Higher education today faces greater challenges than ever before. Some examples of these challenges include an increasing number of competitors, both public and private, operating on a global scale (Shanahan and McParlane, 2005; Brewer and Walker, 2011; Tilak and Kumar, 2022); more students than ever before choosing to pursue higher education in online and transnational programmes (Wessels and Sandler, 2015; Kmiotek-Meier and Powell, 2020); an increasing number of complex regulations to comply with (Cameron and Klopper, 2015); external evaluations that put pressure on institutions to achieve certain teaching and research outcomes (Brewer and Walker, 2011; Veretennik and Okulova, 2023); a society that is increasingly demanding in terms of the knowledge transfer it expects from its higher education institutions or universities (Brewer and Walker, 2011; Tagliaventi and Carli, 2021), as well as the uncertainties arising from new technologies (Kimmons et al., 2023; Setyadi et al., 2025). In addition to this demanding context, some countries and regions face additional challenges such as war or lack of resources (Brewer and Walker, 2011; Jesry et al., 2022). In conclusion, the risks to which universities are currently exposed and the need to manage them are receiving increasing attention.

As a result, whether required by government agencies or initiated by individual institutions, risk management is evident in universities in different regions, including the United Kingdom, Australia, Russia, Ukraine, Ecuador, Kenya, and Malaysia (Amuya and Kariuki, 2023, 2024; Anchundia et al., 2018, Christopher and Sarens, 2015; Edwards, 2012; Huber, 2011; Klochkova et al., 2017; Malki and Aldwais, 2019; Seale, 2017; Yokoyama, 2018; Syreyshchikova et al., 2020; Khaw and Teoh, 2023). Many universities also pursue ISO 9001 certification, which includes a risk analysis requirement in its latest version. In general, then, it appears that risk management is an increasingly common consideration in the world of higher education (Syreyshchikova et al., 2020), which in turn suggests the need for in-depth research into its application.

Prior risk management literature, conducted in other industry contexts, has produced a fairly well-accepted conceptual framework of critical parameters. For example, supply chain risk management studies offer typologies of risk drivers (e. g., human factors, natural factors), performance factors that may be at risk (e. g., operational, financial, reputational outcomes), and the practices or strategies that members of the supply chain can use to mitigate their risks (e. g., strategic stockpiling at different points in the supply chain, versatile work teams) (Ho et al., 2015). Such broad conceptual frameworks help to provide an informed overview of the requirements for risk management. It also helps to identify the key relationships between the variables in the framework that need to be explored. But the distinctiveness of the higher education industry, with its focus on three main missions (teaching, research, knowledge transfer), makes it difficult to extrapolate studies carried out in other sectors to this context. Rather, we need context-specific studies to identify and address the main challenges related to risk management in higher education in particular.

As some authors point out (Amuya and Kariuki, 2023; Edwards, 2012), applying a risk management lens to higher education is not straightforward. Some prior studies analyse a single type of risk, such as the regulatory burden (Edwards, 2012), cyber threat (Jesry et al., 2022), human error (Johan et al., 2019), work-related stress (Kinman and Court, 2010), a failure to meet quality requirements (Brewer and Walker, 2011; Edwards, 2012; Jesry et al., 2022), failures in IT systems (Johan et al., 2019; Aljuhami and Bamasoud, 2021), insufficient internationalization (Beecher and Streitwieser, 2019; Shanahan and McParlane, 2005), or not achieving sufficient levels of security in facilities (Bubka and Coderre, 2010). Yet, with few exceptions (Bamber and Elezi, 2024), this research domain lacks a comprehensive perspective that encompasses multiple types of risk and diverse mitigation strategies. Nor do we find many studies of the relationships of various risk management parameters that take an aggregated approach; most research remains conceptual or based on isolated case studies. To complement existing contributions, we aim to gather information from a wide range of universities in order to identify a comprehensive set of risk mitigation practices and empirically confirm their effectiveness in reducing academic risk.

Accordingly, in the next section, we review some relevant prior literature and develop our conceptual framework. In Section 3, we propose a model of practices that might mitigate the risk of not achieving adequate performance, and then Section 4 details the methodology. With Section 5, we present and discuss the results before moving to the conclusions in Section 6.

Risk refers to the probability of the occurrence of adverse events or catastrophes (e. g., floods, pandemics, economic crises) that have severe impacts on desirable outcomes of any kind (e. g., health, finance, reputation) (Higher Education Funding Council for England [HEFCE], 2001; Society for Risk Analysis [SRA], 2024). Risk management in turn requires multiple activities and phases (e. g., Fan and Stevenson, 2018; Syreyshchikova et al., 2020), specifically, risk identification, risk assessment, and the implementation of risk mitigation strategies. To articulate or analyse risk management for any given sector of activity, we therefore must consider both a catalogue of potential risks and a catalogue of practices or strategies that offer mitigating potential.

Figure 1 depicts our conceptual model, in which we propose a mitigating effect of AUMPs on teaching, research, and knowledge transfer risks.

Through planning and control, organisations can clarify their objectives and establish a time frame for their fulfilment, reducing the risk of failing to achieve teaching, research, and knowledge transfer goals effectively (Yokoyama, 2018; Khaw & Teoh, 2023). For example, by developing degree and training programs, universities can update the capacities of their graduates and ensure they are adjusted to the latest demands of the labour market and society; such developments also serve as guides for research and knowledge transfer efforts (Klochkova et al., 2017). Similarly, teaching development efforts among university staff should have an evident impact on teaching but also might help identify research and knowledge transfer needs (Khaw & Teoh, 2023; Bamber & Elezi, 2024). Encouraging teaching staff to enhance their research profile improves their chances of publications; elements of such quantitative and qualitative training also might inform their teaching and knowledge transfer activities. By developing closer links with students, staff can adjust the university to better meet their demands and those of society, which in turn reduces the risk of not adapting teaching, research, or transfers to the demands of society (Klochkova et al., 2017). Because internationalisation represents a way of learning, updating, and benchmarking, with effects on all areas of university activity, it also should lower the risk of not meeting standards across the three domains of teaching, research, and knowledge transfer (Klochkova et al., 2017). Finally, the services provided to society and the environment are a sources of learning; generating novel ideas can exert an impact on the university’s future knowledge transfer processes and also seems likely to improve teaching activity (which increasingly must include sustainability concepts), as well as providing inspiration for research (Bamber & Elezi, 2024). On the basis of these arguments, we offer a general exploratory hypothesis.

Tables 8–10 contain the results of the estimated regression models. In each table, Model 1 provides the results of the regression model with just the control variables, Models 2-9 indicate the results for each AUMP independently, and Model 10 depicts the results of the stepwise regression. From Table 8, we can determine which variables predict decreased teaching risk. Among the control variables, risk present in both the general university environment (political, economic, sociological, and technological issues) and its specific environment (students, suppliers, alternative training programs) exerts positive, significant effects on teaching risk. In the case of AUMPs, the following practices have negative, significant effects on teaching risk: investing in the development of teaching staff, investing in the development of research staff, improving the resource provision of teaching centres, the incremental role of students in universities, investing in internationalization, and developing programs that help improve society and the environment. In the stepwise regression model, the only significant AUMP is the development of the research staff, implying that this variable has the greatest explanatory power for reducing teaching risk. As we predicted, improving the research profile of academics reduces not only research risk, as expected, but also teaching risk. Attending doctoral programs, research techniques courses, and seminars helps researchers move closer to the frontier of knowledge. Such research development efforts can help prevent educational programs and degrees from becoming obsolete. In turn, the degrees remain attractive to students, and the degree abandonment rate should decline.

According to Table 9, pertaining to reduced research risk, perceived risk in the specific university environment has a significant and positive effect, such that more adverse conditions surrounding the specific university increase research risk. In the case of the AUMPs, we find several that exert significant and negative effects on research risk, namely, investing in the development of research staff, empowering students, internationalization and development of collaboration programs with society on social and environmental issues. The stepwise regression only includes one AUMP in its result: developing collaborative projects with other types of organizations (companies, government agencies, non-profit organizations). Expressing a commitment to society and the environment through these collaborations emerges as the variable with the greatest explanatory power for reducing research risk. Such collaborations help researchers identify the problems that society is trying to solve, such that they discover topics of interest for research and also gain access to valuable data with which to test possible models and hypotheses. These benefits help reduce the risk of poor quality publications or scoring lower in research rankings.

Finally, Table 10 reveals which variables have predictive capacity for reducing the risk surrounding knowledge transfers. Among the control variables, risk in both the general environment and the specific environment have significant and positive effects. Among the AUMPs, those with a confirmed significant, negative effect on knowledge transfer risk include improved planning and control, investing in the development of teaching staff, investing in the development of research staff, increased provision of resources to university centres, increased student empowerment, and internationalization and development of collaboration programs with society (companies, government agencies, non-profit organizations). In this case, the stepwise regression only includes the development of research personnel; this AUMP offers the greatest explanatory capacity for reducing knowledge transfer risk. Developing academics’ research skills can help them identify and find solutions for societal challenges.

Using a risk analysis prism, we seek to identify which practices can help mitigate the risk that a university is not able to fulfil the fundamental missions that define it: teaching, research, and the transfer of knowledge to society. In so doing, we make several contributions to university risk management literature. First, by adopting a broad perspective, this study includes many of the risk management parameters that universities may leverage. On the one hand, we recognize the three main processes that constitute university activity and accordingly account for teaching risk, research risk, and knowledge transfer risk as performance variables. On the other hand, in support of this broad analytical framework, we compile an extensive list of AUMPs, as potential risk-mitigation practices. Furthermore, we account for both risk in the general university environment (political, economic, sociological, and technological factors) and university-specific risk (adversities related to students, suppliers, existing or potential competitors that provide society with other alternatives for higher education).

Second, the empirical evidence affirms the capacity of AUMPs to mitigate risk in university performance and confirm one particular AUMP as an effective solution to teaching and knowledge transfer risk: investing in the development of research personnel. Such efforts might include the development of training programs, attracting graduate students, hiring and promoting academic staff on the basis of their research quality, or collaborating with prestigious external researchers. These practices help not only to reduce research risk, as might be expected, but also indirectly affect teaching risk and knowledge transfer risk. The development of research personnel provides tools for enhancing the attractiveness and relevance of educational offers. Likewise, developing personnel research skills with training programs or by offering incentives for research results can decrease the risk of failing to transfer knowledge to society.

Another AUMP emerges as a main mitigator of research risk: commitment to society and the environment. This commitment implies incentivising collaboration initiatives with other organizations (e. g., companies, non-profit organizations, public entities) to undertake projects linked to sustainability. These collaborations contribute directly to mitigate the risk of failing to transfer knowledge to society; they also indirectly provide research ideas and data that help mitigate research risk. Thus, the transfer of knowledge and value across the university and society works in both directions; both can benefit from collaborative projects. Beyond these two AUMPs that act as the main mitigators of university risks, our study confirms the beneficial influences of several other practices, as we summarise in Table 11.