A study on mediating effect of institute reputation on relationship between institute social responsibility and student loyalty: Exploring concerns in Pakistani private HEIs
Bibliographic record
Abstract
The aim of this study is to determine the mediating role of the institute reputation between institute social responsibility and student loyalty.The present study uses a multi-stage sampling technique, which first creates four clusters; including Karachi, Quetta, Peshawar, and Lahore, and picks one cluster; namely Lahore, randomly for the purpose of the study.Second, proportionate sampling technique is used to calculate the proportionate values of every individual educational institute.Finally, systematic sampling is used to compute the sampling interval k th number of individual universities.Data are collected from the provincial capital city of Punjab province (Lahore) and a total of 511 questionnaires are distributed among postgraduate students under business education discipline of private HEIs, 206 questionnaires are excluded from the sample due to some missing and misleading values, and final analysis is run by using SmartPLS 3.2.8 on 345 questionnaires.Findings elucidate that institutes social responsibility did not have any direct relationship with student loyalty.However, institute social responsibility has indirect relationship with student loyalty thru the mediating variable of institute reputation.Institute reputation also has a significant and positive influence on student loyalty.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".