The exponential growth of digital content has generated massive textual datasets, necessitating advanced analytical approaches. Large Language Models (LLMs) have emerged as tools capable of processing and extracting insights from massive unstructured textual datasets. However, how to leverage LLMs for text-based Information Systems (IS) research is currently unclear. To assist IS research in understanding how to operationalize LLMs, we propose a Text Analytics for Information Systems Research (TAISR) framework. Our proposed framework provides detailed recommendations grounded in IS and LLM literature on how to conduct meaningful text-based IS research. We conducted three case studies in business intelligence using our TAISR framework to demonstrate its application across several IS research contexts. We also outline potential challenges and limitations in adopting LLMs for IS. By offering a systematic approach and evidence of its utility, our TAISR framework contributes to future IS research streams looking to incorporate powerful LLMs for text analytics.

翻译：数字内容的指数级增长催生了海量文本数据集，亟需先进的分析方法。大型语言模型已成为能够处理并从大规模非结构化文本数据集中提取洞见的工具。然而，如何利用大型语言模型开展基于文本的信息系统研究当前尚不明确。为帮助信息系统研究理解如何将大型语言模型付诸实践，我们提出了一种面向信息系统研究的文本分析框架。该框架基于信息系统与大型语言模型文献，就如何开展有意义的基于文本的信息系统研究提供了详细建议。我们运用该框架开展了三项商业智能案例研究，以展示其在多个信息系统研究场景中的应用。同时，我们概述了在信息系统中应用大型语言模型可能面临的挑战与局限。通过提供系统化方法及其效用证据，我们的框架将为未来希望整合强大大型语言模型进行文本分析的信息系统研究方向作出贡献。