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Record W2960189667 · doi:10.5267/j.msl.2019.6.024

A study on mediating effect of institute reputation on relationship between institute social responsibility and student loyalty: Exploring concerns in Pakistani private HEIs

2019· article· en· W2960189667 on OpenAlexvenueno aff
Muhammad Umer Azeem, Che Azlan Taib, Halim Mad Lazim

Bibliographic record

VenueManagement Science Letters · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsReputationLoyaltySocial responsibilityPublic relationsPsychologyBusinessSociologyBusiness administrationMarketingPolitical scienceSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.328
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2019
Admission routes1
Has abstractyes

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