Optimal Sales Channel and Business Model Strategies for a Hotel Considering Two Types of Online Travel Agency

Abstract

This study addresses a pivotal strategic issue in hospitality e-commerce: how hotels can optimize cooperation with heterogeneous online travel agencies (OTAs). Moving beyond the conventional question of whether to cooperate, we investigate the interrelated decisions of which OTA type to partner with (quality-focused vs. price-focused) and which business model to adopt (merchant vs. agency). We develop a game-theoretic model that incorporates key e-commerce factors, including hotel capacity constraints, cross-channel spillover effects, and differential consumer acceptance of OTA types. Our analysis yields a contingent decision framework. We demonstrate that OTA cooperation becomes beneficial only when a hotel’s room capacity exceeds its direct-channel demand. The optimal strategy evolves with capacity: hotels with moderate capacity should partner with a single OTA type—predominantly the quality-focused one—while larger hotels should engage both types to maximize market coverage. In terms of business models, smaller hotels benefit from the risk-shifting merchant model, whereas larger hotels capture higher margins through the agency model. A key finding is the general superiority of a differentiated approach: applying the agency model to quality-focused OTAs and the merchant model to price-focused OTAs. This research provides a structured analytical framework to guide hotel managers in crafting e-commerce platform strategies and offers scholars a foundation for further inquiry into platform competition and contract design in digital marketplaces.

1. Introduction

The strategic relationship between hotels and Online Travel Agencies (OTAs) constitutes a central challenge in global hospitality management. However, the dynamics of this relationship within China—the world’s largest digital travel market—remain insufficiently understood. While existing literature, primarily based on Western contexts, acknowledges OTAs’ dual role in expanding market reach [1], while creating channel conflict [2], the applicability of these findings to China’s unique ecosystem is unclear. This market is characterized by the pronounced dominance of domestic OTAs, which facilitate an estimated 70% of budget hotel room sales [3]. The tension between global theory and local practice is evident in instances such as the dissolved partnership between China Lodging Group and Ctrip.com [3], and in international chains like Marriott launching targeted direct-booking campaigns within China [4]. This gap between theory and practice highlights a critical question for both scholars and industry professionals: Under what specific conditions should hotels in the Chinese market consider cooperating with OTAs?

Recent research has analyzed the fundamental decision of whether to integrate an intermediary sales channel by balancing market expansion benefits against potential conflict [3,4]. However, prior work leaves two more nuanced, sequential questions unanswered.

The first question addresses the high-level channel strategy: Should a hotel focus exclusively on its direct channel, or should it allocate resources to partner with OTAs? This decision concerns the fundamental trade-off between channel control and market reach.

The second question focuses on the operational strategy within OTA partnerships: If a hotel decides to cooperate, what is the optimal strategy for selecting OTA types (quality-focused vs. price-focused) and determining the number of partners? As illustrated in Figure 1, OTAs can be categorized by their target market segment. Quality-focused OTAs (e.g., Ctrip.com, Booking.com) target service-sensitive consumers, typically enabling higher room rates but charging higher commissions. Price-focused OTAs (e.g., Fliggy.com, eLong.com, Agoda.com) target price-sensitive consumers, resulting in lower average rates and commissions. In practice, hotels exhibit varied approaches, partnering with either one type or both. Therefore, we investigate the conditions under which a hotel should cooperate exclusively with quality-focused OTAs, price-focused OTAs, or with both types simultaneously to maximize profitability.

Furthermore, hotels must select a contractual business model for OTA cooperation. The merchant model involves the OTA purchasing rooms at wholesale prices and reselling them, transferring inventory risk to the OTA but lowering the hotel’s per-room profit [3]. Conversely, the agency model involves the OTA acting as a sales agent for a commission; the hotel retains higher per-room revenue but bears the inventory risk [5]. Major platforms utilize these models differently (e.g., Orbitz.com primarily uses the merchant model, Booking.com the agency model, and Ctrip.com both) [2]. This leads to our third research question: What is the optimal business model—merchant, agency, or a hybrid—when cooperating with one or both types of OTAs, and how does this choice interact with the channel selection strategy?

To address these questions, we develop a game-theoretic model that integrates heterogeneous consumer channel preferences and hotel profit maximization under capacity constraints. We analyze the profitability of three channel scenarios (direct-only, single-type OTA, both-type OTA) under two business models (merchant, agency). Our analysis yields a contingent decision framework. Key findings include: (1) OTA cooperation is beneficial only when a hotel’s room capacity exceeds its base direct channel demand; (2) Hotels with moderate capacity should partner with a single OTA type (predominantly quality-focused), while larger hotels benefit from engaging both types; (3) The optimal business model depends on hotel capacity and consumer acceptance levels, with smaller hotels favoring the merchant model and larger hotels favoring the agency or hybrid models.

However, while prior research provides valuable insights into individual aspects of this problem—such as business model selection or the role of capacity [3]—a critical lens is missing. The strategic implication of hotel partnership with heterogeneous OTA types (quality- versus price-focused) remains underexplored, especially in conjunction with other key decisions. This study introduces this vital dimension and constructs a unified analytical framework to investigate the interdependent choices of OTA type, partnership scope, and business model under capacity constraints. By doing so, we move beyond asking whether to cooperate with OTAs, to address the more nuanced questions of with whom, how many, and under which terms.

The remainder of this paper is organized as follows. Section 2 reviews the relevant literature. Section 3 presents the model. Section 4 analyzes optimal choices. Section 5 discusses equilibrium strategies, and Section 6 concludes. All proofs are provided in an Appendix A.

2. Literature Review

2.1. OTA Research in Tourism and Hospitality

Research on OTAs within tourism and hospitality provides the immediate context for this study. This literature stream acknowledges the general tension between channel expansion and conflict but emphasizes industry-specific operational realities, such as service perishability, fixed capacity, and the spatial-temporal nature of consumption [3,4].

A central theme is the well-documented channel conflict between hotels and OTAs. Chennamaneni et al. highlight the trade-off between the market access provided by OTAs and the associated costs, including high commission rates and potential brand dilution [5]. Empirical work, such as the case study of Expedia.com and Choice Hotels by Lee et al. [2], illustrates how OTAs can both broaden reach and cannibalize more profitable direct bookings. This tension has spurred strategic responses, including failed partnerships in specific markets [3] and direct-booking campaigns by major chains [4].

Furthermore, Scholars have also investigated determinants of optimal OTA cooperation. Ye et al. [3] integrate hotel room capacity as a critical moderating variable, a factor paramount to revenue management. Other research underscores the positive “billboard” or spillover effect, where OTA presence enhances brand visibility and stimulates direct sales—a nuance particularly relevant in hospitality [6,7].

Recent research further illuminates the strategic complexity of platform competition. A pivotal contribution by Abhishek et al. [8] established a foundational framework for comparing agency selling and reselling in electronic retailing. Building on this, Wei et al. [9] analyze how competitive e-tailers choose between these online sales formats, revealing that the degree of competition and platform power are decisive factors. More recently, Xu et al. [10] have extended this discourse by examining strategic third-party product entry and mode choice under competition between a platform’s self-operating channels and its marketplace. Collectively, these studies provide a sophisticated analytical framework for understanding strategic interactions in a multi-actor platform ecosystem and underscore the necessity of considering inter-OTA rivalry and strategic format selection in modeling hotel-OTA cooperation.

Despite these advances, two significant gaps persist. First, while empirical models often treat OTAs homogenously, theoretical frameworks that categorize OTAs by strategic focus (e.g., quality vs. price) and analyze multi-type partnerships are lacking. Second, the choice between merchant and agency models is seldom analyzed as an integrated decision jointly made with OTA type selection under capacity constraints.

2.2. Sales Channel Choice and Consumer Behavior

The literature on channel selection establishes that while adding a new sales channel can generate benefits, its profitability is contingent on specific market and operational conditions [11]. Foundational research demonstrates that a direct online channel can profitably coexist with traditional retail through mechanisms like market expansion and efficiency gains [12]. This potential is particularly critical for managing perishable goods, where integrated channel management is essential for aligning supply with volatile demand [13], and where online channels can improve key performance indicators [14].

However, a substantial and nuanced body of work cautions against a universal multi-channel strategy, demonstrating that its success is highly context-dependent [15]. Key moderating factors identified in the literature include the relative bargaining power between channel members [16]; for instance, a dual-channel strategy may not benefit a manufacturer if the retailer holds greater power [17]. Other critical factors encompass the level of consumer acceptance of the online channel [18], channel power dynamics coupled with supplier fairness concerns [19], and product-specific considerations like remanufacturing [20]. In the specific context of hotel distribution, Li et al. [4] find that the optimal strategy is influenced not only by online channel acceptance but also decisively by hotel room capacity and the OTA’s market expansion potential. Furthermore, research on perishable goods highlights that while multi-channel distribution offers benefits, it also introduces complex trade-offs, where mechanisms such as dynamic pricing and commission rates become vital tools for channel control [21]. This body of work collectively underscores that the manufacturer’s decision to introduce its own channel hinges on factors like the profitability and strategic importance of existing third-party retailers [15], and that channel competition and conflict must be carefully managed [22].

The paradigm shift from multichannel to omnichannel retailing has placed consumer behavior at the very core of channel strategy [1]. Contemporary empirical research confirms that channel choice is not monolithic but is shaped by a complex matrix of channel characteristics, consumer personality, and media context [23]. This finding provides robust external validation for our model’s core premise of differential consumer acceptance of quality-focused versus price-focused OTAs. Beyond basic adoption, deeper psychological mechanisms are instrumental. For instance, Yi et al. [24] demonstrate that consumers’ perceptions of fairness can significantly tilt a firm’s optimal choice between direct and agent selling models, reinforcing the necessity of incorporating such consumer-centric variables into channel design.

The analytical scope has productively extended to competitive environments involving multiple retailers. Studies have examined pricing and channel integration strategies for competing dual-channel retailers [25], the broader dynamics of price competition in multichannel retailing [26], and the strategic implications of a manufacturer working with multiple, heterogeneous retailers [27]. Seminal work by Abhishek et al. [8] analyzing a manufacturer and two e-tailers, and by Tsay and Agrawal [28] on channel conflict and coordination, further illuminate the impact of inter-channel competition and demand spillovers. Research also explores the value of strategies like “online-to-store” channels in competitive markets [29]. This collective body of work clarifies the complex, contingent conditions under which channel addition is advantageous.

A critical task remains to adapt these general insights to the hospitality industry’s specific context, defined by perishable inventory and fixed capacity. The operational challenge of optimally allocating finite room capacity across channels finds a strong parallel in advanced omnichannel inventory research. Hassanzadeh and Martínez-de-Albéniz [30] employ an optimal control framework to demonstrate that retailers must dynamically allocate finite inventory across channels to protect revenue, a principle that provides a rigorous theoretical anchor for our treatment of room capacity as a binding constraint. Furthermore, operational models that explicitly integrate consumer channel preference, such as the dual-channel decision model for a shopping complex [31], offer direct methodological support for our utility-based approach to modeling consumer choice between a hotel’s direct channel and different types of OTAs.

2.3. Business Model Selection

Literature on business model selection in distribution channels comprises studies focusing on a single model and those comparing different models. Research in the first stream analyzes optimal strategies under specific contractual forms, such as the agency model with revenue sharing [32,33] or the “name-your-own-price” (NYOP) variant of the merchant model [34].

The second, more pertinent stream directly compares the merchant (reselling) and agency models. The foundational work of Abhishek et al. [8] establishes that the optimal choice depends critically on the nature of cross-channel spillover effects: the agency model is preferable when the spillover is negative, while the merchant model dominates when it is positive. Subsequent research has expanded this contingency view. In the specific context of hotel distribution, research has been further deepened. Ma et al. [35] employ differential game theory to analyze the dynamic game and choice between the agency and merchant models for hotels and OTAs, providing a new modeling perspective and solution to this classical problem. Wei et al. [9] find that the intensity of competition among e-tailers is a decisive factor in format selection. In the specific context of hotel distribution, Ye et al. [3] compare optimal pricing under both models, concluding that the superior business model is condition-dependent. Other studies highlight the influence of factors such as information sharing [36] and product characteristics like perishability and sales cycle length [37].

A critical observation from this literature is that the choice of business model is frequently analyzed in isolation from the strategic decision of which channel partners to select. Furthermore, industry practice shows that major OTA platforms (e.g., Expedia, Booking.com) often flexibly adopt hybrid model strategies based on market conditions and partner types [35], highlighting that the theoretically optimal choice must adapt to complex commercial realities during implementation. Implementing sophisticated channel-model strategies faces significant practical barriers, including operational and technological integration challenges, as systematically identified in omnichannel research [38]. This underscores the importance of coupling theoretical optimization with an assessment of practical feasibility.

2.4. Uniqueness and Contributions of Our Study

Synthesizing the reviewed literature reveals a core theoretical gap, as

Table 1. Parameters and variables examined in the present and previous studies.

Characteristics	Consumer Behavior	Capacity	Channel Spillovers	Multiple Retailers	Internet Channel	Merchant Model	Agency Model
Ye et al. [3]	✓	✓			✓	✓	✓
Abhishek et al. [8]			✓	✓	✓	✓	✓
Wei et al. [9]				✓	✓	✓	✓
Saha et al. [17]		✓		✓	✓	✓
Zhang et al. [18]	✓				✓	✓
Wang et al. [25]					✓	✓	✓
Wu et al. [27]	✓			✓	✓		✓
Yu et al. [31]	✓				✓	✓	✓
Ye et al. [39]		✓			✓	✓	✓
This study	✓	✓	✓	✓	✓	✓	✓

systematically illustrates. Previous studies offer robust insights into isolated decisions—such as whether to add a channel, how consumers choose, or which business model to adopt—yet they consistently treat these choices as separate problems. Consequently, we lack a framework that captures their strategic interdependence. Specifically within hospitality, no study concurrently models the hotel’s joint optimization over (a) the type of OTA partner (quality- vs. price-focused), (b) the number of partners (single vs. dual), and (c) the business model for each partnership, all under the pervasive constraint of finite room capacity.

To bridge this gap, we develop a unified game-theoretic model. Our framework’s primary theoretical contribution is the integrated analysis of these three dimensions. We do not merely add variables; we model how the choice in one dimension fundamentally shapes the optimal strategy in another under capacity constraints. For instance, the appeal of a quality-focused OTA interacts with the risk allocation of the merchant model, and this interaction shifts decisively as available room inventory changes. This holistic approach moves beyond asking if a hotel should partner with OTAs, to provide a contingent framework for determining with whom, how many, and on what terms. By synthesizing perspectives from channel strategy, contract theory, and revenue management, our study offers a more complete analytical foundation for both research and practice in platform-mediated distribution.

3. The Model

3.1. Sequence of Events and Decisions

As shown in Figure 2, the supply chain in this study comprises a hotel with room capacity $k$, a quality-focused OTA (denoted by subscript “1”), and a price-focused OTA (denoted by subscript “2”). The hotel can sell rooms through three channel modes: (A) a single direct channel only; (B) the direct channel plus one type of OTA (either ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$ or ${\mathrm{O}\mathrm{T}\mathrm{A}}_2$); or (C) the direct channel plus both types of OTAs. In Mode C, due to differentiated consumer preferences, demand competition exists between the two OTA channels. Furthermore, OTA channel presence generates a positive cross-channel spillover effect (with coefficient $\tau$) on the direct channel’s demand. Consequently, the hotel must first decide on the channel mode and, if cooperating with OTAs, determine the business model for each partnership (see the decision sequence in Figure 3).

We adopt the following notation: subscripts H and $i$ denote the hotel and the OTA, respectively ($i=\mathrm{1,2}$). Superscript “0” indicates no cooperation with a given OTA type, while M and A denote cooperation under the merchant and agency models, respectively. In Mode C, the hotel could cooperate with ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$ or ${\mathrm{O}\mathrm{T}\mathrm{A}}_2$ through business model $x$ or $y$, where $x\in \left\{0,\mathrm{M},\mathrm{A}\right\}, y\in \left\{0,\mathrm{M},\mathrm{A}\right\}$, thus creating nine business model combinations, namely 00, M0, A0, 0M, 0A, MM, MA, AM, and AA. $p_H$, $Q_H$, and ${\pi }_H^{xy}$ denote the hotel’s direct channel price, potential demand, and profit under the $xy$ business model, respectively. Correspondingly, $p_i$, $Q_i$, and ${\pi }_{{OTA}_i}^{xy}$ denote ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$’s price, potential demand, and profit under the $xy$ business model, respectively.

3.2. Basic Assumptions

To establish the model, we make the following assumptions:

(1)

Market Potential: The base market potential for the hotel’s direct channel is $\overline{Q}$. For analytical clarity and without loss of generality, the total potential consumer population for the OTA channel is normalized to 1 [8].

(2)

Cross-Channel Spillover Effect: We assume that OTA listings generate a positive spillover effect on the hotel’s direct channel demand, a well-documented phenomenon known as the “billboard effect” [6]. Empirical evidence supports this: for instance, a Cornell University study found that listings on Expedia increased total hotel bookings by 9–26%, with 75% of consumers visiting an OTA before a direct booking and 65% of direct bookers consulting an OTA first [6]. Complementary data from the Chinese market, such as traffic skew on Ctrip.com, further confirms that OTA exposure stimulates overall demand [7]. To model this cross-channel spillover, we let the total demand potential attracted by the OTAs be $Q_1+Q_2$. A portion of this demand, ${\tau (Q}_1+Q_2)$, spills over to the direct channel, where the spillover coefficient $\tau (0\le \tau \le 1)$ quantifies the billboard effect’s strength [6]. Thus, the direct channel’s total effective demand becomes ${Q_H=\overline{Q}+\tau (Q}_1+Q_2)$.

(3)

Price Differential and Consumer Heterogeneity: Reflecting the commonly observed market pattern where OTA prices are often lower than direct prices, we normalize the selling price of the direct channel to 1 and assume the price on any OTA channel is less than 1 [39]. This accounts for the typically higher price elasticity of demand in the online channel and the competitive pressure on OTAs to offer attractive rates [39,40]. Consumers are heterogeneous in their valuation $V$ for a hotel room, which is uniformly distributed over [0, 1]. They make purchase decisions to maximize utility [3]. Building on the observation that OTAs employ different strategies to cater to travelers with different reservation values [41], we distinguish between OTA types. A consumer’s acceptance rate for the quality-focused OTA is 1, yielding a valuation of $V$. For the price-focused OTA, the consumer acceptance rate is $\theta (0<\theta <1)$, yielding a valuation of $\theta V$. Since the quality-focused OTA provides superior services and information transparency, we reasonably bound the relative valuation by restricting $\theta$ to the range (0.5, 1), implying the maximum valuation for the quality-focused OTA is at most twice that for the price-focused OTA.

(4)

Commission Rates: Quality-focused OTAs typically charge a higher commission rate ($r_1$) than price-focused OTAs ($r_2$) due to the superior services and marketing they provide. For notational simplicity, we define $\overline{r_1}=1-r_1$ and $\overline{r_2}=1-r_2$ where ${0<r}_2<r_1<0.5$.

3.3. Consumer Utility and Demand Model

According to consumer utility theory, a consumer’s net utility from purchasing a room through the quality-focused OTA is $U_1=1-p_1$, and through the price-focused OTA is $U_2=\theta V-p_2$. As long as $U_1>max (U_2,0)$, that is, $V>max (p_1,{\displaystyle \frac{p_1-p_2}{1-\theta }})$, the consumer will prefer ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$. In contrast, if $U_2>max (U_1,0)$, that is, $V\in ({\displaystyle \frac{p_2}{\theta }},{\displaystyle \frac{p_1-p_2}{1-\theta }})$, the consumer will prefer ${\mathrm{O}\mathrm{T}\mathrm{A}}_2$. Following the relationship between $p_1$ and $p_2$, three zones are identified in Figure 4.

Zone I: $\theta p_1\le p_2$, then ${\displaystyle \frac{p_1-p_2}{1-\theta }}\le p_1\le {\displaystyle \frac{p_2}{\theta }}$. If $V>p_1$, then we have $V>{\displaystyle \frac{p_1-p_2}{1-\theta }}$, that is, $U_1>max (U_2,0)$, and thus only ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$ is used.

Zone II: $p_1-\left(1-\theta \right)\le p_2<\theta p_1<\theta$, then ${\displaystyle \frac{p_2}{\theta }}<p_1<{\displaystyle \frac{p_1-p_2}{1-\theta }}$. If ${\displaystyle \frac{p_2}{\theta }}<V<{\displaystyle \frac{p_1-p_2}{1-\theta }}$, then $U_2>max (U_1,0)$, and thus the fraction ${\displaystyle \frac{p_1-p_2}{1-\theta }}-{\displaystyle \frac{p_2}{\theta }}$ of consumers will prefer ${\mathrm{O}\mathrm{T}\mathrm{A}}_2$. If $V>{\displaystyle \frac{p_1-p_2}{1-\theta }}$, then $U_1>max(U_2,0)$, and thus the fraction $1-{\displaystyle \frac{p_1-p_2}{1-\theta }}$ of consumers will prefer ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$.

Zone III: $p_2<\theta , p_1>1-\theta$ and $p_2< p_1-(1-\theta )$, then ${\displaystyle \frac{p_1-p_2}{1-\theta }}>1>V$, that is $U_2>max (U_1,0)$, and thus only ${\mathrm{O}\mathrm{T}\mathrm{A}}_2$ is used.

The potential demand functions for the two OTAs are given by Equation (1):

$$\left(Q_1, Q_2\right)=\left\{\begin{array}{c}\left(1-p_1, 0\right) (p_1,p_2) \in I\\ \left(1-{\displaystyle \frac{p_1-p_2}{1-\theta }}, {\displaystyle \frac{p_1-p_2}{1-\theta }}-{\displaystyle \frac{p_2}{\theta }}\right) (p_1,p_2) \in II\\ \left(\mathrm{0,1}-{\displaystyle \frac{p_2}{\theta }}\right) (p_1,p_2) \in III\end{array} \right.$$

(1)

These demand functions characterize market segmentation between the two OTA types based on equilibrium prices. Given the pricing decisions of the hotel and the OTAs, we can calculate the profits for all parties, which forms the basis for deriving the hotel’s optimal channel and business model strategy.

4. Model Analysis

4.1. Single Direct Channel

In the scenario of a single direct channel, the hotel does not cooperate with either type of OTA and sells rooms exclusively through its own direct store (corresponding to Mode A in Figure 2). The average demand through this channel is denoted as $\overline{Q}$, and the hotel’s price is normalized to 1 unit for analytical convenience. The hotel’s revenue in this case is derived as follows.

Proposition 1.

The hotel’s revenue under the single direct channel is given by:

$${\pi }_H^{00}=p_H^{00}{Q}_H^{00}=\left\{\begin{array}{c}\overline{Q}, \overline{Q}\le k\\ k, \overline{Q}>k\end{array}\right.$$

(2)

Proposition 1 indicates that if the hotel’s room capacity $k$ does not exceed the average direct channel demand $\overline{Q}$, the hotel should sell all available rooms through its direct channel to maximize profit. Since the price via direct sales is higher than the effective price through OTAs, and all rooms can be sold via the direct channel, cooperation with OTAs is not advantageous in this case.

4.2. Cooperation with One Type of OTA

This section examines the case in which the hotel cooperates with only one type of OTA—either quality-focused or price-focused—while maintaining its direct channel (Mode B in Figure 2). Four possible sales mode configurations arise: M0, 0M, A0, and 0A. We analyze the optimal decisions and profits for the hotel and the OTA under each mode.

4.2.1. Cooperation Under the Merchant Model

Under the merchant model, the OTA commits to purchasing a fixed quantity of rooms monthly at a wholesale price $w$ set by the hotel, and then sets its own retail price to consumers. The interaction is modeled as a two-stage Stackelberg game in which the hotel acts as the leader and the ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$ as the follower [3]. The hotel first determines the optimal wholesale price $w_i^{xy}$ (if $i=1$, then $xy=M0$; if $i=2$, then $xy=0M$) to maximize its profit, after which the ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$ determines the optimal retail price $p_i^{xy}$.

Given that Zone II in Figure 4 represents cooperation with both OTA types, only Zones I and III are relevant here. The supply chain optimization problem is formulated as:

$$\left\{\begin{array}{c}\underset{w_i^{\mathit{xy}}}{\max }{\pi }_H^{xy}=w_i^{xy}{Q}_i+1\times (\overline{Q}+\tau Q_i)\\ s.t. \underset{p_i^{\mathit{xy}}}{\max }{\pi }_{{OTA}_i}^{xy}=(p_i^{xy}-w_i^{xy}{)Q}_i\\ (1+\tau {)Q}_i+\overline{Q}\le k\end{array}\right.$$

(3)

where $\left\{\begin{array}{c}\mathrm{if} i=1, \mathrm{then} xy=M0, { Q}_1=1-p_1^{M0} \\ \mathrm{if} i=2, \mathrm{then} xy=0M,{ Q}_2=1-{\displaystyle \frac{p_2^{0M}}{\theta }} \end{array}\right..$

Lemma 1.

When the hotel cooperates exclusively with ${OTA}_i$ under the merchant model, the optimal price and corresponding profits of the hotel and ${OTA}_i$ are summarized in 

Table 2. Equilibrium outcomes under the merchant model.

Precondition		$w_i^{xy\ast }$	${ p}_i^{xy\ast }$	${ \pi }_H^{xy\ast }$	${ \pi }_{{OTA}_i}^{xy\ast }$
$i=1$	$\tilde{k}\ge {\displaystyle \frac{{\tilde{\tau }}^2}{4}}$	${\displaystyle \frac{\tilde{\tau }}{2}}$	${\displaystyle \frac{2+\tilde{\tau }}{4}}$	$\overline{Q}+{\displaystyle \frac{{\tilde{\tau }}^2}{8}}$	${\displaystyle \frac{{\tilde{\tau }}^2}{16}}$
	$0\le \tilde{k}<{\displaystyle \frac{{\tilde{\tau }}^2}{4}}$	$1-{\displaystyle \frac{2\tilde{k}}{\tilde{\tau }}}$	$1-{\displaystyle \frac{\tilde{k}}{\tilde{\tau }}}$	$\overline{Q}+\tilde{k}(1-{\displaystyle \frac{2\tilde{k}}{{\tilde{\tau }}^2}})$	${\displaystyle \frac{{\tilde{k}}^2}{{\tilde{\tau }}^2}}$
$i=2$	$\tilde{k}\ge {\displaystyle \frac{\tilde{\tau }(\theta +\tau )}{4\theta }}$	${\displaystyle \frac{\theta -\tau }{2}}$	${\displaystyle \frac{3\theta -\tau }{4}}$	$\overline{Q}+{\displaystyle \frac{{(\theta +\tau )}^2}{8\theta }}$	${\displaystyle \frac{{(\theta +\tau )}^2}{16}}$
	$0\le \tilde{k}<{\displaystyle \frac{\tilde{\tau }(\theta +\tau )}{4\theta }}$	$\theta (1-{\displaystyle \frac{2\tilde{k}}{\tilde{\tau }}})$	$\theta (1-{\displaystyle \frac{\tilde{k}}{\tilde{\tau }}})$	$\overline{Q}+\tilde{k}({\displaystyle \frac{\theta +\tau }{\tilde{\tau }}}-{\displaystyle \frac{2\theta \tilde{k}}{{\tilde{\tau }}^2}})$	${\displaystyle \frac{\theta {\tilde{k}}^2}{{\tilde{\tau }}^2}}$

Here, $\tilde{k}=k-\overline{Q}$, $\tilde{\tau }=1+\tau$.

.

4.2.2. Cooperation Under the Agency Model

Under the agency model, the ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$ charges a commission rate on each room sold, and the hotel sets the retail price on the OTA platform. Here, the ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$ acts as the Stackelberg leader and the hotel as the follower [3]. The ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$ first sets $r_i$, and the hotel then determines $p_i^{xy}$ accordingly.

Then, the optimization problem of the hotel in this case is:

$${\displaystyle \genfrac{}{}{0pt}{}{\underset{p_i^{\mathit{xy}}}{\max }{\pi }_H^{xy}={(1-r_i)p}_i^{xy}{Q}_i+1\times (\overline{Q}+\tau Q_i)}{s.t. (1+\tau {)Q}_i+\overline{Q}\le k}}$$

(4)

where $\left\{\begin{array}{c}\mathrm{if} i=1, \mathrm{then} xy=A0, { Q}_1=1-p_1^{A0} \\ \mathrm{if} i=2, \mathrm{then} xy=0A,{ Q}_2=1-{\displaystyle \frac{p_2^{0A}}{\theta }} \end{array}\right..$

Lemma 2.

When the hotel cooperates exclusively with ${OTA}_i$ under the agency model, the optimal price and corresponding profits of the hotel and ${OTA}_i$ are as expressed in 

Table 3. Equilibrium outcomes under the agency model.

Precondition		${ p}_i^{xy\ast }$	${ \pi }_H^{xy\ast }$	${ \pi }_{{OTA}_i}^{xy\ast }$
$i=1$	$\tilde{k}\ge {\displaystyle \frac{\tilde{\tau }(\overline{r_1}+\tau )}{2\overline{r_1}}}$	${\displaystyle \frac{\overline{r_1}-\tau }{2\overline{r_1}}}$	$\overline{Q}+{\displaystyle \frac{{(\overline{r_1}+\tau )}^2}{4\overline{r_1}}}$	${\displaystyle \frac{{r_1(\overline{r_1}}^2-{\tau }^2)}{4{\overline{r_1}}^2}}$
	$0\le \tilde{k}<{\displaystyle \frac{\tilde{\tau }(\overline{r_1}+\tau )}{2\overline{r_1}}}$	$1-{\displaystyle \frac{\tilde{k}}{\tilde{\tau }}}$	$\overline{Q}+{\displaystyle \frac{\overline{r_1}\tilde{k}(\tilde{\tau }-\tilde{k})}{{\tilde{\tau }}^2}}+{\displaystyle \frac{\tau \tilde{k}}{\tilde{\tau }}}$	${\displaystyle \frac{r_1\tilde{k}(\tilde{\tau }-\tilde{k})}{{\tilde{\tau }}^2}}$
$i=2$	$\tilde{k}\ge {\displaystyle \frac{\tilde{\tau }(\theta \overline{r_2}+\tau )}{2\theta \overline{r_2}}}$	${\displaystyle \frac{\theta \overline{r_2}-\tau }{2\overline{r_2}}}$	$\overline{Q}+{\displaystyle \frac{{(\theta \overline{r_2}+\tau )}^2}{4\theta \overline{r_2}}}$	${\displaystyle \frac{{r_2({\theta }^2\overline{r_2}}^2-{\tau }^2)}{4\theta {\overline{r_2}}^2}}$
	$0\le \tilde{k}<{\displaystyle \frac{\tilde{\tau }(\theta \overline{r_2}+\tau )}{2\theta \overline{r_2}}}$	$\theta (1-{\displaystyle \frac{\tilde{k}}{\tilde{\tau }}})$	$\overline{Q}+{\displaystyle \frac{\overline{r_2}\theta \tilde{k}(\tilde{\tau }-\tilde{k})}{{\tilde{\tau }}^2}}+{\displaystyle \frac{\tau \tilde{k}}{\tilde{\tau }}}$	${\displaystyle \frac{r_2\theta \tilde{k}(\tilde{\tau }-\tilde{k})}{{\tilde{\tau }}^2}}$

.

Lemmas 1 and 2 provide the hotel’s optimal pricing and profit under each business model when cooperating with a single OTA type. By comparing revenues across all four cases, we derive the following proposition.

Proposition 2.

If the hotel cooperates with only one type of OTA, the hotel’s optimal platform selection, price, and corresponding profits are as follows:

(1)

If $\theta \le {\displaystyle \frac{\overline{r_1}}{\overline{r_2}}}$,

(a)

If $\tilde{k}\le {\displaystyle \frac{\tilde{\tau }(\overline{r_1}+\tau )}{2\overline{r_2}}}$, then the hotel should cooperate with ${OTA}_1$ under the agency model, and $p_1^{A0\ast }={\displaystyle \frac{\overline{r_1}-\tau }{2\overline{r_1}}}$, ${\pi }_H^{A0\ast }=\overline{Q}+{\displaystyle \frac{{(\overline{r_1}+\tau )}^2}{4\overline{r_1}}}$, ${\pi }_{{OTA}_1}^{A0\ast }={\displaystyle \frac{r_1({\overline{r_1}}^2-{\tau }^2)}{4{\overline{r_1}}^2}}$.

(b)

If ${\displaystyle \frac{r_1\tilde{\tau }}{1+r_1}}\le \tilde{k}<{\displaystyle \frac{\tilde{\tau }(\overline{r_1}+\tau )}{2\overline{r_2}}}$, then the hotel should again cooperate with  ${OTA}_1$ under the agency model, but the optimal price will change, that is, $p_1^{A0\ast }=1-{\displaystyle \frac{\tilde{k}}{\tilde{\tau }}}$, ${\pi }_H^{A0\ast }=\overline{Q}+{\displaystyle \frac{\overline{r_1}\tilde{k}(\tilde{\tau }-\tilde{k})}{{\tilde{\tau }}^2}}+{\displaystyle \frac{\tau \tilde{k}}{\tilde{\tau }}}$, ${\pi }_{{OTA}_1}^{A0\ast }={\displaystyle \frac{r_1\tilde{k}(\tilde{\tau }-\tilde{k})}{{\tilde{\tau }}^2}}$.

(c)

If $0\le \tilde{k}<{\displaystyle \frac{r_1\tilde{\tau }}{1+r_1}}$ then the hotel should cooperate with ${OTA}_1$ under the merchant model, and $w_1^{M0\ast }=1-{\displaystyle \frac{2\tilde{k}}{\tilde{\tau }}}$, $p_1^{M0\ast }=1-{\displaystyle \frac{\tilde{k}}{\tilde{\tau }}}$, ${\pi }_H^{M0\ast }=\overline{Q}+\tilde{k}(1-{\displaystyle \frac{2\tilde{k}}{\tilde{\tau }}})$, ${\pi }_{{OTA}_1}^{M0\ast }={\displaystyle \frac{{\tilde{k}}^2}{{\tilde{\tau }}^2}}$.

(2)

If $\theta >{\displaystyle \frac{\overline{r_1}}{\overline{r_2}}}$

(a)

If $\tilde{k}\ge {\displaystyle \frac{\tilde{\tau }(\theta \overline{r_2}+\tau )}{2\theta \overline{r_2}}}$, then the hotel should cooperate with ${OTA}_2$under the agency model, and $p_2^{0A\ast }={\displaystyle \frac{\theta \overline{r_2}-\tau }{2\overline{r_2}}}$, ${\pi }_H^{0A\ast }=\overline{Q}+{\displaystyle \frac{{(\theta \overline{r_2}+\tau )}^2}{4\theta \overline{r_2}}}$, ${\pi }_{{OTA}_2}^{0A\ast }={\displaystyle \frac{r_2({\theta }^2{\overline{r_2}}^2-{\tau }^2)}{4\theta {\overline{r_2}}^2}}.$.

(b)

If ${\displaystyle \frac{\tilde{\tau }(1-\theta \overline{r_2})}{2-\theta \overline{r_2}}}\le \tilde{k}<{\displaystyle \frac{\tilde{\tau }(\theta \overline{r_2}+\tau )}{2\theta \overline{r_2}}}$, then the hotel should again cooperate with ${OTA}_2$ through the agency model, but the optimal price will change, that is, $p_2^{0A\ast }=\theta (1-{\displaystyle \frac{\tilde{k}}{\tilde{\tau }}})$, ${\pi }_H^{0A\ast }=\overline{Q}+{\displaystyle \frac{\overline{r_2}\theta \tilde{k}(\tilde{\tau }-\tilde{k})}{{\tilde{\tau }}^2}}+{\displaystyle \frac{\tau \tilde{k}}{\tilde{\tau }}}$, ${\pi }_{{OTA}_2}^{0A\ast }={\displaystyle \frac{r_2\theta \tilde{k}(\tilde{\tau }-\tilde{k})}{{\tilde{\tau }}^2}}$.

(c)

If $0\le \tilde{k}<{\displaystyle \frac{\tilde{\tau }(1-\theta \overline{r_2})}{2-\theta \overline{r_2}}}$, then the hotel’s optimal sale platform selection, optimal price, and corresponding profit are the same as Proposition 2 (1C).

Proposition 2 shows that if consumers’ acceptance rate of the price-focused OTA is relatively low ($\theta \le {\displaystyle \frac{\overline{r_1}}{\overline{r_2}}}$), the OTA must set a very low online price to attract them. Consequently, the hotel should not cooperate with the price-focused OTA. When the hotel’s room capacity is large (${\displaystyle \frac{r_1\tilde{\tau }}{1+r_1}}\le \tilde{k}$), it should instead cooperate with the quality-focused OTA through the agency model. In all other cases, the hotel should choose the merchant model with the quality-focused OTA. This preference stems from the higher marginal revenue of direct sales; with a relatively small room capacity, the hotel can strategically adjust room allocations between its direct and the OTA’s indirect channel under this model.

When consumer acceptance rate of the price-focused OTA is high ($\theta >{\displaystyle \frac{\overline{r_1}}{\overline{r_2}}}$) meaning consumers perceive little difference between the two OTAs—the choice shifts. Since the quality-focused OTA charges a higher commission, a hotel with large room capacity (${\displaystyle \frac{\tilde{\tau }(1-\theta \overline{r_2})}{2-\theta \overline{r_2}}}\le \tilde{k}$) should choose the price-focused OTA. Otherwise, the hotel should cooperate with the quality-focused OTA under the merchant model.

4.3. Cooperation with Both Types of OTA

We now consider the case where the hotel cooperates simultaneously with both quality-focused and price-focused OTAs (Mode C in Figure 2). Four business model combinations are possible: MM, MA, AM, AA. The potential demands of the hotel, ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$, and ${\mathrm{O}\mathrm{T}\mathrm{A}}_2$ are ${Q_H=\overline{Q}+\tau (Q}_1+Q_2)$,$Q_1=1-{\displaystyle \frac{p_1-p_2}{1-\theta }}$, and $Q_2={\displaystyle \frac{p_1-p_2}{1-\theta }}-{\displaystyle \frac{p_2}{\theta }}$, respectively.

Next, we analyze the optimal decision and optimal income of the hotel and ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$ under different cooperation modes.

(1)

Cooperation through the MM business model combination

In this combination, the hotel cooperates with both OTAs under the merchant model. Following the structure in Section 4.2.1, the interaction is modeled as a Stackelberg game where the hotel is the leader and each OTA is a follower. Thus, the sets the wholesale prices ${ w}_i^{MM}$ for ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$, each OTA then determines its retail price ${ p}_i^{MM}$ to maximize its own profit.

The optimization problem of the supply chain is formulated as:

$$\left\{\begin{array}{c}\underset{w_1^{\mathit{MM}},w_2^{\mathit{MM}}}{\max }{\pi }_H^{MM}=w_1^{MM}\left(1-{\displaystyle \frac{p_1^{MM}-p_2^{MM}}{1-\theta }}\right)+w_2^{MM}\left({\displaystyle \frac{p_1^{MM}-p_2^{MM}}{1-\theta }}-{\displaystyle \frac{p_2^{MM}}{\theta }}\right)+\overline{Q}+\tau (1-{\displaystyle \frac{p_2^{MM}}{\theta }})\\ s.t. \underset{p_1^{\mathit{MM}}}{\max }{\pi }_{{OTA}_1}^{MM}=(p_1^{MM}-w_1^{MM})\left(1-{\displaystyle \frac{p_1^{MM}-p_2^{MM}}{1-\theta }}\right)\\ \underset{p_2^{\mathit{MM}}}{\max }{\pi }_{{OTA}_2}^{MM}=(p_2^{MM}-w_2^{MM})({\displaystyle \frac{p_1^{MM}-p_2^{MM}}{1-\theta }}-{\displaystyle \frac{p_2^{MM}}{\theta }})\\ p_1^{MM}-\left(1-\theta \right)\le p_2^{MM}\le \theta p_1^{MM},\overline{Q}+\tilde{\tau }(1-{\displaystyle \frac{p_2^{MM}}{\theta }})\le k\end{array}\right.$$

(5)

(2)

Cooperation through the MA business model combination

Similar to Section 4.2.1 and Section 4.2.2, the interaction between the hotel and ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$ is modeled using a leader-follower Stackelberg game. Under the merchant model, and the hotel acts as the leader: it first sets the wholesale price ${ w}_1^{MA}$, to which the ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$ responds by determining its optimal retail price ${ p}_1^{MA}$. Under the agency model, the ${\mathrm{O}\mathrm{T}\mathrm{A}}_2$ assumes the leadership role by setting the commission rate. The hotel, as the follower, subsequently determines its optimal online retail price $p_2^{MA}$ in response to this rate $r_2$.

The optimization problem of the supply chain is formulated as:

$$\left\{\begin{array}{c}\underset{w_1^{\mathit{MA}},p_2^{\mathit{MA}}}{\max }{\pi }_H^{MA}=w_1^{MA}\left(1-{\displaystyle \frac{p_1^{MA}-p_2^{MA}}{1-\theta }}\right)+(1-r_2)p_2^{MA}\left({\displaystyle \frac{p_1^{MA}-p_2^{MA}}{1-\theta }}-{\displaystyle \frac{p_2^{MA}}{\theta }}\right)+\overline{Q}+\tau (1-{\displaystyle \frac{p_2^{MA}}{\theta }})\\ s.t. \underset{p_1^{\mathit{MA}}}{\max }{\pi }_{{OTA}_1}^{MA}=(p_1^{MA}-w_1^{MA})\left(1-{\displaystyle \frac{p_1^{MA}-p_2^{MA}}{1-\theta }}\right)\\ \\ p_1^{MA}-\left(1-\theta \right)\le p_2^{MA}\le \theta p_1^{MA},\overline{Q}+\tilde{\tau }(1-{\displaystyle \frac{p_2^{MA}}{\theta }})\le k\end{array}\right.$$

(6)

(3)

Cooperation through the AM business model combination

Similar to Section 4.3 (2), in cooperation between the hotel and ${\mathrm{O}\mathrm{T}\mathrm{A}}_2$ under the merchant model, the hotel acts as the Stackelberg leader: it sets the wholesale price ${ w}_2^{AM}$, to which ${\mathrm{O}\mathrm{T}\mathrm{A}}_2$ responds with its optimal retail price $p_2^{AM}$. Conversely, in its cooperation with ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$ under the agency model, ${\mathrm{O}\mathrm{T}\mathrm{A}}_1$ assumes leadership by setting the commission rate $r_1$, and the hotel, as the follower, determines its optimal response by setting the online price ${ p}_1^{AM}$.

The optimization problem of the supply chain is formulated as:

$$\left\{\begin{array}{c}\underset{p_1^{\mathit{AM}},w_2^{\mathit{AM}}}{\max }{\pi }_H^{AM}=\left(1-r_1\right)p_1^{AM}\left(1-{\displaystyle \frac{p_1^{AM}-p_2^{AM}}{1-\theta }}\right)+w_1^{AM}\left({\displaystyle \frac{p_1^{AM}-p_2^{AM}}{1-\theta }}-{\displaystyle \frac{p_2^{AM}}{\theta }}\right)+\overline{Q}+\tau (1-{\displaystyle \frac{p_2^{AM}}{\theta }})\\ s.t. \underset{p_2^{\mathit{AM}}}{\max }{\pi }_{{OTA}_2}^{AM}=(p_2^{AM}-w_2^{AM})\left(1-{\displaystyle \frac{p_1^{AM}-p_2^{AM}}{1-\theta }}\right)\\ \\ p_1^{AM}-\left(1-\theta \right)\le p_2^{AM}\le \theta p_1^{AM},\overline{Q}+\tilde{\tau }(1-{\displaystyle \frac{p_2^{AM}}{\theta }})\le k\end{array}\right.$$

(7)

(4)

Cooperation through the AA business model combination:

Similar to Section 4.2.2, under the agency model, ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$ acts as the Stackelberg leader and the hotel acts as the follower. Thus, the hotel will decide the optimal response online price, ${ p}_i^{AA}$, to the ${\mathrm{O}\mathrm{T}\mathrm{A}}_{\mathrm{i}}$’s commission rate, $r_i$.

The optimization problem of the supply chain is formulated as:

$$\left\{\begin{array}{c}\underset{p_1^{\mathit{AA}},p_2^{\mathit{AA}}}{\max }{\pi }_H^{AA}=\overline{r_1}{p}_1^{AA}\left(1-{\displaystyle \frac{p_1^{AA}-p_2^{AA}}{1-\theta }}\right)+\overline{r_2}{p}_2^{AA}\left({\displaystyle \frac{p_1^{AA}-p_2^{AA}}{1-\theta }}-{\displaystyle \frac{p_2^{AA}}{\theta }}\right)+\overline{Q}+\tau \left(1-{\displaystyle \frac{p_2^{AA}}{\theta }}\right)\\ s.t. p_1^{AA}-\left(1-\theta \right)\le p_2^{AA}\le \theta p_1^{AA},\overline{Q}+\tilde{\tau }(1-{\displaystyle \frac{p_2^{AA}}{\theta }})\le k\end{array}\right.$$

(8)

Solving the optimization problems (5)–(8) yields the equilibrium prices, demand allocations, and profits for each business model combination. The following proposition synthesizes the conditions under which cooperating with both OTAs is beneficial and how revenue responds to capacity changes.

Proposition 3.

Under each of the four business model combinations (MM, MA, AM, AA), the hotel’s decision to cooperate with both OTAs, as well as the resulting revenue trend, is governed by a set of capacity thresholds. These thresholds depend on the interplay of: the hotel’s room capacity, the spillover effects of OTA channels, the commission rates imposed by OTAs, and consumer acceptance of price-focused OTA. Specifically, hotels will collaborate with both types of OTAs only when their room capacity is relatively large ($\tilde{k}\ge { k}_1$). As the hotel’s room capacity increases and reaches a threshold level (${ k}_1<\tilde{k}\le { k}_2$), the hotel’s revenue will increase with room capacity. However, once this threshold is exceeded ($\tilde{k}>{ k}_2$), the hotel’s revenue will no longer increase. These thresholds are presented in 

Table 4. Capacity thresholds under different business model combinations.

	Threshold	${\mathit{k}}_1$	${\mathit{k}}_2$
Business Model
$MM$		${\displaystyle \frac{\tilde{\tau }}{4}}$	${\displaystyle \frac{\tilde{\tau }[3\theta +\tau \left(2+\theta \right)]}{2\theta (4-\theta )}}$
$MA$		${\displaystyle \frac{\tilde{\tau }(1-\theta \overline{r_2})}{4\overline{\theta }+\theta { r}_2}}$	$\tilde{\tau }{\displaystyle \frac{4\tau \overline{\theta }+\theta [\overline{\theta }\left(4{\overline{r_2}-r}_2\right)-{{ r}_2}^2\theta ]}{\theta (8\overline{\theta }\overline{r_2}-\theta {{ r}_2}^2)}}$
$AM$		$\mathrm{m}\mathrm{a}\mathrm{x}({\displaystyle \frac{\tilde{\tau }\left(r_1-\overline{\theta }\right)}{\theta \left(2\overline{\theta }+{ r}_1\right)}},{\displaystyle \frac{\tilde{\tau }\left(\overline{\theta }-r_1\right)}{\theta { r}_1+2\left(\overline{\theta }-{ r}_1\right)}})$	${\displaystyle \frac{\tilde{\tau }\left[\theta \right(\overline{\theta }\left(4{-3r}_1\right)-{{ r}_1}^2)+2\tau (\overline{r_1}+r_1\theta -{\theta }^2\left)\right]}{\theta (8\overline{\theta }\overline{r_1}-\theta {{ r}_1}^2)}}$
$AA$		$\mathrm{m}\mathrm{a}\mathrm{x}({\displaystyle \frac{\tilde{\tau }\left(\overline{r_1}-\theta \overline{r_2}\right)}{2\overline{r_1}-\theta \left(\overline{r_1}+\overline{r_2}\right)}},{\displaystyle \frac{\tilde{\tau }(\theta \overline{r_2}-\overline{r_1})}{\theta ({ r}_1-{ r}_2)}})$	${\displaystyle \frac{\tilde{\tau }\left[\theta \right(\overline{\theta }\left(4{-3r}_1\right)-{{ r}_1}^2)+2\tau (\overline{r_1}+r_1\theta -{\theta }^2\left)\right]}{\theta (8\overline{\theta }\overline{r_1}-\theta {{ r}_1}^2)}}$

.

Integrating the findings from Propositions 1 to 3 leads to the following corollary:

Corollary 1.

When the hotel’s room capacity is low ($k<\overline{Q}$), the optimal strategy is to rely exclusively on the direct channel. For larger capacities ($k\ge \overline{Q}$), the hotel should engage in cooperation with OTAs. Furthermore, as room capacity increases, the optimal number of OTA partners tends to rise, reflecting the strategic need to distribute inventory across multiple sales channels when direct channel demand is saturated.

This corollary reinforces the principle that OTA collaboration is a strategic tool for capacity utilization, becoming advantageous primarily when the hotel’s room supply exceeds the baseline demand potential of its direct sales channel.

5. The Hotel’s Equilibrium Strategy

Propositions 1–3 provide the hotel’s optimal pricing strategies and profits for three scenarios: using only a direct channel, cooperating with one type of OTA, and cooperating with both types of OTAs. As illustrated in Section 4.2 and Section 4.3, the optimal selection of sales channels and business models depends on a complex interplay of parameters. To derive more intuitive managerial insights from these analytical results, this section employs numerical simulations to examine how the hotel’s equilibrium strategy is influenced by key factors: the hotel’s room capacity, consumers’ acceptance level of the price-focused OTA, and the spillover effect of the OTA on demand via the direct sales channel.

5.1. The Equilibrium Strategy as a Function of $k$ and $\theta$

To examine the impact of the hotel’s room capacity, $k$, and consumers’ acceptance of the price-focused OTA, $\theta$, on the hotel’s optimal channels and business model selection, we conduct a numerical analysis with the following parameter values: $\overline{Q}=0.1$,${ r}_1=0.15$, ${ r}_2=0.1$, $\tau$ takes the values 0, 0.4, and 0.8. $\theta$ ranges from 0.5 to 1, and $k$ ranges from 0 to 1, as shown in Figure 5 and Figure 6.

First, as Figure 5a–c shows, if the hotel’s room capacity is very small (i.e., $k=0.1$), the hotel should cooperate with only the quality-focused OTA under the merchant model (M0). It avoids the channel competition that often arises from managing multiple distribution channels, while also allowing the hotel to prioritize its more profitable direct sales. Since rooms sold directly yield higher marginal revenue than those sold through OTAs, the merchant model’s wholesale arrangement enables the hotel to allocate the bulk of its limited inventory to its own channel. The choice of a quality-focused OTA further strengthens this strategy: its higher consumer acceptance supports a premium online price, which in turn maintains attractive margins even on OTA sales. Thus, by cooperating with a quality-focused partner under the merchant model, the hotel optimizes profit without overextending its distribution network.

When a hotel’s room capacity grows slightly yet remains modest (i.e., $k=0.2$), a shift in contractual strategy becomes advantageous: the hotel should still partner with only one OTA, but now under the agency model. While the rationale for limiting partnerships remains—avoiding multi-channel competition and preserving focus on the higher-margin direct channel—the agency model introduces new flexibility. Here, the hotel’s capacity is sufficient to absorb the additional direct demand stimulated by the OTA’s presence, eliminating the need for the careful room allocation required under the merchant model. Moreover, the agency model typically offers the hotel a higher marginal revenue per booked room. The choice between a quality-focused or price-focused OTA under this model hinges critically on consumer acceptance. If acceptance of the price-focused OTA is low ($\theta <0.94$), the quality-focused partner is preferable. A low-acceptance price-focused OTA must discount heavily to attract bookings, depressing the hotel’s net revenue per room. In contrast, the quality-focused OTA can sustain a higher retail price, preserving healthier margins for the hotel. Conversely, when consumer acceptance of the price-focused OTA is high ($\theta \ge 0.94$)—meaning guests perceive little difference between the two platforms—its lower commission rate becomes decisive. In this case, partnering with the price-focused OTA under the agency model yields greater profit, as the hotel retains a larger share of a similarly priced sale.

When the hotel’s room capacity is moderate (i.e., $k=0.5$), the optimal strategy continues to depend on consumer acceptance of the price-focused OTA. When this acceptance is low, cooperating with the quality-focused OTA under the agency model remains preferable, following the logic described earlier. However, high consumer acceptance shifts the optimal strategy significantly: the hotel should cooperate with both OTAs under the merchant model (MM). This approach allows the hotel to control the retail price on both platforms, thereby mitigating competition between them and securing higher overall revenue—a critical advantage when consumers perceive the two channels as largely interchangeable.

When capacity is large (i.e., $k=0.7$), the strategic imperative moves beyond selection toward aggregation. To maximize occupancy, the hotel should engage both OTA types. If consumer acceptance of the price-focused OTA is low, a hybrid model (AM) proves most effective. Under this arrangement, the hotel partners with the quality-focused OTA via the agency model to capture higher margins, while using the merchant model with the price-focused OTA. The latter allows the hotel to manage pricing and room allocation for this more discount-sensitive channel. Should consumer acceptance be high, the uniform merchant model (MM) again becomes optimal, as it enables coordinated price control across both platforms.

For very large hotel capacities (i.e., $k>0.81$), cooperation with both OTAs is essential to fill rooms. When consumer acceptance of the price-focused OTA is low, the dual agency model (AA) offers the greatest advantage. Here, the distinct positioning of the two OTAs minimizes direct competition, and the hotel’s ample capacity reduces the need for the inventory control provided by the merchant model. Since the agency model generally yields higher marginal revenue, adopting it across both partners maximizes profit. As consumer acceptance of the price-focused OTA rises to moderate or high levels, the optimal strategy shifts accordingly to the AM or MM, following a similar rationale as in the large-capacity case.

Second, the influence of the cross-channel spillover effect is visually apparent in the sequence of Figure 5a–c. As the spillover parameter ($\tau$) increases, the region where the hotel partners with a single OTA expands considerably. This trend highlights a key strategic insight: a stronger positive spillover enhances the value of a focused channel strategy. For hotels with smaller room capacities, a single OTA partnership is sufficient to generate significant auxiliary demand for the direct channel. Adding a second OTA under these conditions often introduces more competitive friction than incremental benefit. By limiting itself to one partner, the hotel smartly avoids inter-channel conflict while still leveraging the spillover to steer more bookings toward its higher-margin direct sales.

Third, a separate strategic force is observed when examining the impact of commission rates. Comparing the strategy landscapes in Figure 5 (where ${ r}_1=0.15$, ${ r}_2=0.1$) with those in Figure 6 (where ${ r}_1=0.2$, ${ r}_2=0.1$) reveals a clear shift. The higher commission rate for the quality-focused OTA erodes the hotel’s marginal revenue under the related agency model. This erosion makes the price-focused OTA—with its lower commission—relatively more attractive, expanding its optimal region in the strategy map. Concurrently, the overall appeal of the merchant model grows. Since the merchant model’s wholesale price is not directly scaled by a commission, it becomes a strategically safer option when agency-model margins are compressed, leading to a broader zone where merchant-based strategies (like M0 or MM) prevail.

5.2. The Equilibrium Strategy as a Function of $\tau$ and $\theta$

We further examine the interaction between the spillover effect ($\tau$), and consumers’ acceptance of the price-focused OTA ($\theta$). The parameters are set as: $\overline{Q}=0.1$, $k=0.6$, $\tau$ ranges from 0 to 1, and $\theta$ ranges from 0.5 to 1, as shown in Figure 7.

Figure 7a illustrates that when the spillover effect is strong (high $\tau$) and $\theta$ is low, the optimal strategy is A0 (agency model with the quality-focused OTA). A strong spillover makes a single OTA partnership sufficiently effective in boosting overall demand, while a low $\theta$ makes the price-focused OTA less profitable. Conversely, with high $\theta$ and strong $\tau$, cooperating with both OTAs (typically under MM or AM) becomes optimal to fully exploit the high overall demand potential.

When the spillover effect is weak (low $\tau$), the hotel often needs both OTAs to achieve a satisfactory occupancy rate. The choice between AM and MM depends on $\theta$: a lower $\theta$ favors AM (agency with the quality-focused OTA, merchant with the price-focused OTA), while a higher $\theta$ favors MM, allowing the hotel to control prices on both platforms.

Figure 7b shows that an increase in ${ r}_1$ shrinks the dominant region for the A0 strategy and expands the regions where merchant models (M0, MM) are optimal, consistent with the findings in Section 5.1.

5.3. Empirical Survey Research

To assess the practical relevance of our theoretical model, we conducted a survey analyzing the OTA partnership strategies of hotels in China. We selected two major OTAs: Ctrip.com, which emphasizes service and caters to less price-sensitive consumers, and eLong.com, which competes primarily on price. Thus, Ctrip.com corresponds to the quality-focused OTA (${\mathrm{O}\mathrm{T}\mathrm{A}}_1$), and eLong.com to the price-focused OTA (${\mathrm{O}\mathrm{T}\mathrm{A}}_2$) in our model.

Data was collected from the publicly listed inventories of both platforms. After cleaning the data (removing entries with incomplete or invalid information, such as missing room counts), our final sample consisted of 5346 hotels from Ctrip.com and 3187 from eLong.com. A key finding is the significant overlap: 3106 hotels were listed on both platforms. Therefore, the total number of unique hotels with valid data was 5427. Their channel choices were as follows: (1) 57.2% (3106 hotels) cooperated with both OTAs; (2) 41.3% (2240 hotels) cooperated only with Ctrip.com (${\mathrm{O}\mathrm{T}\mathrm{A}}_1$); (3) 1.5% (81 hotels) cooperated only with eLong.com (${\mathrm{O}\mathrm{T}\mathrm{A}}_2$).

This distribution strongly supports Proposition 2, which indicates that when choosing a single OTA partner, hotels overwhelmingly prefer the quality-focused type due to its higher consumer acceptance and revenue potential.

Furthermore, we categorized these 5427 hotels by room capacity (Figure 8). The data reveals a clear trend: the proportion of hotels cooperating with both OTAs increases with room capacity. For hotels with 1–100 rooms, 54.79% used both OTAs. This proportion rises consistently across larger capacity categories. This empirical pattern aligns with Corollary 1 and the numerical analysis in Section 5.1, confirming that larger hotels are more likely to engage multiple OTA channels to manage their higher room inventory.

This survey provides real-world validation for the model’s key predictions regarding channel selection. The data integration involved categorizing hotels by their observed OTA partnerships (the dependent strategy) and their reported room capacity (a key independent parameter,$k$). The positive correlation between capacity and multi-OTA usage is readily interpreted within our analytical framework.

6. Conclusions and Implications

6.1. Conclusions

This study challenges the simplistic view of OTAs as a uniform channel. By modeling the strategic interplay between hotel capacity, heterogeneous OTAs, and alternative business models, we provide a more nuanced framework for channel decision-making. The central insight is that there is no universally optimal strategy; the best path depends critically on a hotel’s specific context, primarily its room inventory relative to direct channel demand.

Our analysis delineates a strategic evolution. For hotels where direct demand saturates capacity, OTA partnerships offer limited value. Once capacity exceeds this point, targeted cooperation with a single, quality-focused OTA typically marks the optimal first step. As capacity grows further, the logic shifts from selection to aggregation, making partnerships with both OTA types advantageous. The choice between merchant and agency models is similarly contingent: the merchant model’s wholesale security appeals to smaller hotels managing risk, while the agency model’s potential for higher margins becomes attractive to larger hotels with robust direct sales. Perhaps most counterintuitively, we find that a one-size-fits-all approach rarely works best when dealing with both OTA types; a differentiated hybrid model often outperforms a uniform contract.

6.2. Theoretical and Practical Implications

Theoretically, this research advances the literature in several ways. First, it moves beyond treating OTAs as homogeneous by introducing a strategic typology (quality-focused vs. price-focused), shifting the research question from “whether to cooperate” to “with whom and how”. Second, it integrates the choice of business model (merchant vs. agency) with the selection of OTA type within a unified analytical framework, bridging channel management and contract theory literature [3,35]. Third, it synthesizes concepts from marketing (channel acceptance) and operations management (capacity allocation) into the context of hotel revenue management.

These insights open new avenues for platform competition research. Our OTA typology provides a needed lens for analyzing platform heterogeneity, while our integrated framework reveals the contingencies that dictate optimal contract-form matching. The empirical patterns from the Chinese market (Section 5.3) lend strong support to the model’s predictions. We see, for instance, the clear predominance of quality-focused OTA partnerships and the tendency for larger hotels to engage multiple platforms. This validation does more than confirm the model’s relevance; it underscores the framework’s potential as a foundation for localized theory-building. Scholars might use this lens to investigate how distinct institutional features—such as platform governance, unique digital trust dynamics, or regulatory interventions in markets like China—systematically shape distribution strategies.

For practitioners, the framework delivers actionable guidance. For Hotel Managers: For Hotel Managers: The study provides a strategic decision-making framework. Managers should first assess their property’s capacity relative to direct channel demand. Small to mid-sized hotels should consider prioritizing partnerships with quality-focused OTAs under the merchant model [35]. Large hotels can strategically use both OTA types, potentially with differentiated business models, to maximize coverage and revenue across segments.

For OTA Platform Operators: For service-oriented, quality-focused OTAs, the findings validate a strategy centered on superior value—justifying their typically higher commissions by demonstrating their ability to deliver higher-yield guests. Their messaging to hotel partners should consistently highlight this quality differential. For price-focused OTAs, the opportunity lies in volume and flexibility. They may find a natural niche with very large hotels that need to fill vast numbers of rooms or in markets where extreme price sensitivity dominates. To attract a wider range of partners, these platforms could benefit from re-evaluating their standard terms; more flexible or tiered commission structures could be a powerful competitive tool [35,42].

For Industry Associations and Policymakers: Our results underscore the importance of maintaining a diverse and competitive OTA landscape. A marketplace that offers hotels genuine choice between different strategic partner types—some competing on service, others on price—fosters healthier competition and innovation than one dominated by monolithic players. This diversity ultimately benefits the entire hospitality ecosystem by preventing channel monopolization and encouraging continuous improvement in service and technology [43].

6.3. Limitations and Future Research

Like all modeling efforts, this study simplifies reality to achieve clarity, creating natural boundaries for extension. First, we assume a positive cross-channel spillover. Exploring scenarios with neutral or even negative spillovers—where OTA presence cannibalizes direct sales—would be a valuable complication, reflecting concerns some hoteliers genuinely hold [44,45]. Second, our model centers on a single hotel’s decision. Introducing competition among multiple hotels on the same platforms would capture the strategic interplay often seen in destination markets and could alter equilibrium strategies. Third, the assumption of deterministic demand sidesteps the critical element of risk. Future work could contrast how the risk-sharing properties of the merchant model (demand risk transferred to OTA) and agency model (demand risk retained by hotel) influence choice under uncertainty.

Beyond these structural considerations, the strategic calculus we propose is likely moderated by important operational and market contexts. Factors such as a hotel’s location (e.g., urban center versus remote resort), pronounced seasonal demand fluctuations, and its primary target market (international or domestic) could systematically influence the optimal choice of OTA partner and business model. Future research integrating these variables would greatly enhance the practical nuance and contextual relevance of distribution strategy frameworks.

Finally, the digital landscape itself is a moving target. The rapid emergence of new platform types—such as social commerce channels (e.g., TikTok) or meta-search engines—continuously reshapes the distribution ecology [46]. Our typology of quality- versus price-focus provides a starting point, but future research must examine how these novel intermediaries fit into and disrupt the strategic calculus we have outlined here.