Initial coin offering (ICO) is a new financing method that has been widely used in cryptocurrency projects. However, it has been reported that nearly 30% of cryptocurrency projects fail during ICO, indicating an important gap in research and an opportunity for more advanced research on ICO project assessment. This study reveals that previous studies primarily used project-related factors to predict ICO success while neglecting social factors such as team information and expert evaluation. Inspired by the knowledge-based theory (KBT) of the firm, we set out to examine the impact of heterogeneous team knowledge and expert evaluation on ICO success. One primary contribution of this study is the design of novel knowledge measures based on KBT. In addition, we propose a deep-learning model – an attention-based bidirectional recurrent neural network (A-BiRNN) – to automatically extract features from online comments. We validate the proposed model on a real-world dataset, and experiments show that the accuracy of the proposed prediction model outperforms those of existing models by more than 6%, highlighting the effectiveness of the proposed approach in predicting ICO success. This study's results provide useful ideas for both investors and ICO platforms to assess the quality of cryptocurrency projects, thus improving information symmetry in ICO markets. Also, this study demonstrates the value of applying KBT in assessing firm performance in ICO markets. The generalized value of the proposed approach should be tested in more business contexts, such as crowdfunding and peer-to-peer (P2P) lending.

首次代币发行（ICO）是一种新的融资方式，在加密货币项目中​​得到了广泛的应用。然而，据报道，近 30% 的加密货币项目在 ICO 期间失败，这表明在研究方面存在重要差距，并有机会对 ICO 项目评估进行更高级的研究。本研究表明，以往的研究主要使用与项目相关的因素来预测 ICO 的成功，而忽略了团队信息和专家评估等社会因素。受公司基于知识的理论 (KBT) 的启发，我们着手研究异构团队知识和专家评估对 ICO 成功的影响。本研究的一个主要贡献是设计了基于 KBT 的新知识度量。此外，我们提出了一种深度学习模型——基于注意力的双向循环神经网络 (A-BiRNN)——从在线评论中自动提取特征。我们在真实世界的数据集上验证了所提出的模型，实验表明，所提出的预测模型的准确性比现有模型高出 6% 以上，突出了所提出的方法在预测 ICO 成功方面的有效性。本研究的结果为投资者和 ICO 平台评估加密货币项目的质量提供了有用的想法，从而改善了 ICO 市场的信息对称性。此外，这项研究证明了应用 KBT 在评估 ICO 市场中的公司业绩方面的价值。所提议方法的广义价值应在更多商业环境中进行测试，例如众筹和点对点 (P2P) 借贷。