{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T01:16:32Z","timestamp":1770686192861,"version":"3.49.0"},"reference-count":41,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2019,9,2]],"date-time":"2019-09-02T00:00:00Z","timestamp":1567382400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ITP"],"published-print":{"date-parts":[[2020,6,18]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>In recent years, short-term sharing accommodation platforms such as Airbnb have made rapid forays in populous cities worldwide, impacting neighborhoods profoundly. Emerging work has focused on demand-side motivations to engage in the sharing economy. The purpose of this paper is to analyze rarely examined supply-side motivations of providers.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>To address this gap and to illuminate understanding of how Airbnb supply is configured and influenced, this study examines spatial patterns and socioeconomic influences on participation in the sharing accommodation economy by Airbnb hosts in New York City (NYC). An exploratory conceptual model of host participation is induced, which posits associations of demographic, economic, employment, social capital attributes, and attitudes toward trust and sustainability with host participation, measured by Airbnb property density in neighborhoods. Methods employed include ordinary least squares (OLS) regression, k-means cluster analysis and spatial analytics.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Spatially, clusters of high host densities are in Manhattan and northern Brooklyn and there is little proportionate change longitudinally. OLS regression findings reveal that gender ratio, black race\/ethnicity, median household income, and professional, scientific, and technical occupation, and attitudes toward sustainability for property types are dominant correlates of property density, while host trust in customers is not supported.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>These results along with differences between Queens and Manhattan boroughs have implications for hosts sharing their homes and for city managers to formulate policies and regulate short-term rental markets in impacted neighborhoods.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The study is novel in conceptualizing and analyzing the supply-side provider motivations of the sharing accommodation economy. Geostatistical analysis of property densities to gauge host participation is novel. Value stems from new insights on NYC\u2019s short-term homesharing market.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/itp-10-2018-0481","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T06:35:20Z","timestamp":1592462120000},"page":"983-1009","source":"Crossref","is-referenced-by-count":18,"title":["Spatial and socioeconomic analysis of host participation in the sharing economy"],"prefix":"10.1108","volume":"33","author":[{"given":"Avijit","family":"Sarkar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehrdad","family":"Koohikamali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James B.","family":"Pick","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2019,9,2]]},"reference":[{"issue":"2","key":"key2021070515372663100_ref001","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1111\/j.1538-4632.1995.tb00338.x","article-title":"Local indicators of spatial association \u2013 LISA","volume":"27","year":"1995","journal-title":"Geographical Analysis"},{"issue":"1992","key":"key2021070515372663100_ref002","first-page":"111","article-title":"The Moran Scatterplot as an ESDA tool to assess local instability in spatial association","volume":"111","year":"1996","journal-title":"Spatial Analytical Perspectives on GIS"},{"key":"key2021070515372663100_ref005","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1086\/666376","article-title":"Access-based consumption: the case of car sharing","volume":"39","year":"2012","journal-title":"Journal of Consumer Research"},{"key":"key2021070515372663100_ref006","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2017.02.029","article-title":"Understanding collaborative consumption: test of a theoretical model","volume":"118","year":"2017","journal-title":"Technological Forecasting and Social Change"},{"key":"#cr-split#-key2021070515372663100_ref007.1","unstructured":"Benner, K. 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