Knowledge-Oriented Leadership and Innovation in Digital Governance

Fatmah Mohmmad H. Alatawi

International Journal of Electronic Government Research2025https://doi.org/10.4018/ijegr.384514article
ABDC C
Weight
0.50

What the paper says

In a developing economy, government policy such as Vision 2030 in Saudi Arabia, can maximise digital transformation and women's empowerment through systemic reforms and e-government to promote inclusive economic growth and social advancement. Nonetheless, women-led businesses often lack institutional support, digital literacy, and knowledge systems for innovation. This study therefore examines the impact of e-government and knowledge management on women's entrepreneurial performance and creativity in Saudi Arabia, employing total interpretive structural modelling to define the relationships between nine critical success factors. Under a resource-based approach, dynamic capabilities theory, and institutional theory, a hierarchical model is created to demonstrate how policy and government-led digital platforms can enhance knowledge-driven innovation. The concept is tested by analysing the relevant literature, deriving strategic recommendations for policymakers, digital ecosystem builders, and startup incubators. Hence, digitally empowered governance is highlighted to promote entrepreneurial resilience, innovation ambidexterity, and the long-term viability of women-led businesses in Saudi Arabia.

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https://doi.org/https://doi.org/10.4018/ijegr.384514

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@article{fatmah2025,
  title        = {{Knowledge-Oriented Leadership and Innovation in Digital Governance}},
  author       = {Fatmah Mohmmad H. Alatawi},
  journal      = {International Journal of Electronic Government Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijegr.384514},
}

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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.