Navigating role identity tensions — IT project managers’ identity work in agile information systems development
Bibliographic record
Abstract
While the management of traditional, plan-based information systems development (ISD) projects lies with the information technology project manager (ITPM), this role is challenged in agile ISD team settings, characterised by self-managed and autonomous teams. Driven by the potential for individual, project, and organisational benefits through a deeper comprehension of these challenges, our study draws upon role identity theories to uncover identity tensions that this role creates and ways in which ITPMs engage in identity work to resolve these tensions. We conducted an in-depth, qualitative study involving interviews with ITPMs operating within agile ISD teams embedded in traditionally-managed organisations. A central contribution of our paper lies in the development of a theoretical model that unpacks how ITPMs navigate identity work tensions by engaging in specific identity work activities. By revealing the interplay between ITPMs’ role identity tensions, identity work activities, and contextual factors, our study moves beyond purely descriptive accounts and offers a nuanced understanding of the emotional and behavioural dimensions of ITPMs in their liminal position. Additionally, our work underscores the pivotal role of ITPMs as boundary spanners, bridging the gap between traditional and agile governance structures to drive digital transformation. For practitioners, we provide agile ISD team design recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".