A case study of the applicability and relevance of a business risk management framework in a higher education quality management

Abstract

Recent local and international concerns by stakeholders for quality in for-profit (such as business firms) and not-for profit institutions (such as universities) are demanding greater oversight of the key risks they face. One regulatory reform, particularly King III Report in South Africa, now significantly requires the expansion of public and private policies relating to effective institutional governance. This reform is geared towards assuming specific risk management responsibilities with respect to responding and ensuring that institutional quality is preserved and enhanced. One response to this growing reform is the emergence of a new paradigm, which is known as institutional risk management (IRM), designed to increase quality, create awareness, identify, mitigate, monitor and report the portfolio of risks facing an institution. In view of the above reform, the study explores the applicability and relevance of an institutional risk management framework for enhancing higher education institutions‟ (HEIs) quality management framework (QMF). This IRM framework forms the lens through which this study explores its relevance and applicability in an HEI context. It was conducted in a historically black South African University. The study employed a mixed research design using both questionnaires (64 respondents) and interviews to elicit the state of the institution‟s risk analysts‟ experiences. During the one and half months of data collection, the process sought to address research questions three and four. The data analyses involved both descriptive and inferencial statistical techniques including other mathematical methods. Meanwhile, literature addressed questions one two and five. One of the findings was that IRM could be used to improve an organisation‟s performance and enhance QMF in an HEI. This framework would include; risk awareness; systematic risk identification and prioritisation; risk mitigation; risk monitoring and reporting; risk planning and operation; and systematic risk measure in an institution. In addition, the research found that the „case-institution‟ in the study had not developed and documented institutional risk management policy and procedures for the use of all staff –thus lacking a close link between institutional risk policies/procedures and its strategic objectives. In addition, the University does well in all the five IRM processes, but, on risk identification, it is relatively weak compared to the rest, thus in both upside and downside risk. Furthermore, although, there are several IRM models that form all IRM framework used in for-profit institutions (banks) as literature revealed, such were not found in the University under study. Another finding also noted that risks in a dynamic institution such as a University could be measured and predicted to enhance quality through risk management models such as the Nicholas risk management model, Bayesian analysis and other statistical measures. In view of the above findings, the study recommends firstly that IRM framework be applied to a non-profit institution such as an HEI. Secondly, although, there were several models that form IRM framework which is used in the for-profit institutions as literature revealed, such (models that form IRM framework) were not found in the University; hence, it is recommended IRM framework be adopted in a South African University context. Thirdly, although Nicholas and Bayesian risk analysis models, which form part of IRM framework, were used to measure and predict risks, these did not incorporate one factor - the rate of change of risk factor with respect to time. Consequently, it is recommended that periodic surveys be conducted on the IRM variables to see how change occurs over time, while suggesting that management set targets for both upside and downside indicators of risk management. Hence, for future research, it is recommended that a particular technique is used to measure and predict risks with the consideration of rate of change of risks (i.e. time and dynamic nature).