EVALUATING MOTIVATION OF CONSTRUCTION WORKERS: A COMPARISON OF FUZZY RULE-BASED MODEL WITH THE TRADITIONAL EXPECTANCY THEORY
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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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".