The impact of social media use on the autonomy and organisational citizenship behaviour of faculty members in Kenyan private universities
Bibliographic record
Abstract
As the impact of social media grows, understanding the mechanisms through which social media affects employee behaviour increases. Employing social capital theory, we investigate the mechanisms through which social media usage affects organisational citizenship behaviour (OCB) of faculty in Kenyan private universities. OCB is an important aspect of universities’ performance, given the high level of autonomy in universities. We develop a theoretical model that posits direct links to OCB of three social media usages (social, cognitive, and hedonic) which affect OCB. We also posit indirect links (using autonomy as a mediator) that affect faculty’s intrinsic motivation for OCB. Using descriptive cross-sectional survey, a mediated model was tested on 388 faculty. Results revealed: 1) social media usage significantly impacts OCB, with social and cognitive having a positive relationship, and hedonic having a negative relationship with OCB; 2) social media usage tends to increase autonomy. Findings of this study contribute towards job performance improvement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".