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Record W3088418194 · doi:10.5539/ijef.v12n10p68

Understanding the Motivating Factors among Future Working Individuals

2020· article· en· W3088418194 on OpenAlexvenueno aff
Mohamad F. Issa

Bibliographic record

VenueInternational Journal of Economics and Finance · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryPromotion (chess)LoyaltyJob securityWork (physics)PsychologyApplied psychologySocial psychologyMarketingMedical educationPublic relationsBusinessEngineeringPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This study has investigated the ranked importance of certain motivational factors of senior level undergraduate and MBA students in the School of Business at The Lebanese International University(LIU). To gain information about the rankings of these motivational factors, a self-administered questionnaire addressing nine motivational factors: (1) Job security, (2) Good salary, (3) personal loyalty to employees from your superiors, (4) interesting work, (5) good working conditions, (6) Promotion and growth in the organization, (7) Full appreciation of work done, (8) freedom to plan and execute work independently,(9) A good match between your job requirements and your abilities and experience was designed and hand delivered to 126 participants in the School of Business. Nearly 70% of the students participated in the survey were registered in the MBA program. The findings of the study suggest that good salary and job security are the highest ranked motivational factors. The findings of the study also support the notion that what motivates workers is different given the environment in which they work.

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)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.222
Teacher spread0.166 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

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