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This study explores the organisational dynamics in a change process across work units in a Swedish municipality. The purpose of this study is to understand how and why co-creation unfolds during efforts to bring different units into one united work unit.

A qualitative longitudinal study was designed using data triangulation for eight months, comprising written reflection texts, meeting protocols and interviews. This study is based on a back-and-forth inductive and abductive grounded theory analysis.

The main results of this study indicate that there was friction in the co-creation process between units, between the members of the change group and supervisors, as well as friction within the change group. Further, the results indicate that communications, relations, supervisor support and governing strategies clashed with work routines and methods, work cultures, roles and responsibilities and that the units had differing views of the needs of the intended target group. This thereby challenged the propensity for change which, in turn, may have limited developmental learning at a workplace and organisational level.

Working across units to find common and new paths and work methods for labour market inclusion proved to be challenging because of contextual circumstances. Crossing and merging organisational boundaries through co-creation processes was demanding because of new expectations from the organisation, as it shifted towards trust-based governance in conjunction with working during a pandemic when social interactions were restricted to digital communication channels.

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.

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.

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.

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.