David Wilkerson, Anne Kuh and Tracy Wilkerson
Provides a “user friendly” benchmarking system which can be used before undergoing organizational change in order to create reference points which will serve as baselines and…
Abstract
Provides a “user friendly” benchmarking system which can be used before undergoing organizational change in order to create reference points which will serve as baselines and goals. First, defines what benchmarking is and looks at the characteristics of a benchmarking system. Reveals the advantages and disadvantages of using Surveys to collect cultural data and examines how to tailor survey questions. Addresses the issue of what action to take once the benchmarking results are known.
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Abstract
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Eyyub Can Odacioglu, Lihong Zhang, Richard Allmendinger and Azar Shahgholian
There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing…
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.