Exploring supervisor and organizational support as predictors of commitment, satisfaction and conversion intention of business interns: testing a mediated path
Bibliographic record
Abstract
Purpose This article presents results of an empirical analysis examining student perceptions of supervisor support and organizational support as predictors of intern organizational commitment, internship job satisfaction and conversion intention. Design/methodology/approach The study involved surveying 160 students serving as interns who are enrolled in business programs at a Midwest US university. This research examined the perceived level of support that students experienced during their internship and the relationships that different types of support had on key outcomes. Findings Results from partial least squares path modeling indicate significant direct effects that perceived supervisor and organizational support have on the aforementioned outcomes. Moreover, perceived organizational support fully mediated the relationship between perceived supervisor support and the outcomes. This research demonstrates that organizations may leave a larger impression on students than do supervisors during business internships (i.e. temporary employment). Originality/value Results add to contemporary literature by concluding that the positive supporting influence that supervisors have on internship outcomes is augmented by organizational support, which can aid employers and faculty in their design of internship programs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".