How Control Configurations and Enactments Shape Legitimacy Perceptions and Compliance Intentions in IS Development Projects
Bibliographic record
Abstract
Managers choose and implement controls to promote employee behavior that contributes to IS development (ISD) project success. Still, ISD project failure rates remain high, suggesting that project controls employed are often not effective. In this regard, existing IS project control research commonly considers how managers configure controls (in terms of control modes and degree) and enact them (control style), whereas the role of employees’ perceptions of the legitimacy of enacted controls remains largely neglected. To address this shortcoming, we conducted a vignette study with 232 participants to quantitatively test a set of hypotheses on how different control modes, degrees, and styles impact employees’ legitimacy perceptions, and ultimately their compliance intentions. Our analysis reveals a significant impact of all three control dimensions on legitimacy perceptions. Moreover, we identify a positive link between legitimacy perceptions and compliance intentions. To increase control effectiveness, our results thus suggest that managers should choose and implement ISD controls in a way that employees perceive as being just and providing them with autonomy.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".