摘要近年来，我国的公益事业迅猛发展，但是人与人之间存在的信任问题导致人们从事公益事业的热情大受打击，由于公益众筹对公益事业的发展具有显著性的推动作用，所以公益众筹项目的成功对公益事业的发展提供了有力的条件。众筹项目筹资成功率的逐年降低，使得公益事业的发展很难冲破瓶颈。本文研究公益众筹项目成功的影响因素，选取“众筹网”上已筹资成功的293个公益众筹项目作为研究样本，基于外界响应度（项目支持数、项目评论数、项目关注数、项目分享数）、项目概况（项目用到的图片和视频数、发起人参与的众筹项目总数）2个维度构建了理论模型，利用描述统计、回归分析的方法对平台中影响项目成功的几个因素进行分析。实证结果显示：项目用到的图片数、项目的关注数、支持数、分享数量几个因素对项目众筹成功具有显著的影响，发起人参与其他的众筹项目的数量以及用户的评论数量对项目众筹成功没有显著影响。86349

毕业论文关键词：众筹；捐赠；影响因素；众筹网

Antecedents of Donation-based Crowd-funding Success: An Empirical Study

Abstract In recent years, the public of welfare undertakings is developing rapidly in our country。 But interpersonal trust problems hit the enthusiasm of the people engaged in public welfare undertakings。 The Donation-based Crowd-funding is significant for the development of public welfare undertakings so that donation-based Crowd-funding Success provides powerful conditions to the development of public welfare undertakings。 This paper studies the antecedents of Donation-based Crowd-funding Success。 We have selected 293 projects which has raised the success as the research sample from Zhongchou。com based on two dimensions including the response degree (project support, project review, project number, project share number), project overview( the number of pictures and videos, the number of project raised by the sponsors) to build a theoretical model。 This paper analyzes the antecedents of Donation-based Crowd-funding Success by using descriptive statistics, regression analysis method in the platform。 The research conclusion show that the number of images, the number of supporting and sharing are significant influence to the project。 The number of project raised by the sponsors and the number of comments on the project to successful completion are not significant。

Keywords: Crowd-funding; Donation; Antecedents; Zhongchou。com 

目录

0 引言 1

1 文献综述 2

1。1 国内文献研究情况 2

1。2 国外文献研究情况 2

2 研究假设 4

2。1 外界响应度 4

2。2 项目概况 5

3 研究设计 6

3。1 样本选择与数据来源 6

3。2 变量测量 7

3。3 研究模型 7

4 样本分析 8

4。1 描述性统计分析 8

4。2 变量相关性分析 9

4。2。1 外界响应度 9

4。2。2 项目概况 10

5 回归分析 11

5。1 测评模型评价 11

5。1。1 数据分析