This paper examines video games, a form of digital innovation, and seeks to predict a successful game based on the composition of game development team members. Team composition is measured with observable features generated from a graph network based on development team information derived from individual team member work on previous games. Features include network features, such as team member closeness, success percentile, and failure percentile, and non-network features, such as the number of games published prior by the studio. We propose a novel framework using these features to predict the chance of success for new games with an accuracy higher than 92%. Further, we investigate important features for prediction and provide model interpretability for practical implementations. We then build a decision support tool that allows video game producers, and associated stakeholders such as investors, to understand how the predictive model decides, predicts, and performs its recommendations. The findings have implications for those seeking to proactively impact digital product performance through graph network-generated features of team composition, where features are directly observable, as opposed to features that are more challenging to observe, such as personalities.

中文翻译：

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使用源自图网络的团队组成特征来预测数字产品性能


本文研究了视频游戏这一数字创新形式，并试图根据游戏开发团队成员的组成来预测一款成功的游戏。团队组成是通过图形网络生成的可观察特征来衡量的，该图形网络基于从单个团队成员在之前游戏中的工作中获得的开发团队信息。特征包括网络特征，例如团队成员的亲密程度、成功百分位数和失败百分位数，以及非网络特征，例如工作室之前发布的游戏数量。我们提出了一个新颖的框架，利用这些特征来预测新游戏的成功机会，准确率高于 92%。此外，我们研究了预测的重要特征，并为实际实现提供了模型可解释性。然后，我们构建了一个决策支持工具，允许视频游戏制作者和相关利益相关者（例如投资者）了解预测模型如何决定、预测和执行其建议。这些发现对于那些寻求通过图网络生成的团队组成特征来主动影响数字产品性能的人来说具有重要意义，其中的特征是可以直接观察到的，而不是那些更难以观察的特征（例如个性）。