An Illinois State University study paired Evidano with manual coding to analyze faculty views of shared governance

Shared governance is widely promoted as the collegial way for universities to make decisions, yet how it works in practice at public universities in Africa is rarely examined. This study asked how faculty perceive shared governance and what outcomes they associate with it, using social capital theory to interpret the networks, trust, and norms that make participation meaningful.

The setting was a large, research-intensive public university in southern Ghana with four colleges, more than 60,000 students, and nearly 1,500 full-time faculty. Its size and age meant governance structures were well established. The article appears in the International Journal of Research and Innovation in Social Science.

The study used an exploratory, single-case design. Through snowball sampling, 12 faculty members were recruited and interviewed individually over Zoom with open-ended questions across an eight-week period.

Recordings were transcribed and de-identified before coding. Because answers were specific and the topic sensitive, participant codes were used throughout to protect confidentiality.

The researcher used multiple coding approaches and added Evidano to complement and enhance the rigor of manual coding. Evidano generated content and thematic analyses across the full set of transcripts, an inductive, whole-dataset approach the author credits with consistent theme detection and a faster analysis.

The author is explicit about the limits: AI tools may miss contextual subtleties, read participant language too literally, or overlook culturally specific nuance. So the researcher iteratively reviewed both the raw transcripts and the AI-generated codes and themes, refining them for context, theoretical alignment, and the participants’ own voices.

Faculty perceptions clustered into three themes. Structured participation with limited influence: faculty sit on committees and attend meetings but feel they have little say over strategic and financial decisions, and junior faculty feel left out of key roles. Inclusivity and collaborative culture: governance is inclusive in principle, but input can feel like a formality. Opportunity to provide input: faculty want a voice on research, curriculum, evaluation, advising, and academic standards, and feel better represented at department level than university-wide.

Two outcome themes followed. Faculty linked their input to stronger academic quality, including curriculum revisions and more continuous and project-based assessment. Governance also built working relationships, trust, and interdisciplinary communication, while weak governance deepened departmental silos.

The recommendations combine cultural change, such as building trust and sincere engagement, with structural change, such as standardizing systems, decentralizing decisions, expanding faculty-led committees, and agreeing on a common definition of shared governance.

This case is a clear example of a hybrid workflow written into the methods section. Evidano provided a whole-dataset first pass; the researcher kept interpretive authority and documented exactly how AI output was checked and refined.

For higher-education and policy researchers working with sensitive institutional interviews, it shows how AI-assisted analysis can sit inside a theory-driven case study while the final themes stay grounded in participants’ lived experience.

The findings come from one anonymized university and 12 faculty recruited by snowball sampling, so they describe perceptions within that institution rather than a representative picture of Ghanaian or African higher education, although the author argues the tensions resonate across Global South contexts.

The study reports perceived outcomes rather than measured ones, and the article does not benchmark Evidano against a manual-only analysis.