Tourism!DestinationImage!Positionsofthe!Sub5 · PDF...

Tourism Destination Image Positions of the Sub-‐

Provincial Districts in China: a Similarity and

Uniqueness Comparison

Master Thesis submitted in fulfillment of the Degree

Master of Science

in International Tourism Management

Submitted to Prof. Dr. Andreas Zins

Shasha LIN

1213001

Vienna, 26-‐11-‐2013

I

AFFIDAVIT

I hereby affirm that this Master’s Thesis represents my own written work and that I have used

no sources and aids other than those indicated. All passages quoted from publications or para-‐

phrased from these sources are properly cited and attributed.

The thesis was not submitted in the same or in a substantially similar version, not even partial-‐

ly, to another examination board and was not published elsewhere.

Date Signature

II

ABSTRACT

Homogenization of tourism images is challenging destination managers in China. In order to

make effective positioning strategies, create differentiated destination images and benefit

from co-‐opetition relationships, it is important to benchmark tourism image positions between

destinations. Few image and positioning studies to date have focused specifically on bench-‐

marking the intended tourism image positions and their extents of projection on marketing

material between large numbers of tourism destinations; and none has analysed these topics

for China. This study addresses these knowledge deficits.

In this research, content analysis is used to analyse the intended image positions of 1/3 sub-‐

provincial districts in China and their projections on the contents of official tourism marketing

websites.

Overall speaking, the sub-‐provincial districts in China have fairly distinctive image positions.

When compared with neighbouring districts, they are more likely to have unique image posi-‐

tions, but also more likely to have higher degrees of similarities once their image positions are

co-‐occurred with others. The commonly used image positioning categories by destinations in

China are cognitive in nature. Affective image positions are less adopted. The MDS graphs as-‐

sist benchmarking by visualizing the similarity distances of image positions between destina-‐

tions. In general, the projections of image positions on the official tourism marketing websites

are not congruent.

III

ACKNOWLEDGEMENTS

Foremost, I would like to express my sincere gratitude to my supervisor Prof. Dr. Andreas Zins

for the continuous guidance and support of my master thesis research and writing, for his pa-‐

tience, motivation, enthusiasm, and immense knowledge. I could not have imagined having a

better supervisor for my master thesis.

Besides my supervisor, I would like to thank Prof. Dr. Josef Mazanec and Dr. Ivo Ponocny for

their professional advice on my data analysis, and my friends in China: Bingjie Zhang, Bingying

Yang, and Ran Chen, for helping me conduct reliability tests.

My sincere thanks also go to Dr. Sabine Sedlacek for being my academic mentor and always

offering me advice and support during my studying in MODUL University Vienna.

Last but not the least, I would like to thank my parents Qingfu Lin and Minxia Liang, for giving

birth to me at the first place and supporting me spiritually throughout my life.

IV

TABLE OF CONTENTS

Affidavit ..................................................................................................................... I

Abstract .................................................................................................................... II

Acknowledgements ................................................................................................. III

List of Tables ........................................................................................................... VII

List of Figures ......................................................................................................... VIII

List of Abbreviations ................................................................................................ IX

1 Introduction ......................................................................................................... 1 1.1 The initiation of this research ................................................................................... 1 1.2 Research purposes ................................................................................................... 2 1.3 Research structure ................................................................................................... 3 1.4 Clarification of some frequently appeared confusing concepts ................................. 4

1.4.1 Region, province and district ................................................................................... 4 1.4.2 DMO and tourism administration ............................................................................ 4

2 Literature review .................................................................................................. 5 2.1 Definition of destination positioning and image ....................................................... 5 2.2 Postioning approaches ............................................................................................. 6 2.3 Categorization of destination positions and images .................................................. 8 2.4 Influences of geography on destination image positioning ..................................... 10 2.5 DMO ...................................................................................................................... 12 2.6 Methodologies of destination positioning and image studies ................................. 13

2.6.1 General methods in both English literature and Chinese literature ...................... 13 2.6.2 Benchmarking ........................................................................................................ 15 2.6.3 Content analysis ..................................................................................................... 16

2.7 Conclusion ............................................................................................................. 19

3 Background review: image positioning of tourism destinations in China ............ 21 3.1 Image positioning problems of Chinese tourism destinations ................................. 21 3.2 DMOs in China ....................................................................................................... 22 3.3 Formal process of making tourism developmet plans ............................................. 22 3.4 Tourism resource geographical regions (TRG Regions) ............................................ 23 3.5 Tourism development and overall economic development levels ........................... 25 3.6 Conclusion ............................................................................................................. 26

4 Research questions and hypotheses ................................................................... 28

V

5 Methodology ..................................................................................................... 29 5.1 Population and sampling ........................................................................................ 29 5.2 Data structure and sources ..................................................................................... 30

5.2.1 Tourism development plans ................................................................................... 30 5.2.2 Official tourism marketing websites ....................................................................... 31

5.3 Data collection and coding ...................................................................................... 31 5.3.1 From tourism plans ................................................................................................. 31 5.3.2 From official tourism marketing websites .............................................................. 32 5.3.3 Data aggregation .................................................................................................... 35

5.4 Intermediate data prepration ................................................................................. 36 5.4.1 Grouping image positions ....................................................................................... 36 5.4.2 Co-‐occurrences of image positions of SP Districts within the same TRG Region ... 37 5.4.3 Co-‐occurrences of image positions of SP Districts in different TRG Regions .......... 38 5.4.4 Calculate proximity values for all pairs of sample districts .................................... 38

5.5 Data analysis techniques ........................................................................................ 39 5.5.1 Hypotheses testing ................................................................................................. 39 5.5.2 Further exploratory analyses .................................................................................. 40 5.5.3 Visualising data analysis results .............................................................................. 40

5.6 Prior study .............................................................................................................. 40

6 Results ............................................................................................................... 42 6.1 Data collection results ............................................................................................ 42 6.2 Data preparation results ......................................................................................... 43

6.2.1 Grouping results of the intended image positions ................................................. 43 6.2.2 Coding results of website contents ........................................................................ 46

6.3 Hypotheses testing ................................................................................................. 46 6.3.1 H1 ........................................................................................................................... 46 6.3.2 H2 ........................................................................................................................... 51 6.3.3 Ms vs. Md ................................................................................................................. 57 6.3.4 H3 ........................................................................................................................... 60 6.3.5 Projection differences between districts in Eastern costal China, Middle China and

Western China .................................................................................................................... 61 6.3.6 H4 ........................................................................................................................... 63

6.4 Similarity distances of image positions between sample districts ........................... 66

7 Discussion .......................................................................................................... 70 7.1 Benchmarking of image positions ........................................................................... 70

7.1.1 General overview ................................................................................................... 70 7.1.2 Uniqueness ............................................................................................................. 70 7.1.3 Similarity ................................................................................................................. 71 7.1.4 Impacts of grouping ................................................................................................ 71

VI

7.1.5 MDS graphs ............................................................................................................ 72 7.2 Projection congruence ............................................................................................ 72 7.3 Validity and reliability Issues .................................................................................. 73

7.3.1 Limitations of data sources .................................................................................... 74 7.3.2 Limitations of primary data coding ........................................................................ 74 7.3.3 Limitations of intermediate data preparation ....................................................... 74 7.3.4 Limitations of data analysis ................................................................................... 75 7.3.5 Suggestions for improvement ................................................................................ 75

8 Conclusion ......................................................................................................... 76 8.1 Summary ................................................................................................................ 76 8.2 Contribution to knowledge .................................................................................... 77 8.3 Managerial Implications ......................................................................................... 77

8.3.1 Implications for developing image positions ......................................................... 77 8.3.2 Implications for improving the extent of projection .............................................. 80

8.4 Future research ...................................................................................................... 80

9 Bibliography ....................................................................................................... 81

Appendices .............................................................................................................. 92 Appendix 1: Distributions of sample districts in each province ......................................... 92 Appendix 2: Distributions of sample districts in each TRG Region .................................... 94 Appendix 3: Intended image positions of sample districts ................................................ 95 Appendix 4: Image positions and their frequencies in all three layers ............................ 101 Appendix 5: Ob, Di and Pi values of sample districts ........................................................ 112 Appendix 6: Ms and Md values of sample districts in all three layers .............................. 122 Appendix 7: Source links of tourism plans and official tourism marketing websites of

sample districts ............................................................................................................. 126

VII

LIST OF TABLES Table 3-‐1 The 10 Tourism Resources Geograhpical Regions and Their Details .......................................... 24 Table 5-‐1 Data Matrix Example of Image Position Projections (Dandong City) ........................................ 33 Table 5-‐2 Data Matrix Example of Sample Districts and Their Image Positions ........................................ 35 Table 5-‐3 Data Matrix Example of Sample Districts and Their Image Position Projections ...................... 35 Table 5-‐4 Aggregated Database Example of Ob, Di and Pi Values of All Sample Districts .......................... 36 Table 6-‐1 Testing Results of H1 by Using Ms values in B1, S1 and T1 ....................................................... 47 Table 6-‐2 Testing Results of H2 by Using Md values in B2, S2 and T2 ....................................................... 52 Table 6-‐3 Wilcoxon Signed Ranks Test Results between Ms and Md Values in All Three Layers ............... 57 Table 6-‐4 Paired Samples T-‐Test Results between Ms and Md Values in the Third Layers ........................ 58 Table 6-‐5 Wilcoxon Signed Ranks Test Result Between Ni value and Np Value of Each District ............... 60 Table 6-‐6 One-‐sample K-‐S Test Results of Pr Values Against Their Mean Value ....................................... 61 Table 6-‐7 The List of Districts with Lowest Projection Extents (Pi <1) ....................................................... 65 Table 6-‐8 The List of Districts with Highest Projection Extents (Pi >9) ...................................................... 66

VIII

LIST OF FIGURES

Figure 1-‐1 Relationships between region, province and district .................................................................. 4 Figure 2-‐1 Tourism destination image formation model ........................................................................... 13 Figure 2-‐2 Different clustering results given by statistical software and researchers’ interpretations ..... 17 Figure 5-‐1 Hierarchy of a general tourism resources category .................................................................. 37 Figure 6-‐1 Data collection results ............................................................................................................... 42 Figure 6-‐2 Relationships between the Base layer, the Second layer and the Third layer .......................... 43 Figure 6-‐3 Frequencies of non-‐unique image positions in the Base layer .................................................. 44 Figure 6-‐4 Ranking of the top 9 non-‐unique image positions in the Second layer. .................................... 45 Figure 6-‐5 Ranking of the top 10 non-‐unique image positions in the Third layer ...................................... 45 Figure 6-‐6 Results of the normal distribution tests of Ms values in B1, S1 and T1 ..................................... 46 Figure 6-‐7 Comparisons of the Ms values in the Base layer among 8 TRG regions ..................................... 48 Figure 6-‐8 Comparisons of the Ms values in the Base layer among 24 provinces ...................................... 48 Figure 6-‐9 Comparisons of the Ms values in the Second layer among 8 TRG regions ................................ 48 Figure 6-‐10 Comparisons of the Ms values in the Second layer among 24 provinces ................................ 49 Figure 6-‐11 Comparisons of the Ms values in the Third layer among 8 TRG regions .................................. 49 Figure 6-‐12 Comparisons of the Ms values in the Second layer among 24 provinces ................................ 49 Figure 6-‐13 Frequencies of Ms values of B1, S1 and T1 in the selected ranges of Ms values ..................... 51 Figure 6-‐14 Results of the normal distribution tests of Md values in B2, S2 and T2 ................................... 52 Figure 6-‐15 Comparisons of the Md values in the Base layer among 9 TRG regions .................................. 53 Figure 6-‐16 Comparisons of the Md values in the Base layer among 25 provinces .................................... 53 Figure 6-‐17 Comparisons of the Md values in the Second layer among 9 TRG regions .............................. 54 Figure 6-‐18 Comparisons of the Md values in the Second layer among 25 provinces ................................ 54 Figure 6-‐19 Comparisons of the Md values in the Third layer among 9 TRG regions ................................. 54 Figure 6-‐20 Comparisons of the Md values in the Second layer among 25 provinces ................................ 55 Figure 6-‐21 Frequencies of Md values of B2, S2 and T2 in the selected ranges of Ms values ..................... 56 Figure 6-‐22 Frequencies of Ms and Md values in all three layers ............................................................... 59 Figure 6-‐23 Normal Distribution Test Results of Pr values ......................................................................... 60 Figure 6-‐24 Comparison results of Pr values between districts in Eastern, Middle and Western China .... 62 Figure 6-‐25 Comparison results of Ob values between districts in Eastern, Middle and Western China ... 62 Figure 6-‐26 Comparison results of Di (mean) values between districts in Eastern Costal China, Middle

China and Western China .................................................................................................................. 63 Figure 6-‐27 One-‐Sample K-‐S Result of H4 .................................................................................................. 64 Figure 6-‐28 Frequencies of districts in different Pi intervals ...................................................................... 64 Figure 6-‐29 MDS results of all sample districts with image positions in the Base layer ............................. 67 Figure 6-‐30 MDS results of sample districts with non-‐unique image positions in the Base layer .............. 68 Figure 6-‐31 MDS results of all sample districts with image positions in the Second layer ........................ 68 Figure 6-‐32 MDS results of all sample districts with image positions in the Third Layer ........................... 69

IX

LIST OF ABBREVIATIONS

BTHS Region TRG Region including Beijing, Tianjin, Hebei Province, Henan Province,

Shanxi Province, Shandong Province and Shaanxi Province

CNTA China National Tourism Administration

CSHH TRG Region including Chongqing, Sichuan Province, Hubei Province

and Hunan Province

D-‐TRG Districts Districts in different TRG Regions

DMO Destination Management Organization

GFH Region TRG Region including Guangdong Province, Fujian Province and Hai-‐

nan Province

GSTP General Specification for Tourism Planning

HMT Region TRG Region including Hong Kong, Macau and Taiwan

IM Region TRG Region including Inner Mongolia Autonomous Region

LJH Region TRG Region including Liaoning Province, Jilin Province and Hei-‐

longjiang Province

LP District District with lowest extent of projection

MDS Multi-‐Dimensional Scale Proxscal

MP District District with highest extent of projection

PTA Provincial Tourism Administration

QT Region TRG Region including Qinghai Province and Tibet Autonomous Region

RTA Municipal/County Tourism Administration

RTO Regional Tourism Office

S-‐TRG Districts Districts within the same TRG Region

SJZAJ Region

TRG Region including Shanghai, Jiangsu Province, Zhejiang Province,

Anhui Province and Jiangxi Province

SP District Sub-‐provincial district

TRG Region Tourism resources geographical region

XNG Region

TRG Region including Xinjiang Uyghur Autonomous Region, Ningxia

Hui Autonomous Region, and Gansu Province

WTO World Trade Organization

YGG Region

TRG Region including Yunnan Province, Guizhou Province and Guangxi

Zhuang Autonomous Region

Tourism Destination Image Positions of the Sub-‐Provincial Districts in China: a Similarity and Uniqueness Comparison

1

1 INTRODUCTION

This introduction section includes four topics: (1) the initiation of this research; (2) research

purposes; (3) the structure of this research; (4) the clarification of some frequently used con-‐

fusing concepts in this research.

1.1 The initiation of this research

According to Zhang et al. (2011), Chinese tourism market is under high-‐speed growth. Its size is

huge consisting of more than 2 billion travellers. More than 97% of the total travelling is do-‐

mestic, which contributes approximately 80% of the total tourism revenues of 1561 billion

yuan. In the most recent five-‐year development plan made by Chinese central government,

tourism is positioned as a strategic pillar of the economy and a vital engine of consumption

(Blanke & Chiesa, 2013). In addition, policies aiming at stimulating tourism demands such as

the Tourism and Leisure Plan for Citizens are released (Zhang et al., 2011). As a consequence,

local governments are encouraged to rank tourism industry as one of their most important

industries for local development, which however, greatly intensifies the competition and de-‐

mands effective strategies.

At the same time, the modernization has made many Chinese tourism destinations like cities

appear similar (Han & Tao, 2005). Many destinations are trapped by the dilemma of encourag-‐

ing commoditization and restricting damaging tourism activities (Yang et al., 2008). One solu-‐

tion is to develop differentiation strategies and distinctive destination image positions. How-‐

ever, many Chinese destination managers have not developed or failed to develop effective

destination positioning strategies (Han & Tao, 2005).

The homogenization of destination images is not the unique issue in China. Globalization and

modernization have resulted in the increasing same looks of most destinations all over the

world (Dann, 2000; Plog, 2000) and made tourists perceive countries as interchangeable, not

matter they are looking for old cities, good beaches, or restful forests (Cohen, 1972). After all,

few tourism products are unique (Murphy & Pritchard, 1997) and competing destinations are

close substitutes in most tourism markets − particularly in those where charter flights and

package deals dominate (Pike, 2004)


2

Nevertheless, being endowed with abundant and unique tourism resources does not neces-‐

sarily mean that this destination is more competitive than another one which has effectively

utilized its although deficient resources (Hsu et al., 2004). In other words, how a destination

use its resources is more important than what it has (Ritchie & Crouch, 2000).

Positioning strategy is one of the most important sources of destination competitive ad-‐

vantages (Keller, 2003). Competitive marketing positions are vital for the long-‐term success of

destination development (Chen & Uysal, 2002). Effective positioning strategy can help a desti-‐

nation prevent direct competition with stronger competitors, delivering unclear messages to

target markets, and suffering from low consumer demands because of the existing negative

image or the absent destination identity (Lovelock, 1991).

In order to gain competitive advantages and become places of status, destinations with substi-‐

tutable tourism resources and development structures should adopt position-‐differentiation

strategies and build images around their unique attributes (Goodall, 1990). Otherwise, destina-‐

tions are more likely to fall into places of commodities that lead to greater substitutability (Gil-‐

bert, 1990). Ries & Trout (1986) suggest that instead of “betterness”, marketers should rather

think “differentness”.

In a regional context, focusing on distinctive features could help neighbouring destinations

develop tourism products that complement each other and encourage regional co-‐marketing

(Uysal et al., 2000). Shi et al. (2005) point out that tourism destinations in China have experi-‐

enced the stages of competition and cooperation; now they are gradually working towards co-‐

opetition (Brandenburger et al., 2003), which means the co-‐existence of both competition and

cooperation. Therefore, it is important to benchmark destinations – in particular the compet-‐

ing or neighbouring destinations – regarding the tourism resources, functions and structures of

tourism industry, as well as existing destination images and image positions (Cheng & Wu,

2004).

1.2 Research purposes

Positions in tourism field could be categorized into positions of travel and tourism arrange-‐

ment (functional positions) and positions of destination images (Govers & Go, 2003; Tierney,

2002). According to Chan & Wang (1996), image positioning is an effective approach for devel-‐

oping new tourism products and attraction sites as well as rejuvenating declining destinations.

In this master study, only positions of destination images are focused.


3

This master thesis is an exploratory research. The primary aim is to compare the intended

tourism image positions that are originally developed by destination managers of the sub-‐

provincial districts in China and identify any patterns of similarity and uniqueness behind. Fur-‐

thermore, the author is also interested in the actual projection extents of these intended im-‐

age positions on promotion material such as the contents on official tourism marketing web-‐

sites.

The research findings are expected to: (1) inform local destination managers in China about

the current status of their destination tourism image positions compared with other destina-‐

tions – from both the planning and implementation perspectives; and (2) give suggestions to

the tourism administrations of both sub-‐provincial level and higher levels on how to better

understand and coordinate the districts under their authorities and how to develop effective

image positioning and projection strategies.

This thesis has two key research questions:

1) To what extent the tourism destination image positions of sub-‐provincial districts in China

differ from each other.

2) To what extent the tourism destinations at sub-‐provincial level in China have projected the

intended image positions on their official tourism marketing websites.

1.3 Research structure

The entire thesis is divided into eight main sections. The first introduction part explains the

initiation and purposes of this research. The second part reviews the tourism literature on the

definition, approaches, and categorization of destination positioning and image, influences of

geography, DMOs, and methodologies used by past destination positioning and image studies.

The third part reviews the background of tourism development and destination image posi-‐

tioning in China. Based on the literature review and background review, four main hypotheses

are formulated for research questions and listed in the fourth part. Then the fifth part de-‐

scribes the methodology of this thesis in detail. In the sixth part, the data analysis results are

presented. Then the results and their limitations are discussed in the seventh part. The final

eighth part provides the overall conclusion, managerial implications and suggestions for fur-‐

ther research.


4

1.4 Clarification of some frequently appeared confusing concepts

1.4.1 Region, province and district

District, province and region are three spatial levels frequently and consistently used in this

master study. In terms of the size, region is the largest whereas district is the smallest. A region

contains one or several provinces. A province has several districts under its authority.

In this research, the unit of analysis is sub-‐provincial districts that belong to provinces. The

provinces are sorted into different geographical regions that are characterized by different

categories of endowed tourism resources. Figure 1-‐1 visulizes their relationships.

Figure 1-‐1 Relationships between region, province and district

1.4.2 DMO and tourism administration

In China, tourism administration is the Destination Management Organization (DMO). Destina-‐

tion managers are the senior officials working in the tourism administrations. In this research,

when talking about China, “DMO” is exchangeable with “tourism administration”; and “desti-‐

nation manager” refers to the “senior official working in the tourism administration”.

Tourism resources geographical region

(TRG Region)

Province(s)

Sub-‐provincial

districts

(SP Districts)


5

2 LITERATURE REVIEW

This chapter reviews the major and latest literature about destination positioning and image

that are published in English or in Chinese. The main review topics include definition, ap-‐

proaches and categorization of destination positioning and image, influences of geography,

DMO, and methodologies used in the past studies. After that, the key enlightenments to this

research are summarized.

2.1 Definition of destination positioning and image

Underpinning in the philosophy of understanding and meeting the unique needs of customers

(Pike & Ryan, 2004), destination positioning is a process of establishing and maintaining a dis-‐

tinctive place about a destination – relative to its competitors – in potential visitors’ minds

(Crompton et al., 1992; Lovelock, 1991; Wind & Robinson, 1972).

In this over-‐communicated society, simplified and focused messages are demanded to defence

clutter (Ries & Trout, 1986). In addition, competitors are part of the external macro-‐

environment, whom the DMO has no control over (Pike & Ryan, 2004). Thus in order to stand

out and reduce risks due to competition, effective positioning strategies should offer customer

problem-‐solving solutions that are different from competitors (Chacko, 1997; DiMingo, 1988).

The types of positioning in tourism field may include positions of travel and tourism arrange-‐

ments (functional positions) as well as positions of destination images (Govers & Go, 2003;

Tierney, 2002). According to Kotler et al. (1993), destination image simplifies huge amount of

information connected with a place. It is one of the key issues concerned by destination posi-‐

tioning (Aaker & Shansby, 1982; Echtner & Ritchie, 1993). Hence the major objectives of any

destination positioning strategy are to change negative images, create new images, or rein-‐

force existing positive and competitive images and determinant attributes (Pike, 2009; Pike &

Ryan, 2004). Many researches suggest that effective destination positioning strategies should

measure existing destination images (Chaudhary, 2000; Fakeye & Crompton, 1991; Rezende-‐

Parker et al., 2003), and study their structures and formation dynamics (Baloglu & McCleary,

1999; Echtner & Ritchie, 1993; Gartner, 1993).


6

2.2 Postioning approaches

“Perception is reality” implies that rather than the reality, what has been perceived is often

more important to people (Chacko & Marcell, 2008). Following this philosophy, the basic posi-‐

tioning approach is to manipulate those images already in people’s mind instead of creating

something new and different (Ries & Trout, 1993) and make them recognize the functional

benefits, emotional benefits (Ritchie and Ritchie, 1998) and self-‐expressive benefits (Aaker,

1996) of purchasing a specific product.

Forced by market competition, destination managers must select and translate destination

attributes into identities and image positions (Govers & Go, 2005) that are attractive, unique,

easy to disperse, desirable to mass tourists, able to stimulate tourists’ curiosities, and con-‐

sistent with tourism trends and hot topics (Jin, 2003).

Ingredients for positioning could be selected from product attributes, price, competition,

product class, user and application (Chacko, 1997) and positioning by product attributes is the

most popular approach (Aaker & Shansby, 1982). However, the attributes enabling product

differentiation may not appeal to consumers (Crompton et al., 1992; Lovelock, 1991), which

infers that comparative advantages are not equal to competitive advantages. Moreover, image

positions are not like trademarks that later registers have no right to use them because

whether a destination image position is desirable or not is eventually judged by tourists (Jin,

2003). Sometimes tourists may favour destinations that though have less diverse or lower

quality tourism resources (Bao & Zhu, 2003).

Without positioning strategies, many destinations though having rich and diverse tourism re-‐

sources and products have sent blurred image messages to target markets (Lovelock, 1991).

Many DMOs are challenged by the task of narrowing down abundant destination attributes to

an essential positioning proposition (Pike, 2009). They tend to be all things to all people with-‐

out carefully considering whether these images and products appeal to tourists or not (Gee &

Makens, 1985), which will result in less incisive and more nebulous images that cause confu-‐

sions (Aaker & Shansby, 1982; Crompton et al., 1992).

Ries (1992) suggests that the most powerful marketing concept should be the word in target

consumers’ minds. In order to be noticed and remembered by people suffering from infor-‐

mation floods, the positioning strategy should focus on only one or few distinctive or powerful

attributes (Aaker & Shansby, 1982; Crompton et al., 1992). Ideal destination image positions


7

should be able to distill the soul feature(s) from higher-‐level rather than simply listing the at-‐

tributes or reinforcing stereotypical images (Daye, 2010; Jin, 2003). Thus destinations should

shift broad-‐based positioning approach to a more targeted and customized way (Heath, 1999).

The overall image of a destination is often intrinsically tied to one or several “iconic” features

that are easy to recognize, powerful and dominant compared to other features; thus they of-‐

ten become juxtaposed and quasi synonymous with the destination itself (Bowie & Buttle,

2004; Judd & Fainstein, 1999; Voase, 1999). Although only a minority of destinations are fortu-‐

nate to have these truly special tourism endowments, most destinations are still able to identi-‐

fy at least one feature to represent itself (Echtner & Ritchie, 1993; Pearce, 1982). Beside physi-‐

cal attributes that are easy to recognize, destination-‐positioning strategies increasingly focus

on promoting uniqueness, holistic appeals and emotional appeals (Echtner & Ritchie, 1993;

Pearce, 1982).

Uniqueness is important for envisaging the overall image of a destination (Echtner & Ritchie,

1993). However, no matter how impressive the image is perceived within the destination, it

might immediately become less influential after comparing with others (Qu et al., 2011). The

key of effective destination positioning is resulting in a unique image that is sustainable, be-‐

lievable, relevant and unable to be surpassed or usurped by competitors (Morgan et al., 2002).

It should continuously enable simplifying information and differentiating images in consumers’

minds (Botha et al., 1999; Buhalis, 2000; Calantone et al., 1989; Cai, 2002; Chon et al., 1991;

Crompton et al., 1992; Echtner & Ritchie, 1993; Fan, 2006; Go & Govers, 2000; Mihalic, 2000;

Morrison & Anderson, 2002; Mykletun et al., 2001; Qu et al., 2011; Ritchie & Ritchie, 1998;

Uysal et al., 2000). As indicated by Gilbert (1990), positioning unique attributes will increase

destination attractiveness and perceived added values, and thus make it a status area rather

than a commodity area. Besides, as long as being perceived as unique, any destination element

can become the representation of destination uniqueness not matter it is a physical feature,

symbol or atmospheric attribute (Echtner & Ritchie, 1993; Pearce, 1982).

In addition, Jin (2003) states that effective positioning strategies depend on the destination

managers’ abilities to develop and match the right strategies to the needs of different source

markets. Because there is only limited number of tourism resources categories to choose from,

she points out that under the circumstances when two destinations are distant to each other

or target different source markets, having similar image positions will not lead to direct com-‐

petition.


8

2.3 Categorization of destination positions and images

Destination image is the key construct of destination positioning. Nevertheless, after reviewing

the literature, the author think that positioning refers more to destination managers’ behav-‐

iours, while image reflects more about tourists’ perceptions; and many researchers study pro-‐

jected destination positions by analysing the existing destination images. Therefore, the cate-‐

gories of destination positions are almost the same as the categories of destination images.

Pike (2004) summarizes 14 positioning categories including leadership, discovery, nature, loca-‐

tion, people, water, self expressive, escape, pleasure, treasure, royal, vibrant, climate, and

culinary. Frochot (2003) develops 10 positioning themes from all sample regions; they are nat-‐

ural, history, authentic, traditional, pure, rural, activities, arts and crafts, wild, and gastronomy.

Govers & Go (2005) sort Dubai’s destination image attributes into 14 categories including “rec-‐

reational facilities and activities”, “experience modern Dubai”, “reflection of modern Dubai”,

“sea, sun, sand experience”, “reflections of heritage”, “reflections of culture”, “outdoor activi-‐

ties”, “outdoor experience”, “reflections of old and new”, “heritage experience”, “hospitality

facilities”, “leisure and recreational experience”, “sea, sun, sand facilities and activities” and

“cultural experience”.

Uysal et al. (2000) sorts 48 destination image categories into 4 dimensions: (1) “activities” in-‐

clude categories of sightseeing, shopping, restaurants, golf and tennis, snow skiing, hiking and

backpacking, canoeing and rafting, bicycling, spectator sports, cultural events, theme parks,

festivals, kids activities, horse racing, hunting, civil war sites, and historic buildings; (2) “places”

include categories of beaches, mountains, cities, resorts, state and national parks, towns and

villages, natural features, countryside, and architecture wonders; (3) “feelings” include catego-‐

ries of rest and relaxation, escape pressure, exciting travel, family friends, new things, roman-‐

tic setting, familiar place, indulge self and family, friendly people, fun and enjoyment, and re-‐

discover self; and (4) “general category” includes categories of variety see and do, attraction

use together, good value, 1st class accommodations and facilities, traveller information, con-‐

venient and easy, high available service, clean and well maintained, ease getting around, good

weather, and well marked roads and attractions.

In addition to the direct and practical utilities, the commoditized destination features could

also be the psychological utility or symbolic significance (Goldman & Wilson, 1983; Watson &

Kopachevsky, 1994), which are similar to the functional benefits, emotional benefits and self-‐

expressive benefits reviewed earlier. Based on the attractiveness, Witt & Moutinho (1989)


9

divide destination elements into three basic categories: (1) “static factors” include natural and

cultivated landscape, climate, means of travelling, as well as historical and local cultural attrac-‐

tions; (2) “dyrzaniic factors” include accommodation, catering, personal attention and service,

entertainment and sport, access to the market, political conditions and trends in tourism; and

(3) “currerzf decision factors” include the marketing, prices and country of origin.

Cognitive image, affective image and conative image are three distinct but hierarchically inter-‐

related types of destination images (Gartner, 1993). Cognitive images, which normally focus on

tangible attributes, have received most research attentions than the other two types (Pearce,

1977; Pike, 2002).

Pike & Ryan (2004) sort New Zealand’s cognitive image categories into four main dimensions:

(1) “good life and infrastructure” includes indicators of good cafes and restaurants, suitable

accommodation, shopping, hot pool bathing, and good value for money; (2) “getting away

from it all” includes indicators of natural scenic beauty, not too touristy, good ocean beaches,

places for walking and tramping, and friendly locals; (3) “outdoor play” includes indicators of

places for swimming or boating, fishing, and adventure activities; and (4) “the weather” in-‐

cludes indicators of good weather, lots to see and do, and close to other holiday destinations.

Qu et al. (2011) conclude 5 dimensions for Oklahoma’s cognitive images: (1) “quality of experi-‐

ences” include categories of easy access to the area, restful and relaxing atmosphere, reason-‐

able cost of hotels and restaurants, scenery and natural wonders, lots of open space, and

friendly local people; (2) “touristic attractions” include categories of local cuisine, state and

theme parks, good place for children and family, welcome centres, good weather, cultural

events and festivals, and good shopping facilities; (3) “environment and infrastructure” include

categories of clean and unspoiled environment, infrastructure, availability of travel infor-‐

mation, easy access to the area, and safe and secure environment; (4) “entertainment and

outdoor activities” include categories of entertainment, nightlife, water sports, and a wide

variety of outdoor activities; (5) “cultural traditions” include categories of native culture and a

taste of local life and culture.

Individual’s favourable, unfavourable or neutral feelings towards an object form his or her

affective attitude (Fishbein, 1967). Russel et al. (1981) develop an eight-‐dimension affective

response grid from 105 common adjectives used to describe environments by factor analysis.

The eight dimensions contain four semantic differential scales: “pleasant and unpleasant”,

“relaxing and distressing”, “arousing and sleepy” and “exciting and gloomy”. “Exciting” is a


10

function of “arousing” and “pleasant” while “distressing” is a synthesis of “sleepy” and “un-‐

pleasant”. Besides, Echtner & Ritchie (1991) propose a three dimensional continuum: “func-‐

tional and psychological”, “attributes and holistic” and “common and unique”.

In addition, “destination brand” such as “World Cultural Heritage” is also one important posi-‐

tioning category because of its ability to identify and differentiate destinations (Keller, 1998).

All the categorization results mentioned above are based on the destination attributes − either

tangible or intangible or both. Destination images can also be categorized according to their

formation processes such as organic image and induced image (Gunn’s, 1988; Gartner, 1993),

and primary image and secondary image (Phelps, 1986; Mansfeld, 1992). But they are not di-‐

rectly related to the focus of this master research and are not discussed in detail here.

Besides, one type of image can also be used to portray other image types. For instance, desti-‐

nation images such as cultural experience, status, cultural identity, communicating and shar-‐

ing, relaxation, excitement, escapism, education and lifestyle can be simultaneously portrayed

by the image of food (Frochot, 2003; Hjalager & Corigliano, 2000; Rimmington & Yüksel, 1998).

2.4 Influences of geography on destination image positioning

This master thesis has taken the influences of geography on destination image positioning into

consideration. Melián-‐González & García-‐Falcón (2003) think that the types and amounts of

resources determine the potentials of developing certain kind(s) of industries in a specified

place. Places are ‘’genetically’’ endowed with unequal abundance of tourism resources, devel-‐

opment support, and basic infrastructure (Ritchie & Crouch, 2000). When people choose from

destinations of same type, they tend to pick the one having higher quality of tourism resources

(Shi et al., 2005)

Zhou & Xiao (2003) point out that destinations that are geographically close are more likely to

have shared context images of landscape, politics, culture, ethnic and religions. This increases

the possibility of image substitutions and competition because tourists often choose only one

out of several destinations with similar perceived images (Li, 2000).

Nevertheless, the locality of a place is fabricated in a unique way by its own landscape, history

and traditions, cultural patterns, community values and power relations (Gregory, 1989; Ley &

Samuels, 1978), which may project distinctive destination images (Bramwell & Rawding, 1996).

Ashworth (1990) studied common and differentiated features of seven Mediterranean country


11

destinations; and confirm that the projected image based on similar features may show varia-‐

tions from place to place (Ashworth & Voogd, 1988; Ashworth & Voogd, 1990; Baloglu, 1996).

In addition, the characters and objectives of DMOs in different places often vary, which may

also lead to different destination images (Cox & Mair, 1988; Shaw, 1993).

According to Li (2000), the geo-‐space could be divided into hierarchical levels. An area at high-‐

er spatial level contains several areas at lower spatial level. The spatial size of higher level is

larger than that of lower level. She thinks that it is easier for people to first recognize and re-‐

member images of places at higher geo-‐spatial level like continent, country and province and

then gradually turn to that at lower levels such as counties and attraction sites. The perceived

images of places at lower geo-‐spatial level are heavily influenced by the perceptions of areas at

upper levels. She also points out that this cognition process is more likely to happen when

tourists are from distant source markets and not clear about the specific circumstances of their

destinations. Zhou & Xiao (2003) conclude two main possible ways of how tourists synthesize

the perceived images of multiple geo-‐spatial levels: (1) context image is clearer than the image

of the specific destinations; (2) the images of “iconic” destinations at the lower geo-‐spatial

levels like counties and attraction sites are regarded as the image of higher level area such as

the whole province.

Besides, Li (2000) think that tourists may easily perceive the images of two destinations as the

same if they are at the same geo-‐spatial level and geographically close to each other; or when

tourists know nothing about certain destination but are familiar with its neighbouring districts,

they tend to perceive that this destination has same images as its neighbours.

The travel motivations and perceived distances about a destination vary among tourists, which

results in different perceived images about a same destination. Li (2000) proposes the concept

“minimum distance of cognition”. According to this concept, people live within their minimum

distance of cognition to certain tourism destination perceive it from a more rational and daily

perspective; and they may not visit this destination even though it is a popular tourist place

because of the overestimated potential travel opportunities. On the other hand, tourists from

distant places often have lower level of and even distorted understandings about this destina-‐

tion, which however results in more imaginations and perceived attractiveness that motivates

tourists to travel there.

Therefore, in order to prevent vicious competition by developing the image positions that are

different from but complementary to that of other destinations, it is vital to take the full influ-‐


12

ences of geography into consideration when conducting positioning and identify the truly dif-‐

ferentiated destination features (Zhou & Xiao, 2003).

2.5 DMO

According to WTO (1999), DMOs could be divided into three hierarchical layers: (1) national

tourism authorities or organizations, (2) regional, provincial or state DMOs, and (3) local DMOs

for smaller districts such as cities or towns.

In the pre-‐Internet era, DMOs were able to centrally control the dissemination of destination

information by effectively influencing the media content (Govers & Go, 2003). But now they

have found it almost impossible; because due to the popularity of Internet, tourists are in-‐

creasingly influenced by online tourism forums and communities that DMOs have no much

control over (Choi et al., 2007).

According to Heath (1999), all activities of RTOs (Regional Tourism Offices) are initiated based

on their positions. Therefore, studying the positioning concepts held by destination managers

is the key to understand DMOs and destination development. In addition, the projected imag-‐

es mirror the destinations’ status quo, which helps understand the travel stimuli that are de-‐

livered by destination marketers (Gartner, 1993; Mackay & Fesenmaier, 2000).

Figure 2-‐1 is the tourism destination image formation model (Govers & Go, 2005). Different

types of gap exsit between different stages of these image formation processes. Many destina-‐

tion image studies emphasize on the effectiveness of projected image, namely, the gap be-‐

tween the projected image and the perceived image. Stabler (1987) mentions that it is im-‐

portant to evaluate how well the tourism images projected on the marketing material and

those perceived by tourists are matched. On the contrary, the processes of how destination

managers develop and project destination image positions are rarely studied, which are the

stage of “Tourism Destination Identity” and the gap between “Tourism Destination Identity”

and “Projection Tourism Destination Image” shown in Figure 2-‐1. This gap infers that a poorly

projected destination image position does not mean that it was poorly designed since the be-‐

ginning. The projection process could change and distort the intended destination image posi-‐

tions.


13

Figure 2-‐1 Tourism destination image formation model (Govers & Go, 2005)

Understanding the world of destination managers could enrich the interpretations of destina-‐

tion image positioning and projection processes. However, this field has not received much

attention from tourism researchers. In addition to the content analysis of destinations’ online

marketing information, Govers & Go (2005) also interviewed destination managers in order to

understand their definition of ‘’appropriate” and answer the question “what kind of image do

decision makers have?”, i.e. “what are the intended image positions”, which is regarded as

“one of the most important questions” by Boulding (1983) but rarely answered.

2.6 Methodologies of destination positioning and image studies

2.6.1 General methods in both English literature and Chinese literature

According to Wei (2012), although few tourism destination researches about China are con-‐

ducted by western scholars, Chinese tourism researchers, who are eager to learn from the

advanced international research achievements for the purpose of promoting tourism devel-‐

opment in China, have been introducing a lot of influential and latest international tourism

literature into China since 1990s. Overall, there are clear differences between the destination

positioning and image studies done by Chinese researchers and western researchers.

Western countries have begun researching destination positioning and image for a longer pe-‐


14

riod and already built up a fairly comprehensive theoretical system. Conventional researches in

this field often use a pre-‐determined list of destination attributes (Choi et al, 1999; Echtner &

Ritchie, 2003; Martin & Rodriguez del Bosque, 2008) to measure latent image dimensions; and

adopt combined structured and unstructured methodologies suggested by Echtner & Ritchie

(1991). The measurements could vary because researchers may conceptualize the destination

image constructs in different ways; and multivariate or bivariate structured techniques are

most frequently used to operationalize these constructs (O’Leary & Deegan, 2005).

Chinese scholars have developed a theoretical system of tourism image positioning and plan-‐

ning in the early 1990s (Wei, 2012) as well as some terms that are rarely mentioned in western

researches such as “analysis of geographical patterns and contexts”, “first impression zone”

and “halo effect zone” (Miao, 2005). After that, few theoretical innovations are made (Wei,

2012) despite Chinese researchers’ enthusiasm in studying destination positioning and image −

average 18.5 new articles about this field are published in China from year 1994 to year 2003

with the overall upward trend (Miao, 2005).

In general, Chinese researchers agree that image is tourists’ rational reflections on a destina-‐

tion and its characteristics, and these subjective impressions are affected by factors such as

personal experience, value and external information. However, they question the incomplete-‐

ness of interpreting destination images solely based on tourists’ subjective impressions be-‐

cause the roles of objective conditions of destinations cannot be ignored (Bai, 2009; Ma & Shu,

1999; Peng, 1998; Wang et al., 1999). In practice, the qualitative analysis of geographical pat-‐

terns and contexts is the first and most frequently used approach by Chinese tourism profes-‐

sionals to conduct destination positioning and ensure resulted positions are closely tied to the

locality (Chan & Wang).

2.6.1.1 Quantative vs. qualitative analysis

Western scholars are proficient in various quantitative methodologies and increasingly tend to

combine quantitative approach with qualitative method when they study destination position-‐

ing and image. Despite being the most frequently used approach, quantitative analysis is not

good at capturing the holistic and psychological impressions; therefore, qualitative methods

have been increasingly recognized and used to complement quantitative methods (Dann,

1996; Echtner & Ritchie, 1993; Mackay & Fesenmaier, 2000; Reilly, 1990).


15

On the contrary, Chinese researchers heavily rely on qualitative analysis and follow similar

methodology, which may lead to similar conclusions and designs of image positions for desti-‐

nations that are actually distinctive (Cheng & Wu). Their main objective is quite practical to

assist local governments in making tourism or socio-‐economic development strategies (Guo,

2003). Therefore, in China there are sophisticated methodologies for categorizing and evaluat-‐

ing tourism resources; but few researches are about the categorization of destination position-‐

ing and images (Wei, 2012).

2.6.1.2 Research targets

In terms of research population, western researchers rank tourists or local consumers as top

options, then they come to tour operators or tourism experts; college students are also fre-‐

quently involved as interviewees (Pike, 2002). In other words, their main focuses are the image

perceptions of demand side. On the contrary, Chinese researchers highly concentrate on posi-‐

tioning and designing destination images from the standpoints of supply side and the tourism

resources orientation. This type of topics has occupied around 76% of the total Chinese litera-‐

ture on destination image (Miao, 2005).

Regarding the geo-‐space, western researchers like studying countries and cities (Pike, 2002).

Only a few of them have conducted image researches by including destinations at regional

level or sub-‐regional level (Ashworth, 1990; Baloglu, 1996). Most Chinese tourism scholars who

analyse existing or design new image positions are interested in destinations at city-‐level − in

particular the provincial capitals and famous tourism cities (Cheng & Wu, 2004; Miao, 2005).

Nevertheless, recent studies are shifting the focuses to those cities and counties having strong

intentions to develop tourism industry (Miao, 2005). There are few researches about destina-‐

tions at regional-‐level or specific attraction sites like mountain, sea and theme park (Cheng &

Wu, 2004).

2.6.2 Benchmarking

Benchmarking is a frequently used approach when study destination positioning and image. It

has two major types in this field: (1) comparing the representations of destination image

across sources such as guidebooks and movies; (2) comparing the existing images of different

destinations.

Cheng & Wu (2004) suggest that it is important to benchmark image positions with other des-‐

tinations − especially the neighbouring districts − in order to ensure their uniqueness. It is criti-‐


16

cal to clarify what should be benchmarked, the goal of benchmarking (Dickinger & Koltringer,

2011), and the suitable benchmarking methodology, which have received only limited atten-‐

tions, however (Wöber, 2002).

2.6.3 Content analysis

2.6.3.1 Representations of destination image

It has been recognized that analysing written and visual contents of tourism marketing materi-‐

al could provide a great deal of information about the projected destination images (Jenkins,

1999; O’Leary & Deegan, 2005). Destination images could be projected through representa-‐

tions such as narratives, photographs, videos, music and virtual tours, which ideally should

reflect rich tourism experiences, and multisensory, fancy and emotional cues (Govers & Go,

2005).

When researchers study destination images by using content analysis, most of them prefer

analysing text and pictures. Only a minority of them have chosen movies or video advertising

as their units of analysis. According to Gretzel & Fesenmaier (2003), narrative is the basis of

projecting destination image, which could be further enhanced by adding photographic mate-‐

rial. Although text analysis is more common, increasing number of researchers have recog-‐

nized the vital roles of photographs in projecting destination images and chosen them as re-‐

search targets (Dann, 1996; Mackay & Couldwell, 2004; Mellinger, 1994; Pritchard & Morgan,

1996; Williams, 2001). Some researches analyse both text and pictures (Choi et al., 2007;

Govers & Go, 2005; Hsu & Song, 2013; Stepchenkova & Morrison, 2006).

Gunn (1997) points out that online contents have become established sources of travel infor-‐

mation and automatically influence tourists’ image formation processes. The large amount of

tourism information available online provides a large data pool for content analysis. Pan et al.

(2007) analyse the text of 40 travel blogs and Carson (2008) examine 76 blog entries about

Australia’s Northern Territories. Stepchenkova & Morrison (2006) analyse both text and pic-‐

tures of 212 websites of tour operators in United States and Russia. Govers & Go (2005) study

online text and pictures about Dubai in order to understand its projected destination images.

2.6.3.2 Techniques of content analysis

According to Mazanec (2010), text of destination image could be analysed through a system of

position co-‐occurrence of connotations and the name of place in text with significant frequen-‐


17

cy. Sophisticated automated computer programs such as CatPac II are useful to calculate the

word frequency and then sort the words into categories. On the other hand, some researchers

such as Carson (2008) prefer conducting this process manually.

When analysing the photographs, qualitative methods such as expert judgment are more

commonly used to extract and separate the multiple meanings hidden behind a single picture

(Choi et al., 2007; Frochot, 2003).

2.6.3.3 Limitations of content analysis

2.6.3.3.1 Objectivity vs. subjectivity

When researchers transform the contents into numbers for quantitative analysis, the inherited

qualitative nature of contents leads to the difficulties in identifying measurable units of analy-‐

sis and ensuring objectivity (Berger, 1998). From the articles the author have reviewed, many

content analyses on destination image are exploratory in nature and rely on researchers’ sub-‐

jective judgment at least to some extent. For instance, extracting keywords and counting their

frequencies are the most basic procedures of content analysis. However, after extraction, the

keywords and their frequencies are not able to explain for themselves and the original con-‐

texts but have to rely on researchers’ interpretations (Choi et al., 2007; Dickinger & Koltringer,

2011; Frochot, 2003). Statistical procedures such as factor analysis are popular in assisting

researchers in identifying the latent dimensions of keywords in a more objective way. Never-‐

theless, there exist interpretation variations between statistical software and researchers; an

example is shown in Figure 2-‐2.

Figure 2-‐2 Different clustering results given by the statistical software and the researchers’ interpretations (Govers & Go, 2005)

One way to improve the objectivity and reliability of the analysis is to recruit a panel. The panel


18

members extract and sort the keywords following the given research methodology, but they

work independently from the researchers. Then the consistency of the results from both sides

is checked to prove the reliability of the researchers’ interpretations (Frochot, 2003).

2.6.3.3.2 Generalizability

The generalization power of many content analyses on destination images is challenged by

following reasons.

First, the sample size of destination is often very small. Many researchers only study one desti-‐

nation as case (Choi et al., 2007). Therefore, the research findings are only able to explain spe-‐

cific destinations rather than applied universally.

Second, many researchers rely on convenience sampling. For instance, when some researchers

study the destinations where majority tourists are non-‐English speakers and most text is writ-‐

ten in non-‐English language, they still only analyse the small amount of information presented

in English (Choi et al., 2007). In another example, there are often long lists of sample frame

when analyse different types of travel websites. In this case, researchers may choose the first

several URLs for analysis instead of making selections based on their true relevancy to the re-‐

search designs (Dickinger & Koltringer, 2011).

Third, good sampling should consider the influences of geography. Some tourism attributes

only appear in specific places. In addition, a large region could offer various types of tourism

products that increase the diversity of perceived images and thus the complexity of analysis

(Frochot, 2003).

Fourth, ideally, a comprehensive content analysis of destination image should include textual,

pictorial and multimedia representations collected from both modern and traditional channels,

such as Internet, TV, newspaper and brochures (Dickinger & Koltringer, 2011). As already re-‐

viewed in section 2.6.3.1, most researchers analyse textual and pictorial contents. While due

to the technical limitations, it is still difficult to analyse other multimedia contents.

2.6.3.3.3 Automated content analysis by computer program

CatPatII or other automated text mining programs are popular among researchers to analyse

contents. However, the validity and reliability of the results from this kind of automated com-‐

puter processing are questioned because the contexts are missing. For example, the same


19

sentiment words could be either positive or negative to different persons or under different

contexts (Dickinger & Koltringer, 2011).

In addition, many computer programs could only analyse certain types of language. For in-‐

stance, CatPatII cannot be used to capture the meanings of Chinese characters.

Moreover, a word or a picture may contain multiple meanings and reflect multiple image cate-‐

gories. For instance, cuisine can also reflect the culture and lifestyle (Frochot, 2003).

2.7 Conclusion

Seven principal points are concluded after literature review:

First, image position is one of the major types of destination position. Having unique and im-‐

pressive image is central to effective destination positioning. Therefore, many image studies

suggest that it is important to measure existing destination images in order to understand

destination positions. This gives the main reason why the specific focus of this master study is

the image positions of destination rather than other types of positions.

Second, geography is one of the major factors affecting destination positioning. It not only

determines the endowments of tourism resources, but also influences the administrative abil-‐

ity of DMOs; both of which are central to the development and implementation of competitive

positioning strategies. Although destinations that are geographically close and sharing same

context identity may show image variations from place to place, they are still less likely to be

perceived as different by tourists who are not familiar with them and live over long distances.

Therefore, in this master study, the author uses three levels of China’s geo-‐spatial hierarchy as

pre-‐determined groups when compare image positions between destinations.

Third, poorly perceived destination images should not be equal to bad intended destination

image positions that were originally designed by destination managers. Hence, the projection

process of intended image positions should not be ignored. Moreover, rare researches on des-‐

tination positioning and image aim at the initiation process and the non-‐distorted meanings of

the intended image positions. Therefore, this master thesis stands on destination managers’

side and studies the intended destination image positions and the gap generated during the

projection process.

Fourth, because of competitors’ critical influences on the effectiveness of positioning strate-‐


20

gies, it is important for a destination to compare its existing position(s) with those of other

destinations. This master study proposes a simple and fast benchmarking approach to com-‐

pare and visualize the similarity distances between a large number of destinations, which is

rare in existing destination positioning and image studies.

Fifth, there are few articles in Chinese or in English about destinations at provincial-‐level and

local-‐level. In this study, districts at sub-‐provincial level in China are the units of analysis.

Sixth, as indicated by the comparison of Chinese and Western literature on destination posi-‐

tioning and image, western scholars quite frequently use quantitative analysis and demand-‐

side population, which ensure the marketing orientation of image positions whereas the validi-‐

ty of results is heavily influenced by the proficiency of process control and the interpretations

are strictly restricted by the nature of data. On the other hand, qualitative analysis is very pop-‐

ular among Chinese researchers because they think that this approach is able to epitomize and

extract typical and unique destination features in a comprehensive and accurate way, which is

barely satisfactory if use quantitative method. However, the Chinese qualitative approaches

often neglect the measurements and understandings of tourists’ expectations and impres-‐

sions, which make it difficult to know whether the intended image positions are attractive or

just the destinations’ own wishful thinking (Ding et al., 2007). Recognizing these limitations,

more and more researchers have applied both the qualitative analysis and the quantitative

analysis in their tourism destination studies, which is also adopted in this master study.

Seventh, content analysis is one of the major methods used to study destination positioning

and image, which has been more popular due to the extensive use of Internet for disseminat-‐

ing tourism information. The text and pictures of tourism marketing material provide great

amount of information that project destination image positions, and are the most common

and popular data sources of content analysis. Automated text mining is a popular content

analysis technique, but the validity and reliability of its results are questioned in terms of miss-‐

ing contexts and available language options. Some researchers prefer conducting content

analysis manually despite higher expected level of subjectivity. One solution is to conduct reli-‐

ability test. Many content analyses on destination positioning and image have limited generali-‐

zation power because of small sample size, use of convenience sampling and neglecting the

influences of geography. This master research has addressed these restrictions, whose details

are described in the methodology chapter.


21

3 BACKGROUND REVIEW: IMAGE POSITIONING OF TOURISM DESTI-‐

NATIONS IN CHINA

This chapter reviews the image positioning problems of Chinese tourism destinations, DMOs in

China, formal process of making tourism development plans, and the classification of regions

that are used as the pre-‐defined groups in this master research.

3.1 Image positioning problems of Chinese tourism destinations

In China there are increasing substitutions among tourism destinations like cities, which is

more obvious among those having same regional contexts, source markets and similar tourism

products (Han & Tao, 2005). One solution is to have image positions that are distinctive from

other competing destinations.

However, Chinese destination managers are facing several problems regarding positioning and

marketing their destination images.

First, the existing image positions of many destinations are unclear (Chen, 2008). Han & Tao

(2005) point out that tourism images of many city destinations − in particular the middle-‐sized

and small cities − are still on the organic stage and lack of systematic planning. What is more

important is that many destination managers have not recognized the importance of image

positioning and the benefits of induced image (Chen, 2008; Han & Tao, 2005).

Second, some destinations have unrealistic image positions because they have neglected mar-‐

ket research and carried out positioning in a blind way (Chen, 2008). For instance, many desti-‐

nation managers think that destination image is the same as creative initiative or slogan rather

than the rational result from systematic positioning process (Han & Tao, 2005).

Third, many image positions are not creative enough to give tourists’ distinctive impressions,

and sometimes the positioning is simply done by duplicating or copying image positions from

other destinations (Chen, 2008). For example, some destination managers think that fancy and

elegant words are good to elevate overall images. However, if these words are overused by

multiple destinations and their true connotations are not clarified, people may negatively per-‐

ceive that the images of these destinations are exaggerated, flashy and superficial and find it

difficult to differentiate them (Jin, 2003).


22

Other problems include: (1) some destinations frequently change their image positions; (2)

there lacks the image position for the whole district or region as a destination; (3) the existing

image positions receive inadequate popularity and public recognitions (Chen, 2008).

3.2 DMOs in China

In China, DMOs are government departments traditionally. In other words, China National

Tourism Administration (CNTA) is the DMO for the whole China; Provincial Tourism Admin-‐

istrations (PTAs) manage provincial-‐level destinations and Municipal or County Tourism Ad-‐

ministrations (RTAs) are the DMOs for sub-‐provincial districts (Feng et al., 2003; Li & Wang,

2010). In this master research, when talking about China, “DMO” is exchangeable with “tour-‐

ism administration”; and “destination manager” refers to the “senior official working in the

tourism administration”.

CNTA is the highest-‐level DMO in China. Its responsibilities include: (1) formulating and imple-‐

menting tourism policies; (2) developing tourism products and markets; (3) developing and

promoting tourism destination positions and images; (4) conducting tourism researches; (5)

handling tourists’ complaints and protecting their legitimate interests; (6) supervising tourism

education and training; (7) guiding tourism administrations at lower levels; and (8) making

tourism plans (CNTA, 2008).

According to CNTA (2008), RTAs are DMOs at base level and directly manage local tourism

attractions and enterprises. PTAs act as coordinators between CNTA and RTAs through sup-‐

porting CNTA to implement tourism legislations and policies and helping RTAs obtain funding

and guidance from CNTA. As sub-‐branches of CNTA, PTAs and RTAs are in charge of: (1) making

regional-‐ or district-‐level tourism development policies and plans; (2) managing tourist assets,

amenities and accessibility of the destinations under their authorities; (3) supervising and

regulating tourism market; and (4) assuring quality of tourism services. Policies and plans

made by PTAs and RTAs have to gain approvals from tourism administrations at upper levels.

3.3 Formal process of making tourism developmet plans

A scientific tourism development plan is essential to the sustainable development of local tour-‐

ism. The plan should clarify the strategic roles of tourism industry and include feasible projects

that best suit local realities; any unrealistic ideas and projects should be prevented (Bao & Zhu,

2003).


23

According to the Chap. III of the Tourism Law 2013, tourism administrations should draft tour-‐

ism development plans based on the local socio-‐economic development plans. When a plan is

for cross-‐regional tourism development, it should be made either by higher-‐level administra-‐

tions or base on the negotiations of relevant local administrations. After being reviewed and

approved by the assessment committees, the plans should be publicized. Besides, the tourism

development plans should include specific tourism image promotion strategies.

The Chinese central government released the General Specification for Tourism Planning

(GSTP) in 2003 to guide administrations making tourism development plans. This standard

requires the tourism administrations to outsource the plan drafting work to professional tour-‐

ism consulting institutions that have been certified by CNTA and consist of experts specialized

in diverse disciplines such as tourism, economy, resources, environment, city planning and

architecture. GSTP (2003) also specifies that before drafting the plans, planners need to first

make systematic analysis about local contexts, tourism resources, source markets, competitors

and so on; base on the analysis results, the main tourism functions, products and images will

be positioned and then enriched by detail working plans.

According to the GSTP (2003), tourism development plan can be classified into national plan,

regional plan, and local plan. Local tourism development plan can be further classified into

provincial plan, municipal plan and county-‐level plan. The local-‐level tourism development

plans should base on the plans of higher-‐levels as well as the local realities. The planning peri-‐

od could range from short term (3-‐5 years) to medium term (5-‐10 years) and to long term (10-‐

20 years).

GSTP (2003) further specifies that when the plan finishes drafting, it will be reviewed and

passed at the meetings organized by the assessment committee and tourism administrations

of higher-‐levels. Only when three fourths of the committee members agree, the plan is then

officially approved. The committee usually cconsists of more than seven members including

the representatives of government departments (no more than 1/3) and local experts (no less

than 1/3). The experts should cover the disciplines of economic analysis, market development,

tourism resources, environmental protection, city planning, engineering and architecture.

3.4 Tourism resource geographical regions (TRG Regions)

Based on the principles of locality, genesis, multi-‐level sequences, pinpointing dominant fac-‐

tors, similarity and integrity of tourism resources, administrative divisions and traffic coordina-‐


24

tion, Chinese tourism researchers have developed several schemes for classifying destinations

all over China into different tourism resources geographical regions since 1980s (Wu, 2001).

This master study adopts the latest scheme developed by Song (1994). He classifies the tour-‐

ism destinations in China into 10 TRG Regions and 77 sub-‐TRG Regions. TRG Regions consist of

complete provinces. While the sub-‐TRG Regions focusing on market appeal may contain dis-‐

tricts across provinces. Table 3-‐1 summarizes the features of the 10 TRG Regions and their

included provinces; the regions names are the abbreviations of their included provinces.

Table 3-‐1 The 10 Tourism Resources Geograhpical Regions and Their Details

Region

name

Province(s)

included

Features of tourism development

LJH

Region

Liaoning

Jilin

Heilongjiang

• Unique Northland snow scenery and numerous natural wonders

such as arctic scenery, volcanic wonders, and animal wonders.

• Many convalescent summer resorts like hot springs and coastline.

• Splendid cultural heritages and colourful ethnic customs. Repre-‐

sentatives of ethnic minorities include Manchu, Korean, Mongolian,

Olunchun, Daur, and Hoche.

BTHS

Region

Beijing

Tianjin

Hebei

Henan

Shanxi

Shandong

Shaanxi

• Dominated by cultural tourism resources that are well integrated

with natural tourism resources.

• Long history of tourism development with relatively concentrated

distribution and excellent geographical mix of tourism resources.

SJZAJ

Region

Shanghai

Jiangsu

Zhejiang

Anhui

Jiangxi

• Rich and high quality of tourism resources.

• Tourism resources are represented by mountain scenery, gardens,

famous cities and religions.

CSHH

Region

Chongqing

Sichuan

Hubei

Hunan

• Numerous natural reserves and charming mountains and peaks.

• Famous for the Three Gorges and the monuments from the Three

Kingdom Period.


25

Region

name

Province(s)

included

Features of tourism development

GFH

Region

Guangdong

Fujian

Hainan

• Tropical views and customs.

• Cultural tourism resources with modern features, combined north-‐

ern and southern China styles, and combined Chinese and foreign

styles.

YGG

Region

Yunnan

Guizhou

Guangxi

• Widely distributed Karst landscapes.

• Multi-‐ethnic enclave and unique ethnic customs.

• Spectacular mountain valleys. The natural landscapes in this region

have few artificial traces and can be called Natural Beauty. Many

natural wonders are unique in China and even in the world.

XNG

Region

Xinjiang

Ningxia

Gansu

• Vast desert, prairie oasis and mountain forests constitute the di-‐

verse but unique landscapes.

• "Western China" scenery that reflects the mysterious natural scen-‐

ery, rugged landscape and exotic customs.

• Fascinating Silk Road.

• Various charming ethnic customs.

IM

Region

Inner Mongolia • Unique grassland scenery, which integrates other types of tourism

resources elements such as mountains, water, forests and desert.

• Ethnic customs (Mongolian).

• Rich heritages from Yuan, Ming and Qing dynasties.

QT

Region

Qinghai

Tibet

• Lots of Snow Mountains and glaciers.

• Charming and unique religious culture. Various temples are the

symbols of the Qinghai-‐Tibet region.

• Unique ethnic customs (Tibetans).

HMT

Region

Hong Kong

Macau

Taiwan

• Hong Kong and Macao: different kinds of combined Chinese and

foreign styles due to the special social contexts.

• Taiwan: natural scenery with tropical and subtropical features.

Note. Adapted from Song (1994).

3.5 Tourism development and overall economic development levels

The development level of tourism industry is highly related to the level and structure of local

economy. Currently, the tourism development stages vary among districts having different


26

types of economic structures. According to Zhang et al. (2011), in the districts with highly com-‐

parative tourism resources advantages such as Sanya, Lijiang and Zhangjiajie, the contributions

of tourism industry to GDP are more than 50%; whereas it is not dominant but important in

terms of coordinating and enhancing other industries in the districts such as Beijing, Shanghai,

Qingdao and Guangzhou.

“Eastern costal China”, “Middle China” and “Western China” are the officially classfied regions

that are used to describe the different economic and social development levels of districts in

China. In Eastern coastal China, destination managers are emphasizing on regional tourism co-‐

development and building international-‐level destinations; while the districts in Middle China

develop tourism in order to assist urbanization process; and enhancing the overall industriali-‐

zation level is the first goal of tourism development for districts in Western China (Zhang et al.,

2011). These different strategic focuses affect the resources and efforts invested in tourism

development and thus may indirectly influence the planning and implementation quality of

destination positioning strategies.

3.6 Conclusion

Seven principal points are concluded after background review:

First, in order to minimize the perceived homogenization, it is important for tourism destina-‐

tions to have distinctive image positions. However, it is a challenging task for Chinese destina-‐

tion managers because of: (1) unclear and unrealistic image positions; (2) positioning without

creativity that impresses tourists; (3) changing positions frequently; (4) lacking image position

for the whole destination; and (4) inadequate popularity and public recognitions of existing

image positions. Benchmarking existing destination image positions could help address these

issues. This master study proposes a simple but systematic approach to do this.

Second, tourism image promotion strategy is one of the essential chapters in the tourism de-‐

velopment plan. In China, making tourism development plans is one of the responsibilities of

DMOs at all levels that are written down in the Tourism Law 2013. DMOs outsource the plan

drafting work to certified tourism consulting institutions. The plans are drafted following the

GSTP (2003) − the standard guideline published by Chinese central government. The final

drafts are reviewed and approved by the assessment committees and then publicized. The

intended destination image positions will be written down in the tourism development plans if

they are already developed. Hence, tourism plans are credible sources for identifying the in-‐


27

tended image positions of destinations.

Third, Chinese tourism researchers have developed several schemes for classifying destina-‐

tions all over China into different tourism resources geographical regions. This master research

adopts the scheme developed by Song (1994). This latest scheme proposes 10 TRG Regions

that contain complete provinces, which are good for data collection for this master research.

These TRG Regions, which have taken the influences of geography into consideration, are pre-‐

determined groups for comparing image positions in this master study.

Fourth, in China, the tourism development stages vary among districts having different levels

and structures of economy. “Eastern costal China”, “Middle China” and “Western China” are

the officially classified regions that are used to describe the different economic and social de-‐

velopment levels of districts in China. In general, the destinations in each of these regions have

different strategic focuses of tourism development that may indirectly influence the planning

and implementation quality of destination positioning strategies. “Eastern costal China”,

“Middle China” and “Western China” are used as pre-‐determined groups for comparing the

projections of intended image positions in this master research.


28

4 RESEARCH QUESTIONS AND HYPOTHESES

Effective image positioning strategies are vital for building competitive advantages and the

success of tourism destination development. Therefore, it is important to understand and

evaluate the existing destination image positions. Currently, there are many sophisticated

tourism researches studying destination positioning and image from the tourists’ perspectives,

but fewer researches have been conducted from the destination managers’ perspectives. In

addition, few articles benchmark image positions between multiple destinations and their

projections on marketing material. In order to generalize the results to larger geo-‐scopes, it is

important to include substantial number of destinations for analysis and take the influences of

geography into consideration.

The key research questions of this master thesis are:

1) To what extent the tourism destination image positions of sub-‐provincial districts in China

differ from each other.

2) To what extent the tourism destinations at sub-‐provincial level in China have projected the

intended image positions on their official tourism marketing websites.

From the key research questions and the literature review, four hypotheses are developed:

H1: The sub-‐provincial districts have rather the similar intended image positions like those of

other sub-‐provincial districts within the same tourism resources geographical region with

an expected degree of congruence of 0.3.

H2: The sub-‐provincial districts have different intended image positions compared to those of

the sub-‐provincial districts in other tourism resource geographical regions with an ex-‐

pected degree of congruence of 0.3.

H3: The intended image positions have been congruently projected on the contents of corre-‐

sponding official tourism marketing websites.

H4: The extent of projection is different between the sub-‐provincial districts.

Besides, unique image positions are identified if they only belong to one district.


29

5 METHODOLOGY

This master thesis is an exploratory research. Content analysis is the primary method of this

research, which has combined both qualitative and quantitative approaches. Qualitative anal-‐

yses are mainly used to code extracted contents of image positions from tourism plans and

marketing information from official tourism websites, as well as sort image positions into dif-‐

ferent groups. Quantitative approach is applied to ensure probability sampling and generaliza-‐

bility, and to calculate and visualize the similarity distances between samples. Because these

two approaches have distinctive but complementary roles in this master research, they are

equally weighted.

In this chapter, the author describes the research methodology step by step in detail. The cov-‐

ered topics include the population and sampling, data structure and sources, data collection

and coding, and data analysis techniques that are used to test hypotheses and answer re-‐

search questions mentioned in the previous chapter.

5.1 Population and sampling

This master thesis stands from the destination managers’ perspectives to study and bench-‐

mark the intended destination image positions of sub-‐provincial districts in China. There are 27

provinces1 and 365 sub-‐provincial districts (SP Districts) in China. This research targets SP Dis-‐

tricts as units of analysis in order to ensure substantial sample size and the generalization

power of the findings. The 365 SP Districts are classified into 9 groups according to the scheme

of tourism resources geographical regions (TRG Regions) reviewed in section 3.4. Due to the

different administration systems, the 10th region including Hong Kong, Taiwan and Macau is

excluded from this research.

122 out of 365 SP Districts are selected. The sampling method combines the multistage sam-‐

pling, stratified sampling and simple random sampling. Since the 9 TRG Regions cover com-‐

1 There are 34 provincial-‐level administrations in China, but only 27 of them are used explicitly in this research. Four provincial-‐level cities: Beijing, Tianjin, Shanghai and Chongqing, are grouped with nearby provinces for the conven-‐ience of selecting samples in this research. Hong Kong, Macau, and Taiwan are also equivalent provincial-‐level dis-‐tricts; but they are not included in this research due to the different administration systems.


30

plete provinces, the quota of SP Districts selected from each province under each TRG Region

is proportional (1/3) to the total number of SP Districts in that province. Then the simple ran-‐

dom sampling is applied to draw the sample districts from each province according to the quo-‐

ta.

5.2 Data structure and sources

In this research, the contents of destination image positions written in the officially approved

tourism development plans are analysed in order to understand the intended image positions

that destination managers want the tourists to perceive. Furthermore, the marketing contents

on the official tourism websites are analysed for the purpose of evaluating whether the in-‐

tended image positions have been congruently projected in text on the official online market-‐

ing platforms of the destinations.

This research only uses secondary and semi-‐structural data rather than primary data. All the

contents analysed are in Chinese. Due to the restricted research resources and network, the

author only relies on the Internet to collect data.

5.2.1 Tourism development plans

The “Tourism Development Master Plan” (Master Plan) and the “12th Five-‐Year Tourism Devel-‐

opment Plan (2011-‐2015)” (Five-‐Year Plan) are two major types of official tourism develop-‐

ment plans for tourism administrations in China. Both plans are required to write down desti-‐

nation image positions unless they have not been developed yet.

The Master Plans have to be drafted by certified tourism consulting institutions and approved

by tourism administrations. They often cover longer period from 10 to 20 years. Nevertheless,

not all DMOs of SP districts have finished their Master Plans. On the other hand, some DMOs

made the Five-‐Year Plans either in addition to or as the substitutions of the Master Plans.

Moreover, not all DMOs have disclosed the entire plans to the public online for free.

If the full version of these two types of tourism plans could not be collected, the last choice is

to use news articles written by credible media that have reported destination image positions

written in the tourism plans. If none of these sources is available for a destination, then it is

assumed as missing value.


31

5.2.2 Official tourism marketing websites

Similar to many other countries, the destination marketing websites in China are usually man-‐

aged by government administrations (Wei, 2006; Wu, 2006; Zhou & Shi, 2005). These websites

reflect real situations of destinations without any commercial descriptions (Qi et al., 2008).

Some destinations such as Beijing and Suzhou have two types of official tourism websites −

official tourism marketing websites and official tourism administration websites. The admin-‐

istration website is responsible for disclosing information such as tourism legislations, policies,

and local tourism development (Feng et al., 2003). While the official tourism websites of other

destinations have double functions of administration and marketing.

Official tourism marketing website is the first choice for extracting the contents for analysis. If

the destinations only have one official tourism website with both administrative and marketing

functions, only the marketing part will be studied. If a DMO does not have its own official web-‐

site, then it is assumed that the projection of the intended image position of this destination is

completely incongruent.

Moreover, the existences of intended image positions are the basic premises of analysing cor-‐

responding websites, because the purpose of analysing the official tourism marketing websites

is to evaluate the projections of the intended destination image positions. In addition to that,

the retrieved time of the extracted marketing contents should come after the effective date of

the corresponding tourism plans where the intended image positions are identified.

5.3 Data collection and coding

In this section, the steps of data extraction and coding for one sample district are explained.

These steps are repeated for all sample districts.

5.3.1 From tourism plans

5.3.1.1 Step 1: Identify and extract contents for analysis

The contents about intended destination image position(s) – the most essential image posi-‐

tion(s) for a destrict as whole destination to distinguish itself – are identified from the tourism

plan. Other image positions for its sub-‐districts and specific tourism products are not consid-‐

ered.


32

Proposed slogans in the tourism plans are excluded. First, if the tourism plans have already

clearly stated the image positions, then there is no need to further analyse slogans whose de-‐

signs are based on the intended image positions. Second, if the plans contain only contents of

slogans without mentioning anything about destination image positions, then it is hard to dis-‐

tinguish whether the slogans could represent the intended image positions. Third, slogans may

use wordings that have not direct linkages with destination image positions, which could dis-‐

tort the essential meanings of intended image positions if analysed.

5.3.1.2 Step 2: Separate image positioning concepts

The extracted contents about image positions are often in the formats of short sentences or

phrases that contain more than one concept. Thus, the original extracted contents are pro-‐

cessed to identify all included image-‐positioning concepts that are independent from each

other.

This concept separation process follows the principle of keeping the complete meanings of the

positioning concepts instead of simply slicing the contents into single word segments. The au-‐

thor believes that it could help improve the accuracy and validity of the data. In order to

achieve this, the author first reads through the entire tourism plan and understands the back-‐

ground, rationales and the implementation plans of the intended image position(s) before

starting the separation process.

5.3.2 From official tourism marketing websites

5.3.2.1 Step 1: Identify and extract contents for analysis

Identify and extract “titles of the marketing-‐related articles”, “slogans” and “descriptions

about destination images” from the official tourism marketing website.

5.3.2.2 Step 2: Transform the contents into numeric data

Create a data matrix like Table 5-‐1 with the already separated intended image position(s) in

the first raw and the extracted website contents in the first column. Then, check whether the

extracted website contents reflect the image position(s).


33

Table 5-‐1 Data Matrix Example of Image Position Projections (Dandong City)

Variables

Total number of occurred

projected positions

(intended & unintended)

Num

ber of projected image

positions that occur for the

first time (intended &

unin-‐

tended)

Intended position 1:

Yalv River

Intended position 2:

Beautiful

Mean

Beautiful Dandongd 1 1 1

Beautiful city with islandsd 2 1 1

Yalv River International

Tourism Festivald 1 1 1

… … … … …

Sum 95a 25b 2c 2c 2

Fair density (Df) 4%

Actual density (Di) 2.11% 2.11% 2.11%

Penetration ratio (Pi) 0.526 0.526 0.526

a Total times of occurrences of all intended and unintended image positions projected by the website contents (Ob). b Total number of projected intended and unintended image positions (Nb). c Total times of occurrences of an intended image position projected by the website contents (Oi). d Extracted contents from the official tourism marketing website.

5.3.2.3 Evaluate the projection success of the intended image position(s)

Several ratios are calculated In order to evaluate the projection congruence of the intended

image positions.

1) Total times of occurrences of an intended image position projected by the website contents

(Oi). However, the direct comparison of Oi between destinations make no sense because the

amount of marketing information presented on the websites may vary a lot from one to an-‐

other due to different website styles, marketing funding and expertise. Benchmarking is only

possible when Oi value is normalized into Di value (see point 4 below).

2) Total times of occurrences of all intended and unintended image positions projected by the

Contentsd (first 3 rows) Calculations (bottom 4 rows)


34

website contents (Ob). The unintended image positions refer to the positioning concepts that

are not written down in the tourism plans.

3) Total number of projected intended and unintended image positions (Nb).

4) Actual projection density of an intended image position (Di). It is the normalized or stand-‐

ardized Oi value, which enables benchmarking between different destinations. Its algorithm

formula is:

Di=Oi

Ob x 100%

5) Fair projection density of all intended and unintended image positions (Df). It is the thresh-‐

old for judging whether an intended image position could be recognized as having been pro-‐

jected by the extracted website contents. Its algorithm formula is:

Df=1 Nb x 100%

6) Penetration ratio showing the extent of projection of an intended image position (Pi). Its

algorithm formula is:

Pi=Di

Df

When Di exceeds Df, or Pi is larger than 1, an intended image position is recognized as having

been congruently projected by the contents on the official tourism marketing website. The

larger Pi value is, the more likely an intended image position is perceived by the viewers.

7) The mean values of Di and Pi for all intended image positions are calculated to show that

whether this destination has projected its intended image positions as a whole on its official

tourism marketing website and the average extent of projection.

All these figures have been calculated for the example district (Dandong City) in Table 5-‐1. In

this example, the penetration ratios for both intended image positions “Yalv River” and “Beau-‐

tiful” are smaller than 1 indicating the incongruent projection practices.


35

5.3.3 Data aggregation

5.3.3.1 Data matrix of sample districts and their image positions (Matrix A)

Repeat the steps in section 5.3.1 for all sample districts and create a data matrix like Table 5-‐2.

In this matrix, all image positions identified from the tourism plans of all sample districts are

put into the first row, and district names are listed in the first column. The numeric value “1”

indicates the match between image position and district.

Table 5-‐2 Data Matrix Example of Sample Districts and Their Image Positions

Positions

Yalv River

Beautiful

Marine and

coastal custom

Island full of treasures

Happiness

Holy land of

Buddhism

Origin of Chi-‐

nese civilization

Fossil kingdom

Dangdong 1 1

Huludao 1 1 1

Chaoyang 1 1 1

5.3.3.2 Data matrix of sample districts and their image position projections (Matrix B)

Repeat the steps in section 5.3.2 for all sample districts. Create a data matrix like Table 5-‐3

based on the transformed Pi values of all sample districts. The first row and first column of this

data matrix is exactly the same as those of Matrix A. The major differences lie on the numeric

values. If the Pi value of a specific image position of a district is larger than 1, then the number

“1” is marked in the corresponding box of this data matrix. If the Pi value is equal or smaller

than 1, then the number “0” is marked in the corresponding data box. The blank box indicates

no relationship between the image position and the district.

Table 5-‐3 Data Matrix Example of Sample Districts and Their Image Position Projections

Positions

Yalv River

Beautiful

Marine and

coastal custom

Island full of treasures

Happiness

Holy land of

Buddhism

Origins of Chi-‐

nese civiliza-‐tion

Fossil kingdom

Dangdong 0 0

Huludao 1 0 0

Chaoyang 1 1 1

District name

District name


36

5.3.3.3 Aggregated database of Ob, Di and Pi value for all sample districts

Put the Ob, Di and Pi values of all sample districts into a single database like Table 5-‐4.

Table 5-‐4 Aggregated Database Example of Ob, Di and Pi Values of All Sample Districts

District name Ob Di (Meana) Pi (Meanb) Intended image position Di Pi

Dandong 95 2.11% 0.526 Yalv River 2.11% 0.526

Beautiful 2.11% 0.526

Huludao 106 11.95% 2.868 Marine and coastal custom 35.85% 8.603

Island full of treasures 0% 0

Happiness 0% 0

Chaoyang 789 8.62% 3.534 Holy land of Buddhism 8.37% 3.430

Origins of Chinese civilization 8.49% 3.482

Fossil kingdom 9% 3.689

a the mean of Di values of all image positions of a sample district b the mean of Pi values of all image positions of a sample district

5.4 Intermediate data prepration

5.4.1 Grouping image positions

There are two grouping stages. In the first stage, separated image-‐positioning concepts are

grouped together if they have same semantic meaning. In addition to the author’s own judge-‐

ment, two volunteers who are professional in Chinese-‐English translation help check the accu-‐

racy of the grouping results in this stage.

In the second grouping stage, the reduced image positioning concepts resulted from the first

grouping stage are further clustered based on their shared specific attributes or sub tourism

resources categories. Factor analysis is also applied to see if it assists data reduction in this

stage.

Figure 5-‐1 shows the hierarchy of a general tourism resources category, which could be divid-‐

ed into sub tourism resources categories and further down into the specific attributes. They


37

are the ingredients for positioning tourism images. When the grouping process is carried out,

the lower layers will be eliminated. In other words, the destinations differentiated themselves

solely based on the specific attributes may be perceived as having similar image because they

use the same (sub) tourism resources category.

Figure 5-‐1 Hierarchy of a general tourism resources category

5.4.2 Co-‐occurrences of image positions of SP Districts within the same TRG Region

In order to test H1, the Matrix A (see section 5.3.3.1) is used to calculate the relative times of

co-‐occurrences of image positions between a SP District and the remaining SP Districts within

the same TRG Region (Ms). Thus, Ms value indicates the similarity of image positions between a

district and the rest of the districts within the same TRG region. Its algorithm formula is:

Ms=1×C1

A1!B1!R1

A1= Total times of occurrences of image positions of the testing district

B1= Total times of occurrences of image positions of the remaining districts within the same TRG Region

R1= Total number of co-‐occurred image positions of the testing district

C1= Total number of districts having co-‐occurred image positions with the testing district

Ms value ranges from 0 to 1. It equals to “0” when there is not any image position co-‐

occurrence between all districts within the same TRG Region. Ms value increases when the

total times of co-‐occurrences increase. When Ms value equals to “1”, it means that all districts

within the same TRG Region have same image positions.

Repeat this calculation and get the Ms values for all sample districts.

A general tourism resources category

Sub tourism resources category 1

Specific a{ribute 1

Specific a{ribute 2

Sub tourism resources category 2

Specific a{ribute 3


38

5.4.3 Co-‐occurrences of image positions of SP Districts in different TRG Regions

In order to test H2, the Matrix A (see section 5.3.3.1) is used again to calculate the relative

times of co-‐occurrences of image positions between a SP District and all SP Districts in other

TRG Regions (Md). Thus, Md value indicates the similarity of image positions between a district

and all districts in other TRG regions. The algorithm formula is:

Md=1×C2

A2!B2!R2

A2= Total times of occurrences of image positions of the testing district

B2= Total times of occurrences of image positions of all districts in other TRG Regions

R2= Total number of co-‐occurred image positions of the testing district

C2= Total number of districts having co-‐occurred image positions with the testing district

Md value ranges from 0 to 1. It equals to “0” when there is not any image position co-‐

occurrence between the testing district and all districts in other TRG Regions. Md value in-‐

creases when the total times of co-‐occurrences increase. When Ms value equals to “1”, it

means that the testing district and all districts in other TRG Regions have same image posi-‐

tions.

Repeat this calculation and get the Md values for all sample districts.

5.4.4 Calculate proximity values for all pairs of sample districts

The proximity of image positions between each pair of sample districts is calculated by using

data in Matrix A (replace the blank data boxes of variables with value “0”). Due to the binary

nature of data, the Jaccard Similarity Coefficient (J) is used. The algorithm formula is:

J = M11

M01!M10!M11

M11= value “2” when an image position occurs in both district A and district B (both have a value of 1).

M01= value “1” when an image position not occurs in district A (value “0”), but occurs in district B (value “1”).

M10= value “1” when an image position occurs in district A (value “1”), but not occurs in district B (value “0”).

When an image position does not occur in both district A and district B, namely, both have

missing values (M00), the matching is not included in the calculation.

The proximity values are used to calculate the similarity distances between sample districts.


39

5.5 Data analysis techniques

5.5.1 Hypotheses testing

Normal distributions of relevant data are checked before running all hypotheses tests in order

to decide whether parametric or non-‐parametric tests should be used.

When testing H1 by one-‐sample test, an Ms value that is closest to the reality is pre-‐

determined as test value. However, it is difficult to make an educated guess without any prior

information such as the threshold of the total number of districts having co-‐occurred image

positions that destination managers will consider changing theirs. Therefore, the determina-‐

tion of test value relies on the author’s reasonable but rough guess. In the real world, although

districts close to each other may have similar context images, they are still able to identify

unique attributes. In addition to that, Chinese destination managers have adopted differentia-‐

tion strategies and try to develop distinctive image positions. Thus, it is reasonable to set a low

Ms test value at 0.3.

The same for H2, an Md value that is closest to the reality is pre-‐determined as test value for

one-‐sample test. Although districts far from each other usually have different context images,

they are likely to have same image positions because of the limited choices of tourism re-‐

sources categories and the less worries about having co-‐occurred images due to different

source markets and less direct competition. Hence, it is also reasonable to set the Md test val-‐

ue at 0.3.

Before testing H3, the total number of intended image position(s) of each sample district (Ni) is

transformed from data in Matrix A and the total number of projected intended image posi-‐

tion(s) of each sample district (Np) is transformed from data in Matrix B. In addition, projection

congruence ratio (Pr) is calculated for each sample district:

Pr =Np

Ni

Then paired samples test is run for H3 by ranking “Ni” and “Np” for each sample district. In

addition to that, one-‐sample test is also run for H3 by using Pr values of all sample districts. The

consistency of their results will strengthen their reliability power.

The differences between Pi (Mean) values of all sample districts are tested by one-‐sample test

to prove H4.


40

5.5.2 Further exploratory analyses

Paired samples test is used to compare the results of H1 and H2 in order to identify whether

the image positions of the sample districts are more similar to those of the districts within

same TRG Region or in different TRG Regions.

It is assumed that districts with advanced economic development and more marketing experts

should better know how to project intended image positions on the promotional material.

Therefore, the sample districts are put into the pre-‐determined groups: “Eastern coastal Chi-‐

na’’, ‘’Middle China’’ and ‘’Western China’’. Then the independent samples tests are used to

compare the differences between these groups regarding: (1) Ob values that infer the total

amount of marketing information on the websites; (2) Di (Mean) values that infer the efforts

destination managers have put on projecting the intended image positions; (3) Pr values that

indicate whether destinations have projected all of its intended image positions.

In order to explore the possible reasons causing different extents of projection (Pi), the specific

sample districts with the highest Pi values (MP Districts) and the lowest Pi values (LP Districts)

are identified. Their Ob values, Di values and Pi values are compared.

5.5.3 Visualising data analysis results

Boxplots and bar charts are used to visualize the comparison results between different groups

and different variables.

In order to calculate and visualize the similarity distances between sample districts, the prox-‐

imity values (see section 5.4.4) are processed by Multi-‐Dimensional Scale Proxscal (MDS) with

euclidean distance algorithm.

5.6 Prior study

The prior study covers the stages from sampling to data coding. The purposes of prior study in

this master thesis are: (1) checking and correcting if there are any problems and neglects in the

proposed methodology; (2) testing the reliability of the measurements before starting formal

data collection and processing; (3) improving the methodology design.

Following the original methodology, the prior study was conducted from 15th August to 20th

August 2013. One SP District was selected from each TRG Region by using simple random sam-‐

pling. The data for 9 districts in total were collected and coded.


41

3 out of the 9 sample districts in the prior study are marked as missing value because no credi-‐

ble information about their intended image positions was found from the Internet. The Pi val-‐

ues of the rest 6 districts show that none of them has congruently projected all of the intended

image positions on their official tourism websites.

Several practical problems have been identified: (1) how to accurately extract and separate the

contents about intended image positions from tourism plans; and (2) how to correctly calcu-‐

late the “Penetration ratio (Pi)”. Then by doing try-‐and-‐error, these problems were solved and

the methodology has been improved.

Due to the expected large database and the constraints of research resources and professional

network, the reliability of measurement is only tested during the prior study instead of in the

formal data collection and processing stage, and only for the step of coding extracted website

contents (see section 5.3.2.2). Two Chinese volunteers helped with the reliability test. After

that, their results were compared with those done by the author. The resulting average con-‐

sistency rate is 93.77%. Considering that these two people are not professional in tourism field

and have not received any relevant training before, the author assumes that 93.77% con-‐

sistency is adequate to prove the reliability of the methodology and the author’s judgment in

this master study.


42

6 RESULTS

By strictly following the methodology described in the previous chapter, rich data analysis re-‐

sults came up and are presented in this chapter.

For the convenience of presenting, two frequently used long phases in this chapter are short-‐

ened: (1) “Districts within the same TRG Region” are called “S-‐TRG Districts” in short; (2) “Dis-‐

tricts in different TRG Regions” are called “D-‐TRG Districts” in short.

6.1 Data collection results

Data collection was conducted from 20th August to 6th September 2013. Valid tourism plans for

83 districts out of 122 sample districts were collected. The distributions of sample districts in

each province and each TRG Region are listed in Appendix 1 and Appendix 2. Regarding the

types of tourism plans, 48 documents are the “Tourism Development Master Plans” and 35

documents are the “12th Twelve-‐Five Year Tourism Development Plans”.

After studying the 83 tourism plans, the intended image positions of 77 sample districts were

identified and extracted, while no existing intended image positions were found for the rest 6

districts. Appendix 3 lists the intended image position(s) of each sample district and those

without existing image position are marked with “NA”. In addition, 2 out of the 77 districts

with valid image positions do not have official tourism websites. Appendix 7 contains the

source links of tourism plans and official tourism marketing websites of all sample districts.

Figure 6-‐1 Data collection results

75

77

83

122

Districts having official tourism website with

Districts with exis}ng intended image posi}ons

Districts with valid tourism plans

Sample districts

Total number


43

6.2 Data preparation results

6.2.1 Grouping results of the intended image positions

Originally, 189 intended image-‐positioning concepts were separated from the extracted con-‐

tents of the 77 tourism plans. Then these 189 positioning concepts were reduced to 164 image

positions by combining those having same semantic meanings, whose results have been

checked by two volunteers professional in Chinese-‐English translation.

Based on the shared characteristics, the 164 image positions (141 cognitive positions and 23

affective positions) were primarily reduced into 100 image positions (79 cognitive positions

and 21 affective positions) and then further into 48 image positions (29 cognitive positions and

19 affective positions). These data aggregations are mainly contributed by grouping the cogni-‐

tive image positions rather than the affective positions.

For the convenience of the explanations, the database with 164 image positions is called “Base

layer”, the aggregated database with 100 image positions is named “Second layer” and the

further aggregated database with 48 image positions is called “Third layer”. Figure 6-‐2 visual-‐

izes the relationships between these three layers. Appendix 4 contains the image positions and

their frequencies in each layer.

Figure 6-‐2 Relationships between the Base layer, the Second layer and the Third layer

Figure 6-‐3 shows the 12 image positions in the Base layer that appear more than one time.

Most image positions are unique in this layer because they could be differentiated by specific

attributes. For example, the image position “Intoxicated landscape of mountain and water” is

distinguished from the general position “Landscape of mountain and water”. However, in the

Second layer and the Third layer, this difference between two image positions due to the spe-‐

cific tourism attribute is not counted any more.

Base layer

(contains 164 image

positions)

Third layer

(contains 48 image

positions)

Second layer

(contains 100 image

positions)

Group concepts

with shared

characteristics

Group concepts

with shared

characteristics


44

On the other hand, the non-‐unique cognitive image positions in this layer rather consist of

general tourism resource categories without being affiliated by sub tourism resources catego-‐

ries or specific attributes. Affective image positions that are used by multiple sample districts

include “Magic”, “Mysterious”, “Beautiful”, “Great Beauty” and “Magnificent”, which are also

commonly used and universally applied affective descriptions.

Figure 6-‐3 Frequencies of non-‐unique image positions in the Base layer. Symbol “*” represents the “affective image position”. There are 164 image positions in this layer.

Figure 6-‐4 and Figure 6-‐5 list the image positions with top frequencies after primary grouping

and secondary grouping processes. It is obvious that sample districts position their images

mainly relying on endowed natural tourism resources especially the landscapes of mountain,

water and sea, as well as cultural tourism resources such as local history, celebrities and tradi-‐

tions. “Leisure” is the only functional-‐based image position in both two rankings. “Beautiful”,

“Magic” and “Mysterious” are still the most frequently used affective image positions, alt-‐

hough their frequencies are much lower than those of the cognitive image positions in the

same layers. This indicates that in general fewer sample districts adopt affective image posi-‐

tions.

Among the 21 still unique image positions in the third layer, 15 of them are affective. The re-‐

maining 7 unique cognitive image positions are “ Fossil kingdom”, “Desert view”, “Forest city

on the plain region”, “Sports city”, “Commercial city”, and “Space science tourism”, which are

either unique endowed general tourism resources categories in specific geographical locations

or functional positions that may be used by other destinations as common functions rather

than unique positioning ingredients.

2 2 2 2 2 2

3 3

4 5 5 5

Magnificent* Great beauty*

Beau}ful* Red tourism

Regimen Green city

Mysterious land* Eco

Magic* Leisure place

Costal and marine customs Landscape of mountain and water

Frequency


45

Besides, around 8.5% of the total sample districts directly use the name of their unique attrac-‐

tions to represent the overall destination image positions. Almost all the attractions whose

names are used as image positions are national-‐wide famous, which at least domestic tourists

could easily associate.

Figure 6-‐4 Ranking of the top 9 non-‐unique image positions in the Second layer. Symbol “*” represents the “affective image position”. There are 100 image positions in this layer.

Figure 6-‐5 Ranking of the top 10 non-‐unique image positions in the Third layer. There are 48 image positions in this layer

4 (2.12%)

5 (2.65%)

5 (2.65%)

6 (3.17%)

6 (3.17%)

6 (3.17%)

7 (3.70%)

7 (3.70%)

10 (5.29%)

Holy land

Magic*

Leisure place

Hometown of celebri}es

Chinese ancient history

Ancient capital

Style of ethinic

Marine, costal and beaches

Landscape of mountain and water

Frequency and total percentage

7 (3.70%)

9 (4.76%)

9 (4.76%)

9 (4.76%)

9 (4.76%)

9 (4.76%)

11 (5.82%)

11 (5.82%)

15 (7.94%)

16 (8.47%)

Style of ethinic

Chinese history

Chinese civiliza}on

Ancient architect

Leisure

Eco

Humani}es and culture

Water

Landscape of mountain, water and pastoral

Image posi}ons named a�er a{rac}ons

Frequency and total percentage


46

6.2.2 Coding results of website contents

Following the steps in section 5.3.2, all image positions are checked if they have been project-‐

ed by the marketing contents extracted from the corresponding official tourism websites and

relevant figures are calculated. Appendix 5 is the complete version of aggregated database of

Ob, Di and Pi values of all sample districts. 2 out of the 77 sample districts having existing image

positions do not have official tourism websites with marketing information. Therefore, their

Ob, Di and Pi values automatically equal to zero indicating the total projection incongruence of

their intended image positions.

6.3 Hypotheses testing

6.3.1 H1

H1: The sub-‐provincial districts have rather the similar intended image positions like those of

other sub-‐provincial districts within the same tourism resources geographical region with

an expected degree of congruence of 0.3.

Following the steps in section 5.4.2, Ms values are calculated for all sample districts in the Base

layer, the Second layer and the Third layer, which are listed in the column B1, S1 and T1 in

Appendix 6. H1 was tested three times with the Ms values in B1, S1 and T1.

The 1-‐Sample K-‐S Test results in Figure 6-‐6 show that Ms values in B1 and S1 are not normally

distributed (p-‐value <0.001); whereas with the p-‐value <0.180, Ms values in T1 are normally

distributed. Therefore, when testing H1 against Ms test value “0.3”, the non-‐parametric 1-‐

Sample K-‐S Test was run for the Ms values in B1 and S1, while 1-‐Sample T-‐Test was conducted

for Ms values in T1.

Figure 6-‐6 Results of the normal distribution tests of Ms values in B1, S1 and T1


47

The Ms mean values were tested against the Ms test value “0.3”. The Ms mean values and the

mean differences against the test value in the Base layer, the Second layer and the Third layer

are listed in Table 6-‐1. The confidence intervals of the mean do not include the observed sam-‐

ple mean for all three layers. Therefore, H1 is rejected for all three grouping layers of intended

image positions. The intended image positions of SP Districts are significantly more different

from those belonging to other S-‐TRG Districts by a ratio of 0.3.

Table 6-‐1 Testing Results of H1 by Using Ms values in B1, S1 and T1

N Mean Std. Deviation

Mean Difference

(test value=0.3) Sig. (2-‐tailed)

Ms (B1) 76 0.0065553 0.0216711 -‐0.2934447 0.000a

Ms (S1) 76 0.0321874 0.04204797 -‐0.2678126 0.000b

Ms (T1) 76 0.0827042 0.07216628 -‐0.2172958 0.000c

a. from 1-‐Sample K-‐S Test b. from 1-‐Sample K-‐S Test c. from 1-‐Sample T-‐Test

The mean values of all three layers are smaller than 0.1. The mean value becomes larger when

the image positions are grouped, which indicates an increased similarity. Nevertheless, the

change is fairly small.

Although in general the intended image positions of sample districts are quite different from

those of the remaining S-‐TRG Districts, there are still certain variations worth being addressed

and may provide some useful information to destination managers. Figure 6-‐7, Figure 6-‐9 and

Figure 6-‐11 are the box plots that visualize the differences of Ms values among the 8 TRG Re-‐

gions in all three grouping layers. The QT Region is not included here because it has only one

sample district with valid data, thus the comparison within this region is inapplicable. Figure 6-‐

8, Figure 6-‐10 and Figure 6-‐12 show the differences among 24 provinces in all three grouping

layers. There are three provinces missed because there is no sample district with valid data in

Gansu Province and Tibet Autonomous Region; and Qinghai Province belongs to QT Region

that the comparison is not applicable.


48

Figure 6-‐7 Comparisons of the Ms values in the Base layer among 8 TRG regions

Figure 6-‐8 Comparisons of the Ms values in the Base layer among 24 provinces

Figure 6-‐9 Comparisons of the Ms values in the Second layer among 8 TRG regions


49

Figure 6-‐10 Comparisons of the Ms values in the Second layer among 24 provinces

Figure 6-‐11 Comparisons of the Ms values in the Third layer among 8 TRG regions

Figure 6-‐12 Comparisons of the Ms values in the Second layer among 24 provinces


50

According to Figure 6-‐7, only three TRG Regions in the Base layer have sample districts with co-‐

occurred image positions with other S-‐TRG Districts. The sample districts in the majority of TRG

Regions are able to distinguish themselves from their neighbors. Compared to other TRG Re-‐

gions, GFH Region (Guangdong, Fujian and Hainan) has the most sample districts having same

image positions with other S-‐TRG Districts. In addition to that, as shown by Figure 6-‐8, Guang-‐

dong Province has the most districts (3 out of 4) sharing same image positions with the re-‐

maining S-‐TRG Districts. Fujian province follows with 2 districts − there are only three sample

districts in this province − having co-‐occurred image positions with the neighbouring S-‐TRG

Districts.

After grouping the image positions into broader tourism resources categories according to the

shared characteristics, many districts are not able to distinguish themselves by specific attrib-‐

utes. Therefore, more TRG Regions and provinces have districts showing higher relative co-‐

occurrence values (Ms) in the Second layer and the Third layer. GFH Region and Guangdong

province still have the highest extents of similarity among their districts.

On the other hand, the uniqueness of image positions of the sample districts in YGG Region

(Yunnan, Guizhou, Guangxi) is much less affected by the grouping processes. It is reasonable to

infer that the sample districts in YGG Region have more unique general tourism resources cat-‐

egories rather than just specific attributes. Therefore, they are still highly distinctive even

though the image positioning concepts are aggregated.

Figure 6-‐13 benchmarks the frequencies of Ms values between different layers in different

ranges of Ms values that are selected according to their deviations. Thus these benchmarking

ranges are only applied to the specific set of Ms values in this master study. Ms value equal to

“0” means that the image position of the testing district is unique. Ms value between 0 and 0.1

still indicates small similarity of image positions. When the Ms value is larger than 0.1, it is per-‐

ceived as fairly large similarity.

In the Base layer (B1), when compared to the S-‐TRG Districts: (1) 68 sample districts (89.5%)

have unique image positions; (2) 5 districts (6.6%) have more than 5% similarity; and (3) 1 dis-‐

trict (1.3%) has fairly large similarity of more than 10%.

In the Second layer (S1), when compared to the S-‐TRG Districts: (1) 38 districts (50%) have

unique image positions; (2) 24 districts (31.6%) have more than 5% similarity; and (3) 4 districts

(5.3%) have fairly large similarity of more than 10%.


51

Figure 6-‐13 Frequencies of Ms values of B1, S1 and T1 in the selected ranges of Ms values

In the Third layer (T1), when compared to the S-‐TRG Districts: (1) only 17 districts (22.4%) do

not have any similarity with other neighbouring districts; (2) 50 districts (68.5%) have more

than 5% similarity; (3) there are already 30 district (39.5%) having more than 10% similarity;

and (4) 4 regions (5.3%) have very large similarity of more than 20%.

To summarize, it is obvious that grouping has increased the possibility of a district to be per-‐

ceived as targeting the same image like other S-‐TRG Districts. In the Base layer, image posi-‐

tions of most districts are unique. While in the Second layer, half of the total districts have

certain degrees of positioning similarities with their neighbours. When come to the Third layer,

nearly 40% of the total sample districts show fairly large similarity of more than 10%. Besides,

the degrees of similarities of two districts have even exceeded 30%.

6.3.2 H2

H2: The sub-‐provincial districts have different intended image positions compared to those of

the sub-‐provincial districts in other tourism resource geographical regions with an ex-‐

pected degree of congruence of 0.3.

Following the steps in section 5.4.3, Md values are calculated for all sample districts in the Base

layer, the Second layer and the Third layer, which are listed in the column B2, S2 and T2 of

Appendix 6. H2 was tested three times with the Md values in B2, S2 and T2.

The 1-‐Sample K-‐S Test results in Figure 6-‐14 show that Md values in B2 are not normally dis-‐

tributed (p-‐value <0.001), whereas Md values in S2 (p-‐value <0.117) and in T1 (p-‐value <0.8)

are normally distributed. Therefore, when testing H2 against Md test value “0.3”, the non-‐

parametric 1-‐Sample K-‐S Test was run for the Md values in B2, while 1-‐Sample T-‐Test was con-‐

ducted for Md values in S2 and T2.

68(89.5%)

5(6.6%) 1(1.3%) 0

38(50%) 24(31.6%)

4(5.3%) 0

17(22.4%)

50(68.5%)

30(39.5%)

4(5.3%)

0

20

40

60

80

0 ≥0.05 ≥0.1 ≥0.2 Freq

uency & Percentage

Selected range of MS value

B1 S1 T1


52

Figure 6-‐14 Results of the normal distribution tests of Md values in B2, S2 and T2

The Md mean values were tested against the Md test value “0.3”. The Ms mean values and the

mean differences against the test value in the Base layer, the Second layer and the Third layer

are listed in Table 6-‐2. The confidence intervals of the mean do not include the observed sam-‐

ple mean for all three layers. Therefore, H2 is accepted for all three grouping layers of intend-‐

ed image positions. The SP Districts have intended image positions that are significantly differ-‐

ent from those belonging to the D-‐TRG Districts as assumed by a ratio of 0.3.

Table 6-‐2 Testing Results of H2 by Using Md values in B2, S2 and T2

N Mean Std. Deviation Mean Difference

(test value=0.3)

Sig. (2-‐tailed)

Md (B2) 77 0.0066352 0.01024961 -‐0.2933648 0.000a

Md (S2) 77 0.0245522 0.02231983 -‐0.27544784 0.000b

Md (T2) 77 0.073458 0.04673629 -‐0.22654197 0.000c

a. from 1-‐Sample K-‐S Test b. from 1-‐Sample T-‐Test c. from 1-‐Sample T-‐Test

The mean values for all three layer are smaller than 0.1. The mean value becomes larger when

the image positions are grouped, which indicates an increased similarity. Nevertheless, the

change is fairly small.

Although in general the intended image positions of sample districts are quite different from

those of the D-‐TRG Districts, there are still certain variations worth being addressed and may

provide some useful information to destination managers. Figure 6-‐15, Figure 6-‐17 and Figure


53

6-‐19 are the box plots that visualize the differences of Md values among the 9 TRG Regions in

all three grouping layers. Figure 6-‐16, Figure 6-‐18 and Figure 6-‐20 show the differences among

25 provinces in all three grouping layers. There are two provinces missed because there is no

sample district with valid data in Gansu Province and Tibet Autonomous Region.

Figure 6-‐15 Comparisons of the Md values in the Base layer among 9 TRG regions

Figure 6-‐16 Comparisons of the Md values in the Base layer among 25 provinces


54

Figure 6-‐17 Comparisons of the Md values in the Second layer among 9 TRG regions

Figure 6-‐18 Comparisons of the Md values in the Second layer among 25 provinces

Figure 6-‐19 Comparisons of the Md values in the Third layer among 9 TRG regions


55

Figure 6-‐20 Comparisons of the Md values in the Second layer among 25 provinces

Different from the comparisons between the S-‐TRG Districts, there are already many sample

districts having same image positions with the D-‐TRG Districts even in the Base layer. When

the image positions are aggregated by grouping into the Second layers and the Third layer

items, the Md values or similarities increase significantly, especially for those TRG regions hav-‐

ing general tourism resources categories such as “ancient culture”, “landscape of mountain

and water” and “coastal”.

Same as the comparisons between S-‐TRG Districts, GFH Region and Guangdong province again

shows highest extents of similarity when image positions of their districts are benchmarked

with those of the D-‐TRG Districts. At the same time, the image positions of the districts in YGG

Region still show its greatest ability to resist the grouping effects and show highest degree of

uniqueness in the Third layer.

Figure 6-‐21 benchmarks the frequencies of Md values between different layers in different

ranges of Md values that are selected according to their deviations. Thus these benchmarking

ranges are only applied to the specific set of Md values in this master study. Md value equal to

“0” means that the image position of the testing district is unique. Md value between 0 and 0.1

still indicates small similarity of image positions. When the Md value is larger than 0.1, it is per-‐

ceived as fairly large similarity.


56

Figure 6-‐21 Frequencies of Md values of B2, S2 and T2 in the selected ranges of Ms values

In the Base layer (B2), when compared to the D-‐TRG Districts: (1) 47 districts (61%) have

unique image positions; and (2) no district has more than 5% similarity.

In the Second layer (S2), when compared to the D-‐TRG Districts: (1) 14 districts (18.2%) have

unique image positions; (2) 10 districts (13%) have more than 5% similarity; and (3) 1 district

(1.3%) has fairly large similarity of more than 10%.

In the Third layer (T1), when compared to the D-‐TRG Districts: (1) only 7 districts (9.1%) do not

have any similarity with the D-‐TRG Districts; (2) 50 districts (64.9%) have more than 5% similar-‐

ity; (3) there are already 24 districts (31.2%) having more than 10% similarity; and (4) 1 district

(1.3%) has very large similarity of more than 20%.

To summarize, it is obvious that grouping has increased the possibility for a district to be per-‐

ceived as targeting the same image like other D-‐TRG Districts. In the Base layer, image posi-‐

tions of more than half sample districts are unique. While in the Second layer, more than half

of the sample districts have certain but small degrees (less than 5%) of positioning similarities

with districts that are far away. When come to the Third layer, more than 30% of the sample

districts show fairly large similarity of more than 10%. There is only one sample district in the

Third layer having around 20% similarity.

47(61%)

0 0 0

14(18.2%) 10(13%)

1(1.3%) 0 7(9.1%)

50(64.9%)

24(31.2%)

1(1.3%) 0

10

20

30

40

50

60

0 ≥0.05 ≥0.1 ≥0.2

Freq

uency & Percentage

Selected range of Md value

B2 S2 T2


57

6.3.3 Ms vs. Md

Table 6-‐3 and Table 6-‐4 present the comparison results between Ms and Md values in all three

layers, which are used to see whether the image position of a district is more similar to those

of the S-‐TRG Districts or D-‐TRG Districts. Wilcoxon Signed Ranks Test was conducted for Ms and

Md values in all three layers. In addition, Paired Sample T-‐Test was run for Ms and Md values in

the Third layer because they are normally distributed; its resulting p-‐value is consistent with

that from the Wilcoxon Signed Ranks Test.

Table 6-‐3 Wilcoxon Signed Ranks Test Results between Ms and Md Values in All Three Layers

N Mean Rank Sum of Ranks Asymp. Sig. (2-‐tailed)

Md -‐ Ms (Base layer)

Negative Ranks 7a 22.71 159 0.206

Positive Ranks 22b 12.55 276

Ties 47c

Total 76

Md -‐ Ms (Second layer)

Negative Ranks 30d 42.47 1274

Positive Ranks 36e 26.03 937 0.282

Ties 10f

Total 76

Md -‐ Ms (Third layer)

Negative Ranks 41g 37.17 1524

Positive Ranks 31h 35.61 1104 0.239

Ties 4i

Total 76

a Md < Ms (Base layer) b Md > Ms (Base layer) c Md = Ms (Base layer) d Md < Ms (Second layer) e Md > Ms (Second layer) f Md = Ms (Second layer) g Md < Ms (Third layer) h Md > Ms (Third layer) i Md = Ms (Third layer)


58

Table 6-‐4 Paired Samples T-‐Test Results between Ms and Md Values in the Third Layers

Paired Differences Sig. (2-‐tailed)

Mean Std. Deviation

Pair: Ms – Md (Third layer) 0.00888457 0.0648348 0.236

The p-‐values of all three layers are larger than 0.05. Therefore, in general whether the image

positions of sample districts are more similar to those belong to the S-‐TRG Districts or D-‐TRG

Districts cannot be concluded.

Nevertheless, looking into the specific patterns of differences shown in Table 6-‐3 may give

some useful information to destination managers.

In the Base layer, there are 47 districts having unique image positions no matter benchmarked

with the S-‐TRG Districts or the D-‐TRG Districts. The image positions of 7 districts show higher

degrees of similarities with those of the S-‐TRG Districts than the D-‐TRG Districts. On the other

hand, the image positions of 22 districts are more similar to those belonging to the D-‐TRG Dis-‐

tricts than the S-‐TRG Districts. In other words, there are 15 more districts whose image posi-‐

tions are more similar to those of the D-‐TRG Districts than the S-‐TRG Districts. However, the

average degree of similarity is smaller when benchmarked with the D-‐TRG Districts than with

the S-‐TRG Districts.

In the Second layer, only 10 districts have unique image positions when compared to all other

sample districts. There are still 6 more districts whose image positions are more similar to

those of the D-‐TRG Districts than the S-‐TRG Districts; and the average degree of similarity is

still smaller when benchmarked with the D-‐TRG Districts than with the S-‐TRG Districts.

In the Third layer, only 4 districts still retain unique image positions when compared to all re-‐

maining sample districts. However, different from the other two layers, 10 more districts are

having image positions more similar to those of the S-‐TRG Districts than the D-‐TRG Districts;

and the difference between their average degrees of similarities is negligible.

In addition, when the image positions are aggregated following the order of grouping process-‐

es: (1) the average degrees of similarities increase for the districts whose image positions are

more similar to those of the D-‐TRG Districts than the S-‐TRG Districts; (2) while for the districts


59

having image positions more similar to those of the S-‐TRG Districts than the D-‐TRG Districts,

the average degree of similarity first increases and then decreases.

Figure 6-‐22 combines the Figure 6-‐13 and the Figure 6-‐21 together in order to compare the

frequencies of districts of all three layers when they are benchmarked within the same TRG

Region and across different TRG Regions in the selected ranges of relative co-‐occurrence val-‐

ues “0”, “≥0.05”, “≥0.1” and “≥0.2”.

Figure 6-‐22 Frequencies of Ms and Md values in all three layers

In all three layers, there are more districts keeping unique image positions when compared to

the S-‐TRG Districts than to the D-‐TRG Districts. However, in the Second Layer, there are more

districts having fairly small degrees of similarities (0-‐0.05) when their image positions are com-‐

pared with those of the D-‐TRG Districts than the S-‐TRG Districts. The situation is similar in the

Third layer; there are more districts whose image positions have fairly larger similarity (≥0.01)

compared to those of the S-‐TRG Districts than the D-‐TRG Districts.

To sum up, when compared with the S-‐TRG Districts, there are more districts having unique

image positions than when benchmarked with the D-‐TRG Districts. When the image positions

of the districts have at least certain degrees of similarities, the degrees are smaller when com-‐

pared with those belonging to the D-‐TRG Districts than the S-‐TRG Districts.

68

5 1 0

47

0 0 0

38

24

4 0

14 10

1 0

17

50

30

4 7

50

24

1 0

10

20

30

40

50

60

70

80

0 ≥0.05 ≥0.1 ≥0.2

Freq

uency

Selected range of relajve co-‐occurrence value

B1

B2

S1

S2

T1

T2


60

6.3.4 H3

H3: The intended image positions have been congruently projected on the contents of corre-‐

sponding official tourism marketing websites.

Wilcoxon Signed Ranks Test was run to test H3 by ranking the total number of intended image

positions (Ni) and the total number of projected intended image positions (Np) of each sample

district. The results are shown in Table 6-‐5.

Table 6-‐5 Wilcoxon Signed Ranks Test Result Between Ni value and Np Value of Each District

N Mean Rank Sum of Ranks Asymp. Sig. (2-‐tailed)

Ni -‐ Np

Negative Ranks 0a 0 0 0.000

Positive Ranks 46b 23.5 1081

Ties 31c

Total 77

a Ni < Np b Ni > Np c Ni = Np

Since the p-‐value <0.001, H3 is rejected. Overall, the intended image positions have not been

congruently projected on the contents of corresponding official tourism marketing websites.

As indicated by Table 6-‐5, only 31 districts have projected all of their intended image positions

on their official websites, while the remaining 46 districts have not congruently projected all of

theirs.

On the other hand, Figure 6-‐23 indicates that the distributions of projection congruence ratios

(Pr) are non-‐normal. Thus, 1-‐Sample K-‐S Test was used to test H3 against the mean value of Pr.

Figure 6-‐23 Normal Distribution Test Results of Pr values


61

Table 6-‐6 shows that the projection congruence ratios are significantly different between sam-‐

ple districts (p-‐value <0.001). Therefore, H3 is rejected again, which confirms the results of

Wilcoxon Signed Ranks Test.

Table 6-‐6 One-‐sample K-‐S Test Results of Pr Values Against Their Mean Value

N Mean Std. Deviation Std. Error Mean Sig.

Projection congruence ratio (Pr) 77 0.6266 0.38051 0.04336 0.000

In addition, when comparing the mean value of Pr to the extreme value “1” − when image po-‐

sitions of all districts have been congruently projected, the difference is around 0.3734. This

value further elaborates the results in Table 6-‐5 that the intended image positions of substan-‐

tial number of districts out of the 46 districts without congruent projections are at least par-‐

tially projected.

6.3.5 Projection differences between districts in Eastern costal China, Middle China and Western China

Due to the non-‐normal distributions, Independent Samples Jonckheere-‐Terpstra Test for Or-‐

dered Alternatives was used to compare the Pr differences between the districts in Eastern

coastal China, Middle China and Western China. Figure 6-‐24 shows that there are only signifi-‐

cant Pr differences between districts in Eastern coastal China and Western China (p-‐value

<0.016). The mean Pr value of the districts in Western China is 48.06, which is much larger than

the mean Pr value “31.39” of the districts in Eastern coastal China. The mean difference indi-‐

cates that districts in Western China are more able to congruently project their intended image

positions than the districts in Eastern coastal China.

Due to the non-‐normal distributions, independent Samples Jonckheere-‐Terpstra Test for Or-‐

dered Alternatives was also run for comparing the differences of Ob values − total times of

occurrences of all intended and unintended image positions projected by the website contents

– between the districts in Eastern coastal China, Middle China and Western China. Figure 6-‐25

shows that there is no significant Ob difference between districts in these three regions (p-‐

value >0.05). In addition, from the boxplot, the main data ranges and mean values of all three

regions are very close, in particular between Eastern coastal China and West China. This indi-‐

cates that the amounts of meaningful marketing contents on the official tourism websites are

fairly close between districts in Eastern coastal China and Western China.


62

Figure 6-‐24 Comparison results of Pr values between districts in Eastern coastal China, Middle China and Western China

Figure 6-‐25 Comparison results of Ob values between districts in Eastern coastal China, Middle China and Western China


63

With normal distributions, independent Samples Kruskal-‐Wallis Test was conducted for com-‐

paring the differences of Di (Mean) values – average actual projection density of all intended

image positions of a district – between the districts in Eastern coastal China, Middle China and

Western China

Figure 6-‐26 shows that there is no significant Di (Mean) difference between districts in Easten

coastal China, Middle China and Western China (p-‐value >0.05). In addition, from the boxplot,

the main data ranges and mean values of all three regions are very close. This indicates that

the general extents of focus on projecting intended image positions are fairly close between

districts in all three regions.

Figure 6-‐26 Comparison results of Di (mean) values between districts in Eastern coastal China,

Middle China and Western China

6.3.6 H4

H4: The extent of projection is different between the sub-‐provincial districts.

With non-‐normal distributions, 1-‐Sample K-‐S Test was run to test the differences of Pi (Mean)

values – average penetration ratio showing the average extent of projection of all intended

image positions of a district – against their mean value. Figure 6-‐27 indicates that the Pi (Mean)

values are significantly different between sample districts (p-‐value <0.007). Therefore, H4 is

accepted.


64

Figure 6-‐27 One-‐Sample K-‐S Result of H4

The Pi (Mean) values range from 0 to 12.399. The higher the value is, the more likely the in-‐

tended positions to be perceived by the website visitors. The mean Pi (Mean) value is 2.6 indi-‐

cating that nearly half of the total sample districts have only moderate Pi (Mean) values range

from 1 to 3.

From Figure 6-‐28, 19 districts have Pi (Mean) values smaller than “1”, which mean that they

have not congruently projected their intended image positions. 22 districts have considerable

Pi (Mean) values larger than “3” and 4 districts have very encouraging Pi (Mean) values larger

than “9”.

Figure 6-‐28 Frequencies of districts in different Pi intervals

In addition, the Di (Mean) value reflects the actual proportion of the contents projecting all

intended image positions of a district on its official tourism website. When Ob value is fixed,

larger Di value will directly increase Pi value. The Di (Mean) values range from 0 to 27.8% with

the mean value of 8.1%. They infer that it is not always the more marketing information on the

websites the better; but the more information focusing on the intended image positions than

other unintended image positions, the better.

Table 6-‐7 and Table 6-‐8 list the key figures of districts with lowest projection extents (LP Dis-‐

tricts) and highest projection extents (MP Districts) in order to find out the possible causes of

projection failure and success.

19 (24.7%)

36 (46.75%)

13 (16.88%) 5 (6.49%) 4 (5.19%)

0

10

20

30

40

0-‐1 ≥1, <3 ≥3, <5 ≥5, <9 >9

Freq

uency & Percentage

Pi interval


65

Table 6-‐7 The List of Districts with Lowest Projection Extents (Pi <1)

District name Pi Ob Nb Ni Oi (Mean)

Wuzhishan (no website) 0 0 0 2 0

Ledong (no website) 0 0 0 2 0

Kunming 0 18 11 1 0

Wuzhou 0 4 4 1 0

Hinggan League 0 55 24 1 0

Shuangyashan 0.2 105 21 2 1

Wuhan 0.2578125 32 11 4 0.75

Pingxiang 0.263888889 36 19 2 0.5

Jiangmen 0.483333333 40 6 3 3.22

Dandong 0.526315789 95 25 2 2

Cangzhou 0.526315789 114 36 3 1.67

Tongling 0.58974359 52 16 2 1.92

Wenchang 0.634615385 13 11 2 0.75

Dazhou 0.752688172 31 10 3 2.33

Dezhou 0.814606742 178 29 1 5

Xi'an 0.825749605 1901 39 2 40.25

Huangshi 0.875 12 7 2 1.5

Bayannur 0.884210526 38 17 5 1.6

Maoming 0.886363636 99 27 4 3.25


66

Table 6-‐8 The List of Districts with Highest Projection Extents (Pi >9)

District name Pi Ob Nb Ni Oi (Mean)

Panzhihua 9.402697495 519 40 1 122

Changzhou 10.19768084 3622 54 1 684

Turpan Prefecture 10.29775281 178 47 1 39

Pingliang 12.3991727 967 88 2 136.25

Almost all LP Districts except Xi’an have very low Ob values or lack of substantial amounts of

meaningful marketing contents. On the contrary, 3 out of the 4 MP Districts have large Ob val-‐

ues or fairly large amounts of meaningful marketing contents on their websites.

In addition, the average times of occurrences of intended image positions or Oi (Mean) values

are very small for all LP Districts. Maybe only 1 or 2 sentences or phases on the official web-‐

sites are about the intended image positions. While on average the MP Districts have much

more both the absolute (Oi) and relative (Oi/Ob) contents about the intended image positions

on their websites.

Besides, as mentioned earlier, when Ob value is fixed, higher Di value will directly increase the

projection extents (Pi). Compare Xi’an (Pi=”0.83”) and Panzhihua (Pi=”9.4”), the marketing con-‐

tents on the websites of both districts project around 40 different image-‐positioning concepts.

Xi’an has much lower Di value (=“40.25/1901”) while Panzhihua shows more focuses on pro-‐

jecting its intended image positions (Di=”122/519”) despite less amount of marketing infor-‐

mation.

Therefore, in order to increase the possibilities of image positions being perceived by website

visitors, it is important to have substantial amounts of marketing contents on the official tour-‐

ism websites while staying focused on the intended image positions.

6.4 Similarity distances of image positions between sample districts

Multi-‐Dimensional Scale Proxscal with euclidean distance algorithm was run three times with

the proximity values of districts in the Base layer, the Second layer and the Third layer. The

resulting MDS graphs visualize the distances between specific districts regarding the similarity


67

of image positions, which make it easier and quicker to pick up specific districts and check their

profiles for comparisons than clustering analysis.

Not all the sample districts have been shown on the graphs because when there are more than

4 cases overlapping in the same location, only the first 4 cases could be seen. Therefore, if a

district could not find its name on the MDS graph, it needs to first search the groups of districts

having more than 4 members and see whether its “distance value” is the same as one of the

groups. Then by following the names of the first 4 districts list in the group having the same

“distance value” with it, this district is able to identify its location on the MDS graph. For ex-‐

ample, in the middle of Figure 6-‐29 where the districts “Chaoyang”, “Shuangyashan”, “Yanbi-‐

an” and “Songyuan” are located, there in fact should have 41 districts.

Figure 6-‐29 MDS results of all sample districts with image positions in the Base layer

Figure 6-‐29 displays the MDS graph of all sample districts with image positions in the Base

layer. The model is not considered good enough because the Normalized Raw Stress is 0.316

(>0.2) and the Tucker’s Coefficient of Congruence is 0.827 (<0.85). Considering that more than

half of the total districts are having only unique image positions in the Base layer, their sub-‐

stantial group size will significantly violate the normal distribution assumption of the euclidean

distance algorithm by being the outsiders. Therefore, MDS was tried again with only 36 dis-‐

tricts whose image positions have certain degrees of similarities with other sample districts.


68

Figure 6-‐30 shows the improved MDS model. This model is considered good because its Nor-‐

malized Raw Stress is 0.132 (<0.15) and the Tucker’s Coefficient of Congruence is 0.932 (>0.9).

Figure 6-‐30 MDS results of sample districts with non-‐unique image positions in the Base layer

Figure 6-‐31 shows the MDS model of all sample districts with image positions in the Second

layer. This model is considered good because its Normalized Raw Stress is 0.146 (<0.15) and

the Tucker’s Coefficient of Congruence is 0.924 (>0.9).

Figure 6-‐31 MDS results of all sample districts with image positions in the Second layer


69

Figure 6-‐32 shows the MDS results of all sample districts with image positions in the Third lay-‐

er. This model is considered very good because its Normalized Raw Stress is 0.106 (≈0.1) and

the Tucker’s Coefficient of Congruence is 0.945 (≈0.95) indicating that the configurations are

close to actual dissimilarities.

Figure 6-‐32 MDS results of all sample districts with image positions in the Third Layer

The distance between two districts is influenced by: (1) the number of the co-‐occurred image

positions between them (Oc); and (2) the total number of their own image positions (Ni). If the

locations of two districts are overlapped on the graph, it means that they have same Ni and Oc;

and their remaining image positions that are not co-‐occurred between them should not over-‐

lap with those of any other districts. For example, in Figure 6-‐29, districts “Xiangxi” and “Lin-‐

cang” are occupying the same location on the graph. Both of them have two image positions

and one of them is shared. The remaining two image positions – one for each of the district –

are unique without any co-‐occurrence with other districts. On the other hand, Alxa has only

one image position and this position is the same as the shared one by Xiangxi and Lincang.

Thus, Alxa is located close to Xiangxi and Lincang but not overlapped with them.

In addition, when image positions are grouped into different layers, the case distributions on

the MDS graphs change a lot because the patterns of co-‐occurrences (Oc) have changed. There

are more diverse co-‐occurrence patterns and thus fewer chances of having overlapped loca-‐

tions.


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7 DISCUSSION

In this chapter, the data analysis results are discussed and their validity and reliability are eval-‐

uated.

7.1 Benchmarking of image positions

7.1.1 General overview

Overall speaking, the sample SP Districts have fairly distinctive image positions when com-‐

pared with other districts either within the same TRG Region or in different TRG Regions. The

author assumes that the similarity of destination image positions between districts is mainly

affected by the diversity and abundance of endowed tourism resources, perceived risks of

market competition, implementations of the image differentiation strategies, and the coordi-‐

nation of the higher-‐level DMOs.

After reading through all the tourism plans of the sample districts, the author find out that

almost all of them have adopted the principle of differentiation as one of their guiding rules for

developing tourism images and products. This may partially explain why the image positions of

sample districts are distinctive in general.

7.1.2 Uniqueness

Second, when compared with districts within the same TRG Region where the endowed tour-‐

ism resources and context images are more similar, it is surprise that more unique image posi-‐

tions are identified than those found when compared with districts in different TRG Regions.

In the author’s opinion, destination managers may perceive more threats from their neigh-‐

bouring districts that share similar context images and source markets, which drives them to

develop differentiated positions in order to prevent direct competition and even build up co-‐

development relationship with these neighbours. This could also be coordinated through the

tourism development plans of the higher-‐level DMOs like provincial tourism administrations

that give the specific orientations of tourism development to the districts under their authori-‐

ties, because the tourism planning of the lower-‐level districts must follow the tourism plans of

the higher-‐level administrations (Tourism law 2013; GSTP, 2003).


71

On the other hand, districts in different TRG Regions may be easily perceived as having dissimi-‐

lar context images that could automatically result in distinctive image positions for each of

them. Furthermore, they are more likely to target different source markets, which could re-‐

duce negative impacts of having co-‐occurred image positions (Jin, 2003). Moreover, a less

known destination could even increase popularity by having the same or analogical image po-‐

sition like that of a popular destination that are far away and have different source markets.

Therefore, the fewer necessities of having unique image positions and the possible benefits of

having similar images with remote and perceived non-‐competing destinations may explain why

more districts in different TRG Regions have co-‐occurred image positions.

7.1.3 Similarity

When only look at the sample districts with co-‐occurred image positions, there are more of

them when compared to the districts in different TRG Regions than to those within the same

TRG Region, which corresponds to the discussion in the above section. Nevertheless, more of

the later ones have larger extents of similarity (Ms values ≥0.05) than that of the former ones

(Md values ≥0.05). It means that if a district has overlapped image positions with other dis-‐

tricts, the total number of these districts is more likely to be larger if they locate within the

same TRG Region than that if they are in different TRG Regions.

In the author’s opinion, this may reflect the strong influences of the shared tourism resources

features and context images, as well as the less effective implementation of image differentia-‐

tion strategies. First, there are fewer distinct categories and attributes of tourism resources to

choose for the districts within the same TRG Region, whereas the potential pool of unique

elements is larger if the districts are in different TRG Regions. Second, if the provincial tourism

plans are not smart enough to develop differentiated image positions for the sub-‐provincial

districts under its authority, these districts are more highly likely to have similar image posi-‐

tions. This is unlikely to happen for the districts belong to different provinces.

7.1.4 Impacts of grouping

The commonly used positioning categories by the sample districts include “landscape of moun-‐

tain and water”, “marine”, “ethnic”, “ancient architects”, “history”, “celebrities”, “eco” and

“leisure”. They are cognitive in nature and contain more subcategories and attributes than

those less frequently used. In addition, only a minority of districts use affective image posi-‐

tions.


72

When the image positions are grouped according to the shared tourism resources (sub) cate-‐

gories, the differentiation effects of specific attributes and even sub tourism resources catego-‐

ries are reduced dramatically and make more districts under the same image positioning cate-‐

gories. This effect has very practical significance in the real life. Tourists usually have very lim-‐

ited knowledge of a specific destination, in particular when they are far away from each other.

In this case, they may equalize the image of this destination to its context image (Li, 2000).

Besides, many people may have already satisfied and made decision if the context image of

this destination fit their needs, and the additional unique specific attributes may make no dif-‐

ference in their eyes.

Therefore, it is better to seek differentiations from the higher tourism resources categories

rather than positioning with only unique specific attributes. Nevertheless, in reality, destina-‐

tions are endowed with unequal amounts and quality levels of tourism resources (Ritchie &

Crouch, 2000). It could be easier for districts with more unique tourism resources categories –

such as the districts of YGG Region (Yunnan, Guizhou, Guangxi) – to be perceived as having

unique images. While the districts with more shared tourism resources categories – such as

the districts of GFH Region (Guangdong, Fujian, Hainan) – have to rely on specific attributes for

image differentiation; and their images are more likely to be perceived as the same by tourists.

7.1.5 MDS graphs

The MDS graphs visualize the similarity between sample districts. With the help of these

graphs, destination managers are able to finish the following tasks more easily and quickly: (1)

identify and compare with competing destinations; (2) figure out how many destinations hav-‐

ing the same image positions with them and the specific names of these destinations; and (3)

analysis whether the co-‐occurrences of image positions are harmful or helpful. If the co-‐

occurred image positions belong to the destinations that are far away and target different

source markets, then they will not cause harmful competition (Jin, 2003). If they belong to the

districts having co-‐development of tourism and co-‐marketing relationship, there is also no big

problem. Only if the districts have competing relationship, their overlapped image positions

could cause vicious competition.

7.2 Projection congruence

In general, the website projections of image positions of the sample districts are not congruent

because majority of them are either not able to project or only capable to partially project


73

their intended positions. There are several possible reasons: (1) lack of substantial amount of

marketing contents on the websites; (2) too many distractions from the marketing information

about the unintended image positioning concepts; (3) destination managers may fail to focus

on their intended image positions. These reasons could be partially verified by the comparative

observations of the districts with highest projection extents (MP Districts) and lowest projec-‐

tion extents (LP Districts). MP Districts are characterized by having substantial amount of

online marketing information and high density of contents projecting their intended image

positions. On the contrary, almost all the LP Districts have poor amount of marketing infor-‐

mation on their websites; and both their absolute and relative times of occurrences of their

intended image positions are very few.

In addition, the assumption that economically advanced districts, which suppose to have more

marketing resources and expertise, have higher congruence ratios of image position projection

is tested as not true in this study. The projection performances of the sample districts in East-‐

ern coastal China are much less satisfactory than those of the districts in Western China. Be-‐

cause there is no difference regarding the total amount of online marketing information and

the actual projection density of intended image positions between districts in Eastern coastal

China and in Western China, the most likely causes of the lower projection congruence ratios

of the districts in Eastern coastal China are the relative overwhelming information about unin-‐

tended image positions and the inadequate focuses on the original image positioning inten-‐

tions.

7.3 Validity and reliability Issues

This study has employed large representative samples through probability sampling, used orig-‐

inal language of population for analysis, and followed very strict methodology from data col-‐

lection to analyses. The use of clearly stated “destination image positions” in the tourism plans

that have obtained official approvals and been published to the public has solved their validity

and reliability problems compared to interviews pursued with key informants of the tourism

administrations.

Therefore, the author thinks that the data analysis results could be generalized to the whole

population. Nevertheless, the degree of generalization is subject to the limitations of this re-‐

search, which are described in detail in the following paragraphs. Most of the limitations could

be greatly improved if more research resources such as funding, time and personnel are se-‐

cured.


74

In addition, due to the exploratory nature of this master study, the casual statements given by

the author in this discussion section are only inferential in nature. Further causal researches

are suggested to confirm these statements.

7.3.1 Limitations of data sources

The validity of image positioning projection is challenged by not including the analyses of pic-‐

torial and other multi-‐media information on the websites. Furthermore, Internet as the only

channel for data collection also increases the possibility of having missing value and unequal

distributions of the collected documents in different provinces and TRG Regions, which will

directly affect the results of data analysis that benchmarks destinations across different prov-‐

inces and TRG Regions. Nevertheless, due to the limited manpower, time and budget, the au-‐

thor has to make the trade-‐off in this study.

7.3.2 Limitations of primary data coding

Considering the potential losses of true meanings and contexts of the contents by simply

adopting automated word segmentation and sorting techniques − especially when deal with

contents in Chinese, the author conducted these processes manually based on the interpreta-‐

tions of the contexts. This is a trade-‐off process because relying on the author’s own subjective

judgment could pose other reliability issues. Although the author read through the whole

planning documents in order to interpret the positioning as closer as possible to the destina-‐

tion managers’, the positioning contents are so highly concrete that the content separation

processes may fail conveying or distort their original meanings to some extents.

The coding of website contents also solely relied on the author’s own judgment. Although a

reliability test was conducted for this coding process during the prior study stage, its function

is quite limited, whose details are described in section 5.6.

7.3.3 Limitations of intermediate data preparation

The image position grouping or aggregation processes are purely subjective and any changes

of grouping could post direct impacts on the results. Due to the special data nature of this re-‐

search, objective data aggregation techniques such as factor analysis are not applicable. Even

though two volunteers professional in Chinese language have helped check grouping results

based on semantic meanings of the Base Layer, they are not able to help with the reliability

test of the grouping results of the Second layer and the Third Layer because they have little


75

knowledge in tourism field. Due to limited professional network, the author could not find any

suitable candidate to help check the reliability of the grouping results of these two layers.

7.3.4 Limitations of data analysis

The determinations of the Ms and Md test values are only based on the author’s reasonable

but rough guesses. However, the guesses are not educated because there is no prior infor-‐

mation directly giving or referring to these test values such as the threshold of the total num-‐

ber of districts having co-‐occurred image positions that destination managers will consider

changing theirs.

7.3.5 Suggestions for improvement

Four main improvements could be done if adequate research resources are secured: (1) in-‐

clude pictures and other multi-‐media information in addition to text as data sources; (2) use

more channels in addition to the Internet to collect data such as directly asking destination

managers to give their planning documents if possible; (3) involve people who are professional

in both Chinese and tourism studies to help with the reliability test of the entire methodology;

(4) interview destination managers to gain more insights about the image position develop-‐

ment and projection processes in order to enrich the interpretations and collect more educat-‐

ed information about the Ms and Md test values.


76

8 CONCLUSION

8.1 Summary

Overall speaking, the sub-‐provincial districts in China have fairly distinctive image positions.

When compared with neighbouring districts, they are more likely to have unique image posi-‐

tions, but also more likely to have higher degrees of similarities once their image positions are

co-‐occurred with others. The situations are opposite when compared with districts that are far

away. The author assumes that the similarities of destination image positions between districts

are mainly affected by the diversity and abundance of endowed tourism resources, perceived

risks of market competition, implementations of the image differentiation strategies, and the

coordination of the higher-‐level DMOs.

The image positioning categories that are commonly used by sub-‐provincial destinations in

China are cognitive in nature and contain more subcategories and attributes than those less

frequently used. Only a minority of them adopt affective image positions. It is better to seek

differentiations from the higher tourism resources categories rather than positioning with only

unique specific attributes.

The MDS graphs that visualize the similarity of image positions between sample districts ena-‐

ble: (1) identifying and comparing with competing destinations; (2) figuring out the status quo

of their image positions; and (3) analysing whether the co-‐occurrences of image positions are

harmful or helpful.

In general, the projections of image positions on the official tourism marketing websites are

not congruent, which is probably because of: (1) lacking substantial amount of marketing con-‐

tents on the websites; (2) too many distractions from the marketing information about unin-‐

tended image positions; (3) destination managers failing to focus on their intended image posi-‐

tions. Being economically advanced with more marketing resources and expertise has no been

proved affecting the extents of projection.

With large representative samples and strictly designed and implemented methodology, the

author thinks that the results and the findings of this master study could be generalized to all

the 365 sub-‐provincial districts in China, although the degree of generalization is subject to

some limitations. The major limitation of this research is the subjectivity when doing content


77

separation and grouping. If more research resources are secured, the limitations could be im-‐

proved by: (1) including pictures and other multi-‐media information in addition to text as data

sources; (2) using more channels in addition to the Internet to collect data; (3) involving people

who are professional in both Chinese and tourism studies to help with the reliability test of the

entire methodology; (4) enriching the interpretations and collecting more educated infor-‐

mation about the test values by interviewing destination managers.

8.2 Contribution to knowledge

Few image and positioning studies to date have focused specifically on benchmarking the in-‐

tended tourism destination image positions and their extents of projection on marketing ma-‐

terial, and none has analysed this topic for China. This study addresses these knowledge defi-‐

cits.

This master study proposes a simple and fast benchmarking approach to compare and visualize

the similarity distances between a large number of destinations, which is rare in existing desti-‐

nation positioning and image studies.

There is little literature, both in Chinese and in English, about destinations and DMOs at pro-‐

vincial-‐level and local-‐level. In this study, districts at sub-‐provincial level in China are the units

of analysis.

Many content analyses on destination positioning and image have little generalization power

because of small sample size, relying on convenience sampling and neglecting influences of

geography. The content analysis in this research has addressed these restrictions and enables

the generalization of the results to the whole population – the 365 sub-‐provincial districts in

China.

8.3 Managerial Implications

8.3.1 Implications for developing image positions

8.3.1.1 Importance of benchmarking image positions

The development of tourism infrastructure and new service approaches such as e-‐commerce

will change the needs and the way people travel that force the tourism enterprises and gov-‐

ernments to make full-‐range innovations including business models, service models, manage-‐

ment models and marketing models, and thus change the overall competition structures be-‐

tween destinations (Zhang et al., 2011). Therefore, it is strategically important for destination


78

mangers to review their image positions from time to time and ensure they remain competi-‐

tive and desirable to tourists.

It is even more important for destinations with fewer comparative advantages of tourism re-‐

sources to understand and benchmark their image positions with other destinations for the

purposes of clarifying the role of tourism industry in their industry development strategies,

namely, should it be the mainstay industry or only the strategic industry facilitating the devel-‐

opment of other industries. As pointed out by Zhang et al. (2011), although the national strat-‐

egy positions tourism industry as the strategic mainstay industry, there is no need for every

local administration to give the same emphasize on tourism industry and has development

model exactly the same as the national one. The local tourism development should make sure

that the planning fits into specific local situations and homogenization issue is avoided. Even if

one tourism destination image position does not appeal to tourists, it may deliver positive

information that is attractive to investors of other industries and help enhance the overall

brand reputation and competitiveness of this destination (Zhang et al., 2011).

With the help of MDS graphs that visualize the degree of similarity between destinations, des-‐

tination managers could further look into the profiles and do analysis regarding: (1) the

sources of similarity, namely, do the co-‐occurred image positions use the general tourism re-‐

source categories or the specific attributes, whether the implementations of differentiation

strategies are effective, and do the destinations have good and unique tourism resources en-‐

dowments to develop strongly distinctive image positions; and (2) whether the co-‐occurrences

of image positions are harmful or helpful to repel or attract existing and potential visitors. All

the information is very important to assist them making decisions about how should they de-‐

velop the new image positions, or whether and how should they adjust existing image posi-‐

tions.

It could be helpful if destinations create their own database to benchmark and sort image posi-‐

tioning profiles of other destinations based on the benchmarking and MDS visualization ap-‐

proaches proposed in this master study. Once established, it is quite easy to benchmark with

specific destinations and analysis their profiles. In addition, maintaining and updating the da-‐

tabase are also simple to operate.


79

8.3.1.2 Orientations of image positioning

In general, destination managers should seek differentiations from the higher tourism re-‐

sources categories rather than positioning with only unique specific attributes in order to best

meet tourists’ diverse expectations.

Besides, it could be more effective to position images with non-‐tourism resources orientations

like the following three approaches. First, position by cultivating affective, emotional and ex-‐

periential feelings, which leave more spaces for imaginations that trigger travel motivations.

According to the results of this study, it is not yet a popular approach and dominated by using

fancy and elegant words that are difficult to tell part the true connotations if used by more

than one destination. Second, develop unconventional position by creatively integrating tour-‐

ism industry with other industries. Tourism resources should not be limited in existing catego-‐

ries. Through innovative ideas, various kinds of resources could be transformed into new tour-‐

ism resources that are not recognized before (Zhang et al., 2011). Third, adopt image positions

that are functional or complementary oriented and become the back stage performer to other

popular tourist regions in order to increase tourism revenue by absorbing more consumption

of accommodation, transportation and shopping. This approach may also transform the com-‐

petition relationship of two places into cooperative and mutual-‐beneficial relationship. Accord-‐

ing to the results of this research, fewer functional image positions are found.

The Chinese central government is encouraging regional economic development, and tourism

industry has become the hot spot of regional cooperation and co-‐development (Zhang et al.,

2011). This is particularly good for little known destinations to take advantage of inter-‐regional

cooperation and co-‐marketing, and benefit from the context images or images of popular

neighbouring destinations. This approach aims at increasing the total visitor flow to the region

consisting of cooperating destinations. Then the participating destinations, especially the less

famous ones, could have larger tourist flow than if working alone, maximize the benefits with

less investment, and naturally strengthen their destination images through the organic image

formation processes (Li, 2000; Zhou & Xiao, 2003). Besides, DMOs of high-‐levels insert signifi-‐

cant influences on this approach by developing the mixes of the image positions for the desti-‐

nations under their authorities. These mixes should enable the differentiation and the com-‐

plementation between the image positions and eventually the co-‐development of the destina-‐

tions.


80

8.3.2 Implications for improving the extent of projection

In order to improve the extent of projection, the destination managers are suggested to:

First, determine the extents they want the intended image positions to be perceived by tour-‐

ists through the promotional material (Pi). Second, record the current amount of marketing

information on their promotional material and roughly estimate that how many image posi-‐

tions are included (Ob). Third, determine the proportion of marketing contents about the in-‐

tended image positions on the promotional material (Di) in order to ensure their adequate

exposures to viewers.

Then whenever there are new inputs or deletions of marketing information, destination man-‐

agers are able to ensure the status of the intended image positions by adjusting their amount

of information according to the pre-‐determined penetration ratio (Pi). For instance, when they

realize that there is too much information about the non-‐intended image positions, they could

balance by either deleting the unwanted information or adding new information that is rele-‐

vant to the intended image positions.

8.4 Future research

Since this research is only exploratory and describing the current status of image positions and

their projections, the causal statements mentioned in the discussion section are only inferen-‐

tial, which deserve the confirmations and further digging in future researches.

More researches are suggested to further explore the world of destination managers such as

what are their true purposes when developing the destination image positions.

In addition, it could be interesting to explore the existing and potential relationships between

the intended image positions of different districts, namely, are they competing, cooperating or

copeting (co-‐existence of competing and cooperating). The factors of coordination by higher-‐

level DMOs, regional cooperation and source markets should be taken into consideration.

Moreover, the actual strategic impacts of the tourism image positions on other industries in

addition to tourism industry also deserve further studying.


81

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APPENDICES

Appendix 1: Distributions of sample districts in each province

No. Province name

Total number of

sub-‐provincial

districts

Number of

randomly

selected sample

districts

Number of

sample districts

with valid data

% of sample

districts with

valid data

1 Liaoning 14 5 3 60%

2 Jilin 10 3 2 66.67%

3 Heilongjiang 14 5 2 40%

4 Hebei (include

Beijing and Tianjin)

13 4 3 75%

5 Shandong 17 6 6 100%

6 Shanxi 11 4 1 25%

7 Shaanxi 10 3 2 66.67%

8 Henan 18 6 5 83.33%

9 Jiangsu

(include Shanghai)

14 5 5 100%

10 Zhejiang 11 4 4 100%

11 Anhui 16 5 4 80%

12 Jiangxi 11 4 3 75%

13 Sichuan

(include Chongqing)

22 7 5 71.43%

14 Hubei 17 6 4 66.67%

15 Hunan 14 5 4 80%

16 Guangdong 21 7 5 71.43%

17 Fujian 9 3 3 100%


93

No. Province name

Total number of

sub-‐provincial

districts

Number of

randomly

selected sample

districts

Number of

sample districts

with valid data

% of sample

districts with

valid data

18 Hainan 21 7 3 42.86%

19 Guizhou 9 3 1 33.33%

20 Yunnan 16 5 4 80%

21 Guangxi Zhuang

Autonomous

Region

14 4 4 100%

22 Gansu 15 5 2 40%

23 Ningxia Hui

Autonomous

Region

5 2 0 0%

24 Xinjiang Uyghur

Autonomous

Region

16 5 3 60%

25 Inner Mongolia

Autonomous

Region

12 4 4 100%

26 Qinghai 8 3 1 33.33%

27 Tibet Autonomous

Region

7 2 0 0%


94

Appendix 2: Distributions of sample districts in each TRG Region

No. Region name

Total number of

sub-‐provincial

districts

Number of

randomly selected

sample districts

Number of

sample districts

with valid data

% of sample

districts with

valid data

1 LJH Region 38 13 7 54%

2 BTSH Region 69 23 17 74%

3 SJZAJ Region 52 18 16 89%

4 CSHH Region 53 18 13 72%

5 GFH Region 51 17 11 65%

6 YGG Region 40 12 9 75%

7 XNG Region 36 12 5 42%

8 IM Region 12 4 4 100%

9 QT Region 15 5 1 20%


95

Appendix 3: Intended image positions of sample districts

No. Name Intended image positions

1 Dandong Yalv River鸭绿江畔 Beautiful 美丽

2 Huludao Coastal and marine customs 海洋及滨海休闲 Hulu Island, a place full of treasure葫芦宝岛 Happiness 幸福

3 Chaoyang Holy land of Buddhism in northeast China东北佛教圣地 Dawn of Chinese civilization中华文明曙光 Fossil kingdom世界化石王国

4 Songyuan Return to nature回归大自然 Dynamic动感 Style of ethnic Meng and ethnic Man蒙满民族风情

5 Yanbian Holy landscape of mountain and water圣洁山水 Style of ethnic Korean and cabaret朝鲜族风情、歌舞之乡

6 Harbin Abode of ice and snow世界冰雪旅游名城 City with Eurasian style欧亚风情之都 Summer resort避暑胜地 Ecological garden city生态园林之城 National historical and cultural city国家历史文化名城

7 Shuangyashan Bei’da’huang北大荒 New “Tianfu (land of abundance)”中国新天府

8 Zhangjiakou A place for passionate vacation激情度假地 Sports city运动城 Grand land大好河山 Fashion时尚

9 Cangzhou Acrobatics杂技 Kung fu 武术 Ecolandscape in marine areas滨海生态

10 Beijing NAa

11 Dezhou Sunshine阳光

12 Jining Confucian culture儒家文化

13 Heze Forest city on the plain region中国平原森林城市 City of peony中国牡丹城

14 Binzhou Hometown of Sunzi孙子故里 Eco生态 Great beauty大美


96


15 Dongying Magic view of Yellow River’s estuary神奇黄河口 Dreamy oil city梦幻石油城 Ecological diversity生态大观园

16 Yantai Golen coast and beaches黄金海岸 Fairyland full of Taoism fairy tales人间仙境 City of grape wine葡萄酒城

17 Linfen Hometown of Emperor Yao尧乡韵 Charismatic Chinese civilization魅力文明

18 Yulin City of frontier fortress边塞明珠 Gorgeous old castles斑斓古堡 Magic神奇

19 Xi'an Chinese ancient capital华夏故都 Landscape of mountain and water山水之城

20 Sanmenxia Great beauty of Sanmenxia大美之峡 Origin of Taoism大道之源

21 Kaifeng Ancient capital of Song dynasty中国宋都 Watertown with north view北方水城 Native land of Chrysanthemum菊乡 Cuisine tourism食府 Ancient capital of seven dynasties七朝古都

22 Puyang Ecological charisma生态韵味

23 Hebi Mountain of immortals仙山 Poetic river诗水 Full of legends传奇

24 Anyang Three "Yang" initiate “Fortune" 三阳开泰

25 Liangyungang Magic with culture of Monkey King and Xufu神奇（孙悟空和徐福） Romantic浪漫 Landscape of mountain and sea山海

26 Changzhou Chinese dragen city with dragen culture充满龙文化的中华龙城

27 Yancheng Wetlands for red-‐crowned cranes and elk东方湿地（丹顶鹤和麋鹿的故乡）

28 Xuzhou Chinese Terracotta Warriors tour 中国兵马俑之旅 Cradle of Han dynasty and its culture style大汉之源 Scenic秀 Magnificent雄


97


29 Huai'an City near Beijing-‐Hangzhou Grand Canal运河之都 Hometown of celebrities名人故里 Ecohome生态家园 Food town of Huaiyang cuisine淮扬美食之乡

30 Jiaxing NAa

31 Quzhou Landscape of mountain and water山水名城 Magic神奇

32 Shaoxing Watertown with south view水城 Ancient capital of Yue Kingdom越都 Diverse humanities culture 丰富的人文文化

33 Hangzhou Leisure place for quality of life and happiness休闲之都（品质生活、人间幸福天堂）

City with oriental style东方

34 Bengbu Hometown of Emperor Yu禹王家园 City near Huai River淮上明珠城 Leisure place休闲 City of jade玉城

35 Huangshan Mount Huangshan黄山 Huizhou and Hui culture徽州

36 Chuzhou Cradle of Ming dynastry大明摇篮 Intoxicated landscape of mountain and water山水醉城

37 Tongling Chinese ancient bronze city中国古铜都 Last green home for Lipotes vexillifer白鳍豚的最后家园

38 Pingxiang Culture of encounter and destiny缘分邂逅 Modern leisure resort for LOHAS and wellness具有现代特点的乐活养生休闲度假胜地

39 Yingtan City of Taoism华夏道都 Landcape of mountain and water山水 City of bronze铜都 Charisma魅力

40 Shangrao Intoxicated landscape of mountain, water and pastoral令人沉醉的山水田园

41 Dazhou Attractive landscape of Qin-‐ba mountain area秦巴胜景 Hometown and history of Ba people巴人故里 Red tourism (Chinese modern revolutionary history) 红色旅游

42 Guangyuan Old town of North Sichuan style川北古镇 History of the Three Kingdoms period三国历史 Hometown of Empress Wu Zetian女皇故里


98


43 Luzhou City of wine中国酒城 Intoxicated beauty醉美

44 Panzhihua Capital full of sunshine and sunny living style阳光活力之都

45 Suining Hometown of Buddhism goodness Guanyin观音故里 Capital of caring and charity慈善爱心都

46 Huanggang Picturesque landscape of mountain and river江山如画 Colorful culture and humanities文化大彩

47 Huangshi Ancient city of mining and metallurgy矿冶古都 Landscape of mountain and water山水

48 Wuhan Culture of bosom friend知音文化 Yellow Crane Tower黄鹤楼 City near Changjiang River江城 Numeral lakes百湖

49 Xiangyang NAa

50 Xiangxi Mysterious land神秘 Picturesque of the ethnic style文化生态风情画境

51 Yongzhou NAa

52 Zhuzhou Holy land of Emperor Yan culture神农福地 Happy place中部欢乐城 City of energy and power动力之都

53 Huaihua Style of ethnics in Wuxi五溪风情 Style of ethnic Dong侗家风情 Old town and old village古城古镇古村落遗产

54 Shantou Coastal and marine customs海滨 Chaozhou culture and home of overseas Chaozhou Chinese潮韵 Leisure place休闲之都 Commercial city商都

55 Yangjiang NAa

56 Jiangmen Home of overseas Chinese侨乡 Ease and comfort逸／自在 Landscape of mountain, sea and river山海江门景

57 Maoming Coastal and marine customs海滨风情 Hometown of Mrs. Xian冼太故里 City of litchies中国荔乡 Landscape of mountain and water云山鉴水


99


58 Meizhou City of Hakka culture世界客家之都 Colourful千色

59 Fuzhou Hot spring温泉 Ancient capital古都 Place full of “Fu” (happiness and forture) 有福之州

60 Putian Holy land of Mazu (Goddess of the Sea) 妈祖圣地 Southern Shaolin Temple南国少林 Coastal and marine customs滨海新城 Landscape of pastoral and water田园水乡

61 Sanming NAa

62 Wuzhishan Holy mountain in South China Sea南海圣山 Aboriginal land of ethnics民族原乡

63 Wenchang Space science tourism航天科普观光 Coastal eco leisure resort海岸生态休闲度假

64 Ledong Li Autono-‐mous County

Beautiful美丽 Leisure place休闲天堂

65 Guiyang Cool summer resort爽爽的 Original true原真

66 Kunming Spring city春城

67 Lincang Homeland of Ethnic Wa世界佤乡 Mysterious land秘境

68 Honghe Railway between Yunnan and Vietnam滇越路 Hani Terrace万世哈尼梯田 Great beauty大美

69 Zhaotong Magnificent磅礡乌蒙 Magic神奇

70 Fangchenggang Coastal and marine customs南疆休闲港湾 Transnational corridors in marine areas边海跨国廊道

71 Wuzhou Shining bright 璀璨

72 Guilin The best landscape of mountain and water山水甲天下 Leisure place世界休闲度假之都

73 Hechi Longevity resorts长寿福地 Regimen养生天堂 Magic神奇


100


74 Pingliang Mount Kongtong and its magic culture神奇崆峒 Regimen养生

75 Baiyin Magic view of Yellow River黄河奇观 Red tourism (Chinese modern revolutionary history) 红色圣地

76 Turpan Turpan grapes吐鲁番的葡萄熟了

77 Tacheng Leisure place休闲

78 Shihezi City of army reclaimation culture军城 Green city绿城 City of poetry诗城

79 Chifeng The origin of northern China civilization中国北方文明之源 Prairie (with biodiversity, nomadic culture and nearest distance to Bei-‐jing) 草原(生态大观、草原文化、距离北京最近最美的内蒙古草原) China dragen city with the jade dragen relic中华第一龙的故乡 Natural museum of biological and geological diversity生物与地质多样性的天然博物馆

Ancient capital of Liao dynasty契丹辽王朝的故都

80 Bayannur Hetao region河套 The northernmost of Yellow River九曲黄河最北处 Mount Yinshan and its magic culture神奇阴山 Prairie style (the magical Urad prairie style) 神奇的乌拉特草原风情 Desert view大漠风采

81 Alxa Mysterious land中国秘境

82 Hinggan Where the heart belongs心所在

83 Haibei Eco 生态 a No intended image position was identified and extracted from the tourism plan


101

Appendix 4: Image positions and their frequencies in all three layers

Third layer Fa Second layer Fa Base layer Fa

Cognitive image positions

Fossil kingdom 世界化石王国

1 Fossil kingdom 世界化石王国

1 Fossil kingdom 世界化石王国

1

Eco 生态

9 Eco/Eco-‐home 生态／生态家园

3 Eco/Eco-‐home 生态／生态家园

3

Ecological garden city / green city

生态园林之城／绿城

2 Ecological garden city / green city 生态园林之城／绿城

2

Return to nature 回归大自然

1 Return to nature 回归大自然

1

Ecological diversity 生态种类景观多样

2 Ecological diversity 生态大观园

1

Natural museum of biological and geological diversity

生物与地质多样性的天然博物馆

1

Ecological charisma 生态韵味

1 Ecological charisma 生态韵味

1

Landscape of mountain, water and pastoral 山水田园风光

15 Landscape of mountain and water

山水

10 Landscape of mountain and water 山水／山水之城／云山鉴水

5

Holy landscape of mountain and water 圣洁山水

1

The best landscape of mountain and water 山水甲天下

1

Intoxicated landscape of mountain and water

山水醉城

1

Mountain of immortals 仙山

1

Poetic river 诗水

1

Landcape of mountain and sea 山海

2 Landscape of mountain, sea and river 山海江门景

1


102


Landcape of mountain and sea 山海

1

Picturesque landscape of mountain and river 江山如画

1 Picturesque landscape of mountain and river 江山如画

1

Intoxicated landscape of mountain, water and pastoral 令人沉醉的山水田园

1 Intoxicated landscape of mountain, water and pastoral 令人沉醉的山水田园

1

Landscape of pastoral and water 田园水乡

1 Landscape of pastoral and water 田园水乡

1

Climate 气候

6 Sunshine 阳光

2 Sunshine 阳光

1

Capital full of sunshine and sunny living style 阳光活力之都

1

Summer resort 避暑

2 Summer resort 避暑胜地

1

Cool summer resort 爽爽的

1

Four seasons 季节

2 Abode of ice and snow 世界冰雪旅游名城

1

Spirng city 春城

1

Water 水

11 Watertown 水城

2 Watertown with south view 水城

1

Watertown with north view 北方水城

1

Marine, coastal and beaches 滨海

7 Coastal and marine customs 海洋／滨海休闲／海滨风情

5

Golden coast and beaches 黄金海岸

1

Ecolandscape in marine areas 滨海生态

1

Numeral lakes 百湖

1 Numeral lakes 百湖

1


103


Hot spring 温泉

1 Hot spring 温泉

1

Desert view 大漠风采/荒漠

1 Desert view 大漠风采

1 Desert view 大漠风采

1

Prairie 草原

2 Prairie 草原

2 Prairie (with biodiversity, nomadic culture and nearest to Beijing)

草原 (生态大观、草原文化、距离北京最近最美的内蒙古草原)

1

Prairie style (the magical Urad prairie style)

神奇的乌拉特草原风情

1

Forest city on the plain region

中国平原森林城市

1 Forest city on the plain region 中国平原森林城市

1 Forest city on the plain region 中国平原森林城市

1

Flower 花

2 Flower 花

2 City of peony 中国牡丹城

1

Native land of chrysanthemum 菊乡

1

Conservation of rare animals 珍稀动物保育

2 Conservation of rare animals 珍稀动物保育

2 Wetlands for red-‐crowned cranes and elk

东方湿地（丹顶鹤和麋鹿的故乡）

1

Last green home for Lipotes vexillifer 白鳍豚的最后家园

1

Leisure 休闲

9 Leisure place 休闲／休闲之都／休闲天堂

／世界休闲度假之都

5 Leisure place 休闲／休闲之都／休闲天堂／

世界休闲度假之都

5

Point out the deeper meaning of leisure tourism

解读了休闲的内涵内容

3 Leisure place for quality of life and happiness

休闲之都 (品质生活、人间幸福天堂)

1

Modern leisure resort for LOHAS and wellness

具现代特点的乐活养生休闲胜地

1

Coastal eco leisure resort 海岸生态休闲度假

1

Ease and comfort 逸／自在

1 Ease and comfort 逸／自在

1


104


Regimen 养生

3 Regimen 养生

3 Longevity resorts 长寿福地

1

Regimen 养生天堂

2

Sports city 运动城

1 Sports city 运动城

1 Sports city 运动城

1

Cuisine tourism 美食

2 Cuisine tourism 美食

2 Food town of Huaiyang cuisine 淮扬美食之乡

1

Cuisine tourism 食府

1

Local specialities 特产

5 Turpan grapes 吐鲁番的葡萄熟了

1 Turpan grapes 吐鲁番的葡萄熟了

1

City of litchies 中国荔乡

1 City of litchies 中国荔乡

1

City of jade 玉城

1 City of jade 玉城

1

City of wine 酒城

2 City of wine 中国酒城

1

City of grape wine 葡萄酒城

1

Ancient architect 古都古城建筑风貌

9 Ancient capital 古都

6 China ancient capital 华夏故都

1

Ancient capital of Yue Kingdom 越都

1

Ancient capital of Liao dynasty 契丹辽王朝的故都

1

Ancient capital of Song dynasty 中国宋都

1

Ancient capital of seven dynasties 七朝古都

1

Ancient capital 古都

1

Old town and architectures 古城镇建筑

3 Old town of North Sichuan style 川北古镇

1


105


Old town and old village 古城古镇古村落遗产

1

Gorgeous old castles 斑斓古堡

1

Chinese civilization 中华文明

9 Origins of Chinese civilization 中华文明之源

3 Charismatic Chinese civilization 魅力文明

1

The origin of northern China civilization 中国北方文明之源

1

Dawn of Chinese civilization 中华文明曙光

1

Dragen culture 龙文化

2 China dragen city with dragen culture 充满龙文化的中华龙城

1

China dragen city with the jade dragen relic 中华第一龙的故乡

1

Confucian culture 儒家文化

1 Confucian culture 儒家文化

1

Taoism 道家道教

3 Fairyland full of Taoism fairy tales 人间仙境

1

Origin of Taoism 大道之源

1

City of Taoism 华夏道都

1

Chinese history 中国历史文化

9 National historical and cultural city 国家历史文化名城

1 National historical and cultural city 国家历史文化名城

1

Chinese ancient history 古代历史文化

6 Hometown and history of Ba people 巴人故里

1

Hometown of Emperor Yu 禹王家园

1

History of the Three Kingdoms period 三国历史

1

Chinese Terracotta Warriors tour 中国兵马俑之旅

1


106


Cradle of Han dynasty and its culture style

大汉之源

1

Cradle of Ming dynasty 大明摇篮

1

Red tourism (Chinese modern revolutionary history）红色旅游／红色圣地

2 Red tourism (Chinese modern revolu-‐tionary history）

红色旅游／红色圣地

2

Hometown of celebrities 名人故里

6 Hometown of celebrities 名人故里

6 Hometown of celebrities 名人故里

1

Hometown of Emperor Yao 古尧都

1

Hometown of Sunzi 孙子故里

1

Hometown of Empress Wu Zetian 女皇故里

1

Hometown of Mrs. Xian 冼太故里

1

Hometown of Buddhism godness Guanyin

观音故里

1

Humanities and culture 人文文化

11 Diversity of humanities and culture

形容人文文化的丰富性

2 Diverse humanities culture 丰富的人文文化

1

Colorful culture and humanities 文化大彩

1

Culture of Fate and encounter 缘分邂逅

1 Culture of encounter and destiny 缘分邂逅

1

Culture of bosom friend 知音文化

1 Culture of bosom friend 知音文化

1

Capital of caring and charity 慈善爱心都

1 Capital of caring and charity 慈善爱心都

1

"Fu" culture 福

2 Happiness 幸福

1

Place full of "Fu" 有福之州

1


107


Kung fu 武术

1 Kung fu 武术

1

Acrobatics 杂技

1 Acrobatics 杂技

1

City of poetry 诗城

1 City of poetry 诗城

1

City of army reclaimation culture

军城

1 City of army reclaimation culture 军城

1

Oriental and occidental customs 东西方风情

2 Oriental and occidental customs 东西方风情

2 City with Eurasian style 欧亚风情之都

1

City with oriental style 东方

1

Style of ethnics 少数民族风情

7 Style of ethnics 少数民族风情

7 Style of ethnic Meng and ethnic Man 蒙满民族风情

1

Aboriginal land of ethnics 民族原乡

1

Homeland of ethnic Wa 世界佤乡

1

Picturesque of the ethnic style 文化生态风情画境

1

Style of ethnics in Wuxi 五溪风情

1

Style of ethnic Dong 侗家风情

1

Style of ethnic Korean and cabaret朝鲜族风情、歌舞之乡

1

Immigrant a nd roots-‐seeking

culture 移民迁徙寻根文化

3 Immigrant and roots-‐seeking culture

移民迁徙寻根文化

3 Chaozhou culture and home of over-‐seas Chaozhou Chinese

潮韵

1

Home of overseas Chinese 侨乡

1

City of Hakka culture 世界客家之都

1


108


Holy land in Chinese culture

圣地

4 Holy land in Chinese culture 圣地

4 Holy land of Emperor Yan culture 神农福地

1

Holy land of Mazu (Goddess of the Sea)

妈祖圣地

1

Holy mountain in South China Sea 南海圣山

1

Holy land of Buddhism in Northeast China 东北佛教圣地

1

Manufacturing industry tourism 工业旅游

5 City of bronze 铜都

2 Chinese ancient bronze city 中国古铜都

1

City of bronze 世界铜都

1

Dreamy oil city 梦幻石油城

1 Dreamy oil city 梦幻石油城

1

The ancient city of mining and metallurgy

矿冶古都

1 The ancient city of mining and metallurgy

矿冶古都

1

City of energy and power 动力之都

1 City of energy and power 动力之都

1

Commercial city 商都

1 Commercial city 商都

1 Commercial city 商都

1

Space science tourism

航天科普观光

1 Space science tourism 航天科普观光

1 Space science tourism 航天科普观光

1

Special location 区位

5 City near river 临河城市

3 City near Huai River 淮上明珠城

1

City near Beijing-‐Hangzhou Grand Canal 运河之都

1

City near Changjiang River 江城

1

Transnational corridors in marine areas 边海跨国廊道

1 Transnational corridors in marine areas 边海跨国廊道

1


109


City of frontier fortress 边塞明珠

1 City of frontier fortress 边塞明珠

1

Image positions named after attractions

直接用专有地名作

为吸引物

16 Yellow river 黄河

3 Magic view of Yellow River’s estuary 神奇黄河口

1

Magic view of Yellow River 黄河奇观

1

The northernmost of Yellow River 九曲黄河最北处

1

Yalv River 鸭绿江畔

1 Yalv River 鸭绿江畔

1

Hulu Island, a place full of treasure

葫芦宝岛

1 Hulu Island, a place full of treasure 葫芦宝岛

1

Bei’da’huang 北大荒

1 Bei’da’huang 北大荒

1

Hetao region 河套

1 Hetao region 河套

1

Attractive landscape of Qin-‐ba mountain area 秦巴胜景

1 Attractive landscape of Qin-‐ba moun-‐tain area 秦巴胜景

1

Railway between Yunnan and Vietnam

滇越路

1 Railway between Yunnan and Vietnam

滇越路

1

Hani Terrace 万世哈尼梯田

1 Hani Terrace 万世哈尼梯田

1

Mount Kongtong and its magic culture

神奇崆峒

1 Mount Kongtong and its magic culture

神奇崆峒

1

Mount Yinshan and its magic culture

神奇阴山

1 Mount Yinshan and its magic culture 神奇阴山

1

Mount Huangshan 黄山

1 Mount Huangshan 黄山

1

Huizhou and Hui culture 徽州

1 Huizhou and Hui culture 徽州

1


110


Southern Shaolin Temple 南国少林

1 Southern Shaolin Temple 南国少林

1

Yellow Crane Tower 黄鹤楼

1 Yellow Crane Tower 黄鹤楼

1

Affective image positions

Beautiful 美丽

6 Beautiful 美丽

2 Beautiful 美丽

2

Intoxicated beauty 醉美

1 Intoxicated beauty 醉美

1

Great beauty 大美

3 Great beauty 大美

2

Great beauty of Sanmenxia 大美之峡

1

Magic 神奇

5 Magic 神奇

5 Magic 神奇

4

Magic with culture of Monkey King and Xufu 神奇（孙悟空和徐福）

1

Scenic 秀

1 Scenic 秀

1 Scenic 秀

1

Magnificent 雄／磅礡乌蒙

2 Magnificent 雄／磅礡乌蒙

2 Magnificent 雄／磅礡乌蒙

2

Grand land 大好河山

1 Grand land 大好河山

1 Grand land 大好河山

1

Colourful 千色

1 Colourful 千色

1 Colourful 千色

1

Original true 原真

1 Original true 原真

1 Original true 原真

1

New "Tianfu" (land of abun-‐

dance) 中国新天府

1 New "Tianfu" (land of abundance) 中国新天府

1 New "Tianfu" (land of abundance) 中国新天府

1

Three "Yang" initiate “Fortune" 三阳开泰

1 Three "Yang" initiate “Fortune" 三阳开泰

1 Three "Yang" initiate “Fortune" 三阳开泰

1


111


Shining bright 璀璨

1 Shining bright 璀璨

1 Shining bright 璀璨

1

Full of legends 传奇

1 Full of legends 传奇

1 Full of legends 传奇

1

Mysterious land 秘境／神秘

3 Mysterious land 秘境／神秘

3 Mysterious land 秘境／神秘

3

Fashion 时尚

1 Fashion 时尚

1 Fashion 时尚

1

Romantic 浪漫

1 Romantic 浪漫

1 Romantic 浪漫

1

Happy place 中部欢乐城

1 Happy place 中部欢乐城

1 Happy place 中部欢乐城

1

A place for passionate vacation 激情度假地

1 A place for passionate vacation 激情度假地

1 A place for passionate vacation 激情度假地

1

Dynamic 动感

1 Dynamic 动感

1 Dynamic 动感

1

Charisma 魅力

1 Charisma 魅力

1 Charisma 魅力

1

Where the heart belongs 心所在

1 Where the heart belongs 心所在

1 Where the heart belongs 心所在

1

a Frequency of the image position


112

Appendix 5: Ob, Di and Pi values of sample districts

District name

Ob Di (Meana) Pi (Meanb) Intended image position Di Pi

Dandong 95 2.11% 0.526 Yalv River 2.11% 0.526

Beautiful 2.11% 0.526

Huludao 106 11.95% 2.868

Coastal and marine customs 35.85% 8.604

Hulu Island, a place full of treasure 0.00% 0

Happiness 0.00% 0

Chaoyang 789 8.62% 3.534

Holy land of Buddhism in northeast China 8.37% 3.43

Dawn of Chinese civilization 8.49% 3.482

Fossil kingdom 9.00% 3.689

Songyuan 85 8.04% 1.929

Return to nature 21.18% 5.082

Dynamic 0.00% 0

Style of ethnic Meng and ethnic Man 2.94% 0.706

Yanbian 388 7.32% 2.856

Holy landscape of mountain and water 11.17% 4.356

Style of ethnic Korean and cabaret 3.48% 1.357

Harbin 239 6.15% 2.214

Abode of ice and snow 9.21% 3.314

City with Eurasian style 5.44% 1.958

Summer resort 0.84% 0.301

Ecological garden city 11.30% 4.067

National historical and cultural city 3.97% 1.431

Shuangya-‐shan 105 0.95% 0.2 Bei’da’huang

1.90% 0.4


113

District name


New “Tianfu (land of abundance)” 0.00% 0

Zhangjiakou 686 2.31% 1.431

A place for passionate vacation 0.73% 0.452

Sports city 1.31% 0.813

Grand land 6.03% 3.736

Fashion 1.17% 0.723

Cangzhou 114 1.46% 0.526

Acrobatics 0.88% 0.316

Kung fu 2.63% 0.947

Ecolandscape in marine areas 0.88% 0.316

Dezhou 178 2.81% 0.815 Sunshine 2.81% 0.815

Jining 138 21.01% 3.572 Confucian culture 21.01% 3.572

Heze 104 17.79% 4.447

Forest city on the plain region 0.00% 0

City of peony 35.58% 8.894

Binzhou 85 8.63% 1.553

Hometown of Sunzi 14.12% 2.541

Eco 11.76% 2.118

Great beauty 0.00% 0

Dongying 81 9.26% 1.204

Magic view of Yellow River’s estuary 14.81% 1.926

Dreamy oil city 1.85% 0.241

Ecological diversity 11.11% 1.444

Yantai 1336 4.89% 3.325 Golen coast and beaches 6.59% 4.479

Fairyland full of Taoism fairy tales 4.27% 2.901


114

District name


City of grape wine 3.82% 2.596

Linfen 51 12.75% 1.784 Hometown of Emperor Yao 13.73% 1.922

Charismatic Chinese civili-‐zation 11.76% 1.647

Yulin 62 6.99% 2.027

City of frontier fortress 4.84% 1.403

Gorgeous old castles 3.23% 0.935

Magic 9.68% 2.806

Xi'an 1901 2.12% 0.826 Chinese ancient capital 1.55% 0.605

Landscape of mountain and water 2.68% 1.046

Sanmenxia 339 5.80% 1.798 Great beauty of Sanmenxia 10.42% 3.231

Origin of Taoism 1.18% 0.366

Kaifeng 219 10.05% 3.717

Ancient capital of Song dynasty 22.83% 8.447

Watertown with north view 1.83% 0.676

Native land of Chrysan-‐themum 5.02% 1.858

Cuisine tourism 11.42% 4.224

Ancient capital of seven dynasties 9.13% 3.379

Puyang 9 27.78% 2.5 Ecological charisma 27.78% 2.5

Hebi 217 7.60% 2.281

Mountain of immortals 17.05% 5.115

Poetic river 3.46% 1.037

Full of legends 2.30% 0.691

Anyang 20 15.00% 1.35 Three "Yang" initiate “Fortune" 15.00% 1.35


115

District name


Liangyun-‐gang

216 6.79% 2.309

Magic with culture of Monkey King and Xufu 9.72% 3.306

Romantic 0.93% 0.315

Landscape of mountain and sea 9.72% 3.306

Changzhou 3622 18.88% 10.198 Chinese dragen city with dragen culture 18.88% 10.198

Yancheng 358 12.57% 5.154 Wetlands for red-‐crowned cranes and elk 12.57% 5.154

Xuzhou 500 4.45% 1.647

Chinese Terracotta Warriors tour 0.60% 0.222

Cradle of Han dynasty and its culture style 16.40% 6.068

Scenic 0.60% 0.222

Magnificent 0.20% 0.074

Huai'an 285 14.34% 3.586

City near Beijing-‐Hangzhou Grand Canal 2.28% 0.57

Hometown of celebrities 42.81% 10.702

Ecohome 3.86% 0.965

Food town of Huaiyang cuisine 8.42% 2.105

Quzhou 624 3.57% 1.498


Magic 0.16% 0.067

Shaoxing 1880 4.34% 2.43

Watertown with south view 2.77% 1.549

Ancient capital of Yue Kingdom 3.99% 2.234

Diverse humanities culture 6.26% 3.507

Hangzhou 982 5.80% 1.915

Leisure place for quality of life and happiness 10.39% 3.428

City with oriental style 1.22% 0.403


116

District name


Bengbu 195 4.94% 1.234

Hometown of Emperor Yu 5.64% 1.41

City near Huai River 2.82% 0.705

Leisure place 10.26% 2.564

City of jade 1.03% 0.256

Huangshan 1033 7.43% 3.269 Mount Huangshan 11.62% 5.111

Huizhou and Hui culture 3.24% 1.427

Chuzhou 388 4.12% 1.856 Cradle of Ming dynastry 5.15% 2.32

Intoxicated landscape of mountain and water 3.09% 1.392

Tongling 52 3.69% 0.59 Chinese ancient bronze city 4.49% 0.029

Last green home for Lipotes vexillifer 71.79% 0.462

Pingxiang 36 1.39% 0.264

Culture of encounter and destiny 2.78% 0.528

Modern leisure resort for LOHAS and wellness 0.00% 0

Yingtan 29 13.79% 1.517

City of Taoism 17.24% 1.897

Landcape of mountain and water 20.69% 2.276

City of bronze 0.00% 0

Charisma 17.24% 1.897

Shangrao 103 9.22% 3.782 Intoxicated landscape of mountain, water and pas-‐toral 9.22% 3.782

Dazhou 31 7.53% 0.753

Attractive landscape of Qin-‐ba mountain area 3.23% 0.323

Hometown and history of Ba people 9.68% 0.968


117

District name


Red tourism (Chinese modern revolutionary history) 9.68% 0.968

Guangyuan 264 7.01% 2.172

Old town of North Sichuan style 3.22% 0.998

History of the Three King-‐doms period 10.61% 3.288

Hometown of Empress Wu Zetian 7.20% 2.231

Luzhou 200 5.13% 1.845 City of wine 4.50% 1.62

Intoxicated beauty 5.75% 2.07

Panzhihua 519 23.51% 9.403 Capital full of sunshine and sunny living style 23.51% 9.403

Suining 94 7.45% 1.564

Hometown of Buddhism goodness Guanyin 12.77% 2.681

Capital of caring and charity 2.13% 0.447

Huanggang 50 19.33% 3.867

Picturesque landscape of mountain and river 14.67% 2.933

Colorful culture and humanities 24.00% 4.8

Huangshi 12 12.50% 0.875

Ancient city of mining and metallurgy 4.17% 0.292


Wuhan 32 2.34% 0.258

Culture of bosom friend 0.00% 0

Yellow Crane Tower 0.00% 0

City near Changjiang River 6.25% 0.688

Numeral lakes 3.13% 0.344

Xiangxi 590 14.49% 3.913 Mysterious land 7.46% 2.014

Picturesque of the ethnic style 21.53% 5.812

Zhuzhou 57 6.14% 1.351 Holy land of Emperor Yan culture 11.40% 2.509


118

District name


Happy place 7.02% 1.544

City of energy and power 0.00% 0

Huaihua 228 6.14% 1.289

Style of ethnics in Wuxi 3.51% 0.737

Style of ethnic Dong 4.82% 1.013

Old town and old village 10.09% 2.118

Shantou 44 8.52% 1.023

Coastal and marine cus-‐toms 20.45% 2.455

Chaozhou culture and home of overseas Chaozhou Chinese 11.36% 1.364


Commercial city 0.00% 0

Jiangmen 40 8.06% 0.483

Home of overseas Chinese 12.50% 0.75

Ease and comfort 0.00% 0

Landscape of mountain, sea and river 11.67% 0.7

Maoming 99 3.28% 0.886


Hometown of Mrs. Xian 2.02% 0.545

City of litchies 3.03% 0.818


Meizhou 2205 13.93% 6.967 City of Hakka culture 20.41% 10.204

Colourful 7.46% 3.73

Fuzhou 226 4.87% 2.336 Hot spring 9.73% 4.673

Ancient capital 4.87% 2.336


119

District name


Place full of “Fu” (happi-‐ness and forture) 0.00% 0

Putian 82 6.86% 1.715

Holy land of Mazu (God-‐dess of the Sea) 17.07% 4.268

Southern Shaolin Temple 2.44% 0.61


Landscape of pastoral and water 3.05% 0.762

Wuzhishan 0 0.00% 0

Holy mountain in South China Sea 0.00% 0

Aboriginal land of ethnics 0.00% 0

Wenchang 13 5.77% 0.635 Space science tourism 0.00% 0

Coastal eco leisure resort 11.54% 1.269

Ledong 0 0.00% 0 Beautiful 0.00% 0

Leisure place 0.00% 0

Guiyang 808 4.58% 5.999 Cool summer resort 4.95% 6.485

Original true 4.21% 5.512

Kunming 18 0.00% 0 Spring city 0.00% 0

Lincang 309 14.08% 6.194 Homeland of Ethnic Wa 23.62% 10.395

Mysterious land 4.53% 1.994

Honghe 798 5.22% 2.872

Railway between Yunnan and Vietnam 3.38% 1.861

Hani Terrace 4.14% 2.274

Great beauty 8.15% 4.48

Zhaotong 195 10.00% 4.5 Magnificent 13.85% 6.231


120

District name


Magic 6.15% 2.769

Fangcheng-‐gang

166 10.84% 2.494


Transnational corridors in marine areas 6.93% 1.593

Wuzhou 4 0.00% 0 Shining bright 0.00% 0

Guilin 621 6.32% 4.74

The best landscape of mountain and water 11.43% 8.575


Hechi 325 4.26% 1.575

Longevity resorts 6.46% 2.391

Regimen 1.23% 0.455

Magic 5.08% 1.878

Pingliang 967 14.09% 12.399

Mount Kongtong and its magic culture 9.26% 8.145

Regimen 18.92% 16.654

Baiyin 166 8.13% 1.708 Magic view of Yellow River 8.43% 1.771 Red tourism (Chinese modern revolutionary history)

7.83% 1.645

Turpan 178 21.91% 10.298 Turpan grapes 21.91% 10.298

Tacheng 241 4.15% 2.199 Leisure place 4.15% 2.199

Shihezi 104 11.22% 2.131

City of army reclaimation culture 25.96% 4.933

Green city 5.77% 1.096

City of poetry 1.92% 0.365

Chifeng 841 3.54% 2.48

The origin of northern China civilization 4.99% 3.496

Prairie (with biodiversity, nomadic culture and near-‐est distance to Beijing) 4.76% 3.329


121

District name


China dragen city with the jade dragen relic 1.55% 1.082

Natural museum of biolog-‐ical and geological diversity 4.64% 3.246

Ancient capital of Liao dynasty 1.78% 1.249

Bayannur 38 4.21% 0.884

Hetao region 1.32% 0.276

The northernmost point of the Yellow River 1.32% 0.276

Mount Yinshan and its magic culture 1.32% 0.276

Prairie style (the magical Urad prairie style) 9.21% 1.934

Desert view 7.89% 1.658

Alxa 426 4.23% 4.437 Mysterious land 4.23% 4.437

Hinggan 55 0.00% 0 Where the heart belongs 0.00% 0

Haibei 235 20.85% 6.047 Eco 20.85% 6.047 a the mean of Di values of all image positions of a sample district b the mean of Pi values of all image positions of a sample district


122

Appendix 6: Ms and Md values of sample districts in all three layers

No. District name B1a S1b T1c B2d S2e T2f

1 Dandong 0 0 0.11111 0.00588 0.00606 0.08974

2 Huludao 0 0 0.11111 0.02339 0.04242 0.12821

3 Chaoyang 0 0 0 0 0.01818 0.06369

4 Songyuan 0 0.05263 0.11765 0 0.0241 0.06369

5 Yanbian 0 0.05263 0.05556 0 0.07317 0.10897

6 Harbin 0 0 0.05556 0.00578 0.01807 0.11538

7 Shuangyashan 0 0 0.11111 0 0 0.05732

8 Zhangjiakou 0 0 0 0 0 0

9 Cangzhou 0 0.02778 0.05714 0 0.03356 0.11511

11 Dezhou 0 0 0 0 0.0068 0.02878

12 Jining 0 0 0.08571 0 0 0.02878

13 Heze 0 0.02778 0.02857 0 0 0

14 Binzhou 0 0.05714 0.12121 0.01987 0.04762 0.10072

15 Dongying 0 0 0.05714 0 0.02027 0.15108

16 Yantai 0 0.05714 0.14706 0 0.03401 0.11511

17 Linfen 0 0.02778 0.11765 0 0.04082 0.05755

18 Yulin 0 0 0.05714 0.01974 0.04054 0.09353

19 Xi'an 0 0.05714 0.08824 0.02649 0.06803 0.1223

20 Sanmenxia 0 0.05714 0.11765 0 0.01361 0.05755

21 Kaifeng 0 0.05714 0.15152 0 0.03378 0.1

22 Puyang 0 0 0.05714 0 0 0.04317

23 Hebi 0 0.02778 0.02857 0 0.0473 0.08571


123


24 Anyang 0 0 0 0 0 0

25 Liangyungang 0 0.02857 0.15152 0 0.02685 0.08511

26 Changzhou 0 0 0.02941 0 0.00676 0.04286

27 Yancheng 0 0.02857 0.02941 0 0 0

28 Xuzhou 0 0.05714 0.05882 0.00645 0.02013 0.03546

29 Huai'an 0 0.02857 0.02941 0.0129 0.06081 0.09286

31 Quzhou 0.02778 0.08824 0.15152 0.03947 0.06081 0.08571

32 Shaoxing 0 0 0.02941 0 0.04054 0.17143

33 Hangzhou 0 0.02857 0.05882 0 0.01351 0.05

34 Bengbu 0 0.08824 0.15625 0.02581 0.04698 0.12143

35 Huangshan 0 0 0 0 0 0.07857

36 Chuzhou 0 0.11765 0.18182 0 0.05405 0.09286

37 Tongling 0 0.05882 0.06061 0 0 0.02128

38 Pingxiang 0 0.02857 0.09091 0 0.00671 0.09286

39 Yingtan 0.02778 0.08824 0.1875 0.01935 0.05333 0.12766

40 Shangrao 0 0 0.11765 0 0 0.06429

41 Dazhou 0 0.04 0.08696 0.0061 0.02516 0.1

42 Guangyuan 0 0.13043 0.13636 0 0.05063 0.1

43 Luzhou 0 0 0 0 0.00629 0.06

44 Panzhihua 0 0 0 0 0.00633 0.02667

45 Suining 0 0.04 0.13043 0 0.02516 0.06667

46 Huanggang 0 0 0.13043 0 0.00629 0.12

47 Huangshi 0 0 0.08696 0.02454 0.05031 0.1

48 Wuhan 0 0 0.13043 0 0.01242 0.14


124


50 Xiangxi Tujia

and Miao

Autonomous

Prefecture

0 0.04 0.04167 0.01227 0.03797 0.04

52 Zhuzhou 0 0 0.04167 0 0.01875 0.03974

53 Huaihua 0 0.08333 0.08696 0 0.03165 0.06667

54 Shantou 0.125 0.21739 0.34783 0.0303 0.04375 0.07947

56 Jiangmen 0 0.08 0.30435 0 0.00625 0.10667

57 Maoming 0.08 0.08 0.20833 0.03636 0.10692 0.18121

58 Meizhou 0 0.08 0.08 0 0 0

59 Fuzhou 0 0 0.12 0 0.03145 0.14765

60 Putian 0.08 0.125 0.26087 0.01205 0.0375 0.20805

62 Wuzhishan 0 0.04 0.04 0 0.0443 0.04698

63 Wenchang 0 0 0.12 0 0.01258 0.03333

64 Ledong Li

Autonomous

County

0.04 0.04 0.12 0.02454 0.02532 0.06711

65 Guiyang 0 0 0.06667 0 0.00595 0.01875

66 Kunming 0 0 0.06667 0 0.00599 0.01887

67 Lincang 0 0 0 0.01163 0.04192 0.04403

68 Honghe 0 0 0 0.00578 0.01183 0.10063

69 Zhaotong 0.05882 0.0625 0.06667 0.01754 0.02395 0.02516

70 Fangchenggang 0 0 0 0.02326 0.03571 0.08805

71 Wuzhou 0 0 0 0 0 0

72 Guilin 0 0 0 0.02326 0.07186 0.13208

73 Hechi 0.05882 0.0625 0.06667 0.01744 0.02395 0.02516


125


74 Pingliang 0 0 0.14286 0.00552 0.00568 0.06587

75 Baiyin 0 0 0.14286 0.00552 0.01714 0.09581

76 Turpan 0 0 0 0 0 0.02395

77 Tacheng 0 0 0 0.02222 0.02286 0.0479

78 Shihezi 0 0 0 0.00549 0.00565 0.09581

79 Chifeng 0 0.09091 0.125 0 0.04624 0.13174

80 Bayannur 0 0.09091 0.125 0 0.01136 0.06548

81 Alxa 0 0 0 0.0113 0.01163 0.01205

82 Hinggan 0 0 0 0 0 0

83 Haibei NAg NAg NAg 0.01064 0.01093 0.04598 a Ms values of sample districts in the Base layer b Ms values of sample districts in the Second layer c Ms values of sample districts in the Third layer d Md values of sample districts in the Base layer e Md values of sample districts in the Second layer f Md values of sample districts in the Third layer g Not applicable


126

Appendix 7: Source links of tourism plans and official tourism marketing

websites of sample districts

Region name

Province name Name of sample district

Link of tourism plan

Link of

official tourism marketing website

LJH Region

Liaoning Dandong http://wenku.baidu.com/view/6d6fb270027

68e9951e73863

http://www.ddtour.go

v.cn/channels/46.html

Huludao http://cxhyplan.blog.163.com/blog/static/1

41295426201022001220926/

http://tour.hld.gov.cn

/#

Chaoyang http://www.cyly.gov.cn/news/News_View.a

sp?NewsID=5793

http://www.cyly.gov.c

n/

Jilin Songyuan http://wenku.baidu.com/view/49440869b8

4ae45c3b358c5f.html

http://www.sylyw.gov

.cn/

Yanbian Korean

Autonomous

Prefecture

http://wenku.baidu.com/view/ffe82ff80242

a8956bece4c3.html

http://www.cybta.co

m/user/index.xhtml?

menu_id=162

Heilongjiang Harbin http://wenku.baidu.com/view/89d0dd50f01

dc281e53af00e.html

http://www.hrblyj.co

m.cn/home.do?event

=init

Shuangyashan http://tieba.baidu.com/p/930182007?pn=1 http://sysjs.gov.cn/bm

/lyj/ly1.asp

BTHS

Region

Hebei (include

Beijing and

Tianjin)

Zhangjiakou http://www.lwcj.com/StudyResut00307_1.h

tm

http://www.zjktour.co

m.cn/

Cangzhou http://www.cztour.gov.cn/czlyzww/ggfz/51

876.shtml

http://www.cztour.go

v.cn/czlyzxw/index.sht

ml

Beijing http://zhengwu.beijing.gov.cn/ghxx/sewgh/

t1204036.htm

http://www.visitbeijin

g.com.cn/

Shandong Dezhou http://www.dzta.gov.cn/n1654682/n1654602/c1655833/content.html?COLLCC=57881

6300&

http://www.dzta.cn/dtssdezhou/

Jining http://wenku.baidu.com/view/5688e84ff7e

c4afe04a1df55.html

http://www.jita.cn/dts

s/city/jining/menu/ind

ex.action

Heze http://ishare.iask.sina.com.cn/f/6495267.ht

ml

http://www.hzta.cn/d

tssheze/menu/index.a

ction


127

Region

name Province name

Name of

sample district Link of tourism plan

Link of official tourism

marketing website

Binzhou http://binzhou.iqilu.com/bzyaowen/2013/0

429/1518923.shtml

http://www.bzta.cn/d

tssbin-‐

zhou/menu/index.acti

on

Dongying http://www.dyta.gov.cn/web201130560.ht

ml

http://www.dyta.gov.cn/web201130554.ht

ml

http://www.dyta.cn/d

tssDongY-‐

ing/menu/index.actio

n

Yantai http://wenku.baidu.com/view/ec763b55ad

02de80d4d84047.html

http://www.ytta.gov.c

n/

Shanxi Linfen http://www.google.com/url?sa=t&rct=j&q=

临汾市十二五&source=web&cd=4&ved=0C

Ec-‐

QFjAD&url=http%3A%2F%2Fwww.lfta.gov.c

n%2Ffiles%2F%25E4%25B8%25B4%25E6%25B1%25BE%25E5%25B8%2582%25E5%258

D%2581%25E4%25BA%258C%25E4%25BA%

2594%25E6%2597%2585%25E6%25B8%25B

8%25E8%25A7%2584%25E5%2588%2592%

25EF%25BC%2588%25E8%25AE%25A8%25E

8%25AE%25BA%25E7%25A8%25BF%25EF%

25BC%2589.doc&ei=AXsXUuWhOoLLtQbXnI

CQDA&usg=AFQjCNGyPs7YmBZBotkPgx5nfS

jgV0gV_g&sig2=NMLwfJWpHoQqiYlFwTqAw

g&bvm=bv.51156542,d.Yms

http://www.lfta.gov.c

n/

Shaanxi Yulin http://wenku.baidu.com/view/3711df084a7302768e9939ee.html?pn=50

http://www.yltravel.gov.cn/

Xi'an http://www.xian-‐

tourism.com/article/?type=detail&id=24217

http://www.xian-‐

tourism.com/

Henan Sanmenxia http://3y.uu456.com/bp-‐

bc6913f47c1cfad6195fa781-‐1.html

http://www.smxly.co

m/info

Kaifeng http://gov.kfta.cn/article/?621cde51632.ht

ml

http://www.kfta.cn/

Puyang http://www.plansky.net/index.php?m=cont

ent&c=index&a=show&catid=9&id=7775

http://www.pytour.go

v.cn/

Hebi http://www.tourceo.com/zhishi/201207/09/content5651.html

http://www.hebily.net

Anyang http://www.anyang.gov.cn/sitegroup/root/

html/ff8080812b8fc534012bc266d55e5d94

/20121221171182759.html

http://www.aylyzx.co

m/


128

Region

name Province name

Name of



marketing website

SJZAJ

Region

Jiangsu (include

Shanghai)

Liangyungang http://www.lygtour.gov.cn/article/85.html http://www.lygtour.cn

Changzhou http://wenku.baidu.com/view/d7a5a987ec3

a87c24028c4ae.html

http://www.cztour.co

m/

Yancheng http://wenku.baidu.com/view/fd81114bf7ec4afe04a1df48.html

http://www.touryc.com/

Xuzhou http://58.218.194.35/xxgkdesc/xxgk_desc.js

p?manuscriptid=E903II0GMWJIVK7P0WK3F

NH08ZY1ROMT&zt=

http://www.xzta.com/

tour/

Huai'an http://www.jsdpc.gov.cn/pub/jsdpc/ghjg/zx

gh/201111/t20111130_244969.htm

http://www.jshatour.c

om/

Zhejiang Jiaxing http://zfxxgk.jiaxing.cn/web/LeaderNewsSh

ow.aspx?FID=81775&CID=8&ID=30

http://www.jxtourism.

com/

Quzhou http://www.plansky.net/index.php?m=cont


846

http://tour.qz.gov.cn/

Shaoxing http://zw.sxtour.gov.cn/zww/hyxx/jhth/917

.html

http://www.sxtour.go

v.cn/

Hangzhou http://www.google.com/url?sa=t&rct=j&q=

杭州市旅游发展总体规划&source=web&c

d=2&cad=rja&ved=0CDMQFjAB&url=http%3

A%2F%2Fwww.gotohz.gov.cn%2Fzwgk%2Fgkml%2Fghjh%2Ffzgh%2F200808%2FP02012

0323471828389005.doc&ei=S5AnUsytAY-‐

VhQfm84HgBw&usg=AFQjCNET_jHMKP9TG

_gNwdiYv6l1tVWxXA&sig2=C32gXvw7SO69

oGpYBZC9ag

http://www.gotohz.co

m/

Anhui Bengbu http://www.google.com/url?sa=t&rct=j&q=

蚌埠市旅游十二五规划&source=web&cd=

8&cad=rja&ved=0CGAQFjAH&url=http%3A%2F%2Fzwgk.bengbu.gov.cn%2FUpFiles%2F

CA002%2FCA00201%2F201202_CA0020108

0220120200522050230823.doc&ei=JAAeUo

XhMuWw0AXq54HwCg&usg=AFQjCNEsrK-‐

bOkqrtdvQy3r-‐

s4tTZ7l9Gg&sig2=RFmdR_dYCYQFu8fXnDZQ

UA&bvm=bv.51156542,d.d2k

http://www.bblyj.com

Huangshan http://ishare.iask.sina.com.cn/f/35322189.h

tml

http://www.hsta.gov.c

n/


129

Region

name Province name

Name of



marketing website

Chuzhou http://wenku.baidu.com/view/247714d9d1

5abe23482f4dfc.html?pn=50

http://www.ahczlyj.go

v.cn/

Tongling http://wenku.baidu.com/view/151913d519

5f312b3169a50c.html

http://zwgk.tl.gov.cn/XxgkNewsHtml/GA037

/200805/GA037010501200805002.html

http://www.tlly.com/

home.asp

Jiangxi Pingxiang http://www.pxlyw.com/Article_Show.asp?A

rticleID=475

http://www.pxlyw.co

m/

Yingtan http://yingtan.gov.cn/bmgkxx/slyoj/fzgh/fzgh/201009/t20100908_88408.htm

http://www.yttour.gov.cn/lyxw/

Shangrao http://www.google.com/url?sa=t&rct=j&q=

上饶市旅游发展总体规划&source=web&c

d=1&cad=rja&ved=0CCwQFjAA&url=http%3

A%2F%2Fwww.zgsr.gov.cn%2Fsrweb%2Fww

w%2Fzfxxgk%2Fb%2Flvj%2Fupfiles%2F1307428133106.doc&ei=LmEgUvraCsi0tAa4jIDoC

w&usg=AFQjCNGo2Sg3rNEG7nuFOhUq3Wq

TVNGLjg&sig2=xs1J8nl6KRscZqLX6Xo58w

http://www.srta.gov.c

n/

CSHH

Region

Sichuan (include

Chongqing)

Dazhou http://www.dazhou.gov.cn/ZJDZ/DZLY/LYXX

/2011/09/19/11592094566.html

http://ta.dzs.cn/

Guangyuan http://wenku.baidu.com/view/a2230e611e

d9ad51f01df218.html

http://www.gysta.gov.

cn/

Luzhou http://www.lzsta.gov.cn/zwgk/ghjh/system/

2011/12/14/000146432.html

http://www.lzsta.gov.

cn/

Panzhihua http://wenku.baidu.com/view/6537d61414

791711cc791722.html?pn=50

http://www.pzhsta.go

v.cn/

Suining http://wenku.baidu.com/view/13ba30e609

75f46527d3e13f.html?pn=1

www.snta.gov.cn/

Hubei Huanggang http://wenku.baidu.com/view/f1929fca402

8915f804dc2b5.html

http://www.hglyj.gov.

cn/

Huangshi http://www.huangshi.gov.cn/zfpd/jhgh/zxg

h/201307/t20130726_153169.html

http://www.hbhsly.go

v.cn/

Wuhan http://ishare.iask.sina.com.cn/f/22938282.h

tml

http://go.wuhan.net.c

n/

Xiangyang http://wlx.xf.cn/publish/cbnews/201108/17

/cb4483_1.shtml

http://www.x.gov.cn/


130

Region

name Province name

Name of



marketing website

Hunan Xiangxi Tujia

and Miao Au-‐

tonomous

Prefecture

http://www.xxz.gov.cn/xxgk/auto247/0066

86875/jhzj/ghjd/201110/t20111018_23127.

html

http://www.xiangxily.

com/

Yongzhou http://bbs.voc.com.cn/topic-‐3930932-‐1-‐

1.html

http://www.yzta.cn/

Zhuzhou http://www.zhuzhou.gov.cn/gk/ghjh/fzgh/1

28182.htm

http://www.zzly.gov.c

n/

Huaihua http://wenku.baidu.com/view/ad398ee910

2de2bd96058817.html

http://www.hhly.gov.c

n/

GFH Region

Guangdong Shantou http://wenku.baidu.com/view/3d7eac8865

29647d27285272.html

www.stly.gov.cn/

Yangjiang http://www.yangjiang.gov.cn/zwgk/jhgh/zxgh/201202/t20120206_64654.htm

No exist

Jiangmen http://zw.jm-‐

tour.com/cn/zwkx.asp?show=384

http://www.jm-‐

tour.com/

Maoming http://www.google.com.hk/url?sa=t&rct=j&

q=茂名市旅游发展总体规划&source=web

&cd=2&cad=rja&ved=0CC8QFjAB&url=http

%3A%2F%2Fwww.mmlyj.com%2Ffckupload%2F1311564621.doc&ei=96clUsPaPMyrhAe

nsID4Cw&usg=AFQjCNFOSTAC71zGduKrSsu

YwfmimZDv2g&sig2=TjoIyujH3Epv-‐

aYLt9nQUQ&bvm=bv.51495398,d.d2k

http://www.mmlyj.co

m/index.php

Meizhou http://www.plansky.net/index.php?m=cont


http://www.mzta.gov.

cn/index.html

Fujian Fuzhou sys.fznews.com.cn/newsimages/2011-‐4-‐

6/20114617751768.doc

http://www.fzta.gov.c

n/

Putian http://www.putian.gov.cn/a/20090805/000

42.shtml

http://www.ptly.gov.c

n/

Sanming http://www.google.com/url?sa=t&rct=j&q=

三明市旅游发展总体规划&source=web&c

d=1&cad=rja&ved=0CCwQFjAA&url=http%3

A%2F%2Fwww.sm.gov.cn%2Fzwgk%2Fzxwj

%2Fzfbgswj%2F200905%2FP020090504421

724219476.doc&ei=L48kUsSiLZOShQfts4CYB

A&usg=AFQjCNGuy5mqaR8zMqA3C3Fdry384ajZJw&sig2=pZPkgwPPOPUfWdMmzpG8d

http://www.smta.cn/i

ndex.htm


131

Region

name Province name

Name of



marketing website w

Hainan Wuzhishan http://wenku.baidu.com/view/6d118e553c

1ec5da50e2701a.html

No exist

Wenchang http://xxgk.hainan.gov.cn/wcxxgk/bgt/2013

04/t20130423_945994.htm

http://www.wenchan

gtour.com

Ledong Li Au-‐

tonomous

County

http://www.hainan.gov.cn/data/news/2009

/12/91347/

No exist

YGG

Region

Guizhou Guiyang http://www.gygov.gov.cn/art/2012/9/3/art

_18332_398207.html

http://travel.gygov.go

v.cn/

Yunnan Kunming http://wenku.baidu.com/view/f546fe2de2b

d960590c677ae.html

http://www.kmta.gov.

cn/

Lincang http://www.tt91.com/detailed_tt91.asp?id=

58917&sid=1448

http://www.lcly.gov.c

n/

Honghe Hani

and Yi Autono-‐

mous Prefec-‐

ture

http://wenku.baidu.com/view/12139bce08a1284ac85043c8.html?pn=201

http://www.honghe.travel/

Zhaotong http://www.ztta.gov.cn/Pages_76_5732.aspx http://www.ztta.gov.cn/

Guangxi Zhuang

Autonomous

Region

Fangchenggang http://www.fcgs.gov.cn/pubinfo/1745.aspx http://fcg.gxta.gov.cn/

Wuzhou http://blog.sina.com.cn/s/blog_b62e163e01

016qpf.html

http://www.wzta.cn/#

Guilin http://wenku.baidu.com/view/12d96fc59ec

3d5bbfd0a74e5?pn=50

http://www.guilin.co

m.cn/

Hechi http://www.hclyw.net/portal.php?mod=view&aid=621

http://www.hclyw.net

XNG

Region

Gansu Pingliang http://php.plmh.cn/index.php?m=content&

c=index&a=show&catid=312&id=107963

http://lypd.plmh.cn/

Baiyin http://www.baiyin.cn/Item/51143.aspx http://www.bylyj.com

.cn/


132

Region

name Province name

Name of



marketing website

Xinjiang Uyghur

Autonomous

Region

Turpan Prefec-‐

ture

http://luyj.tlf.gov.cn/ny.jsp?urltype=news.N

ewsConten-‐

tUrl&wbtreeid=729&wbnewsid=70795

http://luyj.tlf.gov.cn/

Tacheng

(Tarbagatay)

Prefecture

http://www.xjtc.gov.cn/zhengwugongkai/bu

mendongtai/32670/

http://www.tcdqly.co

m/web/default.asp

Shihezi http://wenku.baidu.com/view/14c51bfefab

069dc502201aa

lyj.shz.gov.cn/

IM

Region

Inner Mongolia

Autonomous

Region

Chifeng http://wenku.baidu.com/view/32007cc2d5b

bfd0a795673cd.html?pn=51

http://www.cfly.net/

Bayannur http://www.bynely.gov.cn/news/show.asp?

id=207

http://www.bynely.go

v.cn/

Alxa League http://www.alsmfgw.cn/ReadNews.asp?Ne

wsID=1035

http://www.alsly.com.

cn/

Hinggan League http://www.xaly.gov.cn/zwgk/58755.htm http://www.xaly.gov.c

n/

QT Region

Qinghai Haibei Tibetan

Autonomous

Prefecture

http://qhmy.gov.cn/html/3078/81622.html http://www.qhhbly.co

m/

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