Discussion Paper Series No.143
A theoretical framework of the dynamic propertyof the tourism destination network
Masahiro Yabuta* and Noel Scott**June/2010

Abstract
This paper gives an analytical framework to investigate a dynamic property of the tourism
destination network, which has various patterns from time and space perspectives. A
development pattern of tourism destination network should be a factor to determine thegrowth of each tourism destination, whereas the co-evolution of the tourism destination
network would be fixed by development patterns of tourism destinations and their mutual
relationship. Giving an imaginary network leads to a widely applicable analytical framework to
explain how the tourism destination network develops and how it works for the tourism
development. Analytical procedures include not only the general tools for network analysis but
a fuzzy grading function to evaluate the performance of tourism destination network as well.
Keywords; Networks; Destination management; Tourism development;
Dynamic property of networking;*Faculty of Economics, Chuo University & the University of Queensland,** School of Tourism, the University of Queensland

1

A Theoretical Framework of the Dynamic Property of the Tourism
Destination Network
By
Masahiro Yabuta (Faculty of Economics, Chuo University)&
Noel Scott (School of Tourism, the University of Queensland)
1. Introduction
The major aim of the paper is to give a theoretical framework of the development
pattern of tourism destination network and its outcomes, which allow us to investigate
the dynamic property of the tourism destination network and various patterns from timeand space perspectives. A development pattern of tourism destination network should
be a factor to propel the growth of each tourism destination, whereas the co-evolution of
the tourism destination network would be fixed with development patterns of tourism
destinations and their mutual relationship. A specific feature of the paper is to include
not only a static analytical aspect of tourism network but also its dynamic aspect. Many
researchers of the network analysis have mainly developed a static analysis bytraditional tools of social network analysis, such as distance, centrality and structural
equivalence, which have been cohesively developed by the Social Network Analysis
since 1970s (SNA, see Freeman (1979))1, though few papers are published on tourism
development from the social network perspectives. As far as the tourism network isconcerned, some papers have been developed and also gave empirical aspects, such as
Shin (2006) and Scott et al. (2007, 2008), which recognized a strategic role of the
network analysis on tourism development. A formation process of the tourism sitewould compose a structure of the regional network among areas and its development
pattern should affect the economic development. Though the development path always
includes a dynamic property due to its time dependency, almost papers of the tourism
network were mainly conducted to the static analysis to focus on the structuralcharacteristics of the network. It should be indispensable for the tourism network
analysis to incorporate analytical measures from the long-run perspective.
Many preceding papers on the tourism network mainly have focused on a tourism
site in which a tourism network has been developing with the spreading and deepening

1

A conceptual explanation and mathematical procedures of social network analysis are given by Knoke

and Yang (2008).

2social relationships. After analyzing the network structure, they mentioned theimprovement of networking conditions as measures of regional development. In
particular, their analysis of the weakness or insufficiency of the tourism networking interms of human relationships or business marketing leads to worthwhile proposals fortourism development in a tourism site.
In this paper, however, we shall analyze a tourism destination network, focusing ourattention on the relationship among tourism sites rather than inside of a tourism site.
Geographically, tourism sites compose a specific pattern of their relationship, which
concerns administrative agreements, transportation infrastructures, complement or
substitute properties of tourism services, and business activities (see Part 1 in Scott et al.
(2008)). Various patterns of the relationship among tourism sites perform a tourism
destination network, whose development might be fixed historically and geographically.
Local businesses and organizations at each tourism site, for example, can join andpromote a cooperative marketing to make their tourism services more fascinating tovisitors in the aggregate. Then the visitors can be contended with their visit to thetourism sites that should be linked together effectively through networking. Accordingly,
it becomes essential for these tourism sites to design a strategic pattern of the tourism
destination network, along which tourists can visit and enjoy attractions. In this regard,
not only the transportation network but also the information network, mainlytransmitted by human relationships, should determine the situation and the pattern of
tourism development of each tourism site. Within a tourism destination network, eachtourism area can develop but its development pattern may be different each other not
only because of the divergence in the position of each area in the tourism destination
network but the difference in its capacity of the tourism growth as well. It should benotable that once a tourism site is integrated into a tourism destination network, thedevelopment pattern of the tourism site would be affected not only by its own
development but through changes of tourism business at other areas as well. This
externality, or interdependency, of the tourism destination network should become adecisive factor to determine a development pattern of the tourism sites. Let us give an
example. Kyoto, Kobe and Osaka are the three major tourism sites in Japan. Each ofthem has something different atmosphere each other and many tourists visit there. They
are independent tourism sites but are connected each other through various means ofinterchange2. Using the words of the network analysis, each of three cities can be the2

Since 1990, West Japan Railway Company has carried on a campaign of the link among them, called„the tale of the three medieval cities‟.

3

node as an independent tourism site, and measures for interchanging them can be thelink. Therefore, node and link perform a basic structure of the network among regions
or organizations as well when we focus our attention not on the regional relationship buton the functional relationship of organizations in the region. Network Analysis (NAhereafter) is an analysis of finding the structure and its effectiveness of the systematic
relationship of node and link, and a dynamic NA should be an analysis of investigatinga changing pattern of NA and its outcomes in the tourism site.
Our paper about the tourism destination network has the following analytical
propositions. Firstly, it is assumed that the tourism destination network to be analyzed
should be well-defined within a small area, where a small number of tourism sites arelocated and visitors can access them by a one-day trip, that is two or three-hour journeyby cars or trains. Secondly, each tourism site is assumed to be a tourism complex, which
includes various multiple tourism attractions, such as accommodation, restaurants,
amusement parks and natural parks. Therefore, our attention would be directed towards
not only each development plan of the attractions in each node but also thecomprehensive development plan in a tourism destination network.
The composition of the paper is as follows: Giving a imaginary hypothetical
network, we shall show a widely applicable framework to explain how the tourism
destination network develops and how it works for the tourism development. Analyticalprocedures include not only the general tools for network analysis but a fuzzy grading
to evaluate the performance of tourism destination network as well (Section 2). In thisregard, some technical aspects about dynamic pattern of the tourism destination network
will be investigated. Then, in Section 3, we shall introduce a performance function of
the tourism destination network and apply a factor-decomposition analysis to an
outcome that should be attributable not only to the externality of tourism destination
network but also to the effects by its own growth. Finally, in Section 4 summarizes theconclusions and further remarks.
2 Tourism destination network and its Performance
2.1 Network Analysis and the Dynamic Property
NA and its application to tourism destinations have been developed with various
dimensions of social network analysis (SNA). Actors (or node) and relations (or link)must be the two indispensable elements in SNA (Knoke and Yang (2008, p6)), whichshould influence both individual behaviour and systemic performance (p7). Therefore,
SNA explains both variations in structural relations and their consequences.
Accordingly, tourism network composed by actors and relations has been also focused

4

on relations between actors such as tourism businesses, governments and residents andtheir outcomes, such as cooperative activities to establish management plans andpolicies and destination development (Pavlovich (2003), Dredge (2006a, 2006b)), or
keeping tourism development sustainable (Halme (2001), Gibson et al. (2005), ErkusOztruk and Eraydin (2010)). Halme (2001) applied NA to confirm the effects of tourism
destination network on learning process. Gibosn et al. (2005) gave a dynamic aspect of
tourism destination network from the destination lifecycle perspectives and showed theoutcome indicators related to learning and exchanging, business activities andcommunity. Erkus-Oztruk and Eraydin (2010) analysed the environmental sustainable
tourism development focusing on governance networks, which consists of action
oriented networks and policy & planning networks.
In the tourism network literatures, nodes include all (or a part of) stakeholders andrelations can be composed by various links such as human relationship (including
attitudes, regulations, partnership and participation), information and physicalconnections. Scale of the nodes and strength of the link should prescribe the structure of
network. In this regard, it is notable that a target of tourism development should develop
the welfare of the whole community, not a part of business, without deteriorating theenvironmental resources. Although the overall target must include such macro or
aggregated aspects, it should be important to regard the tourism network as the effective
tools for attaining such goals in the tourism destination. From the SNA perspectives,
developing human relations or deepening public-private partnership should increase thecommunity‟s welfare. However, as many researches admitted, the main incentive forstakeholders to join in the network is still economic one. Not only business people but
also government, or related organizations, tend to think that the tourism development interms of income is important and the tourism destination network should be a tool for
economic development. Even if economy is the most important goal, total welfare of the
tourism destination should be targeted, which would include various factors such ascommunity development, quality of life, environment, jobs, businesses and profits, orsustainability. Then, the tourism destination network should be examined whether andhow much it can contribute producing these benefits.
Baggio (2007), Scott et al. (2007, 2008) and Baggio et al. (2010) proved theeffectiveness of NA and its applicability to the tourism network. Their procedure of
empirical research on tourism sites in Australia or Italy followed an extensive NA,
including the structural quantitative methods by NA, such as density, closeness andcohesiveness. Connections between nodes, mainly key stakeholders in organizations,
include not only physical or Internet Communication Technology (ICT) measures but

5also human relationships of information flows. On the other hand, Chan et al. (2003)
and Pyo (2010) showed an applicability of tourism chain analysis to special tourism
sites where some attractions (or node) are connected (linked) in different means andintensities. Their method was a prototype of NA but without a structural analysis. One
major point they captured is that there are some inefficiently achieved sites and theyshould be improved from some viewpoints, including transportation and tie-ups witheducational activities. The method concerning a kind of dynamic aspects, however, isnotable, which includes special questions of the situations of past, present and future.
Easy access to understand the changing pattern of situations must also be good for NA
to be dynamic although their statistical procedure to use a fuzzy set could enable themto have a small sized sampling. In this paper, we shall integrate the ordinary method of
NA with these tourism chain analyses in order to develop a dynamic aspect of NA. The
step of the analysis is as follows: firstly, a virtual hypothetical site is assumed to give a
NA analysis in order to clarify their network structure. Secondly, to see a dynamic
pattern of network development, incorporate a changing pattern of past-present-future(target) of efficacy of networking. In this regard, to handle a small sample size, a fuzzy
performance grading is introduced. Then, a quantitative measure of the benefit fromtourism destination networks (or chains) will be analysed and their policy implications
will be investigated.

2.2 A Simple Pattern of Network Development (From Mono-polar to Bi-polar)
At the beginning, we shall assume a virtual example of the tourism destination
network, shown in Figure 1. This is a case where a new sub-centre was developed in a
tourism site though initially only single centre of the tourism site exists. The example
indicates a development process that the area „C‟ transits from a periphery towards acentre and the area „A‟ loses its special position in the tourism site. Network
development should be strengthened through various factors; increase in the number of„node‟, increase in the number of „link‟, increase in the value which each „link‟ has,increase in the „direction‟ which each „link‟ has, and changing pattern of the structure of
network, that is a formation of „nodes‟ and „links‟ or a changing pattern of the shape of
networking. These factors may affect the overall outcome in a network region and also
affect the outcome to be attained in each „node‟. Outcomes should be valued fromvarious aspects such as income (or revenue), number of tourists, happiness of people in
the network, density of human relationship, effectiveness, fairness, etc. Therefore, for
an actual pattern of the network development, many steps should be needed to complete

6

NA. As far as this simple case in Figure 1 is concerned, however, we shall focus only
our attention on the changing pattern of links between nodes. As shown in Figure 1, thenetwork is developing due to an increase in links among B, C and D but its structure
seems to be changing drastically. To make the analysis simpler, only binary values andnon-directed links are used.
Figure 1 A Development of the Tourism destination network

The data concerning the network pattern, given as Figure 1, can be collected byasking the evaluators who have professional knowledge about the situation not only of
their network around but also of the development pattern of their activities. The
questions should include such as, “Do you think there was (is or will be) a network
relationship with others?” and “If any, how effective do you think the network worked(work or will work)? Please evaluate the overall effectiveness of the performance ofyour activities, by scoring from 0, when no result, to 100, if perfect results. Questions
about other performance attributes of network should be considered. They include bothfactors of „links‟ and „nodes‟. The factors of „links‟ include cooperative promotion andmarketing, transportations, ICT-related network, human network, and the factors of„node‟ may include attractions, service quality, efficiency and fairness in each „node‟.
For a numerical example, we shall assume the data Table 1 shows, in which the overall

7performances are measured by money term. In this regard, we need to ask evaluators ina node the weights that show relative importance (or contribution) in networking tocompare with other nodes. Specifically, each evaluator in the network would be asked
how much important role your node plays in the network on a scale of hundred. Thenthe weight of the performance can be calculated as a ratio of its score to total scores at
each of time (Table 2).

A
B
C
D

Table 1 Overall performance
Past
Present
Future
50
70
80
40
50
70
40
60
80
30
50
80
Table 2 Weights of the performance
A
B
C
D

Past
65
10
15
10

Present
50
15
25
10

Future35
15
35
15

Arrangements for NA have now been completed. Following the ordinary proceduresof NA, the adjacency matrix and the distance matrix for the network development in
Figure 1 can be given by Table 3 and Table 4. The adjacency matrix shows if there is alink between two nodes or not, and we can easily find in the present, for example, A andB has a link, indicated as „1‟ in (1,2)th element, but B and D has no link, indicated as „0‟in (2,4)th element (or equivalently „0‟ in (4,2)th element for D and B). However, theevaluator in C or D is expecting a new link to be established in the future. On the other
hand, the distance matrix shows a length of the trip between two nodes. From D to C in
the past, we need to trip at least along two links from D to A and from A to C. However,
we can use a shortcut thanks for a newly developed link between D and C, only tripping
along one direct link. Then the (4,3)th element of the distance matrix will be „1‟ in the
future whereas it was „2‟ in the past, meaning that it becomes easier for tourists toaccess to C from D, or inversely, to D from C. The tendency of shortening distanceamong nodes in a tourism site can contribute a tourism development, leading to a morefavourable evaluation at each node for the future, as expected in Table 1.

8

Past
A
B
C
D

A
0
1
1
1

Table 3 Adjacency matrix
Present
A
B
C
D
1
1
1
A
0
0
0
0
B
1
C
1
0
0
0
0
0
0
D
1

Future (Target)
A
B
A
0
1
B
1
0
C
1
1
D
1
0

C
1
1
0
1

B
1
0
1
0

C
1
1
0
0

D
1
0
0
0

D
1
0
1
0

Table 4 Distance matrix
Past

Present

A
B
A
0
1
B
1
0
C
1
2
D
1
2
Future (Target)

C
1
2
0
2

D
1
2
2
0

A
0
1
1
1

C
1
1
0
1

D
1
2
1
0

A
B
C
D

B
1
0
1
2

A
B
C
D

A
0
1
1
1

B
1
0
1
2

C
1
1
0
2

D
1
2
2
0

Then, NA provides a basic structure of this tourism destination network summarizedin Table 5. In Table 5, density means a proportion of actual ties in a network relative tothe maximum number of ties. In general, average distance among nodes decreases whendensity increases.
Table 5 Basic structure of the tourism destination network
Diameter
Average
Density
distance
Past
2.0
1.5
0.5
Present
2.0
1.33
0.67
Future
2.0
1.17
0.83

9

Centrality is a key concept of NA to know about what position a node is in the
network. In a group with human network, for example, we can find a leader who
actually plays a central role in the group though it is hard to ascertain that he or she is in
position as a true leader or close to a leader. NA gives a rigorous definition of what
status of each person, or node, shares in the network. In NA, some prototypes of thecentrality are provided, such as degree centrality, between centrality, closenesscentrality and eigenvector centrality. Among them, closeness centrality, for example,
shows the degree an individual is near all other individuals in a network. In Table 6, thecloseness certainty of each node shows the standardized one. Centralization for
closeness centrality shows the difference between the numbers of links for each node
divided by maximum sum of difference, which shows how much variation there is
between the numbers of links each node has (See Table 6).
Mathematical formula of closeness centrality is given by1g 1(1) Cc (i )  g
or Cc g (i)  g,
 d (i, j ) d (i, j )
j 1

j 1

where d(i,j) is the distance between node i and j, and g is the total number of nodes. As
for Figure 1, g=4 and i, or j, is A, B, C or D. Ccg(i) shows a standardized closenesscentrality. In this regard, centralization for closeness centrality is given byg[Cc (i*)  Cc (i)]
g

(2)

Cc 

g

i 1

[( g  2)( g  1)] /(2 g  3),

where Ccn(i*) shows the maximum value of closeness centrality for all nodes in the
network.
In the past case in Table 6, only the node „A‟ has one so that links are dispersed
around just one node „A‟, which is classified as the case of star structure (Also see
Figure 1). Due to a bipolarization strategy in the tourism site, closeness centrality inboth „A‟ and „C‟ will be 1.0 in the future. In „C‟, closeness centrality has been and will
be increasing sharply. Closeness centrality in „B‟ has already increased and that in „D‟will be expected to increase from 0.6 to 0.75. These changes occur due to a newly
constructed relationship with another node. On the other hand, the bipolarization
strategy has reduced the centralization from 1.0 to 0.75 and will reduce it from 0.75 to
0.42. As shown in Table 6, in the past, there was only one node to which all links weredirected from other nodes when the network had a star structure, then centralization
becomes one, meaning that a bias of centrality is the largest. In this regard, it is notablethat the centralization could be zero when all nodes in the network are completely

10linked together, namely when there is a link between D and B in the future period in acase of Figure 1.
Table 6 Evolution of centralization
Past
Present
Future
A
1.0
1.0
1.0
B
0.6
0.75
0.75
C
0.6
0.75
1.0
D
0.6
0.6
0.75
Centralization
1.0
0.75
0.42
2.3 Evaluation of Outcomes
Next step is to evaluate the performance of tourism destination network andoutcomes of each actor. Through interviews to valuators of each node, somequantitative data to evaluate the performance of tourism development would becollected like as Table 1 which gives a hypothetical example of evaluations only foroverall performance of the tourism activities. Except for the overall performance, there
would be a number of dimensions of outcomes concerning the tourism destination
network, such as a quality of tourism services, number of tourists, employment level,
cooperative atmosphere and brand image in the region, environmental conservation andso on. These data may be given by the officially published data or by a questionnaire
survey, both of which, however, should include the expected values for the future timeof each outcome. As is in the case of Figure 1, a drastic change may be observed when
we look at a small scope of tourism destination network, where the construction of alink between nodes can strongly influence the performance not only of each node but
also of tourism site entirely. Then, the method of numerical analysis extended by Chanet al. (2003) might be useful for NA whereas it was originated for a supply chain
management (SCM). Chan et al. (2003) and Font et al. (2008) provide us ameasurement method of cross-organisational performance that also can be applicable to
NA. Researches on SCM or its application to tourism management (TSCM) naturally
include the network perspectives because they mentioned the relationships among
stakeholders, such as cooperation, competition and channels (Zhang et al. (2009)). The
measurement method of the performance at each node follows the next steps:(Step1) Evaluate the performance by evaluators: ei = (Ei1;past, Ei2;present, Ei3;target),
i=A,B,C,D which are given by Table 1. For example, Ei1= (50,40,40,30).

11

Ei 2  Ei1
10,(Step2) Calculate the Performance Score (PS), which is defined by i 
Ei 3  Ei1
where i  [0,10], i=A,B,C,D. In the case of A, for example, A  (20 / 30) 10  6.67 .
Therefore, each PS shows only an achievement rate of the present performance tocompare with the future target.
(Step3) Estimate the Performance Grades. Evaluations given in Table 1 imply many
ambiguities or incorrect information because each evaluator‟s judgement may besubjective and arbitrary. Then, applying a fuzzy set theory to re-estimating theperformance scores should become a possible and useful procedure to avoid theseambiguities and to reach unbiased evaluations. A typical method is to calculate theexpected value of PS by using both the probability named the degree of belongingness
and a specified fuzzy grade (Figure 2).
Figure 2 Triangular Fuzzy Grades

For example, the PS of B is reported as 3.33. As mentioned above, this data might
be ambiguous. The true PS of B, if any, might be larger (or less) than 3.33. Then, weshall introduce the „triangular fuzzy grade‟ given as Figure 2. Each triangle has an apex
from a to f, and constitutes a membership to which the PS should belong. Then, 3.33 isoutside the range of membership of a, b, c and f, however, it is inside the range ofmembership both of d and e. Using these memberships, the performance grades of B aregiven by Pa=0,Pb=0,Pc=0,Pd=(3.33-2)/(4-2)=0.665, Pe=(4-3.33)/(4-2)=0.335, Pf=0, or in
the form of row vector, (0,0,0,0.665,0.335,0)T. With other calculations of theperformance grades, the fuzzy performance grade matrix becomes
0
0 0
 0
0.33
0
0 0

0.67
0
0.5 0
FPG  

0.67 0.5 1
 0
 0
0.33 0 0


0
0 0
 0

12

(Step4)To calculate the Performance Index (PI): Multiply a measurement result matrix
given by FPG with the weight vector, WT.
0
0 0
 0
 0
 0.3300 0 0.65 0.5 0.35 0.22

0.67
0
0.5 0  0.1 0.15 0.15  0.51
FPG  W T  
0.67 0.5 1 0.15 0.25 0.35 0.24
 0


 0
0.33 0 0  0.1 0.1 0.15 0.03



0
0 0
 0
 0

0
0.17
0.46
0.33
0.05
0

0 
0.12
0.41
  x1 , x2 , x3 
0.43
0.05

0 

Then, the performance measure (Performance Index = PI) can be given bysx
PI i  6 i , s  (10,8,6,4,2,0)
 xij
j 1

where xij (i=1(past), 2(present) and 3(future), j=1,2,…,6) is the jth element of the ith row
vector, xi, of FPG×WT. Each element of vector s shows the scores that correspond toapexes from a to f. In a numerical example of Table 2, PIs are given in Table 7, which .
Table 7 Overall Performance Index
A
B
C
D
PI

Past
50
40
40
30
5.81

Present
70
50
60
50
5.48

Future
80
70
80
80
5.18

PS
6.67
3.33
5.00
4.00

(Step 5) Evaluate the outcome. Applying these steps to the numerical example leads tothe following outcomes: firstly, PI as an aggregate measurement crucially depends on
the weight vector. The difference in relative weights among actors is due to thedifference in viewpoints of how important the individual actor or social actor(government) are about each node. Their evaluations about nodes might change fromtime to time, meaning that the weight vector should be changed due to changing
network structure. Moreover, this changing pattern of relative weights or network
structure should be the key to determine the PI itself. Secondly, although PI is estimatedby using scores at three points in time, i.e. past, present and future (target), it iscorresponding only to performance at present because PS merely indicates a present
situation towards a target. If the target of a node is set lower to compare with anothernode, PS in the node should be large. Moreover, if every actor in all nodes keeps thetarget low, then PI will become large. However, this does not necessarily imply that the13performance is excellent. Thirdly, PS is estimated by using scores which actors gradedfor each period. PS indicates not an absolute score but a relative score because it onlyshows a change rate in scores. This procedure may be preferable when performance
should be calculated with various units.
2.4 Technical extensions
In this section, we shall provide some technical aspects to induce PIs. One is tointroduce an expected performance score in addition to the ordinary performance score
mentioned above. The other is to adjust measures to evaluate outcomes of the network.
Both of them might contribute to a further analysis of NA.
Tourism development or its trend should be important for all stakeholders, among
which investors most seriously watch the performance of each node. In particular, future
performance must be an essential consideration of investment. In this regard, an
expected PS (EPS) should be estimated and compared with (realized) PS. The following
method is to calculate both PSs and PIs, and to compare them each other.
In addition to Performance Score (PS), we shall define the EPS by
E  Ei 2
i  i 3
 10, i  [0,10], i  A, B, C , D
Ei 3  Ei1

Accordingly, EPS means a potential development that each site will be expected toattain. Then, as for the numerical example about Table 1 and 2, PSs and EPSs are given
in Table 8 to follow the five steps explained above. PIs in the lower line of Table 8 how
EPIs in terms of the weight at each period3, which are computed based on EPSs. A
merit of the procedure is that the potential performances of each node can be estimatedand compared with realized past performances. In the sample model, the past-present
performance of B is very low, around 3.3, whereas its EPS becomes larger to about 6.7,
indicating a prosperous future development. As mentioned above, the evaluator‟sweight to show the relative importance of the node in the network should vary as time
elapses. In this regard, it should be re-conformed that PIs are the performance scores toindicate the situation of development process from the past to the present from the longterm, which is from the past to the future, perspectives, whereas EPIs are performance
scores to indicate the situation of development process from the present to the futurefrom the long-term perspective. In this sense, investors or actors at each tourism site
may consider the growth pattern from past to future to be more important. This will
explain why PIs and EPIs show a different changing pattern with respect to the weights.

3

To reach PIs, there might be various ways to use the weights, such as the average weight for two pointsof time, or, simply the weights at present.

14

As for the numerical example of the weights in Table 2, the relative weight of A isdecreasing but these of other nodes are increasing. After changing the network structurefrom mono-polar to bi-polar, the weights, indicating how much each evaluator attaches
importance to each node, tend to change so that a newly developed centre becomes
more important in the network. Moreover, increases of the links between nodes also
contribute to an increasing pattern of EPI. Accordingly, the better performance withincreasing weight at B, C and D leads to offset the less performance with decreasing
weight at A, and this will lead the EPI to change from 4.21 to 4.82.
Table 8 Extended Overall Performance Index
A
B
C
D
PI

Past-Present Present-Future
6.67
3.33
3.33
6.67
5.00
5.00
4.00
6.00
5.81
5.48
5.18
4.21
4.52
4.82

EPS/PS
0.50
2.00
1.00
1.50

Instead of an ordinal procedure of PS, growth rate of performance also can be used
for estimating PIs. This should be preferable when scores are measured in various units
such as money income, number of tourists and so on. In the growth rate term, it is clearthat with changing weights, both realized PI and expected PI are increasing upwards.
This is an outstanding outcome compared with the ordinal measures of PI or PS. This isindicated by Table 9.
Table 9 Extended Overall Performance Index by growth rate
A
B
C
D
PI

Past-Present Present-Future
4.00
1.43
2.50
4.00
5.00
3.33
6.66
6.00
4.27
4.29
4.52
2.43
2.74
3.16

EPS/PS
0.36
1.60
0.67
0.90

3 A Performance Function and Networking
Aforementioned procedure is based on the subjective value judgment to evaluate
the performance both of each node and of the entire network, meaning that someunreliable information should be included even if the use of fuzzy set could adjust the

15actor‟s evaluation of the performance. This procedure, however, can be easily applied tovarious types of performance and network structure to confirm the pattern of tourism
development at each tourism site. Figures of PI or EPI should include a mixed effect of
the node‟s own development with the development of network among nodes. On theother hand, as long as the patterns of links of a tourism destination network could berecognized, a traditional procedure to estimate outcomes, which is the production
function, would be also applicable to NA although some technical issues of the datacollection remain. This section devotes to analyse the procedure by production function
and to compare the two approaches.
3.1 A hypothesis
A major aim to apply NA to tourism development is to give some policy
recommendations in order to improve the structure of a tourism destination network.
Recommendations about location and type of tourist facilities, for example, should bebased on the results of NA (Shin (2006)). However, there must be another aim of NA tobe considered; that is to estimate and analyse the relationship between the developmentof the network structure and its effect on the tourism development. In general, tourism
development in the region should be supported by the development of the network as if
the network composed by nodes and links is an infrastructure. Like other inputs tocontribute to producing products and services, the network will also play the same role
as a contributor. If this is the right conclusion, it can be confirmed that encouraging
investment on improving network in a tourism site will cause the site‟s welfare toincrease.
This leads to a hypothetical logic that improving network or network expansion
should contribute an increase in welfare at each node. Welfare can be estimated byvarious data such as the level of tourism income, the volume of employment and otherrelevant figures. The following procedure is an attempt to quantify the effect of achanging pattern of the network on the overall outcome in terms of income at a tourism
site.
3.2 An Analytical Framework
Various inputs contribute outputs. To make analysis simple, the outcomes of
tourism sector in a tourism site is assumed to be measured by income, or equivalently,
production. The income level is hypothetically determined by(3)yi  f i (ni ,  l ji n j ,  n j ), lij  1 when j directly tied to i, or lij  0 otherwise
j i

j

16

where yi is income, ni is total inputs at node i, such as employment. It is assumed that
there are two layers of effects from network; one is the direct effect which is stemmed
from other nodes that link directly to node i, and the other is the overall externality of
the network4 . In fact, it might be hard to estimate (1) within the non-linear type offormation. Following the Taylor expansion, an evolutional process of the tourism
destination network can be given by a liner function;(4) yi  f i1ni  f i 2  (lij n j  lij n j )  f i 3  (n j )   i .
j ijIn this equation, each partial derivative f i1 , f i 2 and f i 3 mean the effect of change in thesize of node i itself, change in other node‟s size and additional links with node i, andchange in total network, on outcome at node i, respectively.  i is the reminder of linear
approximation, but hereafter no notice is taken.
In the matrix expression, (2) will become(5)Y  F1N  [ F2 Lt N  F2 Lt N ]  F3N 0
{[ F1  F2 Lt ]N  F3N 0 }  F2 Lt N,

where Fk is a diagonal matrix whose components consist of fik. L is a matrix whose
elements consist of lij. ΔL is a matrix whose components are Δlij.ΔN is a vector whose
elements are an increment of capacity of each node and ΔN0 is a vector whose elements
are the sum of all node‟s increments of capacity. N is a vector whose elements are ni.
The first expression in r.h.s of (5) proves that an increment of output can be
decomposed by three factors: the first one is a „direct effect‟ of changes in the size ofcapacity of node itself; the second one is a „network effect‟ via linkage among nodes
through which a changing pattern of network can influence output; and the third one is a„overall effect‟ of a network site. The second expression in (5) separates effects into twoparts; one is the effects due to changes in nodes‟ capability and another is the effects
due to changes in linkage pattern. In this regard, the former might be named as „scaleeffects‟ and the latter might be named „linkage effects‟. A change in output at each node
should occur due to both scale effects and linkage effects.
On the other hand, it is clear that a change in links of the network area iscorresponding to a developing pattern of the network structure because it causes various

4

In (3), only the case of new links with bi-direction is considered. However, it is possible to incorporate

a deepening of the link by setting lij to be more than 1. Moreover, it is also possible to think about oneway directional link between nodes, for example, by setting lij =1 but lii =0. According to this procedure,
we can introduce the valued directed graph into NA.

17centralities such as the closeness centrality to change. Next step is to clarify the
relationship between Δ L and a changing pattern of the centrality. As far as thenumerical example in Figure 1 is concerned, we only focused our attention on„closeness centrality‟, which is formulated by (1) and (2). Numerical results of an
example have already been given in 2.2. From (1), as far as a change in the network
structure do not include a new entry of the nodes into the network, a change ofcentralization index can be induced by(6) Cc (i ) 

1 g

g

( j 1 d (i, j ))g[ j 1 d (i, j )] .
g

2

In this formula, it should be notable that, as far as our simple case is concerned, thedifference of adjacency matrix between two points of time, which is indicated by ΔL, isthe same as the difference of distance matrix at each point of time. In a matrix formula,this equation can be given by(7) C'  D G  L G .
whereΔC’ is a vector whose elements are C' (i) and G is also a vector whose elements
are

1 g
( j 1 d (i, j ))2g. In this regard, D is a matrix whose components are d(i,j). By

definition, D  L . Therefore, factors that affect a changing pattern of the network, inparticular a change in linkages among nodes have the influence on a changing pattern ofvarious outcomes at each node in the network.
3.3 Calculation and Considerations
A specific pattern of network development is examined using the model
framework developed above. It is necessarily for the estimation to give additional
information about the size of each node, which is represented by various data such as
employment, income, capacity of accommodation or attractions and so on. A
representative data about size of node is assumed to be given by the table below.
The question is what made (or will make) the overall performance, for example,
develop. Theoretically, there are two driving forces of development, which are „scaleeffects‟ and „linkage effects‟. To answer this question, we need to estimate parametersin (4) or elements of various matrixes in (5). In the special case, assume that there is nodifference in derivatives f i1 , f i 2 and f i 3 at each node, meaning that there should be acommon multiplier effect of various factors which affect on outcome.
Then a process of the estimation should have the following steps;

18(i) Collect data properly. Even if we could have a good data set about current income
and inputs to produce tourism service, still we need their expectations, which might
be predicted in a master plan with social-economic aspects. In (3), only theinvestment to develop a new linkage by transportation or ITs is considered.
Therefore, the investment to increase connectivity between nodes is excluded5.
(ii) Calculate the values in matrixes and vectors in (4) or (5). This procedure should beapplied to both changing pattern from the Past to the Present and from the Present to
the Future, repeatedly.
(iii) In general, a procedure to estimate equation (5) for each period should be followed.
For the specific numerical example, all data are collectively used for estimating
equation (5). By using the estimated equations, the factors that cause a change inoutcomes are decomposed into two or three parts as given in (5).
It is notable that before following steps from (i) to (iii), we should know thestructure of the tourism destination network and its development pattern. In otherwords, we need the data to draw the network graph like Figure 1 though the weights ofeach node in Table 2 are unnecessary. In this paper, a method to analyse a small sizednetwork, which would have a small number of nodes and links, or a small part of theentire network is investigated. The procedures should be applicable to the network
designs in which an investment to enforce linkages among nodes is planned.
For the simple example given by Figure 1 and Table 1, it should be notable that
only the estimation process is meaningful because the estimates includes various
statistical issues mainly related to the degree of freedom due to a shortage of the sample
data. In this regard, it is assumed that there is no structural change in the production
functions between two periods, Past-Present and Present-Future, so that the data of both
periods can be used for a panel analysis6.
Assume that the input at each node is given by Figure 2 Table, which also shows
matrixes or vectors related to (4) and (5).
Then, the equation (4), or (5), can be estimated by the ordinary least square method
and its result can be expressed as(8)yi  16.08  2.19ni  0.23 (lij n j  lij n j )  0.75 (n j ), s  2.43, r 2  0.855 , F  14.79
j i(6.48)(2.01)

j(0.41)

5

As already noted in footnote 2, the connectivity between nodes can be considered.

6

As shown in Figure 2, the averaged values of inputs for each period are used to estimate.

,19or the result of OLS without intercept is given by(9)yi  2.20ni  0.24 (lij n j  lij n j )  0.05 (n j ), s  2.22, r 2  0.789 , F  208.1
j i(7.21)

j(2.42),(0.39)

where t-values are in parenthesis. All parameters are statistically significant except forthe parameters of total network effect. As far as this example is concerned, it should beclear that the network effects positively affect the performance of a node thorough bothan increase in the size of other nodes and a newly developed linkage between nodes.
Figure 2 Summary Tables of the Data

A
B
C
D

Outout performance

From Past to Present

Past Present Future
50
70
80
40
50
70
40
60
80
30
50
80

dy 1

20

dn 1

5

0

0

0

0

10

dn 2 =

2

dL= 0

0

1

0

dy 3

20

dn 3

7

0

1

0

0

16

dy 4

20

dn 4

9

0

0

0

0

0

0

1

1

1

1

0

0

0

dy 2

=

n1
Input (Size of Node)

A
B
C
D

Past Present Future
20
25
27
15
17
24
13
20
25
10
19
29

n2

22.5
=

16

L=

0
dL t *n=

16.5

18
L t *dn=

5

n3

16.5

1

0

0

0

5

n4

14.5

1

0

0

0

5

From present to Future
dy 1

10

dn 1

2

0

0

0

0

20

dn 2 =

7

dL= 0

0

0

0

dy 3

20

dn 3

5

0

0

0

1

24

dy 4

30

dn 4

10

0

0

1

0

22.5

0

1

1

1

1
1

0
1

1
0

0
0

1

0

0

0

dy 2

=

n1
n2
n3
n4

26
=

20.5
22.5
24

L=

0
dL t *n=0

22
L t *dn=792Moreover, it is also possible to estimate the individual effect of each factor on the
total outcome by the factor decomposition analysis. Applying (9) to the numericalexample leads to a summary table of the factor decompositions as shown by Table 10.
The total outcome can be thoroughly decomposed into three parts except for residuals
because (9) includes no intercept.
Looking at not only the tourism development given in Figure 2 but the developmentof tourism destination network given in Figure 1 as well, Table 10 also indicates us
some noteworthy points as follows: first, the direct effects are relatively large at nodes
where there was (or will be) no development of network-linkages with respect to theirown nodes. In fact, from the present to the future, for example, it is predicted that the20situation of the network around the area „B‟ will never change. Secondly, the overall
effects are relatively large at the area „B‟ and the area „A‟ for each period of time. At
the nodes where the overall effects are relatively large, the network effects are also large.
This is mainly because the numerical example deals with a small sample case where
there is only a little difference between the network effects and the overall effects.
Thirdly, the network effects can be decomposed into two parts as (4) indicates; theeffects via additional links (link effects) and the effects via changes in other node‟s size
(node effects). They are shown by Table 11. In this regard, the area „B‟ and the area „C‟
had the same structural change in the network each other (see Table 6) so that there islittle difference in the network effect in terms of output except for the direct effects. On
the other hand, as far as the area „A‟ is concerned, there is no linkage effects for both
periods because no new linkage was (will be) established. However, the area „A‟ has
played (will play) a role as a centre of tourism site so that a closer linkage of „A‟ withother nodes has contributed (will contribute) to the tourism development at the area „A‟.
Fourthly, it predicts us a policy recommendation for a future strategy of the networking
building for the tourism development. A development plan to build the new linkage
between the area „C‟ and the area „D‟ aiming at the bi-polar structure will contribute to
the tourism development in terms of output, from 9.2% at the area „B‟ to 48.4% at thearea „A‟, or 26.6% in average. In this stage, introducing the cost-benefit analysis tocompare the total network effects with the total investment cost of the networking will
help the governments (or the planners) to make a decision if they invest or not, or how
much they should invest.
Table 10 Factor Decomposition

A
B
C
D

From Past to Present
OutputEstimates Residuals
20
16.56
3.44
10
10.80
-0.80
20
21.69
-1.69
20
22.23
-2.23

Direct Effect
11.01
66.5%
4.41
40.8%
15.42
71.1%
19.83
89.2%

Network Efeect
4.35
26.2%
5.19
48.1%
5.07
23.4%
1.21
5.4%

Overall Effect
1.20
7.2%
1.20
11.1%
1.20
5.5%
1.20
5.4%

A
B
C
D

From Present to Future
OutputEstimates Residuals
10
10.97
-0.97
20
18.36
1.64
20
20.24
-0.24
30
29.20
0.80

Direct Effect
4.41
40.2%
15.42
84.0%
11.01
54.4%
22.03
75.4%

Network Efeect
5.31
48.4%
1.69
9.2%
7.97
39.4%
5.92
20.3%

Overall Effect
1.25
11.4%
1.25
6.8%
1.25
6.2%
1.25
4.3%

21

As mentioned in (5), there is another explanation about the decomposition formula.
Whereas the first expression of (5) decomposes the total effect into three parts, such as
the direct effects, the network effects and the overall effects, the second formation of (5)decomposes into two parts; one is the „scale effects‟ due to changes in nodes‟ capability
and another is the „linkage effects‟ due to changes in linkage pattern. Hence, a change in
output at each node should occur due to both the scale effects and the linkage effects.
The linkage effects are shown in Table 11 and the rest of the effects are classified as thescale effects. Therefore, as shown in Table 11, it is clear that the linkage effects appears
when a linkage between nodes is newly established and that they might be 17% to 37%,
depending on the network structure. This means that establishing a new linkage ordeepening the linkages between nodes can play important role to the tourism
development although the scale effects should be its major driving force.
Table 11 A decomposition of the Network Effect
From Past to Present
Network Efeect
A
B
C
D

4.35
5.19
5.07
1.21

26.2%
48.1%
23.4%
5.4%

Linkage Effect
0.00
0.0%
3.98
36.9%
3.86
17.8%
0.00
0.0%

Node Effect
4.35
26.2%
1.21
11.2%
1.21
5.6%
1.21
5.4%

Linkage Effect
0.00
0.0%
0.00
0.0%
5.80
28.6%
5.43
18.6%

Node Effect
5.31
48.4%
1.69
9.2%
2.17
10.7%
0.48
1.7%

From Present to Future
Network Efeect
A
B
C
D

5.31
1.69
7.97
5.92

48.4%
9.2%
39.4%
20.3%

4. Conclusion and Further Remarks
It is clear that tourism in a region where the nodes of stakeholders are related toeach other can be developed thorough various factors. The factors of the tourism
development mainly come from investment or improvement of capacity building at eachnode. Continual self-evolution of each node must be the most important factor towards
a sustainable development in the region. However, it is also true that in society no nodeor no stakeholder exists alone. Nodes or stakeholders are tied each other in a networkand the network, socially or technically, promotes and shares the public benefits among22

nodes it actually produces. This means that the external effects by networking in a
tourism site should be a vital factor leading to tourism development.
In this paper, following NA, we have provided some technical procedures thatenable us to give a quantitative analysis not only from statistic but also dynamic
perspectives. In particular, the paper is characterized by a dynamic analysis of the
tourism destination network, which assumes the two development stages of periods; oneis the period from the past to the present and another is the period from the present to
the future (or the target). Incorporating two stages of periods, it becomes possible toanalyse the relationship between the network development and the tourism development.
Although the numerical example for the network analysis is too simple, the analyticalmethods predicted here must contribute to evaluate the development plans in the
tourism sites, meaning that the policy measures related both to tourism development andto network development can be also recommended.
As the typical analytical measures, this paper introduces two methods; one is themethod to use the quantitative data, which the actors at each node answer and, therefore,
might be subjective and depending on their personality, memories and expectations. In
this regard, it was proved that incorporating a Fuzzy analysis enabled us to avoid somedegree of ambiguity of the data and the overall performance of the network should be
estimated though there were some prototypes. This method might be easiest way toevaluate the dynamic process of networking and tourism development. However, it isstill in the black box about what factors, including networking, promoted (or will
promote) the tourism development. To analyse the possible factors to promote tourism
development, an output function including not only inputs but also factors of
networking development was introduced. Hence, it was proved that there are threemajor factors to produce tourism development in a network site, such as the selfcapacity building of nodes, the total effect of tourism sites and the network effect oneach node through changing pattern of network structure. This procedure utilizes theofficial data, such as income, employment and lands, at each period of time, meaningthat we should include the data that are predicted in the master plans for the future ateach node. Moreover, it should be also predicted how the network structure will be in
the future. These procedures and data allow us to develop a dynamic analysis of the
tourism destination network. From a research perspective, these two analytical ways
should be applied depending on tourism sites because the possibility of the datacollection may be different from site to site. Although the framework was only applied
to a virtual numerical example, the procedures seem to be useful and feasible, meaning

23

that the network analysis developed here can be applicable to the real tourism
destination network.
The main findings of this research note are as follows. First, a feasible
theoretical framework was given in order to estimate a changing pattern of network
structure and of performance in the tourism site. Relationship between the changing
pattern of networking and its performance was analysed. A numerical example provedthat the network effect might be large in the node which has a large closeness centrality
like „A‟, a centre of network site, but linkage effects might be larger in the periphery of
the centre of the network like „B‟ or „D‟. This means that newly developed linkages
among nodes could contribute more to develop the periphery nodes than centre nodes.
Secondly, it might be proposed from a policy perspective that as far as the virtual case isconcerned, the linkage effect at each node was not so large, probably around from 18 to
29 %. Therefore, it might imply that scale effect was still the key to tourism
development and every stakeholder should do everything in their respective fields
although strengthening linkages among nodes might have positive effects on the tourism
performance.
Many points, including a methodological procedure, yet remain to be discussed.
In this paper, the outcome from the tourism destination network was simply assumed tobe measured by economic variables, such as income, employment and production. In
this regard, it should ask what the goal or policy target for the society to develop thetourism destination network is. The outcomes should be derived from various causes orinputs. The papers about the inputs, as the driving forces of the tourism development,
via promoting networks include the followings. Properties of nodes or actors in the links
are investigated from the businesses and organizations perspectives in Lemmetynen and
Go (2009), Tinley and Lynch (2001), Oztruck (2009), and Novelli et al. (2006). On theother hand, Briedenhann and Wickens (2004), Nash (2006), March and Wilkinson(2009) were focused on the measures to link including some physical measures as roads
or human-related sources of the partnership and cooperative or value-creation process.
In the preceding research, broad perspectives of outcomes include mainly the society,economics, governance and environmental perspectives. Sustainability was analyzed by
Oztruck and Eraydin (2010), Fadeeva (2005), and Halme (2001). Transformation andevolution patterns of relationship between organizations were investigated by Pavlovich(2003), and Scott and Cooper (2007). Moreover, Larsen, et al. (2007), and Gibson et al.
(2005) mentioned the importance of the social life of the people and their connectivity
in the society towards sustainable tourism. The role of the network in the community on
the policy planning and its designs was stressed by Dredge (2006a, 2006b), and Pforr

24(2006). From the tourism development perspectives, the final object, the society targets
should be the sustainable economic prosperity because the social structure is basicallymaintained by the businesses which employ people and support a family in thecommunity. However, if the network development is enforced by the development of
nodes and links, and if this leads to the development of the human relationships or otherrelationships among organizations in the tourism network, it can be proven that thedevelopment of the tourism network should lead to a better economic situation of the
tourism sites. In this sense, any observations about these positive effects of the network
development on the human relationship, the policy planning and the sustainable aspects
can lead to a conclusion that they should indirectly affect an economic or businessdevelopment. Therefore, these intermediate effects, or outcomes, may be classified as
the efficient cause, which Adam Smith once mentioned to explain the important role ofdivision of the labor. Though this task may be beyond us, we should take therelationship between the efficient cause and the final outcomes, though A. Smith named
it the final cause.
There are some technical issues. As already mentioned, a small number ofsample data should be efficiently analysed by using a fuzzy function and by estimating
the performance scores (PS) for each node or the performance index for entire networksite (PI), though there might be some prototypes of them. A problem is if it can be
confirmed how PS or PI is related to a changing pattern of network structure of the
tourism destination network site. Unfortunately, it cannot be estimated in the paper
mainly due to an insufficient degree of freedom of the sample data. Of course, the main
target of this research is to estimate the performance of the tourism sites and investigate
its cause and effects from the network analysis perspectives. The next step is clearly to
put the methods developed here to practical, not virtual, use.
References
Baggio, R.,Scott,N. and Cooper,C. (2010) “Network Science A Review Focused on
Tourism,” Annals of Tourism Research, doi.10.1016/J.annals,2010.02.008.
Briedenhann, J. and Wickens, E. (2004) “Tourism routes as a tool for economic develop
ment of rural areas – vibrant hope or impossible dream?” Tourism Management,
25, 1, 71-79.
Chan,F.T.S., Qi,H.J..,Chan,H.K., Lau,H.C.W. and Ip R.W.L (2003) “A conceptualmodel of performance measurement for supply chains,” Management Decision,
41/7, 635-641.
Dredge, D. (2006a) “Policy networks and the organisation of tourism,” Tourism

25Management, 27, 2, 269-280.
Dredge, D. (2006b) “Networks, conflict and collaborative communities,” Journal of
Sustainable Tourism, 14, 6, 562-581.
Fadeeve,Z. (2005) “Translation of sustainability ideas in tourism networks: Some rolesof cross-sectional networks in change towards sustainable development,” Journalof Cleaner Production, 13,2,175-189.
Freeman, L.C. (1979) “Centrality in social networks conceptual clarification,” Social
Networks, Volume 1, Issue 3, 215-239.
Gibson, L., Lynch, P. and Morrison, A. (2005), “The local destination tourism network:
Development issues,” Tourism and Hospitality Planning and Development, 2, 2,
87-99.
Halme (2001) “Learning for sustainable development in tourism networks,” Business
Strategy and the Environment, 10, 100-114.
Knoke, D. and Yang, S (2008), Social Network Analysis, 2nd edititon, SAGA
Publications.
Larsen,J, Urry, J. and Axhausen, K.W. (2007) “Networks and tourism: Mobile Social
Life,” Annals of Tourism Research, 34, 1, 244-262.
Lemmetyinen,A. and GO, F.M. (2009) “The key capabilities required for managing
tourism business networks,” Tourism Management, 30,1,31-40.
March, R. and Wilkinson, I. (2009) “Conceptual tools for evaluating tourism partnership,” Tourism Management, 30, 3, 455-462.
Nash, R. (2006) “Causal network methodology: Tourism research applications,” Annalsof Tourism Research, 33, 4, 918-938.
Novelli, M. Schnitz, B. and Spencer, T. (2006) “Networks, clusters and Innovation intourism :A UK experience,” Tourism Management, 27, 6, 1141-1152.
Oztruck,E and Eraydin, A.(2010), “Environmental governance for sustainable tourism
development: Collaborative networks and organisation building
Pavlovich, K. (2003) “The evolution and transformation of a tourism destination
network: the Waitomo Caves, New Zealand,” Tourism Management, 24, 2, 203216.
Pforr, C. (2006) “Tourism policy in the making: An Australian network study,”Annalsof Tourism Research, 33, 1, 87-108.
Pyo, S. (In press) “Measuring tourism chain performance,” Service Industries Journal.
Scott,N. and Cooper, C. (2007) “Network analysis as a research tool for understanding
tourism destinations,” Development in Tourism Research, Chapter 14, 199-219.
Scott, N., Cooper, C. and Baggio, R. (2007) “Destination Networks: Four Australian26

Cases,” Annals of Tourism Research, 35,169-188
Scott, N., Baggio, R. Cooper, C. (2008) Network Analysis and Tourism; From theory topractice, Channel View Publications.
Selin, S. and Beason, K. (1991) “Interorganizational relations in tourism,” Annals of
Tourism Research, 18, 4, 639-652.
Shin, H-Y. (2006) “Network characteristics of drive tourism destinations: An
application of network analysis in tourism,” Tourism Management, 27, 10291039.
Tinsely,R. and Lynch,P. (2001) “Small tourism business networks and destination
development,” International Journal of Hospitality Management, 20,4,367-378.
Zhang,X., Song, H. and Huang, G.Q. (2009) “Tourism supply chain management: A
new research agenda,” Tourism Management, 30, 345-358.