Development of a tourism coopetition model: A preliminary Delphi study

Journal of Hospitality and Tourism Management 37 (2018) 78–88

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Development of a tourism coopetition model: A preliminary Delphi study a,∗

Adriana F. Chim-Miki , Rosa M. Batista-Canino a b

T

b

Federal University of Campina Grande, Brazil University of Las Palmas de Gran Canaria, Spain

A R T I C LE I N FO

A B S T R A C T

Keywords: Coopetition model Tourism destination Interorganizational network Delphi technique Indicator

While coopetition behavior has been explored in various studies from different points of view, tourism coopetition and models to measure coopetition capacity have not received similar attention. In this study, we aim to develop an explanatory model of coopetition in tourism destinations. A theoretical review provided a list of factors and environmental conditions to favor coopetition behavior. Later, a Delphi technique was used to better define the model of Tourism Coopetition. An expert panel analyzed a list of 47 indicators derived from coopetition literature grouped in seven factors and narrowed them down to the 30 most important indicators of coopetition related to tourism destinations. The findings show that a coopetition model in a tourism destination is determined by seven factors: co-location, associationism, competition, cooperation, strategic management, coentrepreneurship, and co-production. This research has a twofold implication: it provides a means of monitoring the strengths and weaknesses of a tourism destination toward coopetition behavior and therefore, it can be used to optimize stakeholder relationships to increase the competitiveness in the hospitality and tourism industry, as well as inspiring other researchers in this field.

1. Introduction The first studies on coopetition reveal the formation of value networks among competing companies, complementary firms, suppliers, and/or customers, which compete on benefits despite cooperating for a common goal (Brandenburger & Nalebuff, 1996). It is a broad view on competition because it is not only for profit but for any type of benefit (Barney, Dagnino, Corte, & Tsang, 2017; Dahl, Kock, & LundgrenHenriksson, 2016; Raza-Ullah, Bengtsson, & Kock, 2014). Coopetition interorganizational networks are frequent in any industries. However, some contexts are more propitious, for instance, the tourism destination, because its high degree of complementarity and atomization of supply that induces networking arrangements (Czernek & Czakon, 2016; Della Corte & Aria, 2016). Indeed, a tourism destination as “an area capable to attract tourists as main places to visit” (Della Corte & Aria, 2016, p. 525) depends to partnership structures among public and private sector, i.e., it depends on coopetition networks. The tourists demand a complex offer consisting of different complementary goods and services provided by various entities in a tourism destination, so this complex context is based on interdependence and complementarity which generate a favorable condition to coopetition (Czernek & Czakon, 2016). For this, usually, the tourism destination organizes their development strategies driven by a public

∗

organization, or by a vital business partnership, or in some cases, both (Della Corte & Sciarelli, 2012), resulting in models based on governance or coopetition networks. In this way, a tourism destination acts as a natural coopetition hub that provides a balance between competition and collaboration increasing both stakeholders and tourism destination performance. Coopetition improves the collaborative behavior toward collective advantages, thus producing sectorial development and minimizing the natural tension of competition (Czernek & Czakon, 2016; van der Zee & Vanneste, 2015). At the same time, coopetition behavior influences tourism destination competitiveness, since it is positively correlated with other constructs such as: innovation (Belderbos, Carree, & Lokshin, 2004; Park, Srivastava, & Gnyawali, 2014; Quintana-Garcia & Benavides-Velasco, 2004; Ritala, 2012; Ritala & HurmelinnaLaukkanen, 2009); co-creation of value (Park et al., 2014; Ritala & Hurmelinna-Laukkanen, 2009; Ritala & Tidström, 2014), performance (Hou, Chen, Liu, Sun, & Li, 2015; S.; Kim, Kim, Pae, & Yip, 2013; Lado, Boyd, & Hanlon, 1997; Luo, Slotegraaf, & Pan, 2006); knowledge sharing (Tsai, 2002), and others. Identifying the conditions to generate a coopetition network for the hospitality and tourism industry is essential for its competitiveness. The number of studies on coopetition is growing, but few of them are in the tourism area. Therefore, some questions remain unanswered, such as,

Corresponding author. E-mail addresses: [email protected], [email protected] (A.F. Chim-Miki), [email protected] (R.M. Batista-Canino).

https://doi.org/10.1016/j.jhtm.2018.10.004 Received 9 January 2018; Received in revised form 20 October 2018; Accepted 22 October 2018 1447-6770/ © 2018 CAUTHE - COUNCIL FOR AUSTRALASIAN TOURISM AND HOSPITALITY EDUCATION. Published by Elsevier Ltd All rights reserved.

Journal of Hospitality and Tourism Management 37 (2018) 78–88

A.F. Chim-Miki, R.M. Batista-Canino

2.1. Determinants of coopetition behavior: a synthesis of theoretical framework

what are the indicators of coopetition in a tourism destination or tourism network? To contribute to filling this literature gap, the goal of this study was to identify the factors and indicators that better express and measure coopetition in the tourism destination. This paper provides an approach to develop a monitoring tool to coopetition behavior and for strategic management of tourism destination or tourism network. In order to address these goals, the paper presents the experts' opinion obtained through a qualitative empirical study using the Delphi technique. Consensus among the experts was needed to define the best indicators for measuring the coopetition in tourism destinations. This led us to several research results essential for reaching two different aims: it can be a basis for the development of a coopetition monitor to tourism destinations as a practical tool to support strategic management toward coopetition advantages and provide further insight on tourism coopetition.

Studies that carried out a literature review on coopetition have provided many variables relating to coopetition behavior, sometimes considering coopetition as a resource, capacity or strategy (e.g., ChimMiki & Batista-Canino, 2017c; Luo, 2005). Many of these variables were identified as drivers of coopetition, others as hindrances, but scholars frequently analyzed them separately or through small sets of variables. Based on the variables used in coopetition studies, three theoretical macro lines stood out on the coopetition theoretical framework: the first one comes from theories about agglomerations and productive networks; the source of the second line comes from competition theories, and the third is related to inter-organizational cooperation theories. Thus, the analytical variables of these theoretical lines consolidated by well-known researchers, are legacies that support the conceptual framework of coopetition. On the one hand, this mix of theories had produced a diffused and broad background to study coopetition, one the other hand, it allows to address its complex nature.

2. Coopetition: a paradigm in progress Coopetition occurs when organizations work together to create a higher value (Schoo, 2009) or a market (Schiavone & Simoni, 2011), while, at the same time, competing for all types of benefit generated. Thus, two focuses are underlying in the coopetition research provided by strategy and inter-organizational network research: strategies of collective value generation and individual value appropriation strategies (Gnyawali & Madhavan, 2001). Indeed, in many cases, coopetition represents the current organizational form of the markets (Kylanen & Mariani, 2012; Schoo, 2009). It has become an essential strategy in various industries as it generates the capacity for a more effective response to market changes (Zineldin, 2004). Recent studies highlight this concept as an in-progress paradigm (Lorgnier & Su, 2014), whereas a short time ago it was considered as not being entirely defined (Bengtsson & Kock, 1999). The concept evolved from a simple definition summarized as cooperation and competition simultaneously, to a more comprehensive concept such as: it is a hybrid behavior resulting from the cooperation-competition that occurs between networks, organizations, or within organizations, including relationships between competitors, suppliers, complementary businesses, government agencies, local population, and customers as a result of joint actions to achieve a common goal despite individual interests, thus generating co-production (Chim-Miki & Batista-Canino, 2017a). This broad conceptualization means that is recognized as a new behavior that occurs at different organizational and interorganizational levels, therefore it is a complex and multidimensional concept. In the search to understand coopetition, research on this topic has been focused on the motives, the probability of coopetition, interaction among firms, processes, and results of coopetition (Bengtsson & Kock, 2014). However, studies on measuring coopetition as an explanatory model are still few and far between in the hospitality and tourism industry. Regarding the tourism sector, coopetition is a dyad behavior that assumes cooperation and competition simultaneously, occurring between two or more agents at a tourism destination to promote its development as an integral product (Chim-Miki & Batista-Canino, 2017b; Czakon, Mucha-Kuś, & Sołtysik, 2016). In fact, in a tourism destination, this hybrid behavior of coopetition is recurrent since the organizations mobilize resources together to achieve shared goals. It provides a superior competitive advantage, recognized as the coopetitive advantage (Chen & Paulraj, 2004; Mariani, Buhalis, Longhi, & Vitouladiti, 2014). Therefore, it is primordial to define the coopetition key criteria, i.e., indicators that allow us to check strengths and weaknesses under a relational perspective to highlight opportunities for tourism development based on strategies to generate superior competitive advantages.

2.1.1. Legacies from agglomerations and productive networks theories on coopetition perspectives: co-location, co-entrepreneurship, and co-production Theoretical background of coopetition has a mix of theories related to business agglomerations, such as cluster theory (Porter, 1999), industrial districts (Pyke, Becattini, & Sengerberger, 1990), and networks (Lazzarini, 2007). Because of these theoretical influences, practically, all scholars highlight the common purpose, shared goals, and the degree of interdependence as boosters to coopetition (Czakon et al., 2016; Czernek, Czakon, & Marszałek, 2017) and, they highlight that the density of firms has a role in consolidating coopetition strategies (Della Corte & Aria, 2016). In this way, coopetition empirical studies have analyzed co-location factors, as district size, spatial concentration level, organizational consolidation level, level of SMEs, level of heterogeneity in the supply, degree of shared values, and degree of interdependence among other variables (Bengtsson & Kock, 2000; Brandenburger & Nalebuff, 1996; Della Corte & Sciarelli, 2012; Kylänen & Rusko, 2011; Lorgnier & Su, 2014). Although the variables are related to co-location, they show that not only sharing space is the key to creating a coopetition atmosphere and inter-organizational relationships, since shared values and complementarity level act on the coopetition behavior. On the other hand, the coopetition framework pays attention to the management of tension among partners (Czakon & Czernek, 2016). Scholars suggested that a governance system is necessary to develop a collective efficiency view, as well as, conducting the network as collective entrepreneurship (Tuohino & Konu, 2014). Bengtsson and Kock (2014) indicated that an external actor managing the inter-organizational networks could improve coopetition results. Thus, the tourism destination can be administered as a co-entrepreneurship. With this focus, the variables analyzed by coopetition researchers verified the degree of centralized coordination of projects, shared resources management (Chin, Chan, & Lam, 2008), shared commerce (Kylänen & Rusko, 2011), awareness of governance (Wang & Krakover, 2008), and integration (Della Corte & Aria, 2016; Tuohino & Konu, 2014). Fundamentally, the use of coopetition pursues advantages and positive results in win-win relations, i.e., co-production (Luo, 2005) and empirical studies show the positive results from coopetition strategies on shared production, co-creation of value, and other benefits. Business volume and performance are traditional items for measuring corporate success (Lusch & Laczniak, 1989). In turn, coopetition research following this line, but with focus on co-production, thus the variables are related to the effect on productivity and profitability in firms (Luo, Rindfleisch, & Tse, 2007; Oum, Park, Kim, & Yu, 2004); or financial performance (Kim & Parkhe, 2009). However, such as in the agglomeration theory, the coopetition framework notes that appropriation of advantages is not equal for all partners because coopetition 79

Journal of Hospitality and Tourism Management 37 (2018) 78–88

A.F. Chim-Miki, R.M. Batista-Canino

3. Research design and methodology

does not mean a perfect balance between competition and cooperation (Chim-Miki & Batista-Canino, 2017c).

The research proposes a coopetition model according to a systematic theoretical review on the topic of coopetition. An analysis of 284 papers published between 1996 and 2016, extracted from Scopus and the Web of Science database was performed. This review provided the factors and indicators that were later validated by an expert panel through the Delphi method. The two most extensive databases of reviewed literature provided a complete scan of papers: firstly, from a broad perspective related to sources on strategic and inter-organizational research, and later from a narrow one, under the focus of tourism coopetition. This systematic review substantiated the proposal and its validity. The industrial district and cluster approaches (Hjalager, 2000) used in the coopetition perspective were partially confirmed by our results. They confirm the theoretical background regarding the fact that the number of companies in the coopetitive network can increase competition or improve the complementarity (Barney et al., 2017; Luo, 2005). Previous studies also showed the variety of business activities provides vertical or horizontal networks (Bengtsson & Kock, 2000) and, a firm's spatial concentration is supported by a theoretical approach of shared resources and knowledge (Lazzarini, 2007; Porter, 1990, 1999). The same situation occurs regarding shared values that are highlighted in empirical research on coopetition, as this leads to shared resources and knowledge, as well as, to establishing a common goal (Coote, Forrest, & Tam, 2003; Werner, Dickson, & Hyde, 2015). The Delphi method provides a structured process which obtains the most reliable consensus of expert's opinion. This qualitative method helps us to ensure the better fit of selected variables from literature to the tourism destination coopetitive model pursued. The technique is especially indicated to support the construction of models (Lee & King, 2008) being one of the most common techniques for defining indicators for multidimensional constructs (Hudson, Ritchie, & Timur, 2004). In this sense, through the Delphi technique, Kaynak and Macaulay (1984) defined indicators to measure market potential in tourism; and, Miller (2001) verified indicators of sustainable development in tourism. Kaynak and Macaulay (1984) suggest this method when knowledge is not available or disperse, thus we used it to validate the proposition of a coopetition model submitting the design, the content, and the conceptual basis to an expert panel assessment.

2.1.2. Legacies from competitiveness theory: competition and strategic management Porter's theory is another academic background applicable to the coopetition perspective since the internal and external competition, as well as, the threat of substitutes affects the position in the market (Porter, 1979) that can be an incentive or impeditive to coopetition behavior. In this sense, providing a new approach on competition, Eriksson (2008) points out inter-organizational relationships transit through a continuum of coopetition, which has two extremes: one extreme is cooperation, and another is competition. The same viewpoint is verified in research from Bengtsson and Kock (1999), Lado et al. (1997); Padula and Dagnino (2007) and others who point out the effects of competition nature variables based on Porter's Five forces view. Coopetitive framework can be considered a type of joint strategic management toward competitiveness. In this direction, previous studies on coopetition analyzed common strategies to planning, management (Chin et al., 2008; Dahl et al., 2016), training, and monitoring (Badulescu, Badulescu, & Borma, 2014; Della Corte & Sciarelli, 2012) complex business environments. Also, strategies to lead to co-marketing (Rusko, Merenheimo, & Haanpää, 2013; Wang & Krakover, 2008), value co-creation, and innovation (Ritala, Golnam, & Wegmann, 2014; Ritala & Hurmelinna-Laukkanen, 2009) have been studied as coopetition. These variables used by scholars consider the coopetition as a source of strategic management to competitiveness through diverse ways of improving performance.

2.1.3. Legacies from cooperation theories: cooperation and associationism factors Cooperation is the other extreme in the coopetition continuum defined by Eriksson (2008). Although cooperation is studied through a broad range of foci, in coopetition scholars pay more attention to the success factors for inter-organizational cooperation for generating strategic networks (Cheng, Li, & Love, 2000). Thus, some of those are focused on partnering, for instance, characteristics of partnership success, attributes, communication behavior, and conflict resolution techniques (Mohr & Spekman, 1994). This theoretical legacy applied to the coopetition perspective is expressed in the studies that analyzed the degree of cooperation among companies and that between the public and private sector. Also, the degree of trust (Chang & Chiu, 2016; Chin et al., 2008), real cooperation, and the temporality of cooperation (Badulescu et al., 2014; Della Corte & Aria, 2016; Kylanen & Mariani, 2012). Another legacy from cooperation theories on coopetition studies is the analysis of the business association to combine resources toward opportunities and innovative solutions (Luo 2007; Chin et al., 2008). At the tourism sector, Czakon and Czernek (2016) point out that the tourist demands a tourism destination, that is, different firms are necessary to be able to offer what the tourist wants. Also, in tourism, other conditions lead to coopetition strategies such as those evidenced by Kylänen and Rusko (2011) in their study of tourism shared resources in border areas. Independence among industries is also a highlighted point to conduct associationism, for instance, in the research by Taylor, McRae-Williams, and Lowe (2007), on wine and tourism industries. In summary, scholars analyzed the intentionality as the level of business associationism, number of alliances among enterprises (Badulescu et al., 2014), level of management integrated in the sector (von Friedrichs Grängsjö, 2003), size and strength of business associations, and degree of awareness on coopetition advantages among stakeholders (Badulescu et al., 2014; Della Corte & Sciarelli, 2012; Kylanen & Mariani, 2012; Kylänen & Rusko, 2011).

3.1. Coopetition model proposition The first determinants in the proposed model are basic conditions to generating a coopetition network, that is, the existence of competition, cooperation and co-location. These preconditions are optimized through strategic management allowing joint operation among destination stakeholders as a co-entrepreneurship to achieve goals of coproduction. Table 1 summarizes each factor proposed to develop a coopetition model and, its mains theoretical basis. 3.2. Delphi method: a validation of coopetition model The Delphi technique requires a previous selection of participants for the panel, based on their knowledge of the topic to be assessed. The model considers a tourism destination as a coopetitive network; thus, an intentional sampling of qualified researchers was selected to this end. According to criteria, participants should have published research on the management of tourism destinations or tourism coopetition. The Delphi technique indicates a sample range of ten to thirty participants (Dalkey, 1969), although “most Delphi studies have used between fifteen and twenty respondents.” (Ludwig, 1997, p. 2, p.2). A total of 50 international researchers, selected under the previous considerations, were invited to participate. In the end, 18 experts responded in the first round. However, 16 participants concluded all Delphi rounds. Experts were from Spain – top ranked in publishing 80

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Table 1 A proposition of coopetition factors and theoretical foundations. Source: Elaborated by the authors Factor

Theoretical influences

Co-Location It verifies shared space characteristics that influence the coopetition process.

Theories of Competitiveness, Strategy, Cluster, Alliance Networks and Industrial District; Perspectives of Coopetition and Relational Economic Geography. Some authors are: Bathelt (2006); Bengtsson and Kock (2000); Della Corte and Sciarelli (2012); Hjalager (1999); Kylänen and Rusko (2011); Lazzarini (2007); Lorgnier and Su (2014); Luo (2005); Porter (1990); Tsai (2002).

Competition It verifies the internal and external business environment and the degree of competition because the balance among competitiveness and cooperation acts directly on an entrepreneur's behavior toward coopetition.

Competitiveness and Strategy theories; Coopetition perspectives. Some authors are: Bengtsson and Kock (2000); Padula andDagnino (2007); Della Corte and Sciarelli (2012); Lado et al. (1997); Luo (2005; 2007); Porter (1979); Ritala (2012); Tsai (2002); Wang and Krakover (2008).

Associationism This factor verifies the intention to collaborate among tourism stakeholders, which is demonstrated by the level of articulation around business associations and institutions.

Coopetition and partnering perspectives. Some authors are: Badulescu et al. (2014); Bengtsson, Raza-Ullah, and Vanyushyn (2016) Chin et al. (2008); Della Corte and Sciarelli (2012); Kylänen and Rusko (2011); Lin et al. (2010); Luo (2007); Lorgnier & Su (2014); Mariani (2007); Zineldin (2004).

Cooperation This verifies the real degree of cooperation among stakeholders and between the public and private sector, in order to generate coopetitive advantages for the tourism destination.

Theories of Interorganizational cooperation, Clusters and Networks; Coopetition and partnering perspectives. Some authors are: Badulescu et al. (2014); Bengtsson and Kock (2000; 2014); Cheng et al. (2000); Chin et al. (2008); Della Corte and Sciarelli (2012); Kylanen and Mariani (2012); Mohr and Spekman (1994); Wang and Krakover (2008).

Strategic Management This checks the capacity to generate shared processes and actions among the stakeholders, as well as, the level of governance awareness.

Theories of Strategic management, Governance, Networks; Coopetition perspective; Tourism Destination Competitiveness Models. Some authors are: Badulescu et al. (2014); Chin et al. (2008); Della Corte and Sciarelli (2012); Kylänen and Rusko (2011); Phillips and Moutinho (2000); Porter (1979; 1990).

Co-entrepreneurship This verifies the degree of co-management, i.e., actions at the tourism destination as an integral product.

Theories of Strategic management, Governance, Networks; Industrial District; Coopetition perspective; Co-entrepreneur approaches. Some authors are: Chin et al. (2008); Della Corte and Sciarelli (2012); Kylanen and Mariani (2012); Kylänen and Rusko (2011); Padula and Dagnino (2007); Wang and Krakover (2008).

Destination Co-production This checks tourism co-production, i.e., indicators of tourism development that can generate collective results for both, stakeholders and tourism destination.

Management theories; Approaches of Business Performance, Inter organizational cooperation, Tourism competitiveness. Some authors are: Della Corte and Sciarelli (2012); Kylanen and Mariani (2012); Kim and Parkhe (2009); Luo (2007); Luo et al. (2007); Lusch and Laczniak (1989); Park & Russo (1996); Porter (1990); Oum et al. (2004).

4. Research findings and discussion

tourism academic and non-academic papers -, Brazil, and Taiwan. 83.3% of researchers were affiliated to universities and research centers; while, 16.7% were affiliated to governmental tourism agencies, considering practice and academia. The number of Delphi rounds required depends on the degree of consensus desired by the researcher (Hsu & Sandford, 2007). This study previously defined 80% as the minimum level of consensus for the participants, either to exclude or include indicators in the first round. Therefore, indicators below 80% consensus were conducted at a second round. In this step, a position supported by 2/3 of the experts was accepted. Indicators below this rate were excluded from the model because the lack of consensus indicated a low degree of importance to measure coopetition. A pilot test was performed with a small group of international researchers with similar characteristics to the target group to verify the validity and reliability of the survey tool. The survey instrument was adjusted according to suggestions obtained. Finally, indicators were presented to experts having chosen the best ones to represent coopetition in each coopetition factor. The second round requested that the panelists reaffirmed the answer or modified it according to their position. The primary objective of this Delphi exercise was to develop a coopetition model to be a basis for monitoring with the best indicators. Thus, to obtain new indicators not present in the literature reviewed, but considered significant by the experts, the questionnaire had an open-ended question in each factor to include the expert's suggestion.

Based on the experts' contributions, the forty-seven indicators were reduced to thirty. Indicators with a ranking average located in the fourth quartile were excluded, a cut-off point that represented 25% of the less essential indicators to measure coopetition. The Delphi results provided a ranking of importance of coopetition indicators to the tourism destination. It concentrated the model on the most relevant measures of coopetition capacity. The position and dispersion measures were also verified, because mean, mode and, standard deviation is usually used to analyze the Delphi results. However, in this case, they were not the best statistics for expressing importance. Due to the design and objectives of the research, the percentage for consensus on the ranking attributed to the indicator was better, since the final objective was to exclude indicators to concentrating the model in the most significant variables. 4.1. Co-location factor The literature review indicated co-location as a driver to coopetition. However, according to the experts' selection, co-location factor was centered around five indicators. In the first round, six indicators had a consensus of above 80% – five to include and one to exclude. In the second round two more indicators were excluded (Table 2). On the one hand, these indicators show that the size of the tourism destination influences coopetition, but that it is nevertheless, mediated by a spatial concentration of tourism firms, diversity of the offer, and shared values. On the other hand, excluded indicators show that the 81

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Table 2 Indicators related to the Co-location Factor in the Coopetition Model. Indicators

Spatial concentration level Shared values Degree of complementarity District size Diversity of tourism business activities Degree of geographic location Atomization of offers at destination Age of tourism enterprises

1st Round

2nd Round

Mean/Mode

Consensus

3.3/1.0 3.3/2.0 3.5/2.0 3.7/5.0 3.8/3.0 6.9/7.0 5.7/8.0 5.8/7.0

100% - Included 94.1% - Included 88.2% - Included 100% - Included 88.2% - Included 82.4% - Excluded 58.8% - Included 52.9% - Included

firm's age does not consolidate the co-location. In addition, the geographical location and degree of atomization of supply in the tourism destination do not influence the co-localization factor. These results partially confirm the background used on empirical coopetition studies, that are based on an industrial district and cluster approach (Hjalager, 2000; Lazzarini, 2007). The argument of coopetition studies is the more firms there are in the coopetitive network, the more competition and complementarity there will be (Della Corte & Aria, 2016; Luo, 2005). Furthermore, the variety of business activities provides vertical or horizontal networks (Bengtsson & Kock, 2000) and, a firm's spatial concentration is supported by the theoretical approach of shared resources and knowledge (Porter, 1999). Indeed, shared values are highlighted by coopetition research since they lead to shared resources and knowledge, as well as, to establishing a common goal (Coote et al., 2003). However, results indicated that the consolidation or maturity of the firms is not fundamental to coopetition behavior. The experts also point out a possible non-geographical co-localization. This makes sense because a company located outside of a destination territory or in a virtual space can still be selling the tourism product. Another point contrary to the theoretical approaches, especially industrial district (Hjalager, 2000), was the exclusion of a supply atomization degree. Although the tourism industry is formed by many SMEs that are more inclined to coopetition (Della Corte & Sciarelli, 2012) the expert panel focused on the tourism destination as an integrated management system, therefore the firm size was not an indicator to the degree of coopetition.

Final Result

Consensus INCLUDED INCLUDED INCLUDED INCLUDED INCLUDED EXCLUDED EXCLUDED (No consensus) EXCLUDED (No consensus)

62.5% 62.5%

previously established; therefore, they were excluded. In summary, indicators of the competition factor defined by experts examine the business environment regarding Porter's theories (Porter, 1990), i.e., the internal, external and intra sectorial competition is verified. Results follow the previous findings of tourism coopetition studies, that is, to generate coopetition networks, contexts of higher competition and cooperation are better than contexts with a higher level of cooperation, but a lower level of competition (Della Corte & Sciarelli, 2012). 4.3. Associationism factor According to the theoretical background, the rate of partnering has a positive relationship with the tourism coopetition capacity, representing the intentionality toward coopetition (Kylänen & Rusko, 2011). The associationism factor was defined entirely in the first round by the expert's consensus (Table 4). The selected indicators to the model are related to partnering behavior on the tourism destination. Associative organizations in the tourism destination should be supported by strong associations to provide a positive influence on the coopetition network. The choice confirms the trend in literature previously highlighted on this paper. The degree of awareness of the partnering advantages is the fundamental point on coopetition models (Della Corte & Aria, 2016; Della Corte & Sciarelli, 2012). The eliminated indicator was the 'size of the organizations', which is coherent with that previously chosen to exclude the size of firms. 4.4. Cooperation factor

4.2. Competition factor

Results of the cooperation factor confirm the theoretical basis on inter-organizational studies, as well as, previous findings on coopetition studies. They focus on mutual trust among stakeholders (Cheng et al., 2000; Mohr & Spekman, 1994); and the trust level and commitment toward the consolidation of a coopetition network (Della Corte & Sciarelli, 2012). Thus, from seven indicators for the cooperation factor, the expert panel chose five by consensus in the first round (Table 5). The Delphi result indicated to check the number of cooperation programs available

In the competition factor experts preferred indicators based on the perception of entrepreneurs rather than based on secondary data. That means the perception of entrepreneurs on the competitiveness in the micro-environment generates a predisposition to coopetition behavior. Table 3 shows the competition factor results. Three indicators have obtained consensus in the first round and confirmation in the second round, while two indicators do not achieve the level of consensus Table 3 Indicators related to the Competition Factor in the Coopetition Model. Indicators

1st Round

2nd Round

Mean/Mode

Consensus

Degree of competition among companies in the destination Competitive positioning of the destination according to the stakeholders Degree of intra sectorial competitive pressure of substitute products according to perception of entrepreneurs Competitive positioning of the destination according to the tourism monitor

2.2/1.0 2.7/3.0 2.9/2.0 3.3/5.0

94.4% 88.9% 83.3% 72.2%

Tourism offers in the destination

4.0/4.0

61.1% - Included

82

-

Included Included Included Included

Final Result

Consensus

50% Included 50% Included

INCLUDED INCLUDED INCLUDED EXCLUDED Not consensus EXCLUDED Not consensus

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Table 4 Indicators related to Associationism Factor in the Coopetition Model. 1st Round

Indicators

Propensity to business associationism Propensity for integrated management of the destination Strength of the associations Degree of awareness among businesses and tourism organizations in the destination about the advantages of Associationism Size of associations

Final Result

Mean/Mode

Consensus

1.9/1.0 2.4/2.0 3.1/3.0 3.2/4.0 4.4/5.0

100% - Included 100% - Included 83.3% - Included 83.3% - Included 63.7% - Excluded

INCLUDED INCLUDED INCLUDED INCLUDED EXCLUDED Not consensus

isolated action.

in the tourism destination that represent the propensity degree for collaboration defined by Della Corte and Sciarelli (2012), and Badulescu et al. (2014) in coopetition studies. On the other hand, the excluded indicators in the cooperation factor were also considered as essential to coopetition in previous studies. For instance, regarding the age of cooperation programs, empirical research developed by Kylanen and Mariani (2012) in tourism destinations indicated longer standing cooperation programs could provide better coopetition results. Also, the commitment of the firms to provide monitored data to the sector was not considered relevant to cooperation, although it is a demonstration of collective thinking.

4.6. Co-entrepreneurship factor The choice of indicators in this factor focuses on the centralized coordination of projects and the awareness level of entrepreneurs on shared management. Indeed, several authors of coopetition highlight that these items have a high level of importance for stakeholder engagement on coopetition networks (Chin et al., 2008; Kylänen & Rusko, 2011; Tuohino & Konu, 2014; Wang & Krakover, 2008). The first round achieved a consensus to include three indicators; the second confirmed these inclusions and excluded another three indicators (Table 7). Two indicators included in the model are related to leading to tourism co-entrepreneurship based on coopetition: investment level and the number of regional cooperation projects conducted by a governance institution. These indicators in the previously reviewed literature have a twofold perspective being, at the same time, results and drivers of coopetition. On the other hand, experts excluded three indicators that represented specific management in the group. This means that experts prefer to verify the co-entrepreneurship factor based on broader indicators of management (number of projects, investment level) rather than specific management actions (shared marketing, integrated tourism products and management of shared resources).

4.5. Strategic management factor Results from the expert panel on the strategic management factor follow the theoretical line of strategy as a pivotal point for generating competitive advantages in a tourism destination (Wang & Xiang, 2007). The factor was presented to the expert panel in two sub-factors: private and public management indicators. However, results from the open-ended questions suggested unifying them (See footnote 1 in Table 6). Related to the private indicators the consensus rate included five (Table 6) and, to the public, only one indicator. The remaining five indicators lacking a consensus were confirmed by experts in the second round as items of little importance to coopetition, so they agreed to exclude them. The expert panel recommended measuring the number of joint programs developed as a real result of strategic management. Nevertheless, the measurement is complemented by two indicators that express the perception of businessmen on the level of strategic and participatory management at the tourism destination. Results confirm the literature review on tourism that highlights that this perception affects pre-willingness to work in coopetition networks (Tuohino & Konu, 2014). Regarding the excluded indicators was the level of monitoring of the sector, training programs for human resources and, business training. These indicators represent coopetition actions, but they do not impact on coopetition behavior. Experts used the same viewpoint to public sector indicators. They removed regional policies and level of public investment for the tourism sector. These indicators can be considered as coopetition actions if they represent joint work, but they may also be an

4.7. Co-production factor Finally, Table 8 shows the results of the co-production factor of destination. According to pre-established criteria, three indicators were included. They are derived from ratios of secondary data, and have relation with collective performance to the tourism destination. Results follow the business literature related to performance (Kim & Parkhe, 2009; Lusch & Laczniak, 1989; Oum et al., 2004), but this was adapted to the tourism industry. Tourism density is a result of the ratio between the tourism flows by the total population of the destination. Thus, it is a type of collective production. Also, this ratio allows comparison among destinations of varied sizes. Therefore, it is adequate to a model and future monitor. Two other indicators included are average tourism spending at the destination and participation of the tourism in employment levels at the

Table 5 Indicators of the Cooperation Factor in the Coopetition Model. Indicators

Effective degree of cooperation between the private sector Degree of trust between the actors Degree of cooperation for innovation Degree of cooperation companies at the destination Degree of cooperation between the public and private sector Temporality of cooperation Monitoring of the local tourism sector

1st Round

Final Result

Mean/Mode

Consensus

3.1/3.0 3.1/1.0 3.2/1.0 3.3/2.0 3.3/4.0 5.7/6.0 6.3/7.0

94.4% -Included 83.3% -Included 88.9% -Included 88.9% -Included 100% -Included 72.2% -Excluded 83.3% -Excluded

83

INCLUDED INCLUDED INCLUDED INCLUDED INCLUDED EXCLUDED EXCLUDED

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Table 6 Indicators of Strategic Management Factor in the Coopetition Model. Indicators

2nd Round

Mean/Mode

Consensus

Participatory planning level of the destination Joint Marketing Plan of the destination promoted by private tourism organizations Private joint programs at the destination for effective innovation development Control level and monitoring by the private initiative at the destination

2.8/1.0 3.6/3.0 4.1/6.0 4.2/2.0

83.3% -Included 88.9% -Included 100% -Included 61.1% -Included

62.5%

Joint labor actions to training for tourism

4.9/8.0

61.1% -Included

50%

Co-creating value programs to the destination Joint actions of business training

5.0/5.0 5.6/7.0

72.2% -Included 61.1% -Included

68.8% 50%

Collaboration with networks for territorial development Government actions to contribute to the Tourism Coopetition of the destinationa Public policies aimed at encouraging regional Tourism Coopetitiona

5.7/8.0 1.5/1.0 2.0/2.0

61.1% -Included 82.4% -Included 76.5% -Included

68.8% 50%

2.4/3.0

52.9% Excluded

56.3%

Public investment level in the tourism sector

1

1st Round

a

Final Result

Consensus INCLUDED INCLUDED INCLUDED EXCLUDED Not consensus EXCLUDED Not consensus INCLUDED EXCLUDED Not consensus INCLUDED INCLUDED EXCLUDED No consensus EXCLUDED No consensus

Items of Private Strategic Management. a Items of Public Strategic Management.

empirical studies, do not support their inclusion in the coopetition model. Thus, we did not deem it necessary to use them (e.g. tourism tradition of the destination; the degree of training of managers). One suggested indicator was based on measuring over time, which is an inappropriate measure as a monitor is already developed to be a longitudinal series. However, all comments and suggestions from the expert panel in the open-ended questions were analyzed and they helped us to gain further theoretical knowledge on coopetition.

destination in proportion to total employment. On the contrary, the participants of the panel excluded the indicator that verifies the average stay in the destination. In summary, the choice of indicators was addressed to verify the contribution of the tourism sector to economic and social development in accordance with a collective view.

4.8. Other approaches and a summary of the open-ended questions Additionality, the Delphi survey included seven questions relating to the suitability of the factors proposed in this model (Table 9). The expert panel has agreed that the factors proposed are appropriate to be applied to the tourism destination and have the power to express the theoretical foundation of coopetition, although they are not easily understood or measured. This doubt is understandable since models to measure constructs are usually a challenge to apply. Also, they are in accordance that the first three factors are prerequisites of a coopetition inter-organizational network. Items by open-ended questions were evaluated according to their relevance for the proposed model and its theoretical bases. Appendix 1 summarizes these contributions. Most indicators suggested by the experts were already covered by the proposed indicators, i.e., they were different but had the same focus. Only a few new indicators were mentioned, however, they are not directly related to the coopetition construct and their frequency, both among experts and previous

5. Conclusions and implications This research intended to act on the twofold gap observed in the literature review, namely: (1) the behavior within the tourism destinations is a hybrid form of competition and cooperation simultaneously, which receives little attention in tourism studies; (2) coopetition model studies with a set of indicators to measure a coopetition capacity in a network are scarce. Three theoretical lines were highlighted in this study as sources of fundamentals to understand the complex concept of coopetition: theories about agglomerations and networks; competitiveness theories, and inter-organizational cooperation theories. Our research aim was to propose a way to measure the coopetition in the tourism destination or business network using variables from earlier literature as a reference. Due to the literature gap from this perspective, a qualitative approach

Table 7 Indicators of Co-Entrepreneurship Factor in the Coopetition Model. Indicators

1st Round

2nd Round

Mean/Mode

Consensus

Centralized coordination of projects

2.7/1.0

Regional cooperation projects promoted and developed by the tourism governance

3.3/2.0

Level of investments for the development of tourism destination made by tourism governance

3.9/5.0

Level of awareness of tourism governance on the need to integrate and promote all tourism businesses in a shared management process Number of touristic routes or integrated packages promoted by the tourism destination governance

4.2/3.0

Degree of shared resources management by tourism governance

4.7/6.0

Degree of shared commerce of the destination promoted by governance

4.8/6.0

94.4% Included 83.3% Included 94.4% Included 72.2% Included 61.1% Included 61.0% Excluded 61.0% Excluded

84

4.2/1.0

Final Result

Consensus INCLUDED INCLUDED INCLUDED 87.5%

INCLUDED

68.8%

EXCLUDED

56.3%

EXCLUDED Non consensus

75%

EXCLUDED

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Table 8 Indicators of Co-Production Factor of Destination in the Coopetition Model. Indicators

Participation of tourism in employment rates Average daily spending at the destination Tourism density Average of time permanence at the destination

1st Round

2nd Round

Mean/Mode

Consensus

1.8/1.0 2.3/2.0 2.3/1.0 3.5/4.0

100% -Included 88.2% -Included 76.5% -Included 64.7% -Included

YES

NO

Do all the above factors apply to a tourism destination? Do you consider that all factors together express the fundamentals of the concept of Coopetition? Are all factors easy to understand? Are the indicators measurable? Do you consider the Coopetition system to be formed by the order of the factors shown in Fig. 1? Do you consider that these factors contribute to measure Coopetition on a large scale? Do you consider that the Co-location factors, Competition, and Associationism are prerequisites of Coopetition?

100% 94,4%

0% 5,6%

66,7% 72,2% 88,9%

33,3% 27,8% 11,1%

94,4%

5,6%

94,4%

5,6%

Consensus

68.8% 56.3%

INCLUDED INCLUDED INCLUDED EXCLUDED Not consensus

of two strands of literature, covering: variables of coopetition as the new market strategy and an intrinsic behavior of the tourism destinations; and, variables of coopetition as a capacity of a tourism destination, i.e., it is a measure. Our results compared to previous studies show can be synthesis into two sets of findings. The first set of outcomes was the confirmation of some theoretical approaches extracted from the extensive literature that is applied to empirical coopetition research and the rejection of other assumptions, related to agglomerations theories to the tourism destination. Our scrutiny shows that a tourism destination is a much more complicated coopetition network than others. Transpose approaches from the industrial district and cluster theory to a coopetition model in the tourism destination are not simple, because the co-location, consolidation, atomization, and concentration in the tourism industry have a different viewpoint. The tourist consumes the 'tourism destination,' i.e., a whole product generated by a multitude of organizations. Also, the tourist has a limited budget. Therefore, all firms are competitors, even when they are

Table 9 Filtering device for indicators of Coopetition Model. Questions

Final Result

was a previous step. We used the Delphi technique applied by experts in tourism to obtain a ranking of critical indicators for measuring tourism coopetition. Thus, the contributions of our study lie at the intersection

Fig. 1. Coopetition Model. 85

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However, indicators excluded from the model also are considered coopetition variables in several published papers. It's another finding because these exclusions show that there are variations when occurs transposition of knowledge on coopetition to tourism coopetition, i.e., analysis of the tourism destination as collective entrepreneurship and management as an integral product. General comments from the expert's panel stimulate future research on tourism coopetition, and they recognize coopetition as a natural behavior in this industry, as well as, its influence on tourism development. This finding can indicate an evolution of competitiveness theories should include a relational approach as a resource (coopetition) that generates a capacity (coopetitiveness). It seems that the expert panel envisioned the future use of this construct. The research had two main limitations, the low response rate of the Delphi questionnaire, and the high number of variables used in previous coopetition research. Even so, the study results provide a basis for creating a monitor of coopetition which will contribute to an analysis of tourism destinations competitiveness using the relational perspective. Therefore, it generated implications for further research, such as a comparison between tourism destinations or networks, as monitors of coopetition capacity; or to use this approach as a determinant (pillar) to add to the structure of current tourism competitiveness monitors to lead them to a coopetitiveness level.

complementary. It is another way to analyze the interdependence, which is the primary driver to coopetition. Our findings showed that experts have a focus on the collective view for modeling coopetition. They looked at how to share goals to develop the tourism destination (value co-creation), but, they are also aware of competition by the individual appropriation of value. The indicators selected by experts' panel to a coopetition model pursuit a balance not only cooperation and competition but also in variables of coopetition as a process and result at the same time. Another set of findings were contributions to the tourism literature of modeling of coopetition. The first level of the model expresses the foundations of a coopetition relationship, which represents coopetition as a resource. The middle part of the model measures management tools and strategies that can generate coopetitive advantages, i.e., describe the coopetition as a capacity. The last level of the model represents the coopetition results. In sum, the set provides a tool for measuring coopetition in the tourism destinations. The results provided a synthesis of literature since from the Delphi survey provided a scaling down of 47 indicators to 30. The structure of the coopetition model (Fig. 1) is a set of the best indicators to measure coopetition as a resource, capacity, and result at the same time. This structure differs from other approaches on coopetition which usually focuses on one category. Likewise, the expert panel agreed that the model proposed is appropriate for the tourism industry, and this sector has few studies on coopetition. Understand coopetition as an intrinsic behavior of tourism destination is a relevant lesson learned from this study because the tourism destination is a natural coopetition network. Even more, its development can be based on a bottom-up strategy led by the private sector through a coopetitive network. Integrated management assumes a role of tourism co-entrepreneurship, a term to express the perspective of driving the destination as a collective endeavor. In the end, the indicators included in the coopetition model by the expert panel correspond to variables identified by coopetition literature as essential to generating a favorable context for coopetition behavior.

Declarations of interest None. Acknowledgment We thank the CAPES Foundation, Ministry of Education of Brazil, for the support for research performed through the process No. 0387/ 14-2.

Appendix 1. Synthesis of the indicators suggested by the expert panel (Open-ended questions)

Suggested indicator

Analysis/Status

Technological level of the area

It was considered that the innovation programs express coopetition relations. Meanwhile, the technological level of the area has no influence on the establishment of a relationship of coopetition in any literature analyzed, and only one expert has suggested this indicator. For these reasons, the indicator was not included in the model. The model proposed already includes variables based on the entrepreneurial perception on Degree of internal rivalry between the companies in the territory in relation to the competition for resources (by suppliers, human resources, financial resources, e- competition in the destination, covering competition as a whole, customers, and resources. Therefore, it was considered that this indicator is already included in the model, but with a tc.), different name. Interaction level among stakeholders There are various indicators of interaction among stakeholders in the proposed model, especially in the associationism and cooperation factors. So, it was considered that this indicator was already included in the model. Tourism tradition of the destination This approach considers that the maturity of relations between the stakeholders of the destination influences the formation of a system coopetition. Within this framework, the initial proposal has included indicators which represent the age of the companies, as well as, of the cooperation programs in the tourism sector. These indicators are a way to verify the degree of tourism destination tradition. Although the literature review does not support the tourism tradition as an indicator or explanatory variable of coopetition. Rather, the antiquity represents the stability of the interorganizational network. However, according to the final Delphi results, this indicator was excluded by the panel of experts. Bargaining power of suppliers and customers We consider this perspective is very important to competition. However, because of the difficulty of a direct measure of bargaining power, the proposed model uses indirect measures, such as the market size, the degree of business diversity, the degree of concentration and complementarity entrepreneurial. That is, items that generate several predispositions to operate or not toward coopetition strategies are verified. The bargaining power is implied since it is generated by the structure that the market establishes according to its size, diversity and, complementarity of the offer. Training level of the managers The training degree of managers is not an indicator supported in the coopetition literature, but in management literature. The experience in management of coopetition networks is an indicator in the literature, but we did not include it because this indicator requires that the respondent know the coopetition concept in order to analyze the question.

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Support degree of administration to associations, as well: agreements, credit transfers, The proposed system includes an indicator that identifies the support to business associajoint lines of action tions. It verifies the degree to which the companies provide resources to them. Also, another indicator checks the public strategic management toward the coopetition, including the number of public-private programs. Thus, it was considered that this indicator suggested is included in the model in different ways. Power degree of organizations to participate in the destination governance This indicator is already included in the model within the associationism factor. Tourism products as resulting of cooperation between stakeholders This indicator was in the co-entrepreneurship factor. However, the expert panel decided by its exclusion. Public strategies by the federal government This variable is measured by two indicators in the model proposed. One indicator is in the public strategic management factor and the other in the cooperation factor. New tourist services that arise from the start of the implementation of coopetition It is an indicator time axis which we consider not adequate for our proposal, due to the fact strategy that the final objective is to generate a monitor which is already an overtime measurement. For this reason, it has not been included in the model redesign. Participation of tourism in the income level of the population In the model, we chose to use the level of jobs that tourism generates in the destination comparatively to the total level of employment. Two motives support these decisions. Firstly, this information is easier to obtain from national or local statistics; secondly, the model is being developed to apply to regions/countries in a comparison among multiple destinations (a monitor). The degree of participation in the income level is relative to each region, making it difficult to establish the significant importance of this involvement. Even a single country may require some conversion to establish economic parity that allows a comparison between destinations. For this reason, it has not included in the model. Assessment by the tourist regarding destination quality The analysis perspective used in this proposal is based on the viewpoint of the offer. For this reason, we have not included mixed perception variables.

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Adriana Fumi Chim-Miki, PhD in Tourism, Economics and Management. Associate Professor at Federal University of Campina Grande, Brazil. Faculty of Management and Accounting, Aprigio Veloso Street, 882, Paraíba. 58429900. Brazil. Phone: +55 83 21011034 or +55 53 98130106.

Rosa M. Batista-Canino, Ph.D. in Economic and Management Science, Vice-rector of Entrepreneurship and Employment at University of Las Palmas de Gran Canaria, Spain. Professor of the Doctoral Program in Tourism, Economics and Management, Facultad de Economía, Empresa y Turismo. Módulo C216, Campus de Tafira. 35017 Las Palmas, Phone: +34 928458647.

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