Territorial Support Institutions and Relationship Structure in Tourism Destination Management: A Brazilian Case Study

Instituições de suporte do Território e estrutura de relacionamento na gestão de destinos turísticos: um estudo de caso brasileiro

Instituciones de apoyo territorial y estructura de relaciones en la gestión de destinos turísticos: un estudio de caso brasileño

Abstract:

This study describes the relationship between the role of tourism support institutions, their services and their relationship structure in the tourism destination management of Pirenópolis-GO, in the interior of Brazil. Tourism can be studied as a geographical territory comprising local attractions and different actors. These actors include tourism support institutions (TSIs), such as universities, agencies, trade associations and others. Using a qualitative and quantitative approach, this study involved twenty support institutions, with data collection through semi-structured interviews. The research findings indicate the centrality of some institutions which, nevertheless, do not play a leading role in the network, and that i) Centrality is not related to the provision of services but to the relationships between TSIs; ii) Less investment-intensive activities are more abundant and complementarity of services is not observed among the TSIs; and iii) Destination management is not performed by a destination management organization (DMO), which makes its management more fragmented.

Key-words:
tourism destination management; local support institutions; local support services; inter-organizational relationships

Resumo:

Este estudo descreve a relação entre o papel das instituições de suporte ao turismo, seus serviços e sua estrutura de relacionamento na gestão do destino turístico de Pirenópolis-GO - no interior do Brasil. O turismo pode ser estudado como um território geográfico formado por atrativos locais e diferentes atores. Esses atores incluem instituições de suporte ao turismo, como universidades, agências, associações comerciais e outros. Com abordagem qualitativa e quantitativa, o estudo envolveu 20 instituições de suporte, sendo os dados coletados por meio de entrevistas semiestruturadas. Os resultados da pesquisa indicam a centralidade de algumas instituições, que, no entanto, não desempenham um papel protagonista na rede, e que: i) a centralidade não está relacionada com a prestação de serviços, mas sim com as relações entre as instituições; ii) atividades que necessitam menos investimentos são mais comuns e não se observa complementaridade de serviços entre as instituições; e iii) o gerenciamento de destino não é realizado por uma Organização de Gerenciamento de Destino (DMO), o que torna seu gerenciamento mais fragmentado.

Palavras-chave:
gestão de destinos turísticos; instituições locais de suporte; serviços locais de suporte; relações interorganizacionais

Resumen:

Este estudio describe la relación entre el papel de las instituciones de apoyo al turismo, sus servicios y su estructura de relaciones en la gestión del destino turístico de Pirenópolis-GO en el campo de Brasil. El turismo se puede estudiar como un territorio geográfico formado por atractivos locales y diferentes actores. Estos actores incluyen instituciones de apoyo al turismo (IAC), como universidades, agencias, asociaciones comerciales y otros. Utilizando un enfoque cualitativo y cuantitativo, este estudio involucró a 20 instituciones de apoyo, y los datos se recopilaron a partir de entrevistas semiestructuradas. Los resultados de la investigación señalan la centralidad de algunas instituciones, que, sin embargo, no tienen un papel protagónico en la red, y que i) la centralidad no está relacionada con la prestación de servicios sino con las relaciones entre las IAC. ii) Las actividades menos intensivas en inversión son más abundantes y no se observa complementariedad de servicios entre las IAC. iii) La gestión de destinos no la realiza una organización de gestión de destinos (DMO), lo que hace que su gestión sea más fragmentada.

Palabras clave:
gestión de destinos turísticos; instituciones de apoyo locales; servicios de apoyo local; relaciones interorganizacionales

1 Introduction

The 2018 World Tourism Organization report (UNWTO, 2019) highlights that between 2012 and 2018 tourism grew, on average, more than the global economy and generated more international revenue than the export of manufactured goods. This growth has been noticed by different cities and countries (UNWTO, 2019). For some of these cities, tourism is particularly important (Goffi & Cucculelli, 2019; Vignieri, 2019). Such is the case with Pirenópolis, Brazil

The local growth of tourism depends on many aspects, such as the relationship between different actors in a destination. These relationships have long been recognized as important for the development of a more competitive tourism product (Czernek & Czaron, 2016; Hassan, 2000; Jamal & Getz, 1995; Kylänen & Rusko, 2011; Pavlovich, 2003; Ritchie & Crouch, 2010). This fact was pointed out by Dwyer and Kim (2003) - and later, by Wang and Krakover (2008) and Bornhorst et al. (2010) - who emphasized that a relationship structure between the actors could generate positive results for the destination.

The relationship structure is an integral part of destination management, and it can be practiced by a destination management organization. The stronger the local structure is, the greater its propensity to generate network links (Farias & Hoffmann, 2018; Wang & Krakover, 2008). In this case, the leadership or coordination exercised by an institution locally gives an understanding the structure of the relationship on the one hand (Wang & Krakover, 2008) and the likely local outcome this fact may have on the other (Dwyer & Kim, 2003). Thus, the success of these organizations is linked to the success of their own tourism destinations (Volgger & Pechlaner, 2014).

The relationship structure is interconnected with other territorial constructs, such as support institutions and tourism destination management, which leads to a territorial clustering of companies (Page & Connell, 2006). Due to the intrinsic complementarity of the tourism product (Baggio et al., 2010; Prideaux et al., 2014), companies end up specializing in certain stages of the value chain. By increasing the diversity of the industry locally, tourism support institutions (TSIs) emerge. This is similar to what occurs in the manufacturing sector, as described in Brusco's (1993) and Schmitz’s (1993) pioneering texts. These TSIs offer various services (Dudensing et al., 2011; Iorgulescu & Răvar, 2015; Wang & Krakover, 2008), which can be performed in a coordinated or uncoordinated way, and can have a greater or lesser impact on destination performance (Czernek & Czaron, 2016; Farias & Hoffmann, 2019; Volgger & Pechlaner, 2014).

Despite these interconnections, there are still questions that the available literature has yet to answer. Has the role of TSIs become standardized in different destinations? Does the provision of services by TSIs complement each other, as in the case of companies? Is the relationship structure among TSIs linked to the way destination management is performed? These unanswered questions have resulted in the following research question: What is the relationship between the role of TSIs, their services, their relationship structure, and tourism destination management?

This study describes the relationship between the role of tourism support institutions, their services and their relationship structure in the tourism destination management of Pirenópolis, State of Goiás (GO) in the interior of Brazil.

This study is justified by the increased competitiveness of the tourist industry, which has led to new approaches to the administration of tourist spaces (Sanz-Ibáñez & Anton-Clavé, 2014). Moreover, much of the tourism research has taken place in developed destinations, not developing ones (Benckendorff & Zehrer, 2013; Carmona et al., 2014; Koseoglu et al., 2016; Liu et al., 2014; Merinero-Rodriguez & Pulido-Fernández, 2016; Miki et al., 2012). Thus, studying a destination in a developing country, and a region outside the central tourist flow, can help to understand its management from a theoretical point of view and can help manage the destination from a managerial point of view.

This work is divided into four parts. Besides this introduction, we present a theoretical background that supports the research. We then discuss the method used. Finally, we present and discuss the results, conclusions, limitations and recommendations.

2 Theoretical Background

When it comes to supporting tourism, the literature mostly deals with local communities (Strzelecka et al., 2017) which, in our view, is only part of the question. Support can also come from local tourism institutions. A support institution is any organization that is able to provide services to companies at lower prices because of its expertise and gains of scale (Brusco, 1993). In the case of tourism, most business in Brazil are small companies, and the cost of acquiring information in a market where there is information asymmetry can be prohibitive (Fleischer & Felsenstein, 2000).

TSIs may be businesses or professional associations (Barros & Moreira, 2005; Dayasindhu, 2002); public and private funding organizations (Dayasindhu, 2002); government agencies (Dayasindhu, 2002; Stacke et al., 2012); community associations and councils (Barros & Moreira, 2005); universities (Dayasindhu, 2002; Stacke et al., 2012); research institutions and technological schools (Dayasindhu, 2002; Stacke et al., 2012); or teaching and training centers (Dayasindhu, 2002); DMO (Beritelli et al., 2014; Wang & Krakover, 2008; Yang et al., 2019); among others.

The diversity of actors in a destination, including support institutions, fosters cooperation and network performance (Farias, 2020; Endres, 2003). Hoffmann and Campos (2013) have demonstrated that cities with more institutions also have more services available. However, it cannot be guaranteed that whenever there are a large number of support institutions operating in one destination, there will be a large number of services provided; at least not from the perspective of their diversity (Hoffmann et al., 2016).

The types of services provided by support institutions in a destination include: technical assistance and training courses (Hoffmann & Campos, 2013; Ritchie & Crouch, 2003); technological services and dissemination of technology (Hoffmann & Campos, 2013); initiatives to promote cooperation and coordination between actors (André, 2004; Mitchell & Schreiber, 2007; Wang & Krakover, 2008); transfer of knowledge about products and specific markets (Mitchell & Schreiber, 2007; Wang & Krakover, 2008); capture and recognition of knowledge and opportunities for members of the cluster (Bellandi & Caloffi, 2008); marketing management and destination promotion (Mitchell & Schreiber, 2007; Wang & Krakover, 2008); assistance to combat unemployment in the tourist destination (Nunkoo et al., 2012); ease of access to finance (Dudensing et al., 2011; Schmitz, 1993); management of protected natural areas (Iorgulescu & Răvar, 2015); and advisory and consulting services (Hoffmann & Campos, 2013).

The services provided by support institutions are specific to the companies operating in the destination. Although the activities may not be used by all the companies in a territory with the same frequency or intensity (Mitchell & Schreiber, 2007), the companies that do use them are helping to improve the destination performance. Thus, support institutions can be considered as sources of competitive advantage for clustered companies (Dwyer & Kim, 2003; Hoffmann & Campos, 2013; Hoffmann et al., 2016).

Clusters increase the likelihood of developing cooperation between the actors because TSIs can foster relationships (Nunkoo et al., 2012) and cooperation between the actors (Wang & Krakover, 2008), even creating local networks (Volgger & Pechlaner, 2014). According to Vignieri (2019), sustainable tourism development in small towns, for example, is closely connected with the ability of local public and private actors to collaborate, align strategies and share resources. Thus, local tourism governance is crucial for both destination planning and performance (Vignieri, 2019).

However, more institutions does not necessarily create more cooperative relationships, since in one destination, similar products, such as hosting, may be competing for the same customers (Bengtsson & Kock, 2003). However, sharing information and resources among companies can generate even more innovations (Park et al., 2014). Moreover, sharing between companies and TSIs can also lead to higher performance (Vignieri, 2019). Thus, as Wang and Krakover (2008) point out, among the agents of a destination, relationships are recognized as both competitive and cooperative.

According to Dwyer and Kim (2003), one aspect that makes a destination more competitive is its management, which may be done either by public agencies (Hoffmann & Campos, 2013) by third sector entities (André, 2004) or both (Beritelli et al., 2014; Volgger & Pechlaner, 2014). Dwyer and Kim (2003) also state that these institutions should act in a complementary way to increase the synergy between them. The goal of this management strategy is to make the destination more competitive and thus, foster local development (Dwyer & Kim, 2003; Volgger & Pechlaner, 2014). Therefore, the importance of local government commitment to tourism is clear (Goffi & Cucculelli, 2019).

Destination management requires some degree of coordination, since even with common goals - such as destination promotion (André, 2004) or destination infrastructure problem solving (Kylänen & Rusko, 2011) - companies are competing with each other (Bengtsson & Kock, 2003), and local institutions may have different sources of funding (Hoffmann & Campos, 2013). Thus, they may need to serve the interests of their sponsors. This context is inherent to the destination territory and implies that without some level of coordination among local actors, it may be difficult to crystallize the advantages that the territory could generate, even though they have attractions with recognized tourism value (Vignieri, 2019).

Destination marketing organizations (DMOs) are defined in the literature in two ways. Tsé (2016 apud Yang et al., 2019) define them as organizations that bring together travel agencies, hotels, government and attractions that seek to promote the destination. In this paper, we apply the definition given in the works of Beritelli et al. (2014), and Volgger and Pechlaner (2014), which defines a DMO as a destination management organization. We emphasize that the success of DMOs is positively correlated with the success of the tourism destination itself, according to Volgger and Pechlaner (2014).

One of the roles of the DMO is to interact with different suppliers in order to offer the tourism product (Rodríguez-Girón et al., 2018), which reinforces its coordinating role in the destination. In this kind of structure, in order to develop a new product, it is necessary to involve the DMO as much as the local government (Rodríguez-Girón et al., 2018). For this, the community should be involved, to avoid criticism of actions of the DMOs (Yang et al., 2019).

We observe that a DMO is not necessary for partnerships between tourism actors. The work of Spasojevic et al. (2019) shows that medium size airlines are more likely to partner with local government, tourism providers and other actors than to develop new air routes. This scenario changes for small, or large airlines, or when there are other types of interests. Thus, the airlines do not create a DMO specifically for this purpose, but to use pre-existing partnership to provide the new route (Spasojevic et al., 2019). We must also consider that a destination may not have a single organization. There may be more than one network of companies and institutions, in which prominent actors, including businesses, influence other members by acting as a bridge between them, facilitating communication within the group (Williams & Hristov, 2018).

3 Method

The choice of case. In this paper, we study a tourist destination in Brazil that attracts regional tourism demand (Brasil, 2015). We identified studies on Brazil's inductive destinations, which allow comparisons with previous research. From the universe of 65 inductive destinations (Brasil, 2015), we considered three more criteria for choosing the case: (i) the destination could not be a State capital; ii) the competitiveness index should be the closest to the study median (Brasil, 2015); iii) and it should be accessible to institutions, as recommended by Yin (2001). The destination that met all the criteria was the city of Pirenópolis, in the state of Goiás (GO), in the interior of Brazil.

Survey participants. Eleven member institutions of the City Tourism Council were identified. These institutions were contacted by telephone and the respondent-driven sampling technique (Salganik & Heckathorn, 2004) was applied. This step led to the identification of 12 more institutions. Finally, representatives of 20 institutions agreed to take part in the survey.

Data collection. We recorded interviews with representatives of the institutions (primary data) with their permission. The average length of the interviews was 47 minutes. We used an interview script with structured and semi-structured questions. This script was divided into four parts, as follows.

1. Services provided by the institution. We presented Hoffmann and Campos (2013)’s list of fifteen services and asked representatives how often their institution provides that service, on a scale of one (low frequent) to seven (high frequent). Services scoring between four and seven were considered frequently offered by institutions.

2. Institution relationships. To identify the existing relationships, each representative was presented with a list of all the institutions operating at the tourism destination, and asked to indicate how often he institutions interact with each other, on a scale of one (low frequency) to seven (high frequency). For the present research, a relationship exists when both institutions have marked values of between four and seven.

3. Importance of the institution to local tourism. To identify the collaboration between the institutions and the local tourism, each representative was presented with a list of all the institutions found at the destination, and asked to indicate how those promote collaboration for the development of local tourism, on a scale of one (collaborates a little) to seven (collaborates a lot).

4. Actions of the institutions: We conducted semi-structured surveys, with questions focusing on the following topics:

1. Coordination of local institution;

2. Perception of the institution about local tourism as a product;

3. Actions and influence of the institution on the local tourism destination decision making process;

4. Initiatives by the institution to improve relationships between local actors;

5. Initiatives by the institution to improve relationships between regional actors;

6. Forma tourism destination planning;

7. Informal tourism destination planning;

8. Whether there is a local DMO;

9. Whether there is a regional DMO;

10. Whether there is a local brand;

Data processing. We used two techniques for the data analysis: social network analysis and content analysis. Regarding the social network analysis, we used the software programs R 3.6.1 and RStudio 1.2.1335. We also used the package igraph 1.2.4.1, according to the receommendations of Csárdi and Nepusz (2006), to create and analyze:

1. Quantity and structure of the relationships;

2. Cooperation between the institutions and the tourism destination.

To analyze the relationship network, we used the symmetric model (Gomes & Guimarães, 2008).

Using the package igraph 1.2.4.1, we designed and identified networks characteristics described in the literature (Csárdi & Nepusz, 2006; Tomael & Marteleto, 2006; Wasserman & Faust, 1994):

1. Density;

2. Degree centrality;

3. Closeness centrality;

4. Betweenness centrality; and

5. Higher click.

Regarding the content analysis, we processed contents as suggested by Miles and Huberman (1994). The content analysis was applied to the responses of part four of the survey script (action of the institution). We decided to use the contents to help to discuss data from other parts of the same script.

4 Results

4.1 Institutions Identified

Table 1 presents the institutions identified and considered for the presentation and analysis of the data. Of the 23 TSIs, 11 are members of the City Tourism Council and three belong to the council itself (tourism, culture and environment). Also, of the 23 destination institutions, 14 are from the city, three are from the State of Goiás, and six are national institutions. The three TSIs (IPEC, IPTur, and AGETUR) that did not respond to the survey were cited by the representatives and therefore appear in the results.

Table 1:
Supporting institutions identified in the city of Pirenópolis

N.	Name of the Institution	Initials	Is it part of the tourism board?	Public or private?	Scope of action	Number of members	Number of employees
1	Municipal Secretary of Tourism	SETUR	Yes	Public	City	-*	10
2	Municipal Secretary of Culture	SECUL	Yes	Public	City	-*	13
3	Municipal Secretary of Environment and Agriculture	SECENV	Yes	Public	City	-*	4
4	State of Goiás Public University	UEG	Yes	Public	State**	-*	-*
5	Special Secretariat of Government Affairs	SECGOV	Yes	Public	City	-*	4
6	Pirenópolis Tourism Agency Association	AGTP	Yes	Public	City	10	0
7	Brazilian Hotel Industry Association	ABIH	Yes	Public	Federal**	20	0
8	Brazilian Association of Bars and Restaurants	ABRASEL	Yes	Public	Federal**	20	0
9	Pirenópolis Attraction Association	ATRAC	Yes	Public	City	12	0
10	Association of Artisans of Pirenópolis	Piretur	Yes	Public	City	300	5
11	Touristic guides Association	AGCP	Yes	Private	City	10	0
12	City Tourism Council	COMTUR	-*	Public	City	11***	0
13	City Environment Council	COMDEMA	-*	Public	City	14***	0
14	City Culture Council	COMCULT	-*	Public	City	12***	0
15	Tourist Service Centers	CAT	No	Public	City	-*	5
16	Brazilian Micro and Small Business Support Service	SEBRAE	No	Private	Federal**	-*	2
17	Institute of National Historical and Artistic Heritage	IPHAN	No	Public	Federal**	-*	5
18	Pirenópolis Convention & Visitors Bureau	PCVB	No	Private	Federal**	16	0
19	Social Service of Commerce	SESC	No	Private	Federal**	-*	26
20	Cerrado Institute of Permaculture and Ecovillages	IPEC	No	Private	City	-*	-*
21	Pirenópolis Educational Community	COEPI	No	Private	City	-*	-*
22	Goiás Tourism Research Institute	IPTur	No	Public	State**	-*	-*
23	Goiana Tourism Agency (Former Goiás Tourism)	AGETUR	No	Public	State**	-*	-*

• Note. Source: research data (2020)
• * The question does not relate to institution, or the data were not obtained; ** There are federal, state and city institutions. However, the data collected refer to the performance in Pirenópolis; *** Number of seats on the board. Each chair is represented by one holder and one alternate

4.2 Services Identified

Table 2 presents the services identified in Pirenópolis. The acronyms of the institutions are shown in the first column. The names of the services appear in the first row of the table and are described in Table 3. Table 2 shows that the most frequent services are social activities and political representation, which are present in 80% of the institutions. Also, 65% of the institutions consider that they represent the political interests of their portfolio (when public) or their associates (when private). Table 3 presents the description of services identified in Pirenópolis.

Table 2:
Survey of services provided by support institutions

	IMPRV	PRESS	ASLAW	ASTEC	ASTLC	ASCOM	SACTV	CONTRC	EVENT	IFTRN	INVST	LOBBY	INFSR	TRNNG	SUSTB	Total
SETUR		4					4		4	5		6	7			6
SECUL		7		7		7	7		7	7		7	4			8
SECENV			7	7					7	7		7		6	7	7
UEG				6			7		7	6				7		5
SECGOV	7	6	7				7		7	7		7		7	6	9
AGTP					5							7				2
ABIH	7	7	7	7	7		7			7		7		7	7	10
ABRASEL	7		6	6	6				6	6		7		7	7	9
ATRAC	5			7			7					7		5	5	6
Piretur				7		7			7							3
AGCP	7			6			6					7	5	6	7	7
COMTUR							6					7	5		6	4
COMDEMA	6						7					7	7	7	7	6
COMCULT							5		6	5					4	4
CAT							7					4	7			3
SEBRAE	4	5		7	6	7	4	5	6	5		6				10
IPHAN				6			6							7		3
PCVB	4			7	7		7		4	4			7	4	7	9
SESC						6	6	6		6						4
IPEC*	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
COEPI	7			6			7		7	7			5	7	7	8
IPTur*	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
AGETUR*	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
Total	9	5	4	12	5	4	16	2	11	12	0	13	8	11	11

• Note. Source: research data (2020)
• * Institutions not interviewed

Table 3:
Description of services

Initials	Description of service
IMPRV	Improvement - These are courses that institutions provide to their internal members (employees or associates)
PRESS	Press office
ASLAW	Legal advice
ASTEC	Technical advice, linked to knowledge for problem solving
ASTLC	Technology advisory, affects the development of innovation
ASCOM	Commercial activities, such as fairs and events
SACTV	Social activities
CONTRC	Agreement - Formation of agreements with other TSIs or companies to benefit the members of an institution
EVENT	Event infrastructure
IFTRN	Training infrastructure
INVST	Availability of lines of investment to develop projects, build or acquire goods or services for the group
LOBBY	Political representation
INFSR	Information services
TRNNG	Training - Courses offered to external members to reach other trade groups, not just for internal use
SUSTB	Environmental sustainability actions

• Note. Source: research data (2020)

Table 2 also shows that the TSIs of Pirenópolis offer 123 destination services. Of the 20 institutions interviewed, nine are private and non-governmental, offering 65 services, and 11 are public, offering 58 services. Private and non-governmental TSIs offer 7.2 services each, on average, while public TSIs offer 5.3 services.

Comparing the average services offered between the TSIs that are part of the City Tourism Council (6.5) with those that are not (6.2), we found that being part of the City Tourism Council does not affect the number of services offered. Besides, of the 123 services provided by TSIs, 73 (59%) are provided by city institutions. However, we noticed an average of 7.5 services provided per national institution, whereas city institutions have an average of 5.6.

We also noticed a form of specialization of services within the institutions. Whereas public TSIs focus on events infrastructure (seven out of 11) and information services (five out of eight), private and non-governmental TSIs focus on improvement courses (seven out of nine), technology advisory (five out of five) and agreements (two out of two). National TSIs are the leading providers of technology advisory services (four out of five) and agreements (two out of two), and city TSIs provide social activities (10 out of 16), events infrastructure (seven out of 11), political representation (10 out of 13), information services (seven out of eight) and environmental sustainability actions (eight out of 11).

4.3 Relationship between institutions

As we mentioned earlier, we considered that a relationship exists when both institutions have marked values of between four and seven (highlighted in yellow in Table 4). For the IPEC, IPTur and AGETUR institutions, which were not interviewed, we replicated the values mentioned by the other TSIs when one of them was referred to one of them. Regarding the relationship between the three institutions, it was not possible to perform this procedure. As shown in Table 5, the sum of the relationships of each institution is 182. Thus, the number of tourism destination relationships is half that value (91), since the relationships are two-way.

Table 4:
Relationships between institutions at the destination

N.		23	22	21	20	19	18	17	16	15	14	13	12	11	10	9	8	7	6	5	4	3	2	1
	Institutions	AGETUR	IPTur	COEPI	IPEC	SESC	PCVB	IPHAN	SEBRAE	CAT	COMCULT	COMDEMA	COMTUR	AGCP	Piretur	ATRAC	ABRASEL	ABIH	AGTP	SECGOV	UEG	SECENV	SECUL	SETUR
1	SETUR	5	7	6	1	1	5	4	5	7	5	5	6	6	5	6	5	5	6	6	1	5	6
2	SECUL	5	4	6	2	3	5	6	6	6	7	4	6	5	7	4	6	6	5	5	7	4
3	SECENV	5	1	7	1	1	1	5	4	5	2	7	6	7	1	7	1	7	5	7	7
4	UEG	3	1	1	1	1	3	3	1	3	6	6	6	2	2	2	5	5	2	3
5	SECGOV	6	1	1	4	4	1	7	7	6	6	6	6	4	6	4	4	5	3
6	AGTP	1	1	1	1	1	5	1	7	7	1	1	7	7	1	7	2	3
7	ABIH	6	7	3	1	1	7	5	7	4	4	1	7	4	5	6	1
8	ABRASEL	6	2	3	2	3	3	4	3	5	2	2	5	3	3	5
9	ATRAC	1	1	1	1	1	2	1	3	1	1	1	1	2	1
10	Piretur	1	3	1	1	2	2	2	2	7	7	2	3	4
11	AGCP	2	1	5	1	4	3	4	3	4	4	7	7
12	COMTUR	1	1	4	1	1	6	1	1	1	7	1
13	COMDEMA	1	1	1	1	1	3	1	1	1	1
14	COMCULT	4	1	7	1	1	1	6	1	5
15	CAT	6	6	3	1	4	4	4	6
16	SEBRAE	6	4	5	5	5	5	5
17	IPHAN	2	2	3	2	2	2
18	PCVB	2	1	2	1	1
19	SESC	5	1	4	4
20	IPEC*	-	-	-
21	COEPI	1	1
22	IPTur*	-
23	AGETUR*

• Note. Source: research data (2020)
• * Institutions not interviewed

Table 5:
Number of relationships by institution

Institution	Relationships by institution	Institution	Relationships by institution
SETUR	19	ABRASEL	6
SECUL	15	COMTUR	6
CAT	15	UEG	5
SECENV	12	SESC	5
SEBRAE	12	IPTur	5
SECGOV	11	AGTP	4
ABIH	10	Piretur	4
AGETUR	10	COMDEMA	4
AGCP	9	PCVB	4
COMCULT	7	IPEC	4
IPHAN	7	ATRAC	1
COEPI	7
Total 182

• Note. Source: research data (2020)

The maximum number of relationships in the target is 250. This value is found by calculating the simple combination of 23 distinct elements (TSIs), grouped two through two, and subtracting a value of three from this result. This number (three) refers to the calculation of the simple combination of three distinct elements (relationships between the three non-interviewed institutions), grouped two to two, as shown in Equation 1.

Equation 1 possible relationships in the tourism destination. $c_{\mathrm{23,2}}-c_{\mathrm{3,2}}$ $\frac{23!}{2!(23-2)}-\frac{3!}{2!\left(3-2\right)!}=250$

Table 6 shows the classification of the institutions according to four relationship measurements, as described by Wasserman and Faust (1994). We have used a color intensity scale, where red indicates very intensive and no color indicates a low rate.

Table 6:
Measures of centrality, closeness, higher click and betweenness

Centrality	Closeness	Higher Click	Betweenness
SETUR	SETUR	SETUR	SETUR
SECUL	SECUL	SECUL	SECUL
CAT	CAT	CAT	CAT
SECENV	SECENV	SECENV	SECENV
SEBRAE	SEBRAE	SEBRAE	SEBRAE
SECGOV	SECGOV	SECGOV	SECGOV
ABIH	ABIH	ABIH	ABIH
AGETUR	AGETUR	AGETUR	AGETUR
AGCP	AGCP	AGCP	AGCP
COMCULT	COMCULT	COMCULT	COMCULT
IPHAN	IPHAN	IPHAN	IPHAN
COEPI	COEPI	COEPI	COEPI
ABRASEL	ABRASEL	ABRASEL	ABRASEL
COMTUR	COMTUR	COMTUR	COMTUR
UEG	UEG	UEG	UEG
SESC	SESC	SESC	SESC
IPTur	IPTur	IPTur	IPTur
AGTP	AGTP	AGTP	AGTP
Piretur	Piretur	Piretur	Piretur
COMDEMA	COMDEMA	COMDEMA	COMDEMA
PCVB	PCVB	PCVB	PCVB
IPEC	IPEC	IPEC	IPEC
ATRAC	ATRAC	ATRAC	ATRAC

• Note. Source: research data (2020)

Table 7 shows results of institutions’ evaluation of the TSIs. The representatives were asked how much they thought each institution collaborated with the destination's tourist activities, on a scale of one (collaborates a little) to seven (collaborates a lot). They were allowed to evaluate as many institutions as they wanted, as the respondents might not have known about the work of one or more institutions. In presenting the data from this research step (Table 7), we show only the consolidated scores, in order to protect the respondents confidentiality and obtain the most honest answers possible.

At this time, adjustments were necessary because the people interviewed could not rate their institution. Thus, the interviewed institutions could receive up to 19 marks, while the three institutions that were not interviewed could receive up to 20 marks. The adjustment was therefore made by multiplying the average score by 20, so that in the end, all the institutions received 20 times their average score.

Table 7:
Evaluation of collaboration of institutions with tourism destination

Institution	Maximum number of marks possible	Numbers of marks received	% of marks received	Average marks received (1-7)	Sum of marks with adjustment (Max.140)
CAT	19	19	100.0%	5.9	118.9
SECUL	19	18	94.7%	5.6	111.1
SETUR	19	19	100.0%	5.5	110.5
AGCP	19	16	84.2%	5.4	108.8
ABIH	19	19	100.0%	5.4	108.4
COMCULT	19	16	84.2%	5.2	103,8
AGETUR	20	19	95.0%	5.1	102.1
COMTUR	19	18	94.7%	5.0	100.0
ATRAC	19	19	100.0%	5.0	100.0
SEBRAE	19	18	94.7%	4.9	97.8
AGTP	19	19	100.0%	4.8	96.8
UEG	19	18	94.7%	4.8	96.7
COMDEMA	19	18	94.7%	4.7	94.4
SECENV	19	18	94.7%	4.6	92.2
ABRASEL	19	17	89.5%	4.6	91.8
IPHAN	19	18	94.7%	4.4	88.9
SECGOV	19	17	89.5%	4.4	88.2
Piretur	19	17	89.5%	4.4	87.1
IPTur	20	13	65.0%	3.8	76.9
COEPI	19	16	84.2%	3.8	76.3
PCVB	19	16	84.2%	3.6	71.3
IPEC	20	16	80.0%	2.7	53.8
SESC	19	14	73.7%	2.5	50.0

• Note. Source: research data (2020)

Column four of Table 7 shows the scores attributed, as percentages. A low percentage of possible responses means that an institution is not known to other institutions of the destination. The last column of Table 7 shows the marks received by the institutions, after adjustments, on a scale of zero to 140.

5 Discussion

We found 23 TSIs in Pirenópolis, which can be considered a high number when compared to destinations with a greater tourist influx, such as Balneário Camboriú, studied by Hoffmann and Campos (2013). We believe that the use of the Respondent-Driven Sampling technique (Salganik & Heckathorn, 2004) to identify TSI may have contributed to this. The fact that the number of actors is large may favor the tourism destination (Hoffmann & Campos, 2013).

The institutions found were public, private (Dayasindhu, 2002), and non-governmental actors that can be classified as government agencies (Dayasindhu, 2002; Stacke et al., 2012); universities (Dayasindhu, 2002; Stacke et al., 2012); technological schools (Dayasindhu, 2002; Stacke et al., 2012); community associations and councils (Barros & Moreira, 2005); environmental agencies (SECENV and COMDEMA). We did not find any new kind of organization in the case studied.

We did not find a DMO, as might be expected from the literature (Beritelli et al., 2014; Wang & Krakover, 2008; Yang et al., 2019). We believe the absence of a DMO in Pirenópolis is an interesting result because it is similar to Balneário Camboriú, a popular beach resort in the South of Brazil that is a quite different tourism destination from Pirenópolis - an ecotourism destination in the central zone in Brazil. Yet neither city has a DMO to manage its local tourism. Both cases seem to show us that this structure can be as much an exception as the rule in tourism management in Brazil.

Having TSIs in Pirenópolis is a positive aspect of local tourism, through its ability to provide services to local businesses (Brusco, 1993; Schmitz, 1993). But the lack of a formal or an informal DMO creates some difficulties. The first difficulty pointed out by the representatives is that even though there is a tourism planning for the destination, this is not discussed among the TSIs. But this discussion is important because it is linked to the performance of the tourist destination (Vignieri, 2019). The second concern pointed out by respondents is that there is no brand or marketing of the destination, something which also happens in other destinations (Goffi & Cucculelli, 2019).

And even though respondents consider the performances of SETUR and SECUL to be fairly good, these two institutions are political bodies, run by people with an elective office. Thus, they have political party affiliations that may or may not fit in with the goals of the tourism destination.

The services these institutions provide are also mentioned in the literature: technical assistance and training courses (Hoffmann & Campos, 2013; Ritchie & Crouch, 2003); activities related to environmental management (Iorgulescu & Răvar, 2015); and advisory and consulting services (Hoffmann & Campos, 2013). No activities were found related to knowledge transfer (Bellandi & Caloffi, 2008; Mitchell & Schreiber, 2007; Wang & Krakover, 2008); or marketing and destination promotion (Mitchell & Schreiber, 2007; Wang & Krakover, 2008). This reality may indicate that services can be idiosyncratic and that in the absence of a DMO, the broker role (Belso-Martínez & Díez-Vial, 2018; Boari & Riboldazzi, 2014) is not exercised by any TSI.

Of the five institutions that most provide destination services - SEBRAE (10 services), ABIH (10), ABRASEL (9), PCVB (9) and SECGOV (9) - four are private or non-governmental. Of the 11 institutions that least provide destination services - SETUR (6 services), ATRAC (6), COMDEMA (6), UEG (5), COMTUR (4), COMCULT (4), SESC (4), IPHAN (3), Piretur (3), CAT (3) and AGTP (2) - eight are public. In terms of the three destination councils - COMDEMA (6 services), COMTUR (4) and COMCULT (4) - we note that they individually provide a number of services below the overall average, which is 6.2. This may indicate that boards, while active, are failing to turn discussions into action.

The fact that tourism is a territory-related activity (Page & Connell, 2006), with complementary services (Baggio et al., 2010), can create an enabling environment for business-to-business relationships. This same logic would be expected for the institutions. Our results indicate that these relationships exist, but to a limited extent. Of the 250 possible relationships in the destination, 91 were found, which represents a network density of 0.364. The data in Table 5 are confirmed in Table 6. SETUR, SECUL and CAT have the most frequent relationships. This means that there is more cooperation between some TSIs than others. However, this result is not unusual, although all the secretariats are linked to each other via City Hall and the commitment of local public power to tourism brings benefits to the activity (Goffi & Cucculelli, 2019).

Destination management can be done in different ways (Beritelli et al., 2014; Volgger & Pechlaner, 2014). This management is the responsibility of businesses and the local government, with two different but interconnected objectives: making the destination more competitive and fostering local development (Dwyer & Kim, 2003; Volgger & Pechlaner, 2014). To this scope, management can be involved with such topics as promoting the destination (André, 2004) or improving its infrastructure (Kylänen & Rusko, 2011).

As demonstrated by Spasojevic et al. (2019), no DMO is required for relationships between organizations in tourism. However, having a DMO can improve tourism destination management (Beritelli et al., 2014; Volgger & Pechlaner, 2014). This is because a DMO may have the ability to engage actors in networks, which helps to manage target stakeholders (Volgger & Pechlaner, 2014), improve communication between local actors (Williams & Hristov, 2018), and ensure the offer of the tourist product (Rodríguez-Girón et al., 2018).

6 Conclusion, limitations and recommendations

This study describes the relationship between the role of tourism support institutions, their services and their relationship structure in the tourism destination management of Pirenópolis-GO in the interior of Brazil. Based on our results, we can provide some propositions as a contribution. If we take into account the measures of centrality (Table 6) and collaboration with local tourism (Table 7), we realize that SETUR and SECUL could play a central role. However, the services they provide (Table 2) are not the most numerous. Hence, our first proposition is:

P1: The centrality in the network of relationships between institutions is not related to the number of services they provide.

The literature points out that in tourism, companies specialize in a few activities within the value chain, making them complementary (Baggio et al., 2010; Prideaux et al., 2014). But this does not appear to be the case in the TSIs of Pirenópolis (Table 2). Our data show that low investment-intensive activities are more abundant, and those activities requiring more technical knowledge are rarer. Also, there is no financial support service in the tourist destination. This output was first found in the city of Balneário Camboriú (Hoffmann & Campos, 2013). Our result therefore helps to understand both cases, as there are both “soft” and “hard” services in terms of TSI investment. Although TSIs offer diversified services, as in other destinations in Brazil (Hoffmann & Campos, 2013) and abroad (Dudensing et al., 2011; Iorgulescu & Răvar, 2015), the “hardest” ones, which demand more knowledge and even investment from institutions, are scarcer in Pirenópolis, whereas softer ones are abundant. Thus, we propose the following:

P2. The amount of services provided at a destination by TSIs decreases as the investment required for their supply increases.

Relationships among public organizations are more frequent with their counterparts than among private and non-governmental organizations. As we discussed earlier, more institutions does not necessarily mean more relationships. This is precisely what our results point to, as less than 40% of the possible relationships exist. Thus, we propose the following:

P3. The intensity of TSI relationships within a destination is unrelated to the number of organizations present in the same destination.

A relationship with TSI can bring performance benefits to companies (Vignieri, 2019). And the relationship between organizations, for the development of a DMO, for example, (André, 2004), can bring improvements to the destination as a whole (Volgger & Pechlaner, 2014). Moreover, it is clear that the TSIs that matter most to the destination (Table 7), in the perception of their peers, are not those that provide more services (Table 2) but those that have more relationships (Table 5). We therefore propose the following:

P4. A relationship matters more than offering services in the evaluation of local performance of a TSI by its peers.

Our results showed the centrality of some TSIs but the absence of a DMO. Moreover, despite the need for most of the local actors to become more involved in the tourism destination management, this has not been materialized in practice. Also, although there is a tourist route, of which Pirenópolis is part, the TSIs indicated that they do not meet with TSIs from other tourism destination on the same route. This may be due to a tendency for tourism destination management to become decentralized, as pointed out by Gil et al. (2009) more than ten years ago. Thus, we present our last proposition:

P5: The absence of a DMO makes destination management more fragmented.

As a contribution to tourism management, we can say that tourism destination management is a political process, because different actors can perform complementary roles, but with the same objective: to develop local tourism in order to develop the local economy. We believe a study of the local supply and demand of support services could be a first step to improving the management of tourism destination.

Our contribution to public policy is related to local council management. If the local public power decides to create some local council, to deal with to ecological environment or even to develop new skills for its workforce, it will need to involve local business actors in this process.

Finally, we present a contribution to this teaching theme. As we found in the literature, tourism is a service provided for a group of specialized and complementary service firms. But this idea about complementarity does not match with the support services provided. Therefore, it is necessary to teach that complementarity is connected to the firms, but not with TSIs.

This research has some limitations. As a single case study, it has its own idiosyncrasies - tourist profile, population density, ease of access (Dwyer & Kim, 2003) etc. - which can create different results when compared to other destinations. However, it is worth remembering that case studies are not intended to make generalizations (Gibbert et al., 2008). Naturally, qualitative research through case studies provides more detailed quantitative information (Flyvbjerg, 2006). Moreover, it is emphasized that the quantity and quality of the collected data were sufficient for the scope of the proposed work.

As pointed out by Yin (2001), case studies with a qualitative approach gain more prominence when compared to other cases. Thus, future research may conduct multi-case studies to make comparisons between them as well as with similar previous studies. Another research opportunity would be to analyze and compare the results of our study with research conducted in other countries.

Moreover, it is a recurrent theme in the literature that the most appropriate way to analyze and to understand a tourist destination is by understanding it as a tourism product or tourism cluster, as mentioned earlier. Therefore, another research opportunity would be to explore whether the actors in a destination see their destination as a tourism product. We argue that there may be a correlation between the perception by organizations that they have a tourism product and the willingness to work collaboratively with the actors of other destinations, to form a tourism product cluster.

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Publication Dates

• Publication in this collection
  25 Oct 2021
• Date of issue
  Sep-Dec 2021

History

• Received
  20 Dec 2020
• Accepted
  20 Apr 2021