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.

中文翻译：

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运营管理大文本数据研究：基于扎根理论的主题建模

目的

在推进运营管理 (OM) 方面，对方法论多元化的需求日益增长，特别是随着用于分析大量文本数据的机器学习 (ML) 技术的出现。为了弥补这一知识差距，本文引入了一种将机器学习技术与传统定性方法相结合的新方法，旨在从现有出版物中重建知识。

设计/方法论/途径

在这种基于实用主义的溯因方法中，人机交互分析大数据，作者采用主题建模 (TM)（一种 ML 技术）来实现建构主义扎根理论 (CGT)。部署四步编码流程（原始编码、专家编码、重点编码和理论构建）以力求程序和解释的严谨性。为了演示该方法，作者从开源专业项目管理 (PM) 网站收集了数据，并说明了他们的研究设计和数据分析导致的理论发展。

发现

结果表明，TM 显着提高了研究人员系统地研究和解释大型文本数据生成的代码的能力，从而有助于理论构建。

原创性/价值

本文提出了一种将基于机器学习的技术与人类解释学方法相结合的新方法，用于 OM 的实证研究。该方法利用扎根理论，从海量文本数据中重建潜在知识，揭示已发表数据中隐藏的管理现象，为学者开发商业和管理研究的潜在理论提供了新途径。