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Record W3106754889 · doi:10.6007/ijarbss/v10-i11/8152

The Impact of Flexible Working Arrangements on Millennials: A Conceptual Analysis

2020· article· en· W3106754889 on OpenAlexaboutno aff
Nur Zafira Akma Rozlan, Geetha Subramaniam

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersUniversiti Teknologi MARA
KeywordsBusinessPsychologySociologyAestheticsProcess managementArt

Abstract

fetched live from OpenAlex

Recent statistics show that millennials make up about 40% of the employees struggling with mental health issues at the workplace in Malaysia. Concurrently, inflexible work schedules have been listed as one of the work place hazards in the National Standard of Canada for Psychological Health and Safety in the workplace. Hence, as millennials will constitute a major part of the global workforce in years to come, it is crucial to study the impact of flexible working arrangements on this category of employees. As flexible working arrangements (FWAs) provide a degree of control to the employees to decide their work arrangements in terms of time, place and method of working, it inadvertently allows an increase in autonomy of the employees. This conceptual paper first deliberates how flexible working arrangements may improve well-being and productivity of millennials since this generation values autonomy more than the previous generations. By using Self-Determination Theory at the workplace, this paper describes how flexible working arrangements (FWAs) support the need for autonomy among millennials, leading to improved well-being and productivity. Arguably, this paper proposes autonomy to be the mediator that links flexible working arrangements (FWAs), well-being and productivity. Finally, a theoretical framework is proposed for future research.

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)0.004
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 score0.052
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.494
GPT teacher head0.606
Teacher spread0.112 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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