Competence Retention Analysis: A Technique for Predicting and Managing Retention Within Organizational Training Design and Delivery

Those responsible in organisations for the design and delivery of training require a practical method for the analysis and prediction of skills retention. To address this, a taxonomy of nine psychological domains was developed specifically to provide a finer-grained approach to analysis of the skills required in the performance of trained tasks. An extant predictive model relevant to five of the domains was applied to produce a set of domain retention curves for physical/lower-order cognitive skills. These curves informed the development of a novel Competence Retention Analysis Technique (CRA-T) that incorporates a simple ‘traffic light’ approach indicating workforce proficiency, following a period without practice. CRA-T simplifies the process of understanding skill retention for practitioners by providing an alternative to separate empirical studies. By identifying the psychological domains involved in task performance insights can be gained into the acquisition and retention of these components, allowing the determination of those most at risk of decay. CRA-T is suitable for the analysis of a range of physical/cognitive tasks across sectors, where systematic approaches to training analysis/design for skill retention optimisation are required. CRA-T considers complex cognitive skills, but as no predictive models currently exist, longitudinal research is required to define their retention levels.

Authors

Institutions

Publication Details

Journal
Journal of Cognitive Engineering and Decision Making
Published
2025-09-04
DOI
https://doi.org/10.1177/15553434251372444
Primary Topic
Human Resource Development and Performance Evaluation
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Competence Retention Analysis: A Technique for Predicting and Managing Retention Within Organizational Training Design and Delivery

Victoria Smy, Marie Cahillane, T.D. Anderson, Piers MacLean
Journal of Cognitive Engineering and Decision Making
Human Resource Development and Performance Evaluation
article

Competence Retention Analysis: A Technique for Predicting and Managing Retention Within Organizational Training Design and Delivery

Victoria Smy, Marie Cahillane, T.D. Anderson, Piers MacLean
article en

Abstract

Those responsible in organisations for the design and delivery of training require a practical method for the analysis and prediction of skills retention. To address this, a taxonomy of nine psychological domains was developed specifically to provide a finer-grained approach to analysis of the skills required in the performance of trained tasks. An extant predictive model relevant to five of the domains was applied to produce a set of domain retention curves for physical/lower-order cognitive skills. These curves informed the development of a novel Competence Retention Analysis Technique (CRA-T) that incorporates a simple ‘traffic light’ approach indicating workforce proficiency, following a period without practice. CRA-T simplifies the process of understanding skill retention for practitioners by providing an alternative to separate empirical studies. By identifying the psychological domains involved in task performance insights can be gained into the acquisition and retention of these components, allowing the determination of those most at risk of decay. CRA-T is suitable for the analysis of a range of physical/cognitive tasks across sectors, where systematic approaches to training analysis/design for skill retention optimisation are required. CRA-T considers complex cognitive skills, but as no predictive models currently exist, longitudinal research is required to define their retention levels.

Journal of Cognitive Engineering and Decision MakingVol. 20(1)
BAE Systems (Sweden) (SE), BAE Systems (United Kingdom) (GB), Cranfield University (GB), Defence Academy of the United Kingdom (GB)
Defence Science and Technology Laboratory, Defence Science and Technology Group
Openalex Percentile: Top 74%
Human Resource Development and Performance Evaluation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.