The digital transformation of Chinese companies offers a new frontier for organizational research. Widespread use of workplace platforms creates rich archives of unobtrusive data, providing continuous, real-time insights into organizational life that traditional surveys cannot capture. The central challenge for scholars is turning this data abundance into meaningful theory. This special issue highlights three studies that meet this challenge by using innovative methods to convert granular data into valuable knowledge. The papers employ digital-context experiments, real-time behavioral tracking, and machine-learning-assisted theory building to study phenomena from interpersonal dynamics to crisis productivity. Looking ahead, we explore the potential of unstructured multimodal data and new AI tools to make complex analysis more accessible. We conclude with a research agenda calling for methodological rigor, interdisciplinary collaboration, and a firm balance between technological innovation and theoretical depth.

中国企业的数字化转型为组织研究开辟了新前沿。平台式企业以及大量办公软件的应用创造了丰富的,不受人主观干涉的数据档案, 提供了传统的问卷调查无法捕捉的持续、实时的对于组织生活的记录。管理学者面临的核心挑战是如何从这些数据中找到值得研究的现象和研究问题,继而创建有意义的理论。本专刊重点介绍了三篇论文,展现她们是如何使用有创意的新方法,把颗粒度很细的数据转化为有价值的知识的。这三篇论文分别采用数字情境实验、实时行为追踪、和机器学习辅助构建理论等方法, 来研究从动态人际关系到危机生产力等现象。文章接着展望未来, 探讨了非结构化多模态数据的潜力,以及可以使复杂的数据分析变得更容易的新 AI工具。最后,文章呼吁方法论严谨、跨学科合作, 以及保持技术创新与理论深度之间的平衡。