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Record W2093697814 · doi:10.5539/ijef.v3n5p104

Determinants and Impact of Training: The Case of SMEs in Jordan

2011· article· en· W2093697814 on OpenAlex
Ihab K. Magableh, Radwan Kharabsheh, Khaled Abdelal Al-Zubi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Economics and Finance · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)RevenueBusinessAffect (linguistics)Process (computing)MarketingStage (stratigraphy)Operations managementIndustrial organizationEconomicsFinanceComputer sciencePsychology

Abstract

fetched live from OpenAlex

While the literturte on SMEs performace foucused on the enabling external environement, little research focused on internal factors, mainly training. Moreover, most studies ignored enterprises that don't train their employees. This study addresses this issue and contributes to knowledge in two ways. First, it conceptualises training as a three stage process: in stage one, the manager decides whether to train employees or not. In stage two, mangers who decide to train their employees determine their demand for training measured by training expenditures. In stage three, training impact can be assessed. Secondly, the study identifies the detreminants of training and examines its impact on SMEs performance. The study found that manager's charatcteristics, enterprise's characteristics and other factors affect both the manager's decision in stage one and training expenditures. More importantly, the study found that training has a positive impact on SMEs' performance as measured by profits, revenues and employment growth.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.066
GPT teacher head0.270
Teacher spread0.204 · 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