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

2020 ◽  
pp. 135481661989695 ◽  
Author(s):  
Yuting Chen ◽  
Rong Zhang ◽  
Bin Liu
Keyword(s):  
Social Learning ◽  
Dynamic Pricing ◽  
Positive Impact ◽  
Sharing Economy ◽  
Competitive Market ◽  
Risk Level ◽  
Risk Sensitivity ◽  
Peak Period ◽  
Pricing Decisions ◽  
Pricing Decision

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.

scholarly journals Pricing Decisions on Reward-Based Crowdfunding with Bayesian Review System Facing Strategic Consumers

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Yuting Chen ◽  
Rong Zhang ◽  
Bin Liu
Keyword(s):  
Positive Impact ◽  
Online Reviews ◽  
Equilibrium Analysis ◽  
Review System ◽  
Pricing Policy ◽  
Strategic Consumers ◽  
Pricing Decisions ◽  
Pricing Decision ◽  
Responsive Pricing ◽  
Form Type

Crowdfunding marks a popular and sustainable means by which small and microentrepreneurs obtain financial resources for their innovative project. Consumers increasingly rely on online reviews to make purchase decisions. However, the crowdfunding nowadays lacks a form type of review system. This paper is designed to extend research on the optimal pricing decision with review system for the reward-based crowdfunding. Firstly, a Bayesian analysis is established to construct consumers’ belief update process in presence of review system. Secondly, we take the strategies without the review system as a benchmark to explore the impacts of review system under preannounced pricing and responsive pricing. Finally, through the equilibrium analysis, we find that the review system has a positive impact on the creator under responsive pricing policy. The fraction of favorable review has a large effect on the profit of preannounced pricing. When the fraction is about 80%, the profit is the maximum. Generally speaking, the review system will make more profit for the creator.

scholarly journals Dynamic Pricing Decisions and Seller-Buyer Interactions under Capacity Constraints

Games ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 10 ◽  
Author(s):  
Vincent Mak ◽  
Amnon Rapoport ◽  
Eyran Gisches
Keyword(s):  
Dynamic Pricing ◽  
Capacity Constraints ◽  
Pricing Decisions

scholarly journals On the Impact of HOT Lane Tolling Strategies on Total Traffic Level

2012 ◽  
Vol 48 (3) ◽  
Author(s):  
Soheil Sibdari ◽  
Mansoureh Jeihani
Keyword(s):  
Numerical Analysis ◽  
Dynamic Pricing ◽  
User Equilibrium ◽  
Pricing Strategies ◽  
Peak Period ◽  
Dynamic Strategy ◽  
Traffic Level ◽  
The Right ◽  
The Impact ◽  
High Occupancy Toll

This paper shows how tolling (or pricing) strategies can be used to control the congestion levels of both untolled and high occupancy toll (HOT) lanes. Using a user-equilibrium method, the paper calculates the number of travelers on each route during the peak period and provides a numerical analysis that determines the distribution of travelers for different tolling strategies. It shows that with the right tolling strategy some travelers who initially plan to use the untolled lane during the peak period will change both their routes (i.e., select the HOT lane) and departure times (i.e., depart earlier or later). Using this result, the paper compares static and dynamic pricing strategies and shows that with a dynamic strategy a larger profit can be earned and congestion reduced in the untolled lane.

Dynamic pricing decisions by potential tourists under uncertainty: The effects of tourism advertising

2018 ◽  
Vol 25 (2) ◽  
pp. 213-234 ◽  
Author(s):  
Hongjuan Song ◽  
Yushi Jiang
Keyword(s):  
Dynamic Pricing ◽  
Reference Price ◽  
Risk Preferences ◽  
Uncertain Information ◽  
Informative Advertising ◽  
Pricing Decisions ◽  
Tourism Advertising ◽  
Reference Prices ◽  
Current Price ◽  
Advertising Exposure

The aim of this study is to examine the advertising information learning processes of potential tourists and observe how potential tourists sequentially adjust their perceived reference prices and purchase intentions with different risk preferences and choices with respect to gains (the current price is lower than the consumer’s reference price) or losses (the current price is higher than the reference price). In this study, a Bayesian experiment was conducted to elicit reference prices in the presence of tourism advertising with uncertain information. The findings show that with respect to gains, risk avoiders do not reduce their reference prices as significantly as do risk seekers when exposed to price-informative advertising. Exposure to image advertising changes potential tourists’ risk preferences, and the reference price drops more significantly for risk avoiders than for risk seekers. With respect to losses, informative and image advertising impact the reference price for participants with different risk preferences but not at a statistically significant level.

Sharing Economy for Sustainable Commerce

2020 ◽  
Vol 16 (3) ◽  
pp. 60-73
Author(s):  
Youngkeun Choi
Keyword(s):  
Social Learning ◽  
Learning Theory ◽  
Social Learning Theory ◽  
Self Efficacy ◽  
Sharing Economy ◽  
Tourism Industry ◽  
Adoption Intention

The purpose of this study is to empirically investigate what factors that affect consumers' adoption intention of sharing economy in the tourism industry from the perspective of social learning theory. By presenting the concept of consumers' self-efficacy of sharing economy, a model has been developed that explores the effects that explain the consumers' self-efficacy of sharing economy and their adoption intentions. For this, this study surveys 332 Korean consumers using Airbnb and analyzes the data using AMOS 24. In the results, First, learning from forums and communities, learning from ratings and reviews, and learning from social recommendations increase their self-efficacy of sharing economy. Second, consumers' self-efficacy of sharing economy increases their adoption intention. Finally, learning from forums and communities and learning from social recommendations among the antecedents of consumer's self-efficacy of sharing economy increase their adoption intention through their self-efficacy of sharing economy.

scholarly journals Dynamic Evolution of Safety Regulation of the Ridesharing Industry under Social Media Participation

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 560
Author(s):  
Qipeng Sun ◽  
Tingzhen Li ◽  
Fei Ma ◽  
Xiaozhuang Guo ◽  
Sijie Wang
Keyword(s):  
Social Media ◽  
Information Asymmetry ◽  
Positive Impact ◽  
Evolutionary Game ◽  
Sharing Economy ◽  
Model Solution ◽  
Social Regulation ◽  
Safety Regulation ◽  
Regulatory Issues ◽  
Safety Regulations

The emergence of ridesharing has spread against the background of the sharing economy. There have been a lot of controversies since the emergence of ridesharing, particularly regarding regulatory issues. The safety regulation of the ridesharing industry involves many parties, including governments, platform companies, and society at large. Currently, because of the influence of information asymmetry, it increases the uncertainty of governments’ regulation effect and the difficulty of making regulation measures. Meanwhile, social media, one of the most important forces of social regulation, has not paid enough attention to playing an appropriate role in the safety regulation of the ridesharing industry. Therefore, this study constructs an evolutionary game model between governments and platform companies that concerns the safety regulations of ridesharing passengers under social media participation. The influence path of social media is explored by model solution and numerical simulation. Our results indicate that social media participation has a positive impact on this safety regulation. Specifically, social media participation could reduce governments’ regulatory costs and encourage it to strictly regulate. The exposure of social media could bring losses to platform companies involved and promote platform companies’ investments in improving passengers’ safety. This study provides a decision basis for governments to introduce social media in the safety regulation of the ridesharing industry.

Use of dynamic pricing strategies by Airbnb hosts

2018 ◽  
Vol 30 (1) ◽  
pp. 2-20 ◽  
Author(s):  
Chris Gibbs ◽  
Daniel Guttentag ◽  
Ulrike Gretzel ◽  
Lan Yao ◽  
Jym Morton
Keyword(s):  
Dynamic Pricing ◽  
Design Methodology ◽  
Sharing Economy ◽  
Host Factors ◽  
Pricing Strategies ◽  
High Demand ◽  
Content Type ◽  
Multiple Markets ◽  
Hotel Managers ◽  
Practical Implications

Purpose The purpose of this paper is to provide a comprehensive analysis of dynamic pricing by Airbnb hosts. Design/methodology/approach This study uses attribute and sales information from 39,837 Airbnb listings and hotel data from 1,025 hotels across five markets to test different hypotheses which explore the extent to which Airbnb hosts use dynamic pricing and how their pricing strategies compare to those of hotels. Findings Airbnb is a unique and complex platform in terms of dynamic pricing where hosts make limited use of dynamic pricing strategies, especially as compared to hotels. Notwithstanding their limited use, hosts who own listings in high-demand leisure markets, manage entire places, manage more listings and have more experience vary prices the most. Practical implications This study identified a great need for Airbnb to encourage dynamic pricing among its hosts, but also warned of the potential perils of dynamic pricing in the sharing economy context. The findings also demonstrated challenges for hotel managers interested in actionable information related to Airbnb as a competitor. Originality/value This is the first Airbnb study to use a comprehensive set of data over a continuous period in multiple markets to look at a number of listing and host factors and determine their relation with dynamic pricing strategies.

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