Factors influencing crowdsourcing riders’ satisfaction based on online comments on real-time logistics platform

Zhang, Yi; Shi, Xiaomin; Abdul-Hamid, Zalia; Li, Dan; Zhang, Xinle; Shen, Zhiyuan

doi:10.1080/19427867.2022.2052643

Cited by 9 publications

(8 citation statements)

References 71 publications

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“…It can be seen from the above that women riders are most concerned about reward order information, which agrees with the findings of Y. Zhang et al (2022). According to statistics, each rider is assigned about 100 orders every day, but 45 orders a day is the physiological limit of normal people, so women riders tend to choose orders with high reward amount to boost their profits.…”

Section: Ernie Categorization Modelsupporting

confidence: 83%

“…Measuring sentence uncertainty to assess model complexity, indicates that lower perplexity signifies better generalization (Y. Zhang et al, 2022). Utilizing Python's sklearn for topic modeling on women riders' user-generated content and employing Gibbs sampling with 200 iterations, this study identifies an optimal topic count through perplexity variation.…”

Section: Topic Modelmentioning

confidence: 99%

“…Reward order information directly affects women riders' expected earnings (Y. Zhang et al, 2022). Therefore, ''tip order information'' is the topic that women riders most focus on.…”

Section: Ernie Categorization Modelmentioning

confidence: 99%

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Research on the Impact of the Public Safety Emergencies on Women Riders’ Preference of Shanghai Real-Time Crowdsourcing Logistics Platform

Zhang,

Li,

Liu

2024

Sage Open

Self Cite

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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.

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Section: Ernie Categorization Modelsupporting

confidence: 83%

Section: Topic Modelmentioning

confidence: 99%

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Research on the Impact of the Public Safety Emergencies on Women Riders’ Preference of Shanghai Real-Time Crowdsourcing Logistics Platform

Zhang,

Li,

Liu

2024

Sage Open

Self Cite

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“…However, we note that the majority of the previous studies were carried out using the methods of questionnaire survey (e.g., Xiao and Ke [25]), case study (e.g., Mladenow et al [19]), or literature analysis (e.g., Rai et al [10]). The online review data of crowdworkers were rarely paid attention to (see Zhang and Shi [26] for a notable exception). Therefore, we use the text content mining software ROST-CM for data analysis to better explore the factors affecting crowdworkers' continuous participation in crowdsourcing logistics from the first perspective.…”

Section: Related Literaturementioning

confidence: 99%

“…This is not conducive to the crowdworkers' continued participation. However, the relevant research (e.g., Huang et al [2]; Bin et al [7]; Liang et al [12]; Bin et al [15]; Guo et al [22]; Zhang et al [26]) ignores this vital factor.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

Factors Influencing Crowdworkers’ Continued Participation Behavior in Crowdsourcing Logistics: A Textual Analysis of Comments from Online Platforms

Xie,

Lin,

Deng

et al. 2023

Sustainability

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With the lazy economy’s rise in the digital era, the demand for crowdsourcing logistics delivery is increasing. In this process, the continued participation of crowdworkers has been a considerable challenge. In order to further clarify the influencing factors of crowdworkers’ continuous participation and better and targeted incentives for their participative behavior, we use ROST-CM 6.0 software to conduct textual analysis on 3000 comments from crowdworkers on China’s Meituan and Hummingbird crowdsourcing logistics platforms. The results show that the order dispatch system, reward and punishment system, and platforms’ service are the key factors concerned by crowdworkers. The total negative sentiment among crowdworkers regarding crowdsourcing logistics platforms is close to 20%. We also find that crowdsourcing logistics platforms still have room for improvement in the quantity and quality of orders dispatched, the evaluation factors and the appeal system for reward and punishment rules, and the freedom and flexibility of distribution work. Otherwise, this might lead to a trust issue between crowdworkers and the crowdsourcing logistics platform. Based on the research findings, we recommend that the crowdsourcing logistics platforms should enhance service awareness, provide a better work experience for crowdworkers, and optimize platform functions. The government should act as a regulator as well as a service provider. This paper’s innovations include methodologically, from the perspective of the crowdworkers, online comment texts are used to mine the behavioral factors that influence the crowdworker’s continued participation in crowdsourcing logistics; content-wise, it adds fresh insights to existing research on how the order allocation system and platform reward and punishment mechanisms affect the crowdworkers’ continuous participation behavior.

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Prioritization of crowdsourcing models for last-mile delivery using fuzzy Sugeno–Weber framework

Pamucar,

Lazarević,

Dobrodolac

et al. 2024

Engineering Applications of Artificial Intelligence

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Factors influencing crowdsourcing riders’ satisfaction based on online comments on real-time logistics platform

Cited by 9 publications

References 71 publications

Research on the Impact of the Public Safety Emergencies on Women Riders’ Preference of Shanghai Real-Time Crowdsourcing Logistics Platform

Research on the Impact of the Public Safety Emergencies on Women Riders’ Preference of Shanghai Real-Time Crowdsourcing Logistics Platform

Factors Influencing Crowdworkers’ Continued Participation Behavior in Crowdsourcing Logistics: A Textual Analysis of Comments from Online Platforms

Prioritization of crowdsourcing models for last-mile delivery using fuzzy Sugeno–Weber framework

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