Tourist destinations and cooperative agreements between airlines

Journal of Destination Marketing & Management Tourist Destinations and Cooperative Agreements between Airlines --Manuscript Draft-- Manuscript Number: JDMM-D-20-00621R3 Article Type: Research Paper Section/Category: Keywords: code-share agreement, tourist destination, routes typology, Airlines Corresponding Author: Marta Domínguez-CC University of Seville Faculty of Economics and Business Sciences: Universidad de Sevilla Facultad de Ciencias Economicas y Empresariales SPAIN First Author: Marta Domínguez-CC Order of Authors: Marta Domínguez-CC Cristóbal Casanueva Ángeles Gallego Abstract: The important links that are formed between tourist destinations and the airline industry are evident in the previous literature. These links are active in both directions. The characteristics and the policies of tourist destinations can affect such relevant aspects as the organization of airline route networks, the inauguration of new routes and their expansion. In this study, it is proposed that the value of a tourist destination and the inclusion of airports close to the tourist destination on the routes of an airline are also related to whether that airline decides to plan its route networks with its own resources or to complement those resources with assistance from other partner airlines through cooperative alliances, specifically code-share agreements. Taking into account the assumptions of the Resource-Based and the Relational views, the value of a tourist destination can be a relevant factor in the cooperative behaviour of airlines. Moreover, the routes within which the most valuable tourist destinations are embedded can be of different types. It is therefore of interest to know whether the type of route plays a relevant role in the preference for cooperative behaviour among the routes that include tourist destinations. Around 50,000 routes and the most important global tourist destinations were considered to analyse this behaviour. The results showed that the airlines under study preferred to operate the routes that included valuable tourist destinations with their own resources, although this behaviour was more variable when differentiating between types of routes. Suggested Reviewers: Response to Reviewers: The authors wish to thank the Editor and Reviewer for giving us the opportunity to revised the work and seeing the potential of our work since prior version. We think that your comments have served to clarify our proposals and to structure our work in a better way and, without doubt, to improve the process of communicating the investigation that we have completed. The authors have checked again the manuscript and We think that it is free of all typographical or grammatical errors, that the references are all correct, and that the format conforms to the requirements of the journal. We have checked specifically the detailed list about format question and we expect that all is correct. 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We have checked specifically the detailed list about format question and we expect that all is correct, now. Thank you again for all your help and support. Tourist Destination and Cooperative Agreements between Airlines Highlights  Tourist destination value influences the strategic behaviour of airline firms  Airline firms will tend not to cooperate on routes with top tourist destinations  The type of route influences the cooperative behaviour of Airline firms  Scarce resources prompt cooperation on international routes to top destinations  Airlines prefer to cooperate on routes with no hubs but with top tourist destinations Highlights Tourist Destinations and Cooperative Agreements between Airlines Domínguez-CC, Marta*, C. Casanueva and A. Gallego Department, of Business Administration and Marketing University of Seville, Seville, Spain Keywords: code-share agreement, tourist destination, routes typology, strategic behaviour, Airlines *corresponding author Marta Domínguez-CC Dpto. Administración de Empresas y Marketing Avda. Ramón y Cajal, 1 Sevilla 41018. Spain e-mail: [email protected]s Phone: 34 954 556845 Fax: 34 954557575 Cristóbal Casanueva Facultad de Turismo y Finanzas Avda. S. Francisco Javier s/n Sevilla 41018. Spain e-mail: croc[email protected] Phone: 34 954556170 Fax: 34 954557575 Angeles Gallego Facultad de Turismo y Finanzas Avda. S. Francisco Javier s/n Sevilla 41018. Spain e-mail: [email protected] Phone: 34 954557527 Fax: 34 954557575 Acknowledgements This research has been financed by the Ministerio de Economía y Competitividad, Spain (ECO2017-84364-R). The authors wish to thank the two anonymous Reviewer for their comments and suggestions that have contributed to solid improvements to the article. Title Page (with Author Details) 1 Tourist Destinations and Cooperative Agreements between Airlines The important links that are formed between tourist destinations and the airline industry are evident in the previous literature. These links are active in both directions. The characteristics and the policies of tourist destinations can affect such relevant aspects as the organization of airline route networks, the inauguration of new routes and their expansion. In this study, it is proposed that the value of a tourist destination and the inclusion of airports close to the tourist destination on the routes of an airline are also related to whether that airline decides to plan its route networks with its own resources or to complement those resources with assistance from other partner airlines through cooperative alliances, specifically code-share agreements. Taking into account the assumptions of the Resource-Based and the Relational views, the value of a tourist destination can be a relevant factor in the cooperative behaviour of airlines. Moreover, the routes within which the most valuable tourist destinations are embedded can be of different types. It is therefore of interest to know whether the type of route plays a relevant role in the preference for cooperative behaviour among the routes that include tourist destinations. Around 50,000 routes and the most important global tourist destinations were considered to analyse this behaviour. The results showed that the airlines under study preferred to operate the routes that included valuable tourist destinations with their own resources, although this behaviour was more variable when differentiating between types of routes. Keywords: code-share agreement, tourist destination, route typology, Airlines 1. Introduction The interrelations between tourist destinations and the firms that operate within the tourism industry are beyond all doubt. Their mutual influence is evident. Recent works have underlined the utility of interconnecting studies both on the airline industry and on the tourism industry (Bieger & Wittmer, 2006; Duval, 2013; Pike, Pontes & Kotsi, 2021; Spasojevic, Lohmann & Scott, 2018). In particular, Duval (2013) identified the relation between destinations and airline business models as a principal issue in tourism studies. Bieger & Wittmer (2006) pointed out that tourism is a driving factor and a stimulus for change in air transport and that airlines must consider the business model of tourist destinations. There are works that have shown how airline business decisions affect Manuscript (without Author Details) Click here to view linked References 2 destinations (Spasojevic et al., 2018). However, many authors have underlined the influence of both directions between tourist destinations and transport (Currie & Falconer, 2014; Lohmann & Pearce, 2012) and air transport (Bieger & Wittmer, 2006; Graham, Papatheodorou & Forsyth, 2008; Hvass, 2014; Tang, Weaver, & Lawton, 2017). Certainly, destination marketing and planning managers can influence certain airlines in such a way that they cover a specific flight route that connects certain destinations (Castillo-Manzano, López-Valpuesta & González-Laxe, 2011). It is likewise true that the combined action of governments and airlines can contribute to the growth of a tourist destination (Lohmann., Albers, Koch & Pavlovich., 2009; Pike et al., 2021). Therefore, knowledge of airline network structures is important for tourist destinations. In fact, the development of airline routes will generate direct benefits for tourist destinations (Tan, Koo & Duval, 2017), because tourists constitute a high percentage of airline traffic (Graham et al., 2008). Cooperation with other airlines will facilitate the interconnection of a destination very rapidly and with less need for resources and will provoke new direct and indirect influxes of tourists (Hvass, 2014). The strongest strategic opportunity to have affected airlines over recent years is the tendency to cooperate and to set up agreements and alliances (Castiglioni, Gallego & Galán, 2018). Thus, code-share agreements are the most common type of alliance within the airline industry (Zhou & Chen, 2016), although they coexist with another significant trend: Competition between the three global alliances within the sector, Oneworld, SkyTeam, and Star Alliance (Gomes-Casseres, 1994). There is plenty of literature in which the motives that incentivize airlines to reach code-sharing agreements are emphasized (Evans, 2001; Goetz & Shapiro, 2012). Some are internal to the firm, such as cost reduction, network planning of routes, access to partner resources, etc, while others are external to the firm, such as economic and market- 9 trends among its firms that have transformed the sector. The first of these is the emergence of the low-cost strategy (Castillo-Manzano et al., 2011; Min & Joo, 2015). The second is the increase of cooperative agreements and of alliances in the sector (Castiglioni et al., 2018; Zhou & Chen, 2016). Although there are other alternatives to the growth of an airline, such as mergers and acquisitions, commonly found in other sectors, these strategic directions are often limited due to legal and regulatory restrictions at a national level (Duval, 2013; Oum et al., 2001; Pels, 2001; Rhoades & Lush, 1997; Zhou & Chen, 2016). In fact, the successful airlines (having managed to survive the various market crises and economic cycles) have followed one or another strategy in different ways. Although both are not incompatible, it appears that the low-cost model is better adapted to a type of ‘point-to-point’ network, whilst cooperation broadens and sets up appropriate structures based on the ‘hub-and-spoke’ network, because it is related to the existence of large alliances that compete at a global level (Hvass, 2014; Zhou & Chen, 2016). The growth of cooperative alliances between airlines has increased over the past two decades. Besides, its end is not yet in sight (Evans, 2001; Castiglioni et al., 2018; Goetz & Shapiro, 2012): the number of agreements, the number of partners of each company (alliance portfolio), the percentage of routes with code-sharing, and the incorporation of airlines in large-scale alliances, etc, have all increased. Cooperation has been profusely studied as a strategy in the airline sector for some years (Evans, 2001), as it is a pertinent field of study for relevant strategic concepts (Gimeno, 2004; Gomes-Casseres, 1994; Lazzarini, 2007; Wassmer & Dussauge, 2012). Although there may be vertical or diagonal types of cooperation, the two principal types of alliance between airlines (code-share and global alliances) are of a horizontal type, i.e. arising between rivals (Casanueva et al. 2014; Zhou & Chen, 2016). Competition between groups and clusters that have then formed global alliances within the airline sector has 10 been singled out as one of various strategic approaches that nowadays define the airline market (Gomes-Casseres, 1994; Lazzarini, 2007). Between 1997 and 2000, three large global alliances were created in the airline sector, which have continued their expansion: Star Alliance (26 partners, among which United Airlines, Lufthansa, and SAS), Oneworld (14 partners that include American Airlines, British Airways, and Qantas), and Sky Team (19 partners that include Delta, Air France and KLM). Competition at a global level, with intense rivalry for mainstream long haul and/or international routes that generate significant traffic and higher margins, has emerged between the three largest groups of airlines. In an extraordinary way, this pattern determines the route networks of the individual companies and the connectivity between the network of global alliances, in such a way that any traveller can easily travel between two points on the planet along the itineraries proposed by one or more of those three groups. However, the code-sharing agreements are the most prevalent type of alliance in the sector (Zhou & Chen, 2016). They imply that a passenger from one airline can travel on a flight that is operated by another airline. One airline can therefore share the routes and destinations of its partners. An arrangement that increases and structures its own network of routes (Lordan, Sallan & Simon, 2014). Global alliances are linked and have contributed to the expansion of code-share agreements and both processes have advanced hand in hand (Castiglioni et al., 2018). The motives for which code-share agreements are established between airlines are very varied: risk sharing, accessing new markets, access to and mobilization of partner resources, the generation of economies of scale, the reduction of operational running costs, globally competitive services, the generation of more efficient markets that the passengers can exploit, the consolidation of passenger services and their coordination, the 11 generation of marketing incentives and customer fidelity programs, limitations on rivalry, the expansion of the operational network of the airline and flight schedules, etc. (Casanueva et al., 2014; Evans, 2001; Goetz & Shapiro, 2012; Ito & Lee, 2007; Min & Joo, 2015; Pels, 2001; Zhou & Chen, 2016). 2.3 The value of tourist destinations on decisions to cooperate The features of a tourist destination, its geographic situation, its attractions and the quality of its services determine the average expenditure of tourists, and the function that it plays within the global structure of tourism, among other aspects. The differences with regard to flows of tourism within tourist destinations mean that some destinations are more appreciated than others for tourism firms that wish to establish themselves in those places. Air transport is a paradigmatic case here. An airline business will consider the characteristics of the network nodes, constituted in great measure by tourist destinations, when planning its network of routes (Graham et al., 2008), in such a way that it will prefer routes that have tourist destinations of higher value. The routes will be valuable because they imply a competitive advantage or can generate higher income. Some routes are of a strategic character, because of their importance in the network or because of their competitive value (Hvass, 2014; Lederer & Nambimadom, 1998). The flow of tourists through a destination can condition the behaviour of the airlines. In this sense, the tourist destination will also have a specific value, because of its position in the global network of the airline company, whether as a point of departure or destination, hub or gateway (Lohmann et al., 2009). The decision over cooperation with another airline, in order to arrive at a specific 12 tourist destination, has three different forms: the airline can fly to that destination with its own fleet and crew; it can operate the flight under a code-sharing agreement with another airline (sharing seats on its flights with a partner or occupying those of one of its partner’s flights); or it can follow both paths simultaneously (programming flights with and without code-sharing). According to the RBV, if a destination is high value, the firm will probably operate the routes to that destination without cooperating, in order to exploit the maximum value. If an airline has the necessary resources and the capacity to operate a valuable route or to fly to a valuable tourist destination, it will not need the resources of any partner and it will not need to share part of the income that it generates with that partner. However, according to the Relational View and the studies on Network Resources, if an airline has no resources (such as land-based services and maintenance, slots, market knowledge, political influence, etc) to operate a route or to fly from a strategic destination and can neither access nor buy such resources, then it will have to cooperate with partner airlines and in doing so will access and use their resources. This cooperation has a cost both in economic (it has to compensate the use of resources from other firms) and in competitive terms (dependency on a third party, no exploitation of the competitive advantage, concession of other routes, etc). 2.4 Types of routes, value of the tourist destination and cooperative behaviour The inclusion of a valuable tourist destination among the routes of an airline adds value to that airline. The greater the perceived value of the destination by the airline, the greater its propensity to exploit the route with its own resources and to cooperate in cases where it has no such resources. Nevertheless, the features of the route in itself will 13 condition both the value that is added to the airline (regardless of the value of the tourist destination that is on the route), and the volume of resources needed to operate at those destinations. For example, the flagship airlines usually have their own hubs, their own maintenance services, land-based services, market knowledge and familiarity with local regulations and legislation, relations with local suppliers and a consolidated network structure. Higher income will ensue when an airline initiates internationalization processes and operates long-haul routes, but it is unlikely to possess the aforementioned resources. It may therefore be expected that the different types of routes can in some way influence the relation between the perceived value of a tourist destination and the decision to cooperate or otherwise in a route to that destination. Various classifications of routes may be found in the literature. Hvass (2014) distinguished between strategic routes, feeder routes and point-to-point routes. It is common to distinguish between domestic routes as opposed to international routes (Goetz & Shapiro, 2012; Ito & Lee, 2007; Pitfield, 2007). However, distance has been the most common classification criterion for the study of routes (Bhadra & Hogan, 2008; Chu, 2014). Distance determines the quantity of fuel that a plane must carry, air-crew flight time, the amount of additional time required from the crew, etc. All these factors influence the costs of operating a route, and they therefore influence company performance. Nevertheless, these classifications of the routes according to such criteria (distance, overland or overwater, international or domestic) are overly simplified, if we are to reach an understanding of everything that a route implies for the operational network of an airline. Therefore, an acceptable classification of the routes must be based on a multidimensional perspective. In other words, it must simultaneously take account of the different motives for grouping the routes. Four relevant criteria have previously 14 been pointed out: distance, type of plane, domestic or international flight, and whether any of the nodes between the ties are classed as hubs. Distance is a defining factor of a route (Chu, 2014), because one of the most important running costs is fuel, a cost that is dependent on distance. Hence, Lederer & Nambimadom (1998) considered that fuel was a basic element for the design of routes and networks. Routes can be classified in terms of distance travelled in various ways. The most common ones distinguish between short, medium, and long-haul flights and are set by different organizations or by the airlines themselves that apply their own parameters to set the limits (Miyoshi & Mason, 2009). The type of aircraft is related to the type of route. The number of seats is an initial factor to consider, because it affects the level of investment and the possibility of generating economies of scale, as well as having a relation with the capacity of a route and with the load factor (Babic & Kalic, 2018; Niehaus, Ruehle & Knigge, 2009). The cost of each type of airplane will determine the choice of both route and itinerary and flights will have implications both for the flight crew and for relevant maintenance costs (Bhadra & Hogan, 2008; Lederer & Nambimadom, 1998; Niehaus et al., 2009). Miyoshi and Mason (2009) linked types of routes and types of airplanes to the study of their C02 emissions. Organizations such as ICAO and IATA have the most common classifications based on weight and take-off time, which distinguish between Light, Medium, and Heavy. Both national and international destinations were also studied in various works (Goetz & Shapiro, 2012; Pitfield, 2007). Routes with destinations in other countries are subject to legal and market-related constraints that differ from domestic routes (Oum et al. 2001). Finally, whether the point of departure and/or the destination of the route is a hub will also be of relevance to the airline. The majority of airlines have a network structure 15 based on the hub-and-spoke system, in which those central points on the routes are of fundamental importance (Babic & Kalic, 2018; Lederer & Nambimadom, 1998; Lohmann et al., 2009). It is also important to highlight that important resources and essential operational services for the airlines (offices, maintenance, etc) are located at the hub. The type of route can be a moderating influence or at least have some influence on the above arguments. For example, if it is a principal route for connecting with a tourist destination, the airline will be prepared to occupy a position in it by any means (and cooperation through code-sharing is relatively simple), rather than forego that route. The same happens if corresponding with radial connections that provide access to a relevant tourist destination following arrival at a hub. In fact, each dimension that can characterize a certain type of route can justify the conclusion of agreements and alliances, because of their influence on its value and because they imply a greater or lesser need for resources. These agreements will permit more efficient cost sharing, depending on the length of the route (fuel, crew flight time), and will improve the service that is offered to clients (single sale, luggage management, loyalty programs) (Goetz & Shapiro, 2012; Ito & Lee, 2007). An important aspect in the success of airline operations is the degree to which total flight capacity is used, which therefore makes the use of code-sharing advisable. In addition, the cost per seat and air mile in smaller planes is greater, so the search for efficiency once again incentivizes the use of code-sharing (Lederer & Nambimadom, 1998). Besides, evidence of the advantages of code-sharing for domestic routes (Ito & Lee, 2007) and for international routes (Pitfield, 2007) has also been found. In some cases, the motives are similar and in others they differ (legislation, culture, access to resources in other countries). Finally, the position of the hub will generate a special value for a specific destination (Goetz & Shapiro, 2012; Lohmann et al., 2009). The presence of a 16 hub at the departure point and at the end of the route can be seen in combination with the hub-and-spoke structure itself, the development and the expansion of which has in great measure been due to the increased number of code-shares and has permitted airlines to arrive at and to compete in many markets (Chen & Ren, 2007; Goetz & Shapiro, 2012). 3. Methods Five different datasets were consulted to conduct our empirical study. On the one hand, (1) the principal global tourist destinations of greatest value were identified on the basis of the Top 100 city destinations as ranked in the International Euromonitor market research reports (2012 - 2017 edition), which detail the number of international arrivals that spend a minimum of twenty-four hours and a maximum of one year in a city, staying in collective or private accommodation. On the other hand, all the routes at a global level were studied at a given point in time. The dataset of routes was obtained from (2) the Routes dataset, (3) the Airline database, (4) the Airport database, all of them provided by Openflight, and (5) the ICAO (International Civil Aviation Organization) Aircraft Types and Designators database. The initial data on active routes at a global level comprised almost 61,200 routes with 2012 data sourced from Openflight with information on flight itineraries, on whether they operate code-sharing, on the names and codes of the airline that commercializes the flight (marketer), and on the type of airplane in use. Data were obtained from the same source on the airports present in the earlier routes with their codes and positions and the categories of Light, Medium, and Heavy aircraft in use on its routes were obtained from the ICAO. The integration of all of these databases concluded with a final sample comprising 49,590 routes, as not all the necessary data were available for 11,612 routes. 3.1 Variables: 17  Strategic route: A strategic route was defined as having one of the most important global tourist destinations at either its point of departure and/or its destination. In a similar way to Assaf and Dwyer (2013), the Euromonitor International database, specifically the Top 100 City Destinations Ranking was used to locate the strategic routes. Thus, we identified the most frequently requested cities at a global level in that year. This ranking changed slightly from year to year, however the strategic decisions were taken in both the short and long term. The composition of that ranking up until 2017 was therefore taken into account to test which destinations had stayed in those positions in the long-term. Only 80 cities had stayed in the ranking over those years. Thus, those routes with departure points from and destinations to one of the cities that had remained in the ranking over that period were identified. This dummy variable took a value of 1, if the point of departure and/or the destination of the route was a top city and 0, if otherwise.  Routes with code-sharing: The routes in which the operating carrier was not the airline commercializing the routes were classified as code-share routes (Goetz & Shapiro, 2012). Where a particular route had code-share flights and flights that were directly operated by the company itself, the route was only analysed once as a code-share route. From among the 49,590 routes under analysis, 15,500 were operated under code-share agreements and 34,090 were operated with no codeshares.  Route distance: The distance of a route can be established with information on the airport of origin and destination. This difference between the location of the point of departure and the destination was expressed in degrees, to avoid the bias of considering the Earth as a perfect circumference. Once the distance had been 18 calculated in degrees, it was converted into kilometres, considering one degree to be: 40,075.1612 / 360= 111.319 kilometres. Thus, in a similar way to Chu (2014), we measured distance as a function of kilometres between the departure and the destination airport. Even though airline networks are classified by distance, the disparity of the cut-off points meant that distance can be used here as a continuous variable.  Type of airplane: The routing database generally refers to the specific airplane that operates on the route. The standard ICAO classification system applied to airplane codes (Equipment Code) - L (Light), M (Medium) and H (Heavy) - similar to the one used by IATA (International Air Transport Association), was chosen, in order to classify the different models of airplane, in view of the slight differences with seat allocations (number of priority seats, leg room, etc.) and other specifications such as flight autonomy.  Hub: A dummy variable was employed to indicate whether the departure point and/or destination of a route was a hub for the airline that commercialized the route (value 1). The sample of 49,590 routes included 227 airlines that were crossreferenced with the information available on the hubs of each airline.  Domestic or international nature of the route: A dummy variable was used when comparing the countries of the departure and the destination airport where the value 1 corresponded to international routes (departure and destination not in the same country) and 0, if otherwise. 3.2 Analysis Data analysis was conducted in two phases: (i) the analysis examined whether the value of the route (strategic routes) had some relation with the form (code-share or no 25 distances. In addition, almost one third of these routes included an airline hub. The conditions therefore appear to be in place (market and legislation knowledge, contact with stakeholders, etc.), so that many additional resources for the airline business are unnecessary. The results were not significative in the case of ‘Central International Routes’ (Cluster 3) as a function of the strategic value of the route. These are routes in which high value arising from the features of the (international and long haul) routes are mixed with the need for knowledge to access international markets, but with the other argument that the point of departure and the destination of that route is an airline hub, i.e. a point at which the airline resources are concentrated. Perhaps for that reason the tendency not to cooperate arises, regardless of the value of the tourist destinations on that route. However, nothing in that regard may be confirmed from the statistical analysis. The principal contribution of this study has been its in-depth examination of whether the value of a tourist destination is related with the strategic behaviour of an airline. The results have shown that the overall relation is negative when all the routes at an international level are considered together with the 80 principal global destinations. However, this relation was conditioned by the type of route that is developed. On some types of routes such as ‘Peripheral International Routes’ on which the airlines have no hub, a preference is shown towards cooperation, as well as on ‘Peripheral Short-Haul Routes’ on which they have no hub either. If the importance of the hub is considered in the network structure of the airlines, it may be affirmed that the airlines will follow a strategy of cooperation, in so far as they need to reach the destinations where they have no management resources of their own or the destinations that are not core to their route network. This idea is consistent with those proposed for the completion of code-shares 26 for access to and mobilization of partner resources (Casanueva et al., 2014; Wassmer & Dussauge, 2012). At a theoretical level, the predictions of the Resource Based View (RBV) were that the strategic routes either included valuable tourist destinations or were international or long-distance routes that should be operated with the resources that belong to the airline. The Relational View, which extends and complements the RBV, predicts that if an airline possesses insufficient resources (as happens for international routes or when they have no airline hub), there will be a tendency to cooperate with other companies. Partial support is lent to the arguments of the RBV, in so far as the results have confirmed that the airline-sector firms tended to exploit and to make use of their own resources, in so far as those resources were of greater value to them, because they can contribute to competitive advantage (Barney, 1991; 1997). The results have likewise highlighted that in cases where the most important resources for the operations of the firm are not usually within it, that firm will adopt cooperative strategic behaviour to exploit the resources that its partners can make available to it, which enters into the logic of the Relational View and the Network Resources perspective (Niehaus et al., 2009; Casanueva et al., 2014). At a practical level, the findings can serve to guide actors other than the airlines towards more active participation in the development of routes. The interests and the approach of these actors differ. The airports seek greater benefit, seek to attract new routes from new or existing airlines, to promote changes in those routes and longer stays of the passengers at their installations and to prioritize their best performing routes. The DMO and the tourism authorities will have an interest in increasing the number of passengers arriving at or in transit through their destinations (even converting a communications hub into a tourist destination). Furthermore, they will prioritize the routes that increase the 27 number of new tourist arrivals. Other public authorities and organizations in charge of economic development at a local, regional or national level, together with other private stakeholders with the same ends, will have an approach linked more to increasing connectivity for business travelers and for the local population. Despite these differences, either the separate or the joint activity of these actors to influence the development of airline routes can be fundamental for the growth and transformation of tourist destination. The airports (and other stakeholders, at times jointly) offer financial incentives to the airlines in their route development processes. However, the non-financial incentives have an increasingly important role. Among those incentives is the provision of regulatory and market-related information to airlines, as well as the provision and the promotion of contacts with feeder airlines and other agents. Along these lines, this investigation has shown that, in certain circumstances, it can be beneficial for the tourist destination to facilitate cooperative behavior between airlines for the development of code-sharing agreements. In particular, the airlines show greater willingness to cooperate between each other when operating at tourism destinations that are not relevant or when the routes are strategic, but they lack their own resources (such as knowledge and infrastructure) to access them. Airports and destinations can identify these situations and promote meetings between complementary airlines in these contexts, proposing the development of codeshare agreements as a complement to increasing new flights. In this way, the airports will attract higher volumes of traffic and connectivity; the tourist destinations will have accessed routes and clients of airlines from which a higher number of tourists will ensue either directly or indirectly and they will have gained greater visibility; and the airlines will win with regard to costs, the development of their route network and the use of their capability. Thus, one additional line of non-financial action is introduced, so that airports 28 and DMO can influence the airlines. However, the willingness and the capability to bring that influence to a successful conclusion will depend on various kinds of factors such as the attractiveness of the tourist destination, the area of influence of an airport and its demographic characteristics, the airport services, the size of the airport, its possibility for growth, its form of management, the nature of the routes (international or intercontinental) and their distance, the type of airlines involved, the resource endowment of the airlines, etc (Halpern & Graham, 2015). Moreover, our work can help airlines guide their strategy. In the first place, if they possess sufficient resources, it appears that the most logical approach is to exploit the markets in solitary and the positioning at the tourist destinations, to a greater extent, if those are more valuable or strategic. In second place, they would be better advised to follow a strategy of cooperation whenever the necessary resources are not in their possession, which will permit access to partner resources and their mobilization. This research has some limitations. In the first place, secondary data that are publicly available were used in the study, which means that the analysis was determined by the reliability and the appropriateness of the data that were employed. In second place, the cluster patterns were derived from the aforementioned data at a specific point in time and could vary if the data had been taken at a different point in time. Finally, the distances were considered in kms. between the point of departure and the destination airport (Chu, 2014). 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Journal of Air Transport Management, 58, 50-57. 33 Table 1: Descriptive statistics Frequency Percentage Valid Percentage Accumulated Percentage Code-Share 0 34090 68.7 68.7 68.7 1 15500 31.3 31.3 100.0 Total 49590 100.0 100.0 Plane H 8405 16.9 16.9 19.2 L 567 1.1 1.1 20.4 M 40618 81.9 81.9 100.0 Total 49590 100.0 100.0 International 0 22577 45.5 45.5 45.5 1 27010 54.5 54.5 100.0 Total 49590 100.0 100.0 Hub .00 21786 43.9 43.9 43.9 1.00 27804 56.1 56.1 100.0 Total 49590 100.0 100.0 Strategic routes .00 23383 47.2 47.2 47.2 1.00 26207 52.8 52.8 100.0 Total 49590 100.0 100.0 Minimum Maximum Average Standard Deviation Distance (Km) 10.3 15353.6 1962.7 2151.6 Table 2: Correlations Matrix 1 2 3 4 5 6 1.Code-Share 1 2.Plane -.046** 1 3.Hub -.177** .104** 1 4.International -.110** .337** .049** 1 5.Distance (Km) -.045** .696** .130** .403** 1 6.Strategic Route -.017** .280** .203** .306** .314** 1 34 Table 3: Chi-squared tests of variable independence for the complete sample CODE-SHARE Total 0 1 Strategic Route .00 Count 15883 7499 23382 % Inside SR 67.9% 32.1% 100.0% 1.00 Count 18206 8001 26207 % inside SR 69.5% 30.5% 100.0% Total Count 34089 15500 49589 % inside SR 68.7% 31.3% 100.0% Pearson’s Chi-squared test 13.668a*** a. 0 boxes (.0%) count < 5. Minimum expected recount = 7308.50 Table 4: Two-step cluster analysis CLUSTER Cluster 6 4 5 1 3 2 Size 26,10% 22,60% 17,50% 14,60% 12,90% 6,30% 4090 3537 2739 2283 2014 992 Inputs With hub 0(100%) With hub 1(100%) With hub 0(100%) Distance(Km) 6,198.24 With hub 1(100%) Plane L(46.9%) Distance(Km) 894.25 Distance(Km) 776.96 InternationalR 1 (100%) InternationalR 1 (100%) InternationalR 1 (100%) InternationalR 0 (82.4%) InternationalR 0 (100%) InternationalR 0 (100%) Plane M(100%) Plane H(100%) Plane M(100%) Distance(Km) 1,278.18 Plane M(100%) Plane M(100%) Distance(Km) 1,359.55 With hub 0(67.5%) Distance(Km) 1,388.36 With hub 0(65.7%)