Systemic View of the Role of Higher Educational Institutions in the Great Reset

Abstract

The Great Reset is a concept proposed by the World Economic Forum to rebuild the global economy sustainably following the COVID-19 pandemic, emphasising stakeholder capitalism, greener practices, and more equitable social contracts. However, most of the literature focuses on the role of business and government actors in the Great Reset. At the same time, research that considers the role of higher education institutions (HEIs) is lacking. However, HEIs have the significant capacity to support various aspects of the Great Reset. In this work, we examine different mechanisms by which HEIs could bring change, such as fostering the growth of workforce skills, promoting entrepreneurship and innovation, participating in community development and others. A survey on a sample of university professors, postdoctoral researchers, and research assistants has been conducted with the goal of evaluating the degree to which HEIs have played a role in influencing economic recovery after COVID-19, leveraging the industry 4.0, enhancing regional development, revitalising global cooperation, formulating sustainable business models, restoring environmental well-being, and restructuring social contracts, skills, and employment opportunities. A two-step cluster analysis has been conducted, indicating that HEIs played different roles in the Great Reset process, being the Leaders, Followers and Laggers. We found a significant difference between the various groups of respondents according to their scientific field, position, and region regarding the perception of the role played by their HEIs in influencing economic recovery after COVID-19. The position of the respondents contributed to their perception of the role that their HEIs participated in the process of the Great Reset, with the assistant professors perceiving the most often that their HEIs are Leaders. Additionally, the scientific field and region of the HEI also impacted their role in the Great Reset, with the HEIs from technology and countries outside of Europe being the Leaders most often. On the other hand, Followers and Laggers were equally from social sciences and other fields and European countries.

1. Introduction

The COVID-19 pandemic has fundamentally changed world systems, on the one hand, revealing weaknesses in social, economic, and environmental spheres, while, on the other hand, discovered resilience in the ability to retain the business, educational and governmental processes running with the support of technology [1,2]. However, in addition to the strong efforts to retain the day-to-day running, societal and economic problems that emerged during and after the COVID-19 pandemic highlight how urgently a stronger and fairer system is needed for the world to move forward. This turning point has caused many experts to rethink objectives and incorporate sustainability into recovery plans as a top priority [3,4,5]. Against this background, the World Economic Forum’s “Great Reset” project has become a vital conversation supporting a thorough transformation of world institutions to support more inclusive, sustainable, and resilient economies [6].

Under the Great Reset, capitalism should be reimagined to solve structural problems revealed by the epidemic, including social unrest, environmental damage, and economic disparity [6]. Emphasising the necessity of digital transformation, sustainable economic reforms, and improved social fairness, this project tries to use the crisis as a chance to propel significant and long-lasting improvements, planning the future in which society and environmental well-being are more in line with each other as well as in which economy are stronger [7]. The COVID-19 epidemic has sped up the acceptance of digital technology, underlining the need to increase the digital literacy of the workforce [8]. Another important component is adaptability, which is the proper use of the possibilities given by the Fourth Industrial Revolution to accelerate economic recovery and solve societal issues [8,9].

While companies and governments played a significant role in the process of recovery after the COVID-19 pandemic [10,11], the transformation of societies and economies strongly depends on the active role of Higher Educational Institutions (HEIs), which are specially positioned to inspire creativity, encourage critical thinking, and equip future leaders with the tools and knowledge required to negotiate and adjust in an environment of fast-changing technologies [12]. HEIs may greatly help to create a more sustainable and fair society by supporting multidisciplinary research on worldwide issues and improving digital infrastructure to enable remote learning and cooperation [13]. On the other hand, HEIs must also change their roles to become the centres of community involvement, building alliances between local and international players to handle social concerns. They may significantly help to create a resilient and inclusive future [14].

Having in mind all of the above, this study seeks to provide a systemic outlook on the role that HEIs have played so far in the recovery after the COVID-19 pandemic. For that purpose, a preliminary survey has been conducted on the researchers from 105 HEIs, with the goal of capturing their attitudes on the role that their HEIs played in the Great Reset. Two-step cluster analysis was applied to the collected data, and extracted clusters were compared according to the field of research, region, and position of the respondents. Clustering results indicate that there are three groups of HEIs based on their role in the Great Reset: Leaders, Followers, and Laggers.

After this Introduction, the rest of the paper is organised as follows. A literature review on the notion of the Great Reset, the World Economic Forum’s approach to the Great Reset and related conspiracy theories is provided in the second part of the paper. The third part of the paper explains the employed methodology, followed by the presentation and interpretation of the results in the fourth part. Finally, the fifth part of this paper presents a results-related discussion, followed by the conclusion in the sixth part.

2. Literature Review

2.1. The Notion of the Great Rest

The Great Reset, an initiative launched by the World Economic Forum in response to the COVID-19 pandemic, aims to rethink and rebuild the world’s economy and society. Proposed by World Economic Forum founder Klaus Schwab, the Great Reset advocates for reimagining capitalism to tackle systemic issues highlighted by the pandemic, including economic inequality, environmental degradation, and social instability [6]. Although these systemic issues have been present since the 19th and 20th centuries, as discussed in detail by Piketty [15] in his seminal work Capital in the Twenty-First Century, the COVID-19 pandemic has magnified their urgency, underscoring the need for comprehensive reforms to create a more equitable and resilient global economy.

The Great Reset proposes three main components: steering the market towards fairer outcomes, ensuring investments advance shared goals like equality and sustainability, and harnessing innovations of the Fourth Industrial Revolution to support the public good [6]. The initiative also underscores the importance of healthcare systems’ robustness and the role of digital transformation in fostering more inclusive and productive economies.

The COVID-19 pandemic has profoundly reshaped global systems, exposing vulnerabilities across economic, social, and environmental dimensions [16]. The economic disruptions and social challenges during the pandemic motivated numerous countries, companies, and other entities to become more resilient and able to move forward [17]. The Great Reset was motivated by the numerous changes that occurred after the pandemic, ranging from sustainability to digital transformation, advocating for balanced growth with environmental and societal well-being.

The pandemic motivated the reconsideration of priorities, with a clear emphasis on integrating sustainability into recovery strategies [18]. As economies strive to recover, there is a growing recognition of the interdependence between economic growth and environmental protection. Investments in renewable energy, green infrastructure, and sustainable practices not only stimulate economic growth but also mitigate environmental risks, laying the foundation for a resilient future. Adopting circular economy principles and reducing environmental footprints are seen as crucial steps towards ensuring long-term success in a rapidly changing global landscape, reinforcing the principles of the Great Reset [8].

The rapid advancement of digital technologies was the key to overcoming the pandemic’s negative impact, highlighting the importance of preparing the workforce through enhanced education and digital literacy [19]. In turn, this would enable them to become active participants in the Industry 4.0 transformation, driving economic recovery and effectively addressing societal challenges, another key dimension of the Great Reset.

Governments worldwide are reassessing governance systems to enhance resilience against future shocks, echoing the Great Reset’s call for regional development and stability. By resetting governance frameworks, countries aim to foster sustainable growth and adaptability, which is essential for navigating uncertainties post-pandemic [11].

Holling first introduced the term resilience in 1973 [20] and introduced the concept of ecological resilience, defining it as the amount of disturbance an ecosystem can withstand without changing its self-organised processes and structures [21,22]. United Nations Office for Disaster Risk Reduction [23] defined resilience as the capacity of a system, community, or society to withstand, assimilate, adapt to, and bounce back from the impacts of a hazard promptly and effectively. This includes the safeguarding and reinstatement of its fundamental structures and operations. Walker et al. [24] broadened the definition of resilience as the ability of a system to absorb disturbances and reorganise during periods of change while maintaining its core functions, structure, identity, and feedback mechanisms. Allen et al. [25] define resilience as a characteristic of socio-ecological systems that quantifies their ability to endure disturbances without undergoing significant changes in their processes, structures, and feedback mechanisms. In that manner, the term resilience has become broadly used in the context of various systems, such as financial resilience [26], business and community resilience [27], and security [28]. In this study, we refer to resilience in terms of the socio-ecological systems framework, according to which the Great Reset fosters the ability of worldwide societal, economic, and environmental systems to adjust to change and even flourish after the pandemic.

International cooperation has also been revitalised in response to the pandemic, emphasising the need for collaborative approaches to tackle global challenges. Global cooperation is integral to the Great Reset’s vision of inclusive development and resilient societies, as countries learn from each other’s experiences and adapt best practices to local contexts. Additionally, it is important to create sustainable supply chains so they can serve for a long period.

In conclusion, while the COVID-19 pandemic brought unprecedented challenges, it also catalysed a global reckoning on sustainability, resilience, and equitable development. Embracing these principles not only aids in immediate recovery but also lays the groundwork for a more sustainable and inclusive future aligned with the goals of the Great Reset.

2.2. From Concept to Conspiracy Theory

Despite its intentions, the Great Reset concept has been transformed into a conspiracy theory by some groups. The intensity of the influence of such groups increases primarily due to the increase in the use of social networks during the lockdown [29]. With the accelerated development of large language models (LLM), it is possible very quickly and quite simply to create a “disinformation message” that is very convincing, with the intention of manipulating the receivers of the message in the form of forming attitudes and beliefs. Critics argue that the initiative represents an attempt by global elites to centralise power and control under the guise of public good [30].

The theory suggests that the pandemic is being exploited to implement radical changes that infringe on individual freedoms and property rights. This view gained traction through social media and various online platforms, where misinformation and distrust of government and global organisations thrive. The narrative was further amplified by influential figures and media outlets, contributing to widespread scepticism and fear regarding the true motives behind the Great Reset [29].

2.3. HigherEducational Institutions and the Great Reset

The Great Reset initiative aims to create a more inclusive and sustainable world by rethinking how we approach economic development, social equity, and environmental stewardship. By leveraging the collective efforts of governments, businesses, and civil society, the Great Reset seeks to build a new version of a resilient global economy. HEIs play a significant role in this process, which is defined by the set of dimensional initiatives and activities.

2.3.1. Dimensional Initiatives

In response to the multifaceted challenges presented by the COVID-19 pandemic, a comprehensive set of dimensional initiatives and actions has been proposed by the World Economic Forum to ensure robust economic recovery and societal resilience [6]. Dimensional initiatives are defined as a cornerstone pillar of the Great Reset [6] and are as follows:

• Shaping the Economic Recovery after COVID-19. The economic recovery from COVID-19 should prioritise climate change, as surveys show. This highlights the need to integrate sustainability into economic policies and investments, focusing on renewable energy, green infrastructure, and sustainable practices. Such measures not only stimulate economic growth but also mitigate environmental risks and enhance resilience to future challenges, fostering a robust and sustainable recovery. They have a critical role in driving innovation, fostering critical thinking, and preparing future leaders with the skills and knowledge necessary to navigate and adapt in a rapidly changing world. Also, HEIs are promoting interdisciplinary research on global challenges and enhancing digital infrastructure to support remote learning and collaboration [31]. By embracing these changes, HEIs can better prepare students to contribute to a more sustainable and equitable society.
• Harnessing the Fourth Industrial Revolution after COVID-19. This initiative is focused on educating students to effectively use available technology [32], which is crucial for preparing the workforce to adapt and innovate in a rapidly evolving digital landscape, driving economic growth, and addressing societal challenges effectively.
• Strengthening Regional Development after COVID-19. European governments are resetting governance systems to better cope with future shocks post-COVID-19 [11]. These strategic adjustments aim to strengthen regional development and enhance resilience in the face of uncertainties, fostering sustainable growth and stability across diverse regions.
• Revitalising Global Cooperation after COVID-19. Revitalising global cooperation post-COVID-19 involves learning from international practices and adapting them to meet the specific needs of each country [33]. This fosters effective collaboration across borders, enabling countries to address shared challenges, promote inclusive development, and build resilient societies globally.
• Developing Sustainable Business Models after COVID-19. Developing sustainable business models post-COVID-19 involves emphasising their capacity to support long-term sustainability, such as through the adoption of circular economy principles [34]. HEIs are becoming more and more important in helping to forward sustainable development objectives (SDGs). [35] claim that by means of teaching, research, and community involvement, universities are particularly suited to shape society norms and values towards sustainability. According to [36], universities all over the world are including sustainability in their operations and strategic planning, therefore promoting a sustainable culture on each campus. Embedding sustainability into the central focus of higher education depends on this institutional commitment. Universities may be leaders in other fields by giving sustainability priority, therefore proving the viability and advantages of sustainable living. Furthermore, a study by [37] emphasises the need for institutional evaluation instruments in assessing and improving initiatives for environmental sustainability within universities. These instruments enable organisations to see areas of strength and weakness in their environmental projects, therefore directing ongoing development and creativity. This fits the focus of the Great Reset on re-evaluating and reconstructing society and businesses to be fairer and more robust. HEIs may generate graduates who are not just aware of but also dedicated to sustainable development as they implement more sustainable practices and include sustainability in their courses. Attaching long-term sustainability objectives and tackling world issues like social injustice and climate change depends on this paradigm transformation in education. Institutional policies and practices also mirror the inclusion of sustainability into higher education.
• Restoring the Health of the Environment after COVID-19. Restoring the health of the environment post-COVID-19 is crucial for human well-being, as it is well known that environmental health significantly impacts human health. Prioritising topics such as biodiversity conservation and pollution reduction is essential to mitigate climate change [38]. By focusing on these initiatives, we can foster a healthier environment that supports human health, promotes ecological resilience, and contributes to sustainable development goals globally.
• Redesigning Social Contracts, Skills, and Jobs. The desired skills for effective job performance are shifting, emphasising the ability of workers to utilise various tools to enhance productivity [39]. This adaptation underlines the importance of digital proficiency and technological competence in modern workplaces. By mastering these tools, workers can optimise efficiency, streamline processes, and remain competitive in an evolving job market driven by technological advancements.

2.3.2. Action Activities

The set of action activities supports each of the initiatives. Some of these activities are the core processes of HEIs, such as (i) conducting relevant research, (ii) developing workforce skills, (iii) providing continuing education and retraining programs, and (iv) strengthening online and flexible learning options. However, other processes could be considered pivotal parts of HEI activities. However, the level of their deployment depends on the strategy of each HEI, such as (i) supporting entrepreneurship and innovation, (ii) engaging in community development projects, (iii) policy advocacy, (iv) international collaboration, (v) mental health and well-being support and (vi) sustainability and resilience building.

• Through rigorous and relevant research, HEIs drive advancements in both social and technological fields, tackling current challenges and exploring new frontiers of knowledge. HEIs contribute to advancements in various fields, influence policy, and drive societal progress [19].
• HEIs play an important role in developing workforce skills and equipping students with the specific competencies needed for today’s job market. This is the first-dimensional activity, developing workforce skills. HEIs can provide training programs, workshops, and courses that teach students and professionals the necessary skills to succeed in the current job market [40]. This includes both hard skills, like technical proficiency, and soft skills, such as communication and teamwork.
• Providing continuing education and retraining programs ensures that professionals can adapt to evolving industry demands throughout their careers. During the pandemic, there was a surge in continuing educational programs that allowed individuals to gain valuable skills [41].
• The possibility of online and flexible learning, meeting different learning needs and taking advantage of technological progress in education was crucial in the pandemic era. By developing robust online learning platforms and flexible learning options [42], institutions can make education more accessible to a diverse range of students, including working professionals, remote learners, and those with family commitments. Research reveals that there has been a substantial increase in the adoption of online learning platforms, which has reshaped the educational landscape [43]. This shift towards digital education is not merely a temporary adjustment but a transformative change that is likely to persist and evolve [44].
• Supporting entrepreneurship and innovation is fostered in HEIs by encouraging creative thinking and supporting new ventures within the local community. Moreover, HEIs can serve as hubs for community engagement, fostering partnerships with local and global stakeholders to address societal issues. By aligning their missions with the principles of the Great Reset, HEIs can play a vital role in developing a resilient and inclusive future. Institutions can foster a culture of innovation by offering resources like incubators, mentorship programs, and funding opportunities to students and faculty interested in starting their businesses or developing new technologies.
• Engaging actively in community development projects is also an important area in which HEIs should contribute to improving local infrastructure and addressing social needs. Universities can collaborate with local communities to address social, economic, and environmental challenges. This could involve service-learning projects, research partnerships, and community outreach initiatives that benefit both the institution and the community.
• Advocating for policy changes that enhance educational systems and societal frameworks promotes conducive environments for learning and growth. Educational institutions can engage in policy advocacy to influence legislation and public policy in areas relevant to education, research, and societal well-being. This can include providing expert testimony, conducting policy research, and participating in public debates.
• Through international collaboration, HEIs foster global partnerships in academia and research, facilitating the exchange of ideas and enhancing cultural understanding. Prioritising mental health and well-being includes offering support services to nurture a positive and supportive learning environment, which many HEIs have started to provide as part of their services. Partnering with institutions and organisations worldwide enhances the educational experience through exchange programs, joint research projects, and international conferences, promoting cross-cultural understanding and global cooperation [8].
• Finally, by promoting sustainability and resilience, HEIs integrate environmental awareness and sustainable practices into their operations, contributing to a sustainable future.

3. Methodology

3.1. Research Instrument

The research instrument was developed based on [6], consisting of two sets of variables.

The first set of variables is related to the characteristics of the respondents, including country, university, scientific area, position, and job title.

The second set of variables measured the respondents’ attitudes from 1 to 5 (1 do not agree at all, 5 fully agree) in relation to the role that their university played in various action activities according to the dimensional initiatives.

Action activities were coded from A1 to A10 (

Table 1. Variables measuring the dimensional initiatives.

Variable Code	Dimensions	Measurement
D1	Shaping the Economic Recovery after COVID-19	1 to 5 (1-do not agree at all, 5-fully agree)
D2	Harnessing the Fourth Industrial Revolution after COVID-19
D3	Strengthening Regional Development after COVID-19
D4	Revitalising Global Cooperation after COVID-19
D5	Developing Sustainable Business Models after COVID-19
D6	Restoring the Health of the Environment after COVID-19
D7	Redesigning Social Contracts, Skills and Jobs

Source: Authors’ work, based on [6].

), while dimensional initiative was coded from D1 to D7 (

Table 2. Variables measuring action activities.

Variable Code	Action Activities	Measurement
A1	Developing Workforce Skills	1 to 5 (1-do not agree at all, 5-fully agree)
A2	Supporting Entrepreneurship and Innovation
A3	Engaging in Community Development Projects
A4	Conducting Relevant Research
A5	Providing Continuing Education and Retraining Programs
A6	Policy Advocacy
A7	Strengthening Online and Flexible Learning Options
A8	International Collaboration
A9	Mental Health and Well-being Support
A10	Sustainability and Resilience Building

Source: Authors’ work, based on [6].

). Therefore, the following set of variables was developed. For example, variable D1_A1 measures the attitude of the respondents from 1 to 5 about the role that their university played an active role in Shaping Economic Recovery after COVID-19 (D1) by Developing Workforce Skills (A1). Similarly, the variable D7_A10 measures the attitude of the respondents from 1 to 5 about the role that their university played an active role in Redesigning Social Contracts, Skills and Jobs (D7) by Sustainability and Resilience Building (A10).

This approach resulted in 70 questions divided into 7 sections, each section for a one-dimensional initiative containing 10 questions for each action activity.

The respondents were asked to respond to each of the questions from 1 to 5 to what extent they agreed with the statement “My university played an active role in...”, measured on a scale from 1 to not agree at all to 5 to fully agree.

The respondents were also asked to rate their agreement with the statement “Higher educational institutions should not play a direct role” for each of the dimensional initiatives on a scale of 1 to 5.

3.2. Data

The survey was sent to a sample frame of 1000 university professors and research assistants. This frame was constructed using institutional records, professional directories, or academic databases to ensure that it accurately represents the diversity of the academic staff within the relevant departments or fields of study. The survey was distributed from 1 March to 30 June 2024. During this period, 105 respondents participated in the survey, conveying a response rate of 10.5%, which is within the acceptable range for surveys conducted in academic settings. For example, [36] recorded a similar response rate of 10.88% in their survey of commitment and implementation of sustainable development in HEIs.

Table 3. Respondents’ characteristics.

Characteristic	Modalities	#	%
Scientific field	Social Sciences	80	76.2%
	Technology	12	11.4%
	Other	13	12.4%
Position	Research assistant and postdoctoral researcher	21	20.0%
	Assistant Professor	21	20.0%
	Associate Professor	30	28.6%
	Full Professor	33	31.4%
Region	Europe-EU	77	73.3%
	Europe-Non-EU	17	16.2%
	Other	11	10.5%

Source: Author’s work.

presents an overview of the characteristics of a research cohort, encompassing their scientific fields, academic positions, and geographical regions. The data reveals a focus on Social Sciences, accounting for 76.2% of the sample, while Technology and Other fields comprise 10.5% and 12.4%, respectively. Although the ratio between these groups of sciences is not balanced, it allows for the comparison between them.

Respondents were also asked about their position. Examining academic positions, we observe a relatively balanced distribution across ranks, with Full Professors representing the largest group at 31.4%, followed by Associate Professors at 28.6%. Research assistants, Postdoctoral researchers, and Assistant Professors each constitute 20% of the sample.

Geographically, the study population is concentrated in European Union countries, representing 73.3% of participants, with an additional 16.2% from non-EU European nations. The remaining 10.5% originate from other regions. We have collected 105 answers, with a notable concentration from Croatia, accounting for 42 respondents. From Serbia and the Czech Republic, 7 respondents participated, while from Romania, there were 5 respondents. From Bosnia and Herzegovina and Slovenia, 4 respondents participated, while from Slovakia, there were 3 participants. Countries contributing 2 respondents each included Austria, Brazil, Bulgaria, Denmark, Germany, Montenegro, North Macedonia, Poland, Spain, UAE, and the USA. Albania, Belgium, Hungary, Ireland, Italy, Jordan, Macedonia, Morocco, Russia, South Africa, and Vietnam each had 1 respondent.

3.3. Statistical Analysis

For this research, the two-step cluster methodology was employed [45]. That is a statistical technique for classification data into clusters based on a mixture of continuous and categorical variables. This method involves two main steps: pre-clustering and hierarchical clustering.

The first step includes pre-clustering, in which the algorithm scans the dataset and creates small sub-clusters. This step reduces the size of the problem by grouping similar records into sub-clusters, which serve as the input for the next step. This pre-clustering process uses a sequential clustering algorithm.

The second step involves applying a hierarchical clustering algorithm to the pre-clustered data. This method merges the sub-clusters into a larger cluster. The optimal number of clusters is determined by balancing model fit and complexity, often using criteria like the Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC). Cluster quality has been measured with silhouette coefficient: the Silhouette value measures how similar an object is to its cluster (cohesion) compared to other clusters (separation). It is calculated as:

$$s_i={\displaystyle \frac{b_i-a_i}{\max \left(a_i,b_i\right)}}$$

(1)

$$\overline{S}={\displaystyle \frac{1}{N}}\sum _{i=1}^N{S}_i$$

(2)

where

• S(i) Silhouette coefficients for i-th object;
• b(i) Average of the minimum distance between i-th object in a different cluster (average inter-cluster distance)
• a(i) Average of the minimum distance between i-th object in the same cluster (average intra-cluster distance)
• S The average value for the Silhouette coefficients
• N Total number of observations [46]

The relative Importance of Predictors is computed using a logarithmic transformation of the p-values from statistical tests, given by:

$${VI}_i={\displaystyle \frac{-{\log }_{10}({sig}_i)}{{max}_{j\in \Omega }(-{\log }_{10}({sig}_j))}}$$

(3)

where

-

Ω is the set of predictor and evaluation fields.

-

sig_i is the p-value for the i-th predictor [46].

Cluster analysis has been conducted with the seven continuous variables (D1_avg, D2_avg, D3_avg, D4_avg, D5_avg, D6_avg and D7_avg) and three nominal variables (Region, Position and Main area), with the usage of SPSS ver. 29. Outlier treatment with a noise handling of 25% was deployed. The initial distance change threshold was 0, with maximum branches per leaf node of 8 and maximum tree depth levels of 3. The distance measure was log-likelihood. Schwarz’s Bayesian Criterion (BIC) was used as a clustering criterion. The continuous variables were standardised.

4. Results

4.1. Descriptive Statistics of the Dimension’s Initiatives

Figure 1 shows the mean values of activities according to dimensions. According to the graph, there are some exceptions in segments A5 and A8 for the D6 dimension. For A6, we can see there is a drop in the grade, which is expected, because during the pandemic, people realised not only the importance of the health system but also the importance of recovering their health and the infrastructure related to the provision of health services. The crisis has shown the vulnerabilities of the health system and its connection with political decisions. A8 stands out as an international collaboration activity, and it is obvious that respondents recognised an increase in international collaborations in various projects, research and using other countries’ approaches.

Table 4. Descriptive analysis of the average variables of the dimension initiatives.

Dimensions	Code	N	Mean	Std. Deviation
Shaping the Economic Recovery after COVID-19	D1_avg	105	3.77	0.649
Harnessing the Fourth Industrial Revolution after COVID-19	D2_avg	105	3.69	0.759
Strengthening Regional Development after COVID-19	D3_avg	105	3.70	0.817
Revitalising Global Cooperation after COVID-19	D4_avg	105	3.56	0.762
Developing Sustainable Business Models after COVID-19	D5_avg	105	3.54	0.840
Restoring the Health of the Environment after COVID-19	D6_avg	105	3.47	0.817
Redesigning Social Contracts. Skills and Jobs	D7_avg	105	3.52	0.837

Source: Authors’ work.

presents the average values for action activities across the dimension initiatives. For example, the variable D1_avg was calculated as the average value of the items A1_1, A1_2, …, and A1_10, indicating the average support value for the first dimension initiative D1.

On average, the initiatives across all dimensions received scores ranging from 3.47 to 3.77, with standard deviations indicating moderate variability around these means (0.649 to 0.840), as presented in Figure 2.

The results highlight a consistent level of engagement and activity across multiple initiative dimensions within the sampled context. However, there are variations in how these initiatives are perceived within each dimension, which suggests that there is a generally positive assessment of dimension initiatives.

Shaping the Economic Recovery has the highest mean score at 3.77, indicating it is the most prioritised area. Harnessing the Fourth Industrial Revolution and Strengthening Regional Development are closely followed by mean scores of 3.69 and 3.70, respectively. Revitalising Global Cooperation, Developing Sustainable Business Models, Restoring the Health of the Environment, and Redesigning Social Contracts, Skills, and Jobs have slightly lower mean scores, ranging from 3.47 to 3.56.

4.2. Cluster Analysis

4.2.1. Model Summary

Figure 3 presents a summary of the two-step cluster analysis model and the cluster quality based on Silhouette’s measure of cohesion and separation. It shows Silhouette’s measure of cohesion and separation to be higher than 0.2, indicating that the cluster analysis with 10 independent variables is of fair quality [46].

Table 5. Auto-Clustering.

Number of Clusters	Schwarz’s Bayesian Criterion (BIC)	BIC Change a	The ratio of BIC Changes b	The ratio of Distance Measures c
1	1267.356
2	1184.253	−83.102	1.000	1.602
3	1174.38	−9.873	0.119	1.588
4	1209.52	35.140	−0.423	1.056
5	1248.734	39.214	−0.472	1.294
6	1304.428	55.695	−0.670	1.026
7	1361.521	57.093	−0.687	1.062
8	1421.791	60.269	−0.725	1.291
9	1493.658	71.868	−0.865	1.268
10	1573.948	80.290	−0.966	1.030
11	1655.158	81.210	−0.977	1.084
12	1738.727	83.569	−1.006	1.166
13	1826.292	87.564	−1.054	1.009
14	1914.071	87.779	−1.056	1.000
15	2001.857	87.787	−1.056	1.303

^a The changes are from the previous number of clusters in the table; ^b The ratios of changes are relative to the change for the two-cluster solution; ^c The ratios of distance measures are based on the current number of clusters against the previous number of clusters. Source: Authors’ work.

presents the results of the auto-clustering by showing BIC, BIC change, and the ratio of BIC changes calculated for 15 clusters.

The BIC values indicate the best fit at two clusters, with a slight but significant improvement when moving to three clusters. The ratios of BIC changes and distance measures suggest that adding a third cluster provides some additional benefit. After three clusters, both BIC and the ratios indicate that additional clusters do not significantly improve the model and may even worsen it. Therefore, we can conclude that the optimal number of clusters is three.

4.2.2. Variable Importance

Figure 4 presents all variables of the dimension initiatives (D1_avg to D7_avg), as well as region, position, and main area, which are used in a two-step cluster analysis with predictor importance.

Out of the ten named independent input variables, region, position, and main area (i.e., scientific field) have lower predictor importance than 60%, while the rest have higher prediction importance. Nevertheless, shaping the economic recovery after the COVID-19 variable has the greatest impact on the observed classification.

4.2.3. Clusters

Respondents are assigned to one of the three clusters after conducting the cluster analysis using the two-step methodology. Figure 5 graphically presents cluster sizes and distribution: (i) Cluster 1 with 46.7% of the respondents; (ii) Cluster 2 with 37.1% of the respondents; (iii) Cluster 3 with 16.2% of the respondents.

The results of the mean values analysis of the dimensions according to clusters with ANOVA analysis are presented in

Table 6. Mean values analysis of the dimensions according to clusters with ANOVA analysis.

Dimension Initiatives	Code	C1. Leaders	C2. Moderates	C3. Sceptics	F-test
		Mean (St.Dev)	Mean (St.Dev)	Mean (St.Dev)	(p-Value)
Shaping the Economic Recovery after COVID-19	D1_avg	4.23 (0.391)	3.58 (0.348)	2.87 (0.619)	72.592 (0.001) ***
Harnessing the Fourth Industrial Revolution after COVID-19	D2_avg	4.21 (0.429)	3.49 (0.402)	2.63 (0.838)	65.463 (0.001) ***
Strengthening Regional Development after COVID-19	D3_avg	4.24 (0.451)	3.52 (0.480)	2.54 (0.877)	63.807 (0.001) ***
Revitalising Global Cooperation after COVID-19	D4_avg	4.04 (0.540)	3.39 (0.420)	2.58 (0.827)	46.416 (0.001) ***
Developing Sustainable Business Models after COVID-19	D5_avg	4.09 (0.501)	3.39 (0.432)	2.31 (0.897)	65.955 (0.001) ***
Restoring the Health of the Environment after COVID-19	D6_avg	4.00 (0.640)	3.25 (0.355)	2.46 (0.866)	45.932 (0.001) ***
Redesigning Social Contracts. Skills and Jobs	D7_avg	4.04 (0.557)	3.33 (0.475)	2.44 (0.961)	45.877 (0.001) ***

Note: *** statistically significant at 1%; Source: Authors’ work.

. The table compares the average grades given by three clusters of higher educational institutions.

The first cluster comprises the respondents who perceived that their HEIs contributed to the highest level of the dimension actions, and they were titled Leaders. The second cluster comprises the respondents who perceived that their HEIs contributed to the moderate level of the dimension actions, and they were titled Followers. The third cluster comprises the respondents who perceived that their HEIs contributed to the lowest level of the dimension actions, and they were titled Laggers.

Leaders are consistently assessed with the highest average grades, ranging from 4.00 to 4.24, with relatively low standard deviations, indicating strong and uniform support. Followers were evaluated with intermediate grades, with means between 3.25 and 3.58 and low standard deviations, suggesting moderate but consistent support. Finally, Laggers were awarded with the lowest grades, with averages from 2.31 to 2.87. However, this group accounted for the highest standard deviations among all clusters, reflecting greater variability in answers.

Within Cluster 1 (Leaders), the highest mean is noted in the case of D3, representing the Strengthening Regional Development after the COVID-19 variable. Within both Cluster 2 (Followers) and Cluster 3 (Laggers), the highest mean is noted in the case of D1_avg, representing the Shaping the Economic Recovery after COVID-19 variable. The lowest mean value was observed for the variable D6_avg (Restoring the Health of the Environment after COVID-19) among the Leaders. Among the Followers and Laggers, the lowest mean values were observed for the variable D7_avg (Redesigning Social Contracts. Skills and Jobs). All calculated p-values are significant at the 1% significance level, meaning that there are statistically significant differences between the mean values of the observed three clusters for all observed dimensions (D1–D7).

Figure 6 presents the graphical representation of clusters. Figure 6a. presents information on the cluster size, the significance of the input variables (as shown by the scale from 0% to 100%), and the most prevalent groups of respondents based on the chosen independent variable. The rank of the input predictors based on their significance within each cluster is also graphically presented. Figure 6b graphically presents the distribution of variables according to clusters. It can be noted that the members of Cluster 2 (Followers) were normally distributed for most of the variables, except for the variables D2_avg and D3_avg, which were rightly skewed. Both members of Cluster 1 (Leaders) and Cluster 3 (Laggers) were rightly skewed, but the distributions for Laggers were flatter, indicating the greater dispersion among the answers.

4.2.4. Cluster Comparison

Table 7. Distribution of respondents according to scientific field.

	Social Sciences		Technology		Other		Chi-Square (p-Value)
Cluster	N	%	N	%	N	%	17.012
Advocates	39	48.80%	8	72.71%	1	7.70%	(0.0019)
Moderates	27	33.80%	1	0.91%	11	84.60%
Sceptics	14	17.50%	2	18.18%	1	7.70%
Combined	80	100.00%	11	100.00%	13	100.00%

Source: Authors’ work.

presents the distribution of respondents according to the main area, i.e., the main scientific field of the respondent (Social Sciences, Technology or Other) across three observed clusters. Within the Social Sciences area, 48.80% of respondents were assigned to Cluster 1, 33.80% were assigned to Cluster 2, and 17.50% were assigned to Cluster 3. Within the Technology area, 72.71% of respondents were assigned to Cluster 1, 0.91% to Cluster 2, and 18.18% to Cluster 3. Finally, within the other areas, 7.70% of respondents were assigned to Cluster 1, 84.60% were assigned to Cluster 2, and 7.70% were assigned to Cluster 3. The chi-square test indicates a statistically significant difference in the distribution of respondents across these fields at the 1% level of statistical significance.

Table 8. Distribution of respondents according to position.

	Research Assistant and Postdoctoral Researcher		Assistant Professor		Associate Professor		Full Professor		Chi-Square (p-Value)
Cluster	N	%	N	%	N	%	N	%
Advocates	13	61.91%	18	85.70%	7	23.30%	11	33.30%	17.077848314589
Moderates	3	14.28%	2	9.50%	17	56.70%	17	51.50%	0.00900135834
Sceptics	5	23.81%	1	4.80%	6	20.00%	5	15.20%
Combined	21	100%	21	100%	30	100%	33	100%

Source: Authors’ work.

presents the distribution of respondents according to their work position across three observed clusters. Out of the 21 respondents working in the positions of Research Assistant and Postdoctoral researcher, 61.91% of respondents were assigned to Cluster 1, 14.28% were assigned to Cluster 2, and 23.81% were assigned to Cluster 3. Out of the 21 respondents working in the Assistant Professor position, 85.70% of respondents were assigned to Cluster 1, 9.50% were assigned to Cluster 2, and 4.80% were assigned to Cluster 3. Out of the 30 respondents working in the Associate Professor position, 23.30% of respondents were assigned to Cluster 1, 56.70% were assigned to Cluster 2, and 20.00% were assigned to Cluster 3. Finally, out of the 33 respondents working in the Full Professor position, 33.30% of respondents were assigned to Cluster 1, 51.50% to Cluster 2 and 15.20% to Cluster 3. The chi-square test indicates a statistically significant difference in the distribution of respondents across these fields at the 1% level of statistical significance.

Table 9. Distribution of respondents according to region.

	EU		Non-EU		Other		Chi-Square (p-Value)
Cluster	N	%	N	%	N	%
Advocates	36	46.80%	6	35.30%	7	63.60%	41.487563351132
Moderates	37	48.10%	0	0.00%	2	18.20%	0.001
Sceptics	4	5.20%	11	64.70%	2	18.20%
Combined	77	100.00%	17	100.00%	11	100.00%

Source: Authors’ work.

presents the distribution of respondents according to the region, i.e., whether respondents are from the EU, non-EU, or other countries, across three observed clusters. Within the EU region, 46.80% of respondents are assigned to Cluster 1, 48.10% to Cluster 2, and 5.20% to Cluster 3. Within the non-EU region, 35.30% of respondents were assigned to Cluster 1, none of them were assigned to Cluster 2, and 64.70% of them were assigned to Cluster 3. Finally, within the other regions, 63.60% of respondents were assigned to Cluster 1, 18.20% of them were assigned to Cluster 2, and 18.20% of them were assigned to Cluster 3. The chi-square test indicates a statistically significant difference in the distribution of respondents across these fields at the 1% level of statistical significance.

Figure 7 provides an overview of the characteristics of the region in which HEIs were located, the field of study, and the position of respondents. It shows that Cluster 1 (Leaders) consists mainly of associate professors, research assistants, and postdoctoral researchers who are from other regions and mainly in the field of technology. Cluster 2 (Followers) consists mainly of assistant professors and full professors from the EU region working in other scientific areas. Finally, Cluster 3 (Followers) consists of mainly respondents from non-EU regions.

5. Discussion, Implication, and Conclusions

5.1. Summary of the Research

This study explores multiple approaches through which higher education institutions (HEIs) might drive change, including enhancing worker skills, supporting entrepreneurship and innovation, engaging in community development, and others, as part of the Great Reset initiative proposed to create a more inclusive and sustainable world by rethinking how countries, companies, and individuals approach economic development, social equity, and environmental stewardship.

A survey was conducted on a sample of university professors, postdoctoral researchers, and research assistants to assess the extent to which higher education institutions (HEIs) have contributed to economic recovery following COVID-19. The survey aimed to determine the impact of HEIs in utilising industry 4.0 technologies, promoting regional development, fostering global cooperation, developing sustainable business models, improving environmental well-being, and restructuring social contracts, skills, and employment opportunities. A two-step cluster analysis was performed, which resulted in the extraction of three clusters in relation to the strength of HEIs’s response to the COVID-19 pandemic.

First, the analysis revealed a notable disparity among HEIs according to the level at which they participated in the various dimensions of recovery after the pandemic COVID-19, revealing that three groups of HEIs strongly differentiate according to their distinct roles in the Great Reset process, that we called Leaders, Followers, and Laggers. Members of Cluster 1 (Leaders) consistently provided the average grades for all seven dimension initiatives, ranging from 4.0 to 4.24, indicating that they agree with the statement that their HEIs played a significant role in the recovery after the pandemic. Members of Cluster 2 (Followers) provided average grades ranging from 3.33 to 3.58, indicating that they are neutral with the statement that their HEIs played a significant role in the recovery after the pandemic. Finally, members of Cluster 3 (Laggers) provided average grades ranging from 2.31 to 2.87, indicating that they mainly do not agree with the statement that their HEIs played a significant role in the recovery after the pandemic. Considering that Cluster 1 (Leaders) contained the highest number of HEIs (46.7%), Cluster 2 (Followers) contained 37.1% of HEIs. The smallest number of HEIs (16.2%) was in Cluster 3 (Laggers); the results are moderately optimistic, indicating that around half of HEIs played a strong role in the recovery after the pandemic. On the other hand, a large portion of HEIs still did not engage strongly in the recovery.

Second, the study revealed the difference between the respondents from different scientific fields, positions, and regions in terms of their view of the impact of their HEIs on economic recovery following the COVID-19 pandemic, with the leaders predominantly from the technology field, with assistant professors more frequently considering their HEIs as leaders. Possible explanations may lay in the fact that the assistant professors’ careers might be shaped by their recent admission into academia, which may make them more aware of current trends and creative methods, making them more inclined to recognise and engage in progressive institutional initiatives. Assistant professors may also participate more in multidisciplinary research initiatives that correspond with the Great Reset, establishing their HEIs as leaders in this field.

Furthermore, the involvement of Higher Education Institutions (HEIs) in the Great Reset was influenced by their geographical location. HEIs outside of Europe were most frequently identified as the Leaders in this initiative. However, both Followers and Laggers were comprised of HEIs from European countries, both EU and non-EU.

5.2. Theoretical Implications

The contributions of this study are as follows. First, the conclusion of the study about the notable disparity among HEIs according to the level at which they participated in the various dimensions of recovery after the pandemic COVID-19 confirms the findings of [36] that there are various levels of commitment of HEIs towards the implementation of sustainable development, with strong academic leadership commitments as a leading force for the implementation of various sustainability initiatives. Therefore, this research reveals the existing disparities as disclosed and made more serious by the pandemic, increasing the argument for systemic changes. While different sectors and regions have experienced varying degrees of impact [47], our results shed some light on the fact that the same trend occurs in HEIs, calling for a set of actions to strengthen leadership in HEIs but also governmental-funded research in the area related to the dimension initiatives.

Second, the study found that leading HEIs were predominantly from the Technology field, confirming the previous findings that HEIs from Science, Technology, Engineering, and Mathematics (STEM) are perceived as leaders in technology innovation [48], which is a prerequisite for the implementation of Industry 4.0 [49]. On the other hand, all the research fields were relevant in the implementation of e-learning during the COVID-19 pandemic [43,44]. Although the strongest response from the technical HEIs was expected, the lack of commitment from social sciences HEIs is concerning, considering that the social response was crucial to resilience and recovery during and after the pandemic [50]. Also, technical HEIs may have easier access to funding, both government and business [51], which is a possible additional barrier for HEIs from social sciences in tackling the challenges that emerged as an outcome of the COVID-19 pandemic. Additionally, the results that associate professors were more eager regarding the role of their HEIs in the Great Reset is in line with the findings of [52] that junior researchers are more open to new ideas. At the same time, senior academics may have established research agendas and institutional positions that are less affected by these developing efforts.

The conclusion that the involvement of HEIs in the Great Reset initiative is influenced by their geographical location, with non-European HEIs more frequently identified as Leaders, highlights significant regional disparities in global engagement and leadership. Furthermore, [53] indicates that there is a significant difference between the respondents from northern European countries compared to southern European countries according to the digital transformation, leading to the conclusion that differences are also likely among these two groups of countries. These implications align with existing research, such as the work of [54], who discuss the varying motivations and realities of internationalisation across different regions. Similarly, [55] critically reflect on the massification, diversification, and internationalisation of higher education in China, highlighting how regional and national contexts can significantly influence the development and global positioning of HEIs. These studies suggest that the observed regional disparities in the Great Reset initiative are part of a broader pattern.

5.3. Practical Implications

This study has practical implications for HEIs’ management at the national level, as outlined in Figure 8 and detailed in

Table 10. Detailed elaboration of practical implications for fostering the role of HEIs in the Great Reset.

Initiative	Elaboration	Outcome
HEIs management level
Multidisciplinary approach to research and teaching	Given the importance of various scientific fields to the Great Reset, HEIs should promote and invest in multidisciplinary approaches integrating technology, social, and environmental studies.	Enabling institutions to address complex global challenges more effectively and contribute to more holistic and sustainable solutions.
Reassessing educational curricula and programs	Content related to sustainability, digital literacy, and interdisciplinary collaboration should be incorporated at the course level, i.e., as important learning outcomes.	Novel curricula prepare students to be active participants in shaping a more equitable and sustainable future.
Incentive programs for individual researchers	Develop and implement incentive programs aimed at actively encouraging individual researchers to contribute to initiatives aligned with the Great Reset.	Plethora of grants, awards, and research projects that focus on sustainability, digital transformation, and interdisciplinary collaboration.
National and international level
Strategic investment in technology and innovation	HEIs, particularly in Europe, should increase their investments in technology and innovation.	Enhanced digital infrastructure and integrating new technologies that are pivotal to the Fourth Industrial Revolution.
Funding programs to propel regional development	Funding for specific programs aimed at overcoming the regional disparities highlighted in the study.	Providing targeted support to HEIs in regions that are lagging, particularly in Europe.
Fostering international collaborations with leading HEIs	Funding is needed to foster cooperation with HEIs outside Europe that are recognised as leaders in the Great Reset and to share best practices, knowledge, and resources.	Benefit from strengthening collaborations with these leading institutions, thereby enhancing their own capacities and global engagement.

Source: Authors’ work.

.

The initiatives outlined in Figure 8 and Table 10 have the potential for a strong spillover effect from HEIs to national and international levels, as well as the opposite. The initiatives within HEIs, such as promoting a multidisciplinary approach, reassessing curricula, and incentivising individual researchers, can spill over to the national level by influencing broader educational policies and fostering a culture of innovation and sustainability within the country. For instance, successful multidisciplinary programs within HEIs could inspire national educational reforms that prioritise interdisciplinary learning and research, which could lead to a more holistic approach to addressing national challenges. Conversely, national strategies like strategic investments in technology, regional development funding, and fostering international collaborations can provide HEIs with the resources and frameworks necessary to implement these initiatives effectively. National investments in technology and innovation, for example, can enhance the capacity of HEIs to pursue cutting-edge research. At the same time, international collaborations can broaden the impact of HEI-led initiatives by integrating global best practices into national education and research policies. This bidirectional influence creates a synergistic relationship where HEI initiatives drive national progress, and national policies empower HEIs to play a pivotal role in global challenges like the Great Reset.

5.4. Limitations and Future Research Directions

Although this research extends the body of knowledge, one should acknowledge its limitations. Self-reported data from research assistants, postdoctoral researchers, and university professors posing as key informants for the university might induce bias, depending on their specific position and research field. Furthermore, the clustering technique could not fully reflect the complexity of local and personal reactions to the epidemic. Future research should be conducted on a more varied sample and include longitudinal data that would better grasp the long-term effects of the pandemic and the Great Reset concept. Additionally, the in-depth interviews of the university management would provide a starting point for the investigation of the strategic outlook of HEIs on their role in the Great Reset.

5.5. Concluding Remark

In an era marked by unprecedented challenges and opportunities, the Great Reset initiative has emerged as a crucial framework for rethinking and rebuilding global systems in the aftermath of the COVID-19 pandemic. As a final remark, although the term Great Reset has become intertwined with various conspiracy theories, it is undeniable that future world development strongly depends on a fundamental shift in the narrative, philosophy, and approach to daily living, business operations, and governmental processes, in which HEIs will play a pivotal role.