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Record W2127631587 · doi:10.1111/ijtd.12029

Is transfer of training related to firm performance?

2014· article· en· W2127631587 on OpenAlexaff
Alan M. Saks, Lisa A. Burke‐Smalley

Bibliographic record

VenueInternational Journal of Training and Development · 2014
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransfer of trainingTraining (meteorology)Training and developmentTechnology transferJob performanceKnowledge managementBusinessPsychologyComputer scienceManagementSocial psychologyJob satisfaction

Abstract

fetched live from OpenAlex

The purpose of this study was to bridge the gap between micro‐training research on the transfer of training and macro‐training research on training and firm performance by testing the relationship between transfer of training and firm performance. Training and development professionals completed a survey about the training methods used in their organization (on‐the‐job, classroom, computer‐based), the transfer of training, and their firm's performance. The results indicated that transfer of training was positively related to firm performance and mediated the relationship between training methods and firm performance. The results also suggest that among the three training methods, on‐the‐job training was the most strongly related to transfer of training and firm performance. These results highlight the importance of transfer of training for firm performance and suggest that meaningful organizational outcomes can be obtained by implementing training programs and strategies to facilitate and improve the transfer of training.

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)0.006
metaresearch head score (Gemma)0.043
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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.083
GPT teacher head0.348
Teacher spread0.265 · 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

Citations79
Published2014
Admission routes1
Has abstractyes

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