Antecedents and Consequences of Innovative Work Behavior in Indonesian Higher Education During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has brought various social impacts on the higher education service industry (Higher Education Institutions/HEIs).Changes in the online learning process provide the reality of job stress and the demands of innovative behavior to demonstrate performance at HEIs in Indonesia also require innovative employees as a new approach to work.This study examined how the direct effect of workplace happiness on innovative work behavior and innovative work behavior to work performance, by placing job stress as a mediator on workplace happiness to innovative work behavior.The purposive sampling was employed with the criteria of permanent lecturers and having a National Lecturer Identification Number.This study employed a survey method by which a total 354 lectures of private HEIs participated (Muhammadiyah and Aisiyah College).The research model was tested using PLS_SEM Modelling and descriptive-interpretive coding.The results uncovered that workplace happiness had a positive effect on innovative work behavior, workplace happiness had a negative effect on job stress, job stress had a negative effect on innovative work behavior, innovative work behavior had a positive effect on work performance.In addtion, the mediation result was supported, where job stress mediate the effect of workplace happiness on innovative work behavior.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".