Applying the Discourse of Renewal Theory to Post-Crisis Recovery in Kenyan Higher Education: The Case of Daystar University Open access

Articles Published September 27, 2026
Volume 1, issue 1 (2026), pages 41-54 doi.org/10.66699/01rttt22
View this article
Published in: Current Research in Communication Current Research in Communication journal thumbnail View current issue
0 Views
0 Downloads
Crossref 0

Abstract

The COVID-19 pandemic forced universities to rethink how they communicated with and supported stakeholders beyond the acute phase of crisis. This study examines Daystar University’s post-crisis recovery following the first three waves of COVID-19 in Kenya through the Discourse of Renewal Theory (DRT). Using a convergent parallel mixed-methods design, data were obtained from 271 students and eight university senate members. Findings indicate that Daystar University inadvertently enacted all four DRT dimensions: organizational learning, ethical communication, prospective rather than retrospective vision, and effective organizational rhetoric. These practices supported a rapid transition to virtual learning, accreditation of an online campus by Kenya’s Commission for University Education, strengthened ICT infrastructure, and new academic and administrative innovations. Visionary leadership and preservation of institutional values were central to renewal, although communication consistency and reach remained uneven. The study extends DRT beyond its predominantly Western, private-sector applications into an African higher-education context and demonstrates how crisis can catalyse institutional learning, technological adaptation, stakeholder care, and organizational renewal after disruption, including in resource-constrained university settings across Africa.

Keywords
Discourse of Renewal Theory; crisis communication; post-crisis strategies; higher education; COVID-19; Kenya; organizational renewal
Recent Publications

Recent Publication

  1. Scanning published journal records…
Most Read Articles

Most Read

  1. Calculating article readership…
Recommended Articles

Recommended

  1. Matching titles and keywords…