The influence of job characteristics on IT and non-IT job professional’s turnover intentions
Bibliographic record
Abstract
Purpose – Information technology (IT) professionals and their intentions to leave an organization have been studied by researchers; however, these studies do not compare the turnover intentions of IT professionals with non-IT professionals from the same institution. The purpose of this paper is to examine how IT and non-IT job professionals relate to motivational and social job characteristics and their impact on job satisfaction, job performance and turnover intentions. Design/methodology/approach – Data were collected from IT-shared services employees through a survey and quantitative analyses were performed. Findings – Among the motivational job characteristics, IT professionals experienced greater task significance than the non-IT job holders. With social job characteristics, IT professionals had greater outside interaction than the non-IT professionals. However, the non-IT professionals had greater intentions to leave the IT organization than the IT professionals. Additionally, the study examined the differences of the job characteristics and job outcomes among transactional, transformational, and professional advisory work groups. The professionals and advisory group differed from the other groups in terms of feedback from the job, job satisfaction, and turnover intentions. Research limitations/implications – The findings are based on a small sample. However, it highlights some unique differences in how IT and non-IT job occupants perceive job characteristics and job outcomes. Originality/value – This study compares job characteristics and job outcomes of IT and non-IT job occupations in the same IT work environment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".