Predicting corporate innovation using machine learning and social media data
利用员工在社交媒体上的评价数据，结合可解释的机器学习方法，预测企业创新绩效，发现灵活工时和员工股权是创新的重要预测因素。
This study explores the potential of employee reviews on social media to predict corporate innovation performance. We investigate these relationships using a novel social media dataset and an explainable machine learning approach to assess the predictive value and importance of various employee treatment policies in driving corporate innovation. In addition to traditional patent-based innovation measures, we employ a text-based innovation metric derived from 10-K filings. Our findings reveal that several employee ratings on social media provide valuable insights for predicting corporate innovation. Specifically, we highlight the importance of flexible working hours and employee stock or equity options in predicting patent counts, patent citations, and text-based innovation. Other significant predictors of patent-based innovation include employees' career growth prospects and pride in the company. Furthermore, we find that the ability to work remotely is a strong predictor of text-based innovation but is less significant for patent counts and citations. Our findings reveal notable differences in the key determinants of various types of innovation, contributing to a deeper understanding of how employee experiences associate corporate innovation outcomes. • Employee treatment is crucial for driving corporate innovation. • Social media reviews reveal key HR practices impacting innovation. • Study uses machine learning to uncover key predictors of corporate innovation. • Novel text-based metrics expand beyond traditional patent measures. • Study identifies flexibility and employee share schemes as top innovation drivers.