Airbnb in China: The Impact of Sharing Economy on Chinese Tourism

2017 ◽  
pp. 11-19
Author(s):  
Yumeng Bie ◽  
Jieyu Wang ◽  
Jingyu Wang
Keyword(s):  
Sharing Economy ◽  
The Impact

Principles for sharing economy formation in the digital society

2020 ◽  
Vol 16 (5) ◽  
pp. 800-821
Author(s):  
E.V. Popov ◽  
K.A. Semyachkov
Keyword(s):  
Network Effects ◽  
Sharing Economy ◽  
Network Effect ◽  
Industrial Society ◽  
Economic Activities ◽  
Digital Platforms ◽  
Digital Society ◽  
Modern Economy ◽  
Characteristic Features ◽  
The Impact

Subject. The article addresses economic relations that are formed in various areas of economic application of digital platforms. The target of the research is the modern economy of digital platforms across different economic activities. Objectives. The aim is to systematize principles for share economy formation in the context of the digital society development. Methods. We employ general scientific methods of research. Results. The study shows that the development of digital platforms is one of the most important trends in the development of the modern economy. We classified certain characteristic features of modern digital platforms, analyzed principles for their creation. The paper emphasizes that the network effects achieved through the use of digital platforms are an important factor in the development of the share economy. The network effect describes the impact of the number of the platform users on the value created for each of them. The paper also considers differences in the organization of traditional economy companies and companies that are based on the digital platform model, reveals specifics of changes in socio-economic systems caused by the development of digital platforms, systematizes principles of the sharing economy formation in the context of the digital society development. Conclusions. The analyzed principles for sharing economy development on the basis of digital platforms can be applied to create models for the purpose of forecasting the transformation of economic activity in the post-industrial society.

O Direito do Trabalho (des)conectado nas plataformas digitais

2019 ◽  
Vol 4 (1) ◽  
pp. 246-266
Author(s):  
Murilo Carvalho Sampaio Oliveira
Keyword(s):  
Social Relationships ◽  
Business Model ◽  
Economic Condition ◽  
Labor Law ◽  
Sharing Economy ◽  
Digital Economy ◽  
Business Activity ◽  
Digital Platforms ◽  
The Impact ◽  
Conditions Of Work

RESUMO:Este artigo trata dos impactos das plataformas digitais no Direito do Trabalho, tomando como exemplo sintomático o padrão da plataforma Uber. Inicia discutindo o cenário da economia digital e suas transformações nos modos de organizar a atividade empresarial, caracterizando a disrupção destas tecnologias e examinando criticamente se tais inovações situam-se realmente no discurso de economia do compartilhamento. Adiante, aborda as condições fáticas das plataformas de trabalho, questionando a dimensão formal-jurídica de liberdade e a condição econômica de hipossuficiência. Examina o caso da Uber como paradigma do modelo de organização empresarial desta economia digital e a situação dos seus motoristas tidos como parceiros para, ao final, pontuar algumas conclusões a cerca da necessidade do Direito Trabalho estar conectado com essas novas relações sociaisABSTRACT:This article deals with the impact of digital platforms in Labor Law, taking as a symptomatic example the standards of the Uber platform. It begins by discussing the the digital economy scenario and its transformations in the way business activity organize itself, characterizing the disruption of these technologies and critically examining whether such innovations are really part of the sharing economy speech. Hereinafter, it addresses the factual conditions of work platforms, questioning the formal-legal dimension of freedom and the economic condition of hypo-sufficiency. It examines the case of Uber as a paradigm of a business model organization in the digital economy and the situation of its drivers, taken as partners in order to, in the end of it, point some conclusions about the need of Labor Law to be connected with these new social relationships.

Drivers, Riders, and Service Providers: The Impact of the Sharing Economy on Mobility

2021 ◽  
Author(s):  
Saif Benjaafar ◽  
Harald Bernhard ◽  
Costas Courcoubetis ◽  
Michail Kanakakis ◽  
Spyridon Papafragkos
Keyword(s):  
Operations Management ◽  
Service Providers ◽  
Sharing Economy ◽  
Car Ownership ◽  
Full Time ◽  
Ride Sharing ◽  
Finite Set ◽  
Nonatomic Game ◽  
Payoff Functions ◽  
The Impact

It is widely believed that ride sharing, the practice of sharing a car such that more than one person travels in the car during a journey, has the potential to significantly reduce traffic by filling up cars more efficiently. We introduce a model in which individuals may share rides for a certain fee, paid by the rider(s) to the driver through a ride-sharing platform. Collective decision making is modeled as an anonymous nonatomic game with a finite set of strategies and payoff functions among individuals who are heterogeneous in their income. We examine how ride sharing is organized and how traffic and ownership are affected if a platform, which chooses the seat rental price to maximize either revenue or welfare, is introduced to a population. We find that the ratio of ownership to usage costs determines how ride sharing is organized. If this ratio is low, ride sharing is offered as a peer-to-peer (P2P) service, and if this ratio is high, ride sharing is offered as a business-to-customer (B2C) service. In the P2P case, rides are initiated by drivers only when the drivers need to fulfill their own transportation requirements. In the B2C case, cars are driven all the time by full-time drivers taking rides even if these are not motivated by their private needs. We show that, although the introduction of ride sharing may reduce car ownership, it can lead to an increase in traffic. We also show that traffic and ownership may increase as the ownership cost increases and that a revenue-maximizing platform might prefer a situation in which cars are driven with only a few seats occupied, causing high traffic. We contrast these results with those obtained for a social welfare-maximizing platform. This paper was accepted by Charles Corbett, operations management.

Technological unemployment and the business model of sharing economy in conditions of digitalized economy

2021 ◽  
Vol 21 (1) ◽  
pp. 208-225
Author(s):  
Lyudmila Belova
Keyword(s):  
Business Model ◽  
Industrial Revolution ◽  
Negative Impact ◽  
Well Being ◽  
Sharing Economy ◽  
Technological Unemployment ◽  
The One ◽  
Development Paradigm ◽  
The Impact ◽  
Industrial Revolutions

The article traces the impact of innovation on employment and workers income during industrial revolutions. The aim of the study is to identify the business model that contributes to improving the well-being and reducing negative impact of innovative transformations on employees. To achieve this goal, we analyze: the conceptions of industrial revolutions; the “Engels pause”, which arose during the First Industrial Revolution as a “surge” in inequality due to the contradiction between productivity growth and profit, on the one hand, and the stagnation of workers’ real incomes, on the other; the effect of replacing manual labor with automated one; the problems of technological unemployment; the digital business model of sharing economy. The findings report conclusions concerning the change in economic development paradigm as a result of the replacement of classical consumption models by sharing economy business model, on the prospects of the sharing economy business model in the context of its ability to solve employment problems, overcome technological unemployment and increase employees’ income. The achieved results can be useful for policymakers and corporate structures that design innovative development strategies.

scholarly journals Sustainability of micro-enterprises in the digital economy

2021 ◽  
Vol 250 ◽  
pp. 06008
Author(s):  
Oksana Mukhoryanova ◽  
Larisa Kuleshova ◽  
Nina Rusakova ◽  
Olga Mirgorodskaya
Keyword(s):  
Sustainable Development ◽  
The United States ◽  
Sharing Economy ◽  
Digital Economy ◽  
Small Enterprises ◽  
The European Union ◽  
The Sustainable Development ◽  
Traditional Industry ◽  
The Impact ◽  
Micro Enterprises

This paper aims at investigating the predisposition leading to the sustainability of micro-enterprises in the digital economy, especially the sharing economy. This area represents a new field since the research of the impact of the sharing economy on small enterprises is still in its infancy. We study the role of the entrepreneurial approach and entrepreneurial philosophy of the small business with regard to the digitalization and the sustainable development and growth using examples from the European Union and the United States. Some common features and trends are derived and the outcomes are discussed. Our results point at the fact that by creating an economy for micro-entrepreneurs, the sharing economy thrives on traditional industry disrupted by technology. Since micro-enterprises constitute a backbone of the economy in many developed and developing countries, more research is required to shed the light of the sustainable development of these types of enterprises in the globalized and digitalized world.

scholarly journals Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic

2021 ◽  
Vol 251 ◽  
pp. 01017
Author(s):  
Zhixiang Lu
Keyword(s):  
Machine Learning ◽  
New York ◽  
Regional Differences ◽  
Prediction Accuracy ◽  
Visual Analysis ◽  
Sharing Economy ◽  
Short Term ◽  
Emerging Industries ◽  
Machine Learning Model ◽  
The Impact

With the vigorous development of the sharing economy, the short-term rental industry has also spawned many emerging industries that belong to the sharing economy. However, due to the impact of the COVID-19 pandemic in 2020, many sharing economy industries, including the short-term housing leasing industry, have been affected. This study takes the rental information of 1,004 short-term rental houses in New York in April 2020 as an example, through machine learning and quantitative analysis, we conducted statistical and visual analysis on the impact of different factors on the housing rental status. This project is based on the machine learning model to predict the changes in the rental status of the house on the time series. The results show that the prediction accuracy of the random forest model has reached more than 94%, and the prediction accuracy of the logistic model has reached more than 74%. At the same time, we have further explored the impact of time span differences and regional differences on the housing rental status.

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