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Record W4416755250 · doi:10.64899/2151-0407.1946

An investigation of international students’ satisfaction with their university experience using an expectation confirmation theory lens

2025· article· en· W4416755250 on OpenAlexaff
Mehraz Sarker, Jonathan Worae, Jason D. Edgerton

Bibliographic record

VenueJournal of Comparative & International Higher Education · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOrdered logitOrdinal regressionLogistic regressionAssociation (psychology)Life satisfactionThrough-the-lens metering

Abstract

fetched live from OpenAlex

This study examines how various challenges and support systems influence international students’ satisfaction, as reflected in their intention to recommend their university. Using the Expectation Confirmation Theory (ECT), it examines whether students’ initial expectations moderate the relationships between challenges and satisfaction, and between institutional support and satisfaction. Analysis of survey data (N = 712) indicates that the majority of international students report satisfaction with their university experience. Ordinal logistic regression analyses reveal that perceived institutional support and higher initial expectations are associated with increased likelihood of recommending the university. Conversely, academic and discrimination challenges are associated with decreased likelihood of recommendation. Additionally, the negative association between language challenges and satisfaction is moderated by initial expectations, such that students with higher expectations are less likely to recommend their university if they encountered language difficulties. These findings highlight the critical role of institutional support in shaping students’ experiences. Policy implications include enhancing academic, language, and anti-discrimination support services to address barriers and improve the overall satisfaction and retention of international students.

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)0.000
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 score0.163
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0000.000
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 head0.056
GPT teacher head0.354
Teacher spread0.298 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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