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Record W1983920886 · doi:10.5539/ass.v11n10p126

Explaining Generation-Y Employees’ Turnover in Malaysian Context

2015· article· en· W1983920886 on OpenAlexvenueno aff
Abdelbaset Queiri, Wan Fauziah Wan Yusoff, Nizar Dwaikat

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsLoyaltyNormativeJob satisfactionPsychologyContext (archaeology)Organizational commitmentGeneration yWorkforcePaymentSocial psychologyJob dissatisfactionJob designMarketingBusinessJob performanceEconomicsPolitical science

Abstract

fetched live from OpenAlex

Among the various speculation published in media reports about the reasons why generation-Y workforce in Malaysia changes job frequently include dissatisfaction with pay and fringe benefits, seeking work-life balance, perceived status work-values fit, normative commitment, perceived availability of alternative job and job hopping, This study aims to empirically justify or refute some of the anecdotal information about generation-Y employees’ decision to leave an organisation in the context of Malaysia. Using structural equation modelling with a sample size of 150 respondents, this study revealed that satisfaction with payment and fringe, perceived availability of alternative job and job hopping are significant to generation-Y employees’ intention to quit. Additionally, normative commitment as part of employees’ loyalty is insignificant to generation-Y employees’ intention to quit. This study provides implication to human resource (HR) managers that generation-Y employees’ intention to quit may not be entirely due to HR strategies. Instead, cultural and economic factors play an important role in such decisions. However, there are other reasons that are widely held about generation-Y employees’ intention to quit, which may not be held true or empirically validated. Lastly, normative commitment does not influence their intention to stay or to leave an organisation, as their loyalty is to their personal lives.

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 score0.337
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.039
GPT teacher head0.275
Teacher spread0.237 · 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

Citations48
Published2015
Admission routes1
Has abstractyes

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