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Record W4281864645 · doi:10.5539/ijps.v14n2p64

Transfer Behaviour: Is Intention or Memory First? A Model of the Nearest Training Transfer Antecedents

2022· article· en· W4281864645 on OpenAlexvenueno aff
Saeed Khalifa Alshaali, Kamal Ab Hamid, Ali Ali Al-Ansi

Bibliographic record

VenueInternational Journal of Psychological Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyContext (archaeology)Social psychologyTest (biology)Transfer of learningStructural equation modelingEmpirical researchCognitive psychologyDevelopmental psychologyComputer scienceStatistics

Abstract

fetched live from OpenAlex

In real life, there is a relationship between a person’s intention and memory. In addition, both are crucial antecedents of behaviour. This study puts this concept under empirical analysis. Additionally, high loss of training memory (50% after 24 hours) is a critical problem. Therefore, a weak understanding of intention and memory unity (interchangeable relationship) would exaggerate the transfer behaviour problem. It should be noted that billions of dollars are lost because of the low training implications (transfer). In that context, the researchers raise the question of ‘what comes first: intention or memory?’ and conduct a holistic statistical analysis. They apply a quantitative method (self-report survey) to test five hypotheses of this study’s variables: (i) intention to transfer (behaviour), (ii) training retention (memory), (iii) training transfer (behaviour). The study participants are 425 (population = 52,000) governmental (ministries) employees. The researchers derive and adapt the study questionnaire from reliable resources. They apply statistical analysis using PLS-SEM – SmartPLS software 3.0. All five hypotheses are accepted. This shows a highly interchangeable role of intention and memory against behaviour. However, the results analysis reveals that intention comes first, with a prominent presence of memory. Practically, it is suitable to understand intention and memory in combination, especially in the design phase. This would enhance the professionalism of behaviour control and effectiveness. For the theoretical tendency of the current study, the managerial implication is challenging. However, it opens the door for other interested researchers to specify a clear and smart solution for this case. In addition, this study has several values. It reconciles two theories in different fields: transfer model (training) with theory of planned behaviour (psychology). Mainly, it empirically describes the relationship between the most important behaviour antecedents (intention and memory). It helps to solve two practical problems: low training implication and high loss of training memory.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0160.002

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 head0.156
GPT teacher head0.355
Teacher spread0.199 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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