Impact of Rewards (Intrinsic and extrinsic) on Employee Performance With Special Reference to Courier Companies of City Faisalabad, Pakistan.
Bibliographic record
Abstract
The study has conducted to measure the effect of rewards (Intrinsic & Extrinsic) on employee job performance. The experiences and personal opinions of employees working in different couriers companies were analyzed through questionnaires in the city of Faisalabad. The key objective of the study was to find that the rewards affect the performance of an employee. Statistical package (SPSS) has been used to conduct the analysis of the study. Employees like Field Supervisors, Couriers from selected courier companies (TCS, OCS, Leopard and Express Courier Services) were randomly chosen. The focus of the study was to distribute and highlight an adequate level of incentives to the employees and create balance in distribution of rewards so that every employee contributes his efforts for the growth of the company. This study also focuses on two major rewards, intrinsic and extrinsic rewards. Rewards plays a motivational role in the personality of an employee and urge them to produce loyalty and show good performance By the results study shows that there is a strong relationship between both type of rewards and on employee performance. Concluding, the study has verified and explores further research opportunities that can enhance the understanding of rewards and employees job performance. 
 
 Keywords: Intrinsic Rewards, Extrinsic Rewards, Job Performance, TCS, OCS, Leopard
 & Express Couriers.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".