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

Shahgholian, A, Odacioglu, EC, Zhang, L and Allmendinger, R (2023) Big Textual Data Research for Operations Management: Topic Modeling with Grounded Theory. International Journal of Operations and Production Management. ISSN 0144-3577

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Purpose There is a growing need for methodological plurality in advancing Operations Management (OM) research, 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, we employ Topic Modeling (TM), an ML technique, to enable Constructivist Grounded Theory (CGT). A four-step coding process (Raw Coding, Expert Coding, Focused Coding, and Theoretical Coding) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, we collected data from an open-source professional Project Management (PM) website and illustrated our research design and data analysis leading to theory development. Findings Our 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 research. Using Grounded Theory (GT), this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden in published data, offering a new way for academics to develop potential theories for business and management studies.

Item Type: Article
Additional Information: This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com
Uncontrolled Keywords: Big Data, Grounded Theory, Machine Learning, Topic Modeling
Subjects: H Social Sciences > HF Commerce > HF5001 Business
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Liverpool Business School
Publisher: Emerald
SWORD Depositor: A Symplectic
Date Deposited: 27 Nov 2023 11:36
Last Modified: 09 Jan 2024 12:15
DOI or ID number: 10.1108/IJOPM-03-2023-0239
URI: https://researchonline.ljmu.ac.uk/id/eprint/21953
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