Integrating Tacit Knowledge into Employee Profiles to Improve Talent Review Accuracy: A Case Study of Oil and Gas Industry
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The success of an organization in managing human resources is significantly influenced by the accuracy of its talent review processes, including promotions, rotations, and employee placements. However, these processes often rely solely on explicit data, such as job history and administrative evaluations, which do not fully capture contextual competencies. Tacit knowledge—encompassing field experience, intuition, and problem-solving abilities—plays a crucial role in work effectiveness yet remains largely undocumented in a systematic manner. This study aims to design a system that integrates tacit knowledge into employee profiles using the STAR (Situation, Task, Action, Result) narrative framework to support decision-making in talent reviews. The research adopts a Design Science Research Methodology (DSRM) approach, comprising problem identification, system design, prototype development, and evaluation. A case study was conducted within an operational unit of the oil and gas industry, with data collected through interviews and observations. The findings indicate that integrating STAR narratives into employee profiles enriches the available decision-making data and enhances the precision of talent placement and development. This study provides practical contributions for organizations seeking to build more contextual and knowledge-based human resource management systems, offering a structured approach to capturing tacit knowledge and improving strategic HR decisions.
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