Facility Maintenance Management and Its Effects on Employee Performance: A Positivist Approach
Bibliographic record
Abstract
This study aims to investigate the role facility maintenance management plays on employee performance at a institution of higher learning in the Eastern Cape of South Africa.. This study employed a quantitative research approach, and the data were gathered from 150 employees who were chosen through a random sampling method. The data were analyzed using the Statistical Package for the Social Scientist (SPSS) Version 24.0. The analysis was of frequencies and standard deviations. The study findings revealed that the current facilities at the institution need an upgrade to a level that is conducive, suitable, and adequate for employees to perform their duties satisfactorily to reach the objectives of the institution. An efficient method for preparing, scheduling, and coordinating facility maintenance tasks needs to be applied to ensure effective maintenance service is performed effectively. This empirical study provided fruitful implications for academicians by making a significant contribution to the facility maintenance literature by systematically exploring the effect of facility maintenance management on the employee performance at a higher learning institution within the Eastern Cape province of South Africa. This study, consequently, stands to greatly add new knowledge to the existing literature related to maintenance performance measurement in Africa, a research setting that has been neglected by academic researchers of late.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".