Effect of sharing economy on tourism industry employment

Fang, Bin; Ye, Qiang; Law, Rob

doi:10.1016/j.annals.2015.11.018

Cited by 335 publications

(218 citation statements)

References 6 publications

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“…Several past studies have focused on Airbnb's impacts on the hotel industry, including hotel revenue [17,18] and tourism industry employment [3]. For instance, Zervas, Proserpio and Byers [17] found that the entry of Airbnb into the Texas market had a quantifiable negative impact on local hotel revenues, showing that a 1% increase in Airbnb listings in Texas resulted in a 0.05% decrease in quarterly hotel revenues.…”

Section: Airbnbmentioning

confidence: 99%

“…For instance, Zervas, Proserpio and Byers [17] found that the entry of Airbnb into the Texas market had a quantifiable negative impact on local hotel revenues, showing that a 1% increase in Airbnb listings in Texas resulted in a 0.05% decrease in quarterly hotel revenues. Using the data of a total of 657 distinct listings for Idaho, USA, Fang, Ye and Law [3] investigated the effect of the sharing economy (Airbnb) on the tourism industry, and found that the sharing economy would generate new job positions and benefit the entire tourism industry, but the marginal effect decreases as the size of the sharing economy increases. Past studies have examined the impact of Airbnb on housing cost [15,19].…”

Section: Airbnbmentioning

confidence: 99%

“…The sharing economy can be defined as "peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services" [2]. In the peer-to-peer (P2P) accommodation industry, Airbnb is a prominent example, enabling owners to offer their unoccupied houses or rooms for short-term rental [3]. Founded in 2008, Airbnb has been increasing and growing dramatically and has served more than 150 million guests through over 3 million listings in more than 190 countries in less than a decade [4].…”

Section: Introductionmentioning

confidence: 99%

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Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

et al. 2017

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Airbnb has been increasingly gaining popularity since 2008 due to its low prices and direct interactions with the local community. This paper employed a general linear model (GLM) and a geographically weighted regression (GWR) model to identify the key factors affecting Airbnb listing prices using data sets of 794 samples of Airbnb listings of business units in Metro Nashville, Tennessee. The results showed that the GWR model performs better than the GLM in terms of accuracy and affected variable selections. Statistically significant differences varied across regions in Metro Nashville. The coefficients illustrate a decreasing trend while there is an increase in the distance from the listed units to the convention center, which indicates that Airbnb listing prices are more sensitive to the distance from the convention center in the central area than in other areas. These findings can also provide implications for stakeholders such as Airbnb hosts to gain a better understanding of the market situation and formulate a suitable pricing strategy.

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Section: Airbnbmentioning

confidence: 99%

Section: Airbnbmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

et al. 2017

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“…Instant Book is a listing feature, meaning that a potential guest can book the listing without the host's approval. 12 The superhost badge is awarded to a host if they satisfies a series of requirements set by Airbnb. 13 Response time of a host is transformed into numerical values, so that a faster response corresponds to a larger value.…”

Section: Modeling Review Growthmentioning

confidence: 99%

Sharing Means Renting?: An Entire-Marketplace Analysis of Airbnb

2017

SSRN Journal

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Airbnb, an online marketplace for accommodations, has experienced a staggering growth accompanied by intense debates and scattered regulations around the world. Current discourses, however, are largely focused on opinions rather than empirical evidences. Here, we aim to bridge this gap by presenting the first large-scale measurement study on Airbnb, using a crawled data set containing 2.3 million listings, 1.3 million hosts, and 19.3 million reviews. We measure several key characteristics at the heart of the ongoing debate and the sharing economy. Among others, we find that Airbnb has reached a global yet heterogeneous coverage. The majority of its listings across many countries are entire homes, suggesting that Airbnb is actually more like a rental marketplace rather than a spare-room sharing platform. Analysis on star-ratings reveals that there is a bias toward positive ratings, amplified by a bias toward using positive words in reviews. The extent of such bias is greater than Yelp reviews, which were already shown to exhibit a positive bias. We investigate a key issue-commercial hosts who own multiple listings on Airbnb-repeatedly discussed in the current debate. We find that their existence is prevalent, they are early-movers towards joining Airbnb, and their listings are disproportionately entire homes and located in the US. Our work advances the current understanding of how Airbnb is being used and may serve as an independent and empirical reference to inform the debate.

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“…Reference [7][8][9][10] provides an objective, systematic and holistic review of the sharing economy academic literatures related to tourism and hospitality from 2010 to 2015. Reference [11] suggests that the presence of photos can have a significant impact on guests' decision making.…”

Section: Sociology Aspects Of Sharing Economymentioning

confidence: 99%

Sharing Economy Research

Bing¹,

Zhang²

2017

dtssehs

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Abstract. With the rapid growth of Sharing Economy, this article focuses on a review of Sharing Economy research based on web of science database. Altogether there are 65 records tilted as "sharing economy" or "sharing economies" in recent 10 years, excluding conference papers. Analysis results of relating literatures indicate the following results. (1) Concerning document types, article, editorial material and meeting abstract ranked in Top 3; (2) In view of countries/territories, USA, England, and Switzerland ranked in Top 3; (3) Two funding agencies, i.e., ESRC Management and Business Development Fellowship Grant and Hong Kong Polytechnic University, ranked in Top 1, accounting for 3.077% respectively; (4) As for organizations, Boston College and Massachusetts Institute of Technology MIT ranked in Top 1. Then it was followed by Boston University and Open University UK, accounting for 4.615%; (5) The year of published articles mainly distributed between 2014 and 2017, accounting for 97%; (6) 47.692% of the research focused in Business Economics, then 15.385% in Geography and 13.846% in Government Law; (7) Considering source titles, Cambridge Journal of Regions Economy and Society ranked in Top 1, accounting for 18.182%. Overall, the related research topics can be classified into two aspects of sociology and information technology. In the end, conclusions were made with a summary of key results and new opportunities for future research.

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Effect of sharing economy on tourism industry employment

Cited by 335 publications

References 6 publications

Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

Sharing Means Renting?: An Entire-Marketplace Analysis of Airbnb

Sharing Economy Research

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