Analysis of the sharing economy effect on sustainability in the transportation sector using fuzzy cognitive mapping

JOURNAL OF CLEANER PRODUCTION

卷:311

Article

Nozari, Mojtaba Ahmadi^1,4  Ghadikolaei, Abdolhamid Safaei¹  Govindan, Kannan^2,3,4  Akbari, Vahid⁵ 

[1] Univ Mazandaran, Fac Econ & Adm Sci, Dept Ind Management, Babolsar, Iran

[2] Shanghai Maritime Univ, China Inst FTZ Supply Chain, Shanghai 201306, Peoples R China

[3] Yonsei Univ, Yonsei Frontier Lab, Seoul, South Korea

[4] Univ Southern Denmark, Danish Inst Adv Study, Dept Technol & Innovat, Ctr Sustainable Supply Chain Engn, Campusvej 55, Odense, Denmark

[5] Univ Nottingham, Nottingham Univ Business Sch, Jubilee Campus, Nottingham NG81BB, England

关键词: Sustainability;    Sharing economy;    Transportation sector;    Fuzzy delphi method;    Fuzzy cognitive mapping;   

DOI  :  10.1016/j.jclepro.2021.127331

来源: Elsevier

【 摘 要 】

Along with the significant growth of the sharing economy in recent years, its effect on the economy, society, and the environment at the level of governments, academic communities, and researchers has been widely discussed. One of the main debates is identifying the side or negative effects of the sharing economy as well as its positive effects. Studying the cause-effect relationship of the variables affecting sustainability in the sharing economy context can provide valuable results for firms. For this purpose, fuzzy cognitive maps have been used. Based on the literature review, the sharing economy's essential variables that affect sustainability were identified, and using the Fuzzy Delphi method, these variables were localized. Snapp and Tap30 were selected as two of the largest platforms in the Iranian transportation sector. Fuzzy cognitive maps based on aggregated opinions of experts were inputted to the Mental Modeler online software to drawn the cognitive map of this research. The degree of centrality index, i.e., the summation of input and output degree, was used to identify the most relevant sustainability variables. Consequently, the incentives to make a greater use of the platform, the income of drivers, the monopoly power of platforms, the price of services, the higher service quality of platforms, the use of private vehicles by passengers, and the change of traffic in cities were identified as variables with the greatest effect on stability. Four positive loops and one negative loop were identified among these seven variables in the cause-effect analysis. In the final section, some suggestions are presented based on each loop.

【 授权许可】

Free