标题： 什么最重要？ 基于人工智能的初创企业成功预测因素的定量元分析 显示英文标题

标题： What Matters Most? A Quantitative Meta-Analysis of AI-Based Predictors for Startup Success

摘要： 背景：使用机器学习预测初创企业成功是一个迅速发展的领域，但关于关键预测因素的研究结果往往零散且具有情境特定性。这使得难以辨别稳健的模式，并突显了系统综合证据的必要性。 方法：本研究进行了一项定量元分析，以综合基于人工智能的初创企业评估中的预测因素重要性文献。我们进行了系统综述，以确定最终样本中的13篇实证研究，这些研究报告了可排名的特征重要性。从这些论文中，我们提取并分类了58个独特的预测因素，使用加权重要性评分（WIS）来综合其重要性，该评分平衡了特征的平均排名与其出现频率。我们还进行了调节分析，以研究预测因素的重要性如何随情境变化（例如，成功定义）。 结果：我们的综合分析显示，最一致强大的预测因素是一组基础属性：公司特征（例如，年龄、位置）、投资者结构（例如，投资者质量）、数字和社会牵引力（例如，在线动量）以及融资历史。调节分析进一步显示，这一层次高度依赖于情境。例如，预测近期融资里程碑会提升交易即时情境的重要性，而预测长期退出则更重视基本的公司和投资者特征。 结论：最能预测初创企业成功的因素并非普遍适用，而是取决于初创企业的目标、阶段以及用于评估的数据。我们的研究结果指出文献中可能存在一种“便利偏差”，其中预测因素的重要性可能与数据的可访问性有关。我们最后强调了需要标准化报告实践，以促进该领域更稳健、累积的知识构建。 显示英文摘要

摘要： Background: Predicting startup success with machine learning is a rapidly growing field, yet findings on key predictors are often fragmented and context-specific. This makes it difficult to discern robust patterns and highlights a need for a systematic synthesis of the evidence. Methods: This study conducts a quantitative meta-analysis to synthesize the literature on predictor importance in AI-based startup evaluation. We performed a systematic review to identify a final sample of 13 empirical studies that report rankable feature importance. From these papers, we extracted and categorized 58 unique predictors, synthesizing their importance using a Weighted Importance Score (WIS) that balances a feature's average rank with its frequency of appearance. We also conducted a moderator analysis to investigate how predictor importance changes with context (e.g., success definition). Results: Our aggregate analysis reveals that the most consistently powerful predictors are a quartet of foundational attributes: Firm Characteristics (e.g., age, location), Investor Structure (e.g., investor quality), Digital and Social Traction (e.g., online momentum), and Funding History. The moderator analysis further reveals that this hierarchy is highly context-dependent. For instance, predicting near-term funding milestones elevates the importance of the deal's immediate context, while predicting long-term exits prioritizes fundamental firm and investor characteristics. Conclusion: The factors that best predict startup success are not universal but are contingent on the startup's goals, stage, and the data used for evaluation. Our findings point to a potential "convenience bias" in the literature, where predictor importance may be tied to data accessibility. We conclude by underscoring the need for standardized reporting practices to enable more robust, cumulative knowledge building in the field.