Analysing Tourism

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Analysing tourism stakeholders networksAngelo Presenza and Maria Cipollina

Abstract

Purpose – The purpose of this paper is to analyze the variety of relations existing in tourism networks,identied as complex and mutable entities, where a vast range of stakeholders coexist.Design/methodology/approach – After a deep review on stakeholder theory, the research applies techniques of network analysis to a case study. Specically, the analysis focuses on 354 hospitality rms acting in Molise Region (Italy). Each operator was asked to judge the importance to collaborate with other stakeholders to enhance the effectiveness of their management and marketing activities. The answers highlightthe degree of preference among stakeholders andthe resulting information is thelevel of condence in the network.Findings – Results conrm the importance of intensifying relationships between tourism companies themselves and between them and policy makers. It appears that public stakeholders are more important for both management and marketing activities than private sector, since they place a much higher position in the scale of preference.Research limitations/implications – The paper provides a starting-point for further research about non-quantitative destination performance measurement, such as trust and commitment between the stakeholders in tourism destination, and the use of network analysis’ techniques.

Practical implications – Destination managers and policy-makers may use techniques of network analysis to elaborate useful information for planning and managing the relationships inside the tourism network.

Originality/value – The paper offers a novel approach for developing network analysis in tourism network literature. It explores non-quantitative destination performance measurements and uses management and marketingactivities to analyze relationships between public and private stakeholders.Keywords Tourism management, Tourism, Partnership, Stakeholder analysis Paper type Case study

1. Introduction

In the last decade, changing structures of government and a growing realization of the roleof governance has led to interest in social relations between government, business and civilsociety. Explaining relations between organizations does not derive only from scienticinterest, but also from the practical and normative requirement in which the goal is tohighlight the structural features and the techniques for managing these modern

organizational relationships.

In the tourism literature, an increasing interest in networks is divisible into two main streamsof application. First, networks are understood as a useful framework for analyzing theevolution of business, product development, packaging and opportunities for furtherdevelopment (Tinsley and Lynch, 2001). Second, networks are seen as an important conduitfor managing public-private relationships and understanding structures of tourismgovernance (Palmer, 1996; Tyler and Dinan, 2001; Pforr, 2002). These two streamsnecessarily overlap. Innovative, catalytic producer networks require planning and regulatory

DOI 10.1108/16605371011093845 VOL. 65 NO. 4 2010, pp. 17-30,Q Emerald Group Publishing Limited, ISSN 1660-5373jTOURISM REVIEWjPAGE 17

Angelo Presenza is anAssistant Professor at theUniversity ‘‘G. D’Annunzio’’of Chieti-Pescara, Pescara,Italy. Maria Cipollina is anAssistant Professor at theTourism Research Centre,University of Molise,Termoli, Italy.



environments that are exible and capable of timely response (Dredge, 2006). In tourismnetworks, identied as complex and mutable entities that develop and evolve over time inresponse to environmental and organizational developments and demands (March andWilkinson, 2009), a variety of relations can be identied. According to Pavlovich (2003), suchdense ties encourage conformity, acceptable action, and inclusion, and so they encouragedestination cohesion. Sparse ties among groups on the other hand can excludestakeholders and act as bridges to those players who are external to the destination,facilitating importation of new information into the region and introducing innovation (Scottet al. , 2008).

If collaboration among operators is an effective tool in tourism innovation, then we would alsoneed to consider how it can benet the destination level of organization. The existence ofrelations between two actors implies that the behaviour of one conditions the behaviour ofthe other. The purpose of this paper is to contribute to the understanding of the way therelations and networks connecting the actors involved in a tourist destination affect itsbehaviour and performance.

Analysis of the value-creating network-development process may identify the criticalsuccess factors that enable members of a tourism business network in order to performoptimally. Accordingly, various issues are raised:

[. . .] why and how are the actors motivated to cooperate in the network? What is the value thenetwork actors perceive they obtain from (potential) inter-organizational relationships? How doesthe cooperation among stakeholders contribute to encourage the staged authenticity, which in

turn results in deeper experiences and customer satisfaction? (Lemmetyinen and Go, 2009,p. 34).

In this context, the main aim of this research is to apply state of art network analysis (NA) tostudy the links between tourism destination stakeholders, while distinguishing betweenprivate and public sector actors. Network theory seeks to improve the understanding offormal and informal organizational structures that spanpublicand private sectors and shapecollective actions (Dredge, 2006). As a result, NA is becoming a standard diagnostic andprescriptive tool for management to analyze organizational interaction (Scott et al. , 2008).While there is a growing recent literature focusing on the importance of the relationshipsbetween tourists and service organizations and tourism business (Sautter and Leisen, 1999;Sheehan and Ritchie, 2005; Dredge, 2006; March and Wilkinson, 2009), few studiesexamine the tourism destination from the network point of view applying a NA approach(Shih, 2006; Scott et al. , 2008). Using an NA approach this paper assesses the variety ofrelations existing in tourism stakeholders and the importance of their management andmarketing activities. The structure of the paper is as follow: section 2 briey summarizes theexisting literature and species more in details the aim of our analysis; section 3 denes theNA approach; section 4 describes the used methodology; section 5 shows and discussesthe results; nally, section 6 concludes.

2. Applying stakeholder theory to tourism networks analysis

Recently, various studies focus on the importance of interorganizational networks indestinations and the effects of collaboration among organizations. In essence, networks arecharacterized by a range of participants that surpass organizational boundaries andstructures (Howlett and Ramesh, 1995; Rhodes, 1997; Scott et al. , 2008).

They involve commitment by network members to a set of common goals and, quitepossibly, the sharing of worldviews (Burstein, 1991). This ‘‘connectedness’’, in turn, givesrise to opportunities for the transfer and sharing knowledge, which is an important driver forincreasing innovation and competitiveness. Knowledge and ideas diffuse through businesssystems via the relations and networks connecting economic actors and, as a result, theyallow and restrict what individual actors can and do, know and think. Networks also enableand constrain what we can do with our knowledge and ideas: ‘‘they are the means by whichthe knowledge, skills and resources required to develop, exploit and commercialize newideas are marshaled and coordinated’’ (Wilkinson, 2008, p. 25).

PAGE 18 jTOURISM REVIEWjVOL. 65 NO. 4 2010



An individual rm’s performance depends on the behaviour of others that it is directly andindirectly connected to. This argument is emphasized by ‘‘stakeholder theory’’ pioneered byFreeman (1984), who dened a stakeholder as ‘‘any group or individual who can affect or isaffected by the achievement of the organization’s objectives’’ (Freeman, 1984, p. 46).

From this denition emerges a view of stakeholders that is very broad indeed and goesbeyondthose that have merely formal,ofcial,or contractual ties to theorganization. Freeman(1984) argues that external groups to the organization have an increasing ability to affect theorganization itself. The importance of relationships with these organizations supports the

need for a newstakeholder approach to strategic management (Sheehan andRitchie, 2005).From a managerial perspective, the stakeholder theory posits that the various groups mighthave a direct inuence on managerial decision-making. As most succinctly stated byFreeman (1984, p. 46) ‘‘to be an effective strategist you must deal with those groups that canaffect you, while to be responsive (and effective in the long run) you must deal with thosegroups that you can affect’’. This is even more evident when the eld of interest is touristdestination where the experience and satisfaction of tourists and to the general economicsuccess of the region is directly related to many types of rms andother organizations. Someof these are located in the tourist destination; others are located elsewhere but play animportant role in linking destinations to sources of tourists, including other touristdestinations, as well as to other types of inputs required by a tourist destination to functioneffectively and efciently. The performance of a tourist destination depends on the linksbetween these various component actors, not just on their individual characteristics (March

and Wilkinson, 2009).As the tourism system context becomes increasingly fragmented and volatile, theirstakeholders are pressured to adapt collaboration principles to everyday practice,particularly in the planning and marketing areas. Bramwell and Sharman (1999) identifythree potential benets deriving from consensus-based collaboration:

1. it may avoid the costs of solving conicts among stakeholders;

2. it may legitimate collective actions if stakeholders are involved in the decision-makingprocesses which affect their activities; and

3. the willingness to collaborate may enhance the coordination of policies and relatedactivities.

Contributions related to tourism destination planning stress the need for involving public andprivate actors to gather consensus and to make rms’ and institutions’ strategies convergetowards the same goals (Pforr, 2006). Cooperation, as a dynamic process-oriented strategy,may be a suitable means for managing turbulent planning domains at the local as well as theregional, national and international level (Lemmetyinen and Go, 2009). Dredge (2006)investigates relationships between local government and industry in order to criticallydiscuss the role of networks in fostering or inhibiting public-private sector partnershipbuilding. Furthermore, the interdependence between the actors, in terms of their sales,supplies, information, development and access to other companies elsewhere in thesurrounding network (Ford et al. , 2003), affords small and medium-sized tourism enterprises(SMTEs) the opportunity to mitigate their size disadvantage (Bieger, 2004). In particular, theyare able to address scale and scope issues, thereby actively creating and sustainingcompetitiveness (Lemmetyinen and Go, 2009). Even if critical resources areoften physical, itis knowledge-intensive intangibles such as effective organization and leveraging ofrelationships that add signicant value to rms.

In order to assess the importance of interorganizational networks in tourism destinations andthe effects of collaboration among stakeholders, our NA is based on information by a surveyadministered to200accommodationfacilities,hereafterhospitalityrms, inMolise (aSouthernItalian Region) in the period March-July 2008. In particular we focus on two questions:

1. How important is the relationship with local stakeholders for your management activities?

2. How important is the relationship with local stakeholders for your marketing activities?

VOL. 65 NO. 4 2010jTOURISM REVIEWjPAGE 19



Since the collaboration in both activities, management and marketing, among stakeholdersis very important for the tourist destination, this analysis does not focus on only one questionbut uses all detailed information available and is run separately on the two activities, in orderto test the robustness of results. In this way, we also get a complete paint of the relationshipson the network.

3. Network-analysis approach: denition and tools

NA is a new approach used to describe the structure of links between given entities (namely

nodes), and applies quantitative procedures to calculate various indicators for assessmentof features of a whole network and the position of individuals in the network structure.Network analysis delivers a number of useful outcomes. It provides a means of visualizingand simplifying complex sets of relationships, therefore it is useful in promoting effectivecollaboration within a group (Baggio and Cooper, 2008). In simple words NA provides a richand systematic means of assessing such networks by mapping and analyzing relationshipsamong nodes. It consists of a collection of graphs developed to analyze networks in socialsciences, communication studies, economics, political science, computer networks andmeasures such as cohesion, equivalence (role-groups), power of actors, range of inuence,and brokerage, calculated to summarize characteristics of the actors and the network itself(Shih, 2006). According to Burt (1992), a social network is a group of collaborating entities(i.e. actors) that are related to one another. Mathematically, this is a graph in which eachparticipant in the network is called actor and depicted as a node in the network.

In a general form, a NA consists of a graph G ¼(V , L), with a set of vertices V ¼{1, . . . . . . , n }and a set of lines L¼{1, . . . . . . , l } between pairs of vertices. Positive weights W , indicating thestrength of the relation, are associated with each line and, in addition, information can becontained in a vertex value function P :

N ¼ ðV ; L; W ; P Þ ð1Þ

The size of the network is expressed by the number of vertices, n , and the number of lines, l .Relationships can be reciprocal or directed, in which case an arrow is used to indicate thedirection. This may be positive or negative, indicated with a plus or minus sign. One of themain applications of NA is the identication of the ‘ ‘important’’ nodes in their network(Wasserman and Faust, 1994). The most important or prominent nodes generally occupystrategic locations within a network. The overall distribution of ties and their localconcentration are important parameters and indicators of cohesion, which is a property of

the whole network (Haythornthwaite, 1996). It indicates the presence of strong socializingrelationships among members and, also, the likelihood of their having access to the sameinformation or resources. Measures of cohesion, such as density and centralization, indicatethe extent to which all members of a population interact with all others. The density of anetwork is the number of lines in a simple network, expressed as a proportion of themaximum possible number of lines. It is dened by the quotient g ¼l/l max , where l max is thenumber of lines in a complete network with the same number of vertices. Accordingly, acomplete network is a network with maximum density (De Benedictis and Tajoli, 2009). Sincea vertex can be both a sender and a receiver, the indegreeof a vertex isthe number of arcs itreceives, and the outdegree is the number of arcs it sends. In a network, vertices can begrouped according to their degree and the degree distribution of a network is the frequencydistribution of vertices with degree d ¼0,1, . . . , n 2 1. The idea of the centrality of individualsin their network is one of the earliest to be pursued by network analysts (Scott, 2000), and is

used to acquire the positional features of individual nodes within networks (Shih, 2006). Thestandardized degree centrality ( Cd ) of a vertex is its degree divided by the maximumpossible degree:

Cd ¼ d =ðn 2 1Þ ð2Þ

In directed networks, degree centrality can distinguish between the in-degree ( Cdi ) and theout-degree ( Cdo ) of each node. The degree is a measure of the ‘‘activity’’ of the node. Thein-degree centrality is the number of arcs ending at each node, while the out-degreecentrality is the number of arcs starting from each node.




Another measure of node centrality is the closeness ( Cc ) that is based on distance. Themeasure focuses on how close a node is to all the other nodes in the set of nodes(Wasserman and Faust, 1994). It is the invert sum of the shortest distances between eachnode and every other node:

Cc ¼ 1=ðX j dist ðn i ; n j ÞÞ ð3Þ

where dist ðn i ; n j Þdenotes the distance between a node and the others. Also closeness canbe distinguished in in-closeness and out-closeness, respectively, based on inward and

outward connections. Such denition reects the idea that a node is central if it can quicklyinteract with all other nodes.

Finally, the betweenness centrality ( Cb ) measures the extent to which a particular node liesbetween the various other nodes in the set of nodes (Scott, 2000). It reects how often annode lies on the geodesics between the other nodes of the network:

Cb ¼ X j – k

g i jk

g jk ð4Þ

where g jk is the total number of shortest paths joining any two vertices V k and V j , and g i jk isthe number of those paths that not only connect V k and V j , but also go through V i .

Various techniques can be used to display the graphical data, ranging from the use ofhand-drawn relational maps to diagrams derived using sophisticated statistical techniques.

The basic idea is to analyze nodes and their relative position in the network. The resultantdiagram is then interpreted visually.

In this study, we employ these techniques to analyze the structural characteristics of the linksbetween tourism destination stakeholders. Each tourism destination stakeholder is treatedas node and the attitude for the collaboration between hospitality rms are treated as aseries of links. We construct a matrix representing sociometric choices which describe thepresence or absence of a given type of relation (Degenne and Forse, 1999). Figure 1, forexample, shows the simple case where a hospitality rm answers the question: whichstakeholder (among Regional Government, City Government, Research Institute, Touroperator and Travel agency) is more important in exercising your management activity? Thegraph shows that hospitality rmrst prefers to be in touch with travel agencies and then withCity Government, Research Institutes, Regional Government and Tour operator in sequence.Based on the graph, the asymmetric matrix of this rm can be built, where the rows ( i ) andcolumns ( j ) index stakeholders in the graph. In the matrix, we assign ‘‘1’’ in the ( i , j )th cell ifthere is a direct link from i to j , and a 0 in the cell otherwise. The link, represented by thearrow, moves from the least to the most preferred stakeholder. Summing up the matrix ofevery hospitality rm, we obtain our valued matrix for measuring the indicators and drawingthe graphs of NA.

Figure 1 A simple graph and matrix




It is possible to nd several computer software packages that map relational data. For thisstudy was used UCINET 6.03 (Borgatti et al. , 2002), one of the most popular andcomprehensive program for the analysis of social networks and other proximity data. Theprogram contains dozens of NA indexes (e.g. centrality measures, dyadic cohesionmeasures, positional analysis algorithms, clique nders, etc.), plus general statistical andmultivariate analysis tools, such as multidimensional scaling, correspondence analysis,factor analysis, cluster analysis, multiple regression, etc.

4. Methodology

Data required to apply the NA was derived from a comprehensive survey of hospitality rmsconducted by the Tourism Research Centre of the University of Molise in the periodFebruary-September 2008 in the Molise region of Italy. The region is located in Southern Italyand it is the smallest and youngest region of Italy, bordering with Lazio, Abruzzo, Campaniaand Puglia. In the past few years Molise has realized considerable growth in the tourismsector as compared to other southern regions. With respect to Italy as a whole and allowingfor the last ve-year period, Molise registered an average rate of growth in arrivals that isquite above the average of other Italian regions, ranking after Basilicata and Calabria (Istat,2008). Apart from collecting information necessary for drawing the tourism stakeholdernetwork, this survey also aimed to identify the number and the typologies of theaccommodation businesses, and to assess the quality of the tourism system in Molise.

In the regional register of tourism rms administered by the regional Department of Tourism,354 tourism rms were registered, consisting mostly of hotels, bed and breakfast,agro-tourism and guest houses (Table I). These rms are not equally dispersed through theprovince, as Provence of Campobasso has more rms than the others, even tough the Costhas the highest density (only four municipalities versus 84 in Provence of Campobasso and52 in Provence of Isernia).

Data was collected through questionnaire designed for self-completion. The questionnaireswere administered through several methods – a web site dedicated to this research projectwas created from where respondents were able to download the questionnaire, in somecases it was sent by fax or even posted in the cases where accommodation rms did nothave Internet or fax connection. Finally, in few cases, it was delivered personally. The website was on online for around ve months. During this period a group of 4 researchers of the

Tourism Research Centreadministrated the data collection with the aim to achieve at least 50percent response rate per category of accommodation facilities and geographical areas.

At the end of ve months, the 200 useful responses were obtaining, with an acceptableresponse rate of 56.5 percent.

Table I Distribution of hospitality rms in Molise

Geographical area Categories Province of Campobasso Province of Isernia Coast Total

Hotel 43 22 25 90Residence 0 1 8 9Agri-tourism 39 13 10 62Bed and Breakfast 34 25 12 71

Camping 0 2 12 14Holiday house 16 4 6 26hostel 0 1 0 1Guest house 32 16 10 58Rural Tourism 11 8 0 19Village 2 0 2 4Total 177 92 85 354

Source: Author’s calculations based on data provided by Tourism Research Center (2008) –University of Molise, Italy




The questionnaire consisted of several sections collecting information on rms’characteristics (size, origin, quality, etc) and target market. The part dealing with thestakeholders network consisted of 200 hospitality rms required to answer to the followingtwo questions:

1. How important is the relationship with local stakeholders for your management activities?

2. How important is the relationship with local stakeholders for your marketing activities?

The rms answered to each questions assigning a mark from a minimum of ‘‘1’’ to amaximum of ‘‘10’’ for each stakeholder according to propensity to collaborate with them inexercising their activities. The answers highlight the degree of preference to collaborationamong stakeholders and the resulting information is the level of condence in the network.

5. Results and discussion

In general, most respondents provided ratings of over 6 (out of 10) indicating a highpropensity to collaborate with other tourism destination stakeholders. Tourism servicesagencies are less preferred by the hotel, camping and residence rms, while tour operatorsare less preferred by agri-tourism and bed and breakfast rms. On the other hand, guesthouses are the least interested in research institutes. Hospitality rms seem to prefer tocollaborate with the tourism bureau (or DMO) in exercising their management activity, whilehotels prefer to cooperate with city government and holiday houses favour travel agencies

(in italics in Table II).Comparing Table III to Table II, the average ratings are much lower, indicating that thehospitality rms are less liable to cooperate with stakeholders regarding marketing activitiesthan management activities.

Consistent with the results in Table II, most hospitality rms want to collaborate more with thetourism bureau for their marketing activities. Exceptions are bed and breakfast, campingand residence rms which prefer local or regional governments. Rural tourism rms prefer tocollaborate mostly with travel agencies. However, given their relatively low ratings regardingall stakeholders, they appear not to have the ability or desire to collaborate in general.Conversely, residences are likely to collaborate with all stakeholders in their marketingactivities.

We use Tables II and III for constructing our network matrix. A visual assessment of a global

network can be captured based on the graph approach. Figures 2 and 3 show the networkgraph of the Molise Region, where nodes represent the tourism destination’s stakeholdersand arcs directed between pairs of nodes represent the importance that hospitality rmsassign to relationships for management and marketing activities.

At rst glance, the management activity stakeholders with central position are national travelassociations, regional governments, travel agencies and local tourism associations. Relativeto marketing activities, the more central stakeholders are travel agencies and provincialgovernments. The indicators of NA for the two questions are shown in Tables IV and V,respectively. The indicators of degree centrality show the level of preference that rmsassign to each stakeholder. A higher out-degree centrality indicates a stakeholder is theleast preferred in the business activity. A higher in-degree centrality implies that astakeholder is the most important node. Looking at network indicators relative tomanagement activity (Table IV), the comparison between the in-degree and out-degree ofeach node reveals that the most important stakeholder is the tourism bureau, whereas theleast important are research institutes and tour operators. All others place an intermediateposition in the preference scale of hospitality rms.

Assessing the indicators of in-closeness and out-closeness centralities reveals the extent towhich a particular stakeholder is closer from and to others, respectively. Regionalgovernment, travel agencies and local tourism association seem to hold the mediumposition in the range of preference, consistent with the evidence in Figure 2. Thebetweenness centrality indicator conrms the middle position already identied by the




T a

b l e I I

P r o p e n s i t y t o c o l l a b o r a t e i n m a n a g e m e n t a c t i v i t y ( s a m p l e m e a n )

R e g i o n a l

g o v e r n m e n t

P r o v i n c i a l

g o v e r n m e n t

C i t y

g o v e r n m e n t

T o u r i s m

b u r e a u

T o u r i s m

c o n s o r t i u m

L o c a l

T o u r i s m

A s s o c i a t i o n

N a t i o n a l

T o u r i s m


T o u r

o p e r a t o r

T r a v e l

a g e n c y

T o u r i s m

s e r v i c e s

a g e n c y

O t h e r

o p e r a t o r s

R e s e a r c h

i n s t i t u t e s

G u e s t h o u s e

7 . 6

7

7 . 5

7

8 . 3

3

8 . 5 2

8 . 1

9

7 . 5

2

7 . 6

2

7 . 8

1

7 . 2

4

7 . 0 5

8 . 1

4

6 . 6

7

H o t e l

7 . 2

0

7 . 1

7

8 . 0 0

7 . 9

3

7 . 2

8

7 . 3

5

6 . 7

4

7 . 3

0

7 . 5

2

6 . 6 3

7 . 0

9

6 . 9

4

A g r i - t o u r i s m r m s

6 . 5

0

6 . 1

7

6 . 6

1

6 . 9 2

6 . 6

4

6 . 1

1

6 . 0

0

5 . 9

4

6 . 4

2

6 . 3 1

6 . 4

4

6 . 5

0

B e d b r e a k f a s t

7 . 2

5

7 . 3

3

8 . 0

0

8 . 2 9

7 . 1

1

6 . 6

9

6 . 5

8

6 . 5

3

6 . 6

0

6 . 6 4

6 . 8

5

6 . 7

6

C a m p i n g

7 . 0

0

7 . 2

5

5 . 0

0

8 . 2 5

8 . 1

3

7 . 8

8

4 . 7

5

5 . 6

3

4 . 8

8

4 . 0 0

5 . 8

8

6 . 3

8

H o l i d a y h o u s e

6 . 0

0

4 . 9

2

6 . 2

5

7 . 0

8

6 . 9

2

6 . 8

3

6 . 2

5

6 . 5

8

7 . 5

0

5 . 2 5

6 . 9

2

6 . 6

7

R e s i d e n c e

8 . 3

3

8 . 6

7

9 . 0

0

9 . 3 3

9 . 3

2

9 . 3

1

8 . 8

3

8 . 1

7

8 . 1

7

7 . 6 7

8 . 6

7

8 . 3

3

R u r a l t o u r i s m r m s

7 . 9

1

7 . 0

9

8 . 0

9

8 . 4 5

8 . 2

7

7 . 8

2

7 . 2

7

8 . 2

7

8 . 3

6

8 . 0 0

8 . 2

7

7 . 6

4




T a

b l e I I I

P r o p e n s i t y t o

c o l l a b o r a t e i n m a r k e t i n g a c t i v i t y ( s a m p l e m e a n )

R e g i o n a l

g o v e r n m e n t

P r o v i n c i a l

g o v e r n m e n t

C i t y

g o v e r n m e n t

T o u r i s m B u r e a u

T o u r i s m

c o n s o r t i u m s

L o c a l T o u r i s m


N a t i o n a l

T o u r i s m


T o u r

o p e r a t o r

T r a v e l

a g e n c y

G u e s t h o u s e

7 . 9

0

7 . 7

1

8 . 0

0

8 . 1 0

6 . 3 8

7 . 5

7

7 . 6

7

7 . 4

3

7 . 3

8

H o t e l

7 . 5

0

6 . 9

8

7 . 5

6

7 . 9 6

6 . 4 8

7 . 1

3

6 . 5

7

7 . 3

0

7 . 2

8

A g r i - t o u r i s m r m s

6 . 7

8

6 . 5

5

6 . 5

6

7 . 0 8

6 . 1 7

6 . 4

4

6 . 3

1

5 . 9

2

6 . 0

8

B e d b r e a k f a s t

8 . 5

6

8 . 1

6

8 . 3

8

8 . 1

6

7 . 1 5

7 . 0

2

6 . 8

9

6 . 9

5

6 . 8

0

C a m p i n g

8 . 2

5

8 . 2

4

5 . 5

0

7 . 8

8

7 . 7 5

5 . 8

8

5 . 0

0

4 . 0

0

3 . 3

8

H o l i d a y h o u s e

6 . 5

8

5 . 7

5

6 . 5

0

6 . 7 5

6 . 5 8

6 . 5

8

6 . 4

2

6 . 6

7

6 . 5

8

R e s i d e n c e

8 . 8

3

9 . 0

0

9 . 3 3

9 . 3

2

9 . 3 1

9 . 2

9

7 . 6

7

8 . 8

3

8 . 8

3

R u r a l t o u r i s m r m s

4 . 0

9

5 . 0

0

4 . 8

2

5 . 1

8

3 . 0 0

4 . 8

2

4 . 7

3

5 . 1

8

5 . 3

6




closeness indexes and discloses that themain stakeholders are provincial governments andtourism bureaus.

Regarding the marketing activity (Table V), the most important stakeholder resulting from thecentrality indicator is the tourism bureau, while the least preferred are tour operators, travel

agencies and tourism consortiums. Consistent with Figure 3 are measures of closeness andbetweenness which indicate that provincial government and travel agencies place in anintermediate position in the preference scale of hospitality rms.

In general results so far obtained are consistent with the descriptive statistics, whileacknowledging that simple means are not the best indicators because they give the sameweight to all observations without taking into account their importance. In conclusion, theevidence from NA indicates that the public sector (tourism bureau, regional and provincialgovernments) are more important for both management and marketing activities than

Figure 2 Network graph relative to management activity

Figure 3 Network graph relative to marketing activity




private stakeholders, with exception of travel agencies that seem to also rate highly in thescale of preference.

6. Conclusion

The purpose of this study was to discuss stakeholder theory using state of the art networkanalysis applied to the investigation of the relationships between stakeholders in tourismdestinations. Conclusions can be drawn from the research ndings and the discussionthroughout this paper. The rst result is while tourism literature pays signicant attention tothe issue about quantitative destination performance measurement (Presenza, 2007), socialmeasurement perspective appears less pronounced. Despite the diverse approaches, the

Table IV Network indicators relative to management activity

Degree centrality Closeness centrality Tourism destination’s stakeholders In-degree Out-degree In-closeness Out-closeness Betweenness centrality

City government 8 7 73.3 57.9 9.20Local Tourism Association 8 8 73.3 68.8 10.9National Tourism Association 8 8 61.1 64.8 3.50Other operators 8 8 64.7 61.1 3.83Provincial government 6 6 57.9 64.8 3.24

Regional government 8 10 73.3 68.6 12.9Research Institutes 5 8 57.9 68.8 3.83Tour operator 6 8 61.1 64.8 5.51Tourism Bureau 8 2 55.0 47.8 0.36Tourism consortiums 8 8 61.1 64.8 6.43Tourism services agency 7 8 61.1 61.1 3.72Travel agency 8 7 68.8 68.8 12.5

Descriptive statistics Mean 7.33 7.33 64.1 64.3 6.33Standard deviation 1.02 1.84 6.28 7.05 3.92Sum 88.0 88.0 769 772 76.0Variance 1.06 3.39 39.4 49.7 15.4min. 5.00 2.00 55.0 47.8 0.36max. 8.00 10.0 73.3 78.6 12.9

Table V Network indicators relative to marketing activity

Degree centrality Closeness centrality Tourism destination’s stakeholders In-degree Out-degree In-closeness Out-closeness Betweenness centrality

City government 9 7 66.7 66.7 2.6Local Tourism Association 8 8 72.7 66.7 4.2National Tourism Association 7 7 80.0 66.7 3.5Provincial government 7 7 72.7 80.0 6.0Regional government 7 7 72.7 61.5 2.5Tour operator 6 8 61.5 80.0 2.0Tourism Bureau 8 3 72.7 61.5 1.9Tourism consortiums 5 8 53.3 72.7 1.4

Travel agency 6 8 80.0 72.7 7.9

Descriptive statistics Mean 7.00 7.00 70.3 69.8 3.56Standard deviation 1.16 1.49 8.09 6.6 2.03Sum 63.0 63.0 632 628 32.0Variance 1.33 2.22 65.5 43.5 4.12min 5.00 3.00 53.3 61.5 1.42max 9.00 8.00 80.0 80.0 7.91




success of the collaboration depends on the perception rms have about the convenienceof undertaking joint activities. In other words, rms are willing to cooperate if they perceiverewards to outweigh costs and risks.

While organizing an investigation of networks around structure and relational characteristicsprovides a rich descriptive insights, the ndings indicate the importance of investigating the‘‘softer’’ and less tangible social and cultural aspects of networks; in other words, to developa better understanding of less tangible, cultural aspects that go beyond structure andrelations to explore the dynamics associated with actor strategies, rules of conduct, levels of

institutionalization and power relations.The second conclusion is that a sustainable tourism destination strategy requirescollaborative and inclusionary consensus-building practices. It is therefore necessary toconsider the dimension ‘‘trust’’ (Franch et al. , 2008). Trust is nurtured by commitment,by sharing information and expertise and by consolidating relationships between theparties. It is also an expression of awareness and willingness of actors to be part of thenetwork.

Even reading the results of the survey there is a general conrmation of this assertion.Although with different values, all categories surveyed attribute scores generally positive tothe relations with all stakeholders, relating to both management and marketing activities.

It is reasonable to hypothesize that the presence of informational, interpersonal and ordecisional roles can help to transform the theoretical added value offered by theinter-organizational network in concrete competitiveness in the medium/long term. Thecapability to develop and carry out those roles require specic managerial talent thatcould be regarded as one of the critical success factors: we are thinking to the‘‘facilitators’’, actors able to orchestrate the network sharing, acquiring and deployingknowledge within the net. This also involves the ability to manage in networks, whichembraces supporting cooperation among dispersed actors in order to bring about thedesired outcomes.

The last result is more related to the use of network analysis for the understanding of thetourism destination’s structure. By analyzing structures and linkages, importing analyticaland theoretical techniques, this methodology can help policy and management approachesto highlight limitations and opportunities in destination structures. This paper has illustrateddifferences in measures of interorganizational cohesion investigating the propensity of

hospitality rms to ‘‘open’’ their doors to the stakeholders in order to better manage theiractivities. As competition around the world increases, managers may improve theircompetitive advantage by using NA (Scott et al. , 2008). The visualization of the relationshipsof stakeholders renders the approach especially useful because the structures can be easilyinterpreted by managers and communicated to the destination stakeholders themselves.The evidence from NA highlights that public sector (namely tourism bureau, regional andprovincial governments) are more important for both management and marketing activitiesthan private stakeholders, except for travel agencies that seem to also be rated highly in thescale of preference.

It should be observed that the quantitative tools and methods used here are not completelysufcient to give a complete range of outcomes. Further investigation may therefore be setup. A logical step in extending the research described in this paper is to further rene the

parameters that can be used for calculating more indices proposed by the network analysis.The lack of theoretical and empirical works on this topic suggests that future research shoulddevote more resources and attention to further exploring the importance of simultaneouscooperation and competition among tourism businesses in a destination. With thisbackground work, future studies can contribute to a better understanding of themechanisms of collaboration in the tourism context emphasizing the necessity torepresent the key actors that form the tourist destination and the importance of their roleinside the network, as well as the motivation behind the relationships.




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Corresponding author

Angelo Presenza can be contacted at: [email protected]


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