Performance Based Talent Management System Design Soft System Methodology Approach

Authors

  • Ghina Riskiyanti Department of Management, Faculty of Economics and Management IPB University
  • Musa Hubeis Department of Management, Faculty of Economics and Management IPB University
  • Gendut Suprayitno Department of Management, Faculty of Economics and Management IPB University

DOI:

https://doi.org/10.46799/ajesh.v2i7.105

Keywords:

Talent Management System, Organizational Performance

Abstract

In the dynamic global business competition of today's globalization era, to sustain a competitive advantage, Talent Management has become one of the crucial efforts for organizations to face the future. Talent Management systems have also become a critical issue for modern organizations in their endeavors to optimize employee performance and achieve competitive excellence. The success of an organization is determined by highly talented human resources, and human skills are an integral part of a company's business strategy to develop the organization. This research aims to develop an innovative Talent Management System using the Soft System Methodology (SSM) approach to increase effectiveness and efficiency in human resource management. The research methodology involves structured steps of SSM, including understanding the problem context, identifying the stakeholders involved, formulating problem understanding, and designing the system. Data for this study were collected from various sources, including interviews with top-level managers, employees, and human resources departments, as well as document analysis. The research findings indicate that the implementation of a Performance-Based Talent Management System can provide significant benefits to the organization. With the SSM approach, the changes that occur are more easily accepted and adopted by all involved parties, as it involves collaboration and active participation from stakeholders. The results show that impact evaluation of actions taken is necessary for continuous learning, and implementation with adjustments based on feedback and experience is required.

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Published

2023-07-25