# Optimal Service Commission Contract Design of OTA to Create O2O Model by Cooperation with TTA under Asymmetric Information

Authors: Pingping Shi; Yaogang Hu; Yongfeng Wang
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

This paper studies the service commission contract of an online travel agency (OTA) to integrate the online to offline (O2O) model by cooperation with a traditional travel agency (TTA) under asymmetric information. The principal-agent models are established with symmetric and asymmetric service information, respectively. Further, the impacts of asymmetry information on the revenue of the OTA, TTA, and the whole O2O model and the properties of optimal commission contract are analyzed. The paper notes management implications: (1) OTA designs service commission contracts by weighing the fixed payment and service commission coefficient for different incentives to TTAs with different serviceabilities and (2) because the existence of asymmetric information always leads to the damage of OTA’s expected revenue, OTA should encourage the TTA to disclose private service information.


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## Body

## 1. Introduction

With the rapid development of Internet and information technology, it is a popular and convenient way for tourists to buy more diverse tourism products and services through online travel agencies (OTAs) [1–3]. However, online tourism product homogenization makes OTAs reduce the service quality to attract more tourists, such as cashback and low price, forcing consumption in the experience, which seriously increase tourists’ complaints and hinder the healthy development of online tourism [4–6]. To improve competitive advantage and realize differentiation, some OTAs (e.g., Ctrip) open up offline stores to achieve online to offline (O2O) model for providing personal information and advice to tourists, and some cooperate with traditional travel agencies (TTAs), such as Uzai (http://www.uzai.com/) and ZhongXin TTA, Lvmama (http://www.lvmama.com), and JinJiang TTA [4]. In the process of cooperation between OTAs and TTAs, it is difficult for OTAs to observe TTAs’ service information, which makes the cooperation and incentive problem more complicated. Therefore, how to design the commission contract of TTAs strategically for OTAs is an urgent problem to implement O2O strategy.The O2O model, as a new e-commerce business model, combines online trading and offline experience and has become an important strategy for the development of enterprises in recent years [7]. With the rapid development of OTAs, the online channel plays a crucial role in tourism and hospitality, and the tourism O2O model achieved by the cooperation between OTAs and hotels or airlines is a common phenomenon. Therefore, there is a growing popularity on the cooperation problem between OTAs and hotels or airlines and they have gotten some effective cooperation strategies [1, 8–11]. However, to seek better business opportunities, OTAs have to attach importance to the offline service to achieve differentiation. Although some scientific researchers have demonstrated the importance of TTAs’ advice-offering and OTAs’ attributes in travelers’ booking, little literature in the hospitality and tourism fields has studied the service contract problem of cooperation between OTAs and TTAs.To fill this gap and provide some suggestions for OTAs and TTAs managers on establishing the O2O model through service cooperation, this paper proposes a cooperation model to describe decision interactions of an OTA and a TTA. The OTA and TTA play a principal-agent model in which the OTA, as the principal player, determines the service commission and the TTA, as the agent, determines service effort. In addition, in the O2O model, because the OTA and TTA are relatively independent, TTA’s private information is difficult to disclose and it often hides its own service information to obtain higher revenue. By comparing the case of information symmetry and asymmetry, the impacts of asymmetry information on OTA’s service contract, TTA’s information value, and the revenue of OTA and TTA are analyzed.The rest of this paper is organized as follows. Section2 reviews the related literature. Section 3 describes the service cooperation between an OTA and a TTA. Section 4 analyzes and compares equilibriums of symmetric and asymmetric information and further analyzes equilibriums when TTA’s ability is continuous. Section 5 presents the results of numerical analyses. Section 6 concludes this paper by summarizing some of the managerial implications obtained.


## 2. Literature Review

According to the purpose of studying the service cooperation contract design of OTAs and TTAs, this section reviews three distinct of literature studies about the O2O model, service cooperation, and cooperation between tourism enterprises and OTAs.


### 2.1. O2O Model

Since Alex Rampel put forward the concept of the O2O model, it has attracted wide attention in academia. Although the concept and classification of the O2O model are different because of the scholars’ different research perspectives, it is a business model for the effective integration of online and offline stores [4, 7]. It is believed that O2O is a new business model, which can disclose product information, change consumer brand awareness, and mitigate the adverse effects of product uncertainty on consumers’ shopping decisions [12]. By using 311 respondent data from O2O e-commerce users in Greater Jakarta Area, Savila et al. showed that both multichannel integration and trust have a significant effect on both customer online loyalty and customer offline loyalty that drive customer repurchase intention [13].According to the existing research about the O2O model, it can be divided into two categories: one is to purchase online and then experience offline, which can also be called online to offline; the other is to experience offline and then purchase online, which can also be called offline to online [14]. In the hospitality and tourism industry, because tourists have to experience tourism products offline, the O2O model achieved by the cooperation between OTAs and hotels or airlines is a common phenomenon. From the perspective of the supply chain, scholars more emphasize the sale cooperation between the OTAs and hotels or airlines and have gotten some effective cooperation strategies [1, 8–11]. In the retail industry, as the major characteristic feature of a product is tangible, it can be touched and tried out. The O2O model, achieved by cooperating with others or opening by oneself [14, 15], becomes the main research direction. Considering that the service cost and effort information of the showroom are asymmetry, Jin et al. study the design of the commission contract of offline to online, which is considered that the showroom and online retailer have impacts on consumers’ purchasing decisions, respectively [15]. In practice, online sale efforts and offline services will affect the demand for each other. Therefore, it is difficult to highlight the integrity of the online retailer and store cocreating O2O model by splitting online and offline demand.Despite the impact of technology and the advent of online bookings, offline channels play a pivotal role in the tourism supply chain. Unlike the general retail product, the tourism product of a travel agency cannot be touched, transported, stored, or returned. The functions of offline channels are different between general retailer and travel agency. The offline channels of general retailer provide touching, trying, and even online return service for consumers. The offline channels of travel agency provide scene experience, personal and professional information, and advice to meet travelers’ demands on a continuous basis by gathering and organizing information [16, 17]. The O2O model of travel agency in this paper is an integration of online and offline. Both online sale efforts and offline services will have an impact on the overall demand for the O2O model.


### 2.2. Service Cooperation

The existing literature on service cooperation is mostly concentrated in the supply chain field. Service cooperation not only reduces the competition between direct and retail channels but also improves supply chain revenue [18–21]. Unit service reward and service cost sharing are the main forms of service cooperation. To avoid channel conflict and improve service efficiency, Xiao, Dan, and Zhang studied after-sale service cooperation between direct channel and retailers in which direct channel pays unit service commission to retailers [18]. In the O2O retail market, the service cost sharing mechanism is introduced to coordinate conflicts and achieve a win-win strategy, thereby improving the performance of the entire O2O supply chain [19]. Due to the importance of presales services in purchasing decisions, Zhou et al. introduce that the service cost sharing contract can effectively stimulate the retailer to improve his service level while free riding occurred [20]. Considering that the retailer provides the same service level in both channels or not, Yang and Zhang compare the manufacturer’s optimal profit and retailer’s optimal profit under the condition of different services and the same service and provide differentiated services to enable the system to achieve the optimal profit [21]. However, the completely symmetrical assumptions in the above literature are not currently common practice. The adverse selection and moral hazard are always in the cooperation process [22].


### 2.3. Cooperation between Tourism Enterprises and OTAs

Clearly, the OTAs are an extremely important part of the tourism supply chain, recognized as an online distribution channel for hotels and airlines. A larger number of scholars have emerged to pay attention to the issue of the cooperation problem between hotels, airlines, and OTAs, such as pricing strategy, cooperation form, and coordination strategy [1, 8–11]. Koo et al. point out that the airlines are less likely to use OTA platforms if the airlines have a large loyal consumer base or if the OTA platform is highly competitive [8]. Ling et al. and Guo et al. study the optimal pricing strategy for tourism hotels when they operate their online channel by cooperation with an OTA [1, 9]. Considering the overbooking of hotels, Dong and Ling study the pricing and overbooking strategies of a hotel in the context of cooperation with multiple OTAs and analyze how these strategies influence the cooperation process [10]. The commonly used “first come first serve” form puts hotels in a disadvantageous position especially when the OTAs have much more market attractiveness than the hotels. Xu et al. propose a new form named “setting Online-Exclusive-Rooms” for a hotel to collaborate with a third-party website on room booking service [11].To get a new business opportunity, the TTAs are increasingly aware of the need to open up online markets’ cooperation with OTAs because of the low volume of visits and lack of e-commence operating experience of self-built website [3, 4]. Therefore, a few scholars have provided suggestions on the cooperation between TTA and OTA. Based on the resource-based view, Shi and Long analyze the complementary resources input decisions of OTAs and TTAs cocreating the O2O model, which do not propose a clear form of cooperation [3]. In a supplemental study, Long and Shi study the optimal pricing strategies of a tour operator and an OTA when they achieve the O2O business model through online sale and offline service cooperation to develop the advantage of complementary resources [4].However, no study has addressed the problem of an OTA that wants to achieve its O2O model by cooperating with a TTA with private information. In this situation, the OTA has little information and experience regarding offline service and knows little about how to design a service contract to disclosure TTA’s real information. To enrich the scientific literature and provide some suggestions to OTA on how to pursue offline service, this paper studies the optimal service contract of OTAs under asymmetric information through the analysis of a simple O2O model composed of an OTA and a TTA.


## 2.1. O2O Model

Since Alex Rampel put forward the concept of the O2O model, it has attracted wide attention in academia. Although the concept and classification of the O2O model are different because of the scholars’ different research perspectives, it is a business model for the effective integration of online and offline stores [4, 7]. It is believed that O2O is a new business model, which can disclose product information, change consumer brand awareness, and mitigate the adverse effects of product uncertainty on consumers’ shopping decisions [12]. By using 311 respondent data from O2O e-commerce users in Greater Jakarta Area, Savila et al. showed that both multichannel integration and trust have a significant effect on both customer online loyalty and customer offline loyalty that drive customer repurchase intention [13].According to the existing research about the O2O model, it can be divided into two categories: one is to purchase online and then experience offline, which can also be called online to offline; the other is to experience offline and then purchase online, which can also be called offline to online [14]. In the hospitality and tourism industry, because tourists have to experience tourism products offline, the O2O model achieved by the cooperation between OTAs and hotels or airlines is a common phenomenon. From the perspective of the supply chain, scholars more emphasize the sale cooperation between the OTAs and hotels or airlines and have gotten some effective cooperation strategies [1, 8–11]. In the retail industry, as the major characteristic feature of a product is tangible, it can be touched and tried out. The O2O model, achieved by cooperating with others or opening by oneself [14, 15], becomes the main research direction. Considering that the service cost and effort information of the showroom are asymmetry, Jin et al. study the design of the commission contract of offline to online, which is considered that the showroom and online retailer have impacts on consumers’ purchasing decisions, respectively [15]. In practice, online sale efforts and offline services will affect the demand for each other. Therefore, it is difficult to highlight the integrity of the online retailer and store cocreating O2O model by splitting online and offline demand.Despite the impact of technology and the advent of online bookings, offline channels play a pivotal role in the tourism supply chain. Unlike the general retail product, the tourism product of a travel agency cannot be touched, transported, stored, or returned. The functions of offline channels are different between general retailer and travel agency. The offline channels of general retailer provide touching, trying, and even online return service for consumers. The offline channels of travel agency provide scene experience, personal and professional information, and advice to meet travelers’ demands on a continuous basis by gathering and organizing information [16, 17]. The O2O model of travel agency in this paper is an integration of online and offline. Both online sale efforts and offline services will have an impact on the overall demand for the O2O model.


## 2.2. Service Cooperation

The existing literature on service cooperation is mostly concentrated in the supply chain field. Service cooperation not only reduces the competition between direct and retail channels but also improves supply chain revenue [18–21]. Unit service reward and service cost sharing are the main forms of service cooperation. To avoid channel conflict and improve service efficiency, Xiao, Dan, and Zhang studied after-sale service cooperation between direct channel and retailers in which direct channel pays unit service commission to retailers [18]. In the O2O retail market, the service cost sharing mechanism is introduced to coordinate conflicts and achieve a win-win strategy, thereby improving the performance of the entire O2O supply chain [19]. Due to the importance of presales services in purchasing decisions, Zhou et al. introduce that the service cost sharing contract can effectively stimulate the retailer to improve his service level while free riding occurred [20]. Considering that the retailer provides the same service level in both channels or not, Yang and Zhang compare the manufacturer’s optimal profit and retailer’s optimal profit under the condition of different services and the same service and provide differentiated services to enable the system to achieve the optimal profit [21]. However, the completely symmetrical assumptions in the above literature are not currently common practice. The adverse selection and moral hazard are always in the cooperation process [22].


## 2.3. Cooperation between Tourism Enterprises and OTAs

Clearly, the OTAs are an extremely important part of the tourism supply chain, recognized as an online distribution channel for hotels and airlines. A larger number of scholars have emerged to pay attention to the issue of the cooperation problem between hotels, airlines, and OTAs, such as pricing strategy, cooperation form, and coordination strategy [1, 8–11]. Koo et al. point out that the airlines are less likely to use OTA platforms if the airlines have a large loyal consumer base or if the OTA platform is highly competitive [8]. Ling et al. and Guo et al. study the optimal pricing strategy for tourism hotels when they operate their online channel by cooperation with an OTA [1, 9]. Considering the overbooking of hotels, Dong and Ling study the pricing and overbooking strategies of a hotel in the context of cooperation with multiple OTAs and analyze how these strategies influence the cooperation process [10]. The commonly used “first come first serve” form puts hotels in a disadvantageous position especially when the OTAs have much more market attractiveness than the hotels. Xu et al. propose a new form named “setting Online-Exclusive-Rooms” for a hotel to collaborate with a third-party website on room booking service [11].To get a new business opportunity, the TTAs are increasingly aware of the need to open up online markets’ cooperation with OTAs because of the low volume of visits and lack of e-commence operating experience of self-built website [3, 4]. Therefore, a few scholars have provided suggestions on the cooperation between TTA and OTA. Based on the resource-based view, Shi and Long analyze the complementary resources input decisions of OTAs and TTAs cocreating the O2O model, which do not propose a clear form of cooperation [3]. In a supplemental study, Long and Shi study the optimal pricing strategies of a tour operator and an OTA when they achieve the O2O business model through online sale and offline service cooperation to develop the advantage of complementary resources [4].However, no study has addressed the problem of an OTA that wants to achieve its O2O model by cooperating with a TTA with private information. In this situation, the OTA has little information and experience regarding offline service and knows little about how to design a service contract to disclosure TTA’s real information. To enrich the scientific literature and provide some suggestions to OTA on how to pursue offline service, this paper studies the optimal service contract of OTAs under asymmetric information through the analysis of a simple O2O model composed of an OTA and a TTA.


## 3. Problem Description and Assumptions

In this paper, the O2O model, combining both trading and offline service, achieved by service cooperation of an OTA and a TTA is considered herein. Table1 summarizes the main notation and its definitions used in this paper.Table 1
Notation and definitions.
NotationDefinitionssOSale ability of OTAeOSale effort of OTAC (eO)Sale cost of OTAηOSale cost coefficient of OTAsTServiceability of TTAeTService effort of TTAηTService cost coefficient of TTAC (eT)Unit cost of service effort of TTAQThe demand for O2O modelθMarket scaleξMarket random factorsT (q)Service commission that OTA pays for TTAAFixed paymentBService commission coefficient or the rate of revenue sharingΠ (q), ΠO, ΠTThe revenue of O2O model, OTA, and TTA, respectivelyEΠO, EΠTThe expected revenue of OTA and TTA, respectivelyUT (EΠT)The utility function of TTARTTA’s risk aversion coefficientIThe state of TTA’s serviceability,i = H, LsTiTTA’s serviceability,i = H, LρThe probability that the OTA believes that the TTA’s serviceability is at a high stateNote that the superscript∗denotes the optimal solutions.The OTA makes some effort to induce customers to make reservations through its website, such as ranking position. According to the literature [1], c (eO) is a convex increasing function with dceO/deO>0, d2ceT/deT2>0, and ceO=0. The sale cost of OTA is ceO=ηOeO2/2, where ηO is the OTA’s sale cost coefficient, which is widely used in the cost management literature [23–26].While the TTA provides offline services for tourism products, such as the scene experience and personalized and professional travel advice, the service effort is the cost of hiring salespersons by TTA. According to the literature [4, 25, 26], a strictly convex service function c (eT) is used to depict TTA’s unit cost of service effort, ceT=ηTeT2/2, where ηT is the TTA’s service cost coefficient, dceT/deT>0, and d2ceT/deT2>0.The demand for O2O modelq is influenced by OTA’s saleability sO, sale effort eO, TTA’s serviceability sT, service effort eT, market scale θ , and market random factors ξ.(1)q=fsO,eO+gsT,eT+uθ+ξ,where fsO>0, fsOsO≤0, feO>0, feO,eO≤0, gsT>0, gsTsT≤0, geT>0, geTeT≤0, uθ>0, uθθ≤0, and ξ follows a normal distribution ξ∈N0,σ2.In this paper, according to the literature [27], let f (eO) = sOeO, geT=sTeT, and u (θ) = θ. Thus, the demand for O2O model is q = sOeO + sTeT + θ + ξ. Assuming that the revenue of O2O model is proportional to its demand, the revenue of O2O model can be simplified into Πq=sOeO+sTeT+θ+ξ. Following the studies of Jin et al. [15] and He et al. [28], OTA provides service commission (a, b) to motivate TTA to make service effort, where a represents fixed payment and b denotes service commission coefficient or the rate of revenue sharing. Therefore, given (a, b), the service commission that OTA pays for TTA is Tq=a+bΠq.Based on the above assumptions, the OTA’s revenue function is obtained as follows:(2)ΠO=Πq−Tq−ceO=1−bsOeO+sTeT+θ+ξ−a−ηOeO22.Assuming that OTA is risk-neutral and its utility is equal to expected revenue, the OTA’s expected revenue is(3)EΠO=1−bsOeO+sTeT+θ−a−ηOeO22.The TTA’s revenue is(4)ΠT=Tq−ceT=a+bsOeO+sTeT+θ+ξ−ηTeT22.Assuming that TTA is risk-neutral risk aversion, one form of the utility function dominant in both theoretical and applied work in areas of decision theory and finance is the exponential utility function. Following the literature [29], UTΠT=−e−rΠT, where r denotes TTA’s risk aversion coefficient and r > 0, r = 0, and r < 0 are risk aversion, risk neutrality, and risk preference, respectively. By certainty equivalence method, the revenue of TTA is obtained as follows:(5)EΠT=a+bsOeO+sTeT+θ−ηTeT22−rσ2b22.Due to the uncertainty of the O2O model market demand and equation (5), TTA’s risk cost is rσ2b2/2.


## 4. Equilibrium Solutions and Analysis

This paper mainly studies the service cooperation contract design of OTA creating O2O model by cooperating with TTA under symmetric and asymmetric information. Further, the contract model with continuous type of TTA’s serviceability under information asymmetry is researched. Here, the superscripts “N,” “S,” and “C” represent the case of symmetric information, the discrete type, and continuous type of TTA’s service capability under information asymmetry, respectively.In the process of establishing service cooperation between an OTA and a TTA, the serviceability and effort are TTA’s private information which OTA is difficult to observe. Similar to the study of Li et al. [30], it is assumed that there are two possibilities for TTA’s serviceability: high serviceability sTH and low serviceability sTL, and sTH > sTL.


### 4.1. Equilibrium of Symmetric Information

In the case of symmetric information, the OTA fully knows the state of TTA’s serviceabilitysTi (i = H, L). The OTA designs a service contract (aiN,biN) and sale effort eOiN when the type of TTA’s serviceability is i. Then the expected revenue of OTA EΠOaiN,biN and TTA EΠTiaiN,biN and the optimization problem P1 is(6)maxaiN,biN,eOiNEΠO=1−biNsOeOiN+sTieTiN+θ−aiN−ηOeOiN22,s.t.IR−iaiN+biNsOeOiN+sTieTiN+θ−ηTeTiN22−rσ2biN22≥ΠT¯,where(7)eTiN=argmaxeTaiN+biNsOeOiN+sTieTiN+θ−ηTeTiN22−rσ2biN22.In the above optimization problem P1, the OTA maximizes its revenue. Inequality (IR-i) is individual constraints, assuring that the TTA will join the service cooperation because of exceeding the reservation revenue ΠT¯. Equation (7) denotes the TTA optimizes service effort by maximizing its revenue. The optimal decisions are found out by solving the above optimization problem P1 using the backward induction method.Theorem 1.
In the case of symmetric information, given the type of TTA’s serviceability asi, the OTA’s optimal service contract (aiN∗biN∗), sale effort eOiN∗, and the TTA’s optimal service effort eTiN∗ are(8)biN∗=sTi2sTi2+ηTrσ2,aiN∗=ΠT¯−sO2ηO+θbiN∗+12rσ2−sTi2ηTbiN∗2,eOiN∗=sOηO,eTiN∗=sTi3ηTsTi2+ηTrσ2=sTiηTbiN∗.Theorem1 shows that, (1) with the TTA’s service cost coefficient ηT, market uncertainty σ2, and risk aversion coefficient r increasing, the service commission coefficient biN∗ decreases; (2) when the market scale θ and OTA’s sale ability decreasing sO or OTA’s sale cost coefficient ηO increasing, the OTA’s fixed payment increases aiN∗, so as to encourage TTA to participate in cooperation for cocreating the O2O model. In addition, sO and ηO only affect OTA’s fixed payment aiN∗ but have no effect on service commission coefficient biN∗; (3) the OTA’s online sales effort eOiN∗ is only related to its own saleability sO and cost coefficient ηO and is directly proportional to sO and inversely proportional to ηO; (4) with the service commission coefficient biN∗ and TTA’s serviceability sTi increasing or TTA’s service cost coefficient ηT decreasing, the TTA’s offline service effort eTiN∗ increases.


### 4.2. Equilibrium of Asymmetric Information

In the case of asymmetric information, the TTA surely knows which of the two serviceability states will occur, while the OTA has only a subjective assessment about the likelihood of the two serviceability states. Letρ be the probability that the OTA believes that the TTA’s serviceability is at a high state and 1-ρ be the probability of the low state. Due to its parsimony and tractability for analysis, this type of asymmetric information has been commonly employed in supply chain contracting [15, 27, 30]. In this paper, the OTA is an uninformed party that acts as a principal, and the TTA is an agent that holds private information about its serviceability. The service contract design problem is investigated under asymmetric serviceability information. The goal of the OTA is to design a menu of service contracts so as to maximize its expected profit based on the revelation principle.When the state of TTA’s serviceability isi and OTA gives service contract (aiS,biS) and sale effort eOiS , the revenue of OTA and TTA is EΠOaiS,biS and EΠTiaiS,biS, respectively, and then the optimization problem P2 is(9)maxaHS,bHS,eOHS,aLS,bLS,eOLSEΠO=ρ1−bHSsOeOHS+sTHeTHS+θ−aHS−ηOeOHS22+1−ρ1−bLSsOeOLS+sTHeTLS+θ−aLS−ηOeOLS22s.t.IC−HaHS+bHSsOeOHS+sTHeTHS+θ−ηTeTHS22−rσ2bH22≥aLS+bLSsOeOSS+sTHeTHL+θ−ηTeTHL22−rσ2bL22IC−LaLS+bLSsOeOLS+sTLeTLS+θ−ηTeTLS22−rσ2bL22≥aHS+bHSsOeOHS+sTLeTLH+θ−ηTeTLH22−rσ2bH22,IR−HaHS+bHSsOeOHS+sTHeTHS+θ−ηTeTHS22−rσ2bH22≥ΠT¯,IR−LaLS+bLSsOeOLS+sTLeTLS+θ−ηTeTLS22−rσ2bL22≥ΠT¯,where(10)eTHS=argmaxeaHS+bHSsOeOHS+sTHeTHS+θ−ηTeTHS22−rσ2bH22,(11)eTLS=argmaxeaLS+bLSsOeOLS+sTLeTLS+θ−ηTeTLS22−rσ2bL22.In the above optimization problem P2, the expected revenue of OTAEΠO is the sum of revenue under the cooperation of serviceability states of TTA. Inequalities (IC-i) are incentive compatibility constraints, assuring that the TTA does not pretend to choose the other serviceability state, where eTHL(eTLH) indicates the optimal service effort level of TTA under service reward contract (aLS,bLS) ((aHS,bHS)). Inequalities (IR-i) are individual rationality constraints, assuring that the TTA will join to cocreate O2O model because of exceeding the reservation revenue ΠT¯. Equations (10) and (11) denote the TTA optimizes its service effort eTiN by maximizing its revenue according to OTA’s service cooperation contract (aiSbiS) and sale effort eOiS. The optimal decisions are found out by solving the above optimization problem P2 using the backward induction method.Theorem 2.
Under asymmetric information, the OTA reveals TTA’s serviceability by designing separation contracts. The OTA’s service commission contract, optimal sale effort, and TTA’s service effort are(12)aHS∗=ΠT¯−sO2ηO+θbHS∗+12rσ2−sTH2ηTbHS∗2+12ηTsTH2−sTL2bLS∗2,bHS∗=sTH2sTH2+ηTrσ2,aLS∗=ΠT¯−sO2ηO+θbLS∗+12rσ2−sTL2ηTbLS∗2,bLS∗=1−ρsTL2sTL2−2ρsTL2+ρsTH2+1−ρηTrσ2,eOHS∗=eOLS∗=sOηO,eTHS∗=sTH3ηTsTH2+ηTrσ2,eTLS∗=1−ρsTL3sTL2−2ρsTL2+ρsTH2+1−ρηTrσ2ηT.Theorem2 shows the following:(1)
Whenr⟶+∞, bHS∗=bLS∗=0 and aHS∗=aLS∗=ΠT¯ can be obtained. That is, when TTA has no risk tolerance, its revenue could only be equal to its reservation revenue. When r⟶0, bHS∗=1 and bLS∗=1/1+ρsTH2−sTL2/1−ρsTL2<1. That is, when the TTA tends to be risk-neutral, TTA with high serviceability will obtain the total revenue and if the difference between the two types of TTA’s serviceability or the probability of TTA with high serviceability is greater, the OTA’s service commission coefficient for TTA with low serviceability is lower. When r > 0, with the increase of TTA’s risk aversion coefficient, the service commission coefficient decreases correspondingly, indicating that risk aversion can offset the incentive effect of the service commission coefficient.(2)
According to∂bHS∗/∂sTH≥0∂bLS∗/∂sTL≥0, the service commission coefficient will increase with the increasing TTA’s serviceability.(3)
The service effort of the TTA with low serviceability has no effort on the service commission coefficient the TTA with high serviceability obtains. However, the difference between service effort of TTA with high and low service will affect the service commission coefficient TTA with low serviceability obtains, and the greater the difference is, the smaller the service commission coefficient is.(4)
According to∂bHS∗/∂ρ=0∂bLS∗/∂ρ≤0, the probability ρ has no effect on service commission coefficient that the TTA with high serviceability obtains. However, with the increase of ρ, the service commission coefficient that the TTA with high serviceability obtains decreases.(5)
The relationship between service commission coefficient and TTA’s service cost coefficientηT, the degree of market uncertainty σ2, the degree of risk aversion r, and the relationship between fixed payment and OTA’s saleability and sale cost coefficient are the same under symmetric information, which is omitted here.Corollary 1.
For TTA with different serviceability, the optimal service commission contract parameters have the relationshipsbHS∗≥bLS∗ and aHS∗≤aLS∗.Corollary1 shows that the motivation purpose of OTA service contract is different for TTA with different serviceability. This means that the OTA should balance the motivation cooperation and the incentive of improving service effort for TTA when designing service commission contract. If the TTA is of a low serviceability type, the OTA will give TTA a larger fixed payment to ensure TTA’s initiative in cocreating the O2O model. If the TTA is of a high serviceability state, the OTA will give a larger service commission coefficient. And according to bHS∗−bLS∗=sTH2−sTL2ρsTH2+1−ρηTrσ2/sTH2+ηTrσ2sTL2−2ρsTL2+ρsTH2+1−ρηTrσ2≥0, the greater the difference between service efforts of TTA with high and low service, the bigger the difference of service commission coefficient.


### 4.3. Comparison of Results under Symmetric and Asymmetric Information

#### 4.3.1. Validity of Separation Service Contract

By introducing the results of Theorem2 into incentive compatibility constraints (IC-i) and comparing the TTA’s revenue of TTA false and truthfulness reporting serviceability, it is obtained as(13)EΠTHaHS∗,bHS∗−EΠTHaLS∗,bLS∗=0,EΠTLaLS∗,bLS∗−EΠTLaHS∗,bHS∗=bHS∗−bLS∗bHS∗+bLS∗sTH+sTL2ηT>0.When the TTA with low serviceability pretends to be the TTA with high serviceability, the revenue is strictly lower than that when TTA truthfully reports serviceability (EΠTLaHS∗,bHS∗<EΠTLaLS∗,bLS∗). Therefore, the TTA with low serviceability has no motivation to disguise the TTA with high serviceability. In addition, if the TTA is of a high serviceability type, its false report will not affect its revenue (EΠTHaHS∗,bHS∗=EΠTHaLS∗,bLS∗). Therefore, the TTA with high serviceability has no motivation to disguise TTA with low serviceability. It can be seen that separation of service contract has the characteristics of “self-selection” in the OTA and TTA cocreating O2O model. That is, the TTA with high or low serviceability needs to choose the service contract corresponding to its serviceability type.Corollary 2.
By comparing the expected revenue and the reservation revenue with different serviceability types, it is obtained as(14)EΠTsTL=ΠT¯,EΠTsTH=ΠT¯+sTH2−sTL22ηTbLS∗2.Corollary2 shows that when the TTA is of a low serviceability type, TTA’s revenue is reservation revenue after accepting the service commission contract. However, when the revenue of TTA with high serviceability is greater than reservation revenue, the difference sTH2−sTL2/2ηTbLS∗2 shows that, in order to obtain private information of TTA’s serviceability, the OTA needs to pay TTA information sharing fees. If the OTA does not pay the information fee, TTA may lie about its serviceability and damage the OTA’s expected revenue.


#### 4.3.2. The Impact of Information Asymmetric on Service Contract and Expected Revenue

According to Theorems1 and 2, by comparing and analyzing the service contract, TTA’s optimal service effort, and the revenues of the partners under symmetric and asymmetric information conditions, Theorems 3 and 4 are obtained.Theorem 3.
The optimal service contract, sale effort, and TTA optimal service effort under symmetry and asymmetry information have the following relationships:(1)
aHS∗≥aHN∗,bHN∗=bHS∗(2)
aLS∗≥aLN∗,bLS∗≤bLN∗(3)
eTHN∗=eTHS∗,eTLS∗<eTLN∗(4)
eOiN∗=eOiS∗In Theorem3, (1) and (2) show that the OTA should pay a higher fixed payment to mobilize TTA to participate in cocreating the O2O model under asymmetric information. If the TTA is of a low serviceability type, the OTA lacks motivation to encourage TTA to provide higher service effort, and the service commission coefficient is “distorted downward.” If the TTA is of a high serviceability type, the OTA will provide a higher service commission coefficient to encourage the TTA to provide higher service effort under asymmetrical information. Here, the TTA can obtain additional information rent by “upward distorted” fixed payment. This also means that, due to the existence of asymmetric information, the service contract design of OTA needs to adjust the fixed payment and service commission coefficient to ensure that TTA can participate in cooperation and choose the appropriate level of service efforts that are beneficial to OTA. From (3), the service effort of TTA with the high serviceability is the same in symmetrical and asymmetrical information, and the service effort of TTA with the low serviceability is lower in asymmetrical information than that in symmetrical information. Therefore, the OTA is dominant in encouraging low serviceability type TTA to participate in cooperation, which will also provide incentives to participate in cooperation and service efforts to TTA with high serviceability state. (4) shows that the OTA’s optimal sale effort is the same in symmetrical and asymmetrical information. This is straightforward because the OTA knows its own saleability information.Theorem 4.
There is the following relationship between OTA’s and TTA’s expected revenue under symmetry and asymmetry information:(1)
EΠOiS∗≤EΠOiN∗(2)
EΠTHS∗≥EΠTHN∗(3)
EΠTLS∗=EΠTLN∗Theorem4 shows that the information disadvantage of OTA is detrimental to its expected revenue; the information advantage of TTA does not necessarily generate information rent. When the TTA is the high serviceability type, the increase of fixed payment cost and constant service commission coefficient make OTA pay higher service commission. Therefore, the TTA with high serviceability can get additional information rent from the increased fixed payment. When the TTA is of a low serviceability type, the asymmetric information makes the service commission coefficient tilt downward and fixed payment cost rise, and the rise of fixed payment can make up for the loss of TTA caused by the decline of service commission coefficient. Therefore, the expected revenue of TTA with low serviceability remains unchanged.


### 4.4. Strategic Analysis When TTA’s Service Ability Is Continuous

Assuming that TTAs’ service capability is continuously distributed and satisfiedsT∈H=sT¯,sT¯, FsT and fsT are TTA’s cumulative distribution and distribution density function, respectively, and fsT>0.Due to the fact thatξ is not related to sT, the expected revenue of risk-neutral OTA can be obtained as(15)EΠOsT=∫sT¯sT¯1−bCsTsOeOCsT+sTCeTCsT+θ−aCsT−ηOeOC2sT2fsTdsT.The expected revenue of TTA is(16)EΠTsT=aCsT+bCsTsOeOCsT+sTCeTCsT+θ−ηTeTC2sT2−rσ2bC2sT2.Firstly, determine the service effort level of TTAeTCsT; that is, ∀sT∈H, eTCsT=argmaxeEΠTsT==sTCbCsT/ηT.BringingeTCsT to equation (16), it is obtained as(17)EΠTsT=aCsT+sOeOCsT+θbCsT+12sT2ηT−rσ2bC2sT.Furthermore, whensT is continuous, the optimization problem P3 is(18)maxaC,bC∫sT¯sT¯1−bCsTsOeOCsT+sTCeTCsT+θ−aCsT−ηOeOC2sT2fsTdsTs.t.ICbC⋅sT≥0,(19)ICaC⋅sT+sOeOC⋅sTbCsT+sOeOCsT+θbC⋅sT+sTC2ηT−rσ2bC⋅sT=0,(20)IRaCsT+sOeOCsT+θbCsT+12sT2ηT−rσ2bC2sT≥ΠT¯,where eTCsT=sTCbCsT/ηT.Theorem 5.
Whend/dsT1−FsT/sTfsT<0,(21)aC∗sT=ΠT¯−sO2ηO+θbC∗sT−12sT2ηT−rσ2bC∗2sT+∫sT¯sTωηTbC2ωdω,bC∗sTC=11+ηTrσ2/sTC2+21−FsTC/sTCfsTC,eOC∗=sOηO,eTC∗sT=sTCbC∗sTηT.Theorem5 shows that when the serviceability of TTA is continuous, d/dsT1−FsT/sTfsT<0 is the precondition for the existence of a service commission separation contract. At this time, bC∗sT is strictly increasing, and all TTA can choose appropriate service contracts corresponding to its serviceability type.Corollary 3.
EΠTsT−ΠT¯=∫sT¯sTω/ηTbC∗2ωdω.
Corollary3 shows that, apart from the TTA with the worst serviceability, all other types of TTA can get strict information rent. With the increase of TTA’s serviceability, the information rent and the excepted revenue of TTA increase, which is consistent with the conclusion when TTA’s serviceability is discrete.


## 4.1. Equilibrium of Symmetric Information

In the case of symmetric information, the OTA fully knows the state of TTA’s serviceabilitysTi (i = H, L). The OTA designs a service contract (aiN,biN) and sale effort eOiN when the type of TTA’s serviceability is i. Then the expected revenue of OTA EΠOaiN,biN and TTA EΠTiaiN,biN and the optimization problem P1 is(6)maxaiN,biN,eOiNEΠO=1−biNsOeOiN+sTieTiN+θ−aiN−ηOeOiN22,s.t.IR−iaiN+biNsOeOiN+sTieTiN+θ−ηTeTiN22−rσ2biN22≥ΠT¯,where(7)eTiN=argmaxeTaiN+biNsOeOiN+sTieTiN+θ−ηTeTiN22−rσ2biN22.In the above optimization problem P1, the OTA maximizes its revenue. Inequality (IR-i) is individual constraints, assuring that the TTA will join the service cooperation because of exceeding the reservation revenue ΠT¯. Equation (7) denotes the TTA optimizes service effort by maximizing its revenue. The optimal decisions are found out by solving the above optimization problem P1 using the backward induction method.Theorem 1.
In the case of symmetric information, given the type of TTA’s serviceability asi, the OTA’s optimal service contract (aiN∗biN∗), sale effort eOiN∗, and the TTA’s optimal service effort eTiN∗ are(8)biN∗=sTi2sTi2+ηTrσ2,aiN∗=ΠT¯−sO2ηO+θbiN∗+12rσ2−sTi2ηTbiN∗2,eOiN∗=sOηO,eTiN∗=sTi3ηTsTi2+ηTrσ2=sTiηTbiN∗.Theorem1 shows that, (1) with the TTA’s service cost coefficient ηT, market uncertainty σ2, and risk aversion coefficient r increasing, the service commission coefficient biN∗ decreases; (2) when the market scale θ and OTA’s sale ability decreasing sO or OTA’s sale cost coefficient ηO increasing, the OTA’s fixed payment increases aiN∗, so as to encourage TTA to participate in cooperation for cocreating the O2O model. In addition, sO and ηO only affect OTA’s fixed payment aiN∗ but have no effect on service commission coefficient biN∗; (3) the OTA’s online sales effort eOiN∗ is only related to its own saleability sO and cost coefficient ηO and is directly proportional to sO and inversely proportional to ηO; (4) with the service commission coefficient biN∗ and TTA’s serviceability sTi increasing or TTA’s service cost coefficient ηT decreasing, the TTA’s offline service effort eTiN∗ increases.


## 4.2. Equilibrium of Asymmetric Information

In the case of asymmetric information, the TTA surely knows which of the two serviceability states will occur, while the OTA has only a subjective assessment about the likelihood of the two serviceability states. Letρ be the probability that the OTA believes that the TTA’s serviceability is at a high state and 1-ρ be the probability of the low state. Due to its parsimony and tractability for analysis, this type of asymmetric information has been commonly employed in supply chain contracting [15, 27, 30]. In this paper, the OTA is an uninformed party that acts as a principal, and the TTA is an agent that holds private information about its serviceability. The service contract design problem is investigated under asymmetric serviceability information. The goal of the OTA is to design a menu of service contracts so as to maximize its expected profit based on the revelation principle.When the state of TTA’s serviceability isi and OTA gives service contract (aiS,biS) and sale effort eOiS , the revenue of OTA and TTA is EΠOaiS,biS and EΠTiaiS,biS, respectively, and then the optimization problem P2 is(9)maxaHS,bHS,eOHS,aLS,bLS,eOLSEΠO=ρ1−bHSsOeOHS+sTHeTHS+θ−aHS−ηOeOHS22+1−ρ1−bLSsOeOLS+sTHeTLS+θ−aLS−ηOeOLS22s.t.IC−HaHS+bHSsOeOHS+sTHeTHS+θ−ηTeTHS22−rσ2bH22≥aLS+bLSsOeOSS+sTHeTHL+θ−ηTeTHL22−rσ2bL22IC−LaLS+bLSsOeOLS+sTLeTLS+θ−ηTeTLS22−rσ2bL22≥aHS+bHSsOeOHS+sTLeTLH+θ−ηTeTLH22−rσ2bH22,IR−HaHS+bHSsOeOHS+sTHeTHS+θ−ηTeTHS22−rσ2bH22≥ΠT¯,IR−LaLS+bLSsOeOLS+sTLeTLS+θ−ηTeTLS22−rσ2bL22≥ΠT¯,where(10)eTHS=argmaxeaHS+bHSsOeOHS+sTHeTHS+θ−ηTeTHS22−rσ2bH22,(11)eTLS=argmaxeaLS+bLSsOeOLS+sTLeTLS+θ−ηTeTLS22−rσ2bL22.In the above optimization problem P2, the expected revenue of OTAEΠO is the sum of revenue under the cooperation of serviceability states of TTA. Inequalities (IC-i) are incentive compatibility constraints, assuring that the TTA does not pretend to choose the other serviceability state, where eTHL(eTLH) indicates the optimal service effort level of TTA under service reward contract (aLS,bLS) ((aHS,bHS)). Inequalities (IR-i) are individual rationality constraints, assuring that the TTA will join to cocreate O2O model because of exceeding the reservation revenue ΠT¯. Equations (10) and (11) denote the TTA optimizes its service effort eTiN by maximizing its revenue according to OTA’s service cooperation contract (aiSbiS) and sale effort eOiS. The optimal decisions are found out by solving the above optimization problem P2 using the backward induction method.Theorem 2.
Under asymmetric information, the OTA reveals TTA’s serviceability by designing separation contracts. The OTA’s service commission contract, optimal sale effort, and TTA’s service effort are(12)aHS∗=ΠT¯−sO2ηO+θbHS∗+12rσ2−sTH2ηTbHS∗2+12ηTsTH2−sTL2bLS∗2,bHS∗=sTH2sTH2+ηTrσ2,aLS∗=ΠT¯−sO2ηO+θbLS∗+12rσ2−sTL2ηTbLS∗2,bLS∗=1−ρsTL2sTL2−2ρsTL2+ρsTH2+1−ρηTrσ2,eOHS∗=eOLS∗=sOηO,eTHS∗=sTH3ηTsTH2+ηTrσ2,eTLS∗=1−ρsTL3sTL2−2ρsTL2+ρsTH2+1−ρηTrσ2ηT.Theorem2 shows the following:(1)
Whenr⟶+∞, bHS∗=bLS∗=0 and aHS∗=aLS∗=ΠT¯ can be obtained. That is, when TTA has no risk tolerance, its revenue could only be equal to its reservation revenue. When r⟶0, bHS∗=1 and bLS∗=1/1+ρsTH2−sTL2/1−ρsTL2<1. That is, when the TTA tends to be risk-neutral, TTA with high serviceability will obtain the total revenue and if the difference between the two types of TTA’s serviceability or the probability of TTA with high serviceability is greater, the OTA’s service commission coefficient for TTA with low serviceability is lower. When r > 0, with the increase of TTA’s risk aversion coefficient, the service commission coefficient decreases correspondingly, indicating that risk aversion can offset the incentive effect of the service commission coefficient.(2)
According to∂bHS∗/∂sTH≥0∂bLS∗/∂sTL≥0, the service commission coefficient will increase with the increasing TTA’s serviceability.(3)
The service effort of the TTA with low serviceability has no effort on the service commission coefficient the TTA with high serviceability obtains. However, the difference between service effort of TTA with high and low service will affect the service commission coefficient TTA with low serviceability obtains, and the greater the difference is, the smaller the service commission coefficient is.(4)
According to∂bHS∗/∂ρ=0∂bLS∗/∂ρ≤0, the probability ρ has no effect on service commission coefficient that the TTA with high serviceability obtains. However, with the increase of ρ, the service commission coefficient that the TTA with high serviceability obtains decreases.(5)
The relationship between service commission coefficient and TTA’s service cost coefficientηT, the degree of market uncertainty σ2, the degree of risk aversion r, and the relationship between fixed payment and OTA’s saleability and sale cost coefficient are the same under symmetric information, which is omitted here.Corollary 1.
For TTA with different serviceability, the optimal service commission contract parameters have the relationshipsbHS∗≥bLS∗ and aHS∗≤aLS∗.Corollary1 shows that the motivation purpose of OTA service contract is different for TTA with different serviceability. This means that the OTA should balance the motivation cooperation and the incentive of improving service effort for TTA when designing service commission contract. If the TTA is of a low serviceability type, the OTA will give TTA a larger fixed payment to ensure TTA’s initiative in cocreating the O2O model. If the TTA is of a high serviceability state, the OTA will give a larger service commission coefficient. And according to bHS∗−bLS∗=sTH2−sTL2ρsTH2+1−ρηTrσ2/sTH2+ηTrσ2sTL2−2ρsTL2+ρsTH2+1−ρηTrσ2≥0, the greater the difference between service efforts of TTA with high and low service, the bigger the difference of service commission coefficient.


## 4.3. Comparison of Results under Symmetric and Asymmetric Information

### 4.3.1. Validity of Separation Service Contract

By introducing the results of Theorem2 into incentive compatibility constraints (IC-i) and comparing the TTA’s revenue of TTA false and truthfulness reporting serviceability, it is obtained as(13)EΠTHaHS∗,bHS∗−EΠTHaLS∗,bLS∗=0,EΠTLaLS∗,bLS∗−EΠTLaHS∗,bHS∗=bHS∗−bLS∗bHS∗+bLS∗sTH+sTL2ηT>0.When the TTA with low serviceability pretends to be the TTA with high serviceability, the revenue is strictly lower than that when TTA truthfully reports serviceability (EΠTLaHS∗,bHS∗<EΠTLaLS∗,bLS∗). Therefore, the TTA with low serviceability has no motivation to disguise the TTA with high serviceability. In addition, if the TTA is of a high serviceability type, its false report will not affect its revenue (EΠTHaHS∗,bHS∗=EΠTHaLS∗,bLS∗). Therefore, the TTA with high serviceability has no motivation to disguise TTA with low serviceability. It can be seen that separation of service contract has the characteristics of “self-selection” in the OTA and TTA cocreating O2O model. That is, the TTA with high or low serviceability needs to choose the service contract corresponding to its serviceability type.Corollary 2.
By comparing the expected revenue and the reservation revenue with different serviceability types, it is obtained as(14)EΠTsTL=ΠT¯,EΠTsTH=ΠT¯+sTH2−sTL22ηTbLS∗2.Corollary2 shows that when the TTA is of a low serviceability type, TTA’s revenue is reservation revenue after accepting the service commission contract. However, when the revenue of TTA with high serviceability is greater than reservation revenue, the difference sTH2−sTL2/2ηTbLS∗2 shows that, in order to obtain private information of TTA’s serviceability, the OTA needs to pay TTA information sharing fees. If the OTA does not pay the information fee, TTA may lie about its serviceability and damage the OTA’s expected revenue.


### 4.3.2. The Impact of Information Asymmetric on Service Contract and Expected Revenue

According to Theorems1 and 2, by comparing and analyzing the service contract, TTA’s optimal service effort, and the revenues of the partners under symmetric and asymmetric information conditions, Theorems 3 and 4 are obtained.Theorem 3.
The optimal service contract, sale effort, and TTA optimal service effort under symmetry and asymmetry information have the following relationships:(1)
aHS∗≥aHN∗,bHN∗=bHS∗(2)
aLS∗≥aLN∗,bLS∗≤bLN∗(3)
eTHN∗=eTHS∗,eTLS∗<eTLN∗(4)
eOiN∗=eOiS∗In Theorem3, (1) and (2) show that the OTA should pay a higher fixed payment to mobilize TTA to participate in cocreating the O2O model under asymmetric information. If the TTA is of a low serviceability type, the OTA lacks motivation to encourage TTA to provide higher service effort, and the service commission coefficient is “distorted downward.” If the TTA is of a high serviceability type, the OTA will provide a higher service commission coefficient to encourage the TTA to provide higher service effort under asymmetrical information. Here, the TTA can obtain additional information rent by “upward distorted” fixed payment. This also means that, due to the existence of asymmetric information, the service contract design of OTA needs to adjust the fixed payment and service commission coefficient to ensure that TTA can participate in cooperation and choose the appropriate level of service efforts that are beneficial to OTA. From (3), the service effort of TTA with the high serviceability is the same in symmetrical and asymmetrical information, and the service effort of TTA with the low serviceability is lower in asymmetrical information than that in symmetrical information. Therefore, the OTA is dominant in encouraging low serviceability type TTA to participate in cooperation, which will also provide incentives to participate in cooperation and service efforts to TTA with high serviceability state. (4) shows that the OTA’s optimal sale effort is the same in symmetrical and asymmetrical information. This is straightforward because the OTA knows its own saleability information.Theorem 4.
There is the following relationship between OTA’s and TTA’s expected revenue under symmetry and asymmetry information:(1)
EΠOiS∗≤EΠOiN∗(2)
EΠTHS∗≥EΠTHN∗(3)
EΠTLS∗=EΠTLN∗Theorem4 shows that the information disadvantage of OTA is detrimental to its expected revenue; the information advantage of TTA does not necessarily generate information rent. When the TTA is the high serviceability type, the increase of fixed payment cost and constant service commission coefficient make OTA pay higher service commission. Therefore, the TTA with high serviceability can get additional information rent from the increased fixed payment. When the TTA is of a low serviceability type, the asymmetric information makes the service commission coefficient tilt downward and fixed payment cost rise, and the rise of fixed payment can make up for the loss of TTA caused by the decline of service commission coefficient. Therefore, the expected revenue of TTA with low serviceability remains unchanged.


## 4.3.1. Validity of Separation Service Contract

By introducing the results of Theorem2 into incentive compatibility constraints (IC-i) and comparing the TTA’s revenue of TTA false and truthfulness reporting serviceability, it is obtained as(13)EΠTHaHS∗,bHS∗−EΠTHaLS∗,bLS∗=0,EΠTLaLS∗,bLS∗−EΠTLaHS∗,bHS∗=bHS∗−bLS∗bHS∗+bLS∗sTH+sTL2ηT>0.When the TTA with low serviceability pretends to be the TTA with high serviceability, the revenue is strictly lower than that when TTA truthfully reports serviceability (EΠTLaHS∗,bHS∗<EΠTLaLS∗,bLS∗). Therefore, the TTA with low serviceability has no motivation to disguise the TTA with high serviceability. In addition, if the TTA is of a high serviceability type, its false report will not affect its revenue (EΠTHaHS∗,bHS∗=EΠTHaLS∗,bLS∗). Therefore, the TTA with high serviceability has no motivation to disguise TTA with low serviceability. It can be seen that separation of service contract has the characteristics of “self-selection” in the OTA and TTA cocreating O2O model. That is, the TTA with high or low serviceability needs to choose the service contract corresponding to its serviceability type.Corollary 2.
By comparing the expected revenue and the reservation revenue with different serviceability types, it is obtained as(14)EΠTsTL=ΠT¯,EΠTsTH=ΠT¯+sTH2−sTL22ηTbLS∗2.Corollary2 shows that when the TTA is of a low serviceability type, TTA’s revenue is reservation revenue after accepting the service commission contract. However, when the revenue of TTA with high serviceability is greater than reservation revenue, the difference sTH2−sTL2/2ηTbLS∗2 shows that, in order to obtain private information of TTA’s serviceability, the OTA needs to pay TTA information sharing fees. If the OTA does not pay the information fee, TTA may lie about its serviceability and damage the OTA’s expected revenue.


## 4.3.2. The Impact of Information Asymmetric on Service Contract and Expected Revenue

According to Theorems1 and 2, by comparing and analyzing the service contract, TTA’s optimal service effort, and the revenues of the partners under symmetric and asymmetric information conditions, Theorems 3 and 4 are obtained.Theorem 3.
The optimal service contract, sale effort, and TTA optimal service effort under symmetry and asymmetry information have the following relationships:(1)
aHS∗≥aHN∗,bHN∗=bHS∗(2)
aLS∗≥aLN∗,bLS∗≤bLN∗(3)
eTHN∗=eTHS∗,eTLS∗<eTLN∗(4)
eOiN∗=eOiS∗In Theorem3, (1) and (2) show that the OTA should pay a higher fixed payment to mobilize TTA to participate in cocreating the O2O model under asymmetric information. If the TTA is of a low serviceability type, the OTA lacks motivation to encourage TTA to provide higher service effort, and the service commission coefficient is “distorted downward.” If the TTA is of a high serviceability type, the OTA will provide a higher service commission coefficient to encourage the TTA to provide higher service effort under asymmetrical information. Here, the TTA can obtain additional information rent by “upward distorted” fixed payment. This also means that, due to the existence of asymmetric information, the service contract design of OTA needs to adjust the fixed payment and service commission coefficient to ensure that TTA can participate in cooperation and choose the appropriate level of service efforts that are beneficial to OTA. From (3), the service effort of TTA with the high serviceability is the same in symmetrical and asymmetrical information, and the service effort of TTA with the low serviceability is lower in asymmetrical information than that in symmetrical information. Therefore, the OTA is dominant in encouraging low serviceability type TTA to participate in cooperation, which will also provide incentives to participate in cooperation and service efforts to TTA with high serviceability state. (4) shows that the OTA’s optimal sale effort is the same in symmetrical and asymmetrical information. This is straightforward because the OTA knows its own saleability information.Theorem 4.
There is the following relationship between OTA’s and TTA’s expected revenue under symmetry and asymmetry information:(1)
EΠOiS∗≤EΠOiN∗(2)
EΠTHS∗≥EΠTHN∗(3)
EΠTLS∗=EΠTLN∗Theorem4 shows that the information disadvantage of OTA is detrimental to its expected revenue; the information advantage of TTA does not necessarily generate information rent. When the TTA is the high serviceability type, the increase of fixed payment cost and constant service commission coefficient make OTA pay higher service commission. Therefore, the TTA with high serviceability can get additional information rent from the increased fixed payment. When the TTA is of a low serviceability type, the asymmetric information makes the service commission coefficient tilt downward and fixed payment cost rise, and the rise of fixed payment can make up for the loss of TTA caused by the decline of service commission coefficient. Therefore, the expected revenue of TTA with low serviceability remains unchanged.


## 4.4. Strategic Analysis When TTA’s Service Ability Is Continuous

Assuming that TTAs’ service capability is continuously distributed and satisfiedsT∈H=sT¯,sT¯, FsT and fsT are TTA’s cumulative distribution and distribution density function, respectively, and fsT>0.Due to the fact thatξ is not related to sT, the expected revenue of risk-neutral OTA can be obtained as(15)EΠOsT=∫sT¯sT¯1−bCsTsOeOCsT+sTCeTCsT+θ−aCsT−ηOeOC2sT2fsTdsT.The expected revenue of TTA is(16)EΠTsT=aCsT+bCsTsOeOCsT+sTCeTCsT+θ−ηTeTC2sT2−rσ2bC2sT2.Firstly, determine the service effort level of TTAeTCsT; that is, ∀sT∈H, eTCsT=argmaxeEΠTsT==sTCbCsT/ηT.BringingeTCsT to equation (16), it is obtained as(17)EΠTsT=aCsT+sOeOCsT+θbCsT+12sT2ηT−rσ2bC2sT.Furthermore, whensT is continuous, the optimization problem P3 is(18)maxaC,bC∫sT¯sT¯1−bCsTsOeOCsT+sTCeTCsT+θ−aCsT−ηOeOC2sT2fsTdsTs.t.ICbC⋅sT≥0,(19)ICaC⋅sT+sOeOC⋅sTbCsT+sOeOCsT+θbC⋅sT+sTC2ηT−rσ2bC⋅sT=0,(20)IRaCsT+sOeOCsT+θbCsT+12sT2ηT−rσ2bC2sT≥ΠT¯,where eTCsT=sTCbCsT/ηT.Theorem 5.
Whend/dsT1−FsT/sTfsT<0,(21)aC∗sT=ΠT¯−sO2ηO+θbC∗sT−12sT2ηT−rσ2bC∗2sT+∫sT¯sTωηTbC2ωdω,bC∗sTC=11+ηTrσ2/sTC2+21−FsTC/sTCfsTC,eOC∗=sOηO,eTC∗sT=sTCbC∗sTηT.Theorem5 shows that when the serviceability of TTA is continuous, d/dsT1−FsT/sTfsT<0 is the precondition for the existence of a service commission separation contract. At this time, bC∗sT is strictly increasing, and all TTA can choose appropriate service contracts corresponding to its serviceability type.Corollary 3.
EΠTsT−ΠT¯=∫sT¯sTω/ηTbC∗2ωdω.
Corollary3 shows that, apart from the TTA with the worst serviceability, all other types of TTA can get strict information rent. With the increase of TTA’s serviceability, the information rent and the excepted revenue of TTA increase, which is consistent with the conclusion when TTA’s serviceability is discrete.


## 5. Numerical and Sensitivity Analysis

The impacts of the risk aversion coefficient, serviceability, and asymmetric information on OTA service contract and both TTA and OTA’s revenue are analyzed so as to get more management implications. Considering that the references on cooperation between the TTA and OTA are less and numerical examples are usually hypothetical, the values of parameters are assigned according to Jin et al. [15]. A selected set of parameters is as follows: sO = 0.3, ηO = 0.1, ηT = 0.3, sTH = 0.5, sTL = 0.5, ρ = 0.5, θ = 0, σ2 = 4, and ΠT¯=5. In order to study the impacts of TTA’s risk aversion coefficient r and serviceability sTi on the service commission and revenue of both TTA and OTA, take sTi as the horizontal axis; that is, when sTH = 0.5 and sTL∈0.2,0.5 or when sTL = 0.5 and sTH∈0.5,1, fixed payment, service commission coefficient, and revenue under symmetric information and asymmetric information are plotted, respectively, with r = 0.1, r = 0.5, and r = 0.9. The results of numerical examples are summarized in Figures 1–3.Figure 1
Impacts of r andsTi on fixed payment. (a) sTL=0.5. (b) sTH=0.5.
(a)(b)Figure 2
Impacts of r andsTi on service compensation coefficient. (a) sTL=0.5. (b) sTH=0.5.
(a)(b)Figure 3
Impacts of r andsTi on ΔEΠTiSN∗ and ΔEΠOiSN∗. (a) sTL=0.5. (b) sTH=0.5.
(a)(b)


### 5.1. Analysis of the Parameters of Service Commission Contract

Figure1 shows that (1) when TTA’s serviceability sTi increases, fixed payment of aiS∗ and aiN∗ always decreases, but aiS∗>aiN∗. This is straightforward because OTA needs to give TTA more fixed payment to stimulate its initiative to cooperate and create an O2O model under asymmetric information; (2) when r takes different values, the influence of asymmetric information on fixed payment increases with the increase of the difference between TTA’s serviceability, which indicates that the smaller the difference between TTA’s different serviceability, the smaller the influence of asymmetric information on fixed payment; (3) no matter the serviceability type of TTA is, aiS∗ and aiN∗ increase and the distance between fixed payment curves decreases with the increase in r, indicating that r can alleviate the impact of asymmetric information on fixed payment.Figure2 shows that (1) when TTA’s serviceability sTi increases, service commission coefficient of biS∗ and biN∗ always increases, and bHS∗=bHN∗ and bLS∗<bLN∗. This indicates that the existence of asymmetric information does not affect the service commission coefficient of OTA paid for TTA with high serviceability, but it will reduce the service commission coefficient of OTA paid for TTA with low serviceability; (2) no matter the serviceability type of TTA is, biS∗ and biN∗ decrease with the increase in r, indicating that r can offset the incentive effect of service commission coefficient on TTA; (3) Figure 2(b) shows the distance between service commission coefficient curves decreases with r or sTL increases, which indicates that r can alleviate the impact of asymmetric information on service commission coefficient and the smaller the difference between TTA’s different serviceability, the smaller the influence of asymmetric information on service commission coefficient.


### 5.2. Revenue Analysis of Both TTA and OTA

Figure3 shows that (1) no matter the serviceability type of TTA is, there is ΔEΠOiSN∗<0, indicating that the existence of asymmetric information always leads to loss of OTA expected revenue. ΔEΠTHSN∗>0 and ΔEΠTLSN∗=0 show that only TTA with high serviceability can obtain additional information rent. ΔEΠTHSN∗ and ΔEΠOHSN∗ are symmetric with respect to the 0 value curve, which shows that, in the O2O model composed by TTA and OTA, the existence of asymmetric information transfers part of revenue from OTA to TTA with high service capability; (2) when sTH increases, ΔEΠOiSN∗ firstly decreases and then increases. The changing trend of ΔEΠOHSN∗ depends on whether OTA’s revenue from the improvement of TTA’s serviceability can compensate for the cost of information value; (3) ΔEΠOiSN∗ decreases with r increases, which shows that r can alleviate the adverse effect of OTA’s information disadvantage. ΔEΠTHSN∗ decreases with r increases, which indicates that r can inhibit the information advantage of TTA with high serviceability.


## 5.1. Analysis of the Parameters of Service Commission Contract

Figure1 shows that (1) when TTA’s serviceability sTi increases, fixed payment of aiS∗ and aiN∗ always decreases, but aiS∗>aiN∗. This is straightforward because OTA needs to give TTA more fixed payment to stimulate its initiative to cooperate and create an O2O model under asymmetric information; (2) when r takes different values, the influence of asymmetric information on fixed payment increases with the increase of the difference between TTA’s serviceability, which indicates that the smaller the difference between TTA’s different serviceability, the smaller the influence of asymmetric information on fixed payment; (3) no matter the serviceability type of TTA is, aiS∗ and aiN∗ increase and the distance between fixed payment curves decreases with the increase in r, indicating that r can alleviate the impact of asymmetric information on fixed payment.Figure2 shows that (1) when TTA’s serviceability sTi increases, service commission coefficient of biS∗ and biN∗ always increases, and bHS∗=bHN∗ and bLS∗<bLN∗. This indicates that the existence of asymmetric information does not affect the service commission coefficient of OTA paid for TTA with high serviceability, but it will reduce the service commission coefficient of OTA paid for TTA with low serviceability; (2) no matter the serviceability type of TTA is, biS∗ and biN∗ decrease with the increase in r, indicating that r can offset the incentive effect of service commission coefficient on TTA; (3) Figure 2(b) shows the distance between service commission coefficient curves decreases with r or sTL increases, which indicates that r can alleviate the impact of asymmetric information on service commission coefficient and the smaller the difference between TTA’s different serviceability, the smaller the influence of asymmetric information on service commission coefficient.


## 5.2. Revenue Analysis of Both TTA and OTA

Figure3 shows that (1) no matter the serviceability type of TTA is, there is ΔEΠOiSN∗<0, indicating that the existence of asymmetric information always leads to loss of OTA expected revenue. ΔEΠTHSN∗>0 and ΔEΠTLSN∗=0 show that only TTA with high serviceability can obtain additional information rent. ΔEΠTHSN∗ and ΔEΠOHSN∗ are symmetric with respect to the 0 value curve, which shows that, in the O2O model composed by TTA and OTA, the existence of asymmetric information transfers part of revenue from OTA to TTA with high service capability; (2) when sTH increases, ΔEΠOiSN∗ firstly decreases and then increases. The changing trend of ΔEΠOHSN∗ depends on whether OTA’s revenue from the improvement of TTA’s serviceability can compensate for the cost of information value; (3) ΔEΠOiSN∗ decreases with r increases, which shows that r can alleviate the adverse effect of OTA’s information disadvantage. ΔEΠTHSN∗ decreases with r increases, which indicates that r can inhibit the information advantage of TTA with high serviceability.


## 6. Conclusion and Future Research

The rationality of the OTA service commission contract is an important factor to ensure the efficient operation of the O2O model in the establishment of service cooperation between OTA and TTA. Considering the serviceability information asymmetry, the OTA designs reasonable service commission contracts to differentiate TTA serviceability and motivate TTA to improve service effort level. Some suggestions for establishing a cooperation contract are provided:(1)
When an OTA has low saleability or high sale cost, the OTA will increase the fixed payment to the TTA to support the cocreation of the O2O model. The incentive effect of the service commission coefficient can be offset by the TTA’s service effort cost coefficient, risk aversion coefficient, and the degree of market uncertainty.(2)
When the type of TTA’s serviceability is continuous, the precondition of separation contract isd/dsT1−FsT/sTfsT<0. At this time, the TTA can choose the appropriate contract corresponding to its serviceability type.(3)
In cocreating the O2O model, the existence of asymmetric information always results in the loss of OTA’s expected revenue. Except for TTA with the lowest serviceability, all other types of TTA can obtain strict information rent. The stronger the TTA serviceability is, the more information rent it obtains and the greater its expected revenue is.(4)
The OTA designs service contracts by weighing the different incentives of fixed payments and service commission coefficient to the TTA. When the TTA is a high serviceability type, the OTA sets a high service commission coefficient to encourage the TTA to make more service. When the TTA is of a low serviceability type, the OTA sets a larger fixed payment, stimulating its enthusiasm for participating in cocreating the O2O model.Due to some of its basic assumptions, there are still some limitations of this paper. Firstly, in this paper, the service commission contract is considered as a linear form. In future research, it can be extended by other service cooperation forms, for example, service cost-sharing contract. Secondly, this study only considers that an OTA establishes service cooperation with a TTA or multiple TTAs. The next step is to extend this model, in which two or more OTAs achieve the O2O model by service cooperation with multiple TTAs. Thirdly, the optimal service commission contract is studied from a short-term perspective. However, in some cases, long-term cooperation is more conducive to the stable development of both. For example, He et al. studied sustainable tourism by using evolutionary game models [31, 32]. Although it is more complex and challenging, service cooperation could be discussed from a long-term perspective. Finally, our paper mainly utilizes an analytical approach. Thus, another future research direction is to conduct empirical research to validate our analytical findings.



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*Source: 9934726-2021-05-03.xml*