印永翔Ying, Yung-Hsiang方敏馨Fang, Min-Hsin2019-09-03不公開2019-09-032017http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G0104590160%22.&%22.id.&http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/94903共享經濟結合行動網路、社群媒體與行動支付而興起。自2008年經金融海嘯後逐漸興起到現在已是各行各業蓬勃發展的狀態。近年來，各國政府也將共享經濟議題當作國家發展重點項目之一。 本研究目的旨在探討共享經濟和共享住宿Airbnb所帶來的正負外部性，以及Airbnb和一般傳統飯店業平均價格的關係。首先，以文獻探討整個共享經濟產業的狀況，再獨立探討Airbnb的發展和各國的Airbnb條款，之後確認本研究架構及進行資料的收集，將收集的數據進行量化分析，根據分析結果提出本研究之結論與建議。為達到以上目的，本研究採取網路資料收集，將收集資料經使用STATA14.0套裝軟體將資料進行分析，並驗證研究研果，分別以描述性統計、相關係數、線性區間回歸分析等統計進行驗證分析，並提出結論與建議。 本文以共享經濟（Sharing Economy）為主題，探討其發展以及各國如何面對共享經濟這個不可擋的新興商業浪潮，其中，共享住宿平台Airbnb是共享經濟中的典範企業之一，且在全球191個國家皆有提供房源的服務；近年來Airbnb積極在各國家發展且尋求合法化，但因牽涉到安全、法規和賦稅等問題，各國或各地政府都有更新的的法規來迎接或是限制Airbnb在當地的發展。本研究旨在研究各國景點城市Airbnb與傳統飯店價格的差距，以及影響Airbnb價格的因素為何；透過51個國際城市相關資料的收集為研究樣本，研究結果發現，Airbnb與一般傳統飯店的價格有一倍以上的價差，此幅度的價差會影響許多人在旅遊時的住宿選擇；而租金指數和飯店價格也會影響Airbnb價格，故Airbnb的高度發展也會造成當地租金指數高升和衝擊傳統飯店業。 Airbnb於2013年進軍台灣且於2017年推出繁體中文介面，但在尚未合法化。為因應爾後觀光旅遊和經濟發展，政府政策應要有協助此類共享經濟企業合法化發展的觀念，並且與時俱進更新法規監管安全和稅收問題以期創造雙贏的局面。Sharing economy prospers in conjunction with mobile networks, social media and mobile payments. It has been gradually developed from 2008 financial tsunami and is booming status in every industry nowadays. In recent years, many governments take share of sharing economic issues as one of the key national development projects. The purpose of this study is to explore the development of sharing economy, the positive and negative externality of Airbnb, and the price comparison between Airbnb and the traditional hotels. Firstly, the paper explores the status of the whole sharing economy industry, and then discusses the development of Airbnb and the Airbnb terms of each country. After confirming the research structure and collecting the relevant data, the collected data are quantitatively analyzed, and the conclusions and suggestions of this study are put forward according to the analysis results. In order to achieve the above purpose, this study takes the data collection from various network and the collected data is analyzed by STATA14.0 software. The research results are verified with, descriptive statistics, correlation coefficient, linear interval regression analysis and other statistical verification analysis respectively, and finally put forward conclusions and suggestions for this study. This study is based on a sharing economy, exploring its development and how countries face such an irresistible business trend. Airbnb is one of the typical companies in the sharing economy and has extended its business scale in 191 countries around the world. In recent years, Airbnb tries to actively develop its business in various countries and seek for legalization locally, but because of security, regulations limitation, taxes and other social issues, countries or local governments have updated regulations to meet or limit Airbnb in the local development. This study aims to study price gap between Airbnb& the traditional hotel in those 51 destinations, and what factors will have impacts on Airbnb price level; through relevant data collection in those 51 destinations, the findings found the traditional hotel price is more than double Airbnb price. This price gap will affect many travelers’ accommodation choices. Moreover, rental index and hotel prices will also affect the Airbnb price level. Therefore, Airbnb’s high development will affect local rental index and have certain impact on the traditional hotel industry. Airbnb entered Taiwan in 2013 and launched a traditional Chinese interface in 2017; however, its business has not been legalized by Taiwan government yet. In response to the subsequent prosperous tourism and economic development, the government policy should have the concept to assist the development of such sharing economic enterprises locally. Furthermore, the government can update the out-of-date regulations to solve safety and tax issues for such new business models in order to create a win-win situation.共享經濟共享住宿平台正負外部性相關係數線性回歸分析法Sharing economyAirbnbPositive and negative EternityCorrelation coefficientLinear regression analysis