Big Textual Data Research for Operations Management: Topic Modeling with Grounded Theory

Eyyüb C. Odacioglu, Lihong Zhang, Richard Allmendinger, Azar Shahgholian

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Abstract

Purpose – There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications.
Design/methodology/approach – In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding
and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development.
Findings – The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building.
Originality/value – This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies.
Original languageEnglish
JournalInternational Journal of Operations and Production Management
DOIs
Publication statusPublished - 26 Dec 2023

Keywords

  • Big Data
  • Grounded Theory
  • Machine Learning
  • Topic Modeling
  • Grounded theory
  • Topic modelling
  • Machine learning
  • Big data

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