The roles of mood and conscientiousness in reporting of self‐committed errors on IT projects
Bibliographic record
Abstract
Abstract Over the past two decades, several studies have investigated the factors that lead to and away from individuals' reporting of truthful status information on IT projects. These studies have typically considered the reporting decisions of an individual who is aware of negative status information that is attributed to others' errors. These previous studies have seldom examined the situation in which the individual is considering whether to report information about his or her own self‐committed error on the project. In this study, we consider this largely unexamined phenomenon. In this context, we focus on the influences that different affective states and a personality trait (conscientiousness) can have on error reporting decisions. Specifically, we investigate how different moods (i.e. positive vs. negative) and conscientiousness can influence error reporting decisions in the context of an IT project. Based on the results from a controlled laboratory experiment, we find that individuals in a negative mood are more willing to report their errors compared to individuals in a positive mood. Conscientiousness also positively influences individuals' willingness to report errors, and it also has an indirect effect through cost–benefit differential (i.e. one's perceptions of benefits relative to costs). Additionally, mood is found to moderate the relationship between conscientiousness and willingness to report. We discuss the implication of our findings and directions for future research and for practice. © 2016 John Wiley & Sons Ltd
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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 | 0.003 | 0.031 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".