Enhancing Virtual Team Performance Through a Quantitative-Based Management Model (VTPM)

The rapid growth of globalization, digital transformation, and remote working practices has significantly increased the reliance on virtual teams within modern organizations. Despite their advantages, virtual teams continue to face challenges such as communication barriers, inadequate technology management, unclear role allocation, ineffective strategic planning, and excessive documentation practices, all of which negatively affect team performance. This study aims to identify the key factors affecting virtual team performance and to develop a structured Virtual Team Performance Management (VTPM) model to improve the effectiveness of virtual collaboration. A quantitative research approach was adopted, involving questionnaire-based data collection with 110 respondents who had worked in virtual teams (instrument reliability testing was conducted using Cronbach’s Alpha analysis). The findings indicate that role clarity, effective communication, effective strategic planning, task allocation, and appropriate management of technology are important in the determination of the performance of virtual teams. Specifically, 39.1% of respondents rated communication as “needs improvement,” 63.6% agreed that excessive documentation reduces efficiency, and 56.4% strongly agreed that strategic planning is effective. On this premise, the Virtual Team Performance Management Model (VTPM) has been constituted and validated by three academic and industry experts through expert evaluation. The model received positive evaluations from academic and industry experts regarding its practicality and relevance in virtual team environments. This study provides a comprehensive and empirically informed model that organizations can apply to improve the work of virtual teams.

@article{Kumar2026,
  title     = {Enhancing Virtual Team Performance Through a Quantitative-Based Management Model (VTPM)},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Sathya A/L Nantha Kumar and Zulkefli Mansor},
  doi       = {10.14569/IJACSA.2026.0170546},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170546}
}