Gestión y mitigación del riesgo en universidades latinoamericanas: identificación de prácticas efectivas y factores determinantes

Abstract

[EN] The culture of risk management has evolved significantly in recent decades (Fan & Stevenson, 2018; Wicaksana et al., 2022; Khaw & Teoh, 2023), driven by an increasingly complex, globalized, and interconnected environment. Crises such as the 9/11 terrorist attacks, the 2008 financial collapse, the COVID-19 pandemic, and ongoing geopolitical tensions have underscored the vulnerability of organizations across all sectors and the urgent need to develop systematic capabilities for anticipating and mitigating risks. Higher education institutions (HEIs) are no exception. The university sector faces both general and domain-specific risks that threaten not only their financial stability and reputation but also their core missions of teaching, research, and knowledge transfer. Among these threats are the growing competition arising from the proliferation of public and private institutions and online programs (Shanahan & McParlane, 2005; Brewer & Walker, 2011; Wessels & Sandler, 2015), increasingly complex regulatory frameworks (Cameron & Klopper, 2015), external performance evaluations (Brewer & Walker, 2011), cyberattacks (Jesry et al., 2022), work-related stress (Kinman & Court, 2010), and pressures for self-financing in public universities (Huber, 2011; Stromquist, 2007). These challenges are especially pronounced in Latin America, where political and economic volatility and structural budgetary constraints add further complexity to governance and management (Arim, 2022; Espinosa et al., 2022). Although universities have long been exposed to risk, the growing scope and interdependence of these threats have brought university risk management to the forefront. This shift is reflected in the increasing academic attention to University Risk Management (Huber, 2011; Edwards, 2012; Cristopher & Sarens, 2015; Klochkova et al., 2017; Yokoyama, 2018; Syreyshchikova et al., 2020; Amuya & Kariuki, 2023; Khaw & Teoh, 2023). However, the field remains theoretically fragmented and empirically underdeveloped. While other sectors such as manufacturing or finance have established comprehensive frameworks for identifying and mitigating risks (Ho et al., 2015; Baryannis et al., 2019), universities still lack a consensual model for understanding and managing their specific vulnerabilities. Existing studies often focus on applying the traditional structure of risk management analysis—risk identification, risk assessment, and risk mitigation—to specific sources of risk such as technological failures, insufficient internationalization, or cybersecurity. As a result, they tend to propose isolated sets of mitigation and management practices without integrating them into a systemic framework that enables a holistic understanding and analysis of university risk management. Most contributions remain largely conceptual or are based on single-case studies, while empirical research involving large samples is still rare. Recent works have adopted a more comprehensive approach, offering well-structured literature reviews (e.g., Khaw & Teoh, 2023) that contribute to defining such a systemic framework. However, studies grounded in organizational theories that explain how different variables within this framework interact remain scarce. This dissertation aims to contribute to the growing body of literature on university risk management by pursuing two objectives: (1) to propose a set of management practices that effectively mitigate institutional risks and enhance resilience—conceptualized here as growth in student enrollment—and (2) to draw on institutional theory to identify internationalization as a key driver of university risk management. Conceptual Models and Analyses The research is structured into three studies. The first study addresses Objective (1). It identifies and classifies potential risks across the three core university processes— teaching, research, and knowledge transfer—and explores advanced management practices with the potential to mitigate such risks. To test the proposed relationships, several multiple regression models were applied. Significant correlations were observed in some cases, as detailed in the extended version of this dissertation. To mitigate interpretation issues arising from multicollinearity, separate regression models were estimated for the independent variables, followed by a stepwise regression to identify those with the greatest explanatory power for each dependent variable. The second study addresses Objective (2), proposing internationalization—specifically measured as international faculty secondment—as an antecedent of university risk management, with the formalization of risk management acting as a mediating variable. Analyses were conducted using AMOS software and Structural Equation Modelling (SEM) to test both direct and indirect effects, employing a 90% bias-corrected bootstrap confidence interval with 5,000 resamples. Mediation was deemed present when zero was not contained within the confidence interval of the indirect effects. Partial mediation was established when both direct and indirect effects were significant, while full mediation was confirmed when only the indirect effect was significant. The bootstrapping approach provides a more robust conceptualization of mediation than the traditional methods proposed by Baron and Kenny (1986) and the Sobel test (Hayes, 2018; Cheung & Lau, 2008; Chaudhary & Akhouri, 2018; Irfan et al., 2023). The third study complements the first in addressing Objective (1). It examines how advanced management practices implemented in higher education institutions influenced student growth during and after the COVID-19 pandemic. Covering the 2018–2022 period, the analysis assessed both the pre-pandemic baseline and the evolution of these practices throughout the crisis. To verify this effects, first, eight independent multiple regression models were estimated—one for each management practice—to ensure coefficient stability and avoid collinearity issues. Subsequently, a stepwise regression was conducted to identify variables with unique explanatory power, based on statistical significance (p < 0.05) and incremental contributions to the adjusted R², thereby distinguishing associative from causal effects. To complement this analysis, a one-way ANOVA was performed to compare institutional performance among universities grouped according to their management practice trajectories during the pandemic. Using pre-pandemic distributions as reference points, universities were categorized into three cohorts: those that reduced, maintained, or increased their adoption of advanced management practices. Tukey’s HSD post-hoc tests (α = 0.05) were then applied to assess differences in student growth among these groups, providing a robust evaluation of the differential impact of adaptive management strategies. Data The proposed conceptual models were validated through a survey conducted across four Latin American countries—Peru, Ecuador, Colombia, and Venezuela—covering a population of 379 universities. A structured questionnaire was developed to measure each variable of the study, as described in the following section. Chancellors and members of university governing bodies were selected as key informants because they typically occupy senior strategic and administrative roles (Martínez, 2016; Sánchez, 2022; Davis, 2025), possess in-depth knowledge of the variables under examination, and hold ultimate responsibility for their implementation. Prior to full deployment, the questionnaire and its administration procedure were pre-tested with five Latin American chancellors or vice-chancellors and one European vice- chancellor. The final version was administered online using Qualtrics software. To maximize response rates, the Dillman Method (2000) was employed: first, potential respondents were contacted by phone to introduce the project and request an institutional email address to share the survey link and additional information. Two subsequent rounds of reminders were sent via telephone and email. This process yielded 119 completed questionnaires, representing a 29% response rate and a sampling error of 7.2% at a 95% confidence level. To assess potential non-response bias, an analysis of variance (ANOVA) was performed by comparing the first 25 and last 25 responding universities based on institutional size (measured by the number of students and faculty as reported on their official websites, given the absence of a centralized database). Twelve universities were excluded from the analysis—nine in Venezuela, two in Colombia, and one in Ecuador—due to missing size data, leaving 107 valid responses. The statistical results showed no significant differences between early and late respondents (see Table 4), indicating no evidence of non-response bias (Wagner & Kemmerling, 2010). Several procedural remedies were implemented to mitigate potential common method bias (Podsakoff et al., 2003). Participants were assured of the confidentiality of their responses and informed that there were no “right” or “wrong” answers. Additionally, the questionnaire was designed to clearly separate independent, dependent, and moderator variables. After data collection, Harman’s single-factor test was conducted to assess the presence of common method bias across all construct items. The analysis extracted 11 distinct factors explaining 74.08% of the total variance, suggesting that common method bias was not a concern in this study. Measures To measure the variables in the different models, both reflective and formative scales were used, depending on the nature of each construct. For reflective scales, Confirmatory Factor Analyses (CFA) were conducted to verify unidimensionality, reliability, convergent validity, and discriminant validity. For formative scales, the premises proposed by Jarvis et al. (2003) were examined to ensure their appropriateness. The detailed process of measurement scale development is presented in the extended version of this thesis. Results and Contributions Overall, the results of the proposed model were confirmed, as detailed in the extended version of this dissertation thesis. This first study emphasizes the need to identify, classify, and standardize the analytical parameters of risk management in higher education systems into three categories: (1) factors that cause risks, (2) types of performance parameters that may be at risk, and (3) practices that act as risk mitigators. In other domains—such as supply chain risk management—similar classifications of parameters are already well established; however, in the context of university management, these have yet to be clearly defined. Some previous studies offer key contributions in this regard (e.g., Brewer & Walker, 2011), but a clear conceptual framework for university risk management has not yet been developed. For instance, Brewer and Walker (2011) tend to mix causes and consequences when defining risks—for example, referring to strategic risk as something that affects the university’s long-term performance and is caused by external and governance-related factors. Distinguishing between risks that are specific to their causes and those that are specific to their consequences helps clarify the analysis. Furthermore, Brewer and Walker compile several types of specific risks, but their analysis is not comprehensive: they address competition among universities yet overlook other relevant risk factors such as competitors’ actions, student enrollment levels, or political, economic, and sociological variables. Finally, they do not specify which practices could mitigate such risks nor provide empirical evidence of mitigation effects. Therefore, our findings serve as a stimulus for future academic work aimed at classifying these factors and empirically verifying their interrelationships. The conceptual model and empirical findings of our second article make three key contributions. First, we advance risk management research by emphasizing the often-overlooked role of employees—in our case, faculty members—as active agents in risk mitigation. Their commitment, organizational knowledge, and proactivity significantly influence the development of university risk management systems (González-Zapatero et al., 2024).Second, we refine the University Risk Management framework by defining specific analytical parameters: faculty development as a mitigation practice, University Risk Management Formalisation as a measure of standardization, and international faculty secondment as an antecedent of both. These validated constructs provide a foundation for future empirical research in a field still dominated by conceptual and case-based studies (Khaw & Teoh, 2023). Third, we contribute to the institutional theory debate in higher education by evidencing strong institutional pressures—particularly from leading international universities—that drive isomorphism and standardization. While such pressures risk reinforcing a self-referential academic system, they also represent necessary adaptation mechanisms for institutional survival. International faculty secondment emerges as a strategic response, enabling universities to align with global norms while progressively integrating broader societal concerns such as inequality, sustainability, and social responsibility (Oliver, 2001; Clegg et al., 2021). The third study findings provide robust empirical evidence to the growing field of research on risk management in higher education (Amuya & Kariuki, 2023; Anchundia et al., 2018; Quijije et al., 2018; Edwards, 2012; Huber, 2011; Syreyshchikova et al., 2020), demonstrating that mitigation practices traditionally applied in business contexts are also effective for universities facing external crises. The identification of faculty development as the sole predictor in the stepwise model confirms the strategic importance of investing in highly qualified human capital as a central component of institutional resilience—consistent with institutional theory perspectives on the influence of environmental factors on organizational behavior (DiMaggio & Powell, 1983; Scott, 2008; Collier & Woods, 2011). This evidence reinforces the notion that universities with strong institutional capabilities are better positioned to adapt to external disruptions and sustain performance under conditions of uncertainty. From an institutional theory perspective, the findings confirm that advanced management practices driven by isomorphic legitimization mechanisms strengthen university resilience in times of crisis (DiMaggio & Powell, 1983). Likewise, accreditation schemes appear well designed by incorporating such practices (e.g., strategic planning, faculty development, and social–environmental engagement), acting as external pressures that catalyze and compel institutions to adopt robust management standards. However, university rankings, while capturing visible dimensions such as scientific output and internationalization, overlook critical risk mitigation practices (e.g., resource management during crises or social–environmental commitment), thereby limiting their ability to assess institutional resilience comprehensively. Thus, whereas accreditation frameworks validate a broad spectrum of advanced management practices, rankings continue to prioritize traditional prestige metrics, highlighting the need to integrate indicators of adaptability and sustainability to more accurately reflect institutional performance in volatile environments. Limitations and further research This research is not without limitations, which in turn open several avenues for future inquiry. Although the analyses indicated no evidence of common method bias, future studies could reinforce these findings by incorporating objective indicators—for instance, data on the number and duration of faculty secondments abroad in the second study. While the main goal of this research was to contribute to the existing literature on University Risk Management by developing and empirically validating conceptual models based on large samples, qualitative approaches could further illuminate the mechanisms underlying the observed relationships, thereby enriching the theoretical and empirical foundations of the field. Moreover, the use of additional datasets with panel data would provide opportunities to examine and refine these relationships over time. Expanding the theoretical background could also offer valuable perspectives for gaining deeper insight into the proposed associations, as well as exploring alternative interrelations among the variables. Finally, the investigation of nonlinear (curvilinear) relationships or moderation effects could yield additional understanding of the complex dynamics shaping university risk management