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Tourism Management 45 (2014) 3e15

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Tourism Management

journal homepage: www.elsevier .com/locate/ tourman

Why strategic networks often fail: Some empirical evidencefrom the area of Naples

Valentina Della Corte a,*, Massimo Aria b

aUniversity of Naples Federico II, Department of Economics, Management, Institutions, Naples, ItalybUniversity of Naples Federico II, Department of Economics and Statistics, Naples, Italy

h i g h l i g h t s

� This paper examines the reasons for network failures in an Italian context.� Key determinants include previous experience and an awareness of a need for collaboration.� It is suggested these factors may also apply to other industries.

a r t i c l e i n f o

Article history:Received 21 August 2013Accepted 18 March 2014Available online 19 April 2014

Keywords:Inter-firm collaborationStrategic networks’ failureNetwork governance

* Corresponding author. Tel.: þ39 081675370.E-mail addresses: valentina.dellacorte@unina.it (V

(M. Aria).

http://dx.doi.org/10.1016/j.tourman.2014.03.0100261-5177/� 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

The literature on inter-firm collaboration primarily concentrates on successful cases and tries to explainwhy and how they obtained their success. On the other hand, there are fewer contributions that studythe reasons for inter-firm relationships failure. This paper responds to that deficiency by providing ev-idence as to why inter-firm collaboration fails, and identifies processes where it is possible to pass from adifficult and complex relationship (where mutual trust is not notably present) to situations of recoveryand re-positioning. Empirical evidence is presented from two specific areas in the Campania Region(Italy) to test a proposed theoretical model that identifies the importance of partners’ previous experi-ence and their awareness of the importance of inter-firm cooperation as determinants of the survival andsuccess of the collaboration.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The general literature on inter-firm collaborationmainly focuseson successful cases, endeavouring to explain why they are suc-cessful. The same is also broadly true of studies of tourism net-works. Certainly there are many examples of successful networks.One study, that of Erkus-Öztürk and Eraydin (2010) examined thecase of Antalya, a Turkish seaside destination. Tourism flows to thisarea started in the 1960s, which then became the “second mostattractive province for foreign companies” and Turkey’s most“dense tourism area” (Erkus-Öztürk & Eraydin, 2010). Antalya is anexample of an environmentally sustainable tourism network thatsupports the joint participation of private firms, semi-public or-ganisations and the community in order to develop policies andplanning based on sustainable development. This network isconsidered successful at the local level with regard to the

. Della Corte), aria@unina.it

implementation of sustainable policies, although its national andglobal linkages are still weak (Erkus-Öztürk & Eraydin, 2010).

A further example that Novelli, Schmitz, and Spencer (2006)suggest, is the Healthy Tourism Lifestyle Cluster in the UnitedKingdom. This network was established to position East Sussex as a“healthy lifestyle” destination. The creation of this network has ledsmall and medium enterprises (SMEs) to cooperate with the area’sdifferent actors in order to increase the attractiveness of the entiredestination. This kind of network has improved the quality of theservices, has led to the co-creation of marketing activities and toshared involvement in the area’s annual events (Novelli et al.,2006.)

However, very few contributions provide an in-depth study ofthe reasons for inter-firm relationships’ failure. This gap in theliterature has led us to develop a conceptual and an empiricalinvestigation aimed at identifying the main reasons for alliancesfailing. We also investigate whether there are processes that canhelp them progress from a difficult and complex relationship(without mutual trust) to a new recovery and repositioning situa-tion that can produce successful results.

1 Hold-up problems refer to “the general business problems in which each partyto a contract worries about being forced to accept disadvantageous terms later, afterit has sunk an investment, or worries that its investment may be devalued byothers” (Milgrom & Roberts, 1992). They occur when contractual parties have in-centives for making non-verifiable investments that are relationship-specific(Coase, 1937; Rosenkranz, Schmitz, 2004; Williamson, 1975).

2 In this case, literature recalls the concepts of “hot groups” (Leavitt & Lipman-Blumen, 1995) and “good fights” (Eisendhart, Kawajy, & Bourgeois, 1997).

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This paper’s analysis comprises a review of the literature, thusupdating previous studies, to identify the gaps that still exist. This isfollowed by an empirical study of two specific tourism areas in theCampania Region (Italy). The analysis of this study tests the theo-retical model to assess if partners’ previous experience and theirawareness of the importance of interactions can result in theircollaboration failing, rather than surviving and/or being a success).

2. Literature analysis and theoretical framework

In this paper, we endeavour to answer important researchquestions with reference to networks’ failures:

1) what are the main relational problems in strategic networks’failure?

2) is the trust/distrust relationship between the extremes of acontinuum linear, or are there more complex connections?

3) which managerial decisions and actions could control andovercome distrust and make the system work?

With regard to these research questions, the transaction costaspect always plays a significant role in inter-firm analysis, since itis linked to opportunistic behaviours in contexts in which in-vestments in the relationship are extremely firm-specific (Coase,1987; Williamson, 1981, 1985).

In spite of the meaning and content that many authors attributeto trust (Barney & Hansen, 1994; MacDuffie, 2010), wee in keepingwith the resource-based perspective e consider trust as an output,the result of specific approaches and connected strategic resources(Barney, 1991). In other words, if specific resources and capabilitiesare found in a relationship, reciprocal trust may result in terms of“confident positive expectations regarding another’s conduct”(Lewicki, McAllister, & Bies, 1998 p. 439), rather than “a psycho-logical state comprising the intention to accept vulnerability basedupon positive expectations of the intentions or behaviour ofanother” (Rousseau, Sitkin, Burt, & Camerer, 1998, p. 395).

According to this view, once established, trust itself can becomea resource. This concept is close to that of “mutual trust” (Eberl,2004; Fink, 2005; Mayer, Davis, & Schoorman, 1995; Svensson,2001, 2006) between network actors because the involved stake-holders adjust their behaviours mutually, since their cooperationfirst generates trust and is then based on reciprocal trust. One of themain gaps emerging from studies on mutual trust is that they(Deutsch,1962;Mayer et al., 1995; Newcomb,1956; Svensson, 2001,2006; Walster, Walster, & Berscheid, 1978) develop the issue from adyadic perspective (A trusts B and B trusts A) without taking thenetwork vision and the trust-balanced perspective of each actorinto account.

This sounds like the result of a decision process with deep rootsin cognitive and more rational aspects as well as in affective andmore emotional and instinctive factors (McAlisster, 1995 p. 25).Once trust is generated, this supports many further developmentsin the relationship in terms of reducing governance costs (Bidault &Jarillo, 1997; Gulati and Nickerson, 2008; Rowley, Behrens, &Krackhardt, 2000), since less coordination is required if trust ishigher. It improves the “network climate” by leveraging its har-mony (Rampersad, Quester, & Troshani, 2010; Song, Dyer, & Thieme,2006) as well as through a better “understanding each other’s pointof view” (Gupta, Raj, &Wilemon,1986, p.12). However, its strongestroots are always the parties’ specific competences and capabilities.

Besides trust, another important aspect that should be takeninto account is related to the effects of dependence (Jiang &Henneberg, 2011) in network collaboration. It should be high-lighted that, according to the main assumptions of the resource-dependence theory (Pfeffer & Salancik, 1978), firms are rarely

internally self-sufficient, they therefore try to establish networkrelations that can generate strategic benefits for themselves and forthe network as a whole. The degree of dependence helps estab-lishing long-term relationships if the dependence level is high, andshort-term relationships if the dependence level is low. Provan andSkinner (1989) point out that, according to this view, high levels ofdependence generate high cooperation with little opportunism.MacKenzie (1996) similarly argues that trust and dependence areinterrelated, since risky actions are more predictable if dependenceis high, as they can threaten the relations and the associated ben-efits. If dependence is linked to common goals, partners are moreoriented towards sharing information (Srinivasan & Brush, 2006)and cooperation (Sandhya & Mrinalini, 2004).

In the literature, the main studied concepts regarding situationsin which this virtuous circle does not take place, are those ofuntrust, distrust and mistrust (McAlisster, 1995). Untrust refers tosituations in which one of the parties does not even expect trust-worthy behaviour from a generally trustworthy partner. Mistrustrefers to unintentional untrustful behaviours (Luhmann, 2005;Marsh & Dibben, 2005). Distrust occurs in typical transactioncosts situations as “it is very costly for partners to evaluate thequality of resources and assets the other takes to the exchange(adverse selection e Akerlof, 1970) and/or the quality of the re-sources and assets brought to the relation (moral hazard e

Holmstrom, 1979); besides, they often have to make specific in-vestments, subject to hold-up vulnerabilities (Klein, Crawfors, &Alchian, 1978)” (Della Corte, 2009, p. 416).

The most difficult situation to manage is undoubtedly that ofdistrust, since there are objective reasons not to expect trust from acounterpart (opportunistic behaviours). However, no studies in theliterature undertake an in-depth analysis of the roots of, or themain reasons for, relationships’ failures.1 Moreover, transaction costeconomics (TCE) concentrates on the process and lacks a focus onindividual backgrounds and consequent behaviours. This articlethus analyses partners’ specific resources and competences that,according to resource-based logic, may be non-valuable andtherefore the main reasons for distrustful behaviours.

This problem is exacerbated if more than two parties areinvolved, since there is a complex set of multidirectional relation-ships to control and to manage successfully, with each partyshowing differing approaches to, and behaviours regarding, each ofthe other actors and the network as a whole. This is another sig-nificant gap in the literature, which mainly analyses the problem oftrust in business relationships with reference to two parties’ stra-tegic alliances, rather than trust to networks and business systems.

Furthermore, there is a lively debate on the question whethertrust and distrust differ and should therefore be consideredopposing concepts (Luhmann, 2005; Marsh & Dibben, 2005; Smith,2005), or whether they can co-exist (Lewicki et al., 1998; Moody,Galleta, & Lowry, 2010).2 While the traditional view states thattrust and distrust are contradictory concepts, a new perspectivesupports the underlying “functionally equal” meaning (Erdem,Imai, & Keane, 2003). According to this emerging vision, trust anddistrust may have positive impacts on relationships in terms ofoutcomes. While most scholars agree that trust influences valuecreation in networks positively, some literature contributions on

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distrust point out that the prudence and the providence emergingfrom such partners’ behaviours allow possible negative conse-quences to be identified (Lewicki et al., 1998; Mishra & Spreitzer,1998). We believe that such a situation mainly refers to the part-ner selection phase. There is, however, the risk of falling intotypically mistrustful relations in which the prudence proves to beexcessive.

Whatever the case, there is always a strong link between ex-pectations, decision making and strategic behaviour. Lewicki et al.(1998, p. 439) define distrust as a confident negative expectationregarding the other’s conduct, while trust is a confident positiveexpectation, but both represent an attempt to simplify the socialcontext, thus enabling individuals to move around in their envi-ronment according to their expectations. Inter-firm collaboration’ssuccess is thus based on the partners’ expectations, which, in turn,are linked to the perceived risk of uncertainty and opportunism.

Since partners’ expectations are a key factor for collaborationsuccess, which the literature underlines, they are also a usefulreminder that the social connections between the stakeholders in anetwork can create social capital, which can extensively influencecollaboration results. Social capital has been analysed as a set ofsocial resources embedded in relationships (Burt, 1992); socialcapital thus focuses its attention on relationships because theyrepresent the source of value creation3 within a social structure(Burt, 1992; Coleman, 1990; Lesser, 2012; Wellman & Berkowitz,1988) that shares goals, projects, or long-term aims, and iden-tifies resources to support the stakeholder network’s agenda.

The social relations that firms establishwithin a network help usunderstand the resources, capabilities and competence that createnetwork value, but also the resources that an organisation pos-sesses by being a network member (Adler & Kwon, 2002; Bourdieu& Wacquant, 1992; Koc-Menard, 2009). This refers to the mainassumption of the relational-based view (Dyer & Hatch, 2006; Dyer& Singh, 1998), since “the concept of relational assets is derivedfrom a social capital viewpoint” (Chou & Lee, 2009). A relationalview is based on the assumption that the “distinctive resources ofalliance partners” can generate rents (Dyer & Singh, 1998;Yamakawaa, Yang, & Lin, 2011).

Regarding relational assets, one can maintain that trust andnorm sharing are better developed within a network if the actorsinvolved in the network embrace a relational perspective. Therelational view emerges from firms’ behaviours within a network’srelationships, since they allow the stakeholders to share knowledgeand routines as well as resources and capabilities. Furthermore, arelational view “facilitates long-term cooperation” (Espino-Rodríguez & Rodríguez-Díaz, 2008) and allows the firm to acquirenew experience and knowledge.

Which aspects should be taken into account this regard in orderto analyse and evaluate inter-organisational failure? Factors likeformal agreements/alliances’ termination a declared distrust ofpartners, and an explicit lack of coordination and communicationprocesses in a network’s management can be relevant factors. Inorder to understand potential or actual partners’ strategic thinkingand decision making, it is important to analyse them in twodifferent phases: the selection phase and the management phase.

For a number of reasons linked to the perceived risk and to theirpersonal attitudes, potential partners may not necessarily trust oneanother at the beginning of their relationship (Gulati, 1995; Kogut,Shan, & Walker, 1992; Powell, Koput, Smith and Doerr, 1996). Aspreviously mentioned, transaction cost economics (TCE) theory isthe usual approach with which to analyse and evaluate

3 By value creation we mean the process of creating a multifaceted value basedon different aspects: financial, social, cultural, and competitive factors (market).

opportunistic behaviours. Consequently, studying the issue fromthe perspective of resource-based theory (RBT) is rather new, sinceRBT focuses mainly on the positive contexts of resource sharing andbuilding in inter-firm collaborations (strategic alliances, rather thanstrategic networks). Network failures, or better, unsuccessfulnetwork building therefore requires an analysis of the partners’personal attitudes and backgrounds. Previous models have startedwith an analysis of the partners’ personal resources and compe-tences, which are linked to their experiences, learning and personalattitudes. In particular, Della Corte’s model (2009; p. 416) focuseson relational problems as themain reason for network failures. Thisfocus allows the managerial decisions and actions, which can beput in place to overcome distrust or mistrust problems in both theselection phase and the managerial phase, to be identified. Inter-firm collaboration failures can be measured in terms of “formalagreements/alliances’ closure (or missed start-up), dissatisfactionwith partners’ behaviours. and/or overall network behaviour” (p.416). In this paper, we mainly focus on the causes of partners’collaboration failure.

In the selection phase, there are factors that are directly linkedto previous individual paths and backgrounds, which can influencethe reciprocal level of distrust. Such paths depend on each partner’sbackground and experiences, because they also determine thecreation and development of personal resources and competences.Resources and competences have to be valuable, rare, difficult, orcostly to imitate and used in organisational terms in order togenerate sustainable competitive advantage (Barney, 1986; 1991).When they are found to be non-valuable, they can be labelled asweaknesses. According to this view, partners’ personal approachcould therefore be faced with a problem of non-valuable resourcesfor cooperation and/or of lack of organisational factors to use themappropriately. Partners can even overestimate the risk of failure intheir relationship. These aspects usually depend on specific vari-ables such as the personal attitudes and moral approaches ofpartners’ leaders, their history and reliability, the parties’ experi-ence with inter-firm collaboration, their awareness of the need tonetwork and of other parties’ resources and competences.

The research hypothesis has been developed from these startingfactors and drawn from the trust/distrust theoretical frameworkthat Della Corte proposes: The personal attitudes and moral ap-proaches of focal firms’ leaders, their history and reliability as well astheir experiences with inter-firm collaboration can help reduce initialdistrust in inter-organisational relationships and lead to more stablecooperation frameworks.

Examining the factors that govern inter-firm collaboration, so-cial context as well as the interconnection and the interaction be-tween network actors are a milestone (Gulati, 1998) in the creation(initial phase) and in the management of inter-firm collaboration.With particular reference to the group of actors, it is important tostress that “the willingness of single actors to cooperate does notnecessarily exist a priori” since their awareness of the associatedrisks, such as opportunism, conflicts of interest and asymmetricinformation (Pechlaner & Volgger, 2012; Simmel, 1950), whichresult in the initial distrust ormistrust, can influence the decision tobecome a member of a network or not. While TCE focuses on theprocess, we however study the reasons for this occurring from theRBT perspective by analysing each partner’s individual approach tothe relationship as a function of his or her previous specific expe-riences, resources and competences. According to this view, it isuseful to understand the components that help overcome the initialmistrust or distrust and reduce the perception of the relationshiprisks.

For the purpose of this research, we identify certain factors inthe individual sphere, such as leaders’ personal attitudes andmoralapproaches, their history and reliability as well as their experience

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with inter-firm collaboration, which are significant preconditionsand important factors supporting collaboration during the initialphase. Previous research (Wang & Fesenmaie, 2007) investigatesthe strategic role of individual support in inter-firm collaborationby exploring the extent to which leadership and personal attitudetowards cooperation and communication help reducing initialdistrust. This paper, however, adds new contents regarding the linkbetween partners’ histories and awareness of the importance ofcollaboration and the path of the relationship as well as theeventual role that a governing actor plays in the process.

In terms of social psychology, Tajfel and Turner have made itpossible to identify two hypothetical extremes on a continuum ofsocial interaction (1979): “the interpersonal extreme”, defined asthe “interaction between two or more individuals which is verylargely determined by their individual characteristics and the na-ture of the personal relations between them”, and the “intergroupextreme”, defined as the “interactions which are largely deter-mined by the group membership of the participants and very littlee if at alle by their personal relations or individual characteristics”.Therefore, the social identification and cohesion may allow moreprofitable contexts to be created in terms of the network resources’development.

Social capital’s attributes can thus be classified into three clus-ters (Chow & Chan, 2008):

- the structural dimension, which refers to the connections be-tween social and network relations in order to define who canbe reached and how;

- the relational dimension, which refers to the level of trustdeveloped during interactions between people;

- the cognitive dimension, which refers to the resources thatsupport reciprocal understanding between parties.

The measurement factors of these dimensions vary and refer todifferent aspects of psychological and sociological behaviours(Homans, 1961). As regards the first dimension, the focus is on thenetwork pattern, its density, connectivity and hierarchy. Withreference to the second dimension, the relevant factors are: norms,obligations, trust; their identification raises the actors’ awareness oftheir collective goals. Within the third dimension, knowledgesharing is the most important factor as it allows understanding.

Regarding the partners’ personal attitudes, the literature mainlyrefers to leaders’ attitudes towards cooperation.4 Deutsch (1949)argues that if firm leaders have a specific background of coopera-tive situations, the outcome can be better than if they have expe-riences of competitive situations. Sharing this assumption, Heaveyand Murphy (2012) highlight that leaders’ ability influences thedegree of cooperation in terms of the effectiveness and perfor-mance of the group’s strategies.

Furthermore, regarding leaders’ “moral” approach (which is notopportunistic, but rather a behaviour in which individual and col-lective goals point in the same direction), it is important to definethe meaning and the factors that shape their personal attitudes.This moral perspective takes a Kantian view since “a business or-ganization, like any other organization, is composed of individualpersons and since persons are moral creatures, the interactions ofpersons in an organization are moral interactions and thus aresubject to moral law” (Bowie, 2000). This means that a businessinteraction is not only based on economic factors, but also on re-lationships between individuals subjected to moral rules.

4 Leadership can be defined as a process of social influence inwhich an individualmaximises the efforts of others to achieve a common goal (Kruse, 2013; Northouse,2012).

According to this view, the participative leadership concept is veryimportant (Burns, 1978; Vroom& Jago, 1988). This concept suggeststhat the leader is more a decision proposer than a decision imposer(Bowie, 2000), because the followers’ value plays a predominantrole in the decision making process.

If, on one hand, participative leadership “influences processesamong multiple parties in a systems” (Yukl, 1998, p. 459), it isimportant that a firm’s leader, on the other hand, also exhibits avisionary leadership which supports the sharing of mutual trust.Some scholars (Fyall, Callod, & Edwards, 2003; Gray, 1985) stressthe importance of visionary leadership (Groves, 2006; Rafferty &Griffin, 2004; Stam, van Knippenberg, & Wisse, 2010; Westleyand Mintzberg; 1989), as firm’s leaders should have a clear visionof the potential advantages that can be generated from inter-firmcollaboration. Furthermore, they should recognise the possibil-ities that lie beyond their firm’s boundaries (euristics). In thecontext of inter-firm collaboration, vision has a very specificmeaning that refers to the ability of the firm’s leader to have theright picture of where the organisation’s inter firm-collaboration isleading it. This picture refers to the motivations that drive theleader to enter strategic alliances. According to Child, Faulkner, andTallman (2005), these strategic alliances are associated withtransaction cost economics (Hobbs, 1996; Pimentel Claro, Borin deOliveira Claro, & Hagelaar, 2006; Vosselman, 2012) rather thanresource-based theory (Chin, Peterson, & Brown, 2008; Das & Teng,2000). In other words, such alliances are associated with the risk ofopportunism rather than with resource sharing and co-building,trying to pursue specific strategic goals (Fuller-Love & Thomas,2004; Gulati, 1998; Johanson & Mattson, 1987), reciprocallearning (Holmqvist, 2004; Westerlund & Rajala, 2010), risk mini-mising, new market entries and first-mover advantage.

In network relationships, a leading actor may favour a collabo-ration process and play a significant role in both the strategicplanning and the coordination of the entire system. Such a leadingactor therefore becomes the pivot of the network and is identifiedeither with a leading local firm, or with an ad hoc organizationgoverning thewhole system. According to the specific contexts, thisorganization is generated from both the top-down and the bottom-up processes, which is why it is also called a “governance actor” andis often identified with the “destination organization” label.

Having stressed that leadership plays a significant role inencouraging efficient and productive collaboration (Pittinsky &Simon, 2007, p. 586), it is important to underline other key per-sonal features and their link to inter-firm collaboration. Whileleaders’ history regards their personal background in terms of theirprevious positions, sedimented values and knowledge as well as totheir previous experience with inter-firm collaboration, theconcept of reliability can be analysed by means of internal andexternal lenses. The internal perspective refers to a reliable leaderperceiving situations in which the risk is high and the compensa-tion is low; consequently, he/she never sacrifices the firm in thiscontext. The external lens explains the possibility that, especially inthe initial phase, a reliable leader can assume a central position inthe network, thus making his/her firm the hub firm, allowing it toassume a pivotal role within the network.

Leaders’ own experience with inter-firm collaboration exem-plifies their managerial capability regarding coordinating re-lationships internally and externally (Ritter, Wilkinson, & Johnston,2004), understanding the most appropriate configuration for thenetwork (Svahn, 2004) and conceiving and implementing the rightstrategic and organisational policies. According to this premise, thepaths illustrated in Fig. 1 are found in inter-firm relationships basedon initial distrust or mistrust. It is possible to link a possible evo-lution of the relationship to each path in order to see if problemscan be overcome:

Fig. 1. Trust/distrust framework.Source: Della Corte, Micera, & Tani, 2008; Della Corte, 2009: 418.

5 More precisely, we tried to involve firms operating within the so called 6 A’sthat characterize a tourism offer in a destination: access, accommodation, ame-

V. Della Corte, M. Aria / Tourism Management 45 (2014) 3e15 7

1) The actors don’t trust each other and prefer not to cooperate, thuscontinuing to acquire their individual values (proposition 1):. Eacharea will acquire its own value and all of the non-cooperatingfirms will only generate a total value equal to the sum of theirindividual values. Some may lose, other may gain, but less thanthat which they could obtain through cooperation.

2) The second proposition starts with the question whether anytype of network governance supports the move from adistrustful to a trustful situation: The governance actor of anetwork can influence the process of overcoming distrust byincreasing awareness of the necessity of the counterparts’ strategicresources/competences and by supporting the knowledge transferprocesses and reciprocal relations. These aspects are rather“structural”, because close proximity can reduce moral hazardapproaches and increase awareness of the respective resourcesand competences. Furthermore, such situation requires theintervention of a focal, trustworthy actor to lead the process andsupport the spread of knowledge and trust in the network.

3) Besides this focal actor, reciprocal knowledge can reduce mutualdistrust and help the stakeholders get to know one another well,thus allowing them to move from distrustful to more trustful ap-proaches (proposition 3).

Besides the first situation, in which parties maintain theirnegative approach towards collaboration, the other two processesprovide the possibility of moving to a more trustful situation. Thiscan either happen spontaneously (situation 3), or a governing actorcan instigate it (sit. 2). The questions are, however: what is orshould be this governing actor? What would be the most appro-priate nature (public, private, public and private) of such an actor?What should its main features be?

In this paper, we take these research questions into account inorder to proceed with an empirical test and to draw interestingconclusions.

nities, attractions, ancillary services and assemblage. See Buhalis, 2000; Cooper,Fletcher, Fyall, Gilbert, & Wanhill, 2005; Della Corte, 2013). Most of the samplefirms concentrate on accommodation (hotels) and assemblage (tour operators,travel agencies) activities.

6 According to the product-project-territory model, these factors are useful toidentify the Tourism Local Systems (Della Corte, 2009; Sciarelli, 2007) and, moreprecisely, the territorial dimension.

3. Methods

With respect to the empirical research, we conducted a surveyof a stratified, non-proportional sample of 200 firms operating in

the tourism industry (hotels, restaurants, tour operators, travelagencies)5 in the area of Naples and the Sorrento Peninsula, whichare among the most attractive tourist areas in Italy.

The twomain locations were chosen for exhibiting the followingspecific characteristics6:

U highly developed tourist areas, with interconnections andco-projects with other relevant sectors in the area(manufacturing, craftsmanship, etc.);

U valuable or to-make-valuable resources that characterise thespecific product offers;

U well defined geographical boundaries.

The samples were constructed by taking the following relevantfactors into account:

- the possible conceptualisation of a generalisable phenomenon;- the possibility of examining them, which would require aqualitative analysis of data only obtainable through direct con-tact with firms;

- the concrete possibility to generalise the results.

This method comprised three main phases: the selection of theunits of analysis, the data collection comprising primary (in-terviews) and secondary (promotions, materials, tourist guides,ads) information and the data elaboration.

Table 1 shows the universe of operators e hotel companies andtravel agencies e located in the areas that are the object of theanalysis.

Table 1Hotel facilities and travel agencies Naples City and the Sorrento Peninsula.

Hotel facilities Travel agencies

Naples 147 492Sorrento Peninsula 164 171Total 311 663

Source: Our elaboration from EPT Napoli and Seat Pagine Gialle, 2009.

V. Della Corte, M. Aria / Tourism Management 45 (2014) 3e158

Starting with the list of samples, we identified the hotel com-panies that are members of national associations, such as Feder-alberghi and Confindustria.

On the first “layer”, we identified a sample that is strictlyrepresentative of the universe that we wanted to investigatebecause it presents a good level of homogeneity, even with regardto the hotel facilities and their grading.

To refine the field of research related to hotel facilities, wedecided to only add the facilities with a luxury grading between 4stars and 5 stars to the analysis, as this grading is more represen-tative of the hotel/tourist system in the identified areas. With re-gard to the travel agencies, we started with the universe of firmslocated in the Naples and Sorrento Peninsula areas, but onlyincluded the ones that work with incoming tourism and aremembers of national associations. We thus identified a sample of58 companies, 46 of which are located in the city of Naples and 12in the Sorrento Peninsula.

The second step in the selection process focused on the list ofhotel facilities; travel agencies provided a number of units thatcould be analysed according to the following parameters:

- the accessibility of the information on the companies;- the decision makers’ availability and willingness to collaborate

with the survey.

This level of analysis identified 80 units, of which 55 were hotels(25 in Naples and 30 in the Sorrento Peninsula) and 25 were travelagencies (20 in Naples and 5 in the Sorrento Peninsula).

Data collection took place through 2-h face-to-face interviewswith entrepreneurs or general managers, during which we ac-quired relevant quantitative and qualitative information. Dataelaboration allowed us to test the above-mentioned trust/distrustmodel as a possible tool to analyse and reinforce a networkingperspective. The research was mainly focused on tourist firms inCampania and, specifically, hotels and travel agents as the mostrepresentative of the tourism offer.

The questionnaire comprised three sections:

1 inter-enterprise collaboration: we tried to identify factors that area barrier to achieving cooperative strategies during thedecision-making, negotiation and agreement formulation steps.The aim of this section was therefore to highlight negativeentrepreneurial/managerial approaches during inter-firmcollaboration as functions of previous experiences and specificattitudes;

2 the advantages and disadvantages of inter-enterprise collabora-tion: in this section, we tried to identify the positive and nega-tive implications of collaboration, focussing on the main areas inwhich firms are (even if this has not yet happened) more proneto collaboration. We also examined the hold-up risks and thereasons for collaboration failures;

3 the relationships between single firms and the local networks: inthis section, we studied the interviewees’ opinions on the role ofa real, or eventual, pivotal actor in guiding and coordinating thelocal actors. Our aim was to verify if this governance organisa-tion could support collaboration and overcome persistent

negative behaviours. In other words, we verified if the gover-nance actor could mitigate the distrust of various local firms,thus fostering trust in the network.

3.1. Statistical tools to test the research hypothesis

As underlined in the previous section, we focused on the topic ofbusiness networks’ failure or “missed” start-up by studying thepotential partners’ individual approaches and decision making ininter-firm collaboration. We also tried to verify when and underwhich conditions initial distrust can be overcome. The researchhypothesis is therefore:

The leaders of focal firms’ personal attitudes andmoral approaches,their history and reliability as well as their experience with inter-firmcollaboration can help reduce initial distrust in inter-organisationalrelationships and nurture more stable cooperation frameworks.

Consequently, the following dimensions were studied:

- each partner’s awareness of the need for a network;- each partner’s experience with inter-firm collaboration.

To allow us to study the structure of the inter-dependence be-tween each dimension of interest and a set of items related to thebehaviour and characteristics of the relevant firms, we undertook athree-step data analysis. We employed two different statisticalmethodologies: multiple correspondence analysis (MCA) and hi-erarchical cluster analysis.

Multiple correspondence analysis (MCA) is very popular in thestatistical literature, having reached a high level of developmentand use (Benzécri, 1979; Greenacre, 1984). It is a useful techniquefor structural multivariate categorical analysis. An MCA assignsscores to rows (representing the subjects) and columns (repre-senting the response categories) in a data matrix, yielding aquantification of the information collected in it. This analysis istypically used to analyse questionnaires if the set of questions issufficiently broad and simultaneously diverse enough to coverseveral themes of interest (which are balanced) and lead tomeaningful multidimensional representations.

Cluster analysis is a collection of statistical methods that iden-tifies groups of samples that behave similarly or show similarcharacteristics. Cluster analysis classifies a set of observations intotwo or more mutually exclusive unknown groups on the basis ofcombinations of interval variables. The purpose of cluster analysis isto discover a system that can classify observations into groups, inwhich the group members have properties in common. Clusteranalysis has proven to be very useful in marketing (Larson, 1992) asit describes a company’s efforts. In market research studies, clusteranalysis is often referred to as a segmentation method.

Agglomerative hierarchical cluster methods produce a hierarchyof clusters from small clusters of very similar items to large clustersthat include more dissimilar items. Hierarchical methods usuallyproduce a graphical output known as a dendrogram, or tree, thatshows this hierarchical clustering structure (Ward, 1963).Agglomerative clustering begins by finding the most similar twogroups, based on the distance matrix, and subsequently mergingthem into a single group. This procedure is repeated, step by step,until all the samples have been added to a single large cluster. Thefinal partition is identified by a distance criterion (Fernández &Gómez, 2008). Starting from the bottom part of the dendrogram,the researcher decides to stop the agglomeration process whensuccessive clusters are too far apart to be merged.

In a first step, we used the MCA method to summarise theoriginal set of qualitative variables’ (the questionnaire items)examined dimensions in a reduced number of latent factors on anumeric scale. In the second step of the analysis, these latent

V. Della Corte, M. Aria / Tourism Management 45 (2014) 3e15 9

variables played the role of input variables in the clustering pro-cedure. This method partitions the firms into internally homoge-neous groups in respect of their level of collaboration.Consequently, the output of this integrated procedure is a parti-tioning of the firms, formed by k clusters, which describes theirdifferent typologies with respect to their awareness of the need fora network and their experiences with inter-firm collaboration.

4. Main results and discussion

In order to study the above-mentioned dimensions, we identi-fied two blocks of questions that provide information about thefirm’s awareness of the need for a network and their experienceswith inter-firm collaboration. We performed an MCA of each block,as well as a hierarchical clustering. The clusters’ description and thedependence analysis with the control variables are showed in thetables that follow.

In Table 2, we report the results of an MCA and a cluster analysisof the awareness of the need for a network dimension. The vari-ables that characterise the behaviour of each particular group offirms in each cluster are presented. This allows us to assign a labelto each cluster, thus identifying a typology of firms in respect of theanalysed dimension.

The concentration of specific variables led to the assignment ofthe following label clusters:

1) The “operational” cluster, which just considers collaborationwith reference to operations and product construction. In thisrespect, the tourism product comprises several services (hos-pitality, travel, amenities, etc.) that a tour operator can assemble,that travel agents or internet providers can sell, or that touristscan choose directly. However, there is an obligatory comple-mentarity in the services provided in the studied area. In-teractions are merely functional for this group of firms, becausethis is the way tourism firms operate. They do not collaboratestrategically, nor do they have a joint common vision. They donot consider strategic aspects (see the variables in Table 2), such

Table 2Clusters’ description according to the variables associated with awareness of theneed for a network dimension.

Variables Modalities

Cluster 1(Operational)50 firms

D5 Sales increase NoD6 Compatibility and integration NoD5 Information NoD6 Appropriate planning NoD17 Impact on management OperationalD18 Research and development NoD18 Marketing NoD7 Know-how transfer NoD18 Operations Yes

Cluster 2(Strategic organisational)15 firms

D5 Sales increase YesD7 Contractual clausestipulating non-competition

Yes

D6 Appropriate planning YesD17 Impact on management OrganisationalD18 Operations No

Cluster 3(Knowledge-based)10 firms

D5 Information YesD5 Promotion improvement YesD18 Research and development YesD7 Know-how transfer YesD5 Partner knowledge YesD6 Compatibility and integration YesD6 Communication process YesD18 Sourcing No

Source: our elaboration.

as their compatibility and integration with other local firms,appropriate joint planning, know-how transfer and joint mar-keting. They mention that the impact of this on their manage-ment is operational and that they are only interested inoperational relationships with each other.

2) The “strategic and organisational” cluster is mainly focused onthe opportunities derived from collaboration and co-projectuality in terms of markets (an increase in revenues andmarket share) and the reduction of threats (with com-plementors playing a key role and linked to coopetition strate-giese Nalebuff & Branderburger, 1996; Dagnino & Padula, 2002;Della Corte, 2009). Even highly competitive firms collaborate onspecific projects in respect of such opportunities. The more thispractice occurs, the fewer the threats of competitors in thecompetition arena; this process recalls the complementorsconcept, which refers to traditional threats (competitive forces)that collaboration’s effects reduce. We identified sales increases,non-competition agreements regarding specific initiatives,appropriate planning and the organisational impact of inter-firm collaboration as some of the most relevant variables forthis cluster. On the other hand, firms in this cluster maintainthat they are less interested in operational interactions withother firms. Moreover, most of the firms in this cluster are local,vertically integrated groups; the owned firms have a specificcomplementarity and are thus also focused on operational in-teractions, which is why they seem less interested in coopera-tion with other firms from an operational point of view;

3) The “knowledge-based” cluster is more specifically concentratedon knowledge-based factors such as: information flows,knowledge communication, integration, shared initiativesregarding research and development and reciprocal knowledge.These firms share knowledge and communicate spontaneouslywhen problems arise; this behaviour is not necessarily based onspecific common strategies. The relevant variables they high-lighted were: information, improving promotion, research anddevelopment know-how transfer, compatibility and integration,knowledge exchange and communication. In respect of thevariables with positive modalities, this cluster appears to be theopposite of the operational one.

The majority of the firms does not have a highly strategic view;they mainly consider inter-firm collaboration as a necessity tooperationalize their activities (cluster 1). On the other hand, it isinteresting to note that the most important firms of the SorrentoPeninsula mainly belong to the second cluster, while the majorfirms in the city of Naples belong to the third cluster.

In a second step, we conducted a dependence analysis of theclusters and several control variables by applying a Pearson ChiSquare test. In the following table, we present the control variables,which are characterised by their significant dependence relation-ship with the considered dimension (p-value � 0,05).

In Table 3, a different geo-localisation of the clusters becomesapparent. While the first cluster is almost equally distributedbetween the two examined areas (although mainly found inNaples area), the second is mostly spread throughout the Sor-rento Peninsula, while the third cluster is found in the city ofNaples.

Table 4 shows the local actors’ profound confusion about theexistence of a pivotal actor in the area: While the strategic organ-isational cluster identifies and recognises a leading actor (80%), themajority of the actors in the operational cluster (56.4%) assertsthere is no such actor, but that one is necessary. In the third clusters,some firms recognise such an actor, while others do not in spite oftheir awareness of such an actor’s importance. The perceptionsbetween the clusters therefore differ totally.

Table 5Negative confidence in the possibility of obtaining a net positive value fromcollaboration e systemic offer.

Few tourism offers thatattract tourists

Total

No Yes

Each partner’sawareness ofthe need fora network

Operational 87.3% 12.7% 100.0%Strategic-organisational 80.0% 20.0% 100.0%Knowledge-based 30.0% 70.0% 100.0%

Total 78.8% 21.3% 100.0%

Value df P-value

Pearson’s Chi-squared 16.603 2 .000

Source: our elaboration.

Table 3Distribution of the firms by geographical area regarding each partner’s awareness ofthe need for a network.

Geographical area Total

Naples Sorrento Peninsula

Each partner’sawareness ofthe need fora network

Operational 57.4% 42.6% 100.0%Strategic-organisational

40.0% 60.0% 100.0%

Knowledge-based

80.0% 20.0% 100.0%

Total 57.0% 43.0% 100.0%

Value df P-value

Pearson’s Chi squared 17.203 2 000

Source: our elaboration.

V. Della Corte, M. Aria / Tourism Management 45 (2014) 3e1510

There is also a significant difference between clusters regardingtheir conviction that they could obtain a net positive value fromcollaboration. As shown in Tables 5and 6, and also taking the openresponses and comments into account, the first two clusters arerather sceptical about the local actions and policies at a systemlevel. They maintain that there is a very small possibility that theycould obtain a positive value from collaboration, in terms of pro-jects, the valorisation of local attraction factors as well as themarketing and promotion of the destination.

In this case, the negative confidence in the value that collabo-ration can generate is linked to the lack of a common vision thatemphasises the necessity of resource dependence (Pfeffer &Salancik, 1978). Some scholars (Sandhya & Mrinalini, 2004;Srinivasan & Brush, 2006) underline that if dependence is linkedto achieving common goals, partners are more oriented towardscooperation.

Although the first two label clusters share this commone ratherpessimistic e vision, the knowledge-based one appears moreoptimistic and takes a different approach, which is less traditionaland mainly based on knowledge sharing and fostering. This viewconfirms the theoretical principle that a relational view allows thefirm to acquire new experience and knowledge within a network’srelationships (Espino-Rodríguez & Rodríguez-Díaz, 2008).

According to Table 7, “operational” firms paradoxically maintainthat they have increased their cooperative relationships. However,they mainly refer to the relationships in business-to-businessoperational activities. They often belong to constellation systemsin which there are bigger firms with a series of satellites aroundthem, all of which mainly cooperate in operational terms. Never-theless, they are highly aware of the advantages of collaboration

Table 4Recognition of a pivotal actor.

In your opinion, isthere a pivotal actorregarding the localoffer in your area?

Total

Yes No, butone isnecessary

No, and thisis not necessaryeither

Each partner’sawareness ofthe need fora network

Operational 36.4% 56.4% 7.3% 100.0%Strategic-Organisational

80.0% 20.0% 0.0% 100.0%

Knowledge-based

50.0% 50.0% 0.0% 100.0%

Total 46.3% 48.8% 5..0% 100.0%

Value df P-value

Pearson’s Chi squared 9.906 4 .042

Source: our elaboration.

and of the necessity to foster it. They thus have a rather homoge-neous conviction that it is appropriate to develop collaboration andto start common projects. All of the clusters appear to becommitted to increasing their cooperative relationships.

However, the clusters perceive the role of private firms (Table 8)differently: The strategic and knowledge-based clusters both assertthat private organisations should not play a predominant/leadingrole and instead consider a public organisation or a super-partsentity. It is interesting to explore the reason for these clustersbelieving that a private organisation should not play a pivotal role.This is linked to two main reasons. First, firms may exhibit untrust,distrust and mistrust (McAlisster, 1995) behaviours towards theirnetwork partners. Second, there does not seem to be a highlycapable firm leader who can coordinate the relationships (Ritteret al., 2004) from an external point of view and is sufficientlycharismatic to take on a leading role in the network. The latterreason is a new perspective in the theoretical and empirical fields,since previous studies only ascribe inter-firm relationships’ failureand/or success to trust, untrust, mistrust and distrust behaviours. Inthis respect, the current research bridges a clear research gap, sinceit pursues new horizons in the exploration of the role and theextension of the individual level’s influence on the outcome ofinter-firm collaboration as well as of our understanding of the in-dividual’s role in a network.

On the whole, the results show that the declared awareness ofthe need for cooperation in both areas is merely stated and not trulybelieved: some firms maintain that there is a leader in the areas,while others e operating in the same area e maintain there isn’t.They often complain about the role of local institutions, but aresimultaneously rather sceptical about the notion of a private actorguiding this cooperation. The only cluster more prone to face theinitial mistrust and distrust situations appears to be theknowledge-based one, which, however, is less structured and more

Table 6Negative confidence in the possibility of obtaining a net positive value fromcollaboration.

Promotionalineffectiveness

Total

No Yes

Each partner’sawarenessof the needfor a network

Operational 85.5% 14.5% 100.0%Strategic Organisational 66.7% 33.3% 100.0%Knowledge-based 30.0% 70.0% 100.0%

Total 75.0% 25.0% 100.0%

Value df P-value

Pearson’s Chi squared 14.562 2 .001

Source: our elaboration.

Table 7Deterrence-based trust due to the partner’s and the network’s overall reliability ornotoriousness in respect of cooperative relationships.

Increase in cooperativerelationships

Total

No Yes

Each partner’sawareness ofthe need fora network

Operational 25.5% 74.5% 100.0%Strategic-Organisational 0.0% 100.0% 100.0%Knowledge-based 0.0% 100.0% 100.0%

Total 17.5% 82.5% 100.0%

Value df P-value

Pearson’s Chi squared 7.713 2 .021

Source: our elaboration.

Table 9Clusters’ description according to the variables associated with their inter-firmcollaboration experience.

Inter-firm Collaboration Experience

Variables Modalities

Cluster 1(medium level)20 firms

D10 Impact on local tourismpromotion

Medium tourismpromotion

D10 Impact on local hospitalityactivities

Medium hospitality

D11 Network members’ skillsand capabilities

Medium competences

D2 Increase in local events/initiatives

Yes e project/partnerships

D25 Achieved level ofcollaboration

Adequate level ofcollaboration

D10 Impact on management Medium firm managementD15 Valuation of partnershipsatisfaction

Medium behaviours

D10 Network members’ correctbehaviour

Medium behaviours

D11 Coordination problems NoD16 Valuation of benefits forthe partner

Medium behaviours

D11 Incorrect behaviour NoD10 Bargaining power ofstakeholders

Low economic/politicweigh

D14 Bad coordination No

Cluster 2(High level)43 firms

D10 Network members’ skillsand capabilities

High competences

D10 Impact on local hospitalityactivities

High hospitality

D10 Impact on local tourismpromotion

High tourism promotion

D15 Valuation of partnershipsatisfaction

High behaviours

D10 Impact on management High firm managementD16 Valuation of benefits forthe partner

High behaviours

D14 Resources andcompetences sharing andcreation

Yes

D14 Knowledge, informationand competence exchange

Yes

Cluster 3(No Inter-firm

collaboration)17 firms

D2 Activated partnerships inthe last 3 years

No projects/partnerships

D11 Coordination problems YesD11 Incorrect behaviour YesD15 Valuation of partnershipsatisfaction

Low partnershipsatisfaction

D16 Valuation of benefits forthe partner

Low partner benefits

D25 Achieved level ofcollaboration

Inadequate level ofcooperation

D11 Risk of competence Yes

V. Della Corte, M. Aria / Tourism Management 45 (2014) 3e15 11

spontaneous than the others and, therefore, less guided. Moreover,this is the only cluster withinwhich themajority of firms appears tofavour the idea of a private network.

With regard to the second proposition, we tried to verifywhether previous experiences influence approaches to cooperatingor not cooperating. In order to study the role that previous expe-riences play in the partners’ vision of and approach to inter-firmcollaboration, we used an MCA and hierarchical clustering. Theseanalyses identified the threemain label clusters (Table 9), which arecharacterised by:

- high levels of collaboration: this cluster refers to firms with verysuccessful inter-firm collaboration relationships in strategic,organisational and operational terms. Firms in this cluster reveala medium evaluation of a network’s skills and competences, thelevel of collaboration, the positive impact on management, theplanning and implementing of joint projects, events andinitiatives;

- medium levels of collaboration: this is the cluster that includesthe majority of the firms and also the most the most importantones in both areas. The valuation of this cluster in terms of thedifferent aspects e from their strategy and marketing to theirorganisational behaviour, including the partnerships’ manage-ment and satisfaction e reveals the absence of specific coordi-nation or integration problems;

- no inter-firm collaboration: firms in this cluster do not normallyhave relationships with other organisations in terms of collab-oration. They are, however, rather marginal firms. They show acertain reluctance to cooperate, maintaining that there are dif-ficulties in starting and managing partnerships, and thatknowledge transfer is very risky.

Table 8Deterrence-based trust due to the partner’s and the network’s overall reliability ornotoriousness in respect of private actors.

Private actors’ involvement Total

No Yes

Each partner’sawarenessof the needfor a network

Operational 69.1% 30.9% 100.0%Strategic-Organisational

100.0% 0.0% 100.0%

Knowledge-based

40.0% 60.0% 100.0%

Total 71.3% 28.8% 100.0%

Value df P-value

Pearson’s Chi squared 10.945 2 .004

Source: our elaboration.

transferD11 Information asymmetries Yes

Source: our elaboration.

With regard to the clusters’ geographical distributions(Table 10), there is more extensive experience with collaboration inthe Sorrento Peninsula, where the second cluster shows a higherconcentration. In the Naples area, the experiences seem to havebeen less structured and definite.

Regarding the recognition of a pivotal actor, the high-levelcollaboration clusters again recognise one (60.5%), although themajority of firms in other two clusters (medium level: 55.0%; nocollaboration: 82.4%) maintains that there isn’t one, even if they dofind this important.

With regard to the firms’ view of the nature of an eventualpivotal actor (Table 11), it is interesting that the majority thinks

Table 10Distribution of firm’s by geographical area regarding each partner’s experience ofinter-firm collaboration.

Geographical area Total

Naples Sorrento Peninsula

Each partner’sexperience of

inter-firm collaboration

Medium level 65.0% 35.0% 100.0%High level 40.5% 59.5% 100.0%No inter-firmcollaboration

88.2% 11.8% 100.0%

Total 57.0% 43.0% 100.0%

Value df Sig. asint.

Pearson’s Chi squared 18.452 2 .000

Source: our elaboration.

Table 12Kind of relationship with local tourism firms.

What kind of relationshipdo you have with thelocal tourism firms?

Total

Disregard Cooperation Conflict

Each partner’sexperienceof inter-firmcollaboration

Medium level 0.0% 90.0% 10.0% 100.0%High level 0.0% 100.0% 0.0% 100.0%No inter-firmcollaboration

52.9% 41.2% 5.9% 100.0%

Total 11.3% 85.0% 3.8% 100.0%

Value df Sig. asint.

Pearson’s Chi squared 42.293 4 .000

Source: our elaboration.

V. Della Corte, M. Aria / Tourism Management 45 (2014) 3e1512

that a mixed configuration (public and private) is the mostappropriate. However, while the strategic organisational clusterconsiders private organisations as a possible alternative, the othertwo prefer more public solutions. This result is not very encour-aging in terms of entrepreneurial insight and managerial vision.Small and medium enterprises often automatically trust publicorganisations, but neither of the latter two clusters seems to reallytrust!

With regard to themain activities and functions the intervieweddecision makers believe the pivotal actor should carry out, it isinteresting to note that, contrary to the operational and strategicorganisational clusters, according to which the pivotal actor shouldmainly endorse possible partners to avoid opportunistic behaviours(25.3% and 33.3% respectively), the knowledge-based cluster ap-pears more open from this point of view (13.3%). This cluster as-cribes to the leading actor playing a mainly strategising role anddeveloping territorial marketing activities to attract good in-vestments to the area.

The third cluster (no inter-firm collaboration) shows a disregardfor relationships with other firms (Table 12), even if there are nospecific conflicting relationships. Similar results are found withregard to the local policy makers (Table 13).

The following table shows a very interesting result in terms ofprevious experiences: the local firms maintain that there is no in-tegrated offer regarding the destination. This pessimism is greaterin the second and third clusters (about 84% in the second and 88% inthe third), while the first cluster does not seems to share this fully(only 60% answered “no”) (Tables 14 and 15).

However, the next table shows that the interviewed decisionmakers mentioned that they are increasing their cooperative ac-tions and are very willing to continue pursuing this objective. Thefirst two clusters answered positively regarding whether there hasbeen an increase in the cooperative relationships (95% of the firstcluster and 86% of the second cluster).

Table 11Nature of the eventual pivotal actor.

Main nature of theeventual pivotalactor in the area

Total

Public Private Mixed

Each partner’sawareness ofthe need fora network

Operational 8.0% 24.0% 68.0% 100.0%Strategic-organisational 33.3% 13.3% 53.3% 100.0%Knowledge-based 0.0% 40.0% 60.0% 100.0%

Total 12.0% 24.0% 63.0% 100.0%

Value df P-value

Pearson’s Chi squared 9.750 4 .045

Source: our elaboration.

Consequently, the results show that even if the majority of thesample shows a rather high awareness of the importance ofcollaboration and reveals some experience with inter-firm re-lationships, the firms do not seem to really believe in concretecooperation. That is, there is still a certain distrust or mistrust. Eventhe results of their opinions on a possible pivotal actor to nurturethe cooperation process show that:- most of the interviewed firms do not favour a private pivotalactor, but one that is public or mixed;

- however, they have a widespread negative perception of thelocal institutions in strategic and organisational terms.

Each of these clusters reflects social capital’s attributes (Chow &Chan, 2008), since they reveal a structural dimension, consider theset of actors and their potential interconnections and are verylimited with respect to the relational and the cognitive dimensions.All of these points contribute to their awareness of the need for aleading actor.

This approach confirms that, in spite of the clear advantages andbenefits of inter-firm collaboration, there seems to be a negativeapproach to this in practice, which is mainly due to the absence ofreal, recognised and charismatic governance organisations to guidethe process.

Therefore, the research hypothesis is not fully confirmed. Ana-lysing the different propositions, it emerges, in fact, that:

- Proposition 1 is confirmed by the third cluster, which does notbelieve in inter-firm collaboration and seems uninterested in it.

- Proposition 2 is not confirmed, because the lack of a true, well-structured leading actor that local firms recognise (as explained,there is a difference in opinion and intensity between theclusters, confirming that even though there may be a formalleader, the local actors do not adequately recognised it);

Table 13Kind of relationship with local policy makers.

What kind of relationshipdo you have with thelocal policy makers?

Total

Disregard Collaboration Conflict

Each partner’sexperiencewith inter-firmcollaboration

Medium level 35.0% 60.0% 5.0% 100.0%High level 20.9% 79.1% 0.0% 100.0%No inter-firmcollaboration

76.5% 11.8% 11.8% 100.0%

Total 36.3% 60.0% 3.8% 100.0%

Value df Sig. asint.

Pearson’s Chi squared 24.185 4 .000

Source: our elaboration.

Table 14Negative confidence in the possibility of obtaining a net positive value fromcollaboration.

Few tourism offersthat attract tourists

Total

No Yes

Each partner’sexperiencewith inter-firmcollaboration

Low-medium level 60.0% 40.0% 100.0%High level 83.7% 16.3% 100.0%No inter-firmscollaboration

88.2% 11.8% 100.0%

Total 78.8% 21.3% 100.0%

Value df Sig. asint.

Pearson’s Chi squared 5.751 2 .056

Source: our elaboration.

V. Della Corte, M. Aria / Tourism Management 45 (2014) 3e15 13

- Proposition 3 is partially confirmed, since the majority of thesample (the strategic organisational and knowledge-based firsthierarchical clustering with high and medium levels of experi-ence with collaboration) has experience with inter-firm collab-oration and is aware of the importance of these relationshipsand the advantages they can provide. However, they seem to beaware of the lack of a governance entity that can reinforce andlaunch the destination appropriately.

These results are in line with recent contributions on destina-tion management organisations (DMOs) (Conrady & Buck, 2008;Della Corte & Sciarelli, 2012; Franch, 2010; Kozak e Baloglu, 2011)in which the importance of governance and governance organisa-tions is a widespread and shared idea. Indeed, if a DMO has aneffective governance structure, this can allow it to overcomemistrust and distrust problems, which is a driving factor in a des-tination’s growth through innovation.

To summarise, although the actors would like an ad hoc ormixed (public and private) organisation with a top-down devel-opment process to coordinate their destination management, thereis a lack of a key recognised leading actor. The widespread negativeperception of local institutions in strategic and organisationalterms is linked to the asymmetric vision (Pechlaner & Volgger,2012) that sometimes occurs between private and public actors.This may be due to the inefficiencies that often occur, or even to theabsence of skilful public entities.

On the other hand, actors are well acquainted with the advan-tages that a cooperation process can generate. The results show theneed for a higher level of mutual trust. In keeping with the litera-ture (Deutsch, 1949; Heavey & Murphy, 2012), this research con-firms the assumption that if a group member has a specificbackground concerning cooperative situations, he/she influencesthe cooperation in terms of the group effectiveness, performanceand improving the “network climate”.

Table 15Deterrence-based trust due to the partner’s and the network’s overall reliability ornotoriousness.

Increase in cooperativerelationships

Total

No Yes

Each partner’sexperiencewith inter-firmcollaboration

Medium level 5.0% 95.0% 100.0%High level 14.0% 86.0% 100.0%No inter-firmcollaboration

41.2% 58.8% 100.0%

Total 17.5% 82.5% 100.0%

Value df Sig. asint.

Pearson’s Chi squared 9.140 2 .010

Source: our elaboration.

5. Conclusions and hints for future research

In keeping with transaction cost economics and resource-basedtheory literature, the literature on inter-firm collaboration haslargelyconcentratedonspecific aspects, suchas trust, untrust, distrustandmistrust. The existing literature on the issue has at times ignoredthe socio-psychology perspective and, consequently, the importanceof the individual contribution to network success. This paper origi-nates fromthese criticisms, butmoves fromtheorganisational level ofanalysis to the individual one. It predominantly stresses the individualfeatures of firms’ leaders, which, from a resource-based view, caninfluence a network’s failure and performance. Specifically, we pointout that personal attitudes and previous experience can not onlyimpact a network’s creation, but also its eventual success. In thisrespect, themainconclusionsemerging fromthispaperare concernedwith the understanding of failure, or the missed start-up, of strategicnetworks, thus further verifying that firms’ leaders can influence thegermination of inter-firmcollaboration by reducing the initial distrustand establishing a more stable cooperation. In this landscape, thepersonalattitudes, behavioursandcharacteristicsof afirm’s leaderarean antecedent of inter-firm cooperation.

We conducted our research in the city of Naples and in SorrentoPeninsula (two of the most attractive tourist areas in the south ofItaly) and based it on a survey of a sample of 200firms. The empiricalinvestigation confirms the existence of three clusters with varyingdegrees of cooperation (high level, medium level and a lack of inter-firm collaboration). Although two clusters have experienced someforms of cooperation, they lack a specific leading actor to act as astrategic guide towards a trustful situation. Hence, nobody withinthe network has demonstrated a willingness to build a networkclimate based on trust. The question is: Why aren’t there actualsystems in both the areas, given the actors’ apparent willingness tocooperate and to recognise a leader? The knowledge-based clusterappears as more open minded, but it is still feeble and none of theinterviewed decision makers showed enough talent and/or will-ingness to assume leadership at a territorial level. Finally, the lack ofa governance entity is linked to the absence of a distinctive leader tomanage the destination in the light of criticism of its life cycle.

Returning to the socio-psychological approach examined in theliterature analysis, it is clear that, in both cases, the structuraldimension is feasible, since the overall context is not structuredaccording to an actual networking approach. The cognitivedimension appears to be relevant regarding defining each partner’sapproach towards cooperation. The relational aspect can supportthe process of overcoming initial distrust but, to do so, a tightstrategic governance is necessary. If there is no governance actor, itis very difficult, if not impossible, to move from a situation ofdistrust to a trust situation.

Themodelwas applied to a small, although significant, sample offirms belonging to a specific industry at a local level. It shouldtherefore be extended to other industries and contexts. However,numerous strategic alliances and partnerships characterise thetourism industry due to the complementarity of the services in thetravel experience. Consequently, identifying problems with collab-oration is a significant hint that small and medium enterprisesshould be analysed. It would be interesting, therefore, to extend thesurvey to other industries to obtainmore generalisable results. Suchan approach could be very useful in respect of two aspects:

1) Verifying if, in a certain territory, individual approaches caninduce firms to readily consider concrete possibilities to over-come initial mistrustful, or distrustful situations, in respect ofinteresting networking perspectives. If the local offer is mainlycomposed of SMEs, a leading governing actor seems necessary.

V. Della Corte, M. Aria / Tourism Management 45 (2014) 3e1514

Whether this should be a private, public or mixed (public andprivate) organisation depends on the specific territorial context.

2) Offering local decision makers, who aim to create and developstrategic networks in their territories, a useful approach torecognise the results of their individual behaviour and toconsider other parties in a more focused and committed way.

In conclusion, this research was a major effort, since we decidedto base our study on an empirical analysis and not on existingofficial data. The empirical analysis allowed us to meet, confrontand understand decision makers. We believe this is a very usefultool to create theory that is, as far as possible, linked to the realworld. Our study also analyses the dynamics of small and mediumenterprises in the tourism industry, for which governance is anextremely relevant issue. It would be interesting to apply thisapproach to other industries and to bigger firms for comparisonsand further generalisations. There are also unexplored aspects andquestions to consider, such as: How does a leader of a networkbecomes a leader? Does the leader change over time?Why do someplaces have stronger leaders than others? These topics are far moreimportant in the context of small and medium enterprises, butrequire a specific focus and further study.

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Valentina Della Corte is Associate Professor of BusinessManagement and has won public competition as fullprofessor recently. She received phd at Ca’Foscari Univer-sity. She teaches Tourism Business Management andStrategic Management and Marketing. She is author andreviewer of numerous articles in specialised journals, bothnational and International, of contributions in books withplural authors and of monographic works. She has coor-dinated several research activities and cooperates activelyBachelor, Master degrees and PhD programs in Italy andEurope, also promoting international relations with theentrepreneurial world. She is member of Strategic Man-agement Society and of Academy of Management.

Massimo Aria is assistant professor in Social Statistics atthe Department of Economics and Statistics of the Uni-versity of Naples Federico II. He is a PhD in ComputationalStatistics. He is expert of methods of non-parametric clas-sification and regression, with a particular reference to thetree-based models and to the incremental approaches. Inthe field of Applied Statistics he worked to the planningand realization of sample surveys and the use of methodsof multidimensional data analysis and Data editing for theanalysis of problems connected to social, medical and eco-nomic phenomena. From 2007 He is member of STADresearch group.