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Record W1996512562 · doi:10.1080/15332845.2015.1002070

Psychological Contracts, Perceived Organizational and Supervisor Support: Investigating the Impact on Intent to Leave Among Hospitality Employees in India

2015· article· en· W1996512562 on OpenAlexaff
Priyanko Guchait, Seonghee Cho, James A. Meurs

Bibliographic record

VenueJournal of Human Resources in Hospitality & Tourism · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPerceived organizational supportPsychological contractTransactional leadershipHospitality industryOrganizational commitmentPsychologyTurnoverHospitalityContext (archaeology)PerceptionSupervisorBusinessEmployee researchSocial psychologyMarketingManagementPolitical science

Abstract

fetched live from OpenAlex

This study investigates the effects of perceived organizational support on transactional and relational contracts and how these two types of psychological contracts influence employee intent to leave. Additionally, perceived supervisor support was examined as a predictor of perceived organizational support. Given the high employee turnover rates in the hospitality industry, lack of employee turnover studies in hospitality context, and more importantly, lack of employee turnover studies in countries other than the Western organizational contexts, the current study examines the above relationships with restaurant employees in India. Results showed that perceived supervisor support increased employee perceptions of organizational support, perceived organizational support increased relational psychological contracts but not transactional contracts, and only relational contracts had a significant effect on employees’ intent to leave. Implications of these results and issues for future research are discussed.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.297
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

Citations91
Published2015
Admission routes1
Has abstractyes

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