Signaling Theory And Sharing Economy

In the past decades,along with the rapid innovation and development of mobile terminal,virtual community,and online payment,the internet-based market pulled together Cyberworld and reality.A new type of economic pattern-sharing economy-emerged and revealed great influence to the everyday life of human being.Sharing economy is founded on the concept of decentralization.It provides private individuals with a platform to share the spare assets for free or for a fee.Sharing economy achieved a great development with the fast expansion of internet.It is currently an effective channel to bridge the supply and demand,as well as to promote the sustainable development of experience economy.Sharing economy has already become an integral part of our daily life.A lot of famous brands are based on the concept of sharing economy,such as Airbnb,Uber,Xiaozhu,and DiDi.In contract theory,signaling is the idea that the informative party credibly convey the information to the ignorant party in the effort to improve market efficiency.It is a mean to correct the unbalanced power in the market which is caused by information asymmetry.By delivering signal,the characteristics and quality of the products are shared between the demand and supply side,pushing the market towards equilibrium.Due to its peer-to-peer nature,sharing economy is a market place with great extent of product heterogeneity.The efficiency of the market is,therefore,highly influenced by information asymmetry.Hence,it is very essential to explore the use of signaling theory in the context of sharing economy,in the effort of promoting its healthy and sustainable development.The current study investigates the odds of a sharing accommodation listing being booked and the factors that influence these odds from a supplier’s perspective.In particular,the quality signals used by the hosts and guests are explored and examined.London,UK,is used as the example in the current study for its huge number of Airbnb listings and mature market.Delphi surveys are conducted among eight experts for the classification of listing attributes.Two types of listing attributes,the functional attributes and signal attributes,are identified and discussed.The functional attributes consist of those listing attributes that are directly involved in the consumption process of guests,whereas the signal attributes are provided by the hosts or the platform as quality indicators.A binomial logistic model is adopted to describe the effects of various listing attributes on its market demand.Considering the large volume of data,sequential Bayesian updating method is utilized instead of frequentist(classical)inference for model estimation.The use of sequential Bayesian updating method can avoid the potential p-value failure in big data context,as well as trim the large data set into small,manageable subsets.The current study provides quantitative estimations on the marginal effects of various functional and signal attributes on the booking probability of Airbnb listings.The results show that,in addition to host-specific information such as “Super Host” title and host identity verification,attributes including price,extra charges,discounts,region competitiveness and house rules are all effective signals in the Airbnb market.The impact of signal attribute is more effective in an environment with higher degree of information asymmetry,that is,for the listings without any review comments.On the other hands,the functional attributes have a consistent influence on Airbnb’s market demand in any information environment.The current study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation.The research findings are valuable to current and potential hosts in improving their booking rates and revenue.In addition,government and industrial management organizations can have more efficient strategy and policy planning.For emerging markets such as China,the results of current study can provide mature framework and experience for the healthy development of the sharing accommodation markets.