Enhanced Engagement Nurtured by Determination, Efficacy, and Exchange Dimensions (EENDEED): A Nine-Item Instrument for Measuring Traditional Workplace and Remote Employee Engagement
Bibliographic record
Abstract
With the ongoing pandemic and technological evolutions, remote work has become the new normal. However, existing employee engagement instruments do not take the remote aspect of work into account. This paper proposes a nine-item instrument for assessing employee engagement in general, and remote worker engagement specifically. The instrument was named EENDEED, for enhanced engagement nurtured by determination, efficacy, and exchange dimensions. It was grounded on three theories: (1) self-determination; (2) self-efficacy; and (3) social exchange. After generating the proposed scale grounded on the literature and theory, data were collected from 626 participants in the United States through an online survey. The dataset was split in two random samples of 405 and 195 cases respectively, after eliminating 26 cases with missing values. An exploratory factor analysis (EFA) on the larger sample helped discover its two-factor structure, namely PERFORMANCE and SELF-RELIANCE. A confirmatory factor analysis (CFA) was performed on the second sample, which confirmed the two-factor structure of EENDEED. A final study assessed the reliability and validity of EENDEED. Data were collected through an online survey of 162 employees in the U.S. A CFA was conducted, validating the two-factor structure of the instrument. Construct reliability of the factors and reliability of the scale were ascertained. Face validity, content validity, construct validity, and criterion validity were confirmed. This study fills the gap in current engagement measurements by including the remote worker dimension. Organizations will be better able to identify and address challenges faced by traditional workplace employees and remote workers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".