The Interactive Effects of Status & Self-Construal on Bottom Line Mentality and Unethical Behaviors
Bibliographic record
Abstract
Drawing on moral licensing theory, this research investigates and tests a moderated mediation model unveiling the negative consequences of supervisors' workplace status. We suggest that a supervisor's bottom-line mentality is an underlying mechanism through which employees' perceptions of the supervisor's workplace status leads to unfavorable employee outcomes. We also hypothesize that the supervisor's independent self-construal will moderate the workplace status and the supervisor's bottom-line mentality relationship. Employing a three-wave time-lagged design (n=388), we collected data from employees belonging to the service sector organizations in Pakistan. Utilizing structural equation modeling (SEM) in MPlus, the results show that workplace status is significantly related to employees' intentions to sabotage, expediency, and unethical pro-supervisor behaviors directly and indirectly through the supervisor's bottom line mentality. The results of latent structural equations (LMS) in Mplus also corroborate the moderation and mod-med effects whereby workplace status creates employee's intentions to sabotage, expediency, and unethical pro-supervisor behaviors via the supervisor's bottom line mentality at high levels of independent self-construal. Based on our study's findings, we recommend theoretical and practical avenues for future researchers and managers to deal with the undesirable effects of a supervisor's workplace status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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) | 0.004 | 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".