Existing research on antecedent of funding success mainly focuses on basic project properties such as funding goal, duration, and project category. In this study, we view the process by which project owners raise funds from backers as a persuasion process through project descriptions. Guided by the unimodel theory of persuasion, this study identifies three exemplary antecedents (length, readability, and tone) from the content of project descriptions and two antecedents (past experience and past expertise) from the trustworthy cue of project descriptions. We then investigate their impacts on funding success. Using data collected from Kickstarter, a popular crowdfunding platform, we find that these antecedents are significantly associated with funding success. Empirical results show that the proposed model that incorporated these antecedents can achieve an accuracy of 73 % (70 % in F-measure). The result represents an improvement of roughly 14 percentage points over the baseline model based on informed guessing and 4 percentage points improvement over the mainstream model based on basic project properties (or 44 % improvement of mainstream’s performance over informed guessing). The proposed model also has superior true positive and true negative rates. We also investigate the timeliness of project data and find that old project data is gradually becoming less relevant and losing predictive power to newly created projects. Overall, this study provides evidence that antecedents identified from project descriptions have incremental predictive power and can help project owners evaluate and improve the likelihood of funding success.

中文翻译：

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项目描述和众筹成功：一项探索性研究。

现有的关于资金成功的先例的研究主要集中在基本项目属性上，例如资金目标，期限和项目类别。在本研究中，我们通过项目描述将项目所有者从支持者筹集资金的过程视为说服过程。在说服力单模型理论的指导下，本研究从项目描述的内容中确定了三个示例性前提（长度，可读性和语气），并从项目描述的可信赖线索中确定了两个示例性前提（过去的经验和过去的专业知识）。然后，我们调查它们对资金成功的影响。使用从流行的众筹平台Kickstarter收集的数据，我们发现这些前提与筹款成功显着相关。实证结果表明，所提出的模型结合了这些前提条件，可以达到73％的精度（F测度为70％）。与基于基本项目属性的主流模型相比，基于明智的猜测的结果比基线模型提高了约14个百分点（与基于基本项目属性的主流模型相比，主流模型的提高了4个百分点）。所提出的模型还具有优越的真实阳性率和真实阴性率。我们还调查了项目数据的及时性，发现旧项目数据逐渐变得不那么相关，并且对新创建的项目失去了预测能力。全面的，