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.

Airbnb Host Scaling, Seasonal Patterns, and Competition

“…The study of Gibbs et al, reveals that the host's experience positively influences the adoption of dynamic pricing [27]. Realistically, a multi-listing host has more opportunity to improve her/his experience than a single unit host because the host manages a higher number of transactions [28] and, for this reason, they have more skills to manage also new start-ups [29]. In another study, professional hosts achieve a higher price per night.…”

Impact of coronavirus pandemic on sharing mode of manufacturer

“…Further, Yang, Shao, Wang, and Ye (2020) proposed a novel reward scheme to reduce the potential negative impacts of surge pricing. In addition, Cachon et al, 2017 , Chen et al, 2020b both compare two pricing schemes of static pricing and dynamic pricing. Cachon et al (2017) find that dynamic pricing is better off, while Chen, Zhang, and Liu (2020) find that fixed pricing is better than dynamic pricing under certain conditions.…”

“…In addition, Cachon et al, 2017 , Chen et al, 2020b both compare two pricing schemes of static pricing and dynamic pricing. Cachon et al (2017) find that dynamic pricing is better off, while Chen, Zhang, and Liu (2020) find that fixed pricing is better than dynamic pricing under certain conditions. In this paper, we assume that the car-sharing price is the equilibrium price (market clearing price) to make match the supply and demand in sharing platform, which is consistent with the assumption in Jiang and Tian, 2018 , Tian and Jiang, 2018 .…”

A Systematic Literature Review on Pricing Strategies in the Sharing Economy

“…In the C2C model, sharing activities are realized through the intermediary; in this process, information asymmetry and moral hazard may arise. Information can be disclosed through self-disclosure and the bilateral reputation system [93,109]. To eliminate moral hazard, providers can set a higher price [81], and the intermediary can design a proper deposit and insurance scheme [115].…”