Development of Motivational Strategies to Enhance Productivity in Small and Medium Scale Construction Firms in Ghana
Bibliographic record
Abstract
The study sought to recommends and quantifies the effect of the effective motivational strategies on the productivity of small and medium scale construction firms in Ghana. Also to develop a framework for motivating employee of small and medium construction firms. Data collection was through well structured questionnaire administered to 164 respondents selected through simple random and systematic sampling techniques. The methods of analysis used were descriptive statistics and production function analysis using the Ordinary Least Square (OLS) criterion to estimate the parameters of the production function. The result showed that, majority of the respondents, 21% said job security was the most important driving force in their life so long as job is concern. Besides, it was realised that, an increase in the level of some motivational strategies: Job security, Opportunity for further studies, Employers? good relationship with employees, Involving employees in decision making and Employers? recognition of employees will improve productivity by 0.0255, 0.0342, 0.073, 0.067 and 0.036 respectively. A model for motivating employees was also developed base on the results. Workers of all organisations need to be motivated to facilitate their input towards the attainment of organisational goals. Construction workers like all other worker groups need this sort of motivation to enable them give off their best. It?s incumbent upon management to be able to identify superior performances and reward them accordingly. This would lead to greater effort towards goal attainment.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".