CiteNScope — Scholarly Index & Analytics
Discover / IJBESPUBLICATION RECORD

Evaluation of generative artificial intelligence (GENAI) as a transformative technology for effective and efficient governance, political knowledge, electoral, and democratic processes

Chiji Longinus Ezeji ORCID ↗ · Dominique Emmanuel Uwizeyimana ORCID ↗

International Journal of Business Ecosystem & Strategy · 2025 · Vol. 7 / Issue 3 · 241-254

Research reach

Publication metrics

External links open provider records or DOI searches. Provider counts are not combined. Mendeley readership measures saves, not citations.

Dimensions

Live provider badge. If unavailable, use the Dimensions link above.

CROSSREF CITATIONS1

Observed 21 Oct 2026, 00:14 UTC

OpenAlex · Citations1Cached provider observation

Observed 22 Oct 2026 01:49 UTC

Mendeley · Readers17Cached provider observation

Observed 22 Oct 2026 01:49 UTC

OpenAlex and Mendeley observations refresh on article visits, at most once per 24 hours. Unavailable data is not zero. Google Scholar opens a DOI search.

OpenAlex / Citation year

Annual citations to this article

View provider totals ↑

Citations received by this article, grouped by the year of the citing work. Crossref and OpenAlex lifetime totals remain separate in Publication metrics.

2017–2026 Annual history last updated:

Citations received

OpenAlex · year of the citing work

OpenAlex

Hover over a year or focus the chart and use arrow keys to inspect values.

2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.

View data & coverage

2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Years beyond a saved observation’s window remain unknown (—). Older citations are excluded, so this chart’s sum may differ from the OpenAlex lifetime total. The date above belongs to this annual history; an unsuccessful refresh keeps the previous observation.

Overview

Abstract

The incorporation of generative artificial intelligence in governance, political knowledge, electoral, and democratic processes is essential as the world transitions to a digital paradigm. Numerous nations have adopted Generative AI (GenAI), a disruptive technology that compels electoral bodies to advocate for the integration of such tools into governance, electoral, and democratic processes. Nevertheless, these technologies do not ensure effortless integration or efficient usage owing to intricate socio-cultural and human dynamics. Certain African jurisdictions are ill-prepared for the adoption of these technologies due to factors including underdevelopment, insufficient electrical supply, lack of technology literacy, reluctance to change, and the goals of governing parties. This study examines generative artificial intelligence as a disruptive technology for enhancing governance, political knowledge, electoral processes, and democracy. A mixed-method approach was employed, incorporating surveys and in-person interviews. The analysis of data, debates, and interpretation of findings were grounded in postdigital theory and theme analysis employing an abductive reasoning technique, in alignment with the tenets of critical realism. The study demonstrated that GENAI can influence political knowledge, election processes, and enhance efficiency in government and democracy. Moreover, GENAI, including ChatGPT, can either exacerbate or mitigate societal tendencies that contribute to human division, facilitate the dissemination of misinformation, perpetuate echo chambers, and undermine social and political trust, as well as polarise disparate groups or sets of viewpoints or beliefs. AI offers substantial opportunities but also poses many obstacles, including technical constraints, ethical dilemmas, and social ramifications. The swift progression of AI may disrupt labour markets by automating tasks conventionally executed by people, resulting in job displacement. Implementing AI necessitates significant upskilling and proficiency with digital tools; therefore, governments and organisations must adequately train their personnel to reconcile the disparity between AI's capabilities and users' comprehension. Additionally, there is a requisite for governmental oversight, regulation, and monitoring of AI adoption and utilisation across all facets of its implementation.

Keywords: {'name': 'Artificial, Democratic, Electoral, Evaluation, Generative, Governance, Intelligence, Knowledge, Political Processes, and Technology'}

Authors & affiliations

Chiji Longinus Ezeji
Affiliation not supplied

Dominique Emmanuel Uwizeyimana
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.1108/LHTN-01-2020-0001

Archer, M., Decoteau, C., Gorski, P., Little, D., Porpora, D., Rutzou, T., Smith, C., Steinmetz, G., & Vandenberghe, F. (2016). What is critical realism? Perspectives, 38(2), 4–9.

DOI: 10.48175/IJARSCT-17677

Berry, D., & Dieter, M. (Eds.). Postdigital aesthetics: Art, computation and design (pp. 136–150). Basingstoke: Palgrave Macmillan.

DOI: 10.1007/978-3-031-36336-8_116

DOI: 10.1007/s43681-020-00002-7

DOI: 10.1016/j.tics.2019.02.006

Boucher, P. (2019). How artificial intelligence works. EPRS–European Parliamentary Research Service.

DOI: 10.1111/1475-679X.12294

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York: WW Norton & Company.

DOI: 10.1098/rsta.2018.0080

Daugherty, P. R., & Wilson, H. J. (2018). Human machine: Reimagining work in the age of AI. Boston: Harvard Business Press.

DOI: 10.1007/978-3-030-30371-6

DOI: 10.1002/9781119815075.ch45

DOI: 10.1016/j.techfore.2016.08.019

Gere, C. (2008). Digital culture (Eds.). London: Reaktion Books.

DOI: 10.1109/ICDM.2018.00028

DOI: 10.1080/13645579.2018.1503400

DOI: 10.1093/acrefore/9780190228613.013.109

Ilkou, E., & Koutraki, M. (2020). Symbolic vs sub-symbolic AI methods: Friends or enemies? CIKM workshops.

DOI: 10.1016/j.bushor.2018.03.007

DOI: 10.1038/s41586-021-03819-2

DOI: 10.7551/mitpress/10909.001.0001

DOI: 10.1016/j.giq.2022.101774

McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research projects on artificial intelligence. AI Magazine, 27(4), 12. https://doi.org/10.1609/aimag.v27i4.1904

DOI: 10.2139/ssrn.4637354

DOI: 10.1007/978-1-137-08938-0_2

Negroponte, N. (1998). Beyond digital. New York: Knopf.

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. UK: Crown Publishing Group.

DOI: 10.5749/minnesota/9780816695515.001.0001

PwC (2020). Sizing the prize: What’s the real value of AI for your business and how can you capitalize? https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-proze-report

DOI: 10.1038/s42256-019-0048-x

Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (Eds.). UK: Pearson.

Sutton, R. S. (2020). John McCarthy’s definition of intelligence. Journal of Artificial General Intelligence, 11(2), 66–67.

DOI: 10.1038/s41467-019-14108-y

DOI: 10.3389/frai.2021.550030

Wilson, H. J., & Daugherty, P. R. (2024). Embracing gen AI at work. USA: Harvard Business Review.

DOI: 10.17705/1thci.00185