Peer-to-peer interactions in the sharing economy: Exploring the role of reciprocity within a Chinese social network

2020 ◽  
Vol 28 (3) ◽  
pp. 67-80 ◽  
Author(s):  
Richard G. Starr ◽  
Andrew Q. Zhu ◽  
Catherine Frethey-Bentham ◽  
Roderick J. Brodie
2019 ◽  
Vol 10 (2) ◽  
pp. 28-39 ◽  
Author(s):  
Abbas Strommen-Bakhtiar ◽  
Evgueni Vinogradov

Collaborative consumption facilitated by peer-to-peer platforms has witnessed a rapid expansion in the areas of hospitality and tourism. However, it is very unevenly distributed across countries, regions, cities and neighborhoods. The aim of this article is to investigate why collaborative consumption takes off early and continues flourishing in some regions, while remaining almost non-existent in other regions. The extant literature provides some insights into the effect of demand-side factors on sharing economy. However, this literature largely neglects the role of supply. Informed by the innovation adaption literature, the present study seeks to address this gap. The analysis reveals that regions with a) well-developed tourism industry, b) relatively large number of available properties, and c) situated near the main tourist attractions, tend to have relatively large supply of Airbnb listings and relatively many Airbnb tourists. An early adoption of Airbnb services is also associated with availability of properties to rent out.


2016 ◽  
Vol 2016 (1) ◽  
pp. 12434
Author(s):  
Berrin Erdogan ◽  
Aysegul Karaeminogullari ◽  
Talya N. Bauer ◽  
Allison M Ellis

Author(s):  
Abbas Strommen-Bakhtiar ◽  
Evgueni Vinogradov

Collaborative consumption facilitated by peer-to-peer platforms has witnessed a rapid expansion in the areas of hospitality and tourism. However, it is very unevenly distributed across countries, regions, cities and neighborhoods. The aim of this article is to investigate why collaborative consumption takes off early and continues flourishing in some regions, while remaining almost non-existent in other regions. The extant literature provides some insights into the effect of demand-side factors on sharing economy. However, this literature largely neglects the role of supply. Informed by the innovation adaption literature, the present study seeks to address this gap. The analysis reveals that regions with a) well-developed tourism industry, b) relatively large number of available properties, and c) situated near the main tourist attractions, tend to have relatively large supply of Airbnb listings and relatively many Airbnb tourists. An early adoption of Airbnb services is also associated with availability of properties to rent out.


2017 ◽  
Vol 58 (1) ◽  
pp. 136-148 ◽  
Author(s):  
Graziano Abrate ◽  
Giampaolo Viglia

The emergence of peer-to-peer platforms, known as the sharing economy, has empowered people to market their own products and services. However, there are information asymmetries that make it difficult to evaluate the reputation of the seller a priori. This article examines how sellers have to enhance their personal reputation to optimize revenues. The study proposes a revenue model where, given a frontier that depends on the shared assets, the maximization of revenues depends on reputational factors of the person and of the product. An empirical validation of the framework has been conducted in the context of Airbnb, a popular sharing economy travel platform. The sample comprises 981 establishments across five European cities. The findings suggest the crucial importance of personal reputation along with some distinctive reputational attributes of the product itself. These results emphasize the role of trust and personal branding strategies in peer-to-peer platforms.


2019 ◽  
Author(s):  
Katrine Berg Nødtvedt ◽  
Hallgeir Sjåstad ◽  
Siv Skard ◽  
Helge Thorbjørnsen ◽  
Jay Van Bavel
Keyword(s):  

Humaniora ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Abitassha Az Zahra ◽  
Eko Priyo Purnomo ◽  
Aulia Nur Kasiwi

The research aimed to explain the pattern of social communication on the issue of rejection of the PLTU Batang development policy. It used data on Twitter accounts involved in the rejection of the PLTU Batang development policy. In analyzing existing data, qualitative methods and social analysis networks were used. To see social networks in the rejection of the PLTU Batang development policy, the research used the NodeXL application to find out the patterns of social communication networks in #TolakPLTUBatang. From the results, it can be seen that in the dissemination of social networking information, the @praditya_wibby account is the most central account in the social network and has a strong influence on the social network. The @praditya_wibby account has a role in moving the community through Twitter to make a critical social movement. This means that in the current digital era, democracy enters a new form through the movement of public opinion delivery through social media. Besides, by encouraging the role of online news, the distribution of information becomes faster to form new perceptions of an issue. This is evident from the correlation network where the @praditya_wibby account has correlations with several compass online media accounts, tirto.id, okezonenews, vice, antaranews, BBCIndonesia, and CNN Indonesia.


Author(s):  
Matthew O. Jackson ◽  
Brian W. Rogers ◽  
Yves Zenou

What is the role of social networks in driving persistent differences between races and genders in education and labor market outcomes? What is the role of homophily in such differences? Why is such homophily seen even if it ends up with negative consequences in terms of labor markets? This chapter discusses social network analysis from the perspective of economics. The chapter is organized around the theme of externalities: the effects that one’s behavior has on others’ welfare. Externalities underlie the interdependencies that make networks interesting to social scientists. This chapter discusses network formation, as well as interactions between people’s behaviors within a given network, and the implications in a variety of settings. Finally, the chapter highlights some empirical challenges inherent in the statistical analysis of network-based data.


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