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A configurational model of reward-based crowdfunding project characteristics and operational approaches to delivery performance

机译:基于奖励的众多项目特征的配置模型和交付性能的操作方法

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摘要

Reward-based crowdfunding projects promise funders various kinds of rewards for contributing to their funding goals. After successful funding, the funders expect the project owners to deliver the promised rewards within the specified delivery time. Contrary to crowdfunding research based on conventional symmetric thinking, this research investigates the crowdfunded projects using asymmetric analytics to identify different configurational paths to delivery performance. Using data from the Kickstarter crowdfunding platform, Qualitative Comparative Analysis (QCA) was conducted for the antecedent factors, including project venture status, promised lead time, and sourcing as well as production approaches. The findings indicate that the configurational models, with high consistency and coverage, are sufficient antecedent conditions for on-time and late delivery. Further, the results confirm that a single antecedent condition cannot solely produce an outcome but should be incorporated with other conditions to produce an intended outcome. The findings offer insights into the setting of realistic promised lead times that can be combined with other antecedent conditions to achieve on-time delivery. This research supports the complexity theory and extends its application to crowdfunding research by emphasizing that the approaches for on-time reward delivery performance are unique and not mirror opposites of those applied in late reward delivery performance. Further implications are drawn for crowdfunding stakeholders.
机译:以奖励为基础的众筹项目承诺为其提供资金目标提供各种奖励。成功经过资金后,资助人预计项目所有者在指定的交货时间内将承诺的奖励提供。与基于传统对称思维的众筹研究相反,本研究调查了使用非对称分析来确定不同的配置路径以识别交付性能的不同配置路径。使用来自Kickstarter Crowdfunding平台的数据,对前所未有的因素进行了定性比较分析(QCA),包括项目风险状况,承诺的汇流时间和采购以及生产方法。调查结果表明,具有高一致性和覆盖的配置模型是准时和延迟交付的足够的先行条件。此外,结果证实,单一的前一种病症不能单独产生结果,但应纳入其他条件以产生预期结果。调查结果提供了对现实承诺的交付时间的洞察,这些交货时间可以与其他先行条件相结合以实现准时交货。本研究支持复杂性理论,并通过强调准时奖励交付性能的方法是独一无二的,而不是镜像在奖励奖励性能中的那些方法的镜像反对方法,将其应用于众所周知的研究。为众筹的利益相关者绘制了进一步的影响。

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