Remote Employee Engagement and Organizational Leadership Culture, Measured By EENDEED, a Validated Instrument
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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