Coordination Analysis of Sustainable Dual-Channel Tourism Supply Chain with the Consideration of the Effect of Service Quality

Abstract

The development of the Internet has changed the way that tourism products are sold. More and more tourism product providers (TPPs) use online travel agencies (OTAs) and offline travel agencies (TA) to sell their tourism products. With increasing consumption, service quality has also become an important factor for consumers to choose tourism products. Analysing the effect of service quality on pricing, demand, and profits under centralised and decentralised decisions helps the tourism industry development more sustainably. The study found that the level of the TPP service quality has a positive effect on channel pricing and demand, while the effects of the OTA and TA service quality on channel pricing and profits are affected by the sensitivity of consumer testing to cross-channel prices and services. The results of a comparative analysis of centralised and decentralised decisions show that supply chain members cannot reach an optimal state under the decentralised decision method. Supply chain optimisation is achieved through wholesale price contracts coordinating the distribution of profits among supply chain members. This study used numerical examples to verify the validity of the relevant conclusions and to the coordination mechanism, and propose some managerial implications. This work is pioneering in that it develops a dual-channel tourism supply chain with OTA channel participation, taking into account the service quality of all the members in the tourism supply chain. It suggests further research direction in studying sustainable tourism supply chain development.

1. Introduction

The rapid development of online travel platforms means that in recent years tourists have tended to make their purchases online [1]. More and more travel product suppliers (TPPs) such as hotels and airlines have started to work with online travel agents (OTAs) to expand the sales market and improve profitability [2,3]. At the same time, traditional offline travel agents (TAs) are irreplaceable, and a considerable number of loyal customers are attracted by the powerful advantages of scenario-based, experienced and visualised real services. TPPs thus attempted to adopt dual-channel strategies to sell their tourism products to tourists through OTAs and TAs in order to meet market demands and maintain the sustainable development of the business. The intense competition in the global travel industry is dominated by components of supply chains, however, not individual companies, and this also causes competition among channels while OTAs are increasing their market share, so the TPPs and TAs in the supply chain need to find ways to improve their competitive advantage and regain lost markets [4,5]. Cooperation between multiple business partners in the tourism supply chain is one way for TPPs and TAs to sustain their business success in the tourism market [6]. TPPs and TAs, however, and even OTAs, face many supply chain issues when coordinating with multiple business partners. Addressing these issues can help tourism product suppliers coordinate dual-channel tourism strategies sustainably.

In the increasingly competitive tourism market, providing high-quality service is one of the key factors for the sustainable development of tourism enterprises [7,8]. The service efforts of tourism enterprises need to be customer-oriented [9]. Since service quality is one of the most important components of tourism enterprises as regards increasing their efficiency and profitability, they offer superior service in order to compete in the market [10]. Pricing competition is a major issue for tourism supply chain channels, if they want to compete in the market [11]. Members of tourism supply chains, therefore, need to improve their service quality and reduce their prices on the other hand. Improving service quality means increasing costs, however, and while keeping overall profits among the supply chain unchanged, the service price needs to rise. On the other hand, service delivery is not individual, but holistic, and therefore does not depend only on individual supply chain members improving their service quality. Various tourism enterprises compete for tourists for their own interests, affecting the allocation of supply chain resources. The distribution of profits among supply chain members is not necessarily equal, however, and determining how to balance the interests of all parties is the key to the success of coordination with multiple business partners.

The aim of this study is to develop a dual-channel tourism supply chain model to answer the following questions for the successful coordination of a tourism service supply chain.

(1)

How can pricing decisions be made, considering the effect of service quality factors in the operation of tourism enterprises?

(2)

How does service quality affect the profits of members of the tourism supply chain?

(3)

How can the distribution of benefits be optimised to provide high-quality and efficient tourism services through the coordination of the tourism service supply chain?

This study contributes to tourism supply chain research by providing researchers with a pricing decision model regarding the effect of service quality that they can extend to conduct further studies. This study also contributes to our knowledge in showing how to maximise the profits of a tourism supply chain through contract coordination with multiple business partners. This study helps to realise the Pareto improvement in order to support the sustainability of the tourism supply chain by adopting dual-channel strategies.

2. Literature Review

Based on the above research questions, this work mainly reviews the tourism supply chain, the pricing decision, and coordination of the tourism supply chain, and the impact of service quality on the tourism supply chain.

2.1. Tourism Supply Chain

The essential characteristics of the tourism supply chain are different from those of the product supply chain [12]. The classic supply chain is mostly based on the manufacturing industry and its physical products, while the tourism supply chain is mainly based on services. Scholars have carried out extensive research on the definition and composition of the tourism supply chain. Tapper and Font [13] suggested that the tourism supply chain involved many components–not only accommodation, transportation, and excursions, but also bars and restaurants, handicrafts, food production, waste disposal, and the infrastructure to support tourism. Li et al. [14] pointed out that the core of the tourism supply chain was tourism attractions, which formed a whole that included tourism product design, production, and sales, and finally supports tourists to visit and promotes various forms of consumption. Organisations and enterprises include network chain structures. Zhang et al. [15] defined a tourism supply chain as a network of tourism organisations involved in a range of different activities, ranging from the provision of whole components of tourism products/services, such as flights and accommodation at tourist receptions, to the sales of tourism products in tourist areas. A tourism supply chain consists of a series of organisations, such as the suppliers’ resorts, their transportation and hospitality, souvenir shops, travel agencies, and even the public sector, providing tourists with various goods and services [16]. Xu et al. [17] defined the tourism supply chain as the supply chain structure of tourism products, from tourism suppliers to tourism agents to tourists. Sustainability practices in the tourism supply chain are therefore those practices followed by the focal organisation with their suppliers and customers in order to improve their performance by meeting the environmental, economic, and social objectives of the supply chain [18]. However, tourism supply chain participants are often autonomous, and independent enterprises frequently have conflicting objectives [15]. The tourism supply chain coordination takes the form of contractual relationships among individual firms to perform tasks to achieve supply chain goals. Therefore, the sustainable tourism supply chain takes full account of current and future economic, social, and environmental impacts, addressing the needs of the visitors, the industry, the environment, and destinations through the tourism supply chain coordination [15,18].

With the development of e-commerce, the Internet has become a very important factor in the supply chain of tourism products [19], and the core position of OTA is increasingly obvious. Christodoulidou et al. [20] explored the way that OTAs act as travel intermediaries to build relationships with travel suppliers throughout the distribution value chain. Yan and Hong [21] took the network supplier as the core, studied how to construct the tourism supply chain, analysed the problems existing in the travel agency supply chain, and suggested ways to construct a network supplier supply chain. Huang and Shu [22] suggested that the online tourism service provider was central to meeting the needs of tourists through an analysis of the components of the tourism supply chain, the structural model, and the driving mechanism of the tourism supply chain in the network environment. Guo and Zhang [23] established a tourism supply chain with an e-commerce platform as the core, and the consumer information sharing and coordination mode were studied. Through the coordination, performance can be improved, and greater profitability can be obtained to achieve a sustainable tourism supply chain [15].

2.2. Tourism Supply Chain Decision and Coordination

Some scholars have used different game methods to study supply chain decisions, optimisation, and coordination [24,25,26]. Furthermore, there exist some studies in the field of tourism supply chains. For instance, Huang [27] used the Stackelberg game and Nash game model to analyse decision about hotel prices under different power structures in the tourism supply chain. Yang et al. [28] explored a two-level tourism supply chain consisting of hotels and OTAs, constructing centralised and decentralised models based on Bertrand and Stackelberg games. The results showed that when the wholesale price is below a certain level, the profits of hotels and OTAs under the Stackelberg game are better than those under the Bertrand game. Zhao and Chen [29] considered the effect of tourist preferences on the demand function of groups and individual passenger markets, and comparatively analysed the vertical cooperation advertising strategy of the tourism supply chain, including a single scenic spot and a single travel agency under the game of Nash and Steinberg. Based on the difference in the power of supply chain members, Huang et al. [30] studied the need for local operators and tourism operators to determine the optimal wholesale price in the tourism supply chain under the local operator-dominated Stackelberg, tourism operator-dominated Stackelberg, and the static Nash games, carbon emission reduction levels and retail prices for travel package strategies.

Some scholars have also considered decision and contract coordination issues under the influence of different factors on the tourism supply chain from various perspectives. Lv et al. [31] constructed a tourism supply chain model for oversupply in off-season tourism and insufficient supply in the peak tourism season, composed of tourism suppliers and intermediaries by using the theory of channel rights, and discussed the optimal strategies of suppliers and intermediaries in two modes. Dong et al. [32] considered developed tourism destinations, and constructed a tourism supply chain model consisting of one tour operator and one less developed and one relatively mature tourism destination. The equilibrium solutions and benefits of all members under decentralised decisions were analysed under different cooperation modes, and quantity discount contracts were used to coordinate the tourism supply chain. Jena and Jog [11] considered the pricing and profit issues of the tourism supply chain under the influence of advertising on the channel members’ demand, and designed two coordination mechanisms: cooperative advertising and a two-part fee system. Wan et al. [33], according to the altruistic preferences of decision-makers, discussed the optimal pricing strategies and coordination contract of providers of low carbon tourism products and services (TCP), and an OTA. Liu et al. [34] investigated the pricing and environmental governance efficiency decision and channel coordination of a dual tourism supply chain with corporate social responsibility, and designed a coordination mechanism. Some studies investigated the effect of decision-makers’ altruistic preferences and consumers’ low-carbon preferences on the decision behaviours at all levels of the dual-channel environmental hotel supply chain. For instance, Wan et al. [35] built a dual-channel supply chain network equilibrium model including hotels, OTA platforms, and demand markets. Ma et al. [36] considered the influence of the green tourism experience on the demand for tourism products, constructed a green tourism supply chain consisting of a scenic location and a travel agency, and studied the joint green tourism service, pricing and advertising issues with travel agencies. They analysed the effect of tourists’ green tourism experience based on three different scenarios; wholesale fares, sharing ratios on the optimal decision, and the performance of the tourism supply chain.

2.3. The Impact of Service Quality on the Tourism Supply Chain

Service quality is an abstract concept. Parasuraman et al. [37] identified five dimensions of service quality: tangibility, reliability, responsiveness, assurance, and empathy. Ghobadian et al. [38] defined “quality” in a service organisation as a measure of how well the service provided meets customer expectations. Gummesson [39] defined the concept of service quality from the perspective of perception as the degree to which consumer demand is satisfied, and this concept has also been the most widely recognised. Under the current customer-oriented market trend, service quality has gradually become the source of competitive advantage, scientific research into service has become an important field, and service quality has become the primary factor for the success of an enterprise [40]. By implementing global ISO standards in service management, it is possible to ensure the efficiency of service companies in terms of products, services and international supply chains, thereby increasing customer satisfaction [41].

Ţîţu et al. [42] believed that quality tourism became one of the development directions of the global tourism industry in the future, and tourism quality should be widely concerned. Some scholars use the SERVQUAL scale or make appropriate adjustments as a dimension of tourism service quality [43,44]. Some scholars have studied the dimension of service quality from the perspective of tourism providers, such as hotels, tour guides, and package tours [45,46,47]. Service quality, as one of the main factors affecting the competitiveness of tourism enterprises, has been widely studied by scholars. Tian-Cole and Cromption [48] suggested that tourism managers strived to improve their service quality and customer satisfaction, and that such efforts create loyal customers and tourists. Loyal tourists return to a destination and recommended it to others. Haghkhah et al. [49] reviewed and summarised the concepts and dimensions of service quality in tourism, and specifically studied the effect of service quality on tourism and its degree of influence on customer satisfaction. Kachwala et al. [50] argued that service quality could improve the competitiveness of tourism companies, and gain customer loyalty. Ferri Sanz et al. [51] reviewed the scales most commonly used to measure service quality in sustainable tourism destinations, highlighting the effect of variables on the service quality perceptions of tourists with special needs in sustainable tourism. Most of these are empirical studies. Lai et al. [52] conducted a literature review on service quality in hospitality and tourism, and found that the rapid growth of internet technologies means that electronic tourism offers research opportunities. They pointed out that the adoption of e-services can be affected by different e-service factors, including the coordination of tourism supply chains.

A few scholars have undertaken research on the decision of service quality in the tourism supply chains. Yang et al. [53] believed that the quality of tourism service is a basic guarantee of the sustainable development of tourism. They studied the decisions of the optimal service quality of travel agencies in a package tourism supply chain composed of theme parks and travel agencies. Yang et al. [28] explored a two-level tourism supply chain consisting of hotels and online travel agencies, constructing a centralised and decentralised model based on the Stackelberg game dominated by Bertrand and travel agencies. Research shows that when the wholesale price is below a certain level, the profits obtained by hotels and online travel agencies under the Stackelberg game are better than those under the Bertrand game. Peng et al. [54] constructed a two-level customised tourism service supply chain consisting of tourist attraction providers and two travel agencies, the joint services of the basic service provided by tourist attraction providers, and the value-added services provided by travel agencies. In the case of dependent demand, the pricing strategy and service innovation-decision problem are studied under the same wholesale price and different wholesale prices. Jena and Meena [55] believed that in competitive practice, price-sensitive demand and service-sensitive demand play an important role in a tourist’s choice of travel packages. Their results showed that the profit generated under competition was higher than the profit without competition, and the design of the surplus-sharing contract coordination could improve the overall profit of the tourism supply chain. Huang and Zhang [56] believed that the emergence of group tour service quality problems meant that quality control was increasingly important in tourism supply chains. A dual-channel tourism supply chain model consisting of group tours and network channels was constructed based on tourists’ quality preferences. They discussed the effect of quality control on pricing and additional services when the group agency provides additional services as a differentiation strategy.

By sorting out the previous research, the main references are summarised and compared, as shown in

Table 1. List and comparison of main key related references.

Author	Supply Chain Structure	Whether Consider Service Factors	Game Process	Coordination
Dong, J., Shi, Y., Liang, L., and Wu, H. [32]	One tour operator and two tourism destinations	N\A	Stackelberg	Quantity discount contract
Yang, L., Ji, J., and Chen, K. [28]	Dual-Channel	N\A	Bertrand and Stackelberg	Wholesale
Jena, S. K., and Jog, D. [11]	Two-Echelon TSC	N\A	Local operator, tour operator, integrated Stackelberg	Cooperative advertising Two-part tariff contract
Peng, H., He, Y., and Xu, F. [54]	Two-echelon tourism supply chain	TAP and TA provide services	Stackelberg	N\A
Jena, S. K., and Meena, P. L. [55]	Two-Echelon TSC	Only tour operator provides services	Stackelberg	Sharing Surplus
Wan, X., Jiang, B., Qin, M., and Du, Y. [33]	Single-Channel	N\A	Stackelberg	Revenue-sharing contracts
Huang, L., and Zhang, M. [56]	Dual-Channel	Only tour retailer provides extra service	Stackelberg	Two-part tariff contract
Huang, X., Zhu, S., and Wang, J. [30]	Single-Channel	N\A	Stackelberg and Nash	N\A
This study	Dual-Channel Consider OTA participation on sale	Consider TP, OTA, TA provide services	Stackelberg	Wholesale Price, Income distribution contract

.

The difference can be shown as follows. The existing literature mostly focuses on single-channel tourism supply chain research, and some scholars have also conducted research on dual-channel tourism supply chains that combine being online and offline, in which the supply chain composed of offline distributors is the main channel. However, in reality, tourism product sales channels and channel members are more abundant. The members of the dual-channel tourism supply chain constructed in this study include tourism suppliers who provide tourism products and services, as well as OTA platforms that sell online and travel agencies that sell offline. Hence the supply chain is more complete than those in previous studies.

Most tourism supply chain research takes into account price competition. These studies have assumed that the market demand is only affected by the tourism price, without considering the effect of service quality. There are fewer studies on the effect of service quality on the tourism supply chain, however, especially considering the participation of OTA channels in sales. Even though some studies have focused on the service quality of single-channel members, they have not comprehensively considered the effect of the service quality of all members on the tourism supply chain. The procedural nature of tourism products means that the quality of tourism services is produced through behavioural contact and emotional communication between tourism enterprises and tourists. The quality of tourism service includes not only the quality of the process, but also the quality of the result, including the service quality experienced by tourism consumers when purchasing tourism products through different channels, and the service quality of tourism products provided by tourism providers. The upstream tourism suppliers and downstream distributors collaborate and affect each other, and the service quality perceived by tourists will not only affect the channels through which tourism consumers purchase tourism products, but also affect the selection of tourism products provided by tourism suppliers. It is, therefore, necessary to study the use of contract coordination to optimise the performance of the tourism supply chain with the consideration of the quality of service.

In order to fill the research gap, this work constructs a dual-channel supply chain composed of a single TPP, an OTA, and a TA in combination with the development status of the travel supply chain. TPP makes centralised and decentralised decisions for products and services. OTA and TA make product sales and services. This study aims to optimise the tourism supply chain through design contract coordination. It is expected that effective policy suggestions can be made for the healthy development of tourism.

3. Research Methods

This study involves three steps. It first presents the problem description and assumptions. Secondly, it derives the model formulation and took model comparison analysis under the decentralised and centralised decision-making scenarios, including supply chain coordination. Lastly, this work makes a numerical analysis of the effect of the TPP service quality on channel price, channel demand, and profits. Then, it further analyses the effect of the OTA and TA service quality on channel prices, channel demand, and profits. It also compares the results of centralised decisions and decentralised decisions under contract coordination.

3.1. The Problem Description

Figure 1 demonstrates a dual-channel supply chain consisting of a TPP, an OTA (online channel), and a TA (offline channel). Under the dual-channel travel supply chain, TPP uses OTA (online channel $o$) and TA (offline channel $t$) to sell tourism products at the same time.

The TPP provides the TA with travel products at discounted prices $\mathrm{w}$, for offline sales. It uses the OTA channel to sell tourism products online and pays commissions to the OTA based on a certain percentage of the sales volume $k(k<0<1)$. The prices of travel products for online and offline channel sales are $p_o$, $p_t$.

The service effort made by the OTA and TA is an effective way to expand market demand, and higher service quality will be more favoured by consumers. The service quality level does not refer to the probability of meeting demand but refers to the support in marketing or sales, which will have a positive effect on the demand for the product (higher service quality means higher demand). The demand of each sales channel is an increasing function of its competitor’s selling price and its own service quality level, and a decreasing function of its own selling price and its competitor’s service quality level. Referring to the demand function constructed by Tsay and Agrawal [57] considering the effect of market demand by service quality, the demand function constructed considering the influence of the OTA and TA service quality is:

$$\begin{array}{c}{D}_o=A\left(1-\alpha \right)-\beta p_o+\sigma p_t+\gamma s_o-\lambda s_t\\ D_t=\alpha A-\beta p_t+\sigma p_o+\gamma s_t-\lambda s_o\end{array}$$

The procedural nature of tourism products determines that the quality of tourism services is generated in the behavioural contact and emotional communication between tourism enterprises and tourists. The quality of tourism service includes not only the service quality experienced by tourism consumers when purchasing tourism products through the OTA or TA channels, but also the service quality of tourism products provided by the TPP, such as the spaciousness of hotel rooms, the facilities available at tourist attractions, and luxury cars for travel. Considering the influence of the OTA and TA service quality, referring to the linear demand function constructed by Dan et al. [58] and Wang and Hu [59], the demand function of each channel is constructed considering the joint influence of price and the service quality offered by the TPP, OTA, and TA.

$$\begin{array}{c}{D}_o=A\left(1-\alpha \right)-\beta p_o+\sigma p_t+\gamma s_o-\lambda s_t+\delta s_s\end{array}$$

(1)

$$D_t=\alpha A-\beta p_t+\sigma p_o+\gamma s_t-\lambda s_o+\delta s_s$$

(2)

Use $s_i\left(i=s,o,t\right)$ to describe the service quality level of the TPP, OTA, and TA. Service cost $c\left(s_i\right)$ is related to its service quality level, and increases with the improvement of service quality level, and the rate of increase shows an upward trend. Considering the concave feature of service cost, according to Tsay and Agrawal [57], Xiao et al. [60], Giri and Sarker [61], and other settings for service cost, let it be a quadratic function, $c\left(s_s\right)=\mu s_s^2/2$, $c\left(s_o\right)=\theta s_o^2/2$, $c\left(s_t\right)=\epsilon s_t^2/2$, and $\mu \left(\mu >0\right),\theta \left(\theta >0\right),\epsilon (\epsilon >0)$, are the service quality control cost coefficients of the TPP, OTA, and TA. $A$ stands for potential market demand. $\alpha (0<\alpha <1)$ is channel preference for consumers. $\alpha A$ represents the market demand of those who are loyal to purchasing tourism products through traditional offline channels. $\left(1-\alpha \right)A$ refers to those consumers who are loyal to online channels for online purchases. The market demand for tourism products is always affected by the service quality level and product pricing of tourism companies. Usually, consumers are price-sensitive, and high sales prices will reduce market demand. $\beta ,\gamma$ measure the responsiveness of each channel’s market demand to its own prices and services. $\sigma ,\lambda$ are indicators with which to measure the competitive intensity of competing channels in terms of pricing and service behaviour and $\beta >\sigma >0$, $\gamma >\lambda >0$, which shows that the response of channel demand to the product price and service quality level of its own channel is greater than that of another channel. $\delta (\delta >0)$ is the consumer’s sensitivity to the TPP service quality. Market demand will increase with an increase in TPP service quality.

3.2. Assumptions

The assumptions of this model are as follows.

(1)

Considering the dual-channel travel supply chain, the OTA and TA provide consumers with the same single tourism product, regardless of tourism product mix or bundled sales.

(2)

The tourism supply chain members have no risk preference, are all risk-neutral rational people, regardless of shortages, and both TPP and TA aim to maximise profits.

(3)

$p_i\left(i=o,t\right)>w$, tourism products are sold at a higher price than the discounted price, so as to ensure that the tourism business is profitable.

(4)

Since the TPP masters tourism resources, the TPP is the leader and the TA is the follower in a Stackelberg game, and it is assumed that consumers have different channel preferences.

Assuming that the TPP bears the operating cost of the tourism products of all channels, the operating cost of a unit tourism product is c. The revenue functions of the TPP, TA, and OTA are:

$${\mathsf{\Pi }}_s=p_o{D}_o+w{D}_t-k{p}_o{D}_o-c\left(D_o+D_t\right)-\mu s_s^2/2$$

(3)

$${\mathsf{\Pi }}_t=\left(p_t-w\right)D_t-\epsilon s_t^2/2$$

(4)

$${\mathsf{\Pi }}_o=k{p}_o{D}_o-\theta s_o{}^2/2$$

(5)

The first and second terms in Equation (3) represent the revenue obtained by the TPP through the online and offline channels, respectively. The third term represents the commission paid to the OTA, and the fourth term represents the operating cost of the TPP; Equation (4) is the sales revenue of the TA offline channel minus service costs. Equation (5) is the OTA online channel commission income minus service cost. Since the sales quantity of tourism products in this study is mainly reflected in the demand function, and the demand is a linear function of the price and the level of service quality, this work, therefore, considers offline, online, and wholesale prices as decision variables.

Table 2. Main variables and their meanings.

Decision Variables	Description
$p_t$	Offline sales price of tourism products
$p_o$	Online sales price of tourism products
$w$	Wholesale price from the TPP to the TA
Symbol	Description
$s_s$	The TPP service quality
$s_o$	The OTA online channel service quality level
$s_t$	The TA offline channel service quality level
$A$	Potential market demand
$\alpha$	$\mathrm{Consumer}\mathrm{preference}\mathrm{for}\mathrm{offline}\mathrm{channels}\left(0<\alpha <1\right)$
$\beta$	Price Sensitivity of Demand
$\sigma$	$\mathrm{Cross}\mathrm{price}\mathrm{sensitivity}(\beta >\sigma >0)$
$\gamma$	Sensitivity of channel demand to service quality
$\lambda$	$\mathrm{Sensitivity}\mathrm{of}\mathrm{Service}\mathrm{Quality}\mathrm{of}\mathrm{Competing}\mathrm{Channels}(\gamma >\lambda >0)$
$\mu$	$\mathrm{TPP}\mathrm{service}\cos \mathrm{t}\mathrm{factor}\left(\mu >0\right)$
$\theta$	$\mathrm{OTA}\mathrm{service}\cos \mathrm{t}\mathrm{factor}\left(\theta >0\right)$
$\epsilon$	$\mathrm{TA}\mathrm{service}\cos \mathrm{t}\mathrm{factor}\left(\epsilon >0\right)$
$c\left(s_s\right)$	$\mathrm{TPP}\mathrm{service}\cos \mathrm{t}c\left(s_o\right)=\theta s_o^2/2$
$c\left(s_o\right)$	$\mathrm{OTA}\mathrm{service}\cos \mathrm{t}c\left(s_o\right)=\theta s_o^2/2$
$c\left(s_t\right)$	$\mathrm{TA}\mathrm{service}\cos \mathrm{t}c\left(s_t\right)=\epsilon s_t^2/2$
$k$	$\mathrm{The}\mathrm{percentage}\mathrm{of}\mathrm{commission}\mathrm{paid}\mathrm{by}\mathrm{TPP}\mathrm{to}\mathrm{OTA}(0<k<1)$
$D_o$	Online channel demand
$D_t$	Offline channel demand
${\mathsf{\Pi }}_s$	Profit of the TPP
${\mathsf{\Pi }}_t$	Profit of the TA
${\mathsf{\Pi }}_o$	Profit of the OTA
$\mathsf{\Pi }$	Total profit of the TSC

shows the main variables and their meanings in the text. The total profit of the supply chain is:

$$\mathsf{\Pi }={\mathsf{\Pi }}_s+{\mathsf{\Pi }}_t+{\mathsf{\Pi }}_o$$

(6)

The subscript “$o,s,t$” represents the OTA, TPP, and TA, respectively; the superscript “$C,D,O$” represents the centralized, decentralized, and coordinated models, respectively, and the superscript “*” represents the optimality.

4. Model Formulation and Analysis

4.1. Decentralized Decision

Under the decentralised decision-making dominated by tourism suppliers, in the dual-channel tourism supply chain, the TPP act as the leading enterprise to make decisions, while the TA is the follower. The specific decision-making sequence is as follows: first, the TPP decides the sales price of the online channel $p_o$ and the wholesale price given to the TA $w$, and then the TA determines the sales price of the offline channel after observing the behaviour of the travel supplier $p_t$. The model was derived by reverse induction.

Under the decentralised decision, the TPP determines the online sales price and the wholesale price, and then the TA determines its offline sales price.

Theorem 1.

The optimal strategies for the members of the dual-channel tourism supply chain are:

$$p_o^{D^{*}}=\frac{\begin{array}{c}4\beta \left(1-k\right)\left(\lambda s_t-\sigma s_s-\gamma s_o\right)-\sigma \left(4-3k\right)\left(\gamma s_t-\delta s_s+\lambda s_o\right)+\\ \left(\sigma -\beta \right)\left(4c\left(\sigma +\beta \right)-ck\sigma \right)-4A\beta \left(1-\alpha \right)\left(1-k\right)-A\alpha \sigma \left(4-3k\right)\end{array}}{8{\beta }^{2}k-8{\beta }^2+k^2{\sigma }^2-8k{\sigma }^2+8{\sigma }^2}$$

$$w^{D^{\ast }}=\frac{\begin{array}{c}(1-k)\left(a_1{s}_o-a_2{s}_s-a_3{s}_t\right)+(1-k)\left(A\alpha k{\sigma }^2+4\beta c{\sigma }^2-4{\beta }^{3}c-4A\alpha {\beta }^2\right)\\ +(1-\alpha )\left(6A\beta k\sigma -4A\beta \sigma -2A\beta k^2\sigma \right)+\beta ck{\sigma }^2(\sigma -2\beta )+ck{\sigma }^3\end{array}}{\beta \left(8{\beta }^{2}k-8{\beta }^2+k^2{\sigma }^2-8k{\sigma }^2+8{\sigma }^2\right)}$$

$$p_t^{D^{\ast }}=\frac{\begin{array}{c}(1-k)\left(a_1{s}_o-a_2{s}_s-a_3{s}_t\right)+(1-k)\left(2A\alpha {\sigma }^2-2{\beta }^{3}c-6A\alpha {\beta }^2\right)\\ +(1-\alpha )\left(5A\beta k\sigma -4A\beta \sigma -A\beta k^2\sigma \right)+\beta c\sigma (2(\sigma -\beta )-c(\sigma +\beta ))+2c{\sigma }^2\end{array}}{\beta \left(8{\beta }^{2}k-8{\beta }^2+k^2{\sigma }^2-8k{\sigma }^2+8{\sigma }^2\right)}$$

where

$$\begin{gathered} a_1=4\lambda {\beta }^2+2\gamma \beta k\sigma -4\gamma \beta \sigma -\lambda k{\sigma }^2 \\ {a}_2=4\delta {\beta }^2-2\delta \beta k\sigma +4\delta \beta \sigma -\delta k{\sigma }^2 \\ {a}_3=4\gamma {\beta }^2+2\lambda \beta k\sigma -4\lambda \beta \sigma -\gamma k{\sigma }^2 \\ {b}_1=6\lambda {\beta }^2+\gamma \beta k\sigma -4\gamma \beta \sigma -2\lambda {\sigma }^2 \\ {b}_2=6\delta {\beta }^2-\delta \beta k\sigma +4\delta \beta \sigma -2\delta {\sigma }^2 \\ {b}_3=6\gamma {\beta }^2+\lambda \beta k\sigma -4\lambda \beta \sigma -2\gamma {\sigma }^2 \end{gathered}$$

Proof. 

The second-order partial derivative of $p_t$ about ${\mathsf{\Pi }}_t^D$, $\begin{array}{c}\frac{{\partial }^2{\mathsf{\Pi }}_t^D}{\partial p_t^2}=-2\beta <0\end{array}$ the profit function follows a concave function, let $\begin{array}{c}\frac{\partial {\mathsf{\Pi }}_t^D}{\partial p_t}=0\end{array}$ get $\begin{array}{c}{p}_t^D=\frac{A\alpha +\delta s_s+\sigma p_o+\beta w+\gamma s_t-\lambda s_o}{2\beta }\end{array}$.

Substitute $p_t$ into $\begin{array}{c}{\mathsf{\Pi }}_s^D\end{array}$, find the second-order partial derivative of $\begin{array}{c}{\mathsf{\Pi }}_s^D\end{array}$ with respect to $w,p_o$. The Hessian matrix can be obtained:

$$\begin{array}{c}{H}_{\left(w,p_o\right)}=\left[\begin{array}{cc}-\beta & \sigma -\frac{k\sigma }{2}\\ \sigma -\frac{k\sigma }{2}& 2k\left(\beta -\frac{{\sigma }^2}{2\beta }\right)-2\beta +\frac{{\sigma }^2}{\beta }\end{array}\right]\end{array}$$

The Sequential Principal Minor of $\begin{array}{c}{H}_{\left(w,p_o\right)}\end{array}$:

$$\begin{array}{c}H1=-\beta ,H2=8\left({\beta }^2-{\sigma }^2\right)\left(1-k\right)-{\sigma }^2{k}^2\end{array}$$

If $0<k<\frac{\sqrt{2}\sqrt{2{\beta }^4-3{\beta }^2{\sigma }^2+{\sigma }^4}-4{\beta }^2+4{\sigma }^2}{{\sigma }^2}$, then $\begin{array}{c}H1<0,H2>0\end{array}$.

The Hessian matrix is negative definite, the profit function follows a concave function.

Let $\begin{array}{c}\frac{\partial {\mathsf{\Pi }}_s^D}{\partial p_o}=0,\frac{\partial {\mathsf{\Pi }}_s^D}{\partial w}=0\end{array}$, solving the simultaneous equations, we get $\begin{array}{c}{p}_o^{D^{*}},w^{D^{*}}\end{array}$, substitute $p_t$ and we can get $\begin{array}{c}{p}_t^{D^{*}}\end{array}$. □

Bring the optimal pricing results $\begin{array}{c}\begin{array}{c}{p}_o^{D^{*}},p_t^{D^{*}},w^{D^{*}}\end{array}\end{array}$ into Equations (1) and (2) to obtain the demands of both online and offline channels under the decentralised decision $\begin{array}{c}\begin{array}{c}{D}_o^{D^{*}},D_t^{D^{*}}\end{array}\end{array}$, and then bring them into Equations (3)–(6) to obtain the TPP, OTA, TA optimal profits $\begin{array}{c}\begin{array}{c}{\mathsf{\Pi }}_s^{D^{*}},{\mathsf{\Pi }}_o^{D^{*}},{\mathsf{\Pi }}_t^{D^{*}},{\mathsf{\Pi }}^{D^{*}}\end{array}\end{array}$.

Proposition 1.

Under the decentralized decision.

(1)

The service quality of the TPP is always positively correlated with the sales price of each channel $\begin{array}{c}\begin{array}{c}\frac{\partial p_t^{D^{*}}}{\partial s_s}>0\end{array}\end{array}$, $\begin{array}{c}\begin{array}{c}\frac{\partial p_o^{D^{*}}}{\partial s_s}>0\end{array}\end{array}$;

(2)

The service quality of the TA is positively correlated with the price of the offline channels. The effect of the OTA service quality on the online channel prices is related to consumer sensitivity to price and service quality.

$$\begin{gathered} \{\begin{array}{c}ab>\frac{4-3k}{4-4k},4\beta \gamma -4\lambda \sigma -4\beta \gamma k+3k\lambda \sigma >0,\frac{\partial p_o^{D^{*}}}{\partial s_o}>0\\ ab<\frac{4-3k}{4-4k},4\beta \lambda -4\gamma \sigma -4\beta k\lambda +3\gamma k\sigma <0,\frac{\partial p_o^{D^{*}}}{\partial s_o}<0\end{array} \\ \begin{array}{c}b>\frac{4a-ak}{6a^2-2},6{\beta }^2\gamma -2\gamma {\sigma }^2-4\beta \lambda \sigma +\beta k\lambda \sigma >0,\frac{\partial p_t^{D^{*}}}{\partial s_t}>0\end{array}; \end{gathered}$$

(3)

The sales prices of the online and offline channels and the service quality of cross-channel are also affected by consumer perceptions of price and service quality.

$$\begin{gathered} \{\begin{array}{c}\frac{a}{b}>\frac{4-3k}{4-4k},4\beta \lambda -4\gamma \sigma -4\beta k\lambda +3\gamma k\sigma <0,\frac{\partial p_o^{D^{*}}}{\partial s_t}>0\\ \frac{a}{b}<\frac{4-3k}{4-4k},4\beta \lambda -4\gamma \sigma -4\beta k\lambda +3\gamma k\sigma >0,\frac{\partial p_o^{D^{*}}}{\partial s_t}<0\end{array} \\ \{\begin{array}{c}b>\frac{6a^2-2}{4a-ak},6{\beta }^2\lambda -2\lambda {\sigma }^2-4\beta \gamma \sigma +\beta \gamma k\sigma <0,\frac{\partial p_t^{D^{*}}}{\partial s_o}>0\\ 1<b<\frac{6a^2-2}{4a-ak},6{\beta }^2\lambda -2\lambda {\sigma }^2-4\beta \gamma \sigma +\beta \gamma k\sigma >0,\frac{\partial p_t^{D^{*}}}{\partial s_o}<0\end{array} \end{gathered}$$

where

$$(a=\frac{\beta }{\sigma },b=\frac{\gamma }{\lambda }).$$

Proof. 

Known quantity $\begin{array}{c}\beta >\sigma ,\gamma >\lambda ,0<k<1,8\left({\beta }^2-{\sigma }^2\right)\left(1-k\right)-{\sigma }^2{k}^2>0\end{array}$,

(1)

$\begin{array}{c}\frac{\partial p_t^{D^{*}}}{\partial s_s}=\frac{\delta \left(4\beta \left(1-k\right)+\sigma \left(4-3k\right)\right)}{8\left({\beta }^2-{\sigma }^2\right)\left(1-k\right)-{\sigma }^2{k}^2}>0\end{array}$

$\begin{array}{c}\frac{\partial p_o^{D^{*}}}{\partial s_s}=\frac{\delta \left(1-k\right)(\beta \sigma \left(4-k\right)+2\left(3{\beta }^2-{\sigma }^2\right)}{8\left({\beta }^2-{\sigma }^2\right)\left(1-k\right)-{\sigma }^2{k}^2}>0\end{array}$

(2)

$\begin{array}{c}\frac{\partial p_o^{D^{*}}}{\partial s_o}=\frac{4\beta \gamma -4\lambda \sigma -4\beta \gamma k+3k\lambda \sigma }{8\left({\beta }^2-{\sigma }^2\right)\left(1-k\right)-{\sigma }^2{k}^2}\end{array}$

$\begin{array}{c}\frac{\partial p_t^{D^{*}}}{\partial s_t}=\frac{\left(1-k\right)\left(6{\beta }^2\gamma -2\gamma {\sigma }^2-4\beta \lambda \sigma +\beta k\lambda \sigma \right)}{\beta \left(8\left({\beta }^2-{\sigma }^2\right)\left(1-k\right)-{\sigma }^2{k}^2\right)}\end{array}$

Judging the positive and negative conditions of $\begin{array}{c}4\beta \gamma -4\lambda \sigma -4\beta \gamma k+3k\lambda \sigma \end{array}$ and $\begin{array}{c}6{\beta }^2\gamma -2\gamma {\sigma }^2-4\beta \lambda \sigma +\beta k\lambda \sigma \end{array}$, the conclusion proved.

(3)

$\begin{array}{c}\frac{\partial p_o^{D^{*}}}{\partial s_t}=-\frac{4\beta \lambda -4\gamma \sigma -4\beta k\lambda +3\gamma k\sigma }{8\left({\beta }^2-{\sigma }^2\right)\left(1-k\right)-{\sigma }^2{k}^2}\end{array}$

$\begin{array}{c}\frac{\partial p_t^{D^{*}}}{\partial s_o}=-\frac{\left(1-k\right)\left(6{\beta }^2\lambda -2\lambda {\sigma }^2-4\beta \gamma \sigma +\beta \gamma k\sigma \right)}{\beta \left(8\left({\beta }^2-{\sigma }^2\right)\left(1-k\right)-{\sigma }^2{k}^2\right)}\end{array}$

Judging the positive and negative conditions of $\begin{array}{c}4\beta \lambda -4\gamma \sigma -4\beta k\lambda +3\gamma k\sigma \end{array}$ and $\begin{array}{c}6{\beta }^2\lambda -2\lambda {\sigma }^2-4\beta \gamma \sigma +\beta \gamma k\sigma \end{array}$, the conclusion proved. □

Proposition 1 shows that the sales price of the TA is always positively related to its own service quality level. The level of service quality will have a spill over effect on the sales price of competitive channels, and the degree of influence depends on the magnitude of the effect of price and service quality on demand. When the effect of service quality on demand is greater than a certain range, consumers are service-sensitive. Both OTA and TA hope to seize the market by improving the service quality, however, improving service levels will inevitably increase costs. The increase will eventually be passed on to consumers, and the sales price of goods will be increased through various channels.

4.2. Centralized Decision

Under the centralised decision, the decision is made with the goal of maximising the overall profit of the supply chain, and the TPP and TA jointly determine the product sales price.

Theorem 2.

$$\begin{gathered} \begin{array}{c}{p}_o^{C^{*}}=\frac{A\beta +{\beta }^{2}c-c{\sigma }^2-A\alpha \beta +A\alpha \sigma +\beta \delta s_s+\beta \gamma s_o-\beta \lambda s_t+\delta s_s\sigma +\gamma s_t\sigma -\lambda s_o\sigma }{2\left({\beta }^2-{\sigma }^2\right)}\end{array} \\ \begin{array}{c}{p}_t^{C^{*}}=\frac{A\sigma +{\beta }^{2}c-c{\sigma }^2+A\alpha \beta -A\alpha \sigma +\beta \delta s_s+\beta \gamma s_t-\beta \lambda s_o+\delta s_s\sigma +\gamma s_o\sigma -\lambda s_t\sigma }{2\left({\beta }^2-{\sigma }^2\right)}\end{array} \end{gathered}$$

Proof. 

$\begin{array}{c}{\mathsf{\Pi }}^C={\mathsf{\Pi }}_s+{\mathsf{\Pi }}_t+{\mathsf{\Pi }}_o=D_o{p}_o+D_t{p}_t-\frac{\theta s_o{}^2}{2}-\frac{\mu s_s{}^2}{2}-\frac{\epsilon s_t{}^2}{2}-c\left(D_o+D_t\right)\end{array}$, find the second-order partial derivative of ${\mathsf{\Pi }}^C$ with respect to $p_o,p_t$

$$\begin{array}{c}{H}_{\left(p_o,p_t\right)}=\left[\begin{array}{cc}-2\beta & 2\sigma \\ 2\sigma & -2\beta \end{array}\right]\end{array}$$

The Sequential Principal Minor of $\begin{array}{c}{H}_{\left(p_o,p_t\right)}:H_1=-2\beta <0,H_2=4\left({\beta }^2-{\sigma }^2\right)>0\end{array}$.

This is true because, from the operating conditions, the determinant is greater than zero, and the members of the principal diagonal are negative. Hence, the objective function is a concave function:

Let $\begin{array}{c}\frac{\partial {\mathsf{\Pi }}^C}{\partial p_o}=0,\frac{\partial {\mathsf{\Pi }}^C}{\partial p_t}=0\end{array}$, solving the simultaneous equations, we get $\begin{array}{c}{p}_o^{C^{*}},p_t^{C^{*}}\end{array}$. □

Bring the optimal pricing results $\begin{array}{c}{p}_o^{C^{*}},p_t^{C^{*}}\end{array}$ into Equations (1) and (2) to obtain the demand of online and offline channels under decentralized decision $\begin{array}{c}{D}_o^{C^{*}},D_t^{C^{*}}\end{array}$, and then bring them into Equations (6) to obtain profit $\begin{array}{c}{\mathsf{\Pi }}^{C^{*}}\end{array}$.

Proposition 2.

Compared with the price under centralized decisions.

When$\begin{array}{c}\alpha \ge \frac{A+\gamma s_o-\gamma s_t+\lambda s_o-\lambda s_t}{2A}\end{array}$, then$\begin{array}{c}\alpha \ge \frac{1}{2}\end{array}$, and$\begin{array}{c}{p}_o^{C^{*}}\le p_t^{C^{*}}\end{array}$, without the quality of service factor. Otherwise$\begin{array}{c}{p}_o^{C^{*}}>p_t^{C^{*}}\end{array}$.

Proposition 2 shows that, under the centralised decision, online and offline channel sales are influenced by consumer channel preferences and service quality levels. When the effect of service quality factor is not considered, the online channel will lower sales prices to attract consumers, if consumer preferences for the offline channel are more than one half of those for the online channel.

Proposition 3.

Under the centralized decision,

(1) 

The service quality of the TPP has a positive effect on the price and demand of both channels$\begin{array}{c}\frac{\partial D_o^{C^{*}}}{\partial s_s}=\frac{\partial D_t^{C^{*}}}{\partial s_s}>0\end{array}$, $\begin{array}{c}\frac{\partial p_o^{C^{*}}}{\partial s_s}=\frac{\partial p_t^{C^{*}}}{\partial s_s}>0\end{array}$;

(2) 

The quality of channel service is positively related to its own channel price and demand$\begin{array}{c}\frac{\partial D_o^{C^{*}}}{\partial s_o}=\frac{\partial D_t^{C^{*}}}{\partial s_t}>0\end{array}$, $\begin{array}{c}\frac{\partial p_o^{C^{*}}}{\partial s_o}=\frac{\partial p_t^{C^{*}}}{\partial s_t}>0\end{array}$;

(3) 

The service quality of competitive channels is negatively related to the channel demand, and the effect on the price is related to the type of consumers$\begin{array}{c}\frac{\partial D_o^{C^{*}}}{\partial s_t}=\frac{\partial D_t^{C^{*}}}{\partial s_o}<0\end{array}$, When$\begin{array}{c}\frac{\beta }{\sigma }>\frac{\gamma }{\lambda }\end{array},\begin{array}{c}\frac{\partial p_o^{C^{*}}}{\partial s_t}<0\end{array},\begin{array}{c}\frac{\partial p_t^{C^{*}}}{\partial s_o}<0\end{array}$, $\begin{array}{c}\frac{\beta }{\sigma }<\frac{\gamma }{\lambda }\end{array},\begin{array}{c}\frac{\partial p_o^{C^{*}}}{\partial s_t}>0\end{array},\begin{array}{c}\frac{\partial p_t^{C^{*}}}{\partial s_o}>0\end{array}$.

Proof. 

(1)

$\begin{array}{c}\frac{\partial D_o^{C^{*}}}{\partial s_s}=\frac{\partial D_t^{C^{*}}}{\partial s_s}=\frac{\delta }{2}>0,\frac{\partial p_o^{C^{*}}}{\partial s_s}=\frac{\partial p_t^{C^{*}}}{\partial s_s}=\frac{\delta }{2\left(\beta -\sigma \right)}>0\end{array}$;

(2)

$\begin{array}{c}\frac{\partial D_o^{C^{*}}}{\partial s_o}=\frac{\partial D_t^{C^{*}}}{\partial s_t}=\frac{\gamma }{2}>0,\frac{\partial p_o^{C^{*}}}{\partial s_o}=\frac{\partial p_t^{C^{*}}}{\partial s_t}=\frac{\beta \gamma -\lambda \sigma }{2\left({\beta }^2-{\sigma }^2\right)}>0\end{array}$;

(3)

$\begin{array}{c}\frac{\partial D_o^{C^{*}}}{\partial s_t}=\frac{\partial D_t^{C^{*}}}{\partial s_o}=-\frac{\lambda }{2}<0\end{array}$,

$\begin{array}{c}\frac{\partial p_o^{C^{*}}}{\partial s_t}=\frac{\partial p_t^{C^{*}}}{\partial s_o}=-\frac{\beta \lambda -\gamma \sigma }{2\left({\beta }^2-{\sigma }^2\right)}\end{array}$

when $\begin{array}{c}\frac{\beta }{\sigma }>\frac{\gamma }{\lambda },\frac{\partial p_o^{C^{*}}}{\partial s_t}<0,\frac{\partial p_t^{C^{*}}}{\partial s_o}<0\end{array}$,

$\begin{array}{c}\frac{\beta }{\sigma }<\frac{\gamma }{\lambda },\frac{\partial p_o^{C^{*}}}{\partial s_t}>0,\frac{\partial p_t^{C^{*}}}{\partial s_o}>0\end{array}$. □

Proposition 3 shows that under the centralized decision:

(1)

The improvement of the TPP’s service quality will win more consumers. Consumers are willing to buy tourism products through any channel, the market demand of the channel will increase, and the price will also increase;

(2)

In order to deal with channel conflicts, the OTA and TA win more consumers and increase market share by improving service quality, but service quality improvement will also bring a cost increase, and the OTA and TA will balance the extra service cost by increasing the price of goods;

(3)

The improvement in the service quality of the competitive channel will lead to a decrease in the demand for their own channel. When the service level of their own channel is constant, the service sensitivity of the competitive channel is greater than that to the price of the competitive channel, the improvement of the service quality of the competitive channel will increase the sales volume of the competitive channel. The OTA or TA will attract consumers by reducing prices. When consumers are not sensitive to pricing, the improvement of the service quality of competing channels reduces the demand for their own channels. At this time, their own channels can only rely on raising prices to maintain profits.

4.3. Model Comparison Analysis

Proposition 4.

In the dual-channel supply chain based on service level, there is a threshold between the service quality of the TPP and the service quality of the OTA and TA. When the service quality of the TPP is higher than the threshold, the channel demand under the centralised decision is greater than that under the decentralised decision. Otherwise, it is the opposite.

Proof. 

$$\begin{gathered} \begin{array}{c}{D}_o^{C^{*}}-D_o^{D^{*}}=\frac{4c{\sigma }^4+s_o{r}_1+s_s{r}_2+s_t{r}_3+r_4}{2\beta \left(8{\beta }^{2}k-8{\beta }^2+k^2{\sigma }^2-8k{\sigma }^2+8{\sigma }^2\right)}\end{array} \\ \begin{array}{c}{D}_t^{C^{*}}-D_t^{D^{*}}=\frac{4c\left({\sigma }^3+{\beta }^3\right)+s_o{m}_1-s_s{m}_2+s_t{m}_3+m_4}{2\left(8{\beta }^{2}k-8{\beta }^2+k^2{\sigma }^2-8k{\sigma }^2+8{\sigma }^2\right)}\end{array} \end{gathered}$$

where

$$\begin{gathered} r_1=-4\lambda \left(1-k\right){\sigma }^3+\beta \gamma k\left(2-k\right){\sigma }^2+2{\beta }^2\lambda \left(2-3k\right)\sigma \\ {r}_2=4\delta \left(1-k\right){\sigma }^3+\delta \beta k\left(2-k\right){\sigma }^2-\delta 2{\beta }^2\left(2-3k\right)\sigma \\ {r}_3=4\gamma \left(1-k\right){\sigma }^3-\beta k\lambda \left(2-k\right){\sigma }^2-2{\beta }^2\gamma \left(2-3k\right)\sigma \\ \begin{array}{c}{r}_4=\left(4\left(A\alpha -\beta c\right)\left(1-k\right)+\beta ck\left(2-k\right)\right){\sigma }^3+\left({\beta }^{2}c\left(k^2-4-8k\right)+A\beta k\left(2-k\right)\left(1-\alpha \right)\right){\sigma }^2\\ +\left(2{\beta }^2\left(A\alpha -\beta c\right)\left(3k-2\right)\right)\sigma +8{\beta }^{4}ck\end{array} \\ {m}_1=\lambda \left(k^2+2k-4\right){\sigma }^2+2\beta \left(1-k\right)\left(\gamma \sigma k+2\beta \lambda \right) \\ {m}_2=\delta \left(k^2+2k-4\right){\sigma }^2+2\delta \beta k\left(1-k\right)\left(2\beta -k\sigma \right) \\ {m}_3=\gamma \left(k^2+2k-4\right){\sigma }^2+2\beta \left(1-k\right)\left(k\lambda \sigma +2\beta \gamma \right) \\ \begin{array}{c}\begin{array}{cc}{m}_4& =ck\left(k-6\right){\sigma }^3+\left(A\alpha \left(4-2k-k^2\right)-4\beta c-2\left(1-k-k^2\right)\right){\sigma }^2\\ & +\left(2A\beta k\left(1-k\right)\left(1-\alpha \right)-2{\beta }^{2}c-\left(2-3k\right)\right)\sigma +4A\alpha {\beta }^2\left(k-1\right)-4{\beta }^{3}ck\end{array}\end{array} \end{gathered}$$

when $\begin{array}{c}\begin{array}{ccc}{s}_s& <-\frac{4c{\sigma }^2+s_o{r}_1+s_t{r}_3+r_4}{r_2},D_o^{C^{*}}-D_o^{D^{*}}& >0\end{array}\end{array}$;

$$\begin{array}{c}\begin{array}{c}{s}_s\ge -\frac{4c{\sigma }^2+s_o{r}_1+s_t{r}_3+r_4}{r_2},D_o^{C^{*}}-D_o^{D^{*}}\le 0\end{array}\end{array}.$$

when $\begin{array}{c}\begin{array}{ccc}{s}_s& >\frac{4*c*\left({\sigma }^3+{\beta }^3\right)+s_o{m}_1+s_t{m}_3+m_4}{m_2},D_t^{C^{*}}-D_t^{D^{*}}& >0\end{array}\end{array}$;

$$\begin{array}{c}\begin{array}{c}{s}_s\le \frac{4*c*\left({\sigma }^3+{\beta }^3\right)+s_o{m}_1+s_t{m}_3+m_4}{m_2},D_t^{C^{*}}-D_t^{D^{*}}\le 0\end{array}\end{array}.$$

□

Proposition 4 shows that online and offline channel sales are affected by the service quality of supply chain members under a centralised decision and a decentralised decision. There is a threshold for service quality among members, and the size of the threshold is also related to other introduction coefficients. It can be understood that the decision behaviour of supply chain members with the goal of maximising their own benefits under the decentralised control mode will intensify the competition between the two channels. The OTA and TA will raise their investment in providing a high level of service quality. When the service quality investment of the OTA and TA is higher than the threshold of the TPP service quality investment, the channel demand under the decentralised decision will increase and exceed the channel demand under the centralised decision.

Proposition 5.

Comparing the models under both the centralised decision and the decentralised decision,

$$\begin{gathered} \begin{array}{c}\mathsf{\Delta }\mathsf{\Pi }={\mathsf{\Pi }}^{C^{*}}-{\mathsf{\Pi }}^{D^{*}}={\mathsf{\Pi }}^{C^{*}}-\left({\mathsf{\Pi }}_s^{D^{*}}+{\mathsf{\Pi }}_o^{D^{*}}+{\mathsf{\Pi }}_t^{D^{*}}\right)>0\end{array} \\ \begin{array}{c}{\mathsf{\Pi }}^{C^{*}}>{\mathsf{\Pi }}^{D^{*}}\end{array} \end{gathered}$$

Proposition 5 shows that the revenue of the dual-channel supply chain under the centralised decision is greater than that under the decentralised decision. This is because the TPP and TA in the supply chain under the decentralised decision are independent. The pursuit of profit maximisation makes the supply chain an inefficient state, and the centralised decision of the supply chain is difficult to achieve.

4.4. Supply Chain Coordination

It is well known that a centralised decision only presents optimal supply chain profit, and does not provide a specific profit distribution mechanism for supply chain members. When the TPP is acting as a leader, it is necessary to make sure that the supply chain member under the contract can achieve the Pareto improvement to ensure that the TPP is willing to provide a lower discount price. That is, their own profits with the contract under a centralised decision should not be less than those obtained under a decentralised decision.

Under the wholesale price contract and income distribution contract: the wholesale price provided by the TPP under a coordinated decision is w^o, the sum of profits obtained by the TPP, OTA, and TA is allocated to the TPP in proportion t₁, the OTA according to a certain proportion t₂, and the rest is distributed to the TA. We then need to determine the optimal wholesale price w^o and the value range of the distribution proportion t₁ and t₂.

To achieve Pareto optimality, the following conditions must be met:

$$\begin{gathered} \begin{array}{c}{\mathsf{\Pi }}_s^{O^{*}}\left(w^o,p_o^{C^{*}}\right)=t_1{\mathsf{\Pi }}^{C^{*}}\ge {\mathsf{\Pi }}_s^{D^{*}}\end{array} \\ \begin{array}{c}{\mathsf{\Pi }}_o^{O^{*}}=t_2{\mathsf{\Pi }}^{C^{*}}\ge {\mathsf{\Pi }}_o^{D^{*}}\end{array} \\ \begin{array}{c}{\mathsf{\Pi }}_t^{O^{*}}\left(w^o,p_o^{C^{*}}\right)=\left(1-t_1-t_2\right){\mathsf{\Pi }}^{C^{*}}\ge {\mathsf{\Pi }}_t^{D^{*}}\end{array} \end{gathered}$$

Replacing $\begin{array}{c}{p}_o^{O^{*}}=p_o^{C^{*}},p_t^{O^{*}}=p_t^{C^{*}}\end{array}$ with $\begin{array}{c}{\mathsf{\Pi }}_s^{O^{*}}\left(w^o,p_o^{C^{*}}\right)=t_1{\mathsf{\Pi }}^{C^{*}}\end{array}$, we obtain the wholesale price under the coordination decision $\begin{array}{c}{w}^{O^{*}}\left(t_1\right)\end{array}$.

Next, we need to determine the value range of income distribution proportion. The basis of implementing the contract between the TPP and TA is that the profits of supply chain members after implementing the contract are greater than those under the decentralised decision.

$$\begin{gathered} \begin{array}{c}{t}_1\ge \frac{{\mathsf{\Pi }}_s^{D^{*}}}{{\mathsf{\Pi }}^{C^{*}}}\end{array} \\ \begin{array}{c}{t}_2\ge \frac{{\mathsf{\Pi }}_o^{D^{*}}}{{\mathsf{\Pi }}^{C^{*}}}\end{array} \\ \begin{array}{c}\left(1-t_1-t_2\right)\ge \frac{{\mathsf{\Pi }}_t^{D^{*}}}{{\mathsf{\Pi }}^{C^{*}}}\end{array}. \end{gathered}$$

From the above analysis, we can obtain the value range of $t_1$ and $t_2$, and can ensure that the TPP and OTA accept the contract. The value of a specific distribution proportion should be determined according to the negotiation ability of supply chain members.

5. Numerical Analysis

The above calculation results and related conclusions are further demonstrated and explained by analysing examples abstracted from the real-world case, and the effect of each parameter change on the income, profit distribution and price of both parties is also analysed.

This section mainly discusses the effect of service quality on price strategy, ignoring the effect of consumer channel preferences for the time being, and thus sets $\begin{array}{c}\alpha =0.5\end{array}$. Referring to Ctrip’s commission charge for hotels of about 20% [62], let $\begin{array}{c}k=0.2\end{array}$. Based on the studies of Anderson and Bao [63] and Peng et al. [54], this study assumes that the tourists’ sensitivities to the corresponding prices and service quality are standardized as $\begin{array}{c}\beta =1,\gamma =1\end{array}$, and the range of the competition coefficient is: $\begin{array}{c}0<\lambda <1,0<\sigma <1\end{array}$. Let $\begin{array}{c}c=10,A=50,\delta =0.85\end{array}$. The figures were abstracted from real business in China.

5.1. Analysis of the Effect of the TPP Service Quality

Under the condition that the service quality of the OTA and TA is constant, assuming $\begin{array}{c}{s}_t=5,s_o=5,\lambda =0.2,\sigma =0.45,s_s\end{array}$ changes within (0, 50), the changes of channel price, channel demand, and profit with the TPP service quality are shown in Figure 2a–d.

As can be seen from Figure 2a,b, when consumers have the same preference for the two channels, the dual-channel sales price under the centralised decision is equal to the channel demand, whether under the centralised decision or the decentralised decision. With the improvement of the TPP service quality, both the sales price and the demand for dual-channel price increases, and Point (1) of Proposition 1 and Proposition 2 is verified. When the quality of service provided by the OTA and TA is a certain value, according to the above parameter settings, it can be known that the critical value of $s_s$ is 7.5855, when $\begin{array}{c}{s}_s\ge 7.5855,D_o^{C^{*}}\le D_o^{D^{*}}\end{array}$, when $\begin{array}{c}{s}_s<7.5855,D_o^{C^{*}}>D_o^{D^{*}}\end{array}$. Proposition 3 can be verified.

As can be seen from Figure 2c,d, under the decentralised decision, an improvement in TPP service quality can attract more consumers to buy tourism products, and the profits of the OTA and TA will increase, but service quality should not be too high for the TPP. The excessive pursuit of service quality will bring more cost burdens to the TPP but will reduce the profit of the TPP. Under the centralised decision, the overall profit of the supply chain is always greater than that under the decentralised decision, which also verifies Proposition 4.

5.2. Analysis of the Effect of the OTA and TA Service Quality

(1)

The effect of the OTA and TA service quality on channel prices.

Under the condition that the service quality of TPP and TA is constant, suppose $\begin{array}{c}{s}_s=35\end{array}$, There is a threshold between competition coefficients $\begin{array}{c}\lambda \end{array}$ and $\begin{array}{c}\sigma \end{array}$, respectively; check and calculate when $\begin{array}{c}\lambda =0.45,\sigma =0.8\end{array}$ and $\begin{array}{c}\lambda =0.45,\sigma =0.2\end{array}$:

When $\begin{array}{c}{s}_t=5,s_o\end{array}$ changing within (0,20), the channel price changes with OTA service quality, as shown in Figure 3a,b;

When $\begin{array}{c}{s}_o=5,s_t\end{array}$ changing within (0,20), the channel price changes with TA service quality, as shown in Figure 3a,b.

It can be seen from Figure 3 and Figure 4 that under the decentralised decision, the effect of OTA service quality on online channel prices and offline channel prices is limited by the relationship between the price sensitivity coefficient and the service sensitivity coefficient of competitive channels. According to parameter settings, when $\begin{array}{c}\sigma =0.8\end{array}$, at this time $\begin{array}{c}\frac{1}{\sigma \lambda }>1.0625\end{array}$ and $\begin{array}{c}6\lambda -2\lambda {\sigma }^2-4\sigma +k\sigma <0\end{array}$, the OTA service. The improvement in quality will increase the online and offline prices. When $\begin{array}{c}\sigma =0.2\end{array}$, at this time $\begin{array}{c}\frac{1}{\sigma \lambda }>1.0625\end{array}$ and $\begin{array}{c}6\lambda -2\lambda {\sigma }^2-4\sigma +k\sigma >0\end{array}$, the online channel price will rise with the OTA service quality, while the offline channel price will increase and decrease with the OTA service quality.

In the same way, when $\begin{array}{c}\sigma =0.8\end{array}$, $\begin{array}{c}\frac{\lambda }{\sigma }>1.0625\end{array}$, the improvement in TA service quality will increase the price of the online channel. When $\begin{array}{c}\sigma =0.2\end{array}$, $\begin{array}{c}\frac{\lambda }{\sigma }<1.0625\end{array}$, the price of the online channel will decrease with the improvement of the TA service quality, and always $\begin{array}{c}6-2{\sigma }^2-4\lambda \sigma +k\lambda \sigma >0\end{array}$. Therefore, no matter how $\sigma$ and $\lambda$ changes, the price of the offline channel will increase. Both will improve with the improvement of the TA service quality, and the (2) and (3) views of Proposition 1 have been verified.

Under the centralised decision, the price of the online channel increases with the improvement in OTA service quality, and the price of the offline channel increases with the improvement in TA service quality; while the price changes of competitive channels depend on the relationship between competitive channel prices and service sensitivity coefficients. When the price sensitivity coefficient of the competitive channel is greater than the threshold of the service sensitivity coefficient, the offline channel price will increase with the increase in OTA service quality. The price of the online channel increases with the increase in TA service quality; when the price sensitivity coefficient of competitive channels is less than the threshold of the service sensitivity coefficient, the price of the offline channel decreases with the increase of the OTA service quality, and the price of the online channel decreases with the increase of the TA service quality. As the quality increases, the price of the online channel decreases, and Points (2) and (3) of Proposition 2 are verified.

(2)

Effect of the OTA and TA service quality on the channel demand.

Under the condition of certain service quality of the TPP and TA, suppose $\begin{array}{c}{s}_s=35\end{array},\begin{array}{c}\lambda =0.45,\sigma =0.2\end{array}$:

When $\begin{array}{c}{s}_t=5,s_o\end{array}$ changing within (0, 20), the channel demand changes with the OTA service quality, as shown in Figure 5a.

When $\begin{array}{c}{s}_o=5,s_t\end{array}$ changing within (0, 20), the channel demand changes with the TA service quality, as shown in Figure 5b.

As can be seen from Figure 5: whether under the centralised decision or decentralised decision, the improvement in OTA or TA service quality will have a positive effect on the demand for this channel. The demand for competitive channels will decrease with the improvement in service quality.

(3)

The effect of the OTA and TA service quality on profits.

Suppose $\begin{array}{c}\lambda =0.45,\sigma =0.2,s_s=35,s_o{s}_t\end{array}$ all change at [0, 20], and the combined effect of the OTA and TA service quality on profits under the decentralised decision and the centralised decision is shown in Figure 6a,b.

As can be seen from Figure 6a, under the decentralised decision, the profit of the TPP will increase with the improvement in the service quality of the OTA and TA. The profits of the OTA and TA always decrease with the improvement of the service quality of the competing channels, and the improvement in the service quality of their own channels will make their own profits rise first and then decline. The improvement in service quality brings profits and also increases the cost, and the excessive pursuit of service quality will reduce profits.

It can be seen from Figure 6b that under the centralised decision, the total profit of the tourism supply chain will increase with the improvement of the OTA’s and TA’s service quality. Under the decentralised decision, the total profit of the tourism supply chain will increase with the improvement of the OTA service quality, firstly increase and then decrease with the improvement of the TA service quality, and the centralised decision is always greater than the total profit of the tourism supply chain under the decentralised decision. The members of the supply chain under the decentralised decision pursue the maximisation of their own interests, and the supply chain is in a state of inefficiency.

5.3. Comparative Analysis under Contract Coordination

The following is an example analysis in order to more clearly show the changes in the TPP, OTA, TA decision and profit changes after the coordination, and the parameter values are as follows:

$$\begin{array}{c}\alpha =0.75,\sigma =0.78,k=0.2,\lambda =0.3,s_t=8,s_o=8,s_s=20\end{array}$$

First, we can obtain the results of the centralised decision and decentralised decision from

Table 3. The results of centralized decision and decentralized decision.

	$\begin{array}{c}{\mathit{p}}_{\mathit{o}}^{*}\end{array}$	$\begin{array}{c}{\mathit{p}}_{\mathit{t}}^{*}\end{array}$	$\begin{array}{c}{\mathit{w}}^{*}\end{array}$	$\begin{array}{c}{\mathit{D}}_{\mathit{o}}^{*}\end{array}$	$\begin{array}{c}{\mathit{D}}_{\mathit{t}}^{*}\end{array}$	$\begin{array}{c}{\mathbf{\Pi }}_{\mathit{s}}^{*}\end{array}$	$\begin{array}{c}{\mathbf{\Pi }}_{\mathit{o}}^{*}\end{array}$	$\begin{array}{c}{\mathbf{\Pi }}_{\mathit{t}}^{*}\end{array}$	$\begin{array}{c}{\mathbf{\Pi }}^{*}\end{array}$
Centralized decision	113.99	121.01	N/A	18.50	31.00	N/A	N/A	N/A	5348.6
Decentralized decision	119.63	136.25	116.08	24.74	20.17	4284.2	560.00	374.66	5218.8

.

Under the coordinated decision, the proportion of income distribution should meet $\begin{array}{c}{t}_1\ge 0.8010,t_2\ge 0.1047,t_3\ge 0.07\end{array}$.

Assuming $\begin{array}{c}{t}_2=0.1047,t_1\left[0.8010,0.8253\right]\end{array}$ changes, and the values in

Table 4. The effect of allocation ratio on the profit of each member and the overall supply chain.

$\begin{array}{c}{\mathit{t}}_1\end{array}$	$\begin{array}{c}{\mathit{w}}^{{\mathit{O}}^{*}}\end{array}$	$\begin{array}{c}{\mathbf{\Pi }}_{\mathit{s}}^{{\mathit{O}}^{*}}\end{array}$	$\begin{array}{c}{\mathbf{\Pi }}_{\mathit{o}}^{{\mathit{O}}^{*}}\end{array}$	$\begin{array}{c}{\mathbf{\Pi }}_{\mathit{t}}^{{\mathit{O}}^{*}}\end{array}$	$\begin{array}{c}{\mathbf{\Pi }}^{{\mathit{O}}^{*}}\end{array}$
0.801	98.22	4284.3	560	504.38	5348.6
0.804	98.73	4300.3	560	488.33	5348.6
0.807	99.25	4316.4	560	472.29	5348.6
0.81	99.77	4332.4	560	456.24	5348.6
0.813	100.29	4348.4	560	440.19	5348.6
0.816	100.81	4364.5	560	424.15	5348.6
0.819	101.32	4380.5	560	408.1	5348.6
0.822	101.84	4396.6	560	392.06	5348.6
0.825	102.36	4412.6	560	376.01	5348.6
0.8253	102.41	4413.94	560	374.66	5348.6

can be obtained.

It can be seen from Table 4 that when the income distribution ratio $\begin{array}{c}{t}_2=0.1047\end{array}$, the income under the OTA coordinated decision is exactly the same as that under the decentralised decision. When the income distribution ratio $\begin{array}{c}{t}_1=0.8010\end{array}$, the income under the TPP coordinated decision is equal to that under the decentralised decision. When the income distribution ratio $\begin{array}{c}{t}_1=0.8253\end{array}$, then $\begin{array}{c}{t}_3=0.07\end{array}$, at this time, the income under the TA coordinated decision is equal to that under the decentralised decision.

Therefore, when $\begin{array}{c}{t}_1\left[0.8010,0.8253\right]\end{array}$ changes, the benefits of the TPP and TA are higher than those under the decentralised decision. Under the coordination decision, it can be found that the Pareto optimisation of supply chain members can be realised when the wholesale price range is $\begin{array}{c}{w}^{O^{*}}\left[98.22,102.41\right]\end{array}$.

6. Conclusions and Managerial Implications

6.1. Conclusions

This study discussed the dual-channel tourism supply chain of price and service competition. Tourism suppliers sell tourism products through OTA platforms and traditional travel agencies. Consumer demand between online and offline channels is sensitive to price and service quality. Based on the dual-channel supply chain pricing decision model of the TPP, OTA, and TA serving simultaneously, this work analysed the effect of service quality on pricing and optimal profit under centralised decisions and decentralised decisions. This study further compared and analysed the most efficient supply chain members under decentralised and centralised decisions. Under a decentralised decision, supply chain members all aim to maximise their own profits, and this behaviour causes the overall profit of the supply chain to be sub-optimal. The overall profit of the supply chain under the centralised decision is always higher than that under the decentralised decision. There is room for improvement. The study found that wholesale price contracts and coordinating the distribution of profits among supply chain members can provide a good solution to the decision problem of online and offline dual channels. The upstream and downstream enterprises in the supply chain seek to maximise their own benefits, which leads to the double marginal effect. That is, the supply chain profit under the decentralised decision cannot achieve global optimality so as to solve the double marginal effect of the supply chain caused by the decentralised decision and achieve win-win cooperation for both parties. Finally, it further verified the coordination effect of the two pricing mechanisms through numerical example analysis and analysed the effect of service quality on pricing, demand, and profit under different decisions.

Under the dual-channel tourism supply chain where TPP, OTA, and TA work together for service quality, the research indicates:

(1)

The effect of service quality on price and demand: whether under centralised decision-making or decentralised decision-making, TPP’s service quality and service quality have a positive effect on online and offline channel sales prices and channel demand. This shows that travel consumers are more willing to pay for high-quality travel experiences. For the OTA and TA, the quality of channel services will also affect the price and demand of channels in the process of selling tourism products. The demand for the online channel will increase with an improvement in OTA service quality. The demand for the lower channel will increase with an improvement in TA service quality. With the improvement of the OTA service quality, the sales price of the channel will also increase with an improvement in the service quality of the channel, but the price of the competitive channel will not necessarily decrease, and the price of the competitive channel will change, as it is affected by consumer sensitivity to price and service. Improving the service quality of the OTA and TA will increase the profit of the TPP. The profits of the OTA and TA always decrease with an improvement in the service quality of competing channels, and an improvement in the service quality of their own channels will make their own profits first rise and then decline.

(2)

The effect of service quality on profits: the improvement of TPP service quality can attract more consumers to buy travel products, and the profits of OTAs and TAs will increase. The service quality of TPP should not be too high, however. The excessive pursuit of service quality will increase the cost burden for TPP, but will reduce TPP’s profit. TPP service is therefore always beneficial to OTA and TA, but under certain conditions, it is beneficial to TPP. This conclusion is roughly the same as that of Peng et al. [54]. The overall profit of the supply chain will also increase with an improvement in TPP service quality.

An improvement in the service quality of OTA and TA will increase the profit of TPP. The profit of OTA and TA will always decrease with an improvement in the service quality of the competitive channel, and an improvement in the service quality of its own channel will make its own profit rise first and then decline. The improvement of service quality brings profits and also increases expenditure. The excessive pursuit of service quality will reduce profits. Improving the service quality of OTA and TA will increase the total profit of the tourism supply chain under centralised decision-making.

The total profit of the dual-channel tourism supply chain under decentralised decision-making is always less than the overall profit under centralised decision-making. In the supply chain under decentralised decision-making, travel suppliers and travel agencies make independent decisions, and each pursues the maximisation of interests, which makes the supply chain in a state of inefficiency, and there is room for improvement in the supply chain.

(3)

By designing a wholesale price contract, TPP provides a lower wholesale price, so that the revenue of the tourism supply chain under the coordination mechanism can reach the revenue level of the tourism supply chain under centralised decision-making. Under the wholesale price contract, the profits obtained by all members of the supply chain are higher than the profits obtained by each member under pre-contract decentralised decision-making. The wholesale price contract can perfectly coordinate the dual-channel tourism supply chain, which contributes to the research on the sustainable development of the tourism supply chain.

6.2. Theoretical Contributions

This study contributes to tourism supply chain sustainable development research by constructing a dual-channel supply chain pricing decision model that considers the effect of service quality factors among tourism product suppliers, online sale channels, and offline sale channels. By analysing the effect of the service quality of the TPP, OTA, and TA, this model shows how the selling price and channel demand are affected. This study provides researchers with a research model which they can extend to conduct further studies.

In a high-quality tourism service environment, this study also contributes, to our knowledge, in showing how to optimise the profits of the tourism supply chain under contract coordination. The results demonstrate that centralised decisions can maximise the overall profit of a supply chain, especially working with multiple business partners including online and offline channels. This study further shows that in order to achieve the Pareto improvement, the tourism product suppliers should take on the role of leaders to coordinate a lower discount price with other members.

6.3. Practical Implications

Designing the wholesale price contract and income distribution contract means that the dual-channel tourism supply chain dominated by TPPs can be coordinated, so that after TPPs and TAs are coordinated, the profits obtained are all greater than those under decentralised decisions. By coordinating contracts, a “win-win” can be achieved.

In real business management, both online and offline channels, tourism consumers are willing to pay for better services. TPPs should therefore pay more attention to improving the service quality of tourism products, such as the hotel environment and scenic locations. Facility updates and so on can attract more consumers. When consumers purchase more through OTAs or TAs, the price of the channel will increase accordingly. The profits of OTAs and TAs will also increase, but due to the improvement of service quality, will also be accompanied by the generation of service costs; therefore, for TPPs, it is necessary to pay attention to the strength of service quality improvement and thus to avoid the excessive pursuit of service quality causing unaffordable costs. The development of reasonable service quality is therefore conducive to the sustainable development of the tourism supply chain.

In actual operation and management, TPPs always hope that OTAs and TAs can make service improvements, thereby bringing about an increase in profits. The OTAs will improve the security of the network environment, convenient payment methods, travel information queries, and so on. Additional services, humanistic care, and other ways to improve the service quality of the sales process should be used to attract more consumers to purchase tourism products through this channel, so as to obtain more profits. Due to the existing sales competition between OTAs and TAs, however, the service quality of the competing channel will also seriously affect the demand and profit of this channel. Since the service will also generate service costs, the excessive reliance on improving service quality to obtain profits will make it impossible to make ends meet. OTAs and TAs should therefore weigh consumer preferences for price and service when using services to occupy the market, and provide differentiated services within the threshold range of service levels for consumers with different preferences, and then formulate appropriate price strategies. When the service level of the competitive channel changes, the service level of the competitive channel and the type of consumers should be comprehensively considered, and the price strategy of the channel should be adjusted in time.

6.4. Limitations

Although this study fully considers the decisions of the tourism supply chain for multi-member services in the supply chain, and has a certain practical management significance, there are some deficiencies. Since the quantification of tourism service quality is relatively complex, it could be further studied in the future. The measurement of service quality needs to be more precise and accurate. In addition, different game modes and the randomness of needs could be studied in the future. The theoretical model could be further verified in a real situation to demonstrate its practical contribution. This study only considered one kind of contract coordination, and further studies could verify other coordination contracts such as wholesale-price contracts, quantity-discount contracts, revenue sharing contracts, and price subsidy contracts.