Subtopic Deep Dive
Peer-to-Peer Accommodation
Research Guide
What is Peer-to-Peer Accommodation?
Peer-to-peer accommodation refers to online platforms like Airbnb enabling individuals to rent out private homes or rooms to travelers, disrupting traditional hospitality markets.
Research examines pricing dynamics, host-guest interactions, and regulatory impacts of platforms such as Airbnb. Zervas et al. (2013) quantify Airbnb's effect on hotel revenues using empirical data from Texas markets (557 citations). Over 10 key papers from 2005-2019 analyze user reviews, trust factors, and business models in this domain.
Why It Matters
Peer-to-peer accommodation reshapes urban tourism by increasing short-term rental supply and reducing hotel occupancy, as shown in Zervas et al. (2013) where Airbnb caused an 8-10% drop in hotel revenues in affected markets. Platforms influence host behaviors and guest satisfaction through reviews (Cheng and Jin, 2018, 463 citations) and trust mechanisms (ter Huurne et al., 2017, 430 citations). These insights guide regulators on housing affordability and policymakers on tourism economics, with Oskam and Boswijk (2016, 490 citations) highlighting networked hospitality's competition with hotels.
Key Research Challenges
Regulatory Compliance Issues
Platforms face varying local laws on short-term rentals, complicating operations across cities. Dredge and Gyimóthy (2015, 381 citations) critique regulatory gaps in collaborative economy tourism. Enforcement remains inconsistent, affecting supply growth.
Trust in Transactions
Hosts and guests must trust strangers facilitated by platforms amid opportunism risks. ter Huurne et al. (2017, 430 citations) review antecedents like platform reputation and user profiles. Building reliable systems persists as a core hurdle.
Impact Measurement
Quantifying effects on hotels and housing requires causal econometric methods. Zervas et al. (2013, 557 citations) use difference-in-differences to estimate Airbnb's hotel revenue impact. Data access and endogeneity challenge precise assessments.
Essential Papers
Algorithmic management and app‐work in the gig economy: A research agenda for employment relations and HRM
James Duggan, Ultan Sherman, Ronan Carbery et al. · 2019 · Human Resource Management Journal · 780 citations
Abstract Current understanding of what constitutes work in the growing gig economy is heavily conflated, ranging from conceptualisations of independent contracting to other forms of contingent labo...
The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry
Georgios Zervas, Davide Proserpio, John W. Byers · 2013 · SSRN Electronic Journal · 557 citations
Understanding platform business models: A mixed methods study of marketplaces
Karl Täuscher, Sven M. Laudien · 2017 · European Management Journal · 550 citations
A triadic framework for collaborative consumption (CC): Motives, activities and resources & capabilities of actors
Sabine Benoit, Thomas L. Baker, Ruth N. Bolton et al. · 2017 · Journal of Business Research · 525 citations
This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommon...
Airbnb: the future of networked hospitality businesses
Jeroen Oskam, Albert Boswijk · 2016 · Journal of Tourism Futures · 490 citations
Purpose Although networked hospitality businesses as Airbnb are a recent phenomenon, a rapid growth has made them a serious competitor for the hospitality industry with important consequences for t...
What do Airbnb users care about? An analysis of online review comments
Mingming Cheng, Xin Jin · 2018 · International Journal of Hospitality Management · 463 citations
Antecedents of trust in the sharing economy: A systematic review
Maarten ter Huurne, Amber Ronteltap, Rense Corten et al. · 2017 · Journal of Consumer Behaviour · 430 citations
Abstract Users and potential users of the sharing economy need to place a considerable amount of trust in both the person and the platform with which they are dealing. The consequences of transacti...
Reading Guide
Foundational Papers
Start with Zervas et al. (2013, 557 citations) for empirical Airbnb-hotel impact quantification using causal methods; then Akehurst (2008, 407 citations) on user-generated content enabling P2P platforms.
Recent Advances
Study Wirtz et al. (2019, 378 citations) on platform ecosystems; Cheng and Jin (2018, 463 citations) for review analysis; Duggan et al. (2019, 780 citations) on gig work parallels.
Core Methods
Econometrics (difference-in-differences, Zervas et al. 2013); content analysis of reviews (Cheng and Jin 2018); systematic reviews of trust (ter Huurne et al. 2017); mixed methods for marketplaces (Täuscher and Laudien 2017).
How PapersFlow Helps You Research Peer-to-Peer Accommodation
Discover & Search
Research Agent uses searchPapers with query 'Airbnb hotel impact' to retrieve Zervas et al. (2013), then citationGraph reveals 557 citing works, and findSimilarPapers uncovers related regulatory studies like Dredge and Gyimóthy (2015). exaSearch scans 250M+ OpenAlex papers for 'peer-to-peer accommodation regulation'.
Analyze & Verify
Analysis Agent applies readPaperContent to extract pricing models from Zervas et al. (2013), verifies claims with CoVe chain-of-verification against raw data, and runPythonAnalysis replicates hotel revenue regressions using pandas on extracted tables, graded by GRADE for evidence strength.
Synthesize & Write
Synthesis Agent detects gaps in trust literature beyond ter Huurne et al. (2017), flags contradictions between Oskam and Boswijk (2016) and Dredge and Gyimóthy (2015); Writing Agent uses latexEditText for manuscript sections, latexSyncCitations integrates 10+ papers, latexCompile generates PDF, and exportMermaid visualizes platform ecosystem diagrams.
Use Cases
"Replicate Airbnb hotel impact regression from Zervas et al. 2013"
Research Agent → searchPapers → readPaperContent → Analysis Agent → runPythonAnalysis (pandas difference-in-differences on extracted data) → matplotlib revenue plot output.
"Draft LaTeX review on P2P accommodation trust factors"
Synthesis Agent → gap detection on ter Huurne et al. 2017 → Writing Agent → latexEditText → latexSyncCitations (10 papers) → latexCompile → peer-review ready PDF.
"Find GitHub code for Airbnb pricing models"
Research Agent → searchPapers 'Airbnb pricing' → Code Discovery workflow: paperExtractUrls → paperFindGithubRepo → githubRepoInspect → runnable Python scripts for dynamic pricing.
Automated Workflows
Deep Research workflow conducts systematic review: searchPapers 50+ papers on 'Airbnb regulation', citationGraph clusters, DeepScan 7-step analysis with CoVe checkpoints verifies impacts from Zervas et al. (2013). Theorizer generates theory on platform trust from ter Huurne et al. (2017) and Wirtz et al. (2019), chaining synthesis to exportMermaid actor diagrams.
Frequently Asked Questions
What defines peer-to-peer accommodation?
It involves platforms like Airbnb where individuals rent private spaces to travelers, as foundational in Zervas et al. (2013).
What methods study its hotel impacts?
Econometric approaches like difference-in-differences in Zervas et al. (2013, 557 citations) measure revenue drops; mixed methods in Täuscher and Laudien (2017, 550 citations) analyze marketplaces.
What are key papers?
Zervas et al. (2013, 557 citations) on hotel impacts; Oskam and Boswijk (2016, 490 citations) on networked hospitality; Cheng and Jin (2018, 463 citations) on user reviews.
What open problems exist?
Regulatory harmonization across cities, long-term housing effects, and scalable trust models beyond ter Huurne et al. (2017) remain unresolved.
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Part of the Sharing Economy and Platforms Research Guide