Towards African Metaphors and Proverbs Expert Systems for International Development Projects

Wangai Njoroge Mambo

Journal of Information Systems and Technology Management2026https://doi.org/10.4301/s1807-1775202623001article
ABDC C
Weight
0.50

What the paper says

Some participation and collaboration problems between local beneficiaries and foreign experts in international development projects (IDPs) are due to two groups using different types of knowledge: indigenous and global respectively. Study proposes creating locally low cost indigenous knowledge expert systems and startups incrementally to solve participation and collaboration problems that can be evolved and scaled up in a sustainable way. Method nexus for evolutionary prototyping product and startup creation is explored. Future IDPs will be supported by artificial intelligence enabled tools requiring some basic local AI understanding. Complementary indigenous knowledge expert systems can create synergy with IDPs artificial intelligence systems but will require local ES development capabilities to be built and evolved in advance.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.4301/s1807-1775202623001

Or copy a formatted citation

@article{wangai2026,
  title        = {{Towards African Metaphors and Proverbs Expert Systems for International Development Projects}},
  author       = {Wangai Njoroge Mambo},
  journal      = {Journal of Information Systems and Technology Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4301/s1807-1775202623001},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Towards African Metaphors and Proverbs Expert Systems for International Development Projects

Flags are reviewed by the Arbiter methodology team within 5 business days.


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.