IT Professionals: To engage or not to engage? That is the question!
Bibliographic record
Abstract
There is a paucity of literature on IT professionals that specifically emphasizes what influences employee engagement. Employee engagement is described as a positive, work-related state of mind exhibited by high levels of energy, dedication, persistence, and happy absorption (Schaufeli et.al. 2002b) manifested in business growth and profitability (McEwen 1998). Half of all U.S. workers are not fully engaged in their work and some are totally disengaged (Balogun and Johnson, 2004). When compared to the general employee population, lack of engagement is more of an issue for IT employees, (Treadwell & Alexander, 2011) who found that only 26% of IT employees reported full engagement and 22 % admitted to outright disengagement. We examined the behavioral competencies of IT professionals and their relationships and interactions with the organizational environment and employee engagement. We collected survey data from 795 IT professionals (individual contributors and managers) in the United States and Canada. Our results indicate that specific behavioral competencies and are affected differently by distinguishing factors within the organization environment. We reveal how unique attributes of an organization environment affect how one engages in the organization. We discovered that particular controls such as age and gender do have not influence, while other controls such as role type and years of experience do influence 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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".