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Record W1999860363 · doi:10.5539/ass.v10n7p57

Service Quality, Satisfaction and Student Loyalty in Malaysian Private Education

2014· article· en· W1999860363 on OpenAlexvenueno aff
Shaheen Mansori, Anthony Vaz, Zarina Mizam Mohd Ismail

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLoyaltyService qualityBusinessMarketingService (business)Quality (philosophy)Services marketing

Abstract

fetched live from OpenAlex

The changing of the global demographic trends clearly suggests a growing demand for a quality higher education. In fact, several studies have been carried out in the past few years to explore the factors that have effects on the customer satisfaction and its consequences in various industries. In the same manner, this study also explores the five factors of service quality (SERVQUAL) and their relationship with the level of student satisfaction and their loyalty as well as the intervening role of satisfaction in the relationships between SERVQUAL factors and loyalty among undergraduate students.The study used self-administered questionnaires to test the proposed model and data collected from 460 questionnaires were analysed. The questionnaires were distributed at various private universities and colleges in Malaysia based on convenience sampling. The results of this study are in accordance with prior studies in this field as SERVQUAL factors do influence the level of customers’ satisfaction in the service industry. This study shows that the level of students overall satisfaction is mostly affected by tangibility. This finding indicates that the physical facility on the campus plays a major role in satisfying the students. The results also show that tangibility has the highest influence (directly and indirectly) on the students’ intention to continue to a higher level of studies and/or spreading good word of mouth about the institution to their friends and the society. However, to generalize the results of this study, consideration should be made to the limitation of the number of the private institutions where the samples are collected. It is suggested that for further study, more samples should be taken from a larger number of institutions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.399
Teacher spread0.373 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2014
Admission routes1
Has abstractyes

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