Project leader's interactive use of controls, team learning behaviour and IT project performance: the moderating role of process accountability
Bibliographic record
Abstract
Purpose This study aims to understand how project leaders' interactive use of the project management control systems (MCS) impact IT project performance, by examining the mechanisms through which this relationship is enacted. Design/methodology/approach Data were collected from a cross-sectional survey of 109 IT project managers working in Canadian and USA-based organizations. A moderated mediation model was analysed by hierarchical component reflective-formative measurement modelling using PLS-SEM. Findings Results suggest that the leader's interactive use of project MCS is associated with IT project performance, and this relationship is partially mediated by team learning behaviour. In addition, the relationship between the interactive use of project MCS and team learning behaviour is moderated by the organization's emphasis on process accountability, with the effect being stronger under the conditions of higher emphasis on process accountability. Originality/value This study contributes to the literature on the use of controls in the IT project-based business environments by explaining how the project leader's style of use of controls influences project team learning behaviour that in turn impacts project performance. Additionally, this study extends the project governance and accountability literature by identifying and empirically examining how the perceptions of project leader's institutionalized organizational accountability arrangements moderate the impact of the interactive use of control systems on team learning behaviour. A methodological contribution of the study is the scale development to measure leader's perceptions about the organization's emphasis on process accountability.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".