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

ABSTRACT

Real-time logistics (RTL), which is mainly organized by crowdsourcing, has grown rapidly in recent years. Crowdsourcing riders are the main undertakers of RTL. This paper uses crowdsourcing riders’ online comments as data sources, and uses text mining techniques such as sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling to analyze the factors that bring satisfaction and dissatisfaction to riders. The research results show that in addition to basic income, riders expect the platform to provide them with better services, skills training and safety insurance before work can bring satisfaction to riders. The lack of timely information feedback on the current platform and inaccurate order matching are the reasons for the dissatisfaction of riders. Research also shows that riders can easily gain a sense of accomplishment to help others in the process of completing RTL distribution. Interactions with merchants and customers will also affect riders’ satisfaction.

KEYWORDS:

• Real-time logistics
• crowdsourcing riders’ satisfaction
• text mining
• platform management

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. Real-time intra-city delivery can be divided into C-end customers and B-end businesses according to the source of orders. This refers to running errands for end consumers.

2. Shanghai IResearch Market Consulting Co., LTD. 2020. China real-time logistics industry research report. (https://report.iresearch.cn/report/202006/3588.shtml)

3. Sharing Economy Research Center of State Information Center. 2021. China’s Sharing Economy Development Report. (http://www.sic.gov.cn/News/557/10779.htm)

4. World Bank Group. 2019. World Development Report 2019: The Changing Nature of Work. Washington, DC.

5. Mktcreator. 2019. Crowdsourcing Riders’ Survival Report. (https://socialbeta.com/t/report-delivery-worker-survival-20190328)

6. McKinsey Global Institute. 2016. Independent work: Choice, necessity, and the gig economy.

7. China Federation of Logistics & Purchasing. 2017. Survey report on Logistics and Express Workers of E-commerce in China. (http://www.chinawuliu.com.cn/lhhzq/201704/29/320924.shtml)

8. A Chinese word segmentation package. (https://github. com/fxsjy/jieba)

9. ‘The long tail effect’. The long tail refers to the individualized, scattered, small amount of demand that is so large that the business value of the individual cannot be underestimated.

10. A word embedding model released by Google in 2013. (https://github.com/danielfrg/word2vec)

11. A Chinese sentiment analysis dictionary released by HowNet in 2007.

12. A package that can make wordclouds. (https://github.com/timdream/wordcloud2.js)

13. A Natural Language Processing package of python. (https://github.com/lda-project/lda)

Additional information

Funding

This work was supported by the National Social Science Fund of China [project name The impact of online community on user experience of real-time crowdsourcing logistics platform, grant numbers 22BGL124].