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Enhancing post-training evaluation of annual performance agreement training: A fusion of fsQCA and artificial neural network approach

Author

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  • Md Zohurul Islam
  • Munshi Muhammad Abdul Kader Jilani
  • Mohammad Rezaul Karim

Abstract

This study aims to enhance the post-training evaluation of the annual performance agreement (APA) training organized by the Bangladesh Public Administration Training Centre (BPATC), the apex training institute for civil servants. Utilizing fuzzy-set qualitative comparative analysis (fsQCA) and artificial neural network (ANN) techniques within Kirkpatrick’s four-stage model framework, data were collected from a self-administered questionnaire survey of 71 in-service civil servants who participated in the APA training program. This study employs an asymmetric, non-linear model analyzed through a configurational approach and ANN to explore interrelationships among the four Kirkpatrick levels namely, reaction, learning, behavior, and results. Findings indicate that trainees were satisfied across all levels, identifying a non-linear relationship among these levels in post-training evaluation process. The research highlights that "learning skills" are most significant in the APA post-training evaluation, followed by behavior, results, and reaction. Theoretically, this research advances Kirkpatrick’s model and adds to the literature on public service post-training evaluation. Practically, it recommends prioritizing strategies that address cognitive barriers to enhance training effectiveness. This study’s innovative approach lies in its concurrent use of fsQCA and ANN methods to analyze the success or failure of APA-related trainees, offering alternative pathways to desired outcomes and contrasting traditional quantitative methods that provide a single solution. The findings have practical implications for public service training institutions and bureaucratic policymakers involved in capacity development, guiding the creation of more effective in-service training courses for public officials. The methodology and analysis can be applied in other contexts, allowing bureaucratic policymakers to replicate these findings in their learning institutes to identify unique configurations that lead to successful or unsuccessful training outcomes, adopt effective strategies, and avoid detrimental ones.

Suggested Citation

  • Md Zohurul Islam & Munshi Muhammad Abdul Kader Jilani & Mohammad Rezaul Karim, 2024. "Enhancing post-training evaluation of annual performance agreement training: A fusion of fsQCA and artificial neural network approach," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-29, June.
    • Handle: RePEc:plo:pone00:0305916
    DOI: 10.1371/journal.pone.0305916
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    References listed on IDEAS

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