EVALUATING MOTIVATION OF CONSTRUCTION WORKERS: A COMPARISON OF FUZZY RULE-BASED MODEL WITH THE TRADITIONAL EXPECTANCY THEORY
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Bibliographic record
Abstract
Measuring workers’ performance and the level of motivation is of paramount importance as the productivity of workers at a workplace primarily depends upon their level of motivation. However, measuring the level of workers’ motivation at workplace is not always straightforward because the workers’ motivation is a function of various personal and external factors. This paper proposes a fuzzy rule-based model for evaluating the motivation level of construction workers using their working patterns. The motivation level evaluated using the fuzzy rule-based model was compared with motivational levels determined by the traditional Vroom’s expectancy theory or Expectancy-Instrumentality-Valence (EIV) method. EIV method used questionnaire surveys and interviews to determine workers’ motivation. The results of fuzzy rule-based models aligned closely with the EIV model, especially for the middle range of motivation levels. Compared to traditional EIV model, the fuzzy rule-based system is simple to implement and found to be very pragmatic in construction field settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it