From the Balanced Measure of Psychological Needs (BMPN) to Employee Engagement: Indicators that Matter
Bibliographic record
Abstract
The purpose of this study was to investigate if confidence, interest, authenticity, and loneliness as independent variables, could help predict employee engagement, the dependent variable. In this setting, the independent variables were indicators of the Balanced Measure of Psychological Needs (BMPN) and the dependent variable was obtained using the Utrecht Work Engagement Scale (UWES-9). After surveying 151 participants in the United States, 17 responses were removed from the final dataset during data and assumptions validation. A multiple regression model was created using the remaining 134 valid cases. Our findings confirmed the existence of a statistically significant relationship between confidence, interest, and authenticity in predicting employee engagement. Only, we could not establish a statistically significant relationship between loneliness and engagement, in contrast to some prior research studies. These findings have significant implications for practitioners and researchers as documented in this article. For example, the findings can be useful for employees in determining their future career path, as they need to first look at what interests them. Indeed, interest was identified as the greatest determinant of engagement as compared to the other three predictors. These findings also suggest that managers can keep their employees engaged by assigning them functions or tasks that are aligned with their interests.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".