AI for Qualitative Research: A Hands-On Guide for Management Scholars

Author:   Diana Garcia Quevedo ,  Josue Kuri
Publisher:   Springer Nature Switzerland AG
ISBN:  

9783032088710


Pages:   174
Publication Date:   23 January 2026
Format:   Hardback
Availability:   Not yet available   Availability explained
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AI for Qualitative Research: A Hands-On Guide for Management Scholars


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Author:   Diana Garcia Quevedo ,  Josue Kuri
Publisher:   Springer Nature Switzerland AG
Imprint:   Palgrave Macmillan
ISBN:  

9783032088710


ISBN 10:   3032088712
Pages:   174
Publication Date:   23 January 2026
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

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.

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Author Information

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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