Price Determinants on Airbnb: How Reputation Pays Off in the Sharing Economy

Teubner, Timm; Hawlitschek, Florian; Dann, David

doi:10.22381/jsme5420173

Cited by 130 publications

(54 citation statements)

References 16 publications

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“…For instance, they lack the experience and reputation necessary to reach platform-based premiums, in terms of hierarchical status markers. Research by Teubner, et al (2017 on Airbnb listings in Germany, found that host reputation resulted in price premiums, with similar results being presented by Ikkala and Lampinen (2015), and Wang and Nicolau (2017). In particular, Liang, et al (2017) found that the Airbnb 'SuperHost' badge meant that hosts would set higher prices, with a 6.73 percent markup on price.…”

Section: Implications For Users-by-proxymentioning

confidence: 54%

“…As such, it is important for potential users to build up a reputation, where the duration of membership can be important. Teubner, et al (2017) found that duration of membership on Airbnb was significantly and positively correlated with price, with each month of membership translating into a markup of around US$0.5. Guo, et al (2014) similarly argued that the amount of trust increases with greater number of ratings, where a low number of ratings might raise reliability doubts.…”

Section: Implications For Users-by-proxymentioning

confidence: 97%

“…Proxy users have to take responsibility for any co-hosts or co-guests. The role of reputation mechanisms, such as ratings and reviews (Teubner, et al, 2017;Zervas, et al, 2015), also means that proxy users are liable to be rated negatively due to any unfavorable behavior on behalf of their guests. Due to the lack of feedback reciprocity on the user-by-proxy, there is less incentive to behave most appropriately for the benefit of positive ratings.…”

Section: Implications For Proxy Usersmentioning

confidence: 99%

See 2 more Smart Citations

Sharing by proxy: Invisible users in the sharing economy

Newlands

Lutz

Hoffmann³

2018

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With the future of work increasingly data-driven, platforms automate decisions based on the collection of vast quantities of user data. However, non-users constitute a challenge as they provide little to no data for either platforms or other users. We focus on a category of (non-)users that has not received any attention in research: users-by-proxy. Users-by-proxy make use of sharing services but they are not themselves part of the sharing transaction. Platforms cannot analyze their behavior to tailor services or allocate labor most effectively. Users-by-proxy also have significant implications for trust and reputation mechanisms. In this conceptual contribution, we provide a definition of users-by-proxy as a third category between users and non-users, developing a typology of users-by-proxy based on motives of non-/use. We focus on the ramifications of users-by-proxy for the future of work and their significance for the limits of data-driven decision-making.

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Section: Implications For Users-by-proxymentioning

confidence: 54%

Section: Implications For Users-by-proxymentioning

confidence: 97%

Section: Implications For Proxy Usersmentioning

confidence: 99%

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Sharing by proxy: Invisible users in the sharing economy

Newlands

Lutz

Hoffmann³

2018

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“…For example, Houser and Wooders [57] found that seller reputation in eBay auctions has a positive influence on the final auction price. In a sharing economy context also, it was found that a provider's reputation has a positive influence on an Airbnb listing price [58].…”

Section: Discussionmentioning

confidence: 98%

Reputation Effects in Socially Driven Sharing Economy Transactions

et al. 2018

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Reputation has often been proposed as the central mechanism that creates trust in the sharing economy. However, some sharing platforms that focus primarily on social rather than economically driven exchanges have managed to facilitate exchanges between users without the use of a reputation system. This could indicate that socially driven exchanges are in less need of reputation systems and that having sufficient trust is less problematic. We examine the effect of seller reputation on sales and price as proxies for trust, using a large dataset from a Dutch meal-sharing platform. This platform aims to stimulate social interactions between people via meal sharing. Multilevel regression analyses were used to test the association of reputation with trust. Our main empirical results are that reputation affects both sales and price positively, consistent with the existing reputation literature. We also found evidence of the presence of an information effect, i.e., the influence of reputation on sharing decreases when additional profile information is provided (e.g., a profile photo, a product description). Our results thus confirm the effectiveness of reputation in more socially driven exchanges also. Consequently, platform owners are advised to use reputation on their platform to increase sharing between its users.

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“…Rather, they rely on things like reputation cues, which may imply the presence of costly signals in service providers. Thus, signaling theory is also useful for explaining how reputation cues act as signals to cue specific beliefs about service providers (Slee 2017;Teubner et al 2017).…”

Section: Signaling Theorymentioning

confidence: 99%

Reputation Cues as Signals in the Sharing Economy

2020

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Reputation cues, like star ratings, signal qualities of service providers in the sharing economy and may affect user behavior. Guided by concepts from signaling theory and using a repeated measures experiment (N = 221), this study manipulated the level of star ratings of ride sharing drivers. Intuitive findings are perceived service quality and willingness to use the service provider are higher when the star rating is high versus low. Extending prior work, perceived service quality mediates the effect of reputation on willingness, explaining 83% of the total effect. Also, the direct effect of reputation cues on perceived service quality depends, albeit weakly (η2p = 0.02), on how much users say they pay attention to them. These novel findings clarify the kinds of mental processing that occur when users of shared services evaluate reputation cues. We discuss findings in terms of costly signaling and consider practical implications for users and providers.

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Price Determinants on Airbnb: How Reputation Pays Off in the Sharing Economy

Cited by 130 publications

References 16 publications

Sharing by proxy: Invisible users in the sharing economy

Sharing by proxy: Invisible users in the sharing economy

Reputation Effects in Socially Driven Sharing Economy Transactions

Reputation Cues as Signals in the Sharing Economy

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