Chapter 1 Introduction -- Chapter 2 Overview of Artificial Intelligence, Machine Learning, Natural Language Processing, and Large Language Models -- Chapter 3 Natural Language Processing in Management Research -- Chapter 4 Ethical Considerations -- Chapter 5 Systems and Tools to Use NLP and LLMs: Getting Started -- Chapter 6 Using LLMs in Qualitative Analysis -- Chapter 7 Data Evaluation and Validation -- Chapter 8 Classification -- Chapter 9 Clustering and Topic Modeling -- Chapter 10 Information Retrieval (IR) and Retrieval-Augmented Generation (RAG) -- Chapter 11 Perspectives on LLMs in Management and Qualitative Research.

Open Access

This open access book will guide qualitative researchers in the social sciences with little to no coding experience in leveraging large language models (LLMs). Responding to a lack of instructional materials that recognize the need to equip qualitative researchers with the most advanced tools, this book offers a research-focused guide to harness the power of LLMs. The content is divided into two parts, beginning with an introduction to LLMs, natural language processing, and machine learning, as well as a historical and ethical perspective on the use of AI in research. The second part of the book serves as a hands-on guide, providing step-by-step instructions for the use of LLMs to analyze large datasets. It is written with practical cases, taken from management sciences, and emphasizes maintaining a close connection to the data throughout the process. It will be highly valuable to researchers in management studies, as well as in the wider social sciences. Diana Garcia Quevedo is a Visiting Professor at Clemson University, South Carolina, US, and a recipient of the Stand Up for Science fund at ESCP Business School, where she studies innovation in entrepreneurship and green venturing. Her research focuses on the impact of women entrepreneurs on the economy and society. She also works on new methods for qualitative research, particularly large language models, to analyze large amounts of online data inductively. Josue Kuri is a Principal Scientist at Amazon Web Services (AWS), where he leads cloud infrastructure planning automation efforts. At AWS, he pioneered the use of machine learning for network forecasting and the development of a digital twin platform to optimize large-scale digital infrastructure. Prior to AWS, he worked at Google and Facebook (now Meta) on the operational and strategic planning of network infrastructure, including investments in submarine cables. Additionally, he pursues an interest in expanding the use of AI in research and education.

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