Remote Employee Engagement and Organizational Leadership Culture, Measured By EENDEED, a Validated Instrument
Bibliographic record
Abstract
With the current post-pandemic unpredictable work environment characterized by remote and hybrid work, the leadership culture of an organization is important in fostering a desirable working environment. Such a culture of leadership is modeled by leaders of the organization and instilled in new leaders, as leadership helps motivate, inspire, and engage employees. The purpose of this study was to analyze if the four types of leadership culture (mentoring, risk-taking, result-oriented, and coordinating) as determined by the Organizational Culture Assessment Instrument (OCAI) have a direct influence on the level of engagement of employees. To analyze the influence of organizational leadership culture on remote employee engagement, this study implemented a quantitative non-experimental correlational design. Remote employee engagement was measured using a validated instrument called EENDEED (Enhanced Engagement Nurtured by Determination, Efficacy, and Exchange Dimensions). Data were collected through an online survey from 325 participants, all remote workers in organizations within the United States and a multiple regression analysis was conducted. The findings of this study confirmed that there was a statistically significant relationship between an organization’s leadership culture and its employees’ level of engagement. In other words, the organization leadership culture as defined by OCAI contributes to employee engagement. Mentoring was shown to be the highest contributor in employee engagement. In other words, a mentoring-based leadership culture produced more engaged employees. While risk-taking and coordinating produced a statistically significant positive contribution to employee engagement, a result-oriented culture was not significant in contributing to employee engagement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".