -0.05865

SH CSI 17 -0.18369 -0.05435 -0.20978 -0.32963 -0.23392 -0.25888

SH CSI 18 -0.14398 -0.03285 -0.0746 -0.046 -0.08214 -0.20032

SH CSI 19 -0.04057 0.062217 0.087074 -0.07105 -0.1949 -0.19169

SH CSI 20 -0.04742 0.045102 -0.28358 -0.24256 -0.07053 -0.28134

SH CSI 21 0.074948 0.176629 -0.25043 -0.17726 -0.00184 -0.1111

SH CSI 22 0.031018 0.100556 -0.21302 -0.16898 0.055175 -0.11607

SH CSI 23 0.102984 0.051896 -0.15777 -0.14364 0.02842 -0.08442

SH CSI 24 -0.09753 0.069455 -0.43729 -0.29116 -0.22625 -0.40713

SH CSI 25 0.036258 0.058524 -0.11585 -0.03866 -0.13239 -0.10513

SH CSI 26 0.158963 0.126821 0.229366 0.374066 0.075263 0.215792

SH CSI 27 -0.06588 0.04505 0.173602 -0.07267 -0.06784 0.257581

SH CSI 28 0.285262 0.250734 -0.00775 0.19942 0.036418 -0.02061

SH CSI 29 0.094412 0.129467 0.036308 0.09705 -0.02934 0.004677

SH CSI 30 0.235161 0.303691 -0.08865 0.02168 -0.0116 -0.01873

SH CSI 31 1 0.548857 0.043867 0.196687 0.0855 0.076242

SH CSI 32 0.548857 1 -0.00467 0.009107 -0.03115 0.04924

SH CSI 33 0.043867 -0.00467 1 0.575557 0.329761 0.40587

SH CSI 34 0.196687 0.009107 0.575557 1 0.390302 0.259667

SH CSI 35 0.0855 -0.03115 0.329761 0.390302 1 0.55982

SH CSI 36 0.076242 0.04924 0.40587 0.259667 0.55982 1

SH CSI 37 -0.04729 0.023355 0.266158 0.132354 0.201932 0.400369

SH CSI 38 -0.07182 -0.03519 0.10887 0.022072 0.101346 0.14102

SH CSI 39 0.107089 -0.00335 0.506363 0.470278 0.391008 0.367425

Correlation

SH CSI 40 -0.02112 0.075195 -0.20179 -0.27563 -0.25619 -0.1356

72

Correlation Matrix SH CSI 37 SH CSI 38 SH CSI 39 SH CSI 40

SH CSI 01 0.100394 0.24923 0.102622 0.01186

SH CSI 02 0.151943 0.1214 0.171759 0.087681

SH CSI 03 0.330955 0.194888 0.376568 0.018698

SH CSI 04 0.311958 0.251114 0.210017 0.080959

SH CSI 05 -0.07669 -0.12407 -0.14946 0.202147

SH CSI 06 0.232156 0.189153 0.397655 -0.06718

SH CSI 07 0.018949 0.25548 -0.06936 -0.13938

SH CSI 08 0.3238 0.128561 0.295995 0.193496

SH CSI 09 0.363952 0.527268 0.401304 -0.21592

SH CSI 10 0.141651 0.228221 0.23785 -0.22883

SH CSI 11 0.200306 0.281485 0.252226 -0.21056

SH CSI 12 0.250098 0.588595 0.244111 -0.30351

SH CSI 13 0.229355 0.074189 0.184583 0.28503

SH CSI 14 -0.08177 0.145494 -0.12923 0.098963

SH CSI 15 0.146172 0.078793 -0.06907 0.093642

SH CSI 16 0.137662 0.030218 0.036125 -0.03466

SH CSI 17 0.034302 0.297275 -0.2465 -0.02727

SH CSI 18 0.092869 0.119205 -0.01003 -0.02646

SH CSI 19 0.085598 -0.01783 -0.02546 -0.06896

SH CSI 20 -0.09436 -0.04399 -0.14685 0.118116

SH CSI 21 0.064906 0.028196 -0.12211 0.135859

SH CSI 22 -0.04794 -0.06865 -0.19658 0.058149

SH CSI 23 0.005501 -0.32063 -0.07201 0.026669

SH CSI 24 -0.21173 0.072377 -0.49965 0.045132

SH CSI 25 0.069303 0.107976 -0.06913 -0.09133

SH CSI 26 0.09702 -0.19871 0.174095 0.199637

SH CSI 27 0.135907 0.337204 0.084299 -0.18495

SH CSI 28 -0.23075 -0.25368 0.05051 -0.14726

SH CSI 29 0.00028 -0.16082 0.092559 0.066304

SH CSI 30 -0.0063 -0.14699 0.118007 0.094883

SH CSI 31 -0.04729 -0.07182 0.107089 -0.02112

SH CSI 32 0.023355 -0.03519 -0.00335 0.075195

SH CSI 33 0.266158 0.10887 0.506363 -0.20179

SH CSI 34 0.132354 0.022072 0.470278 -0.27563

SH CSI 35 0.201932 0.101346 0.391008 -0.25619

SH CSI 36 0.400369 0.14102 0.367425 -0.1356

SH CSI 37 1 0.474271 0.474215 -0.13257

SH CSI 38 0.474271 1 0.2292 -0.35028

SH CSI 39 0.474215 0.2292 1 -0.25562

Correlation

SH CSI 40 -0.13257 -0.35028 -0.25562 1

73

KMO and Bartlett's Test

.608

3602.776

780

.000

Kaiser-Meyer-Olkin Measure of SamplingAdequacy.

Approx. Chi-Square

df

Sig.

Bartlett's Test ofSphericity

Scree Plot

Component Number

39373533312927252321191715131197531

Eig

enva

lue

7

6

5

4

3

2

1

0

74

Communalities

1.000 .793

1.000 .752

1.000 .752

1.000 .751

1.000 .755

1.000 .682

1.000 .721

1.000 .794

1.000 .726

1.000 .801

1.000 .662

1.000 .761

1.000 .740

1.000 .703

1.000 .688

1.000 .784

1.000 .665

1.000 .780

1.000 .783

1.000 .747

1.000 .676

1.000 .803

1.000 .760

1.000 .775

1.000 .635

1.000 .801

1.000 .680

1.000 .720

1.000 .681

1.000 .600

1.000 .753

1.000 .768

1.000 .696

1.000 .758

1.000 .753

1.000 .706

1.000 .808

1.000 .810

1.000 .713

1.000 .782

SH CSI 01

SH CSI 02

SH CSI 03

SH CSI 04

SH CSI 05

SH CSI 06

SH CSI 07

SH CSI 08

SH CSI 09

SH CSI 10

SH CSI 11

SH CSI 12

SH CSI 13

SH CSI 14

SH CSI 15

SH CSI 16

SH CSI 17

SH CSI 18

SH CSI 19

SH CSI 20

SH CSI 21

SH CSI 22

SH CSI 23

SH CSI 24

SH CSI 25

SH CSI 26

SH CSI 27

SH CSI 28

SH CSI 29

SH CSI 30

SH CSI 31

SH CSI 32

SH CSI 33

SH CSI 34

SH CSI 35

SH CSI 36

SH CSI 37

SH CSI 38

SH CSI 39

SH CSI 40

Initial Extraction

Extraction Method: Principal Component Analysis.

75

Total Variance Explained

6.045 15.113 15.113 6.045 15.113 15.113

5.065 12.663 27.776 5.065 12.663 27.776

3.229 8.073 35.849 3.229 8.073 35.849

2.486 6.216 42.064 2.486 6.216 42.064

2.360 5.901 47.965 2.360 5.901 47.965

2.160 5.401 53.366 2.160 5.401 53.366

1.899 4.747 58.112 1.899 4.747 58.112

1.513 3.783 61.895 1.513 3.783 61.895

1.324 3.310 65.205 1.324 3.310 65.205

1.222 3.055 68.260 1.222 3.055 68.260

1.141 2.853 71.113 1.141 2.853 71.113

1.074 2.686 73.799 1.074 2.686 73.799

.955 2.387 76.186

.879 2.198 78.383

.842 2.105 80.488

.717 1.792 82.280

.696 1.740 84.019

.639 1.598 85.617

.596 1.490 87.107

.526 1.316 88.423

.498 1.246 89.669

.447 1.119 90.787

.409 1.023 91.810

.395 .987 92.797

.349 .872 93.669

.331 .828 94.497

.283 .707 95.204

.259 .647 95.851

.229 .572 96.423

.204 .510 96.934

.199 .496 97.430

.188 .471 97.901

.153 .383 98.284

.148 .369 98.653

.129 .323 98.976

.111 .277 99.253

9.277E-02 .232 99.485

8.516E-02 .213 99.698

6.649E-02 .166 99.864

5.450E-02 .136 100.000

Component1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

Total % of Variance Cumulative % Total % of Variance Cumulative %

Initial Eigenvalues Extraction Sums of Squared Loadings


76

Component Matrixa

Component

1 2 3 4 5 6

SH CSI 01 0.525 0.346

SH CSI 02 0.530 0.351

SH CSI 03 0.692

SH CSI 04 0.606 0.368

SH CSI 05 0.445

SH CSI 06 0.644

SH CSI 07 0.518 0.529

SH CSI 08 0.552 0.391

SH CSI 09 0.755

SH CSI 10 0.640

SH CSI 11 0.593

SH CSI 12 0.568 -0.374 0.363

SH CSI 13 0.334 -0.391

SH CSI 14 0.483 -0.535

SH CSI 15 0.655

SH CSI 16 0.636 0.333 -0.306

SH CSI 17 0.632 -0.300

SH CSI 18 0.541 0.397

SH CSI 19 0.373 -0.529

SH CSI 20 0.680

SH CSI 21 0.689 0.334

SH CSI 22 0.631 -0.328

SH CSI 23 0.465 0.413

SH CSI 24 -0.445 0.569 0.329

SH CSI 25 0.380 0.368

SH CSI 26 0.577

SH CSI 27 0.356 0.364

SH CSI 28 0.330

SH CSI 29

SH CSI 30 0.322 0.372

SH CSI 31 0.405

SH CSI 32 0.320 0.325

SH CSI 33 0.419 -0.445 0.320

SH CSI 34 -0.511 0.450 -0.407

SH CSI 35 0.338 0.391 -0.325

SH CSI 36 0.366 -0.399 0.368

SH CSI 37 0.548

SH CSI 38 0.534 -0.350 0.417 -0.308

SH CSI 39 0.584 -0.347 0.393

SH CSI 40 0.548


12 components extracted.

77

Component Matrixa

Component

7 8 9 10 11 12

SH CSI 01 -0.380

SH CSI 02 0.302

SH CSI 03

SH CSI 04 -0.321

SH CSI 05 -0.334 0.365

SH CSI 06

SH CSI 07

SH CSI 08 -0.347

SH CSI 09

SH CSI 10 0.309 0.302

SH CSI 11

SH CSI 12

SH CSI 13

SH CSI 14

SH CSI 15

SH CSI 16

SH CSI 17 0.318

SH CSI 18

SH CSI 19 0.406

SH CSI 20

SH CSI 21

SH CSI 22

SH CSI 23 0.364 -0.315

SH CSI 24

SH CSI 25 0.378

SH CSI 26 0.371

SH CSI 27 0.315 -0.319

SH CSI 28 0.456

SH CSI 29 0.307 0.568

SH CSI 30 0.451

SH CSI 31 0.343 -0.356 -0.452

SH CSI 32 0.310 -0.441

SH CSI 33

SH CSI 34

SH CSI 35 -0.449

SH CSI 36 -0.326

SH CSI 37 0.316 -0.465

SH CSI 38

SH CSI 39

SH CSI 40 -0.398 0.311


a. 12 components extracted.

78

Shanghai CSI - Factor Rotation (1st trail)

Rotated Component Matrixa

.682

.733

.697 .301

.776

.434

.667

.722

.585 .381

.407 -.394 .390 .404

.373 -.386 .437

.532

-.344 .458 .490

.347 .601

.745

.664

.784

.438 -.322 -.349

.564 .377

.519 -.334

.694

.752

.794

.536 -.304

.501 -.518 .376

-.360 .337 .334

.322 .506

-.358 .490

.577

.419

.494

.496

.544

.717

.699 -.354

.676

.650

.479

-.301 .698

.742

-.388 -.475

SH CSI 01

SH CSI 02

SH CSI 03

SH CSI 04

SH CSI 05

SH CSI 06

SH CSI 07

SH CSI 08

SH CSI 09

SH CSI 10

SH CSI 11

SH CSI 12

SH CSI 13

SH CSI 14

SH CSI 15

SH CSI 16

SH CSI 17

SH CSI 18

SH CSI 19

SH CSI 20

SH CSI 21

SH CSI 22

SH CSI 23

SH CSI 24

SH CSI 25

SH CSI 26

SH CSI 27

SH CSI 28

SH CSI 29

SH CSI 30

SH CSI 31

SH CSI 32

SH CSI 33

SH CSI 34

SH CSI 35

SH CSI 36

SH CSI 37

SH CSI 38

SH CSI 39

SH CSI 40

1 2 3 4 5 6

Component

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 8 iterations.a.

79

Shanghai CSI - Factor Rotation (2nd trail)

Rotated Component Matrix a

.755

.759

.778

.722

.728

.677

.351 -.328 .327

.735

.733

.811

.684

.757

.805 .356

.614 -.447

.395 -.615

.677

.700

.733

.691

.722

.795

.595

.692

SH CSI01SH CSI02SH CSI04SH CSI05SH CSI06SH CSI07SH CSI11SH CSI14SH CSI15SH CSI16SH CSI20SH CSI21SH CSI22SH CSI28SH CSI29SH CSI30SH CSI31SH CSI32SH CSI33SH CSI35SH CSI36SH CSI37SH CSI39

1 2 3 4 5 6Component



80

Shanghai CSI - Factor Rotation (3rd trail)


.822

.750

.757

.777

.312 .731

.725

.848

.796

.856

.631 .340

.732

.668

.788

.798

.703

.692

.786

.619

.715

SH CSI01SH CSI02SH CSI04SH CSI05SH CSI06SH CSI07SH CSI14SH CSI15SH CSI16SH CSI20SH CSI21SH CSI30SH CSI31SH CSI32SH CSI33SH CSI35SH CSI36SH CSI37SH CSI39




81

Shanghai CSI - Factor Rotation (4th trail)


.902

.668

.805

.807

.751

.729

.844

.872

.690

.649

.796

.809

.725

.656 .455

.758 .332

.644

.756

SH CSI01SH CSI02SH CSI04SH CSI05SH CSI07SH CSI14SH CSI15SH CSI16SH CSI21SH CSI30SH CSI31SH CSI32SH CSI33SH CSI35SH CSI36SH CSI37SH CSI39




82

Shanghai CSI - Factor Rotation (5th and the final trail)

Communalities

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

SH CSI01SH CSI02SH CSI04SH CSI05SH CSI07SH CSI14SH CSI15SH CSI16SH CSI21SH CSI30SH CSI31SH CSI32SH CSI33SH CSI37SH CSI39

Initial


83


2.584 17.224 17.224 2.129 14.194 14.1942.209 14.727 31.952 2.020 13.467 27.6611.823 12.155 44.106 1.888 12.586 40.2471.608 10.721 54.828 1.786 11.910 52.1571.192 7.945 62.773 1.456 9.709 61.866.917 6.114 68.887 1.053 7.021 68.887.861 5.740 74.627.811 5.405 80.031.656 4.376 84.408.539 3.593 88.000.485 3.234 91.234.435 2.899 94.134.360 2.399 96.533.277 1.844 98.377.243 1.623 100.000

Component123456789101112131415

Total% ofVariance

Cumulative% Total

% ofVariance

Cumulative%

Initial Eigenvalues Rotation Sums of Squared Loadings


Component Matrix a

6 components extracted.a.

84


.893

.690

.799

.803

.763

.939

.848

.884

.702

.640

.802

.808

.774

.708

.831

SH CSI01SH CSI02SH CSI04SH CSI05SH CSI07SH CSI14SH CSI15SH CSI16SH CSI21SH CSI30SH CSI31SH CSI32SH CSI33SH CSI37SH CSI39




Component Transformation Matrix

.592 .704 .387 .016 -.044 -.052

.622 -.174 -.584 .324 .218 .299-.161 -.097 .403 .882 .157 -.003-.352 .512 -.335 .022 .692 -.150.261 -.423 .452 -.324 .665 -.041-.211 .157 .174 -.110 .068 .940

Component123456

1 2 3 4 5 6


85

10.4.3 Cronbach’s alpha Reliability method: Shanghai CSI

Factor 1: Novelty and fashion-conscious consumer

R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)

Mean Std Dev Cases

1. SHCSI15 3.1267 1.0574 150.0 2. SHCSI16 2.6933 1.0294 150.0 3. SHCSI21 3.2267 1.1652 150.0

N ofStatistics for Mean Variance Std Dev Variables SCALE 9.0467 7.2126 2.6856 3

Item-total Statistics

Scale Scale Corrected Mean Variance Item- Alpha if Item if Item Total if Item Deleted Deleted Correlation Deleted

SHCSI15 5.9200 3.4835 .6615 .6121SHCSI16 6.3533 3.5857 .6585 .6191SHCSI21 5.8200 3.6788 .4869 .8160

Reliability Coefficients

N of Cases = 150.0 N of Items = 3

Alpha = .7647

86

Factor 2: Perfectionistic and high-quality conscious consumer


Mean Std Dev Cases

1. SHCSI01 4.3400 .9471 150.0 2. SHCSI02 3.9467 .8731 150.0 3. SHCSI04 4.3800 .6822 150.0







Alpha = .7283

87

Factor 3: Habitual and brand-loyal consumer


Mean Std Dev Cases

1. SHCSI33 2.6600 1.0221 150.0 2. SHCSI37 3.4600 1.0969 150.0 3. SHCSI39 2.6467 1.0371 150.0







Alpha = .6791

88

Factor 4: Impulsive and careless consumer


Mean Std Dev Cases

1. SHCSI30 2.7067 .7377 150.0 2. SHCSI31 2.7667 .7634 150.0 3. SHCSI32 2.5600 .6183 150.0







Alpha = .6189

89

Factor 5: Price conscious and value for money consumer


Mean Std Dev Cases

1. SHCSI05 3.8267 .9606 150.0 2. SHCSI07 3.3733 1.0462 150.0




SHCSI05 3.3733 1.0946 .3119 .SHCSI07 3.8267 .9228 .3119 .



Alpha = .4742

90

10.4.4 Decision-making styles of Hong Kong university consumers

HK CSI - Factor Analysis

91

Correlation Matrix HK CSI 01 HK CSI 02 HK CSI 03 HK CSI 04 HK CSI 05 HK CSI 06

HK CSI 01 1.000 0.181 0.318 0.237 -0.040 0.260

HK CSI 02 0.181 1.000 0.467 0.141 0.046 0.329

HK CSI 03 0.318 0.467 1.000 0.320 -0.041 0.398

HK CSI 04 0.237 0.141 0.320 1.000 -0.016 0.285

HK CSI 05 -0.040 0.046 -0.041 -0.016 1.000 0.161

HK CSI 06 0.260 0.329 0.398 0.285 0.161 1.000

HK CSI 07 -0.027 -0.039 -0.049 -0.082 0.393 0.198

HK CSI 08 0.088 0.353 0.224 0.154 0.034 0.365

HK CSI 09 0.036 0.208 0.224 0.040 -0.289 0.139

HK CSI 10 0.026 0.077 0.096 -0.043 -0.329 0.022

HK CSI 11 0.005 0.014 0.036 0.008 -0.147 0.056

HK CSI 12 -0.039 0.132 0.119 -0.049 -0.147 0.148

HK CSI 13 -0.116 0.061 0.127 -0.058 -0.070 0.061

HK CSI 14 -0.099 -0.080 0.114 -0.063 -0.265 -0.028

HK CSI 15 0.050 0.044 -0.020 -0.091 -0.023 0.100

HK CSI 16 0.044 0.060 0.127 0.007 -0.157 -0.042

HK CSI 17 -0.016 0.129 0.127 0.041 -0.267 0.057

HK CSI 18 0.169 0.125 0.034 0.169 -0.045 0.146

HK CSI 19 0.160 0.045 0.117 0.198 -0.098 0.160

HK CSI 20 0.015 -0.057 -0.129 0.102 0.243 0.121

HK CSI 21 0.000 0.114 0.059 0.081 0.163 0.205

HK CSI 22 -0.087 -0.092 -0.113 -0.016 0.174 0.030

HK CSI 23 0.070 0.091 0.184 0.054 -0.088 0.168

HK CSI 24 -0.057 0.047 -0.090 0.058 0.259 0.013

HK CSI 25 0.088 0.076 0.039 0.159 0.107 0.150

HK CSI 26 -0.098 0.040 0.056 -0.010 0.058 0.116

HK CSI 27 0.087 0.096 0.075 0.054 0.182 0.119

HK CSI 28 0.010 -0.089 0.022 0.013 0.050 0.117

HK CSI 29 -0.011 0.014 -0.065 0.041 -0.016 0.005

HK CSI 30 -0.057 -0.020 -0.043 -0.134 0.122 0.026

HK CSI 31 -0.073 -0.051 -0.239 -0.034 0.182 -0.013

HK CSI 32 -0.013 0.026 0.021 -0.050 0.022 0.129

HK CSI 33 -0.094 -0.030 0.196 0.017 -0.105 0.009

HK CSI 34 0.011 0.045 0.128 0.066 -0.082 0.097

HK CSI 35 0.090 0.198 0.241 0.029 -0.121 0.230

HK CSI 36 0.022 0.027 0.111 -0.015 -0.048 0.156

HK CSI 37 -0.003 0.105 0.132 0.060 -0.089 0.245

HK CSI 38 0.063 0.053 0.032 0.001 -0.078 0.186

HK CSI 39 -0.043 -0.001 0.023 -0.020 -0.063 0.068

Correlation

HK CSI 40 0.050 0.017 0.153 0.149 0.135 -0.013

92


HK CSI 01 -0.027 0.088 0.036 0.026 0.005 -0.039

HK CSI 02 -0.039 0.353 0.208 0.077 0.014 0.132

HK CSI 03 -0.049 0.224 0.224 0.096 0.036 0.119

HK CSI 04 -0.082 0.154 0.040 -0.043 0.008 -0.049

HK CSI 05 0.393 0.034 -0.289 -0.329 -0.147 -0.147

HK CSI 06 0.198 0.365 0.139 0.022 0.056 0.148

HK CSI 07 1.000 -0.047 -0.086 -0.168 0.100 0.006

HK CSI 08 -0.047 1.000 0.209 0.160 0.161 0.216

HK CSI 09 -0.086 0.209 1.000 0.543 0.291 0.329

HK CSI 10 -0.168 0.160 0.543 1.000 0.463 0.355

HK CSI 11 0.100 0.161 0.291 0.463 1.000 0.381

HK CSI 12 0.006 0.216 0.329 0.355 0.381 1.000

HK CSI 13 0.115 0.049 0.222 0.307 0.469 0.527

HK CSI 14 -0.036 0.055 0.292 0.369 0.380 0.434

HK CSI 15 0.032 0.061 0.126 0.158 0.071 0.114

HK CSI 16 -0.126 0.020 0.273 0.287 0.176 0.116

HK CSI 17 -0.072 -0.014 0.462 0.411 0.277 0.271

HK CSI 18 0.132 0.120 0.192 0.119 0.053 0.132

HK CSI 19 -0.067 -0.008 0.010 -0.090 0.052 0.011

HK CSI 20 0.150 0.166 -0.055 -0.152 0.022 0.103

HK CSI 21 0.278 0.089 0.052 -0.071 0.107 0.113

HK CSI 22 0.032 -0.082 -0.200 -0.077 -0.052 -0.135

HK CSI 23 0.000 0.048 0.139 0.072 0.124 0.083

HK CSI 24 0.328 -0.046 -0.063 -0.044 -0.027 -0.117

HK CSI 25 0.236 0.116 -0.010 -0.114 0.047 0.105

HK CSI 26 0.108 -0.002 -0.095 -0.089 0.002 0.058

HK CSI 27 0.300 0.108 0.015 -0.121 -0.155 -0.090

HK CSI 28 0.097 0.090 0.021 -0.032 -0.054 -0.058

HK CSI 29 -0.050 0.011 0.019 -0.029 -0.014 -0.060

HK CSI 30 0.179 0.115 -0.011 0.023 0.192 0.150

HK CSI 31 0.118 -0.067 -0.049 -0.133 -0.163 -0.113

HK CSI 32 0.106 0.032 0.112 0.094 0.055 0.031

HK CSI 33 0.212 0.052 0.154 0.112 0.254 0.170

HK CSI 34 0.130 0.023 0.092 -0.024 0.141 0.119

HK CSI 35 -0.015 0.216 0.183 0.219 0.248 0.333

HK CSI 36 -0.075 0.244 0.103 0.060 0.073 0.203

HK CSI 37 0.027 0.236 0.196 0.051 0.125 0.173

HK CSI 38 0.029 0.075 0.259 0.109 0.202 0.226

HK CSI 39 -0.021 0.195 0.150 0.043 0.127 0.172

Correlation

HK CSI 40 0.050 -0.027 -0.151 -0.266 -0.094 -0.219

93


HK CSI 01 -0.116 -0.099 0.050 0.044 -0.016 0.169

HK CSI 02 0.061 -0.080 0.044 0.060 0.129 0.125

HK CSI 03 0.127 0.114 -0.020 0.127 0.127 0.034

HK CSI 04 -0.058 -0.063 -0.091 0.007 0.041 0.169

HK CSI 05 -0.070 -0.265 -0.023 -0.157 -0.267 -0.045

HK CSI 06 0.061 -0.028 0.100 -0.042 0.057 0.146

HK CSI 07 0.115 -0.036 0.032 -0.126 -0.072 0.132

HK CSI 08 0.049 0.055 0.061 0.020 -0.014 0.120

HK CSI 09 0.222 0.292 0.126 0.273 0.462 0.192

HK CSI 10 0.307 0.369 0.158 0.287 0.411 0.119

HK CSI 11 0.469 0.380 0.071 0.176 0.277 0.053

HK CSI 12 0.527 0.434 0.114 0.116 0.271 0.132

HK CSI 13 1.000 0.494 0.183 0.123 0.320 0.078

HK CSI 14 0.494 1.000 0.137 0.215 0.290 -0.053

HK CSI 15 0.183 0.137 1.000 0.652 0.399 0.301

HK CSI 16 0.123 0.215 0.652 1.000 0.514 0.208

HK CSI 17 0.320 0.290 0.399 0.514 1.000 0.300

HK CSI 18 0.078 -0.053 0.301 0.208 0.300 1.000

HK CSI 19 0.049 0.012 0.161 0.212 0.146 0.235

HK CSI 20 -0.010 -0.086 0.322 0.294 0.054 0.258

HK CSI 21 0.103 -0.071 0.317 0.222 0.174 0.279

HK CSI 22 0.011 -0.089 0.295 0.287 0.041 0.149

HK CSI 23 0.187 0.026 0.241 0.141 0.127 0.017

HK CSI 24 -0.099 -0.214 0.111 0.149 -0.022 0.071

HK CSI 25 0.225 0.043 -0.068 -0.062 -0.041 0.018

HK CSI 26 0.129 0.139 -0.163 -0.181 -0.088 -0.204

HK CSI 27 -0.100 -0.046 -0.002 -0.115 -0.186 0.017

HK CSI 28 0.010 -0.046 0.061 -0.038 0.031 0.052

HK CSI 29 -0.066 -0.140 0.055 0.061 -0.083 -0.014

HK CSI 30 0.182 0.102 0.002 -0.072 0.082 -0.059

HK CSI 31 -0.064 -0.019 0.001 -0.106 -0.065 -0.023

HK CSI 32 0.027 0.132 -0.033 -0.021 0.086 0.050

HK CSI 33 0.272 0.162 0.072 0.092 0.132 0.064

HK CSI 34 0.134 0.029 -0.181 -0.175 -0.004 0.020

HK CSI 35 0.255 0.350 0.071 0.035 0.291 -0.021

HK CSI 36 0.032 0.211 -0.102 -0.161 -0.050 -0.092

HK CSI 37 0.168 0.093 0.146 0.011 0.058 0.110

HK CSI 38 -0.116 -0.099 0.050 0.044 -0.016 0.169

HK CSI 39 0.061 -0.080 0.044 0.060 0.129 0.125

Correlation

HK CSI 40 0.127 0.114 -0.020 0.127 0.127 0.034

94


HK CSI 01 0.160 0.015 0.000 -0.087 0.070 -0.057

HK CSI 02 0.045 -0.057 0.114 -0.092 0.091 0.047

HK CSI 03 0.117 -0.129 0.059 -0.113 0.184 -0.090

HK CSI 04 0.198 0.102 0.081 -0.016 0.054 0.058

HK CSI 05 -0.098 0.243 0.163 0.174 -0.088 0.259

HK CSI 06 0.160 0.121 0.205 0.030 0.168 0.013

HK CSI 07 -0.067 0.150 0.278 0.032 0.000 0.328

HK CSI 08 -0.008 0.166 0.089 -0.082 0.048 -0.046

HK CSI 09 0.010 -0.055 0.052 -0.200 0.139 -0.063

HK CSI 10 -0.090 -0.152 -0.071 -0.077 0.072 -0.044

HK CSI 11 0.052 0.022 0.107 -0.052 0.124 -0.027

HK CSI 12 0.011 0.103 0.113 -0.135 0.083 -0.117

HK CSI 13 0.049 -0.010 0.103 0.011 0.187 -0.099

HK CSI 14 0.012 -0.086 -0.071 -0.089 0.026 -0.214

HK CSI 15 0.161 0.322 0.317 0.295 0.241 0.111

HK CSI 16 0.212 0.294 0.222 0.287 0.141 0.149

HK CSI 17 0.146 0.054 0.174 0.041 0.127 -0.022

HK CSI 18 0.235 0.258 0.279 0.149 0.017 0.071

HK CSI 19 1.000 0.239 0.158 0.119 0.253 0.033

HK CSI 20 0.239 1.000 0.526 0.541 0.173 0.327

HK CSI 21 0.158 0.526 1.000 0.454 0.359 0.322

HK CSI 22 0.119 0.541 0.454 1.000 0.184 0.406

HK CSI 23 0.253 0.173 0.359 0.184 1.000 0.008

HK CSI 24 0.033 0.327 0.322 0.406 0.008 1.000

HK CSI 25 0.110 0.030 0.055 -0.128 0.025 -0.014

HK CSI 26 -0.115 -0.136 0.122 -0.086 0.040 -0.030

HK CSI 27 0.032 0.104 0.039 -0.052 0.064 0.105

HK CSI 28 0.017 -0.043 -0.045 -0.003 0.091 0.045

HK CSI 29 -0.028 0.125 0.147 0.025 -0.056 0.120

HK CSI 30 -0.184 -0.072 0.022 -0.155 -0.058 -0.033

HK CSI 31 0.045 0.129 0.039 0.123 0.023 0.044

HK CSI 32 -0.112 -0.040 0.027 -0.010 -0.040 0.067

HK CSI 33 0.051 -0.178 -0.030 -0.145 0.072 -0.010

HK CSI 34 -0.013 -0.176 -0.060 -0.214 0.096 0.034

HK CSI 35 -0.034 -0.098 0.051 -0.094 0.076 -0.062

HK CSI 36 -0.118 -0.136 -0.109 -0.228 -0.011 -0.163

HK CSI 37 0.183 0.036 0.107 -0.030 0.174 -0.260

HK CSI 38 0.108 -0.030 0.056 -0.103 0.092 -0.198

HK CSI 39 -0.048 -0.061 0.010 -0.064 0.103 -0.245

Correlation

HK CSI 40 -0.027 0.052 -0.073 0.051 -0.212 0.025

95


HK CSI 01 0.088 -0.098 0.087 0.010 -0.011 -0.057

HK CSI 02 0.076 0.040 0.096 -0.089 0.014 -0.020

HK CSI 03 0.039 0.056 0.075 0.022 -0.065 -0.043

HK CSI 04 0.159 -0.010 0.054 0.013 0.041 -0.134

HK CSI 05 0.107 0.058 0.182 0.050 -0.016 0.122

HK CSI 06 0.150 0.116 0.119 0.117 0.005 0.026

HK CSI 07 0.236 0.108 0.300 0.097 -0.050 0.179

HK CSI 08 0.116 -0.002 0.108 0.090 0.011 0.115

HK CSI 09 -0.010 -0.095 0.015 0.021 0.019 -0.011

HK CSI 10 -0.114 -0.089 -0.121 -0.032 -0.029 0.023

HK CSI 11 0.047 0.002 -0.155 -0.054 -0.014 0.192

HK CSI 12 0.105 0.058 -0.090 -0.058 -0.060 0.150

HK CSI 13 0.225 0.129 -0.100 0.010 -0.066 0.182

HK CSI 14 0.043 0.139 -0.046 -0.046 -0.140 0.102

HK CSI 15 -0.068 -0.163 -0.002 0.061 0.055 0.002

HK CSI 16 -0.062 -0.181 -0.115 -0.038 0.061 -0.072

HK CSI 17 -0.041 -0.088 -0.186 0.031 -0.083 0.082

HK CSI 18 0.018 -0.204 0.017 0.052 -0.014 -0.059

HK CSI 19 0.110 -0.115 0.032 0.017 -0.028 -0.184

HK CSI 20 0.030 -0.136 0.104 -0.043 0.125 -0.072

HK CSI 21 0.055 0.122 0.039 -0.045 0.147 0.022

HK CSI 22 -0.128 -0.086 -0.052 -0.003 0.025 -0.155

HK CSI 23 0.025 0.040 0.064 0.091 -0.056 -0.058

HK CSI 24 -0.014 -0.030 0.105 0.045 0.120 -0.033

HK CSI 25 1.000 0.365 0.134 -0.017 -0.093 0.131

HK CSI 26 0.365 1.000 0.029 -0.056 -0.009 -0.018

HK CSI 27 0.134 0.029 1.000 -0.032 -0.043 0.005

HK CSI 28 -0.017 -0.056 -0.032 1.000 -0.085 -0.020

HK CSI 29 -0.093 -0.009 -0.043 -0.085 1.000 -0.051

HK CSI 30 0.131 -0.018 0.005 -0.020 -0.051 1.000

HK CSI 31 0.060 0.147 0.157 0.017 -0.029 -0.063

HK CSI 32 0.046 -0.017 0.022 0.153 -0.054 -0.015

HK CSI 33 0.097 0.090 0.022 0.014 -0.091 0.092

HK CSI 34 0.173 0.228 0.089 0.100 -0.070 0.028

HK CSI 35 0.036 0.173 0.066 -0.029 -0.031 0.122

HK CSI 36 0.045 0.170 -0.076 -0.024 0.066 0.127

HK CSI 37 0.072 0.123 0.082 0.126 -0.036 -0.015

HK CSI 38 0.014 0.064 0.040 0.140 -0.128 0.006

HK CSI 39 0.058 0.220 -0.009 0.119 0.001 -0.004

Correlation

HK CSI 40 -0.009 -0.001 0.049 -0.179 0.111 -0.059

96


HK CSI 01 -0.073 -0.013 -0.094 0.011 0.090 0.022

HK CSI 02 -0.051 0.026 -0.030 0.045 0.198 0.027

HK CSI 03 -0.239 0.021 0.196 0.128 0.241 0.111

HK CSI 04 -0.034 -0.050 0.017 0.066 0.029 -0.015

HK CSI 05 0.182 0.022 -0.105 -0.082 -0.121 -0.048

HK CSI 06 -0.013 0.129 0.009 0.097 0.230 0.156

HK CSI 07 0.118 0.106 0.212 0.130 -0.015 -0.075

HK CSI 08 -0.067 0.032 0.052 0.023 0.216 0.244

HK CSI 09 -0.049 0.112 0.154 0.092 0.183 0.103

HK CSI 10 -0.133 0.094 0.112 -0.024 0.219 0.060

HK CSI 11 -0.163 0.055 0.254 0.141 0.248 0.073

HK CSI 12 -0.113 0.031 0.170 0.119 0.333 0.203

HK CSI 13 -0.064 0.027 0.272 0.134 0.255 0.032

HK CSI 14 -0.019 0.132 0.162 0.029 0.350 0.211

HK CSI 15 0.001 -0.033 0.072 -0.181 0.071 -0.102

HK CSI 16 -0.106 -0.021 0.092 -0.175 0.035 -0.161

HK CSI 17 -0.065 0.086 0.132 -0.004 0.291 -0.050

HK CSI 18 -0.023 0.050 0.064 0.020 -0.021 -0.092

HK CSI 19 0.045 -0.112 0.051 -0.013 -0.034 -0.118

HK CSI 20 0.129 -0.040 -0.178 -0.176 -0.098 -0.136

HK CSI 21 0.039 0.027 -0.030 -0.060 0.051 -0.109

HK CSI 22 0.123 -0.010 -0.145 -0.214 -0.094 -0.228

HK CSI 23 0.023 -0.040 0.072 0.096 0.076 -0.011

HK CSI 24 0.044 0.067 -0.010 0.034 -0.062 -0.163

HK CSI 25 0.060 0.046 0.097 0.173 0.036 0.045

HK CSI 26 0.147 -0.017 0.090 0.228 0.173 0.170

HK CSI 27 0.157 0.022 0.022 0.089 0.066 -0.076

HK CSI 28 0.017 0.153 0.014 0.100 -0.029 -0.024

HK CSI 29 -0.029 -0.054 -0.091 -0.070 -0.031 0.066

HK CSI 30 -0.063 -0.015 0.092 0.028 0.122 0.127

HK CSI 31 1.000 0.095 -0.164 0.034 -0.052 -0.096

HK CSI 32 0.095 1.000 0.060 0.095 0.065 0.064

HK CSI 33 -0.164 0.060 1.000 0.354 0.156 0.173

HK CSI 34 0.034 0.095 0.354 1.000 0.180 0.320

HK CSI 35 -0.052 0.065 0.156 0.180 1.000 0.420

HK CSI 36 -0.096 0.064 0.173 0.320 0.420 1.000

HK CSI 37 0.138 -0.015 0.271 0.042 0.072 0.015

HK CSI 38 0.057 0.088 0.358 0.239 0.169 0.095

HK CSI 39 0.058 -0.009 0.152 0.113 0.186 0.107

Correlation

HK CSI 40 -0.016 -0.025 -0.045 -0.100 -0.103 -0.025

97

Correlation Matrix HK CSI 37 HK CSI 38 HK CSI 39 HK CSI 40

HK CSI 01 -0.003 0.063 -0.043 0.050

HK CSI 02 0.105 0.053 -0.001 0.017

HK CSI 03 0.132 0.032 0.023 0.153

HK CSI 04 0.060 0.001 -0.020 0.149

HK CSI 05 -0.089 -0.078 -0.063 0.135

HK CSI 06 0.245 0.186 0.068 -0.013

HK CSI 07 0.027 0.029 -0.021 0.050

HK CSI 08 0.236 0.075 0.195 -0.027

HK CSI 09 0.196 0.259 0.150 -0.151

HK CSI 10 0.051 0.109 0.043 -0.266

HK CSI 11 0.125 0.202 0.127 -0.094

HK CSI 12 0.173 0.226 0.172 -0.219

HK CSI 13 0.168 0.227 0.169 -0.060

HK CSI 14 0.093 0.137 0.209 -0.022

HK CSI 15 0.146 0.031 0.005 -0.294

HK CSI 16 0.011 0.029 0.062 -0.203

HK CSI 17 0.058 0.144 0.028 -0.153

HK CSI 18 0.110 0.170 -0.070 -0.100

HK CSI 19 0.183 0.108 -0.048 -0.027

HK CSI 20 0.036 -0.030 -0.061 0.052

HK CSI 21 0.107 0.056 0.010 -0.073

HK CSI 22 -0.030 -0.103 -0.064 0.051

HK CSI 23 0.174 0.092 0.103 -0.212

HK CSI 24 -0.260 -0.198 -0.245 0.025

HK CSI 25 0.072 0.014 0.058 -0.009

HK CSI 26 0.123 0.064 0.220 -0.001

HK CSI 27 0.082 0.040 -0.009 0.049

HK CSI 28 0.126 0.140 0.119 -0.179

HK CSI 29 -0.036 -0.128 0.001 0.111

HK CSI 30 -0.015 0.006 -0.004 -0.059

HK CSI 31 0.138 0.057 0.058 -0.016

HK CSI 32 -0.015 0.088 -0.009 -0.025

HK CSI 33 0.271 0.358 0.152 -0.045

HK CSI 34 0.042 0.239 0.113 -0.100

HK CSI 35 0.072 0.169 0.186 -0.103

HK CSI 36 0.015 0.095 0.107 -0.025

HK CSI 37 1.000 0.548 0.399 -0.141

HK CSI 38 0.548 1.000 0.493 -0.118

HK CSI 39 0.399 0.493 1.000 -0.023

Correlation

HK CSI 40 -0.141 -0.118 -0.023 1.000

98

KMO and Bartlett's Test

.649

1868.653

780

.000

Kaiser-Meyer-Olkin Measure of SamplingAdequacy.

Approx. Chi-Square

df

Sig.

Bartlett's Test ofSphericity

Scree Plot

Component Number

39373533312927252321191715131197531

Eig

enva

lue

6

5

4

3

2

1

0

99

Communalities

1.000 .451

1.000 .641

1.000 .793

1.000 .548

1.000 .607

1.000 .621

1.000 .715

1.000 .611

1.000 .689

1.000 .710

1.000 .651

1.000 .602

1.000 .690

1.000 .739

1.000 .778

1.000 .767

1.000 .676

1.000 .626

1.000 .605

1.000 .766

1.000 .672

1.000 .714

1.000 .733

1.000 .673

1.000 .724

1.000 .758

1.000 .733

1.000 .643

1.000 .573

1.000 .571

1.000 .634

1.000 .591

1.000 .735

1.000 .688

1.000 .662

1.000 .773

1.000 .712

1.000 .714

1.000 .641

1.000 .765

HK CSI 01

HK CSI 02

HK CSI 03

HK CSI 04

HK CSI 05

HK CSI 06

HK CSI 07

HK CSI 08

HK CSI 09

HK CSI 10

HK CSI 11

HK CSI 12

HK CSI 13

HK CSI 14

HK CSI 15

HK CSI 16

HK CSI 17

HK CSI 18

HK CSI 19

HK CSI 20

HK CSI 21

HK CSI 22

HK CSI 23

HK CSI 24

HK CSI 25

HK CSI 26

HK CSI 27

HK CSI 28

HK CSI 29

HK CSI 30

HK CSI 31

HK CSI 32

HK CSI 33

HK CSI 34

HK CSI 35

HK CSI 36

HK CSI 37

HK CSI 38

HK CSI 39

HK CSI 40

Initial Extraction


100


4.902 12.255 12.255 4.902 12.255 12.255

3.565 8.911 21.166 3.565 8.911 21.166

2.931 7.326 28.492 2.931 7.326 28.492

2.367 5.919 34.411 2.367 5.919 34.411

1.967 4.916 39.327 1.967 4.916 39.327

1.568 3.921 43.248 1.568 3.921 43.248

1.491 3.728 46.976 1.491 3.728 46.976

1.332 3.329 50.306 1.332 3.329 50.306

1.281 3.203 53.508 1.281 3.203 53.508

1.241 3.102 56.610 1.241 3.102 56.610

1.141 2.852 59.463 1.141 2.852 59.463

1.130 2.826 62.289 1.130 2.826 62.289

1.064 2.659 64.948 1.064 2.659 64.948

1.015 2.537 67.485 1.015 2.537 67.485

.931 2.327 69.812

.894 2.234 72.046

.826 2.065 74.111

.805 2.012 76.124

.771 1.928 78.051

.739 1.848 79.900

.706 1.765 81.665

.651 1.629 83.293

.624 1.560 84.853

.616 1.539 86.393

.546 1.366 87.758

.514 1.286 89.044

.495 1.237 90.281

.463 1.157 91.437

.452 1.130 92.567

.399 .999 93.566

.383 .958 94.524

.316 .791 95.315

.304 .761 96.076

.278 .694 96.770

.263 .658 97.428

.255 .637 98.065

.239 .598 98.664

.212 .529 99.193

.175 .438 99.631

.147 .369 100.000

Component1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

Total % of Variance Cumulative % Total % of Variance Cumulative %

Initial Eigenvalues Extraction Sums of Squared Loadings


101

Component Matrixa

Component

1 2 3 4 5 6 7 8

HK CSI 01 -0.510

HK CSI 02 0.335 -0.442

HK CSI 03 0.367 0.344 -0.540

HK CSI 04 0.333 -0.454

HK CSI 05 -0.334 0.453

HK CSI 06 0.320 0.587

HK CSI 07 0.454 0.491 -0.357

HK CSI 08 0.353 0.346 0.467

HK CSI 09 0.627

HK CSI 10 0.584 -0.416

HK CSI 11 0.580

HK CSI 12 0.642

HK CSI 13 0.606 0.350

HK CSI 14 0.565

HK CSI 15 0.338 0.603

HK CSI 16 0.384 0.545 -0.391

HK CSI 17 0.588 -0.342

HK CSI 18 0.444 -0.328

HK CSI 19 0.370 -0.310 -0.431

HK CSI 20 0.739

HK CSI 21 0.662

HK CSI 22 0.690

HK CSI 23 0.301 0.307 0.354

HK CSI 24 0.499 0.367

HK CSI 25 0.431 -0.335

HK CSI 26 0.365 0.302 0.359 0.302

HK CSI 27 0.434

HK CSI 28 -0.401

HK CSI 29

HK CSI 30 0.350 -0.316

HK CSI 31

HK CSI 32 -0.367

HK CSI 33 0.417 -0.327

HK CSI 34 -0.314 0.334 0.429

HK CSI 35 0.519

HK CSI 36 -0.402 0.358

HK CSI 37 0.422 -0.592

HK CSI 38 0.486 -0.535

HK CSI 39 0.358 -0.448

HK CSI 40 -0.317


102

Component Matrixa

Component

9 10 11 12 13 14

HK CSI 01

HK CSI 02

HK CSI 03 -0.422

HK CSI 04

HK CSI 05

HK CSI 06

HK CSI 07

HK CSI 08

HK CSI 09

HK CSI 10

HK CSI 11

HK CSI 12

HK CSI 13

HK CSI 14

HK CSI 15

HK CSI 16

HK CSI 17

HK CSI 18 0.361

HK CSI 19

HK CSI 20

HK CSI 21

HK CSI 22

HK CSI 23 -0.397

HK CSI 24

HK CSI 25 -0.329 0.356

HK CSI 26 0.327

HK CSI 27 -0.426 -0.386

HK CSI 28 0.481

HK CSI 29 0.420 -0.328

HK CSI 30 -0.422

HK CSI 31 -0.435 0.360

HK CSI 32 0.454 0.323

HK CSI 33 0.416

HK CSI 34

HK CSI 35 0.380

HK CSI 36 0.370

HK CSI 37

HK CSI 38

HK CSI 39

HK CSI 40 0.449


a. 14 components extracted.

103

HK CSI - Factor Rotation (1st trail)

Rotated Component Matrixa

.554

.640

.735

.562

.608

.641

.702

.497

.519

.638 -.377

.665

.678

.678

.667

.671

.308 .608 -.371

.551 .357 -.319

.463

.382

.722

.670 .334

.678

.357

.436 .352 -.387

.450

.435

.360

.310

.338

.367 .337

.529

.310 -.384

.758

.751

.631

HK CSI 01

HK CSI 02

HK CSI 03

HK CSI 04

HK CSI 05

HK CSI 06

HK CSI 07

HK CSI 08

HK CSI 09

HK CSI 10

HK CSI 11

HK CSI 12

HK CSI 13

HK CSI 14

HK CSI 15

HK CSI 16

HK CSI 17

HK CSI 18

HK CSI 19

HK CSI 20

HK CSI 21

HK CSI 22

HK CSI 23

HK CSI 24

HK CSI 25

HK CSI 26

HK CSI 27

HK CSI 28

HK CSI 29

HK CSI 30

HK CSI 31

HK CSI 32

HK CSI 33

HK CSI 34

HK CSI 35

HK CSI 36

HK CSI 37

HK CSI 38

HK CSI 39

HK CSI 40

1 2 3 4 5

Component



104

HK CSI - Factor Rotation (2nd trail)


.560

.673

.726

.551

.645

.658 .301

.651

.508

.460 -.364

.665

.733

.773

.762

.641

.568

.772

.669

.595

-.416 .545

.519

.795

.817

.752

HK CSI01HK CSI02HK CSI03HK CSI04HK CSI05HK CSI06HK CSI07HK CSI08HK CSI09HK CSI11HK CSI12HK CSI13HK CSI14HK CSI15HK CSI18HK CSI20HK CSI22HK CSI25HK CSI26HK CSI35HK CSI37HK CSI38HK CSI39

1 2 3 4 5Component



105

HK CSI - Factor Rotation (3rd and the final trail)

Communalities

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

HK CSI01HK CSI02HK CSI03HK CSI04HK CSI05HK CSI07HK CSI08HK CSI11HK CSI12HK CSI13HK CSI14HK CSI15HK CSI18HK CSI20HK CSI22HK CSI25HK CSI35HK CSI37HK CSI38HK CSI39

Initial


106


3.316 16.578 16.578 2.765 13.824 13.8242.142 10.711 27.289 2.197 10.986 24.8102.089 10.443 37.731 2.045 10.227 35.0371.581 7.905 45.636 2.021 10.105 45.1421.501 7.505 53.140 1.600 7.999 53.1401.126 5.630 58.770.994 4.969 63.740.909 4.546 68.285.821 4.103 72.389.795 3.973 76.362.683 3.416 79.778.649 3.243 83.020.596 2.981 86.002.541 2.706 88.707.520 2.600 91.307.439 2.194 93.502.395 1.976 95.477.329 1.645 97.123.317 1.587 98.710.258 1.290 100.000

Component1234567891011121314151617181920

Total% ofVariance

Cumulative% Total

% ofVariance

Cumulative%

Initial Eigenvalues Rotation Sums of Squared Loadings


Component Matrix a

5 components extracted.a.

107


.582

.692

.733

.573

.706

.770

.502

.666

.734

.786

.764

.675

.553

.786

.729

.594

.538

.797

.827

.752

HK CSI01HK CSI02HK CSI03HK CSI04HK CSI05HK CSI07HK CSI08HK CSI11HK CSI12HK CSI13HK CSI14HK CSI15HK CSI18HK CSI20HK CSI22HK CSI25HK CSI35HK CSI37HK CSI38HK CSI39

1 2 3 4 5Component



108

10.4.5 Cronbach’s alpha Reliability method: Hong Kong CSI

Factor 1: Brand conscious and price equals quality consumer


Mean Std Dev Cases

1. HKCSI11 2.8467 .9675 150.0 2. HKCSI12 3.1200 .8507 150.0 3. HKCSI13 2.9933 .8553 150.0 4. HKCSI14 2.3867 .8009 150.0 5. HKCSI35 3.0600 1.0182 150.0




HKCSI11 11.5600 6.7581 .4933 .7151HKCSI12 11.2867 6.8904 .5735 .6859HKCSI13 11.4133 6.7810 .5979 .6770HKCSI14 12.0200 7.1070 .5684 .6902HKCSI35 11.3467 7.0602 .3842 .7606



Alpha = .7501

109

Factor 2: Perfectionistic and high-quality conscious consumer


Mean Std Dev Cases

1. HKCSI01 4.1133 .8072 150.0 2. HKCSI02 3.7600 .7389 150.0 3. HKCSI03 3.7267 .7226 150.0 4. HKCSI04 4.1200 .6647 150.0 5. HKCSI08 3.2667 .9944 150.0




HKCSI01 14.8733 4.4872 .2871 .5908HKCSI02 15.2267 4.2033 .4526 .5072HKCSI03 15.2600 4.0997 .5122 .4786HKCSI04 14.8667 4.7740 .3114 .5769HKCSI08 15.7200 3.9479 .2996 .6033



Alpha = .6066

110

Factor 3: Novelty and fashion-conscious consumer


Mean Std Dev Cases

1. HKCSI15 2.8267 .9813 150.0 2. HKCSI18 3.8267 .7836 150.0 3. HKCSI20 3.6600 1.0022 150.0 4. HKCSI22 3.4200 .9712 150.0




HKCSI15 10.9067 4.2463 .4101 .5951HKCSI18 9.9067 5.1590 .3076 .6537HKCSI20 10.0733 3.7731 .5369 .4980HKCSI22 10.3133 4.0824 .4695 .5515



Alpha = .6491

111

Factor 4: Habitual and brand-loyal consumer


Mean Std Dev Cases

1. HKCSI37 3.0800 .9378 150.0 2. HKCSI38 3.3267 .9796 150.0 3. HKCSI39 2.7200 1.0108 150.0




HKCSI37 6.0467 2.9575 .5470 .6601HKCSI38 5.8000 2.6577 .6211 .5693HKCSI39 6.4067 2.8469 .5079 .7080



Alpha = .7339

112

Factor 5: Price conscious and value for money consumer


Mean Std Dev Cases

1. HKCSI05 3.5933 1.0561 150.0 2. HKCSI07 3.3933 1.1167 150.0 3. HKCSI25 3.7200 .8284 150.0




HKCSI05 7.1133 2.3696 .3423 .3684HKCSI07 7.3133 1.9884 .4327 .1879HKCSI25 6.9867 3.2884 .2075 .5632



Alpha = .5055

113

10.4.6 Comparison of decision-making styles between Shanghai and Hong Kong

university consumer

T-Test 1: Brand conscious and price equals quality consumer

Group Statistics

150 2.3933 .75881 .06196

150 2.8813 .63799 .05209

PlaceShanghai

Hong Kong

Brand consciousand price equalsquality consumer

N Mean Std. DeviationStd. Error

Mean

Independent Samples Test

12.970 .000 -6.029 298 .000 -.4880 .08095 -.64730 -.32870

-6.029 289.465 .000 -.4880 .08095 -.64732 -.32868

Equal variancesassumed

Equal variancesnot assumed

Brand consciousand price equalsquality consumer

F Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

114

T-Test 2: Perfectionistic and high-quality conscious consumer

Group Statistics

150 4.2222 .67739 .05531

150 3.7973 .49480 .04040

PlaceShanghai

Hong Kong

Perfectionistic andhigh-qualityconscious consumer


Mean


15.225 .000 6.203 298 .000 .4249 .06849 .29010 .55968

6.203 272.765 .000 .4249 .06849 .29005 .55973



Perfectionistic andhigh-qualityconscious consumer

F Sig.





Difference


115

T-Test 3: Novelty and fashion-conscious consumer

Group Statistics

150 3.0156 .89521 .07309

150 3.4333 .65517 .05349

PlaceShanghai

Hong Kong

Novelty andfashion-consciousconsumer


Mean


10.653 .001 -4.612 298 .000 -.4178 .09058 -.59603 -.23952

-4.612 273.033 .000 -.4178 .09058 -.59610 -.23946



Novelty andfashion-consciousconsumer

F Sig.





Difference


116

T-Test 4: Habitual and brand-loyal consumer

Group Statistics

150 2.9222 .82143 .06707

150 3.0422 .78890 .06441

PlaceShanghai

Hong Kong

Habitual andbrand-loyal consumer


Mean


.812 .368 -1.290 298 .198 -.1200 .09299 -.30300 .06300

-1.290 297.515 .198 -.1200 .09299 -.30300 .06300



Habitual andbrand-loyal consumer

F Sig.





Difference


117

T-Test 5: Price conscious and value for money consumer

Group Statistics

150 3.6000 .81306 .06639

150 3.5689 .71476 .05836

PlaceShanghai

Hong Kong

Price consciousand value formoney consumer


Mean


1.209 .272 .352 298 .725 .0311 .08839 -.14284 .20506

.352 293.185 .725 .0311 .08839 -.14285 .20507



Price consciousand value formoney consumer

F Sig.





Difference


118

T-Test 6: Impulsive and careless consumer

Group Statistics

150 2.6778 .53431 .04363

150 .0000 .00000 .00000

PlaceShanghai

Hong Kong

Impulsive andcareless consumer


Mean


282.813 .000 61.380 298 .000 2.6778 .04363 2.59192 2.76363

61.380 149.000 .000 2.6778 .04363 2.59157 2.76398



Impulsive andcareless consumer

F Sig.





Difference


Documents

Consumer Decision-making Styles: Comparison Between