Marine tourism omnichannel coordination Open Access

With the rapid development of information and communication technology, the acquisition of tourism product information has become more prevalent and diversified. Tourists can easily search for information and buy tourism products through travel agents (TAs), especially online TAs (OTAs). Recently, large pure-play OTAs in China, such as Trip.com Group and LY.COM, have started to set up offline physical stores to expand the customer market. These stores provide tourists with thoughtful pre-sales consultation and experience services. In this way, TAs can track and accumulate the data associated with the whole shopping process of tourists, grasp the changes in customers that affect their decision-making and timely interact with tourists to improve their shopping experience. Therefore, a tourism omnichannel can be defined as a platform that seamlessly integrates the online experience, network management technology, marketing skills and other resources to sell various forms of travel services to customers through either virtual sites or tangible stores (Verhoef et al., 2015; Long and Shi, 2017).

Bundling is the practice of marketing two or more products and/or services in a single package for a special price (Guiltinan, 1987). In the travel industry, TAs design and provide a variety of tourism to meet the diverse needs of tourists (Dong et al., 2014). They usually combine two or more independent tourism products into a bundled tour package sold as a whole at a discounted price lower than the sum of separate products' prices. Bundle sale of tourism products is beneficial not only to tourists but also to products providers. On one hand, with all the needed services such as tour scenic, flight and accommodation provided, the shopping process for tourists is more convenient and traveling expenses can be saved (Kim et al., 2009; Bujisic et al., 2015). On the other hand, since the price of the bundled tour package is usually lower than the sum of separate tourism products prices, the bundled package tends to stimulate the demand of tourists, thus boosting the revenue of TA.

Marine tourism refers to all kinds of tourism and leisure activities carried out in coastal areas, offshore waters, deep seas and oceans. The long distance and special geographical location determine that it contains multiple tourism elements and consumption items, so the bundling strategy is particularly suitable for marine tourism. For example, bundling bathing beaches with buffets, or resort hotels with recreational activities such as surfing and diving are more popular than single marine tourist programs. The same service can be given to tourists and they just need to pay about two-thirds of the price when purchased separately.

A high level of service efforts in marine tourism omnichannel is a critical success factor with few studies. These efforts are made through the whole process of both online purchase and offline experience, involving online services such as information availability, security, the response speed of Internet platform, as well as offline services such as pre-sales consulting, the sophistication of infrastructure, touring experience and time spent waiting in line. Since service efforts are made by key stakeholders in marine tourism omnichannel with many different elements, formulating these efforts into quantitative functions are quite subtle and will be addressed in Section 3.

In marine tourism omnichannel, pricing with service effort level has become important factor affecting the purchase decisions of tourists. However, current marine tourism products in the market are an admixture of good and evil, such as low-price online group-buying that reduces the level of service effort and hidden consumption in tourism destinations. Such behavior has negative influences on the experience of tourists and the image of tourist operations. Therefore, it is meaningful to study how TA and TS determine the reasonable price and service effort levels to obtain the maximum profit. Specifically, this study intends to address the following research questions:

To answer these research questions, a two-echelon marine tourism omnichannel consisting of two TSs and a TA is designed for the game-theoretical modeling study, both TSs and TA jointly provide comprehensive service to tourists such as pre-sale consultation, scenario experience and tourist reception, etc. Through the construction of omnichannel mode, the price, service effort level and profits under decentralized and coordination situations are compared and analyzed. Finally, a revenue-sharing contract is designed to realize the coordination of marine tourism omnichannel.

The rest of this article is organized as follows. The corresponding literature is reviewed in Section 2. In Section 3, we define modeling notation and assumptions. Section 4 gives model formulations and solutions in the Stackelberg game, Nash game and Coordination scenarios, respectively. Then, the numerical examples and sensitivity analyses are conducted in Section 5. Section 6 discusses some managerial insights. Finally, we conclude this paper and propose some research prospects in Section 7.

Tourism supply chain is a network of tourism organizations which conduct different kinds of tourist activities and have different types of business relationships (Kaukal et al., 2000; Alford, 2005; Zhang et al., 2009). In the literature on service in the tourism supply chain, there are different views on the service supplier. Some scholars consider TS as the service supplier and the service in tourist destinations has a positive impact on customers’ satisfaction and loyalty (Youn and Kim, 2017; Rahman and Zailani, 2017; Zhang et al., 2019a, b). For example, in marine ecosystem services, the symbolic and aesthetic value and coastal and offshore leisure activities provided by the marine play a significant role in marine tourism (Culhane et al., 2020).

Others hold the idea that TA, as the provider of comprehensive tourism services, involves in the tourism supply chain (Yildirim et al., 2018). But in reality, an ideal touring experience cannot be achieved without TA and TS working together to provide a comprehensive service experience, such as the service quality of both TA and TSs in the tourism supply chain (Long and Shi, 2017), the joint green tourism service, pricing and advertising problem of TA and TS in a green tourism supply chain (Ma et al., 2021) and a service supply chain composed of a hotel providing offline services and a platform responsible for pricing, online services and advertising investment (He et al., 2022).

Originating in the USA, the concept of omnichannel operations has developed rapidly in recent years. Relevant literature mainly focus on customers' experience, operational strategies and marketing effect, such as the impact of the integration quality of different channels on the omnichannel user's value experience (Shen et al., 2018), the influencing factors of customers' channel choice intentions in the omnichannel retail environment (Xu and Jackson, 2019), customers' perception of channels on their choice (Singh and Jang, 2020), the influence of offline experience store, online virtual exhibition hall and inventory information disclosure on inventory decisions (Gao and Su, 2017), inventory and pricing decisions of omnichannel retailers supporting returns and cancellations (Zhang et al., 2018), product pricing strategies under BOPS (Buy Online and Pick up in Store) mode (Cao et al., 2016a, b; Niu et al., 2019; Zhang et al., 2019a, b; Kong et al., 2020), the benefits of offline showrooms (Bell et al., 2018), the mutual promotional effects of cross-channel subsidy policies on operational decisions and performances (Chen et al., 2019) and the impact of adopting integrated management service on the performance of the assembly system with direct omnichannel (Chen and Peng, 2021).

Research on the bundling strategy can be traced back to its advantages of it in market segmentation and price discrimination (Adams and Yellen, 1976; Schmalensee, 1984). As an attractive and profitable marketing strategy, numerous scholars have investigated bundling pricing from different perspectives, including customer behavior (Prasad et al., 2017; Luo et al., 2017; Gayer et al., 2021), the types of products (Meyer and Shankar, 2016; Honhon and Pan, 2017; Jena and Ghadge, 2020; Taleizadeh et al., 2020), demand uncertainty (Chen and Zhang, 2015; Talebian et al., 2020), limited stock (Cao et al., 2016a, b; Gökgür and Karabatı, 2019) and market power (Pan and Zhou, 2017; Giri et al., 2020).

Here, we are more concerned about the application of bundling strategies in complementary product pricing. Relevant literature can be divided into two categories: one is the products provided by the same supplier, such as the bundling pricing problem of a manufacturer selling multiple products through a retailer (Pan and Zhou, 2017), the impact of two platforms' bundling strategy on their respective enterprises (Giri et al., 2020), the bundling strategy in a joint economic lot-sizing model with two complementary products (Hemmati et al., 2021). And the other is the products provided by different suppliers, such as the bundling strategy with complementary products considering channel competition (Li et al., 2018), and the pricing strategy for complementary products in a green supply chain (Shan et al., 2020).

Marine tourism includes coastal and maritime tourism. Coastal tourism includes tourism activities based on leisure activities such as swimming, surfing and sunbathing at the seashore, as well as land-based tourism near the seashore and suppliers and manufacturing industries related to these activities. Maritime tourism covers tourism activities based on water rather than land (e.g. boating, yachting, cruises and sailing) and includes the operation of land-based facilities, equipment manufacturing and services required for this part of the tourism industry (Gamage, 2016).

It is found that there are three major categories of studies on marine tourism. The first category is marine tourism resources and products, such as the classification of marine tourism products and resources (Gonzalez, 2021), the development strategies of marine cultural creative products (Qiu, 2020; Cao, 2020), the impact of marine tourism resources development (Wang and Zhang, 2019) and the marketing strategies and paths of marine green tourism (Shen, 2020). The second category is the impact of marine tourism on destinations. Most scholars believe that marine tourism activities cause ecological pollution to the destinations, such as air pollution, land pollution, deterioration of water quality and reduction of biodiversity (Burak et al., 2004; Kurniawan et al., 2016; Catlin and Jones, 2010; D'Lima et al., 2016; Liu et al., 2017). Other scholars believe that marine tourism activities can attract investment and stimulate the development of the local economy (Maria et al., 2017; Liu and Cao, 2018). And the last one is sustainable development, such as the new development mode of marine tourism based on visual design (Pu and Meng, 2019) and big data mining technology (Yan, 2020), the study on the dependence and impact between human activities and marine ecosystem services (Bryhn et al., 2020), and the challenges for marine tourism development (Tsilimigkas and Rempis, 2021).

Through relevant literature review, it is found that a few existing literature focuses on pricing strategies in marine tourism omnichannel. Besides, most of the service effort literature considered either the supplier or the retailer, but not both together. Different from previous research, this paper explores the impacts of the product bundling effects on the key supply chain decisions in the marine tourism omnichannel and explores the cooperative mechanism for it. This study will fill up the gap of the past research and provide new managerial insights to the marine tourism industry.

In this paper, we consider a marine tourism omnichannel consisting of two TSs and a TA who distributes products both online and offline. TSs are considered to provide complementary products to TA, respectively, and TA combines the two into a new product package. According to Figure 1, two sales channels are involved. One is an offline channel set to attract tourists to consult and experience physical stores, and the other is an online platform where tourists can choose and compare marine tourism products they are interested in directly.

Tourism supplier 1 (TS1) provides product 1 at a unit cost of $c1$ and tourism supplier 2 (TS2) provides product 2 at a unit cost of $c2$⁠. TS1 and TS2 sell their products to TA at the wholesale price $w1$ and $w2$⁠, $w=w1+w2$⁠. Here, the subscript “$i$” $(i=1,2)$ represents different marine tourism products from two different TSs. The prices of two complementary products are $p1$ and $p2$⁠, respectively. According to Lim et al. (2012), $p1$ and $p2$ are priced according to product cost segmentation, that is, $p1=p∙c1c1+c2$⁠, $p2=p∙c2c1+c2$⁠. The bundling price of these two products sold online or offline is $p$⁠. The online channel generates certain costs such as advertising and website operations, and the offline channel needs to pay for rent, decoration and staff salaries. So, we denote the fixed operating costs of online and offline channels as $con$ and $coff$⁠.

In addition, TSs and TA all provide a series of services to tourists, their service effort levels of them are denoted as $e1$⁠, $e2$ and $eT$⁠, respectively. In practice, a strictly convex service function $c(e)$ is used to depict the unit cost of TSs' service effort levels. One form commonly adopted in previous literature is given as (Tsay and Agrawal, 2000): $c(ei)=12kiei2(i=1,2)$⁠, where $ki$ is the service effort cost coefficient of TS. Similarly, the convex service function of TA is: $c(eT)=12kTeT2$⁠, where $kT$ is service effort cost coefficient of TA.

The sales volume of tourism products online and offline can be expressed as follows:

The total sales volume of tourism products is:

The parameter $a$ represents the maximum potential market demand for marine tourism products. $λ(0<λ<1)$ represents online-channel market share. Correspondingly, $1−λ$ represents the market share of offline-channel. The parameter $b(b>1)$ represents the price elasticity coefficient of tourism demand. The cross-price elasticity coefficient of tourism demand in different sales channels can be represented by the parameter $d(d>1)$⁠, and $b>d>1$⁠. Let $θ=b−d.$ Moreover, parameter $g$ represents the impact coefficient of TSs’ and TAs’ service effort levels. To simplify the calculation, we set the coefficient of TSs’ service effort level the same as TAs’ (Table 1).

According to the above hypotheses, the profit function of TSs and TA can be described as follows:

The profit function of marine tourism omnichannel can be described as follows:

In this section, six scenarios are considered in total. First, we construct several models to achieve the optimal price of marine tourism products and the maximum profit of TSs and TA by Stackelberg and Nash game. Then, a revenue-sharing contract is considered to coordinate the profit distribution of each member and improve the overall profit in marine tourism omnichannel.

In TSs-leading Stackelberg decision models, the problem is analyzed as a Stackelberg game where TSs act as the leader and TA acts as the follower. Here we consider two scenarios. The first one is that TSs decide their service effort levels and the wholesale prices in sequence, and then TA decides its service effort level and the bundling price in sequence. Another is that TSs and TA decide their own service effort levels in sequence, and then they decide the wholesale prices and bundling prices for customers.

The decision sequence of TSs-TA sequential decision model (abbreviated as SS) is shown in Figure 2. First, TSs decide their service effort levels. Then, the wholesale prices for TA are decided by TSs to maximize their profits. Next, TA decides its service effort level both online and offline according to the pricing and service effort strategies of TSs. Finally, TA decides its bundling price.

When taking TSs-TA sequential decision model, the optimal profit function of marine tourism omnichannel can be formulated as:

Solving this two-stage Stackelberg game, we can obtain the equilibrium TSs' service effort levels $e1SS$ and $e2SS$⁠, the equilibrium wholesale prices $w1SS$ and $w2SS$⁠, the equilibrium TA's service effort level $eTSS$⁠, the equilibrium bundling price $pSS$ and the equilibrium demand online and offline $qonSS$ and $qoffSS$⁠; on this basis, we can obtain the equilibrium profits of TS1, TS2 and TA and marine tourism omnichannel $Π1SS$, $Π2SS$⁠, $ΠTSS$ and $ΠSCSS$ (Table 2). Superscript “SS” represents the solutions/outcomes in the model SS, the same as below.

The decision sequence of the TSs-TA alternating decision model (abbreviated as SA) is shown in Figure 3. First, TSs decide the service effort levels. Then, TA decides its service effort level both online and offline according to the service effort strategies of TSs. Next, the wholesale prices to TA are decided by TSs. Finally, TA decides its bundling price.

When taking TSs-TA sequential decision model, the optimal profit function of marine tourism omnichannel can be formulated as:

Solving this two-stage Stackelberg game, we can obtain the equilibrium TSs’ service effort levels $e1SA$ and $e2SA$⁠, the equilibrium TAs’ service effort level $eTSA$⁠, the equilibrium wholesale prices $w1SA$ and $w2SA$⁠, the equilibrium bundling price $pSA$ and the equilibrium demand online and offline $qonSA$ and $qoffSA$⁠; on this basis, we can obtain the equilibrium profits of TS1, TS2, TA and marine tourism omnichannel $Π1SA$, $Π2SA$⁠, $ΠTSA$ and $ΠSCSA$ (Table 2).

In TA-leading Stackelberg decision models, the problem is analyzed as a Stackelberg game where TA acts as the leader and TSs act as followers. Here we consider two scenarios. The first one is that TA decides its service effort level and the bundling price in sequence, and then TSs decide their service effort levels and wholesale prices in sequence. Another is that TA and TSs decide their own service effort level in sequence, and then decide the bundling price and wholesale prices in sequence.

The order of TA-TSs sequential decision model (abbreviated as AS) is shown in Figure 4. First, TA decides the service effort level both online and offline. Then, the bundling price for customers is decided by TA. Next, TSs decide their service effort levels. Finally, the wholesale prices are decided by TSs.

When taking TA-TSs sequential decision model, the optimal profit function of marine tourism omnichannel can be formulated as:

Solving this two-stage Stackelberg game, we can obtain the equilibrium TAs’ service effort level $eTAS$⁠, the equilibrium bundling price $pAS$⁠, the equilibrium TSs' service effort levels $e1AS$ and $e2AS$⁠, the equilibrium wholesale prices $w1AS$ and $w2AS$ and the equilibrium demand online and offline $qonAS$ and $qoffAS$⁠; on this basis, we can obtain the equilibrium profits of TS1, TS2, TA and marine tourism omnichannel $Π1SA$, $Π2AS$⁠, $ΠTAS$ and $ΠSCAS$ (Table 3).