Large Language Models for Conducting Advanced Text Analytics Information
  Systems Research
Benjamin M. Ampel Chi-Heng Yang James Hu Hsinchun Chen
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.