 Research on the Impact of the Public Safety Emergencies on Women Riders’ Preference of Shanghai Real-Time Crowdsourcing Logistics PlatformYi Zhang, Dan Li and Shengren Liu SAGE Open, 2024, vol. 14, issue 2, 21582440241255804 Abstract: This study explores the impact of public safety emergencies on the preferences of women riders within Shanghai’s real-time crowdsourcing logistics platform. It employs quantitative research methods, utilizing LDA topic modeling and ERNIE categorization model for data analysis. The research identifies six key topics influencing riders’ preferences: Tip Order Information, Sharing and Volunteering, Epidemic Delivery Rules, Quality of Work and Life, Epidemic Control Measures, and Liability Exemption and Reward. The study reveals a cognitive bias among riders towards positive utilities, indicating a generally optimistic emotional state which influences their utility preferences. The findings suggest that the riders prioritize social interests and responsibilities during the pandemic, demonstrating adaptability to new work environments and appreciation for supportive measures by platforms. The study provides insights into the nuances of women riders’ preferences, emphasizing the need for targeted strategies by platforms and authorities to enhance job satisfaction and address challenges faced by women riders. Keywords: women riders’ preference; real-time crowdsourcing logistics; topic classify; sentiment preference (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:14:y:2024:i:2:p:21582440241255804 DOI: 10.1177/21582440241255804 Access Statistics for this article More articles in SAGE Open |
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