When team play matters: Building revenue management in tourism destinations

Abstract

Competition between tourism destinations is intensifying, and collaboration between stakeholders can increase destination appeal. Until now, such collaboration has limited itself to governance and marketing. To advance an earlier proposal of destination revenue management (RM), we develop a conceptual framework of instigators and limiters to such cooperation between tourism operators. Next, we synthesize the framework with behavioral game theory (BGT), an extension of classical game theory that challenges the utility maximization-based outcomes of the classical version. BGT incorporates additional aspects, such as reciprocity and fairness, into bargaining and cooperation and supports the feasibility of forming a RM alliance. Based on BGT findings, our synthesis provides theoretical and practical insights into how destinations can improve their competitiveness through cooperation in two important RM areas, pricing and demand creation.

Keywords

behavioral game theory revenue management stakeholder cooperation tourism destination

Introduction

By 2020, tourism arrivals worldwide are expected to surpass 1.5 billion visitors and the economic contribution from tourism to form 10% of global gross domestic product (UNWTO, 2018). At the same time, tourism destinations compete in a global environment that offers seemingly endless options for a visitor (Sheng, 2011). This environment requires a destination not only to capture the attention of this potential visitor but also to accompany her through the purchase decision. The importance of this guidance is further emphasized as congestion and overtourism might negatively impact destinations that could otherwise be highly profitable in the long term (Bouchon and Rauscher, 2019; Joppe, 2018).

To cope with these challenges, destinations must advance visitor segmentation to incorporate the time and space constraints of the digital era visitor and improve their revenue management. Visitor spending capacity and actual spend directly impact destination performance. While private operators, for example, hotels and transportation companies, have used revenue management tools for decades, destination level application of these tools remains rare (Beal et al., 2018). Furthermore, most of the existing collaboration between tourism operators has, until now, linked with marketing campaigns (Sheng, 2011; Slivar, 2018).

Hotel revenue management has evolved from a room focus to emphasize customers; in this approach, a single business aims to maximize combined profits from a client across revenue streams (e.g. room, food and beverage, spa) or clusters of properties (McGuire, 2015; Noone et al., 2017; Wang et al., 2015; Zheng and Forgacs, 2017). As noted by Noone et al. (2017: 1), in a hotel context “the practice of revenue management will evolve into the more accurate and expansive notion of strategic profit management.” In a similar strategic manner, destination-centric revenue management (shortened to destination RM from here onward) (Kuokkanen, 2013) encompassed independent operators in a tourism destination to maximize revenue and income through cooperation in two important subareas: pricing and demand creation. The concept extended these practices beyond individual companies and proposed strategic cooperation across ownership boundaries to attract additional customers and spending and respond to competition between destinations. The concept resonates with Market Basket Analysis, an approach where customer purchases are analyzed across related product categories for better optimization potential (Solnet et al., 2016). To progress such evolution in practice, this article develops a conceptual model that offers further advice on how to create and manage the stakeholder collaboration that forms the backbone of destination RM.

Destination RM benefits stakeholders in several ways. First, voluntary collaboration in the form of joint offerings and strategic pricing across independent businesses can contribute to demand creation and increase visitor spend, both at the booking phase and at the destination (Kuokkanen, 2016). Second, destinations increasingly aim at offering cross-destination visitor experiences through storytelling that encompasses multiple stakeholders and reaches beyond earlier practices of destination marketing (Kim and Youn, 2017). Creation of such stories requires cooperation at a level deeper than before, and collaboration in pricing and demand creation can support this relationship. Third, destination RM can also help to rebalance demand between high and low seasons (Kuokkanen, 2016). For these reasons, practical implementation of the concept requires further investigation. However, a gap lies in knowledge about the feasibility and management of destination collaboration that includes joint decisions and revenue and expense sharing between cooperation partners.

Our aim is to narrow this gap by focusing on human behavior in cooperation. Building on the existing literature on tourism destinations and stakeholder cooperation, this article proposes a conceptual model for implementing destination RM practices and addressing anticipated challenges based on behavioral game theory (BGT; Camerer, 2003). BGT builds on economics research that extends classical game theory to imitate human behavior and relaxes the strict utility maximization restriction of classical economics. Based on the model, we propose how to build cooperation and revenue culture between destination stakeholders with a focus on the two specified subareas of the discipline.

We contribute to theory in three ways. First, we illustrate how tourism destinations can engage in and manage stakeholder collaboration in pricing and demand creation, and we include proposals for motivating counterparts and mitigating factors that limit cooperation. Second, the article specifies the challenges in initiating such practices between independent tourism businesses within a destination, and it serves as a theoretical basis for the future research and implementation. Third, we introduce BGT to the field of revenue management. Earlier studies have used classical game theory to analyze competitive situations, but the use of the behavioral version has been mentioned only as a desirable future development. Considering the coopetitive nature of tourism destinations (Bengtsson and Kock, 2014), BGT is a valuable inclusion in the theoretical toolkit. From a practitioner perspective, our model can offer advice to destinations that must respond to the ever-increasing competition with novel initiatives and take the discipline to the strategic direction proposed in earlier literature (Kimes, 2017).

Classical versus BGT in tourism and hospitality research

The purpose of BGT (Camerer, 2003) is to extend classical game theory (Von Neumann and Morgenstern, 1944) to imitate real-life human behavior and lift the restrictions related to strict utility maximization in classical economics. The latter was criticized for lack of realism already long before the birth of BGT (Shubik, 1970). Tourism and hospitality research has adopted classical game theory earlier. Zhang et al. (2009) suggested its application to tourism supply chains, and the authors subsequently provided mathematical models of multistage interactions between theme parks, accommodation providers, and tour operators (Huang et al., 2010; Yang et al., 2008, 2009). Competition between destinations (Sheng, 2011) and tourism destination countries (Tran and Thompson, 2015) has also attracted game theory-based analysis. Tavares et al. (2015) evaluated decisions linked with investment in tourism promotion from a game theoretical standpoint. Khalilzadeh and Wang (2018) focused on the impact of coalitions on collaboration outcomes through a game theoretic approach and highlighted the importance of fairness and stability as incentives for better cooperation. In revenue management, Arenoe et al. (2015) analyzed competitive hotel pricing through game theory. These examples suggest the suitability of game theory to analyze tourism business and revenue management, but they focus on the classical version of the theory.

Destination RM, conceptualized by Kuokkanen (2013), involves joint pricing and demand creation and calls for sharing incremental profits between partners. It does not include all the traditional revenue management components, such as capacity control, and it leaves all partners the freedom to sell their services individually. Thus, the concept is based on voluntary cooperation that creates additional offers for customers and aims to maximize profits and create new demand with its emphasis depending on the season. As BGT encompasses other-regarding behavior in the traditional game theory (Gintis, 2014) and focuses particularly on profit maximization in collaborative or coopetitive environments, we argue that it can provide substantial value to the development of destination RM that requires cooperation between stakeholders.

Cooperation in tourism destinations

Tourism research has already applied a destination stakeholder model to mega-events (Timur and Getz, 2008), community-based tourism (Rawat et al., 2015; Sakata and Prideaux, 2013), destination marketing (Paskaleva-Shapira, 2007), and urban destination management (Buhalis, 2000). However, a revenue management perspective is missing. Encompassing certain revenue management practices in destination cooperation can provide enhanced financial optimization opportunities, but similar to all collaboration, such change will also require careful management to instigate cooperation and overcome factors that limit it.

To analyze destination stakeholder cooperation, we must first define a destination. From a methodological point of view, the definition of a destination remains liminal due to multiple alternatives of scale, accessibility, political system, and stakeholder collaboration (Bouchon and Lew, 2014; De Carlo et al., 2008). As a result, destinations have become complex entities with loose boundaries. They can be defined from a geographical angle (e.g. Côte d’Azur, Europe), from an administrative angle (often matching a DMO structure), or from a market angle (e.g. wine route, World Heritage site). In this article, we adopt the administrative perspective, since DMOs and tourism infrastructure follow this approach (Slivar, 2018). Local, regional, and national governments have established regulatory and promotional boards that correspond to their jurisdictions, even when they do not always match the market-perceived tourism destinations (such as a coastline that may be split into several administrative units). However, in the future research, the analysis could be extended to the market-based definitions.

Sheehan and Ritchie (2005) argued that hotels are the key stakeholder group of a destination. Gnanapala (2016) included travel agents in his assessment and considered the two groups as interdependent. The key stakeholders, however defined, seldom exist in a vacuum; they require other stakeholders to prosper. Varying models of ownership add complexity to initiating stakeholder collaboration. For example, some accommodation providers belong to large chains while the majority of such operators are independent (Slivar, 2018). Network hospitality has further introduced new relationships between the stakeholders and included new counterparts, for example, the couch surfing movement (Molz, 2012). Since our objective is to optimize revenue management cooperation within a destination, we focus only on stakeholders that aim at profits or at covering their operating expenses and exclude the broader networks that exist.

Destination RM involves sharing both costs and revenue (Kuokkanen, 2016), and this differs from joint marketing activities that only involve costs (Qu et al., 2011; Wang and Xiang, 2007). While in the destination RM model stakeholders jointly create and offer packages for advance purchase and aim to benefit from pricing psychology before and during visitation, the packages are only additional to the normal business of the various collaborating operators. A customer will always have a choice between a joint offer and an individual purchase of goods and services. Therefore, the collaboration does not equal a cartel-like situation where visitors would have only one option. The joint offer price represents a discount relative to the sum of the individual prices and compensates a customer willing to commit in advance (Kuokkanen, 2016). Customers may buy their accommodation, meals, activities, and event tickets separately. Subsequently, destination RM increases the choice available to consumers and benefits both the businesses involved and the visitors.

As BGT focuses on other-regarding behavior, the background of each “player” becomes crucial in analyzing cooperation dynamics. Furthermore, it is crucial to consider aspects that motivate or hinder potential collaboration. Therefore, to facilitate destination-centric approach to revenue management, we focus on three key aspects: stakeholder background, cooperation instigators, and elements that limit cooperation (Figure 1). In the next section, we further divide the aspects into detailed factors based on literature.

Figure 1.

Revenue management cooperation in tourism destinations.

Stakeholders and their backgrounds

Stakeholder management has evolved from a company-centric practice to an approach that incorporates multiple levels and types of counterparts involved in or impacted by a business, and variables of engagement, power, and interest form the backbone of this approach (Sheehan and Ritchie, 2005). Stakeholder management aims to define the degree of formalization and intensity of relationships between business entities (Merinero-Rodríguez and Pulido-Fernández, 2016). It can help a destination to surmount the political, economic, social, and administrative barriers that isolate its components and allow enhanced competitiveness. Power generally links with the importance of a business to a destination, with large companies wielding influence over others. Engagement and interest create the motivation for a stakeholder to participate in potential collaboration. Yang et al. (2018) argued that partners with similar characteristics and interests work better toward a common goal, while differences in size hamper collaboration. Business seasonality can prompt additional interest in improving low-season performance through cooperation (Sainaghi, 2013). Therefore, we define business size and seasonality as the two first stakeholder background factors that affect the initiation of a revenue management alliance.

In the conventional customer-centric view of revenue management, single businesses use revenue management techniques to improve their individual performances. However, their actions, based on separate customer interactions, may not lead to optimal performance (Kuokkanen, 2013). Additionally, independent operator IT systems tend to be outdated, and data recorded in different departments of a business are not shared efficiently within the organization (Beal et al., 2018). Although data sources are available, destination operators do not always use them to support revenue analytics. Furthermore, online travel agencies (OTA) may be more efficient in collecting and analyzing data on customer behavior. Even the hotel industry, bar large international chains, lacks behind in the ability to analyze big data to support managerial decisions (Haynes and Egan, 2016). This creates a significant challenge: independent hotels, representing 50–70% of conventional hospitality operators in developed countries, are particularly underequipped (UNWTO, 2018).

Beal et al. (2018) emphasized the efficient use of data analytics that would allow businesses to extract valuable customer information and integrate it with revenue management. To achieve optimal results, independent tourism operators must develop their business intelligence to support sophisticated pricing and segmentation decisions (Mariani et al., 2018). Destination RM can further contribute to this, but first the challenge created by different levels of pricing practice sophistication between potential alliance members requires attention. This forms the third stakeholder background factor in our framework.

Instigators

Brent Ritchie and Crouch (2010) argued the need to assess destination competitiveness and called for tools to allow performance comparison. However, the complexity of relationships between destination stakeholders and a lack of trust and time to align stakeholder goals constrained their efforts to achieve this. Therefore, we identify trust and cultural proximity as cooperation instigators. Studies further suggest that collaboration between stakeholders depends on its necessity or the dependency between the stakeholders (Presenza and Cipollina, 2010). As an example, Disney theme parks have initiated revenue management experiments, such as dual admission policy for reducing lineups (Ko and Park, 2019). As the entities involved fall under single ownership, the case does not represent destination RM. However, it serves as an example of the potential benefits revenue management collaboration can create when applied to a group with mutual trust and common culture. Even within a single business, such as Disney, these two cannot be taken for granted due to the challenges in aligning incentives of various business units, and thus the example supports the importance of the two instigators.

Stakeholders in small destinations are more likely to know each other and develop collaboration that involves joint monetary transactions. An example of independent tourism business cooperation is a ski resort where operators combine lift passes and hotel rooms and share the resulting revenue (Sainaghi, 2013). Community-based tourism initiatives in Nepal serve as another example where nearly uniform guesthouses maintain a single price throughout the community to ensure fair sharing of hiker revenue (Pandey, 2011). Kuokkanen (2016) argued that destinations with a comprehensive service bundle and a clear value proposition, such as Arosa, can be more resilient than their competitors at times of economic volatility. In the abovementioned examples, clear agreements on how to share the costs and the revenue are vital for cooperation, and we identify them as two further instigators. Likely due to the challenges in creating such agreements, these efforts have remained localized and they have not reached the level of destination RM; advanced joint pricing and demand creation tactics are thus far missing.

Creating and maintaining the instigators identified is challenging in the scale of a large destination. Therefore, independent stakeholders need a reliable third party to maintain clarity on revenue and expense sharing and to facilitate alliance creation through leadership that fosters trust and common culture. This party must draw motivation from the success of the local economy and manage to provide data analysis and decision-making support. Komppula (2016) maintained that individual decision-makers, such as entrepreneurs, business managers, and influential politicians, can develop leadership at a destination. Beal et al. (2018) placed the emphasis on the leading role of institutional tourism players in transforming themselves to enablers of improved destination performance. Either way, endogenous leadership must exist to allow the four instigators to emerge in large destinations.

Limiters

As noted earlier, the role of leadership stands out in existing destination cooperation literature. DMOs may be in the best position to provide leadership, develop legitimacy, and supply information and resources (Beritelli and Bieger, 2014; Saito and Ruhanen, 2017; Wang and Krakover, 2008). When voluntary cooperation encompasses revenue management, all these elements turn into a critical success factor without which a destination RM alliance is bound to fail. The type of management or governance is crucial for cooperation (Pierre, 2009), and thus lack of coordination and support are detrimental to destination RM. Earlier, we identified leadership and business culture as instigators to cooperation, but without proper management, the basis for collaboration weakens markedly. Therefore, we propose lack of coordination and support the first factor that limits cooperation.

Unclear rules of cooperation may also limit destination RM. As an example, Singapore Tourism Board (STB), a public agent, operates a highly centralized model that expects a high level of compliance from operators. Since 2005, Singapore has combined a long-term tourism infrastructure development strategy with a high net-worth visitor target (CLC, 2015), and it has developed integrated payment systems able to extract optimal yield from visitors (STB, 2016). With such systems, STB is able to mitigate the risks of cooperation by enforcing clear rules on all participants. However, the model is not easy to replicate in other sociopolitical environments due to its semi-authoritarian nature. Despite this, the Singapore case supports the role of clear rules in the initiation of revenue management collaboration, and we identify lack of such rules as a potential limiter to destination RM.

Differences in business culture between tourism operators are likely to influence the level of collaboration, and this is particularly true in the case of neighboring border regions, when a lifestyle orientation is pitted against profit maximization or when active and passive styles of conducting business coincide (Weidenfeld, 2013). For example, a property belonging to an international hotel chain and a diving shop run by an owner whose primary motive for business is to live at the destination might find working together hard due to their different approaches to business. While similar cultures of doing business bolster cooperation, important differences can stand in the way of considering destination RM, and they form our final limiting factor.

BGT insights for destination RM

Our synthesis between BGT and stakeholder collaboration, the main focus of this article, concentrates on the various aspects of collaboration under information and incentive asymmetries applicable to tourism destinations. BGT offers theoretical and applicable insights for addressing the aspects and factors of stakeholder collaboration identified in our framework. As BGT emphasizes the human aspect of economic behavior, it incorporates the crucial psychological factors that are missing from classical economics, rendering the latter an unsuitable theoretical basis.

Table 1 lists the key components of destination stakeholder collaboration in revenue management proposed in Figure 1. It connects them with games under BGT to identify components that can provide guidance for developing a destination RM alliance. Such advice allows stakeholders to anticipate and mitigate issues that may arise in cooperation that involves profit sharing.

Table 1.

Application of BGT to destination RM.

Aspect		Factor		Relevant BGT component*	Application of BGT to destination RM
1.	Stakeholder background	1.1	Relative business size	Dictator game	Stronger stakeholders could take advantage of weaker ones, but even in absolute power situations, this is not inevitable.
		1.2	Seasonality	Bargaining games with asymmetric losses from waiting and outside options	Businesses who suffer least from seasonality could lack motivation to cooperate and, therefore, must understand the benefits of collaboration; asymmetries can benefit an alliance.
		1.3	Pricing practice sophistication	Bargaining games under incomplete information	Importance of training alliance members in pricing techniques and fostering communication.
2.	Instigators	2.1	Clarity on sharing joint revenue	Ultimatum game	BGT experiments support a tendency toward equal sharing of revenue.
		2.2	Clarity on sharing joint expenses	Entry fee game	Advance payments strengthen partner motivation to collaborate.
		2.3	Mutual trust	Trustee game	Fair treatment, incentive alignment, and guilt from breaking promises fundamental to trust creation.
		2.4	Similar cultures of doing business	Common culture game	Common culture instigates faster agreement and fluid cooperation.
3.	Limiters	3.1	Lack of coordination and centralized support	Stag hunt game	Two-way communication increases coordination and collaboration efficiency.
		3.2	Unclear rules of cooperation	Game with random termination	The threat of a random termination of an alliance due to unclear rules impacts bargaining negatively.
		3.3	Diverging cultures of doing business	Weak link game with interaction	Heterogeneous groups tend toward collapsed cooperation.

Note: BGT: behavioral game theory; destination RM: destination-centric revenue management.

* Component games based on Camerer (2003)

Aspect 1: Stakeholder background

Differences in business size, seasonality patterns, and pricing practice sophistication form the first aspect of developing revenue management cooperation in a tourism destination. All the three factors create fundamental imbalances between collaborating businesses, and changing the factors themselves would be hard or impossible. Therefore, we apply BGT to evaluate their relevance and potential mitigation.

Factor 1.1: Relative business size

Collaboration dynamics are likely to favor larger businesses within an alliance. A dominant position, or a reasonable fear of a dominant block within an alliance, could stifle emerging collaboration. Differences in business size are impossible to avoid, but BGT offers evidence to back up the feasibility of destination RM between businesses of different scale.

In a dictator game (Kahneman et al., 1986), two players (the Proposer and the Responder) have an amount of money to split between them. The Responder must accept any split the Proposer suggests, providing the game its name. According to classical game theory, the Proposer would keep everything; as no incentive to share exists, this decision maximizes the utility the Proposer can achieve. However, laboratory and field experiments demonstrate that proposers give away 10–50% of the money they received, with identification of the players (vs. anonymity) resulting in the most generous outcomes (Frey and Bohnet, 1995; Gächter, 2004). Therefore, even in a situation of absolute power, humans do not purely maximize utility in the manner classical economic theory suggests.

Gintis (2014) summarizes this in the division between self- and other-regarding behavior and notes that predictions based on the classical, self-regarding game theory mostly fail. The reason is that in the real world, strategic interaction trumps pure power differences. Social norms and interaction play a crucial role in dictator behavior, and other-regarding behavior may be a result of a need to appear fair and avoid inequality (Andreoni and Berman, 2009; Bahr and Requate, 2014; Kim and Kim, 2019). Grech and Nax (2020) maintain that current research does not allow segregating the effects of strategic thinking from those of social norms. Nevertheless, this BGT finding supports the feasibility of destination RM even when the size and the relative power of collaborating businesses differ significantly, with the findings strongly generalizable also outside a laboratory environment (Gintis, 2014).

Factor 1.2: Seasonality

Revenue management collaboration is aimed not only at increasing overall demand and visitor spend but also at mitigating the impacts of seasonality. However, in many destinations, certain businesses suffer more severely from seasonality than others, for example, as a result of a customer base that consists mostly of one visitor type. Consequently, one or several collaboration partners may be more robust against the adverse effects of seasonality, lowering their incentive to cooperate.

A cluster of BGT games focuses on situations where players may choose to wait before reaching an agreement while the delays cause asymmetric losses. Classic economics maintains that in these situations the player with the lowest waiting cost will dominate. However, as summarized in BGT, the threat of potential alternative deals outside the game, also known as deal-me-out options, changes this interaction, and results in fairer division of gains (Camerer, 2003). In destination RM, each company has the option to focus only on its individual business, and this creates a credible deal-me-out option. According to Finus and McGinty (2019), cost or revenue asymmetries between cooperation partners may, in fact, increase the stability and benefits of an alliance. Therefore, we propose seasonality not to form a serious threat to successful cooperation, although all partners must understand the positive value the cooperation creates (more specifically, the value that would be lost if some partners left the alliance).

Factor 1.3: Pricing practice sophistication

Pricing methods and their sophistication differ fundamentally between businesses. For example, revenue management and dynamic pricing are standard practices in hotels. As a result, hoteliers are likely to be more aware of destination demand and pricing patterns compared with other operators. At the other end of the spectrum, local retailers, event organizers, and other small entrepreneurs may not employ or even be familiar with these practices. Therefore, the level of pricing knowledge varies significantly.

Differences in knowledge are reflected in games with bargaining under incomplete information, a game where one of the players is given information the other does not possess. In such games, the less-informed player tends to accept a deal too early and with suboptimal terms (Camerer, 2003). However, communication will alter this dynamic and generally enhances the efficiency of deal-making (Valley et al., 2002). Furthermore, in such games, more sophisticated players tend to teach those who are less advanced, particularly if they know they will replay the game with the same counterparts (Camerer and Ho, 2015). Therefore, we propose training and clear communication on pricing to reduce the knowledge gap between collaboration partners and enable successful destination RM, with part of the training likely to happen naturally after cooperation has started.

Aspect 2: Instigators

Our collaboration framework identifies several factors that can instigate cooperation if addressed correctly. These consist of establishing clear rules for sharing joint revenue and expenses, developing mutual trust among collaboration partners and fostering similar cultures of doing business across companies. While the instigators are not mandatory to address, an alliance is less likely to function efficiently without them.

Factor 2.1: Clarity on sharing joint revenue

In an ultimatum game (Güth et al., 1982), two players (the Proposer and the Responder) must agree on how to divide a given amount of money. Similar to the dictator game, the first player proposes a division, but the second player may either accept or reject the offer. In the case of an acceptance, both players receive the amounts defined by the Proposer. In the case of a rejection, both players receive nothing. Extensive studies around this game show that with multiple rounds of play, the offers average 37% of the total amount, perceived as fair by the players (Camerer and Ho, 2015). The possibility to reject an offer and punish a player who violates the fairness norm, known as negative reciprocity, is essential in the ultimatum game. This punishment may be monetary (rejection of an offer) or verbal (threats outside the game, Cooper and Kagel, 2013). However, disagreement exists on whether monetary penalty or nonbinding threats are more efficient for game outcome (Masclet et al., 2013; Xiao and Houser, 2009).

Destination RM can also be considered a game where both parties either receive additional revenue or fail to cooperate with no additional revenue earned. As the gross margins of various business types differ significantly, gross profit would often replace revenue as the object of sharing. The results of ultimatum games suggest that with multiple rounds of negotiation, destination stakeholders are likely to converge toward a split perceived not to violate norms. This will, however, require facilitation by a DMO or other coordinating body to create full comprehension of the “ultimatum” at play and to prompt the required reoccurring negotiations and “off-game” communication (threats of withdrawal from the cooperation) that can replace monetary punishments.

Factor 2.2: Clarity on sharing joint expenses

While destination RM fundamentally differs from destination marketing efforts, it also involves expenses to collaboration partners. As suggested in the concept, discounts to attract early purchase of in-destination services can create demand and increase spending during the actual visit (Kuokkanen, 2016). Therefore, collaboration partners must agree on how to allocate such discounts that effectively become destination RM expenses for the alliance.

BGT approaches such a situation under the category of entry fee games. Designed based on the sunk cost fallacy (Kahneman and Tversky, 1979), these games test whether a requirement to pay a fee to participate in a game increases participants’ engagement in it. While the participation fee is a sunk cost that cannot be recuperated, results of several experiments support the notion that such fees deepen participants’ commitment (Camerer, 2003). Furthermore, reduced rates of return arising from a membership (a type of additional expense) increase group productivity and welfare (Aimone et al., 2013). We propose these findings to apply also to a revenue management alliance, and thus entry expenses, when clearly communicated at the beginning of the cooperation, can be beneficial for establishing destination RM and engaging partners in it.

Factor 2.3: Mutual trust

Trust between partners is fundamental for successful cooperation. A trustee game simulates a situation where one player (the Investor) has to rely on a second player (the Trustee) to earn return on investment. In the basic form of the game, trust does not pay off, as the investors generally collect less than 100% of their investment (Cooper and Kagel, 2013). However, the results reflect the rules of the game. Returning money back to the Trustee is a variation of the dictator game, and the Investor has no means to punish an untrustworthy Trustee. Despite this absolute power, the amounts returned are on the average 40% higher than in the dictator game (Cooper and Kagel, 2013). Therefore, trustworthiness can be instigated through arrangements that foster it.

While earlier research indicated that pre-play promises increase returns in a trust game (Charness and Dufvenberg, 2006), Chen and Houser (2019) maintained that promises or promise chains do not promote trustworthiness or cooperation. Instead, the authors discovered that incentive alignment between cooperation partners generates trust, rendering promises unnecessary. Furthermore, promises contingent on other factors do not impact the behavior of cooperation partners in choosing selfish or promised action. According to Chen and Houser, trust can be created through incentive alignment but not inter-alliance promises. However, promises are not useless: Breaking promises causes guilt, and aversion to such guilt impacts the behavior of a promisor significantly (Ederer and Stremitzer, 2017), a potential explanation to the discrepancy presented earlier.

Factor 2.4: Similar cultures of doing business

As discussed earlier, business culture can play a significant role in creating a functional alliance. In common culture games, players are taught to react to stimuli in similar ways (common culture) while completing a task and then transferred to play the game with participants who are not familiar with the stimuli. Weber and Camerer (2003) demonstrated how experienced players underestimated the time to complete the same tasks with new partners not familiar with the “culture.”

The finding applies directly to destination RM, where collaboration becomes smooth if alliance members are comfortable working with each other. Therefore, the initiation phase must include efforts to familiarize partners with each other. The partners must also be accountable for their actions; research that obscures the relationship between actors and actions in cooperation suggests that poor accountability reduces collaboration efficiency (Dana et al., 2007). Transparency will help in developing common cooperation practice and benefit all stakeholders.

Aspect 3: Limiters

The third aspect in our synthesis consists of factors that can substantially limit the depth and efficiency of collaboration once an alliance has commenced and potentially cause its termination. Lack of coordination and centralized support and unclear cooperation rules can cause friction between collaboration partners. Diverging business cultures may also be highly detrimental to destination RM, limiting potential benefits.

Factor 3.1: Lack of coordination and support

While lack of coordination appears an obvious limiter to collaboration, its specific consequences and their value can be hard to define. This complicates investment in a central body that coordinates the alliance. BGT includes games that offer insights into this topic.

In a stag hunt game (Hofstadter, 1986), two players must choose to cooperate for better returns. However, the game differs from the classical prisoner’s dilemma, as rejecting cooperation is not the dominant alternative for either player. Therefore, the ability of one player to assure the other of the commitment to cooperate becomes crucial in the game, with one-way communication increasing efficiency (reaching the optimal outcome) to 50% and two-way communication to 91% (Camerer, 2003). Stag hunt belongs to a generalized group of weak link games that can be extended to multiple players, with the payoff of the group depending on the weakest link. Experiments suggest that larger group sizes require particularly careful coordination to achieve efficient outcomes; the impact of signaling deteriorates as more players send signals to each other (Gintis, 2014). This emphasizes the value of a coordinator that facilitates and supports communication, with technology allowing shared instant messages between the partners to reduce the distortion created by one-to-one exchanges.

Factor 3.2: Unclear rules of cooperation

Closely related to lack of coordination, unclear rules of cooperation also pose a serious threat to destination RM. We draw a connection between this limiter and the BGT component of bargaining games with random termination. These games simulate situations where bargaining may be abruptly terminated; the termination threat leads to inefficient deals with a high average rate of cooperation refusals across a range of experiments (36%, Camerer, 2003). Normann and Wallace (2012) demonstrated that in a standard prisoner’s dilemma, a longer expected game length was significant in increasing cooperation, even when termination was still a possibility.

Unclear rules of cooperation could result in stakeholders doubting the continuity of the alliance, and thus, BGT highlights the importance of clear rules. Fréchette and Yuksel (2017) noted that patience among collaboration partners could also mitigate the possibility of random termination. However, creating a patient attitude toward revenue management among destination stakeholders would likely require more resources than clarifying rules of cooperation, and thus, clear rules are the preferred alternative. Some ambiguity, inevitable in the real world, should not form a prohibitive obstacle to destination RM. According to Krockow et al. (2018), cooperation is reduced only under extreme conditions of termination likelihood.

Factor 3.3: Diverging cultures of doing business

While the last factor that limits destination RM is the opposite of instigator Factor 2.4, its consequences differ from those discussed earlier. We draw a connection between diverging business cultures and a second type of a weak link game. In a version of the game where players either communicate in small groups that remain steady throughout the game, or interact with all players, the role of divergence is emphasized. While the steady small groups reach optimum outcomes roughly seven of eight times, the game that allows interaction between all players finally converges to nearly zero efficiency (Camerer, 2003). In a seminal experiment, Herrmann et al. (2008) demonstrated significant cultural differences of punishment in a cooperation game, and Gintis (2014) further synthesized how culture affects behavior in cooperation games.

Based on these findings, we propose that divergence in business cultures can be detrimental to cooperation. This effect reaches beyond common culture as an instigator detailed in Factor 2.4. For example, divergence in punishment, as discussed earlier, can stop large groups of counterparts from cooperating, while lack of differences in punishment does not instigate collaboration. This emphasizes the need for investment in narrowing the differences between alliance members.

Theoretical and managerial implications of BGT on destination RM

Collaboration between stakeholders in destination governance and marketing has been an active area of research for some time, and it has employed several game theory-based approaches. However, we propose that BGT can offer critical insights for initiating and sustaining a destination RM alliance, a novel type of tourism operator collaboration proposed in earlier literature. We will next discuss the theoretical and practical implications of our synthesis.

Classical economics would suggest destination RM risky or outright impossible due to power imbalances within an alliance that shares monetary inflows and outflows. Our first contribution is to cast doubt on such assessment, as the experiments BGT builds on suggest the contrary. In these experiments, human participants are able to see beyond short-term utility maximization, and they are willing to share profits even when no forcible requirement to do this exists. The concept of destination RM proposed in earlier literature thus seems feasible from a behavioral aspect, as both the intrinsic and incidental aspects of cooperation in our framework can be addressed through appropriate actions. However, empirical studies are needed to validate this claim. BGT supports the potential of creating joint pricing and demand creation actions, but without experiments in the field of application the model remains theoretical.

Destination RM is a tool that can help tourism businesses to respond to changing business situations promptly through cooperation. Our second theoretical contribution focuses on addressing the key factors and aspects relevant to building deeper collaboration between tourism operators. The stakeholders must trust each other to allow cooperation that involves financial in- and outflows, and communication and reciprocity between partners can both facilitate trust creation. Clear definition of the upfront costs (entry fee), accountability, and well-aligned incentives between the partners are crucial for cooperation, as supported by BGT. As the number of collaboration partners grows, the role of continuous communication and clear rules becomes critical, as the risk of one partner abandoning the alliance and jeopardizing benefits for everyone grows.

Coordination is crucial for successful destination RM. Depending on the destination, the local DMO could assume the coordinator role, but in some cases, an association of local business owners could also adopt the position. Withdrawal from cooperation represents the only concrete punishment that can complement verbal threats and guilt creation. As no formal mechanism to impose sanctions exists, the coordinating body must enjoy the trust of the alliance. The coordinator must direct resources into reducing business culture differences and developing communication; without a shared understanding of cooperation significance, individual businesses may fail to recognize its benefits and potentially initiate a vicious circle that stymies the opportunity for everyone to perform better. Supported by behavioral economics, we maintain that destination RM is possible when all members appreciate its advantages and act in a mutually beneficial manner.

Free riding, or stakeholders reaping the benefits of collaboration without participation, is a potential problem with the proposed approach. Certain stakeholders may choose to stay outside expense sharing and simply rely on additional demand or visitor spending created by others, as also suggested in public goods game research (Fischbacher and Gächter, 2010). Such behavior is unavoidable, but the model can survive as long as only small-scale businesses focused on in-destination sales (such as retailers) become free riders. However, a critical mass of engaged partners who are involved at the booking stage must participate. The visibility of the joint marketing that targets advance purchase should reduce free riding, as only alliance members benefit from the additional advance demand. Furthermore, Herbst et al. (2015) proposed that endogenous alliance formation strengthens cooperation and reduces free riding, a finding encouraging for voluntary destination RM.

Due to its negotiation orientation, BGT can also offer direct advice on how to facilitate revenue management collaboration. First, it is essential to create collaboration guidelines between alliance members and develop trust between them. Second, before commencing the alliance, thorough training aimed at levelling differences in partner sophistication and creating a common revenue management culture is required. The latter coincides with an evolving topic in total hotel revenue management (Zheng and Forgacs, 2017). Extending the notion of total revenue management culture to tourism destinations thus fits this trend in the development of the discipline. Successful destination RM would, effectively, push total revenue management beyond business boundaries and support the views that the discipline is transforming into strategic profit management (Kimes, 2017; Noone et al., 2017).

Conclusions, limitations, and further research

Our synthesis between the proposed factors of destination RM collaboration and BGT suggests that the challenges created by imbalances between businesses in an alliance do not impede successful cooperation. First, contradicting with classical game theory, individuals are not only selfish utility maximizers. As proposed in BGT, people are capable of reciprocity in relationships that build on trust, but a credible threat of punishment to free riders must also exist. Second, while successful collaboration faces many obstacles, these can be modeled as behavioral games. Subsequently, BGT research offers insights into addressing these challenges.

Our work is limited by its conceptual nature. While the games under BGT mirror practical business negotiations, it is impossible to evaluate whether the theoretical notions convert into practice in a tourism destination setting. Therefore, we call for empirical research on the topic. First, the cooperation framework must be tested to verify the literature-based aspects and factors through observation of real-life cooperation, such as the examples discussed earlier. The willingness of destination stakeholders to participate in destination-wide actions can be mapped through interviews. Next, the games must be adapted to a destination RM environment and played with tourism stakeholders to test the theoretical BGT insights and to discover how to apply those theories in practice. Such simulated scenarios would also offer valuable inputs to a DMO or other central organization that coordinates an alliance at a later stage. Finally, training programs that target the desired outcomes (or avoid certain outcomes) must be developed to create the environment required for successful cooperation.

There are several practical questions to answer before the concept can be operationalized. As gross margins between businesses differ, the financial basis for revenue and cost sharing requires definition. BGT suggests that such sharing is possible, but it cannot provide actual rules for the calculations, particularly when cost structures differ between partners. In terms of capacity control, each stakeholder in the alliance must dedicate an amount of capacity for the joint offer, and an agreement to manage this capacity must exist. We draw a parallel between capacity allocation for destination RM and capacity sold through OTAs. Practices and systems that can cope with the latter case are already in place, and revenue management cooperation requires a similar approach. Finally, cost rigidity varies between businesses; for example, some operators are less vulnerable to low demand as they are able to reduce staff quickly. Such structural differences need consideration. These questions fall under traditional finance and business control, and as BGT cannot contribute there, they remain areas for further development.

Destination RM could also provide helpful in tackling broader tourism challenges, such as destination overcrowding and environmental sustainability. Such collaboration could level the peaks and troughs of visitation by inducing natural transfers of demand between seasons, and improved profits would help to address the environmental issues tourism creates. More balanced tourist arrivals would be advantageous to all stakeholders in a destination. Philosophically, destination RM could lead to a perception of a destination as a single tourism entity, where technology can facilitate more efficient solutions to the societal externalities tourism can produce.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Henri Kuokkanen

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