Fixed,flexible,and dynamics pricing decisions of Airbnb mode with social learning

Abstract

The rise of the sharing economy has changed the traditional way of providing service to consumers. Airbnb is the most successful peer-to-peer model in the hospitality industry. This article investigates how to conduct strategic dynamic pricing in a competitive market by considering market conditions, quality, and risk sensitivity. Our research yields three main conclusions. First, we observe that the higher the risk level suppliers face, the more profit they will get; the lower the risk level consumers face, the more utilities they obtain. Second, we find that fixed pricing may be optimal or near-optimal for the platform when market size is small, the accommodation quality is better, and consumers’ reliability is low. Otherwise, a flexible pricing strategy is optimal. Finally, we extend the research into dynamic pricing decision in presence of Bayesian social learning and propose that the less-perfect accommodation requires social learning more urgently. In tourism peak period, social learning has less positive impact when the Airbnb accommodation is much perfect. These conclusions provide useful guidance on how the Airbnb and hotel can take advantage of the competitive market.

Keywords

Airbnb Bayesian social learning crowdsourcing-supply sharing platform hotel sharing economy two-sided market

Introduction

Over the past few years, the sharing economy, also known as peer-to-peer economy, has gained popularity and enabled service industry businesses to develop sustainable ways to serve potential consumers (Geissinger et al., 2019; Li et al., 2019; Wang et al., 2019). We take Airbnb as an example to discover the pricing decision of a crowdsourcing-supply sharing platform in the competitive market. Airbnb has obtained over 2,000,000 projects in more than 34,000 cities and 190 countries since it was founded in 2008, becoming one of the biggest peer-to-peer platforms (Airbnb, 2016; Gyódi, 2019). However, under crowdsourcing-supply sharing platform, the durability, performance, or other unobservable attributes of the product are not readily verifiable at the time of purchase. As recent surveys suggest that up to 69% of consumers now consult peer reviews before deciding whether to purchase a product (Feldman et al., 2018). Luckily, the proliferation of online forums and platform hosting product reviews are providing consumers with unprecedented ease-of-access to the post-purchase opinions of their peers (Feldman et al., 2018). These trends translate into increasing pressure to understand how sharing economy pricing policies interact with social learning.

The pricing decision on sharing economy is different from the traditional economy. A sharing platform links both supply side and demand side and balances the demand and supply by charging different service fees from suppliers and consumers. The Airbnb platform decides the pricing strategy of the service fee and provides the optimal rental prices for the landlords. Under the flexible pricing strategy, the rental price for the consumer and the wage for supply are decision variables. Unlike the traditional supply chain, the sharing platform has greater uncertainty on both the demand and the supply side. In this article, we compare the flexible-pricing and fixed-pricing policy by considering the seasonal fluctuations in tourism in supply and demand side.

Taking the above into account, in this article, we consider two main research questions. First, how will the platform’s optimal pricing decisions depend on the seasonal fluctuations in tourism, risk, quality, and so on? Second, how should a crowdsourcing-supply platform incorporate social learning into its pricing policies and how does social learning impact on the utilities of consumers, the profit of suppliers, and platform? To investigate these questions, we develop a dynamic, game-theoretic model and Bayesian social learning model that captures the interactions between social learning and platform strategies. Our equilibrium analysis of this model yields insights along three dimensions, which can be summarized as follows.

First, we confirm that in equilibrium, the higher the risk level suppliers face, the more profit they will get. Conversely, we establish that the lower the risk level consumers face, the more utilities they obtain. Second, we show that fixed pricing can only be optimal or near-optimal for the platform when market size is small, the accommodation quality is better, and consumers’ trust degree is low. Otherwise, a flexible pricing strategy is dominated. Those results complement the researches of Hu and Yun (2017) who study the sharing economy platform theoretically for the first time. Third, social learning can benefit all sharing economic members. In tourism peak period, social learning has a significant positive impact on the platform, consumers, and suppliers when the Airbnb accommodation is less prefect but has less impact when the accommodation is better.

This article aims to explore the optimal pricing strategy for the sharing economy by considering different market conditions in the two-sided market. And we illustrate how social learning affects the profit of suppliers, consumers, and the platform. The rest of this article proceeds as follows. In the second section, we present related literature reviews. The basic model in the different market conditions is proposed in the third section. In the fourth section, the flexible pricing is taken as the benchmark to explore the gap between flexible pricing and fixed pricing. In the fifth section, the dynamic pricing decision considering social learning is presented. The last section concludes the article.

Literature review

Our work is primarily connected to several domains in the existing literature: research on sharing economy, especially the researches of Airbnb and Uber; peer-to-peer service platform and two-sided market; revenue management problems and supply chain coordinating contract theory; and dynamic pricing based on social learning.

The sharing economy and tourism demand model has been studied both empirically and analytically in the literature. Hu and Yun (2017) study an on-demand matching platform which crowdsources a service from independent suppliers and sells it to customers. Gunter and Önder (2018) identify key determinants of Airbnb demand and quantify their marginal contributions in terms of demand elasticities. Gunter et al. (2020) employ a one-way fixed-effects spatial Durbin model and study whether the relationship between Airbnb and the traditional accommodation industry is of a substitutional or of a complementary nature. Önder et al. (2019) show that Airbnb accommodations are shown to indeed be the more affordable alternative, which is consistent with our results. Airbnb data, prices, and locations of hotels in Tallinn, as well as spatial information such as distance to points of interest and so on, are used in hedonic price regression models. According to this research, we take those factors into consideration in our supplier’s profit and consumer’s utility. Zekan et al. (2019) make a contribution to robustness by introducing an interactivity note to the base model, thus inspecting the results for corroboration/discrepancies and going beyond the static analyses that are common in data envelopment analysis modeling. Cai et al. (2019) examine the impacts of five groups of explanatory variables on Airbnb price in Hong Kong: listing attributes, host attributes, rental policies, listing reputation, and listing location. In Hong Kong, only low-end Airbnb rentals benefit from locational factors, indicating the heterogeneous effects of location on Airbnb pricing. Zervas et al. (2017) show that the impact is nonuniform, with lower priced hotels and hotels that do not cater to business travelers being the most affected. Dogru et al. (2019) discuss that an increase in supply while keeping demand relatively constant would decrease prices and revenues in a competitive market. In our study, we consider more comprehensive model and complement those results. We identify that increasing in supply does not always have negative impact on the price and revenues. Our work is a first attempt to discuss the pricing decision on Airbnb platform in a modeling work and extends the above works to explore the optimal dynamic pricing by considering the impact of social learning and the competition from the local hotel.

This article also relates to revenue management and supply chain problems (Li et al., 2018). Taylor (2016) derives the demand rate and the number of available suppliers in equilibrium for a given price and wage and then maximizes the platform’s profit by optimizing the price and wage with two-point distributions for both customers’ and suppliers’ valuations. Bai et al. (2018) establish a queuing model to maximize the social welfare by examining the available amount of supply, customers’ waiting cost, and service rate, which affects the optimal price and wage as well as the system performance. Our work aims at discovering the optimal dynamic pricing and revenue decision for the platform and maximizing the profit of platform, suppliers, consumers, and social welfare.

There is a considerable literature on the peer-to-peer service platform and two-sided market research. Some existing empirical and analytical works have studied on how to subsidize different market players to accelerate the growth of a peer-to-peer platform (Einav et al., 2016; Hawlitschek et al., 2018; Moreno and Terwiesch, 2014; Seamans and Zhu, 2013; Snir and Hitt, 2003; Yoganarasimhan, 2013). Fraiberger and Sundararajan (2017) consider the interaction between ownership and sharing on the peer-to-peer platform. Cohen et al. (2016) analyze the transaction data of Uber to measure consumers’ surplus. Besides, the two-sided market has attracted significant academic interest. Rochet and Tirole (2003) establish a competition model between two platforms with transaction volume that is appropriate for a two-sided market platform with a long-term goal in the form of demand and supply. Hu and Yun (2017) study the pricing decisions of an on-demand matching platform that adapt to the changing market conditions. We extend the work to explore the dynamic pricing decision on the Airbnb platform by considering the suppliers “product” attribute and social learning under two-sided market.

Social learning has attracted significant academic interest. As Deloitte and Touche’s survey shows, about 43% of consumers were reinforced of their original purchase decision by reviews, and 43% of consumers changed their intentions about which product to buy, and 9% of consumers even abandoned their purchase decisions because of negative reviews. Chen and Chang (2018) seeks to understand the factors affecting the purchase intention on Airbnb users in terms of five key factors: rating, rating volume, review, information quality, and media richness. Banerjee (1992) and Bikhchandani et al. (1992) deal with the update process as the scenario where a consumer observes a signal and the decisions of the consumers who decided before him. Here, consumers are rational and update their beliefs in a Bayesian way. Kwark et al. (2014) view the reviews as information mitigating the uncertainty in product quality to consumers’ needs and investigate how this additional information affects upstream product competition. Ifrach et al. (2014) analyze a Bayesian model where both the quality of product and reviews can assume only two possible values, and they provided conditions for learning. Our article yields new insights into how social learning impacts the platform profit and dynamic pricing decision by constructing the information update process in a Bayesian analysis.

The basic model

Considering a sharing platform that connects the demand with crowdsourcing supply, we take the accommodation sharing platform, Airbnb, as an example (Figure 1). This model also suits to the other crowdsourcing-supply sharing platforms. As for tourism, the market condition varies from seasons to seasons. Especially, the tourists will increase rapidly in the vacation or during big events. k is defined as the scenarios of tourism market condition, the larger k is, the larger market demand is. In a two-sided market, a crowdsourcing-supply sharing platform is developed by an intermediary firm to make sharing-economy activities possible (Hu and Yun, 2016). There are three parties in the two-sided market, the demand side, the supply side, and the platform. In such a market, the platform coordinates the consumers and suppliers who are heterogeneous. The utilities of demand and the profit of supply side are characterized as follows.

Figure 1.

The two-sided platform.

On the demand side, we consider consumers who face the potential reservation of Airbnb accommodation A and a hotel B. The Airbnb accommodation A and the hotel B are imperfect substitutes. Each accommodation in the Airbnb and hotel is characterized by a quality attribute and a fit attribute (Kwark et al., 2014). The quality attribute includes service, furniture, decoration, and so on. Every consumer prefers high quality to low quality. The fit attribute describes the preferences of different consumers, including geographic location. v is denoted as the valuation of the hotel B. $Q v$ is denoted as the valuation of the Airbnb accommodation A, where Q is defined as the quality difference between A and B. When $Q > 1$ , the quality of A is better than B. Otherwise, the quality of B is better than A. Accommodation is not perfect for consumers, so consumers will incur unsuitable costs. Here, we establish the Hoteling model to model the relationship between consumers’ utility and fit attribute. To simplify the analysis, we assume that there is a line of length l, and A and B are located at the two ends of the line, 0 and 1, respectively. The difference between consumer preference and the accommodation fit attribute is denoted as t, $t \in [0, 1]$ . $δ$ is defined as unsuitable costs for the consumer. Denoting the price of renting the accommodation on the Airbnb per day as p and the price of booking a room in the hotel per day as p₀. The consumer’s net utilities (consumer’s surplus) of A or B are equal to its valuation minus its price, which is expressed as follows:

S_{A} = ρ_{d} Q v - δ t - p

S_{B} = v - δ (1 - t) - p_{0}

where $ρ_{d}$ is the risk sensitive of renting the accommodation on the Airbnb. Only if $S_{A} \geq S_{B}$ , the consumers would like to rent an accommodation A on the Airbnb instead of booking an accommodation B in the hotel.

On the supply side, the supplier joins in the market and sharing his accommodation only if the revenue is larger than his or her opportunity cost (also called willingness-to-sell). The cost of the accommodation’s quality is $\frac{Q^{2}}{2}$ , and the risk sensitive of sharing his accommodation for consumers is $ρ_{s}$ . We denote the wage that the supplier obtains as w. The condition that the supplier sharing their accommodation on the Airbnb should be satisfied that the opportunity cost not exceed the net profit.

As shown in Figure 1, the crowdsourcing-supply sharing platform is serviced as the intermediary firm that connects suppliers and consumers. As mentioned above, p is the price of the accommodation on the Airbnb, and w is the wage of the supplier. Under the fixed pricing, the platform charges a certain rate $θ$ of the price as the service fee, and thus the wage for the supplier is $(1 - θ) p$ .

Fixed versus flexible pricing

We consider the selection from two popular pricing policies, fixed pricing and flexible pricing. We focus on exploring the optimal pricing strategies of the platform’s service fee. Meanwhile, the Airbnb would provide the reference optimal pricing decision for the suppliers. Thus, the rental price is also a decision variable. Under the flexible pricing, the platform’s service fees are flexible in different tourism market conditions. While, under the fixed-pricing policy, the wage offered to the supplier is $(1 - θ) p$ . Suppose that the tourism market condition $k \in [0, K]$ .

It is instructive to begin with a brief description of the interaction between the suppliers, the platform, and the consumers under the flexible-pricing policy. X is denoted as a consumer’s valuation, and Y is denoted as a supplier’s opportunity cost.

On the demand side, only if $S_{A} \geq S_{B}$ , the consumer will decide to rent an accommodation in the Airbnb. Next, we discuss the demand function and the conditional probability density function (PDF) in three cases, responsively. d₀ represents the numbers of the potential customers; F_X and F_Y are the cumulative distribution function.

Suppose that the demand distribution function $F (Δ)$ ( $F (Δ) > 0$ ) is continuous with density $f (Δ) > 0$ on $(0, \infty)$ . The hazard rate $h (Δ) \equiv \frac{f (Δ)}{\bar{F} (Δ)}$ is increasing in $Δ$ , where $\bar{F} (Δ) = 1 - F (Δ)$ . And the generalized failure rate $H (Δ) = Δ h (Δ)$ is monotonically increasing in $Δ$ . Thus, $h (Δ) > 0$ .

(i) Case 1: $ρ_{d} Q - 1 > 0$

The condition that consumers will choose the Airbnb accommodation should be satisfied

X \geq v_{1} = \frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1}

Thus, the demand function in case 1 can be express as

d_{1} (p) = d_{0} P (X \geq v_{1}) = d_{0} (1 - F_{X} (v_{1})) = d_{0} (1 - F_{X} (\frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1}))

And according to Hu and Yun (2017), on the condition $v > v_{1}$ , the PDF of X can be expressed as

f_{X | X \geq v_{1}} (v) = \frac{f_{X} (v)}{1 - F_{X} (v_{1})} = \frac{f_{X} (v)}{1 - F_{X} (\frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1})}

(ii) Case 2: $ρ_{d} Q - 1 < 0$

The condition that consumers prefer the Airbnb accommodation to the hotel should be satisfied

p \leq X \leq v_{2} = \frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1}

Thus, the demand function when $ρ_{d} Q - 1 < 0$ is

d_{2} (p) = d_{0} P (X \leq v_{2}) = d_{0} (F_{X} (v_{2}) - F_{X} (p)) = d_{0} (F_{X} (\frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1}) - F_{X} (p))

The conditional PDF of X is

f_{X | p \leq X < v_{2}} (v) = \frac{f_{X} (v)}{F_{X} (v_{2}) - F_{X} (p)} = \frac{f_{X} (v)}{F_{X} (\frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1}) - F_{X} (p)}

(iii) Case 3: $ρ_{d} Q - 1 = 0$

The condition that consumers would like to rent the Airbnb accommodation is

{\begin{matrix} p < - 2 δ t + p_{0} + δ \\ X \geq v_{3} = p \end{matrix}

And the demand function is

d_{3} (p) = d_{0} P (X \geq v_{3}) = d_{0} (1 - F_{X} (v_{3})) = d_{0} (1 - F_{X} (p))

The conditional PDF of X is

f_{X | X \geq p} (v) = \frac{f_{X} (v)}{1 - F_{X} (p)}

Similarly, on the supply side, only if the opportunity cost of the supplier is lower than the net revenue, the supplier will enter the market and share their accommodation on the Airbnb, $Y \leq ρ_{s} w - \frac{Q^{2}}{2}$ . Suppose that s₀ represents the numbers of the potential suppliers, F_Y is the cumulative distribution function.

Thus, the supply function can be expressed as

s (w) = s_{0} P (Y \leq ρ_{s} w - \frac{Q^{2}}{2}) = s_{0} F_{Y} (ρ_{s} w - \frac{Q^{2}}{2})

According to Hu and Yun (2017), the PDF of Y is

f_{Y | Y \leq ρ_{s} w - \frac{Q^{2}}{2}} (c) = \frac{f_{Y} (c)}{F_{Y} (ρ_{s} w - \frac{Q^{2}}{2})}

Under the flexible pricing policy, similar to the newsvendor model, the profit of the Airbnb platform is

π = (p - w) min {d (p), s (w)}

where $d (p)$ is the demand function and $s (w)$ is the supply function. The transaction in this model characterized as the match of demand and supply. Here, $min {d (p), s (w)}$ is defined as the number of successful transactions.

Then, we consider the total surplus of demand and supply side and the social welfare, respectively. Besides, similar to the revenue-sharing contracts, the platform should maximize the profit of the suppliers and itself, because the platform will get no profit without a supplier. The four kinds of profit will discuss in the following.

(i) Case 1: $ρ_{d} Q - 1 > 0$

First, the profit of the supply side is

U^{s} = min {d (p), s (w)} \int_{\underline{c}}^{ρ_{s} w - \frac{Q^{2}}{2}} \frac{(ρ_{s} w - \frac{Q^{2}}{2} - c) f_{Y} (c)}{F_{Y} (ρ_{s} w - \frac{Q^{2}}{2})} d c

where $\underline{c}$ is the minimum opportunity cost of the supplier.

Second, the utility of the demand side is

U^{d} = min {d (p), s (w)} \int_{\frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1}}^{\bar{v}} \frac{(ρ_{d} Q v - δ t - p) f_{X} (v)}{1 - F_{X} (\frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1})} d v

where $\bar{v}$ is the maximum valuation of the consumer.

Third, the total profit of the platform and supply side can be characterized as follows

U = U^{s} + (p - w) min {d (p), s (w)} = min {d (p), s (w)} (p - w + \int_{\underline{c}}^{ρ_{s} w - \frac{Q^{2}}{2}} \frac{(ρ_{s} w - \frac{Q^{2}}{2} - c) f_{Y} (c)}{F_{Y} (ρ_{s} w - \frac{Q^{2}}{2})} d c) .

Fourth, the social welfare W includes the profit of the supply side and platform and the consumer’s utility.

\begin{array}{l} W & = U^{s} + U^{d} + (p - w) min {d (p), s (w)} \\ = min {d (p), s (w)} (p - w + \int_{\underline{c}}^{ρ_{s} w - \frac{Q^{2}}{2}} \frac{(ρ_{s} w - \frac{Q^{2}}{2} - c) f_{Y} (c)}{F_{Y} (ρ_{s} w - \frac{Q^{2}}{2})} d c + \int_{\frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1}}^{\bar{v}} \frac{(ρ_{d} Q v - δ t - p) f_{X} (v)}{1 - F_{X} (\frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1})} d v) \end{array}

Similarly, these utilities under cases 2 and 3 are presented in Online Appendix.

Lemma 1. In absence of the Airbnb in the market, the optimal price of the hotel $p_{0}^{*}$ exists and $\frac{1}{h_{X} (δ + p_{0})} \leq p_{0}^{*} \leq \frac{1}{h_{X} (p_{0})}$ in the case 1, where $p_{0}^{*}$ is uniquely given by $p_{0} h_{X} (δ (1 - t) + p_{0}) = 1$ . And the hotel’s profit $π_{B}$ is concave in p₀.

Proof: To see Online Appendix.

Lemma 1 indicates that p₀ increases by the decreasing of hotel’s unsuitable costs. When $δ$ tends to be 0, $lim_{δ \to 0} p_{0}^{*} h_{X} (p_{0}^{*}) = 1$ . Without the Airbnb in the market, the hotel price only depends on the demand distribution function when the misfit cost is ignored. The profit of the hotel is benefit from p₀ when p₀ is small, but is hurt when it is larger. Lemma 1 suggests that it is important for the hotel manager to decide the favorable location or reduce the transport cost for consumers by providing additional value-added service (e.g. pick-up service).

Proposition 1. Under the encroachment of the Airbnb, (i) the optimal profit of hotel is increasing with the price of Airbnb in case 1, but it is decreasing in case 2; (ii) the impact of the Airbnb’s encroachment is increasing because of the high-quality accommodation on the Airbnb; and (iii) the optimal pricing decision exists and $\frac{ρ_{d} Q - 1}{h_{X} (\frac{p + δ - p_{0}}{ρ_{d} Q - 1})} \leq p_{0}^{*} \leq \frac{ρ_{d} Q - 1}{h_{X} (\frac{p - δ - p_{0}}{ρ_{d} Q - 1})}$ in case 1, where p₀ is the solution of $\frac{ρ_{d} Q - 1}{p_{0}} = h_{X} (\frac{p + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1})$ . And the impact of the Airbnb encroachment is concave in p₀.

Proof: To see Online Appendix.

Proposition 1 shows that the impact of the Airbnb increases as the Airbnb accommodation’s quality increases. The quality of Airbnb is better than the hotel in case 1 and less perfect than the hotel in case 2. As the quality of the Airbnb accommodation improved, consumers will prefer the Airbnb accommodation. An immediate implication is that the Airbnb platform should encourage suppliers to improve the accommodation quality and attract more consumers, while strategically enhancing the competitiveness of the platform and expanding its market size.

Corollary 1. Suppose F_X is the cumulative distribution function of uniform distribution $v \in U [0, a]$ . The optimal price of the hotel without the Airbnb is $p_{0}^{*} = \frac{1}{2} (a - δ + t δ)$ and the hotel’s optimal profit is $π_{B}^{*} = \frac{{(a + (t - 1) δ)}^{2}}{4 a}$ . After the encroachment of Airbnb, the optimal price of the hotel is $p ′_{0}^{*} = \frac{1}{2} (p - δ + 2 t δ)$ and the optimal profit is ${π'}_{B}^{*} = \frac{{(p + (t - 1) δ)}^{2}}{4 a (Q ρ_{d} - 1)}$ , where p is the price of the Airbnb.

Proof: To see Online Appendix.

As stated in lemma 1, proposition 1, and corollary 1, the hotel never benefits from encroachment. When $Q ρ_{d} > 2$ and $p''_{0}^{*} = \frac{a + p - a Q ρ_{d} + δ (3 t + Q ρ_{d} - Q t ρ_{d} - 2)}{4 - 2 Q ρ_{d}}$ , the impact of encroachment is minimum ${(π_{B} - {π'}_{B})}^{*} = \frac{a + p - a Q ρ_{d} + δ {(3 t + Q ρ_{d} - Q t ρ_{d} - 2)}^{2}}{4 a (2 - 3 Q ρ_{d} + Q^{2} ρ_{d}^{2})}$ . This suggests that the hotels should improve the quality, service, security, and so on to strengthen their competitiveness. Similarly, for the Airbnb, it is necessary to take advantage of the two-sided platform, which will explore in the following model.

Proposition 2. Suppose that the risk sensitive of demand and supply side is $ρ_{d} = ρ_{s} = ρ$ ; the distribution of demand and supply follows $N (μ_{d}, σ)$ and $N (μ_{s}, σ)$ , respectively. Under the flexible pricing policy, the optimal profit of the platform exists. The optimal price for the consumers and wage for the suppliers locate in $\underline{ξ} < w^{*} < p^{*} < \bar{ξ}$ .

Proof: To see Online Appendix.

Proposition 3. Under the flexible pricing strategy, the optimal utility $U^{*}$ is existed in case 1. And as p₀ or $ρ_{d}$ increases, the U increases.

Proof: To see Online Appendix.

It’s important for the two-sided matching platform to maintain good relationship with their independent suppliers; like revenue-sharing contracts, contracts tend to align the platform’s incentive to maximize its surplus with the suppliers (Hu and Yun, 2017). Thus, in the following problem, we regard maximizing the utility on the supply side plus the platform’s profit U as an important target for the platform’s incentive. When $s (w) < d (p)$ , U is monotonically increasing with p and the optimal $p^{*}$ satisfies $s (w^{*}) = d (p^{*})$ ; when $s (w) > d (p)$ , it has an optimal utility. Similarly, the optimal utility $U^{*}$ is existed in cases 2 and 3. Proposition 3 confirms that higher $ρ_{d}$ and p₀ will provide more profit for the platform and suppliers. This implies that the more trust consumers have, the more profit the platform and suppliers will gain. On the one hand, the suppliers would offer more real information to make their accommodations more reliable. On the other hand, the platform should increase its reliability, reduce the risk, and provide more indemnification for consumers. The platform could encourage the suppliers to post more personal information by implementing some corresponding policies. Moreover, it is significant to match the demand and supply, and the platform and suppliers will obtain the optimal profit when they are balanced.

Proposition 4. Under the fixed pricing strategy, the optimal utility $U^{*}$ is also existed. When (i) s < d, $p^{*}$ satisfices $θ = p^{*} s_{0} (F_{Y} (\underline{c}) - p^{*} θ ρ_{s} {F'}_{Y} (- \frac{Q^{2}}{2} + (p^{*} - p^{*} θ) ρ_{s}))$ and (ii) when s > d and $1 > θ > \frac{s_{0} F_{Y} (- \frac{Q^{2}}{2} - p (- 1 + θ) ρ_{s}) F_{Y} (\underline{c})}{1 + s_{0} F_{Y} (- \frac{Q^{2}}{2} - p (- 1 + θ) ρ_{s}) F_{Y} (\underline{c})}$ , $p^{*}$ satisfices $d_{0} (1 - F_{X} (\frac{p^{*} + 2 δ t - p_{0} - δ}{ρ_{d} Q - 1})) = s_{0} F_{Y} (ρ_{s} (1 - θ) p^{*} - \frac{Q^{2}}{2})$ .

Proof: To see Online Appendix.

Under the fixed pricing strategy, the platform is the leader who set a fixed service fee for suppliers and consumers. Suppliers decide their optimal price for consumers, and the platform may provide an optimal price for reference. To visualize the problem, we discuss the fixed and flexible under the impact of different parameters in the following.

We conduct sensitivity analysis to understand the impacts of key model parameters, for example, market condition, quality of Airbnb accommodation, and risk sensitives degree. We start our investigation from the tourism market condition k, which plays a key role in evaluating the market size.

We assume that F_X is the cumulative distribution function of the normal distribution with mean $μ_{d}$ and standard deviation $σ_{d}$ . F_Y is the cumulative distribution function of the normal distribution with mean $μ_{S}$ and standard deviation $σ_{s}$ .

(a) The impact of the scenarios of tourism market condition k

Visitors will increase rapidly during holidays or big events, which is known as the tourism seasonal fluctuations. The larger k is, the larger market demand is. Suppose that $μ_{d} = 9 + 2 k$ ; $μ_{s} = 14 + k$ ; $σ_{d} = 3 + \frac{1}{2} k$ ; $σ_{s} = 5 + \frac{1}{3} k$ , which means the supply and demand distribution functions are depend on k. From Figures 2 to 4, we analyze the optimal utilities under fixed pricing and flexible pricing strategies in different cases, respectively. We set $Q = 2$ in case 1, $Q = 0.5$ in case 2, and $Q = 1$ in case 3.

Figure 2.

The impact of k under fixed and flexible pricing strategies in case 1.

Figure 3.

The impact of k under fixed and flexible pricing strategies in case 2.

Figure 4.

The impact of k under fixed and flexible pricing strategies in case 3.

Proposition 5. In case 1, the flexible pricing tends to provide better utility for platform and suppliers (U). And the total utility of platform and suppliers U increases in k under flexible pricing. The fixed pricing may be optimal or near-optimal for the platform when k is small. When k increases, it is harmful to consumers in both pricing strategies.

As shown in Figure 2(a), as k goes up, the fixed pricing will benefit the suppliers in case 1. Comparing to the total profit of platform and supplier U (see Figure 2(c)), the platform sacrifices its profit under fixed pricing strategy to maximize the utility of suppliers, which is almost impossible because the platform is the leader. Under the flexible pricing strategy, the total profit of the platform and the supplier is higher and this revenue-sharing contract takes full advantage of the increasing k. As for the supplier, the profit U_s benefits from the increasing k when k is not too high but decreases when k is higher than the threshold. Figure 2(b) shows that the consumer’s utility U_d decreases as k increases and the increasing demand leads to the increasing price for consumers so that they obtain fewer utilities. A further implication based on proposition 5 and Figure 2 that it is necessary for the platform to predict the tourism market condition. When facing the peak period in tourism, the platform should adjust its pricing strategy to attract more suppliers and push information to potential suppliers.

Proposition 6. In case 2, the fixed pricing benefits the platform and suppliers when k is not too high, but the flexible pricing benefits them when k is larger.

Under the flexible pricing, it is interesting that the utility of the suppliers U_s remains constant when k is larger in case 2 (Figure 3(a)). Similar to Figure 2(a), the suppliers take advantage of fixed pricing, but this is on the premise that the platform sacrifices its interests. In case 2 (the consumer’s valuation of the Airbnb accommodation is lower than the hotel), the tendency of the consumer’s utility U_d is similar to that in case 1, but U_d in case 2 is much smaller than that in case 1. As shown in Figure 3(c), the fixed pricing benefits the platform and suppliers when k is not too high. Since U_d is small, the curve of social welfare is the same as U.

Figures 2 and 3 provide some useful managerial implications: the flexible pricing always benefits consumers and this result is driven by the fact that the price under the flexible pricing strategy is lower than the fixed pricing. In both cases, when k is small, the fixed pricing can be optimal or near-optimal for the platform and suppliers. It suggests that the platform can conduct the fixed pricing strategy in normal time and carry out the flexible pricing strategy when larger market size is realized. Those results complement the previous researches (Hu and Yun, 2017).

Case 3 represents the valuation of the Airbnb accommodation and the hotel is consistent. In case 3, U and W under flexible pricing are much larger than fixed pricing. That’s why the red line can’t find in Figure 4(c) and (d). For the consumer, the supplier, or the platform, the flexible pricing is the dominating one and their utilities are reduced as k decreases.

(b) The impact of the accommodation quality Q

On the supply side, the higher quality level causes a higher cost for the supplier. On the demand side, the consumers are in favor of a high-quality accommodation so that the accommodation’s price is higher. To simplify the analysis, we assume that $ρ_{d} = ρ_{s} = 1$ . Hence, $Q > 1$ in case 1 and $0 < Q < 1$ in case 2.

Proposition 7. The utility of the platform and supplier increases with Q in case 1.

In Figure 5(b), as Q increases, the consumer’s utility U_d increases when Q is small and it decreases when Q is high. In case 1, the supplier’s utility and the platform’s profit are increasing with Q. When Q is much higher, the fixed pricing will benefit the platform and supplier, but it is bad for the consumers. When Q is less higher, the flexible pricing takes full advantage of the increasing Q.

Figure 5.

The impact of Q under fixed and flexible pricing strategies in case 1.

Proposition 8. As Q increases, U_d increases, but U_s and U decrease in case 2, and the flexible pricing strategy is the dominating one.

In case 2, the flexible pricing is the domination strategy; the tendencies of the utilities are different from those in case 1. As Q increases, U_d increases, but U_s and U decrease. Thus, in case 2, the improvement of quality will benefit the consumers and hurt the profit of the supply side and platform. Considering the analysis in cases 1 and 2, the intuition is to strategically improve the quality of the accommodation.

Propositions 7 and 8 provide a useful managerial implication: When Q is small, flexible pricing is better. In contrast, fixed pricing offers more profit for the platform and supplier. Based on Figures 5 and 6, it shows that Airbnb gains more profit from high-quality accommodations, which suggests that the platform can carry out a service fee discrimination mechanism and encourage suppliers to improve the accommodations’ quality. The platform should provide some advice on the improvement through the investigation about consumers’ preference or subsidize the suppliers who would like to improve the quality.

Figure 6.

The impact of Q under fixed and flexible pricing strategies in case 2.

(i) $ρ_{d}$

$ρ_{d}$ mainly impacts the consumer’s utility. Thus, hereafter, we only discuss the tendency of U_d with $ρ_{d}$ . In case 1, we set $Q = 2$ and $0.5 < ρ_{d} < 1$ . In case 2, $Q = 0.5$ .

Proposition 9. The increasing of $ρ_{d}$ benefits the demand side in cases 1 and 2.

In case 1 (Figure 7), with the increasing of $ρ_{d}$ , the advantage of flexible pricing is more obvious. When $ρ_{d}$ is larger, the consumers trust the Airbnb accommodation more. Consumers obtain more satisfaction from larger $ρ_{d}$ . In case 2, when $ρ_{d}$ is extremely small, the utilities under fixed and flexible pricing are close. When $ρ_{d}$ becomes larger, the gap between fixed and flexible pricing becomes larger. In summary, $ρ_{d}$ has a positive impact on the consumer’s utility. An immediate implication is that the Airbnb should increase the platform and suppliers’ reliability, improve consumers’ experience, and increase consumer loyalty degree, which may be implemented in two ways. On the one hand, the platform can post the reviews from previous consumers, which assists users learn more unobservable attribution. The platform and suppliers will gain more benefit from high-reliability accommodation, which will discuss in the next section. One the other hand, the Airbnb ought to examine the information of the accommodation and eliminate false information. The Airbnb can reward the high-reliability suppliers and punish low-reliability suppliers by charging a different service fee.

(ii) $ρ_{s}$

Figure 7.

The impact of ρd under fixed and flexible pricing strategies in case 1 (a) and case 2 (b).

Since $ρ_{s}$ is the risk sensitive of the supplier, we only consider its impact for U_s, U, and W.

In cases 1 (Figure 8) and 2 (Figure 9), we observe that, contrary to conventional wisdom, the utilities of suppliers and platform are decreasing with $ρ_{s}$ , the higher risk may benefit the suppliers. When $ρ_{s}$ is not too high, the flexible price is domination. When $ρ_{s}$ becomes larger, the fixed pricing seems to be better in case 1. With the increasing of $ρ_{s}$ , the fixed pricing can be the optimal or near-optimal strategy.

Figure 8.

The impact of ρs under fixed and flexible pricing strategies in case 1.

Figure 9.

The impact of ρs under fixed and flexible pricing strategies in case 2.

Dynamic pricing with Bayesian social learning

In this section, we discuss the dynamic pricing of the Airbnb and suppliers with Bayesian social learning. The previous consumer who rented this Airbnb accommodation posted his experienced accommodation quality through the review, and the landlord who shared his accommodation to this consumer posted his satisfaction for the consumer’s quality by the review at the same time. Thus, the reviews become an effective way to discover the unobserved quality of landlord and consumers. When posting a review, consumers report their quality perceptions rather than net utility (Xu and Fuqiang, 2018). Consumers observe the reviews of the previous consumers and update their belief over the accommodation from q₀ to q₁ by Bayes’ rule (Papanastasiou and Savva, 2016). We denote m_i as the unobservable quality of the Airbnb accommodation for consumer i through the Internet, which can be learned only after consumers experience the accommodation. Similarly, we use n_i to denote the unobservable quality of the consumer for landlord i. We assume that the distributions of l_i and m_i are normal, $l_{i} \sim N (μ_{q}, σ_{q 0}^{2})$ and $m_{i} \sim N (μ_{h}, σ_{h 0}^{2})$ . The average quality review for consumers is $\bar{l} = \sum_{i = 1}^{n_{q}} l_{i}$ and the average quality review for a landlord is $\bar{m} = \sum_{i = 1}^{n_{h}} m_{i}$ .

q₁ and h₁ are random variables, since they depend on the unobservable realization of the accommodation quality q_r and the consumer quality h_r. According to Bayes’ rule, the posterior of accommodation quality and consumer quality are normally distributed, $q_{1} | q_{r} \sim N (q_{1}, σ_{q 1}^{2})$ and $h_{1} | h_{r} \sim N (h_{1}, σ_{h 1}^{2})$ , with

\begin{array}{l} q_{1} = \frac{n_{q} γ_{q}}{n_{q} γ_{q} + 1} \bar{l}, σ_{q 1}^{2} = \frac{σ_{q 0}^{2}}{n_{q} γ_{q} + 1} σ_{q 0}^{2} \\ h_{1} = \frac{n_{h} γ_{h}}{n_{h} γ_{h} + 1} \bar{m}, σ_{h 1}^{2} = \frac{σ_{h 0}^{2}}{n_{h} γ_{h} + 1} σ_{h 0}^{2} \end{array}

Lemma 2. Suppose that n₁ accommodation reviews are available to consumers remaining in the market and n₂ consumer quality reviews are available to landlords (suppliers). The rational beliefs of consumers and suppliers can be represented by the following distribution

q_{1} \sim N (0, \frac{n_{q} γ_{q}}{n_{q} γ_{q} + 1} σ_{q 0}^{2}), h_{1} \sim N (0, \frac{n_{h} γ_{h}}{n_{h} γ_{h} + 1} σ_{h 0}^{2})

where γ_{q} = \frac{σ_{q 0}^{2}}{τ_{q}^{2}} and γ_{h} = \frac{σ_{h 0}^{2}}{τ_{h}^{2}} .

Proof: To see Online Appendix.

Four kinds of utilities are presented in Online Appendix as well. Next, we compare the consumer’s and supplier’s utility and social welfare with and without social learning, respectively.

Proposition 10. Social learning always benefits consumers, platform, and suppliers. The social learning has less impact with larger k when the Airbnb accommodation is more perfect than the hotel.

As shown in Figure 10(a) and proposition 10, the green line is the optimal utility in the presence of social learning. Due to social learning, the utilities of consumers, platform, and suppliers are all increasing. However, it is different from the situation without social learning that the utilities tend to decrease with the increasing of k. This is an interesting finding because it means social learning has less impact during the tourist season. Because of the explosion of the demand, the price of the accommodation is increasing rapidly, and consumers hard to rent an accommodation at an acceptable price. In case 1, the quality of the Airbnb is higher than the hotel, and the valuation and price even become higher because of social learning. Thus, some kinds of low-type consumers will choose the hotel and abandon the Airbnb accommodation since the price of the accommodation is unaffordable. This well explains why the profit for the Airbnb and suppliers tend to go down with k increases.

Figure 10.

Compare the optimal utility with and without social learning in case 1.

Proposition 11. As k increases, the total utility of the supplier and platform U increases with social learning when the Airbnb accommodation is less perfect than the hotel. The social learning always benefits the platform and supplier.

It is worth noting that the tendency of Us, U, and W under social learning in case 2 (Figure 11) is increasing with k increases. Social learning will reduce the risk and uncertainty of the accommodation. Thus, in case 2, consumers will gain more utility by social learning, which increases the demand of the Airbnb and bring more profit for the platform and suppliers.

Figure 11.

Compare the optimal utility with and without social learning in case 2.

The social learning benefits consumers, suppliers, and the platform generally, which reveals that social learning is an effective way to develop the sharing economy and help the platform enhance its competitiveness. This result calls attention to the important advantage of social learning for the less-perfect accommodations, which suggests that the platform may encourage low-quality housing improvement services and provide more influential social learning. Since the social learning will improve utilities for consumers, the consumers have a good experience for their trip with social learning. Thus, the social learning will promote the development of local tourism.

Conclusion

This article studies the interaction between two-sided social learning and the platform’s pricing policies by considering the competitive market, the seasonal market condition, the quality, and the risk sensitive for the first time. Under this modeling framework, we first identify the negative impact for the hotel industry because of the encroachment of the Airbnb. Secondly, we find that fixed pricing may be optimal or near-optimal for the platform when market size is small, the accommodation quality is better, and consumers’ trust degree is low. Otherwise, flexible pricing is dominated. Hu and Yun (2017) don’t take the quality, risk sensitive of the consumers and suppliers, and social learning into consideration. Our results complement the researches of Hu and Yun (2017) who study the sharing economy platform theoretically for the first time. Thirdly, we also observe that the higher risk may benefit the suppliers; conversely, we observe that consumers tend to obtain more utilities from lower risk. Finally, we characterize the social learning in Airbnb as a Bayesian updating process and show that social learning benefits all sharing economic players and has a significant positive impact on the less-prefect accommodation. However, in the peak period of tourism, the social learning has less positive impact on the much-prefect accommodation.

Our study has several managerial implications. As for the hotels, managers may enhance their competitiveness in the presence of Airbnb by providing their special service and increasing security for consumers. As for tourism, it is essential to promote social learning, especially in dull season. As for Airbnb, it is necessary for the platform to predict the market condition. It suggests that the platform can conduct the fixed pricing strategy in normal time and carry out the flexible pricing strategy when larger market size is realized. Airbnb should make full use of the positive impact of social learning and encourage less-perfect accommodation to provide more reliable information. Moreover, the platform may increase the consumers’ trust in the platform and suppliers and increase consumer loyalty degree. The platform can implement a service fee discrimination mechanism to stimulate the improvement of low-quality suppliers.

There are some limitations in our article. In the model, we consider Airbnb and hotels as substitutes. But they are imperfect substitutes and have much difference that can be simply described by the parameter Q. Besides, this article lacks of empirical and data-derived pricing method. This research can be extended in several directions. For instance, as the rapid improvement of technology, the Blockchain tends to be introduced into the Airbnb. A natural question is how should Airbnb exploit the technology advantage. As we have shown, the accommodations’ location and transportation cost may influence the accommodations’ price. Another interesting future research is how to combine this accommodation renting platform with the car hiring platform like Uber, which will reduce the cost and improve consumers’ satisfaction. We expect our results, which are essentially driven by two-sided completion market, to apply more generally.

Supplemental material

supplement_material - Fixed, flexible, and dynamics pricing decisions of Airbnb mode with social learning

supplement_material for Fixed, flexible, and dynamics pricing decisions of Airbnb mode with social learning by Yuting Chen, Rong Zhang and Bin Liu in Tourism Economics

Footnotes

Acknowledgements

The authors gratefully acknowledge the support from National Natural and Science Foundation of China, Science and Technology Ministry of China for Cruise Program, Ministry of Education of China for Humanities and Social Sciences Foundation, China Scholarship Council, Shanghai Maritime University Top-Notch Innovative Talent Project, and Shanghai Maritime University Academic Newcomer Project.

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Natural and Science Foundation of China under grant number 71971134, Science and Technology Ministry of China for Cruise Program under grant number 2018-473, Ministry of Education of China for Humanities and Social Sciences Foundation under grant number 18YJA630143, China Scholarship Council under grant number 201908310077, Shanghai Maritime University Top-Notch Innovative Talent Project under grant number 2019YBR001, and Shanghai Maritime University Academic Newcomer Project.

ORCID iD

Bin Liu

Supplemental material

Supplemental material for this article is available online.

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