基于多活动DEA的公益众筹平台运营效率分析

doi:10.12341/jssms21581

系统科学与数学 ›› 2022, Vol. 42 ›› Issue (11): 2959-2972.DOI: 10.12341/jssms21581

• • 上一篇

基于多活动DEA的公益众筹平台运营效率分析

周忠宝¹, 王皓¹, 江资斌¹, 肖和录², 刘文斌³

1. 湖南大学工商管理学院, 长沙 410082;
2. 湖南师范大学, 长沙 410081;
3. 北京师范大学-香港浸会大学联合国际学院工商管理学部, 珠海 519087

收稿日期:2021-10-13 修回日期:2022-05-08 发布日期:2022-12-13
基金资助:
湖南省社科基金(SKZ2021192)资助课题.

PDF ( 11 ) 引用

周忠宝, 王皓, 江资斌, 肖和录, 刘文斌. 基于多活动DEA的公益众筹平台运营效率分析[J]. 系统科学与数学, 2022, 42(11): 2959-2972.

ZHOU Zhongbao, WANG Hao, JIANG Zibin, XIAO Helu, LIU Wenbin. Operational Efficiency Analysis of Public Welfare Crowd-Funding Platforms Based on Multi-Activity DEA[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(11): 2959-2972.

Operational Efficiency Analysis of Public Welfare Crowd-Funding Platforms Based on Multi-Activity DEA

ZHOU Zhongbao¹, WANG Hao¹, JIANG Zibin¹, XIAO Helu², LIU Wenbin³

1. School of Business Administration, Hunan University, Changsha 410082;
2. Business School, Hunan Normal University, Changsha 410081;
3. DBM, BNU-HKBU United International College, Zhuhai 519087

Received:2021-10-13 Revised:2022-05-08 Published:2022-12-13

中国互联网公益众筹平台近年来得到了快速发展, 如何有效地评价众筹平台的运营效率成为公益捐赠者、筹款人以及平台管理者所关注的问题. 文章以16家互联网公益众筹平台为研究对象, 根据项目的文本描述将平台划分为疾病救助、教育助学、自然保护和扶贫救灾4个板块,建立了相应的投入产出指标体系. 在此基础上,构建了基于方向距离函数的可处理共享投入及并行结构的多活动MDEA模型,设计了相应的求解算法来评价平台及各板块的运营效率和规模收益.实证结果表明, 阿里巴巴公益平台在4个板块上表现都比较好,可以作为优先选取的对象. 而广益联募、微博微公益、中国社会扶贫网等,其管理者可以通过控制成本或增大规模等措施来提高运营效率. 最后,从平台管理者、政府部门、捐款人以及筹款人4个角度提出相应的建议.

关键词： 公益众筹平台, 多活动DEA模型, 并行系统, 共享投入, 规模收益, 遗传算法

With the rapid development of public welfare crowd-funding platforms, how to effectively evaluate the operating performance of these platforms has become an important issue for public welfare donors, fundraisers, and platform managers. We select 16 platforms as samples, and divide their activities into four categories: Disease assistance, education aid, nature protection and poverty alleviation by the detailed information of fundraising data. Then we construct the input-output indexes and a multi-activity DEA model based on directional distance function to analyze the performance and returns to scale of each platform and their multiple-activities. The empirical results show that Alibaba's public welfare platform performs relatively well in the four sectors and can be used as a priority. As for Guangyi Joint Funding, Weibo Public Welfare, China Social Poverty Alleviation Network, etc., its managers can improve operational efficiency by controlling costs or increasing scale. In the end, we propose suggestions from the viewpoints of platform manager, government, donors and fundraisers.

Key words: Public welfare crowd-funding platforms, multi-activity DEA, parallel systems, shared inputs, returns to scale, genetic algorithm

MR(2010)主题分类:

90B50

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