ENHANCING JOB ROTATION ONBOARDING IN THE MALAYSIAN PUBLIC SECTOR: A NotebookLM Large Language Model Case Study

Muhammad Sukri Bin Ramli

No gjv9r_v1, OSF Preprints from Center for Open Science

Abstract: Mandatory job rotations are a cornerstone of the Malaysian civil service, designed to enhance governance, reduce integrity risks, and foster organizational agility. However, these rotations present significant onboarding challenges, requiring employees to rapidly adapt to diverse roles and complex responsibilities, particularly in 'hot seat' and high-risk-to-corruption positions. This study focuses on the Jabatan Kastam Diraja Malaysia (JKDM), where the need for efficient onboarding is heightened by the structured tenure of job rotations. The necessity to quickly acclimate to new roles within a defined period, especially in sensitive positions, underscores the urgency of effective onboarding strategies. To address the inherent onboarding complexities, particularly in navigating intricate customs regulations, this research proposes leveraging Large Language Models (LLMs), with a specific focus on NotebookLM. NotebookLM's ability to ingest and summarize extensive regulatory documents, coupled with features like interactive training modules and AI-powered Q&A, offers a dynamic, personalized learning experience. This approach aims to surpass traditional training limitations, streamlining onboarding, enhancing knowledge transfer, and boosting productivity within JKDM. The study outlines an implementation plan, including a pilot program and department-wide rollout, with expected outcomes of improved onboarding efficiency, enhanced knowledge sharing, and increased operational effectiveness, ultimately contributing to a more agile and integrity-driven public service. Figure

Date: 2025-03-03
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:gjv9r_v1

DOI: 10.31219/osf.io/gjv9r_v1

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