Bibliographic record
Abstract
Purpose This paper aims to discuss the appropriate uses of bonuses and award in recruiting and motivating project employees. Design/methodology/approach It is a conceptual discussion of human resources management (HRM) practices, supported by the author’s professional experience and observations in real-life project settings. Findings Bonuses and awards not only provide extrinsic financial rewards but also provide positive feedback to recipients. Extrinsic financial benefits (such as sign-on bonus, and retention bonus) may enhance the total compensation package and positively affect an employee’s job-related decision at least for the short term. He/she may accept a job offer or choose to stay on a project longer until the completion of a critical milestone because of the bonuses. However, positive recognition of employee performance (through the use of spot award, holiday award, or non-financial certificate of appreciation) is also a useful means to motivate employees. In addition, managers on international assignments need to pay attention to practices specific to host countries. Practical implications The practices discussed in this paper are based on real-life experience and observations. When they are used properly in conjunction with other HRM arrangements, bonuses and awards can be used to mitigate and delay turnover, and to motivate employees to increase their work performance. Originality/value This paper not only draws on theories and information from the HRM and project management literature but also draws from the author’s own management experience. Thus, the relevance and validity of the proposed concepts and practices have been proven in actual functional and project management settings.
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 | 0.027 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".