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Project description and crowdfunding success: an exploratory study

机译:项目描述和众筹成功:探索性研究

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摘要

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.
机译: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的前提(长度,可读性和基调),并从项目描述的可信线索中确定了两个示例性的前提(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资成功显着相关。实证结果表明,结合了这些前提条件的拟议模型可以实现73%的准确性(F测度为70%)。与基于基本项目属性的基础模型相比,基于明智的猜测的基线模型的结果比基于基线模型的结果提高了约14个百分点(或与基于基础知识的属性相比,主流模型的绩效提高了44%)。所提出的模型还具有优越的真实正和真实负率。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。总体而言,这项研究提供的证据表明,从项目描述中识别出的前因具有渐进的预测能力,可以帮助项目所有者评估并提高融资成功的可能性。

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