The exponential growth of digital content has created a need for advanced analytical approaches to process and extract insights from massive unstructured textual datasets. Large Language Models (LLMs) have emerged as powerful tools capable of addressing this challenge. However, researchers in the field of Information Systems (IS) are still unsure about how to effectively leverage LLMs for text-based IS research. To address this gap, we propose a Text Analytics for Information Systems Research (TAISR) framework that provides detailed recommendations grounded in IS and LLM literature on how to conduct meaningful text-based IS research.

The TAISR framework is designed to facilitate the operationalization of LLMs in IS research. It offers a systematic approach that researchers can follow to conduct text analysis using LLMs. By following this framework, researchers can ensure that their analysis is rigorous, reliable, and generates meaningful insights. The framework is flexible and can be applied to various research contexts within the IS domain.

Case Studies in Business Intelligence

To demonstrate the application of our TAISR framework, we conducted three case studies in the field of business intelligence. These case studies showcased how LLMs can be used to analyze large volumes of textual data in different business contexts. The results of these case studies highlighted the potential of LLMs in uncovering valuable insights from unstructured text data.

The first case study focused on sentiment analysis of customer reviews. By applying LLMs, we were able to identify patterns in customer feedback and gain a deeper understanding of customer sentiments towards products and services. This information can be valuable for businesses to improve their offerings and enhance customer satisfaction.

The second case study explored topic modeling in social media data. LLMs allowed us to automatically extract and categorize various topics discussed by users on social media platforms. This analysis can help businesses identify emerging trends, monitor brand reputation, and understand customer preferences.

The third case study delved into text classification for fraud detection. By training LLMs on historical data, we were able to develop a predictive model that can automatically identify potentially fraudulent transactions based on textual information. This approach can assist businesses in proactively preventing financial losses due to fraudulent activities.

Challenges and Limitations

While the TAISR framework offers a promising approach to leverage LLMs for text analytics in IS research, there are several challenges and limitations that need to be considered. One major challenge is the interpretability of LLMs. These models are often considered as “black boxes,” making it difficult to understand how they arrive at their predictions. This lack of interpretability can hinder trust and acceptance of LLM-based findings in IS research.

Another limitation is the requirement for large computational resources to train and deploy LLMs. These models have a high computational cost, which may pose constraints for researchers with limited access to computational infrastructure. Additionally, the ethical implications of using LLMs for text analysis should be carefully considered, including issues related to privacy, data bias, and fairness.

Future Directions

Despite these challenges and limitations, the TAISR framework opens up exciting opportunities for future IS research. Incorporating LLMs into text analytics can enable researchers to uncover deep insights from unstructured textual data that were previously inaccessible. By following the recommendations provided in the TAISR framework, researchers can ensure the rigor and validity of their analysis, leading to more informed decision-making in various IS domains.

In the future, researchers can further extend the TAISR framework by addressing the challenges of interpretability and computational resources. Developing techniques to enhance the interpretability of LLMs can increase trust in their findings and improve their acceptance in the IS research community. Additionally, exploring ways to reduce the computational cost of LLMs or developing alternative models that can achieve similar results with less computational requirements would make it more accessible to a broader range of researchers.

Overall, the TAISR framework paves the way for a new era of text-based IS research, leveraging the power of LLMs to unlock valuable insights from massive textual datasets. By addressing the challenges and limitations, researchers can fully harness the potential of LLMs and drive advancements in the field of Information Systems.

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