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Leveraging competency mapping for optimal performance: a deep dive into the ITeS sector and strategic recommendations

Divya Pant et al.

International Journal of Information Systems and Change Management2025https://doi.org/10.1504/ijiscm.2025.148632article
AJG 1ABDC C
Weight
0.50

What the paper says

The purpose of the study is to delve into the relationship between individual performance and competency mapping in the information technology enabled services (ITeS) industry. A survey of 500 professionals was conducted through a variety of sampling methodologies. R Studio and SPSS 22 were two examples of statistical tools used to analyse the data. Important competencies for competency mapping were determined, such as technical proficiency, capacity for problem-solving, aptitude for communication, flexibility, and leadership traits. To increase both organisational and individual performance, the researchers found areas in the competency mapping procedures that may use improvement. The study offered a useful method for analysing crucial competencies and how they affect organisational goals, which most likely entailed creating models or frameworks for evaluating and enhancing competence mapping procedures. The results demonstrated the beneficial effects of competency mapping on output, worker satisfaction, income levels, and employee engagement.

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https://doi.org/https://doi.org/10.1504/ijiscm.2025.148632

Or copy a formatted citation

@article{divya2025,
  title        = {{Leveraging competency mapping for optimal performance: a deep dive into the ITeS sector and strategic recommendations}},
  author       = {Divya Pant et al.},
  journal      = {International Journal of Information Systems and Change Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijiscm.2025.148632},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.