AI 中文总结
研究GenAI对知识工作生产力的影响，通过对跨国工业组织128名知识工作者的随机实地实验，发现GenAI能提高效率，对质量影响因任务而异，还会改变质量差异，为理解其生产力影响提供了更细致的视角。
AI 中文摘要
生成式人工智能（GenAI）的兴起引发了人们对其提高知识工作生产力潜力的高度期望。基于任务-技术匹配（TTF）理论，我们通过实证研究了GenAI对不同任务类型的生产力影响程度。我们对一家跨国工业组织的128名知识工作者进行了随机实地实验。参与者完成了三项代表性知识工作任务（知识获取、打包和创作），分别使用或不使用GenAI。结果表明，GenAI始终能提高任务效率。但其对质量的影响因任务而异：知识打包和创作任务的质量提高，而知识获取任务的质量下降。此外，GenAI倾向于减少知识打包和创作任务的质量差异，主要使表现较差的知识工作者受益。然而，它增加了知识获取任务的质量差异。这些发现有助于更细致、有区别地理解GenAI的生产力影响，对研究和实践都有启示。
英文摘要
The rise of generative artificial intelligence (GenAI) has fueled high expectations regarding its potential to enhance knowledge work productivity in terms of efficiency and quality. Building on task-technology fit (TTF) theory, we empirically examine the extent of GenAI's productivity effect for different task types. We conducted a randomized lab-in-the-field experiment with 128 knowledge workers from a multinational industrial organization. Participants completed three representative knowledge work tasks (knowledge acquisition, packaging, and creation), either with or without GenAI. Results show that GenAI consistently increases efficiency across tasks. However, its impact on quality is task-contingent: quality increases for knowledge packaging and creation but declines for knowledge acquisition. Furthermore, GenAI tends to reduce quality variance for knowledge packaging and creation, primarily benefiting lower-performing knowledge workers. However, it increases quality variance for knowledge acquisition. These findings contribute to a more granular, differentiated understanding of GenAI's productivity impact and hold implications for research and practice alike.
