چه چیزی در پاریس درحال رخ دادن است؟ Airbnb، هتلها و بازار پاریس: یک مطالعه موردی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 29
Airbnb (که یک وب سایت اینترنتی برای ارائه خدمات گردشگری و مهمانداری می باشد) دربین محققان و دانشمندان حوزه گردشگری و مهمانداری مورد بحث های شدید و پُرحرارت قرار گرفته است. به منظور درک تاثیر به اشتراک گذاری P2P روی چشم انداز گردشگری و مهمانداری، ابتدا فهمیدن جزئیات این بازار حائز اهمیت است. بنابراین در این مقاله ما توسعه بازار Airbnb را درطی هفت سال گذشته در پاریس که محبوب ترین مقصد گردشگری برای مهمانان Airbnb با بیش از 40000 اجاره نامه اقامتی می باشد بررسی می کنیم. این یادداشت تحقیقی یک خلاصه ای از یافته ها ما درباره بازار پاریس می باشد. مطالعه ما الگوهای مختلف رشد و فصلی بودن برای Airbnb و هتلها ونیز بی شباهتی های موجود در مکان جغرافیایی آنها را نشان می دهد. یافته ها بیانگر این هستند که دو محصول در رقابت مستقیم باهم نیستند و رابطه آنها ممکن است پیچیده تر از آن چیزی باشد که قبلا" تصور می شد. ما اعتقاد داریم که بررسی ماهیت رابطه رقابتی می تواند یک مسیر ارزشمند برای بررسی بیشتر باشد.
|مقاله ترجمه شده|
Motivations and constraints of Airbnb consumers: Findings from a mixed-methods approach
انگیزه ها و محدودیت های مشتریان bnb هوایی: یافته هایی از یک دیدگاه روشهای ترکیبی-2018
Airbnb is widely recognized as a disruptive innovation in the tourism industry. While separate studies have examined various factors affecting consumers adoption of Airbnb, the literature has largely focused on a handful of factors in isolation. Adopting a sequential mixed-methods approach, this study proposes a comprehensive conceptual model integrating the literature and findings of a qualitative study and subsequently tests the model via a national survey. The results suggest that, for motivations, price value, enjoyment, and home benefits significantly explain overall attitude toward Airbnb. As for constraints, distrust is the only factor that significantly predicts overall attitude, while insecurity is directly related to behavioral intentions. Overall attitude, perceived behavioral control, and subjective norms, such as social influence and trend affinity, predict behavioral intentions. This study contributes to the literature by simultaneously examining the predictive power of both motivations and constraints of Airbnb consumers in explaining overall attitude and purchase behavior.
keywords: Sharing economy |Airbnb |Motivations |Constraints |Hotel |Tourism
When guests trust hosts for their words: Host description and trust in sharing economy
وقتی که مهمانها به میزبان ها برای کلماتشان اعتماد می کنند: توصیف میزبان و اعتماد در اقتصاد مشترک-2018
In order to better understand the dynamics of user behavior in the sharing economy platform, a multi-stage study was conducted on how Airbnb hosts articulate themselves online and how consumers respond to different host self-presentation patterns. First, using text mining techniques on a large dataset consisting descriptions of Airbnb hosts in 14 major cities in the United States, two patterns of host self-presentation were identified. Hosts generally present themselves online as (1) a well-traveled individual, eager to meet new people or (2) an individual of a certain profession. This contributes to the conceptualization of profile as promise framework for online self-presentation in mixed-mode interactions involving peer-to-peer accommodation platform. Second, consumers respond to the two host self-presentation strategies differently, demonstrating higher levels of perceived trustworthiness in and intention to book from well-traveled hosts. This has direct strategic implications for effective self-marketing of “amateur” tourism players as well as for the role of residents as resources in tourism destinations.
keywords: Airbnb |Sharing economy |Peer-to-peer accommodation |Host self-presentation |Self-marketing |Trustworthiness
What makes an Airbnb host a superhost? Empirical evidence from San Francisco and the Bay Area
چه چیزی یک bnb هوایی را یک سوپرهاست می کند؟ شواهد تجربی از سان فرانسیسکو و ناحیه بای-2018
Using data on Airbnb listings from San Francisco and the Bay Area, the present study investigates the relative importance of the four criteria that need to be fulfilled to obtain the Airbnb superhost status. In order to quantify the marginal contributions of the four criteria, different index models of binary response (logit, probit, and IV probit, which allows for the endogeneity of Airbnb demand) are applied. The results, which are consistent across models, show that in San Francisco and the Bay Area obtaining (and maintaining) excellent ratings is, by far, the most important criterion, followed by reliable cancellation behavior of the host, host responsiveness, and sufficient Airbnb demand. Moreover, commercial Airbnb providers are more likely to obtain the superhost status.
keywords: Airbnb |Binary response index models |Revealing preferences |Sharing economy |Superhost
The performance of the P2P finance industry in China
عملکرد صنعت مالی P2P در چین-2018
Online peer-to-peer (P2P) lending occurs at the intersection of the sharing economy and e-commerce, and has developed into an immense finance industry in China. This study evaluates the business performance of the P2P finance industry and is the first to examine P2P lending activities from an efficiency perspective. We apply an improved version of the modified slacks-based measure that accommodates non-controllable inputs, undesirable inputs and outputs under a two-dimensional growth and operating efficiency paradigm. The results confirm the presence of contradictions between two types of efficiency in P2P platforms. They also show that listed companies, platforms with venture capital investment, and platforms funded by state-owned capital exhibit higher growth efficiency, while platforms with financial group involvement and diversified ownership show increased operating efficiency. Further, management incentives and the relative economic level of the platform location have no significant impact on efficiency.
keywords: Business performance| Peer-to-peer lending| Electronic commerce| Data envelopment analysis| Growth efficiency|Operating efficiency| k-means clustering| Management science| Non-parametric frontier methods| Quadrant analysis
The limits of trust-free systems: A literature review on blockchain technology and trust in the sharing economy
محدودیت های سیستمهای اعتماد آزاد: یک مرور روی منابع علمی فناوری زنجیره سیاه و اعتماد در به اشتراک گذاری اقتصاد-2018
At the tip of the hype cycle, trust-free systems based on blockchain technology promise to revolutionize interactions between peers that require high degrees of trust, usually facilitated by third party providers. Peer-to-peer platforms for resource sharing represent a frequently discussed field of application for “trust-free” blockchain technology. However, trust between peers plays a crucial and complex role in virtually all sharing economy interactions. In this article, we hence shed light on how these conflicting notions may be resolved and explore the potential of blockchain technology for dissolving the issue of trust in the sharing economy. By means of a dual literature review we find that 1) the conceptualization of trust differs substantially between the contexts of blockchain and the sharing economy, 2) blockchain technology is to some degree suitable to replace trust in platform providers, and that 3) trust-free systems are hardly transferable to sharing economy interactions and will crucially depend on the development of trusted interfaces for blockchain-based sharing economy ecosystems.
keywords: Blockchain |Sharing economy |Trust |Trust-free system |Literature review
Opportunities or threats: The rise of Online Collaborative Consumption (OCC) and its impact on new car sales
فرصت ها یا تهدیدها: افزایش مصرف مشارکتی آنلاین و تاثیر آن روی فروش خودروهای جدید-2018
Online collaborative consumption models, such as Uber and Airbnb, have emerged as popular peer-to-peer platforms in the sharing economy. The recent introduction of ride-hailing apps for smartphones has generated a powerful medium for passengers to call cars effortlessly and flexible job opportunities for drivers. A central question surrounding the introduction of online collaborative consumption regards its impact on incumbent firms. For example, ride-hailing services could discourage private car ownership, potentially leading to a subsequent decline of new car sales. Our study investigates whether the adoption of a leading ride-hailing platform, Didi Chuxing, increases or decreases new car sales shortly after the platform’s entries across 51 cities in China. Our empirical results suggest that the initial entry of a dominant ride-hailing company like Didi Chuxing positively impacts new car sales in the short run. However, we suspect that this positive effect will be transitory. Whether the auto industry can leverage the ride-hailing platforms to achieve sustainable benefits remains to be seen in the long run.
keywords: Causal empiricism |Collaborative consumption models |Difference-in-difference (DID) model |Falsification examination |Hierarchical duration model (HDM) |Network effects |Placebo dummy |Ride-hailing services |Propensity-score matching (PSM) |Sharing economy |Two-sided platforms
Mine is yours? Using sentiment analysis to explore the degree of risk in the sharing economy
مال من مال شماست؟ استفاده از تحلیل احساسات برای بررسی درجه خطر در به اشتراک گذاری اقتصاد-2018
The sharing economy is a new business model of e-commerce that stimulates new thinking in different ways. However, security and privacy are the most critical problems in the sharing economy. Stakeholders in this area need to build trust through online reviews. It may be risky when most people make decisions by reading fewer reviews. This research considers the emotions of comments in online reviews, and discovers the positive-negative sentiment ratio based on sentiment analysis. The sentiment ratio matches the level of risk, and customers view it as being suitable decision-making. The results suggest that the selected rankings were different between the base sentiment ratio and the rating stars for accommodations. In addition, different generation customers may make different decisions when they are shown pictures, room information, and the sentiment ratio for online reviews. Generation Z (under 20) and Generation Y (21–34) paid more attention to reviews, cost, and cleanliness. Generation X (35–49) paid attention to cleanliness, reviews, and total stars. The three generations all indicated the importance of online reviews for decision-making. On the other hand, high-risk people are more likely to be affected by the negative reviews, and make different decisions.
keywords: Online risk |Sharing economy |Sentiment analysis
Environmental benefits of bike sharing: A big data-based analysis
مزایای زیست محیطی اشتراک دوچرخه: تحلیل مبتنی بر داده های بزرگ-2018
Bike sharing is a new form of transport and is becoming increasingly popular in cities around the world. This study aims to quantitatively estimate the environmental benefits of bike sharing. Using big data techniques, we estimate the impacts of bike sharing on energy use and carbon dioxide (CO2) and nitrogen oxide (NOX) emissions in Shanghai from a spatiotemporal perspective. In 2016, bike sharing in Shanghai saved 8358 tonnes of petrol and decreased CO2 and NOX emissions by 25,240 and 64 tonnes, respectively. From a spatial perspective, en vironmental benefits are much higher in more developed districts in Shanghai where population density is usually higher. From a temporal perspective, there are obvious morning and evening peaks of the environmental benefits of bike sharing, and evening peaks are higher than morning peaks. Bike sharing has great potential to reduce energy consumption and emissions based on its rapid development.
Keywords: Bike sharing ، Sharing economy ، Energy consumption ، Carbon emissions ، Air pollution ، Big data
Be a “Superhost”: The importance of badge systems for peer-to-peer rental accommodations
یک "Superhost" باشید: اهمیت سیستم های امضاء برای تطابق نظیر به نظیر-2017
Many sharing-economy websites like Airbnb that offer vacation-rental options for travelers are very popular. However, few studies targeting the vacation-rental industry have investigated online reviews. To narrow this gap, this study focuses mainly on the gamification design developed by Airbnb that awards a “Superhost” badge to hosts who receive good reviews and observes how this can impact an accom modations review volume and ratings. All available information regarding Airbnb accommodation offered in Hong Kong was retrieved from Airbnbs website. We then constructed a negative binomial model and a Tobit model with different independent variables and controlled a set of variables relating to accommodation characteristics. The results show that an accommodation with the “Superhost” badge is more likely to receive reviews and higher ratings. In addition, guests are willing to spend more on “Superhost” accommodations. Based on our findings, we present implications for research and host practice.
Keywords: Airbnb | Gamification | Review volume | Rating | Online reputation | Sharing economy | Hospitality