Maximizing Human Capital: Talent Decision-Making Using Information Technology

In the current fiercely competitive landscape, an organization’s ability to succeed depends on its ability to leverage information technology to support personnel decisions that optimise the use of its people resources. This research examines five different strategies for optimising human capital through the use of information technology within the framework of multi-criteria decision-making (MCDM). Alternatively, you can leverage data-driven performance monitoring systems, artificial intelligence-driven talent acquisition platforms, virtual reality (VR) onboarding and training simulations, predictive analytics tools for succession planning and talent forecasting, and machine learning algorithms for skill assessment and development. Eight criteria—efficacy, efficiency, accuracy, accessibility, scalability, ethical concerns, influence on the organization’s success, and trend adaptability—were developed to assess these options. We may determine the weights associated with each choice and rate them by applying the entropy weighted WASPAS (weighted aggregated sum product assessment) approach on top of the T-spherical fuzzy set (T-SFS) theory. This study adds to our understanding of how businesses could utilize information technology wisely to enhance human resource management in addition to providing guidance on how to assess various approaches based on how well they perform across a variety of metrics. Human resource specialists and organizational leaders may make use of the useful suggestions made by the study to improve personnel decision-making procedures and to make the most of their workforce’s potential in the digital age.

@article{Zhang2024,
  title     = {Maximizing Human Capital: Talent Decision-Making Using Information Technology},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Rui Zhang and Xiaobai Li and Gang Liu},
  doi       = {10.14569/IJACSA.2024.0150665},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150665}
}