What are the Hierarchical Motivations of Chinese Travellers? A MEC Study applying Soft and Hard Laddering Techniques

1

What are the Hierarchical Motivations of Chinese Travellers? A MEC

Study applying Soft and Hard Laddering Techniques

Shan Jiang 1, Noel Scott

2, & Peiyi Ding

2

1 Capital Normal University, China 2 Griffith University, Gold Coast, Australia

[email protected], [email protected], [email protected]

Abstract — A Means-end Chain (MEC) approach is used

in this research to examine the outbound travel

motivation at the attributes/ consequences/ values levels,

as well as the structure and interrelationships between

them, in order to link between the destination attributes

with motivational drivers. A two-step process was used

with 60 in-depth interviews using a soft-laddering method,

and 600 hard laddering surveys conducted. Six key

means-end chains emerged from the data analysis serving

as the basis for understanding the underlying motivations

of Chinese travellers to travel outbound. This study not

only enriches the literature of travel motivation, but also

provides meaningful insights into the research on the

Chinese outbound leisure travel market, which is an

emerging market with strong potential for future growth.

Keywords— Chinese travel motivation, soft laddering,

hard laddering, MEC theory

I. INTRODUCTION

China’s tourism industry is among the fastest-

growing contributor to its national economy as well as

providing growing revenue to many tourism

destinations. According to the data from China

National Tourism Administration [1], by the first three

quarters of the year 2014, Chinese outbound tourism

expenditure rose to USD 155 billion, increasing 20.8%

compared to the previous year. At the same time,

Chinese outbound tourism had reached 115 million

trips in 2014, up 17.5% from the previous year. A

recent survey and analysis of the travel behaviour of

Chinese outbound tourists shows that there has been a

slight decline in outbound Chinese tourists’ satisfaction

on services in general, including their satisfaction with

complaint handling [2]. This indicates that the

outbound market may be maturing and expecting more

personalized service during their overseas trip. It also

suggests that tourism marketers both within China and

abroad should research the psychological and cultural

characteristics, to better understand traveller

motivations for choosing a specific tourism destination.

II. LITERATURE REVIEW

Travel motivation may be regarded as a subset of

the wider human motivation and is the “total network

of biological and cultural forces that give value and

direction to travel choice, behaviour, and experience”

[3] (p.40). Travel motivation represents the “whys and

the wherefores of travel in general, or of a specific

choice in particular.” [4] (p.233). It is essential for

industry operators to understand motivation in order to

offer more personalized services and memorable

experiences to customers, and as a result obtain more

repeat business [5].

A. Motivation Studies on Chinese Outbound Market

Research on the Chinese outbound travel market has

evolved quite rapidly and dramatically over the last

decade. Studies on the travel motivation of this market

have used a variety of different models or theories; the

push and pull model [6][7], grounded theory [8],

expectation, motivation, and attitude (EMA) model [9].

Studies have examined the relationship of motivation

with variables including travel-related characteristics,

expectation, attitude, past experience, perceived

constraints, satisfaction, and personal values

[7][9][10]-[12].

Travel motivation seeks to answer the question of

why people travel [13]. A conclusive explanation of

travel motivation research is challenging because of

both the variety of human needs embodied in

motivation and also methodological difficulties in

obtaining data [14]. Admitting their limitations, it is

believed that MEC theory and the laddering techniques

are advantageous methods for understanding travel

motivation, especially they are helpful in discovering

hidden meanings of tourists’ behaviour, which could

shed light on important relations among travel

motivations.

B. Introduction of MEC Theory

The means-end chain (MEC) model is based on

expectancy-value theory [15], widely used in

marketing research to understand consumer behaviour.

A MEC is defined as a model that seeks to explain how

a product or service selection facilitates the

achievement of desired end states. Such a model

consists of elements that represent the major consumer

processes that link values to behaviour [16]. The MEC

model describes product choices that consumers that

maximize their desired consequences and minimize

undesired consequences. For the finer-grained analysis

of the mental representations regarding the product,

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each basic level of abstraction may be divided into two

sub-levels, leading to a MEC with six levels [17],

ordered from low to high abstract (Figure 1).

MEC is a very useful approach for exploring

psychological factors involved in consumer behaviour

and it has been applied in the tourism field [18][19].

Their research topic, research method, research

implication, and main limitations or further research

possibilities are summarized in the following table

(Table I).

C. Soft Laddering and Hard Laddering

Laddering is a technique associated with MEC

theory [20]. Laddering refers to an in-depth, one-to-one

interviewing technique used to develop an

understanding of how consumers translate the attributes

of products into meaningful associations with respect

to self [21]. The essence of this technique is that

consumers are asked for the concrete choice criteria

they think are important when choosing a product (such

as a vacation trip). Laddering assumes that elements

can be sequentially elicited from the respondent to

cause the respondent to think critically about the

connections between the product’s attributes and

personal motivation. There are two approaches to

laddering, soft laddering and hard laddering. Soft

laddering is an inductive analysis technique to uncover

ladders, as the natural flow of speech of the respondents

is restricted as little as possible [18]. In contrast, hard

laddering refers to using data collection techniques of

‘paper-and-pencil’ or survey format, where the

respondent is forced to produce ladders one by one.

Reference [22] has argued that while MEC is widely

used, there is a lack of theoretical development besides

a refinement of methodology. At the same time,

although means-end chain theory has been applied in

tourism research for different studies, there is as yet

seldom research on leisure travel motivation using this

approach.

III. RESEARCH PURPOSES

This research aims to contribute to the tourism

literature of Chinese outbound travel motivation by

using the means-end chain approach to distinguish

motivation at different hierarchical levels, exploring

the interrelations of motivation items, and the

underlying values that drive motivations. Specifically,

the research purposes of this study is to:

1) investigate the motivations structure of Chinese

tourists, in terms of attributes, consequences, and

values levels;

2) analyse the relationships among motivations and

reveal dominant MECs that determine the salient

motivations; and

3) discuss the specific features of Chinese outbound

market based on the analysis on its travel motivations.

IV. METHODOLOGY

In order to provide validity and rigor to this study,

soft and hard laddering methods are both used. The soft

laddering technique was used first as it is less likely to

impose relationships than other techniques [23], and

makes the reconstruction of meaning in the coding

phase easier [24]. Then hard laddering approaches were

applied to confirm, extend and validate the findings.

A. Soft Laddering – Measurement Scale Development

In the qualitative soft laddering research conducted

in 2012, 60 interviews were undertaken in three

different cities in China: Beijing, Shanghai, and

Qingdao. After 60 interviews no new motivation items

were identified and the data then were input into NVivo

9.2 for content analysis. The content analysis procedure

involves preparing transcripts, determining the

motivation items at each level, and summarizing the

key elements in three different levels (attributes,

consequences, and values). Compared with attribute

and consequence, motivation at the values level is more

difficult to determine due to its deeply abstract nature.

Some existing universal value sets drawn from classic

work (e.g., [25]-[27]) were used to identify travel

motivations at value level in the data coding.

Typologies of Chinese values were also referred [28]-

[31]. Based on the content analysis result, 203

motivation concepts were identified in total, and then

were grouped and summarized into 58 content codes

(see Table II).

B. Hard Laddering – Online Questionnaire

Based on the qualitative research results, an online

survey was designed with two sections: the first

concerning demographic characteristics, and the

second section, hard-laddering questions examined

travel motivation at six levels.

Firstly, three of the respondents’ ideal/favourite

destinations (in order) were requested, and then

sequentially for each destination mentioned the

respondents were asked to choose 1-3 most important

reasons attracting them to visit (motivation attribute

level). Then by continually asking “why is this

(motivation) important to you?” the on-line survey

requires the respondents to indicate which upper level

motivations (1-3 as before) are relevant. This enables

the direct and indirect connections between

predetermined concept codes to be determined. The

respondents are asked this question until a motivation

at the terminal values level is found. If the respondents

cannot provide another motivation, they can choose

“N/A” to end one of the ladders.

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FIG. 1 A TYPICAL SIX-LEVEL MEC MODEL

TABLE I

PREVIOUS TOURISM STUDIES WITH MEANS-END CHAIN THEORY

Authors & Year Research topic Methodology Implication, limitation, or further study

Klenosky, Gengler,

and Mulvey (1993)

[32]

Ski destination

choice Laddering three ski

destinations chosen

from preference;

LadderMap program;

HVM

Additional research on ME analysis should be

explored to refine and extend the methodological and

analytical procedures and examine other important

leisure and tourism research issues.

Jansen-Verbeke and

van Rekom (1996)

[33]

Motivation and

behaviour patterns

of museum visitors

Kelly Grid interviews,

Laddering, HVM;

questionnaires

The study implied that the motivation and behaviour

patterns of museum visitors offer interesting clues

when developing an attractive urban tourism product.

Klenosky,

Frauman, Norman,

and Gengler (1998)

[34]

Park visitors' usage

of specific

interpretive service

offerings

Laddering interview;

HVM

Findings have limited generalizability due to the

respondents involved, the sample size, the setting,

and the specific interpretive services that were

examined.

Mattila (1999) [35] Cross-cultural

hospitality

accommodation

choice

Laddering No detailed description on data collection and data

analysis process. Sample may not be big enough to

do the quantitative analysis.

Botschen, Thelen,

and Pieters (1999)

[36]

Benefits segments

in the service

industry

paper-and-pencil

laddering

It would be interesting to see research comparing the

results of hard and soft laddering.

Crotts and van

Rekom (1999) [37]

The underlying

reasons for visiting

a fine arts museum

Repertory grid

methodology of triad

sort task

The study implied that the MEC is a useful tool in

segmenting visitor populations in terms of their needs

and motivations and in deriving positioning strategies

that will appeal most to those segments.

McIntosh (1999)

[38] Tourists visiting

three theme parks Laddering interview

and survey research

The insightfulness on cultural tourists as itinerant

``encoders'' of historical and cultural information and

experiences requires further empirical testing.

Thyne (2001) [39] Values of museum

visitors Laddering interview Additional research should include more extensive

interviews with museum patrons, concentrating on

their motivation-based values for visiting the

museum.

Jewell and Crotts

(2001, 2009)

[40][41]

The underlying

motives and needs

of visitors to a

heritage site

Laddering interview;

HVM; questionnaire;

Associated Pattern

Technique (APT)

In 2001 study, the study is limited in the small size

(n=20) and the single heritage site. Further research

is also suggested in non-visitors; in 2009 study, the

combined methodology might be used in various

contexts of further study.

Frauman and

Cunningham (2001)

[42]

Experiences of

users of a greenway Self-administered

questionnaire; factor

and correlation analysis

Further research should measure and address possible

linkages between attributes, benefits, and values.

Klenosky (2002)

[43] How and why the

push and pull

factors are related

with MEC

Laddering interview;

HVM

The limitation is that it did not confirm if the

consequence and value derived from attributes are

the push factors or not, and it suggested that a MEC

study focuses explicitly on push factors for future

research.

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TABLE II

SUMMARY CONTENT CODES OF INTERVIEW DATA

Concrete Attributes Abstract Attributes

history

culture

art

local customs

natural scenery

interaction with nature

shopping

entertainment

local food

local building

local food

self-driving travel

visit friends and relatives

unique style and

characteristics

fame

sense of mystique

good environment

convenience

fineness

famous-brand

maturity of service facility

suitable for child

advancement

Functional Consequence Psycho-social Consequences

being close to the nature

experiencing differences

for child's self-

improvement (a rounded

individual)

getting a deeper and

comprehensive knowledge

seeking and comparing the

differences with China

being together with my

friends

rewarding oneself

escaping from routine

relaxation

memory

the sense of commonality

enrichment of one's life

curiosity/interest/novelty

mental-refreshing

enjoyment /comfort

reinforcement of interpersonal

relationships

excitement

go-as-you-please

nature/culture shocking

becoming different to others

Instrumental Values Terminal Values

learning the knowledge

self-improvement of

capability

exploration of unknown

world

good personal relationships

self-confidence

self-cultivation and

courtesy

experienced person

reciprocation of greetings,

favours, and gifts

self-achievement

happiness

hedonic/pleasure

self-realization

social status

true friendship and love

the healthy life

the beauty of the world

the exciting life

wisdom

C. Validity and Reliability Consideration

(1) Pre-test & Pilot Test. Traditional soft laddering

requires expertise, and time and practice are needed to

train the interviewer [36][44]. To develop this skill

and improve interview questions, a pre-test was

conducted with 10 interviewees. Then a pilot test with

20 interviewees was conducted to further identify any

weaknesses in the researchers’ research skill in

laddering technique, content analysis, and

Hierarchical Value Map (HVM) analysis. Based on

the results of these two tests the interview questions

were modified to better explore the interviewees’

motivation concepts.

(2) Language Translation. The interviews were

conducted in Chinese language, using Chinese

concepts, and therefore there may be issues of

decentring of meanings. Because this research is

presented in English, an accurate and appropriate

translation from Chinese to English is important and

necessary. To achieve research validity, and maintain

consistency at this stage, reliability checks across

multiple coders were utilized. Specifically, a Chinese

and English version of content codes with a brief

description for each code was prepared by the first

author who is a native Chinese speaker, and the second

author as a native Australia speaker were requested to

check the descriptions, and the third author, who

speaks Chinese but has lived in Australia for many

years, also compared the two versions.

D. Sampling

A leisure traveller is defined as “a person who

travels to a destination (involving an overnight stay

and 24 hours away from home) which incorporates

leisure and recreation activities” [45] (p. 24). In this

research context respondents were Chinese people

planning to undertake leisure travel to an outbound

destination. In addition, because previous travel

experience may provide insights when studying travel

motivation [46][47], people who had outbound travel

experience and also had the intention to travel

outbound again within one year were chosen.

E. Data Collection

Quantitative research using an online surveys was

conducted in the period of March 2014 to July 2014.

The survey was conducted with the assistance of the

Shanghai office of an international market research

company. Due to validity and reliability consideration,

the respondents were required to answer a series of

screening questions at the beginning of the survey

such as their demographic characteristics, previous

travel experience, as well as their future outbound

travel intentions. For each of the three cities: some 200

respondents from Shanghai, Beijing and Guangzhou

provided data, and in total there were 600 respondents’

information were collected.

V. DATA ANALYSIS

The data analysis was conducted using Excel

Analysis, SPSS analysis, and HVM analysis.

Specifically, (1) a Summary Implication Matrix (SIM)

was prepared in Excel to display the number of times

each coded motivation item connected to another

motivation item, including direct connections and

indirect connections. Direct connection refers to the

connection where an element directly precedes

another element with no elements in between the two;

indirect connection refers to the connection where an

element precedes another in a ladder but one or more

additional elements are between them [48]. Because

direct connection indicates a direct relation between

concepts, whereas indirect connections reflect a

general association between concepts, both direct

connections and indirect connections should be

counted [48].These connections were summarized to

provide dominant perceptual orientations. (2) Next a

Hierarchical Value Map (HVM) was used to illustrate

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dominant links between attribute-consequence-value

concepts. A HVM represents the number of

connections between the elements and the meaningful

chains; allowing the key means-end chains to be

identified based on the numbers of all direct and

indirect relations.

A. Respondents Characteristics

As shown in Table III, in total 600 Chinese citizens

provided valid online survey data, of which 56% were

female and 44% male. Of these respondents, 200 were

from each of Beijing, Shanghai, and Guangdong. The

respondents were company managers or supervisors

(59.8%), aged between 25 and 45 (90.8% in total), and

the majority h a bachelor degree (69.3%). The modal

annual income was between 200,000 RMB and

500,000 RMB (about USD 32,000- 80,000) (58.2%),

and 79.2% of the respondents were married with one

child.

TABLE IIII

CHARACTERISTICS OF THE RESPONDENTS (N=600)

Characteristics % Characteristics %

Gender

Male

Female

44

56

Location

Beijing

Guangzhou

Shanghai

33.3

33.3

33.3

Age

18- 24

25-35

36-45

46-55

56-60

3

54.3

36.5

5.5

0.7

Occupation

Government

officials

Government staff

Company manager

Company staff

Worker/Server

Professional

Freelance

Housewife Private businessmen

Student

0.7

1.8

59.8

25.3

0.3

9.2

0.5

0.5

0.8

1

Marital Status

Married with child

Married without

child

Single

79.2

10

10.8

Education

High school

College degree

Bachelor degree

Master degree

PhD degree

1

10.8

69.3

17.3

1.5

Annual Income

(RMB)

200,000-500,000

500,001-800,000

800,001-1,100,000

Above 1,100,001

58.2

16.5

17.8

7.5

B. Key Means End Chains Based on the 600 respondents’ travel motivations,

A SIM (Appendix 1) was constructed to display the

number of times each coded motivation item

connected to another motivation item. The

connections include direct connection and indirect

connections.

Six chains were identified on the HVM, as shown

in Figure 2 to Figure 7. These six key MECs can be

divided into the three motivation levels to gain insight

into the underlying motivations of Chinese travellers

in visiting a particular destination.

Appendix II shows the summary of direct and

indirect relations of motivation for each key MECs.

For example, in MEC1 there are three paths

connecting “Natural Scenery—Hedonic/Pleasure”.

There are 338 direct linkages between ‘natural scenery’

and ‘unique style and characteristics’. In all, there are

1904 direct relations and 2045 indirect relations in this

branch.

1) MEC1: “natural scenery —hedonic/pleasure”

FIG. 2 HVM OF MEC1: “NATURAL SCENERY —

HEDONIC/PLEASURE”

‘Natural scenery’ is one of the most important

travel motivation noted at the level of concrete

attribute. In MEC1, some respondents think that

‘Natural scenery’ is attractive because of its ‘unique

style and characteristics’, which gives the respondents

a feeling of ‘being close to the nature’.

They want the feeling of ‘being close to the nature’

because it can bring a benefit of ‘enjoyment and

comfort’, and help the respondents’ to satisfy their

terminal value of ‘hedonic/pleasure’. In all, there are

1094 direct and 2045 indirect relations in this branch

of MEC 1.

At the same time, some respondents feel that

‘natural scenery’ is attractive is provides a ‘good

environment’, which can give them a feeling of

‘relaxation’. Relaxation is important because it can

make the respondents ‘go-as-you-please’, or feel

‘mental-refreshed’, both of which can help to achieve

the terminal value of ‘hedonic/pleasure’. Specifically,

there are 1094 direct and 1863 indirect relations in the

second branch of MEC 1, and there are 1054 direct

and 1854 indirect relations in the third branch of MEC

1.

2) MEC2: “natural scenery —happiness”

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FIG. 3 HVM OF MEC2: “NATURAL SCENERY — HAPPINESS”

In the second MEC (shown in Figure 3), ‘natural

scenery’ is linked to ‘good environment’ (abstract

attribute), which leads to the functional consequence

of ‘being close to the nature’, and then ‘enjoyment

/comfort’ as the psychosocial consequence. Distinct

from MEC1, ‘enjoyment /comfort’ can further satisfy

the terminal value of “happiness”. In all, there are

1234 direct and 2013 indirect relations in this MEC

3) MEC3: “local customs —the exciting life”

FIG. 4 HVM OF MEC3: “LOCAL CUSTOMS —THE EXCITING LIFE”

As for the MEC3 (see Figure 4), some respondents

want to see ‘local customs’ because they seek ‘unique

style and characteristics’. For these respondents,

‘experiencing difference’ is important to satisfied

their ‘curiosity/ interest/ novelty’, so as to realize their

motivation at the instrumental values level of

‘exploration of unknown world’. ‘Exploration of

unknown world’ is important because it can help them

realize the terminal value of ‘exciting life’. In all, there

are 1221 direct and 2529 indirect relations in this

MEC.

4) MEC 4: “local food — self-realization”

FIG. 5 HVM OF MEC4: “LOCAL FOOD — SELF-REALIZATION”

The MEC4, as shown above in Figure 5, concerns

respondents’ personal interests in ‘local food’. In this

MEC, there are two paths. Some respondents seek the

‘unique style and characteristics’ of the ‘local food’,

because they want to ‘experience differences’ during

their outbound travel; For other respondents, ‘fame’ is

an important abstract attribute because they wish to

‘getting a deeper and comprehensive knowledge’

during their outbound travel. All the respondents in

this MEC want to seek ‘enrichment of one's life’ as

their motivation at the consequences level, so that they

can be an ‘experienced person’ and can satisfy their

terminal value motivation of ‘self-realization’. In

summary, there are 1186 direct and 3153 indirect

relations in this MEC.

5) MEC 5: “natural scenery —the beauty of the

world"

FIG. 6 HVM OF MEC5: “NATURAL SCENERY —THE BEAUTY OF

THE WORLD”

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In the MEC 5 (Figure 6), ‘natural scenery’ is

attractive to the respondents because of its ‘unique

style and characteristics’. This is strongly linked to the

consequence of ‘seeking and comparing the

differences with China’. Respondents seek this

consequence because they want to experience

‘nature/culture shock’, and they believe that this can

help them to appreciate ‘the beauty of the world’ as

the terminal value. In this MEC, there are totally 1009

direct and 2159 indirect relations.

6) MEC 6: “shopping —hedonic/pleasure”

FIG. 7 HVM OF MEC6: “SHOPPING —HEDONIC/PLEASURE”

In the MEC 6 (Figure 7), ‘Shopping’ while

outbound travel is another important motivation in this

research. ‘Famous-brand’ is attractive to the

respondents because this can help them on ‘rewarding

themself’, so that bring the benefit of ‘enjoyment and

comfort’ at the consequences level, and ultimately

achieve their ‘hedonic/pleasure’. In this MEC, there

are in total 866 direct and 1309 indirect relations.

VI. DISCUSSION

(1) By applying the laddering technique, this

current research was designed to look past motivations

at the attribute or consequence level, to investigate

motivation at the values level. According to the

analysis results, ‘unique style and characteristics’

(N=560), good environment’ (N=532) and ‘natural

scenery’ (N=462) are the three of most important

travel motivations at attributes level for Chinese

outbound travellers. At the same time, some of the

travel motivations at consequence level, such as

‘experiencing differences’ (N=541), ‘reinforcement of

interpersonal relationships’ (N=524), and ‘enrichment

of one's life’ (N=507) are also important (500

respondents mentioned these). At the values level,

‘hedonic/pleasure’ (N=534), ‘the beauty of the world’

(N=529), and ‘the exciting life’ (N=523) were the top

three motivations chosen.

The motivations identified in this research are quite

similar to those revealed in previous research on

leisure travel motivation for both Western and

Chinese tourists (e.g., [49][50][11]). Reference [51]

suggest that there are only a handful of tourism

motivations. The same individual may be motivated

by any of these factors depending on the particular

context of a tourist activity or particular destination

visited. Destination attributes are important to

travellers only when the attributes lead to perceived

consequences and end with the value they strive to

achieve. Indeed, some previous researchers focus on

‘push’ factors and overlook the fact that the

destination attributes have effect by arousing tourists’

interest and travel needs [52]. This implies that

destination attributes can be linked to multiple themes

and values [53].

(2) As shown in both the on-line survey data and

the HVMs, different attributes may lead to the same

consequence or value, and the same attribute may lead

to different consequences or values. That is because

during survey stage, every respondent was allowed to

choose one to three motivations in terms under the

laddering process. When these motivation ladders

were summarized, it is possible that different

attributes from different respondents could lead to the

same consequence. Figure 8 shows an example of

such motivation ladders.

FIG. 8 ONE ATTRIBUTE MAY LEAD TO DIFFERENT CONSEQUENCES

Likewise, different motivations at lower level could

generate the same motivation at a higher level. For

example, ‘experiencing differences’ can be connected

to ‘natural scenery’, ‘history/culture/art’, ‘local

customs’, or ‘local building’ (Figure 9).

FIG. 9 DIFFERENT ATTRIBUTES MAY LEAD TO ONE CONSEQUENCE

In the previous research, travel motivations

identified are seldom discussed in terms of their inter-

relationships. Specifically, reference [54] noted that

individuals might travel outbound for similar reasons,

while when choosing a specific destination, they have

different reasons and levels of importance of these

reasons. This phenomenon could not be fully

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explained by previous theory such as push and pull

theory. Another example is that, in prior studies,

cluster analysis is a popular and useful tool to classify

tourists’ motivations, while the clustering was based

on the similarity of motivation items themselves, not

the deeper reasons behind them. As a result, if a group

of respondents mentioned similar motivation such as

novelty, it did not means that they were pursuing the

same goal or end. Indeed, the MEC approach used

here is an ongoing attempt to find causal explanations

of behaviour by exploring and constructing

motivations at a higher level of a motivational

hierarchy.

VII. CONCLUSION

To conclude, using the means-end approach, this

research explored travel motivation to explain the

salient motivations for Chinese outbound travellers,

and also to explore why those motivations are

important by presenting the relationship between

levels of travel motivations in an MEC. In this way, in

addition to describing travel motivation in hierarchical

levels, this research helps to understand the tourist

motivation construct. The discussion in this article

hence complements, but differs from, recent

examinations of travel motivation of leisure tourists.

In study, key motivations of Chinese outbound leisure

tourists are found and shed the light on reasons for

choosing a specific destination, as well as provide

more useful information on travel behaviour [43]. The

paper contributes to the body of knowledge of

motivation studies by better understanding

respondents’ motives and the true underlying need or

motives being sought [40].

At the methodological level, this study confirms the

usefulness of MEC theory and its laddering technique

as a method to examine travel motivation. It is

recognized that the wide range of human needs leads

to the complex nature of travel motivation, and

scholars indicated the methodological difficulties to

detect the multidimensional determinants that shape

tourist flow [55][56]. In the tourism research field,

previous research methods, such as standard list of

motivation items or focus groups, have been applied

in a number of settings [37]. It is argued that with both

qualitative and quantitative laddering techniques this

study allows the respondents to express the motivation

in their own words, and encourages respondents to

think about the underlying motivations. This research

contributes to the motivation research field by

providing insights on methodology in terms of the

connection between destination attributes,

consequence motivation, and personal value as long-

term motivation.

Moreover, this research explored respondent’s pre-

travel motivation in order to obtain thoughts related to

their leisure travel and, in this way, to enhance the

research reliability [57]. It is suggested that

motivation is dynamic flow of action [58], and could

change during the period of its formation [55]. Due to

the differences of travel motivation at the different

stages, the destination marketers need to focus on the

initial travel motivations tourists hold before a leisure

travel. It is argued that distinguishing different stages

of motivation is salient for practical or academic

research, while some studies have undertaken

recollection measurement to recall or report travel

motivation during or after the travel (e.g.,

[59],[60],[6]). It should be emphasized here that

paying no attention to the motivation stage could

violate the research purpose and impact the quality of

data because the research on travel motivation during

or after travel is no longer the initial travel motivation

for this travel. This study explicitly conducted into

motivation research at a specific (early) motivation

stage with a criteria that respondents must have the

travel intention and just examined their travel

motivation before the next travel, to elicit information

for outbound travel.

Practically, with the insights gained from these

findings, this research provides useful information to

target the Chinese outbound leisure market. Indeed,

this research not only provides insight into the

preferred attributes motivating respondents to

outbound leisure travel, but also indicates what kinds

of benefit the respondents would like to get from these

specific attributes, and what values could be satisfied

by these benefits or consequences, to provide a

complete motivational picture. It has been suggested

that it is effective to segment tourism market based on

tourism product attributes (e.g., [61][62]), benefits the

consumer expects from the tourism product (e.g.,

[63][64]), or personal value [65][66] respectively.

There is no research on segmentation based on the

previous three variables as a whole. It is believed that

in this way, the result is more comprehensive and

distinct in terms of practical usage in the marketing

field. Therefore it may let us better understand this

group, in order to fulfil their motivations and satisfy

their needs behind these superficial motivations.

VIII. LIMITATIONS AND FURTHER RESEARCH

This research has a number of limitations. As an

explorative attempt to understand the motivations of

Chinese outbound leisure tourists, the first is due to

the online-survey sampling method used in this

research. Sampling within three major cities—Beijing,

Shanhai, and Gangzhou – was utilized to identify the

sample using online screening questions before the

survey. Other people who were not located at these

three cities are excluded due to time and budget

limitation. Following on from this research limitation,

much work remains, be it extension or replication of

research on travel motivation involving different

sampling methods. At the same time, it is suggested

that comparison of study results between hard

laddering and soft laddering may be useful [67][68].

Therefore, further research could conduct with the

tourists across different culture background, to

9

explore the effect of differences or similarity in travel

motivation. Furthermore, it is recommended to extend

this research by studying specific tourism destinations

(e.g. specific outbound destinations of different

countries). Through these ways, different individuals

and groups could better understand this market with

highlighting different levels of importance by

applying this framework.

(Please note that the appendices may not be published

as the supplementary material)

APPENDIX 1: SUMMARY IMPLICATION MATRIX (SIM)

FOR FORMAL DATA

APPENDIX 2. SUMMARY OF DIRECT AND INDIRECT

RELATIONS OF SIX MECS

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APPENDIX I : SUMMARY IMPLICATION MATRIX (SIM) FOR FORMAL DATA � � � � � � � � � � � � � � � � � � � � �� A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 C01 C02 C03 C04 C05 C06 C07 C08 C09 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 �

A01 191 64 107 120 24 28 23 28 20 48 0.214 0.231 0.12 0.207 0.217 0.102 0.183 0.161 0.209 0.086 0.118 0.222 0.199 0.192 0.219 0.092 0.116 0.189 0.207 0.168�A02 215 82 88 114 16 49 28 30 34 78 0.243 0.274 0.137 0.237 0.261 0.124 0.221 0.179 0.246 0.098 0.133 0.261 0.233 0.225 0.261 0.104 0.128 0.217 0.242 0.175�A03 115 56 61 42 7 71 36 6 14 28 0.151 0.169 0.087 0.154 0.161 0.087 0.136 0.118 0.155 0.068 0.092 0.158 0.145 0.146 0.169 0.076 0.092 0.141 0.148 0.117�A04 274 61 133 177 28 44 14 73 31 35 0.307 0.33 0.149 0.27 0.296 0.152 0.254 0.224 0.288 0.119 0.174 0.318 0.273 0.273 0.318 0.131 0.158 0.269 0.293 0.208�A05 338 76 108 372 14 61 13 66 52 25 0.417 0.425 0.193 0.342 0.385 0.194 0.321 0.291 0.385 0.148 0.221 0.402 0.352 0.35 0.418 0.163 0.203 0.353 0.377 0.287�A06 168 27 66 188 16 13 8 65 72 20 0.231 0.228 0.126 0.199 0.216 0.125 0.185 0.178 0.223 0.096 0.14 0.225 0.211 0.202 0.285 0.1 0.11 0.208 0.212 0.168�A07 52 26 32 44 20 9 26 100 28 48 0.127 0.145 0.071 0.128 0.143 0.084 0.128 0.101 0.14 0.066 0.07 0.14 0.133 0.133 0.147 0.065 0.073 0.127 0.128 0.1�A08 51 65 12 53 144 115 212 77 6 38 0.245 0.287 0.137 0.241 0.272 0.127 0.257 0.182 0.26 0.107 0.136 0.262 0.241 0.233 0.282 0.12 0.134 0.23 0.242 0.207�A09 133 49 57 46 2 44 10 36 9 32 0.148 0.168 0.083 0.15 0.165 0.078 0.136 0.117 0.15 0.064 0.087 0.161 0.142 0.142 0.164 0.071 0.082 0.137 0.15 0.107�A10 228 93 31 68 78 159 35 70 47 16 0.3 0.334 0.16 0.279 0.306 0.167 0.274 0.223 0.309 0.117 0.17 0.318 0.279 0.27 0.327 0.137 0.149 0.282 0.28 0.229�A11 21 7 6 33 24 0 0 31 3 24 0.054 0.056 0.037 0.048 0.052 0.036 0.046 0.045 0.051 0.024 0.036 0.054 0.047 0.044 0.056 0.03 0.027 0.051 0.054 0.045�A12 � � � � � � � � � � 4 15 9 19 10 36 4 10 26 0.027 0.044 0.053 0.043 0.045 0.049 0.034 0.029 0.044 0.047 0.04�A13 � � � � � � � � � � 255 333 37 142 238 53 55 106 170 0.18 0.261 0.481 0.425 0.419 0.495 0.196 0.238 0.421 0.436 0.34�A14 � � � � � � � � � � 66 172 33 153 142 13 111 43 84 0.141 0.178 0.313 0.285 0.27 0.314 0.15 0.171 0.276 0.283 0.238�A15 � � � � � � � � � � 112 201 28 141 124 8 41 110 83 0.134 0.174 0.318 0.284 0.283 0.321 0.153 0.176 0.282 0.295 0.238�A16 � � � � � � � � � � 382 145 48 61 96 38 64 172 256 0.188 0.256 0.461 0.415 0.408 0.481 0.199 0.239 0.414 0.423 0.334�A17 � � � � � � � � � � 36 79 17 52 83 26 110 48 94 0.093 0.13 0.225 0.205 0.198 0.234 0.11 0.122 0.192 0.197 0.174�A18 � � � � � � � � � � 62 184 27 121 100 36 161 50 92 0.138 0.195 0.331 0.3 0.3 0.341 0.152 0.176 0.295 0.306 0.236�A19 � � � � � � � � � � 18 105 14 93 122 14 195 23 40 0.118 0.135 0.246 0.231 0.229 0.263 0.123 0.144 0.225 0.229 0.194�A20 � � � � � � � � � � 37 140 50 117 106 62 71 61 129 0.139 0.188 0.306 0.279 0.28 0.311 0.152 0.171 0.281 0.277 0.224�A21 � � � � � � � � � � 56 29 135 22 33 78 13 21 54 0.088 0.121 0.169 0.159 0.155 0.176 0.109 0.099 0.157 0.167 0.129�A22 � � � � � � � � � � 13 114 43 122 144 9 55 41 45 0.103 0.138 0.232 0.217 0.208 0.239 0.119 0.129 0.206 0.218 0.191�C01 � � � � � � � � � � � � � � � � � � � 8 64 136 147 111 271 12 38 178 211 31�C02 � � � � � � � � � � � � � � � � � � � 21 27 208 199 91 152 30 102 90 177 155�C03 � � � � � � � � � � � � � � � � � � � 27 48 99 84 31 39 56 22 47 63 52�C04 � � � � � � � � � � � � � � � � � � � 54 59 246 147 54 75 53 38 30 168 111�C05 � � � � � � � � � � � � � � � � � � � 30 26 220 199 38 83 29 68 45 220 145�C06 � � � � � � � � � � � � � � � � � � � 43 133 44 25 52 111 71 18 57 20 22�C07 � � � � � � � � � � � � � � � � � � � 78 19 159 73 167 199 30 59 67 43 54�

C08 � � � � � � � � � � � � � � � � � � � 21 24 93 67 154 177 28 50 144 51 52�C09 � � � � � � � � � � � � � � � � � � � 12 25 82 36 251 302 28 39 254 29 45�C10 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C11 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C12 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C13 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C14 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C15 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C16 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C17 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C18 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C19 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �C20 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �V01 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �V02 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �V03 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �V04 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �V05 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �V06 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �V07 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �V08 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �

APPENDIX I : SUMMARY IMPLICATION MATRIX (SIM) FOR FORMAL DATA (Cont.) � � � � � � � �� V01 V02 V03 V04 V05 V06 V07 V08 V09 V10 V11 V12 V13 V14 V15 V16 V17 V18 �

A01 0.116 0.119 0.122 0.187 0.093 0.169 0.153 0.039 0.153 0.121 0.121 0.174 0.14 0.089 0.093 0.161 0.165 0.136�A02 0.133 0.126 0.138 0.223 0.1 0.205 0.168 0.045 0.175 0.134 0.14 0.21 0.156 0.101 0.093 0.184 0.19 0.158�A03 0.091 0.085 0.093 0.139 0.078 0.126 0.107 0.031 0.117 0.092 0.091 0.133 0.101 0.07 0.067 0.111 0.127 0.103�A04 0.158 0.143 0.16 0.265 0.125 0.248 0.206 0.049 0.213 0.163 0.163 0.256 0.187 0.121 0.123 0.219 0.236 0.179�A05 0.196 0.183 0.202 0.35 0.15 0.314 0.259 0.068 0.279 0.213 0.217 0.316 0.228 0.158 0.155 0.296 0.298 0.23�A06 0.113 0.119 0.126 0.202 0.097 0.176 0.156 0.045 0.164 0.137 0.131 0.186 0.134 0.107 0.106 0.169 0.174 0.137�A07 0.072 0.063 0.078 0.116 0.059 0.115 0.085 0.028 0.1 0.069 0.078 0.111 0.089 0.059 0.054 0.099 0.104 0.089�A08 0.136 0.132 0.14 0.226 0.11 0.219 0.172 0.049 0.191 0.134 0.145 0.222 0.175 0.122 0.101 0.195 0.198 0.157�A09 0.084 0.083 0.076 0.132 0.062 0.125 0.101 0.022 0.104 0.077 0.085 0.127 0.086 0.056 0.06 0.112 0.123 0.097�A10 0.154 0.149 0.166 0.264 0.125 0.25 0.21 0.048 0.208 0.167 0.166 0.25 0.186 0.128 0.124 0.223 0.238 0.177�A11 0.031 0.031 0.037 0.048 0.026 0.043 0.04 0.012 0.038 0.039 0.035 0.045 0.038 0.03 0.025 0.038 0.042 0.031�A12 0.036 0.03 0.035 0.044 0.032 0.048 0.042 0.013 0.043 0.033 0.029 0.05 0.032 0.03 0.021 0.038 0.042 0.035�A13 0.24 0.228 0.248 0.41 0.185 0.381 0.312 0.082 0.33 0.247 0.259 0.384 0.28 0.19 0.19 0.346 0.355 0.282�A14 0.177 0.167 0.176 0.271 0.13 0.25 0.222 0.066 0.231 0.169 0.171 0.259 0.205 0.137 0.128 0.237 0.248 0.193�A15 0.178 0.168 0.184 0.273 0.14 0.243 0.217 0.065 0.233 0.174 0.183 0.263 0.203 0.142 0.13 0.233 0.237 0.196�A16 0.236 0.228 0.246 0.399 0.185 0.362 0.298 0.086 0.324 0.249 0.249 0.37 0.274 0.196 0.181 0.341 0.346 0.269�A17 0.122 0.124 0.138 0.192 0.099 0.183 0.153 0.052 0.158 0.127 0.13 0.183 0.144 0.108 0.107 0.162 0.171 0.133�A18 0.172 0.171 0.173 0.283 0.141 0.27 0.216 0.06 0.235 0.186 0.184 0.271 0.2 0.146 0.143 0.237 0.252 0.203�A19 0.135 0.137 0.148 0.22 0.119 0.202 0.165 0.051 0.185 0.138 0.146 0.212 0.169 0.115 0.099 0.191 0.188 0.153�A20 0.164 0.164 0.181 0.261 0.145 0.244 0.214 0.06 0.22 0.178 0.18 0.254 0.195 0.149 0.14 0.221 0.246 0.192�A21 0.106 0.1 0.101 0.147 0.096 0.139 0.132 0.049 0.128 0.109 0.097 0.145 0.118 0.093 0.092 0.126 0.131 0.109�A22 0.131 0.13 0.138 0.194 0.11 0.181 0.164 0.056 0.171 0.145 0.149 0.19 0.148 0.112 0.105 0.166 0.179 0.143�C01 0.22 0.218 0.231 0.376 0.182 0.342 0.289 0.084 0.305 0.235 0.239 0.352 0.265 0.189 0.175 0.32 0.322 0.254�C02 0.244 0.234 0.256 0.399 0.192 0.376 0.307 0.088 0.332 0.248 0.257 0.383 0.286 0.199 0.188 0.339 0.356 0.28�C03 0.143 0.142 0.141 0.186 0.126 0.182 0.171 0.067 0.173 0.149 0.136 0.184 0.16 0.12 0.121 0.165 0.178 0.142�C04 0.231 0.22 0.228 0.341 0.181 0.319 0.273 0.085 0.299 0.227 0.227 0.334 0.255 0.188 0.17 0.292 0.309 0.253�C05 0.237 0.223 0.237 0.377 0.183 0.352 0.288 0.087 0.313 0.234 0.238 0.361 0.269 0.189 0.174 0.319 0.327 0.269�C06 0.127 0.135 0.14 0.184 0.131 0.188 0.172 0.059 0.17 0.147 0.135 0.197 0.153 0.133 0.116 0.163 0.185 0.145�C07 0.207 0.198 0.212 0.319 0.167 0.298 0.256 0.69 0.268 0.209 0.216 0.308 0.238 0.166 0.157 0.276 0.285 0.237�C08 0.184 0.194 0.208 0.286 0.155 0.258 0.24 0.08 0.252 0.209 0.203 0.281 0.223 0.158 0.163 0.252 0.261 0.195�C09 0.221 0.221 0.237 0.37 0.179 0.331 0.289 0.086 0.307 0.237 0.245 0.345 0.26 0.187 0.179 0.31 0.327 0.261�C10 32 22 19 29 7 48 26 7 20 46 40 28 15 39 26 55 53 18�C11 12 22 29 26 55 29 29 23 15 81 95 24 13 73 56 59 55 17�C12 67 56 47 110 27 165 100 14 36 81 104 74 28 37 63 146 136 39�C13 64 34 24 198 12 94 42 9 32 56 114 36 13 13 44 178 134 27�C14 36 66 57 21 26 58 74 6 32 112 172 77 19 24 128 73 110 24�C15 16 29 39 32 16 22 31 8 33 209 260 39 10 39 185 133 144 21�C16 14 37 21 13 95 40 38 21 24 15 25 51 53 20 16 19 14 18�C17 18 16 26 92 10 39 18 3 37 51 73 21 9 15 17 63 92 26�C18 7 31 57 55 18 22 30 10 36 116 212 60 10 36 98 84 133 16�C19 84 31 25 180 5 118 72 5 17 39 74 31 17 17 37 213 107 36�C20 36 58 78 52 27 113 85 6 58 27 42 87 56 23 20 23 63 37�V01 � � � � � � � � 92 27 28 132 64 12 41 54 63 116�V02 � � � � � � � � 88 61 50 127 73 28 64 37 64 44�V03 � � � � � � � � 111 80 48 126 81 30 44 23 57 67�V04 � � � � � � � � 86 50 122 80 16 16 30 287 209 74�V05 � � � � � � � � 30 63 30 49 84 108 40 15 48 23�V06 � � � � � � � � 149 66 53 178 125 24 41 51 130 125�V07 � � � � � � � � 105 53 45 174 99 42 62 38 74 100�V08 � � � � � � � � 17 21 29 21 26 50 25 8 21 16�� � � � � � � � � � � � � � � � � � � �

APPENDIX 2. SUMMARY OF DIRECT AND INDIRECT RELATIONS OF SIX MECS

TABLE 1. THE CONTENT CODE LIST

Code

Number Content Code

Code

Number Content Code

A04 local customs A13 unique style and characteristics

A05 natural scenery A14 fame

A08 shopping A16 good environment

A10 local food A19 famous-brand

C01 being close to the nature C12 enrichment of one's life

C02 experiencing differences C13 curiosity/interest/novelty

C04 getting a deeper and

comprehensive knowledge C14 mental-refreshing

C05 seeking and comparing the

differences with China C15 enjoyment /comfort

C07 rewarding oneself C18 go-as-you-please

C09 relaxation C19 nature/culture shocking

V04 exploration of unknown world V10 happiness

V06 experienced person V11 hedonic/pleasure

V12 self-realization

V16 the beauty of the world

V17 the exciting life

TABLE 2. MEC OF “NATURAL SCENERY —HEDONIC/PLEASURE”.

A05a A13 C01 C15 V11 sub-total

A05 0.000 338.000 0.417 0.418 0.217 338.1052

A13 0.000 0.000 225.000 0.495 0.259 225.0754

C01 0.000 0.000 0.000 271.000 0.239 271.0239

C15 0.000 0.000 0.000 0.000 260.000 260.0000

V11 0.000 0.000 0.000 0.000 0.000 0.0000

total 1094.2045

Note: this table shows the MEC of “natural scenery —unique style and characteristics — being close to the nature — enjoyment /comfort — hedonic/pleasure”.

A05 A16 C09 C18 V11 sub-total

A05 0.000 372.000 0.385 0.353 0.217 372.0955

A16 0.000 0.000 256.000 0.414 0.249 256.0663

C09 0.000 0.000 0.000 254.000 0.245 254.0245

C18 0.000 0.000 0.000 0.000 212.000 212.0000

V11 0.000 0.000 0.000 0.000 0.000 0.0000

total 1094.1863

Note: this table shows the MEC of “natural scenery —good environment — relaxation — go-as-you-please —hedonic/pleasure”.

A05 A16 C09 C14 V11 sub-total

A05 0.000 372.000 0.385 0.350 0.217 372.0952

A16 0.000 0.000 256.000 0.408 0.249 256.0657

C09 0.000 0.000 0.000 251.000 0.245 254.0245

C14 0.000 0.000 0.000 0.000 172.000 172.0000

V11 0.000 0.000 0.000 0.000 0.000 0.0000

total 1054.1854

Note: this table shows the MEC of “natural scenery —good environment — relaxation — mental-refreshing —hedonic/pleasure”.

TABLE 3. MEC OF “NATURAL SCENERY —HAPPINESS”.

A05 A16 C01 C15 V10 sub-total

A05 0.000 372.00 0.417 0.418 0.213 372.1048

A16 0.000 0.000 382.000 0.481 0.249 382.0730

C01 0.000 0.000 0.000 271.000 0.235 271.0235

C15 0.000 0.000 0.000 0.000 209.000 209.0000

V10 0.000 0.000 0.000 0.000 0.000 0.0000

total 1234.2013

Note: this table shows the MEC of “natural scenery —good environment —being close to the nature — enjoyment /comfort —happiness”.

TABLE 4. MEC OF “LOCAL CUSTOMS —THE EXCITING LIFE”.

A04 A13 C02 C13 V04 V17 sub-total

A04 0.000 274.00 0.330 0.273 0.265 0.236 274.1104

A13 0.000 0.000 333.000 0.425 0.410 0.355 333.1190

C02 0.000 0.000 0.000 199.000 0.399 0.356 271.0235

C13 0.000 0.000 0.000 0.000 198.000 134.000 134.0000

V04 0.000 0.000 0.000 0.000 0.000 209.000 209.0000

V17 0.000 0.000 0.000 0.000 0.000 0.000 0.0000

total 1221.2529

Note: this table shows the MEC of “local customs—unique style and characteristics — experiencing differences— curiosity/ interest/ novelty —exploration of unknown world —the exciting life”.

TABLE 5. MEC OF “LOCAL FOOD — SELF-REALIZATION”.

A10 A13 C02 C12 V06 V12 sub-total

A10 0.000 228.00 0.330 0.318 0.250 0.250 228.1148

A13 0.000 0.000 333.000 0.481 0.381 0.384 333.1246

C02 0.000 0.000 0.000 208.000 0.376 0.383 208.0759

C12 0.000 0.000 0.000 0.000 165.000 74.000 239.0000

V06 0.000 0.000 0.000 0.000 0.000 178.000 178.0000

V12 0.000 0.000 0.000 0.000 0.000 0.000 0.0000

total 1186.3153

Note: this table shows the MEC of “local food —unique style and characteristics — experiencing differences —enrichment of one's life — experienced person — self-realization”.

A10 A14 C04 C12 V06 V12 sub-total

A10 0.000 93.00 0.279 0.273 0.265 0.236 274.1104

A14 0.000 0.000 153.000 0.425 0.410 0.355 333.1190

C04 0.000 0.000 0.000 246.000 0.399 0.356 271.0235

C12 0.000 0.000 0.000 0.000 165.000 134.000 134.0000

V06 0.000 0.000 0.000 0.000 0.000 178.000 178.0000

V12 0.000 0.000 0.000 0.000 0.000 0.000 0.0000

total 1190.2529

Note: this table shows the MEC of “local food —fame — getting a deeper and comprehensive knowledge —enrichment of one's life — experienced person — self-realization”.

TABLE 6. MEC OF “NATURAL SCENERY —THE BEAUTY OF THE WORLD".

A05 A13 C05 C19 V16 sub-total

A05 0.000 338.000 0.385 0.377 0.296 338.1058

A13 0.000 0.000 238.000 0.436 0.346 238.0782

C05 0.000 0.000 0.000 220.000 0.319 220.0319

C19 0.000 0.000 0.000 0.000 213.000 213.0000

V16 0.000 0.000 0.000 0.000 0.000 0.0000

total 1009.2159

Note: this table shows the MEC of “natural scenery—unique style and characteristics —seeking and comparing the differences with China — nature/culture shocking —the beauty of the world”.

TABLE 7. MEC OF “SHOPPING —HEDONIC/PLEASURE”.

A08 A19 C07 C15 V11 sub-total

A08 0.000 212.000 0.257 0.282 0.145 212.0684

A19 0.000 0.000 195.000 0.263 0.146 195.0409

C07 0.000 0.000 0.000 199.000 0.216 199.0216

C15 0.000 0.000 0.000 0.000 260.000 260.0000

V11 0.000 0.000 0.000 0.000 0.000 0.0000

total 866.1309

Note: this table shows the MEC of “shopping—famous-brand— rewarding oneself—enjoyment /comfort—hedonic/pleasure”.