A conceptual framework for private higher educational institutions to respond to disruptions in South Africa.

Abstract

During the unprecedented COVID-19 pandemic, global societies experienced widespread disruption and uncertainty, significantly affecting higher education. This "black swan" event tested the resilience of higher education institutions, necessitating an involuntary shift in instructional practices. This study explores the impact of pandemic disruptions on student and staff experiences in local PHEIs, with a focus on their operational flexibility and capacity to navigate turbulent circumstances. The research employs a mixed-methods approach, involving a sample size of 381 students and 316 staff members from various demographic backgrounds. We administered the survey using reliable measures to ensure high response rates. The findings indicate that lower-level students, such as undergraduates, faced greater difficulties in adapting to online learning compared to higher-level students, such as master's and doctoral candidates. The challenges included limited access to technology and resources, as well as difficulties maintaining engagement and motivation. In contrast, staff members initially reported high levels of support adequacy, but these ratings decreased with increased years of experience, possibly due to burnout and evolving expectations. Leaders within PHEIs highlighted the need for enhanced training and preparedness to manage disruptions effectively. They identified specific challenges, such as political and economic factors, system changes, and the absence of specialised tools for disruption management. Insights from leaders included the importance of scenario planning, robust communication strategies, and fostering a culture of adaptability and resilience. We developed a comprehensive framework for disruption management as a guiding beacon for navigating disruptive encounters. We created a machine-learning-based predictive model using a binary classification tree to predict disruption risks within this framework. The model was trained on variables such as potential impact, probability of occurrence, warning index, and relevance to the education sector, achieving high accuracy in classifying disruption risks. Despite limitations like region-specific focus and challenges of data collection during a pandemic, this study provides valuable insights into proactive strategies, support significance, effective leadership, and predictive models for disruption management in higher education. This research contributes to the understanding of disruption management in PHEIs and provides practical tools for enhancing institutional resilience.