Application of Motivation in Nigeria Construction Industry: Factor Analysis Approach
Bibliographic record
Abstract
Motivation application by industry players is expedient for effective workforce towards meeting organisation goal. This study identified motivation variables in accordance with Herzberg theory. This was used to survey factors that influence supervisors’ productivity as well as determining its application by contractors in Nigeria construction firms. Quantitative research design approach was employed with same questionnaire to supervisors and contractors. 174 questionnaires were administered to supervisors and 105 was filled and returned which constitute 60% success rate. Moreover, 16 questionnaires were administered to contractors and 12 was filled and returned which constitute 75% success rate. Analysis was done by descriptive statistics and Exploratory Factor Analysis (EFA). The outcome reveals that supervisors are mostly motivated by job security with mean score of 4.11 and standard deviation of .95 and least motivated by overtime with mean value of 2.82 and standard deviation of 1.14. Moreover, the most potent factor influencing their productivity is financial reward. However, the analysis of contractors’ application of motivation reveals that they operate non financial reward. The paper recommends relating motivation application to workers needs as a way of enhancing productivity in the sector. Furthermore, enactment of employment protection legislations for job security should be enhanced to guide against arbitrary dismissal or retrenchment in the sector.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".