Big textual data research for operations management: topic modelling with grounded theory

Abstract

PurposeThere 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/approachIn 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.FindingsThe 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/valueThis 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.

Guest editorial: The 6th world conference on production and operations management

“…Technological integration and innovation: Results of this special issue suggest that future research could explore the integration of emerging digital technologies into P&OM practices, with a focus on harnessing their transformative potential to enhance operational efficiency, transparency and agility. For instance, as demonstrated by Odacioglu et al .’s (2024) paper by applying topic-modeling analysis, practitioners can make informed decisions based on insights derived from processing textual data. This suggest that practitioners can actively participate in the research process, providing expert opinions to enhance the effectiveness of decision-making processes for their operations and projects (e.g.…”

“…Technological advancements and data analytics: In the era of big data, the ability to extract actionable insights from vast textual datasets has emerged as a critical challenge for P&OM researchers. Odacioglu et al (2024) paper titled “Big textual data research for operations management: topic modelling with grounded theory” introduces a methodological innovation that integrates machine learning techniques with grounded theory to glean actionable insights from voluminous textual data. Drawing on the principles of constructivist grounded theory, the authors offer an example of application of their methodological innovation, by employing topic modeling to analyze textual data sourced from a professional project management website.…”

AI for Management and Organization Research: Examples and Reflections from Project Studies

“…amounts of unstructured data (Odacioglu et al, 2023); for example, thousands of pages of newspaper articles, social media posts, or government reports, opening opportunities to address different research questions and analyze new datasets, that were until now simply too laborious to work with conventional data analysis methods. Second, as Grimes et al (2023) suggest, we can use GenAI for inductive exploration of large datasets through simple prompts, asking the algorithm to identify patterns that we might not have seen.…”