Unveiling the Role of Arab Universities in Advancing Sustainable Development Goals: A Multi-Dimensional Analysis

Abstract

In the global pursuit of sustainability, higher education institutions serve as powerful actors, leveraging their multifaceted contributions to advance the implementation of the United Nations’ Sustainable Development Goals (SDGs). This study employed a multi-dimensional analysis to evaluate the prioritization of these goals within the institutional framework of Arab universities and identify the key factors that drive their involvement with these goals. The methodology used involved a quantitative approach, utilizing a survey questionnaire to collect data from faculty members representing 30 public universities across the Arab region. The results of the study regarding prioritization analysis reveal that SDG4 (Quality Education) and SDG 8 (Decent Work and Economic Growth) are top priorities across all universities, with prioritization scores exceeding 65.4. Furthermore, the results of the ordinal logistic regression analysis demonstrate that institutional governance and research productivity are significant factors that influence Arab universities’ engagement with the SDGs. The findings of this study have important implications for higher education policies, practices, and interventions, aimed at fostering university engagement with the Sustainable Development Goals.

1. Introduction

In the pursuit of global sustainability, the United Nations’ Sustainable Development Goals (SDGs) serve as a beacon, guiding collective efforts towards a more equitable, prosperous, and environmentally sound future. Universities play a crucial role in this regard by supporting knowledge generation, dissemination, and societal engagement [1,2,3,4]. Within the Arab region, universities have a significant influence, serving not only as centers of higher learning but also as vital contributors to regional development agendas. However, while the importance of universities in advancing sustainable development is widely recognized, a comprehensive understanding of their specific roles and contributions within the Arab context remains relatively underexplored. Against this backdrop, this study examined on a scholarly basis the multifaceted engagement of universities in the Arab region with the SDGs. The inquiry was motivated by the need to clarify how universities can effectively support and accelerate the achievement of the SDGs, thereby strengthening the effectiveness of multi-sectoral partnerships at the national level. At the core of the study lies an exploration of four pivotal functional areas of universities: institutional capacity and governance, university–community partnerships and collaboration, research productivity, and teaching and learning. Through meticulous examination of the practices and strategies deployed within these functional domains, our research sought to uncover the mechanisms by which universities contribute to the advancement of the SDGs. We recognize that universities play a dual role as both agents of change and beneficiaries of sustainable development initiatives. Understanding the complex interplay between university activities and sustainable development outcomes is essential for planning well-informed policies and strategies aimed at fostering harmonious partnerships between academia, government, and civil society. This study aimed to move beyond simply documenting university contributions to the Sustainable Development Goals (SDGs) by conducting a comparative analysis to identify similarities and differences among universities in the Arab region. Such analysis holds the promise of providing valuable insights into the contextual factors that shape university engagement with sustainable development goals, leading to the design of appropriate interventions and best practices to enhance their effectiveness. Guided by two overarching research questions, this study sought to advance scholarly discourse on the relationship between universities and sustainable development in the Arab region. Through rigorous empirical investigation and theoretical analysis, the study aimed to offer evidence-based recommendations for maximizing the transformative potential of universities as drivers of sustainable development in the Arab world.

The rate of progress towards achieving all of these goals is not as fast as expected, as many SDG indicators and targets are still off-track [5,6,7]. According to the 2023 UN Sustainable Development Progress Report, only 15% of the SDGs are on track, progress on more than 50% of targets is weak and insufficient, and 30% have stalled or reserved. In general, there is a large body of research demonstrating that numerous challenges have impeded the successful implementation of SDGs in many countries worldwide. These challenges include but are not limited to the lack of multi-stakeholders engagement, unclear policy guidelines [8], the escalating impacts of climate change [9], ineffective governance structures and lack of political commitment [10], disagreements on local priorities [11], lack of integration, and difficulties of measurement [12]. It is also worth noting that the implementation of the SDGs to date shows significant gaps, especially with respect to measuring countries’ performance and progress towards achieving all these goals [13,14,15]. To address these challenges, it is imperative that all UN Member States should urgently prioritize the integration of the SDGs into national policies, strategies, plans, and public investments. Particular emphasis should be placed on leveraging the power of cross-sectoral and multi-stakeholder partnerships and collaboration to reduce the SDGs implementation gaps. Diverse government institutions, civil society organizations, academia, and private sector partners should be involved to take on significant roles in the SDG implementation framework. Within this context of partnership, higher education institutions (HEIs) are expected to play a leading role in accelerating the achievement of the 17 SDGs [16,17,18,19].

The current study aims to explore the essential role of universities in the Arab region in fostering and accelerating the achievement of the Sustainable Development Goals (SDGs). It seeks to enhance the effectiveness of multi-sectoral partnerships at the national level, particularly in terms of monitoring, evaluating, and reporting progress towards the SDGs. The research will focus on four key functional domains of universities: institutional capacity and governance, university–community partnerships and collaboration, research productivity, and teaching and learning. By examining practices within each of these areas, the study aims to assess their potential contributions to advancing the SDGs. Additionally, it will evaluate the similarities and differences in how universities in the region contribute to the SDGs. Guided by two fundamental research questions, the study aims to provide insights into the mechanisms through which universities can act as catalysts for sustainable development within their respective contexts.

There are two main pathways of involvement through which the higher education sector impacts the SDG framework [12,18,20]. First, the importance of higher education to the SDGs is explicitly formulated as a stand-alone education goal under SDG4 (Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all) and education-related targets among other goals. There are two targets under SDG4 that are closely related to higher education, namely target 4.3 and target 4.b. Target 4.3 specifically states, “By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university”. Target 4.b was developed as a necessary implementation measure of target 4.3, which aims to increase the number of scholarships and to expand accessibility of higher education [21]. Second, the higher education sector has been recognized as an influential driver for implementing the full set of the 17 SDGs not just for its educational role. For instance, higher education institutions, particularly universities, can significantly contribute to the realization of all the SDGs by providing the necessary cutting-edge academic knowledge in both teaching and research and the evidence-based solutions and innovations required to address the SDGs implementation challenges [12,22,23]. Furthermore, a wide range of functional areas has been identified by SDSN through which higher education institutions can contribute towards the achievement of the SDGs. These areas include institutional capacity and governance, university–community partnerships, research and development, and teaching and learning [20].

In general, there is a growing global recognition among policymakers and academics of the need to incentivize higher education institutions to take on a leading role in the multi-sectoral partnership efforts required for the successful implementation of the SDGs. UNESCO has been at the forefront of promoting the integration of SDGs within higher education institutions, providing extensive resources and guidelines to support this endeavor. UNESCO’s work emphasizes the transformative potential of universities in fostering sustainable development. In “Education for Sustainable Development Goals: Learning Objectives” [24], universities are encouraged to embed SDG-related content into their curricula, promote interdisciplinary research, and engage with local communities to address sustainability issues. The “Framework for the Implementation of Education for Sustainable Development (ESD) beyond 2019” [25] outlines strategic approaches for higher education institutions to contribute to the SDGs, including policy advocacy, capacity building, and the establishment of sustainable campus practices. As a result, a growing body of academic research has focused on how universities engage with the SDG framework and how their institutional and academic practices align with the SDGs. Examples of this engagement include incorporating sustainable development issues into the strategic planning of higher education institutions [26], advancing SDG competencies in higher education [22], integrating the SDGs into university academic programs [27], exploring the power of research for achieving the SDGs [28], aligning universities mission and activities with the SDG framework [29], embedding SDGs into college curriculum and students learning activities [30], incorporating SDG into the structures and missions of university-based centers [31], and integrating SDGs in extra-curricular and co-curricular activities [32]. Overall, the results of these studies demonstrate how university-generated knowledge can be used to deliver practical measures and actions needed to accelerate the SDGs implementation.

Some other important works in the field of universities and sustainable development goals include “Universities as the engine of transformational sustainability toward delivering the sustainable development goals: ‘Living labs’ for sustainability” by [29], which provides a comprehensive overview of the role of universities in advancing sustainability; “Universities and Sustainable Development: The Role of University Research in Fostering Sustainable Development” by [33], which explores the contributions of university research to sustainable development goals; and “The crosscutting impact of higher education on the Sustainable Development Goals” by [12] and “Sustainability in Higher Education: From Doublethink and Newspeak to Critical Thinking and Meaningful Learning” by [34], which delve into the pedagogical challenges and opportunities for integrating sustainability into higher education curricula. These articles represent foundational contributions to the literature, offering theoretical frameworks, empirical evidence, and practical insights into the role of universities in advancing sustainable development goals.

Despite the large number of studies on university engagement in the SDGs, there is not much empirical evidence to support this engagement in the Arab region, where almost all countries face immense sustainable development challenges [35,36,37]. It is worth indicating that the majority of the existing literature focuses on linking only a portion of university operations to the SDGs. For instance, ref. [35] assessed the extent of establishing an institutional framework for achieving sustainable development in Saudi Arabian universities. Therefore, this study sought to bridge this critical gap by exploring how the SDGs are being integrated into university practices. Unlike previous studies, this study addresses the integration of the SDGs in four functional areas of universities in the Arab region rather than just one functional area.

2. The Present Study

The Arab region as a whole has demonstrated very low performance on almost all SDGs; and most courtiers are not expected to meet most of these goals by the deadline due to several enormous economic, environmental, social, political, and technological challenges. Numerous regional and global reports have prominently revealed that many countries in the region are far off-track to achieve the SDG targets [7,36,37]. For example, recent reports on the Global SDG Index show that many countries did not score high in terms of SDGs performance (see Figure 1, Figure 2 and Figure 3). The slow progress in implementing the SDGs was also emphasized in the 2022 Arab Region SDG Index and Dashboard Report [36]. This report shows that a total of 19 Arab countries have not yet achieved a single SDG. It also reveals that SDG5 (Gender Equality), SDG2 (Zero Hunger) and SDG8 (Decent Work and Economic Growth) are the most significant challenges across the region.

It is worth indicating that SDG performance varies widely at sub-regional levels, with very low performance being particularly prevalent in all low-income Arab countries (Comoros, Djibouti, Mauritania, Somalia, Sudan, and Yemen). In contrast, the middle-income countries (Algeria, Egypt, Iraq, Jordan, Lebanon, Libya, Morocco, Palestine, Syrian, and Tunisia), and the high-income countries (Bahrain, Kuwait, Qatar, Saudi Arabia, Sultanate of Oman, and the United Arab Emirates) have shown some progress across some SDGs. For example, the 2022 Arab Region SDG Index and Dashboard Report demonstrated that five low-income countries along with two of the middle-income countries (Syria and Libya) each have 10 or more SDGs in the red category on the SDG Dashboard, indicating that they are far from achieving these goals.

The low performance and implementation rates of SDGs have created an urgent need to accelerate efforts and collaboration actions at the sub-national, national, and regional levels in all areas of the SDGs in order to make significant progress in the remaining years. One key action to accelerate the achievement of SDG targets in the region is to establish effective cross-sectoral and multi-stakeholder partnerships, as this will significantly accelerate the implementation, monitoring, evaluation, and reporting of the SDGs. There is no doubt that higher education institutions, particularly universities, are at the heart of all SDG implementation processes.

The importance of universities as key actors in advancing SDGs development goals is highlighted by emphasizing the need for integrated approaches that encompass institutional governance, community engagement, research, and education. Additionally, there is recognition of the unique contextual factors within the Arab region that influence university contributions to sustainable development, warranting tailored strategies and interventions. This context aims to explore how universities in the Arab region are effective in supporting and accelerating the achievement of the SDGs, thus contributing to enhancing the effectiveness of multi-sectoral partnerships at the national level in monitoring, evaluating, and reporting on progress towards the SDGs. To achieve this goal, four functional areas of the university must be considered, including institutional governance, partnership with the community, research productivity, and teaching and learning. Within this context, the present study attempted to assess how university practices in each functional area can contribute to the SDGs. Furthermore, an assessment of similarities and differences in university contributions to SDGs were also considered. Based on these considerations, the study was guided by the following research questions (RQ):

RQ1: 

To what extent do universities in the Arab region prioritize the SDGs and integrate them into their institutional and program practices?

RQ2: 

What are the key drivers of Arab universities’ contribution to the sustainable development goals?

3. Method

This study is descriptive in nature, utilizing a quantitative research approach to collect and analyze numerical data with the aim of describing the main features of university contributions towards the SDGs. The statistical data analysis involved descriptive statistics, Cronbach’s alpha, and ordinal logistic regression. Cronbach’s alpha was used to measure the internal consistency of the survey items. Descriptive statistics were used to summarize faculty members’ perceptions of Arab universities’ contributions towards the SDGs. Ordinal logistic regression was used to explore the factors driving Arab universities’ contribution to advancing the SDGs

3.1. Data Collection Procedures

This study adopted a cross-sectional data collection design with a self-developed questionnaire. The questionnaire was developed based on two practical guides for integrating SDGs into higher education institutions [20,38], along with some current perspectives on the role of higher education in achieving the SDGs [12,22]. To ensure content validity, seven faculty members from various academic specializations reviewed the initial questionnaire version. They confirmed that the questionnaire items were adequate in addressing the research questions. Additionally, a pilot study involving 30 faculty members was conducted to assess the internal consistency of the questionnaire. The results show that Cronbach’s alpha values ranged from 0.843 to 0.915 for the sub-scales, with the total scale having a Cronbach’s alpha of 0.921. These values indicate that the instrument has a very good internal consistency [39]. Following the reviewers’ feedback and pre-test results, we made revisions to some of the questionnaire items, resulting in the finalization of the questionnaire.

The final version of the questionnaire consists of three sections. The Section 1 includes demographic information for participating faculty members, such as gender, country, academic rank, and specialization. This section also contains categorical questions to collect faculty members’ feedback on aspects related to the dependent variable “the contribution of universities to the SDGs”. In the Section 2, a priority rating scale question was created to gather data that can help answer the question “To what extent do universities in the Arab region prioritize the SDGs?” Faculty members were required to rank each of the 17 SDGs on a scale from 1 to 17, with 1 indicating the highest level of priority and 17 indicating the lowest level of priority. The Section 3 includes 17 measurement items that aim to identify how universities can contribute to the achievement of the SDGs. This section is further divided into four sub-sections, each covering one of the university’s core functions and operations. These include institutional governance (3 items), university–community partnerships (4 items), research productivity (4 items), and teaching and learning (4 items). A 5-point Likert scale was developed to assess the extent to which faculty members agreed or disagreed with each item in this section (1 = strongly disagree, 2 = disagree, 3 = unsure, 4 = agree, and 5 = strongly agree).

3.2. Participants and Sample Description

Data collection for this study took place between February and April 2023. The questionnaire was created on the Google Forms platform and distributed through online delivery using a snowball non-probability sampling method. The questionnaire link was shared with faculty members through various social media platforms such as WhatsApp, Twitter, and Facebook. To increase the response rate, participants were asked to share the link with their colleagues who are involved in teaching or research positions at universities in the Arab region.

A total of 428 faculty members from 30 public universities in 14 Arab countries participated in this study. In terms of the profile of the participating faculty members, 341 (79.7%) were males, and 87 (20.3%) were females. Regarding participants’ academic rank, the majority of respondents were assistant professors (62.9%, n = 269), with the remaining participants being lecturers (10.2%, n = 44), associate professors (17.8%, n = 76), and full professors (9.1%, n = 39). The areas of specialization included humanities and social science (22.4%, n = 96), health sciences (7.2%, n = 31), economic and business (21.3%, n = 91), engineering and technology (17.3%, n = 74), natural sciences (18.2%, n = 78), and agricultural and environmental sciences (13.6%, n = 58).

Table 1. Distribution of participating faculty members by countries.

Country	No. of Universities	Participating Faculty
		N	%
Algeria	2	28	6.5
Egypt	3	48	11.2
Iraq	2	27	6.3
Jordan	3	36	8.4
Kuwait	1	20	4.7
Morocco	2	29	6.8
Oman	2	30	7.0
Qatar	1	25	5.8
Saudi Arabia	3	41	9.6
Sudan	3	45	10.5
Syria	2	21	4.9
Tunisia	2	26	6.1
United Arab Emirates	2	32	7.5
Yemen	2	20	4.7

presents the distribution of participating faculty members by countries and number of universities.

3.3. Data Analysis Methodology

3.3.1. Sustainable Development Goal Prioritization Score

To address the first question of this study, each sustainable development goal (SDG) was assigned a rank ($R_j$), with j ranging from 1 to 17 based on faculty member’s evaluation. Next, the average ranking for each SDG (${\overline{Y}}_{{SDG}_j}$) was calculated by summing all ranks assigned to that goal and dividing by the total number of participants. A mathematical formula was then applied to convert the prioritized ranks into a 100-point scoring system. The resulting score was named the Sustainable Development Goal priority score (${SDG}_{P.score}$).

$${SDG}_{P.score}=\frac{100}{nR}\left[nR+1-R_j\right]$$

where $nR$ represents the number of ranks, and $R_j$ is from 1 to $nR$. The values of the SDG priority score ranged from a minimum value of 100/17 to a maximum of 100.

3.3.2. Ordinal Logistic Regression Analysis

Model Development

To investigate the effectiveness of universities in the Arab world in supporting the achievement of the SDGs, an ordinal logistic regression (OLR) model was utilized. OLR is a valuable predictive modeling technique, especially when the dependent variable is measured on an ordinal scale. The ordinal dependent variable has three or more ordered categories. Examples include a five-point agreement scale, a seven-point satisfaction scale, or response variables with options such as never, sometimes, usually, and always. Independent variables or predictors in this model may be either quantitative and/or qualitative explanatory variables. In essence, OLR is a type of regression analysis technique that is widely used for modeling or predicting the cumulative probability of an event (or a particular outcome) occurring in each category of the ordinal dependent variable based on the observed data of a set of independent variables. The unknown parameters of the OLR model are typically estimated using the maximum likelihood (ML) probabilistic framework. In this framework, the dependent variable (Y) is initially transformed into a logit link function. The logit function is defined as the natural logarithm (ln) of the odds of a polychotomous categorical outcome occurring. This transformation helps in finding a set of estimated parameters that produce the most accurate predicted values of the outcome variable Y based the sample data of the independent variables [40,41].

For a particular outcome variable (Y) with J-ordered categories assigned values 1, 2, …, k and a set of X-explanatory variables (covariates), the cumulative probability of (Y) being less than or equal to a specific category (j) can be formulated by the following functional form:

$$Pro\left(Y\le y_{j}x\right)=\frac{{\mathrm{e}}^{({\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+{\beta }_3{X}_3\dots +{\beta }_k{X}_k)}}{1+{\mathrm{e}}^{({\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+{\beta }_3{X}_3\dots +{\beta }_k{X}_k)}},{\mathrm{R}}_{\mathrm{Y}}=\left\{0,1,\dots ,\mathrm{k}\right\}$$

where Y represents a linear combination function of the X-predictor variables and can be re-expressed by the following logit transformation:

$$Y=\mathrm{logit}\left({\prod }_{\mathrm{j}}\right)=\log \left[\frac{{\prod }_{\mathrm{j}}}{1-{\prod }_{\mathrm{j}}}\right]$$

$$Y=\log \left[\frac{Y\le y_{j}x}{Y>y_{j}x}\right]={\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+{\beta }_3{X}_3\dots +{\beta }_k{X}_k,j=1,2,\dots ,k$$

where ${\prod }_{\mathrm{j}}=\mathrm{Pro}\left(\mathrm{Y}\le {\mathrm{y}}_{\mathrm{j}}\mathrm{x}\right)$ is the predicted cumulative probability of the event ($\mathrm{Y}\le {\mathrm{y}}_{\mathrm{j}}$). ${\mathsf{\beta }}_0$ is the intercept parameter. ${\mathsf{\beta }}_0$, ${\mathsf{\beta }}_1,{\mathsf{\beta }}_2,$…,${\mathsf{\beta }}_{\mathrm{k}}$ are the unknown OLR coefficients corresponding to the model’s predictors ${\mathrm{X}}_1,{\mathrm{X}}_2,$…, ${\mathrm{X}}_{\mathrm{k}}$. The maximum likelihood estimate of each regression coefficient $\left({\mathsf{\beta }}_{\mathrm{i}}\right)$ shows whether there is a relationship between each independent variable and the ordinal response variable for a specific category of that predictor variable compared to the identified reference category of the response variable. Alternatively, the estimated OLR coefficient can be interpreted as the average change in the log odds of a particular category (or categories) of an ordinal response variable (Y) associated with a one-unit change in each independent variable in the model, given that the other independent variables remain constant [42]. The estimation process of the OLR model also includes calculating the odds ratio (OR) for each estimated regression coefficient $\left({\mathsf{\beta }}_{\mathrm{i}}\right)$ by applying an exponential transformation to that regression coefficient ($\mathrm{O}\mathrm{R}=\mathrm{E}\mathrm{x}\mathrm{p}\left(\mathsf{\beta }\right)$). The odds ratio is specifically used to understand the impact of each predictor variable. It describes how much a particular ordinal outcome variable increases or decreases as a result of a one-unit change in the corresponding predictor variable, assuming all other predictors in the model remain constant. The odds ratio in the OLR model can be calculated by $({\prod }_{\mathrm{j}}/1-{\prod }_{\mathrm{j}})$, which indicates the probability that a particular event occurs relative to the probability that the event does not occur. In summary, odds ratios greater than one illustrate a positive relationship between the ordinal response variable and the corresponding predictor variable (meaning the event or outcome of interest is more likely to occur), while odds ratios less than one indicate a negative relationship (meaning the event or outcome of interest is less likely to occur).

Variables Determination and Measurement

The OLR model considered in this study includes “the contribution of universities to SDGs” as the dependent (outcome) variable with three ordered categories. Faculty members were required to rate their opinions on a scale from 1 to 3, where 1 = the university tends to have a low impact on accelerating the achievement of SDGs, 2 = the university has a moderate impact, and 3 represents a high impact. During the estimation stage, the first category is identified as a reference category. Based on the model’s data, the odds ratio for the estimated OLR model represents the cumulative odds (or increased likelihood) of the higher category (high impact) compared to all lower categories combined for each unit increase in the dependent variable. In predicting university contributions towards the SDGs, four predictor factors are considered. These factors include institutional governance, partnership with community, research productivity, and teaching and learning.

3.3.3. Ordinal Logistic Regression Model Estimation

In this study, the parameters of the OLR model were obtained using the maximum likelihood estimation (MLE) method [43,44,45]. The main objective of this method is to find a parameter estimate that maximizes the likelihood function, which is the probability that particular levels of the categorical response variable can be predicted based on the observed data of the independent variables included in the model. Key outputs of the estimated OLR model include the odds ratio (OR) with a 95% confidence interval and the p-value for each regression coefficient $\left({\mathsf{\beta }}_{\mathrm{i}}\right)$ as well as the results of various statistical tests to assess the predictive performance of the fitted OLR model. These tests encompass the likelihood ratio (LR) test, the Wald chi-square test, the Omnibus tests of model coefficients, the Hosmer–Lemeshow test, Cox and Snell R-square, and Nagelkerke R-square. The likelihood ratio (LR) test is conducted to evaluate the overall statistical significance of the coefficients $\left({\mathsf{\beta }}_{\mathrm{i}}\right)$ in the model (i.e., to test whether a set of explanatory variables are collectively significant or not). This test compares the log likelihood function of a full model (considering all predictor variables) against the null model that includes only the intercept (a model without predictors). Rejecting the null hypothesis in this test indicates that at least one of the coefficients is significantly different from zero. The Wald chi-square test, along with the associated probabilities (p-values), is used to determine if each predictor variable in the OLR model has a statistically significant impact on the ordinal outcome variable or not. This test assesses the significance of individual coefficient. The null hypothesis for this test states that a particular $\left({\mathsf{\beta }}_{\mathrm{i}}\right)$ coefficient is equal to zero. The Omnibus tests of model coefficients, along with the corresponding chi-square goodness-of-fit test, are used to indicate overall model performance (i.e., testing for the significance of all model parameters simultaneously). The Hosmer–Lemeshow test is also used to test and assess the goodness-of-fit of the estimated OLR model (i.e., determining whether the overall model provides an adequate fit to the dataset). In this test, a chi-square statistic is calculated; an insignificant chi-square value indicates that the model adequately fits the data. Furthermore, the Cox and Snell R-Square and the Nagelkerke R-Square methods were used to evaluate how much of the variation in the OLR model (university contribution to SDGs model) is explained by the existing predictor variables.

4. Results

4.1. The SDGs Prioritization Scores

Faculty members’ responses regarding the prioritization of the SDGs were analyzed to gain insights into the relative importance assigned by universities in the Arab region to each SDG. Based on the minimum (17.1) and maximum (89.6) SDG prioritization scores, priorities were categorized into three groups: low priority (ranging from 17.1 to less than 41.3), moderate priority (ranging from 41.3 to less than 65.4), and high priority (ranging from 65.4 to 89.6). Additionally, three color ranges (red, orange, and green) were used to visually represent these priority levels. The red range indicated the lowest priority level, the orange range denoted a moderate priority level, and the green range highlighted the highest level of priority.

The results of the SDG priority scores at the regional level (

Table 2. Descriptive statistics for the SDGs prioritization score.

SDGs		Mean	SD	Min.	Max.
SDG1	No Poverty	61.1	18.033	22.4	78.8
SDG2	No Hunger	40.7	15.819	17.1	61.4
SDG3	Good Health and Well-being	70.8	21.603	24.1	88.2
SDG4	Quality Education	80.2	7.052	67.9	89.6
SDG5	Gender Equality	42.1	11.157	26.2	60.8
SDG6	Clean Water and Sanitation	52.8	10.866	32.9	71.1
SDG7	Affordable and Clean Energy	61.2	13.580	36.2	80.5
SDG8	Decent Work and Economic Growth	75.4	9.515	58.6	87.3
SDG9	Industry, Innovation and Infrastructures	51.7	7.986	34.7	64.2
SDG10	Reduced Inequalities	53.7	5.047	46.9	63.3
SDG11	Sustainable Cities and Communities	74.5	9.770	57.3	85.6
SDG12	Responsible Consumption and Production	74.7	11.614	52.2	88.1
SDG13	Climate Action	74.3	10.797	56.3	87.9
SDG14	Life below Water	56.4	5.329	44.4	62.1
SDG15	Life on Land	39.4	15.155	17.4	56.0
SDG16	Peace, Justice, and Strong Institutions	60.2	11.003	44.4	74.8
SDG17	Partnerships for Goals	61.3	5.104	51.6	69.6

) show that the lowest-priority group includes Life on Land, No Hunger, and Gender Equality. The moderate-priority group includes Industry, Innovation, and Infrastructure; Clean Water and Sanitation; Reduced Inequalities; Life below Water; Peace, Justice, and Strong Institutions; No Poverty, Affordable and Clean Energy; and Partnerships for Goals. The high-priority group includes Good Health and Well-being, Climate Action, Sustainable Cities and Communities, Responsible Consumption and Production, Decent Work and Economic Growth, and Quality Education. Based on the average score estimates, SDG 4 (Quality Education) stands out as a top priority for all universities in the sample with an average score of 80.2, followed by SDG8 (Decent Work and Economic Growth) with an average score of 75.4. This underscores the importance of education in promoting sustainable development and tackling systemic challenges. For SDG8, its rank reflects the significance of economic empowerment and job creation in the region’s development agenda. In contrast, SDG15 (Life on Land), SDG2 (No Hunger), and SDG5 (Gender Equality) received the lowest priority levels. Several factors contribute to this outcome, including limited resource allocation, varying research focuses, policy influences, perceptions of urgency, awareness gaps, and cultural attitudes. Addressing these challenges will require efforts in advocacy, interdisciplinary collaboration, and educational outreach to ensure that all SDGs receive the necessary attention and resources.

In Figure 4, the visual representation of the SDG priority levels shows that out of the total 238 SDG priority scores calculated for all universities in the sample, 29 (12.2%) fall within the red color range, 117 (49.1%) fall within the orange color range, and 92 (38.6%) fall within the green color range. Moreover, it is clear that the universities from Qatar had the highest number of SDG scores in the green color band (11 SDG scores), followed by universities in the UAE, which had 10 scores in this color range. The results also indicate that four out of five high-income countries and one middle-income country (Tunisia) did not receive any values in the red color range. In contrast, two low-income countries (Sudan and Yemen) and one middle-income country had the highest number of SDG priority scores in the red band (five, eight, and six, respectively). Sudan, Syria, and Yemen only scored one SDG priority score in the green range (SDG4: Quality Education). By utilizing these color ranges, stakeholders can easily differentiae the priority level of each SDG. This visual representation facilitates decision-making processes and resource-allocation strategies, enabling universities and other relevant entities to concentrate their efforts on SDGs that are deemed more important in the Arab region.

At the sub-regional level, Figure 5 shows that, in general, the SDGs received higher levels of priority among universities in high-income countries (HICs) than among universities in middle-income countries (MICs) and low-income countries (LICs). The mean, minimum, maximum, and standard deviation for the SDG priority score values are as follows: 70.0, 41.2, 89.6, and 12.373 for the HICs; 58.9, 19.2, 84.6, and 16.112 for the MICs; and 43.1, 17.1, 68.6, and 15.112 for the LICs. According to the F-test, there are statistically significant differences between the three sub-regions in terms of the average SDG priority score values for each of the 17 goals.

At the country level, the results in

Table 3. Prioritization scores of the SDGs at country level.

Countries	G-1 Score	G-2 Score	G-3 Score	G-4 Score	G-5 Score	G-6 Score	G-7 Score	G-8 Score	G-9 Score	G-10 Score	G-11 Score	G-12 Score	G-13 Score	G-14 Score	G-15 Score	G-16 Score	G-17 Score
Qatar	78.8	61.4	88.2	89.6	60.5	71.1	77.9	87.3	64.2	63.3	85.6	87.5	87.8	61.6	54.4	74.8	67.5
UAE	72.4	59.9	87.1	87.7	60.8	70.4	80.5	85.1	60.5	57.7	85.5	88.1	87.9	62.1	54.6	74.4	64.0
Saudi Arabia	67.3	53.5	82.5	86.4	54.7	64.7	78.0	81.9	58.4	55.5	80.2	86.7	81.6	61.0	56.0	68.3	63.8
Oman	72.2	51.8	85.5	85.1	48.2	60.0	73.1	77.5	58.0	62.2	78.4	82.4	84.5	61.4	53.1	72.9	63.1
Tunisia	71.5	46.4	83.9	82.4	44.3	48.9	62.9	82.4	52.5	54.5	82.4	81.4	84.6	58.8	43.4	65.8	60.4
Jordan	67.6	45.8	80.2	85.0	45.8	54.1	56.9	79.6	52.8	51.5	78.1	77.5	75.0	53.8	41.0	58.5	69.6
Kuwait	70.9	52.1	82.9	84.7	41.2	54.7	70.3	73.5	55.9	54.1	71.5	80.3	67.4	59.7	53.5	65.0	65.0
Morocco	71.2	46.0	83.8	81.3	40.2	47.3	64.7	82.6	51.5	49.5	79.9	76.9	79.9	58.6	46.5	63.9	62.1
Algeria	71.8	41.6	80.7	80.5	38.4	49.6	54.8	73.5	50.6	48.9	75.4	73.7	73.7	56.1	41.4	59.0	60.5
Egypt	65.7	36.9	78.1	76.1	37.9	52.1	56.5	78.8	51.7	48.3	77.5	72.3	75.4	57.6	33.5	57.8	60.7
Iraq	57.5	20.3	56.6	75.2	31.4	48.6	50.5	74.7	49.2	49.5	74.1	70.8	65.6	52.9	21.6	48.4	61.0
Syria	35.6	19.3	40.9	72.3	29.7	41.5	47.1	59.7	34.7	53.2	59.1	60.5	60.8	53.2	17.6	45.1	55.5
Sudan	29.8	18.2	37.0	68.6	29.5	43.1	46.9	58.6	42.5	46.9	57.3	52.2	56.3	48.2	17.4	44.8	51.6
Yemen	22.4	17.1	24.1	67.9	26.2	32.9	36.2	60.3	41.2	56.5	57.4	55.0	59.4	44.4	17.4	44.4	52.9

show greater variation in terms of SDG prioritization scores. These scores range from very low priority in Yemen, where SDG2 (No Hunger) received a score of 17.1, to very high priority in Qatar, where SDG4 (Quality Education) received a score of 89.6. The selected universities from Qatar and the United Arab Emirates received the highest SDG priority scores at 74.2 and 72.9, respectively. Conversely, the selected universities from Sudan and Yemen received the lowest SDG scores at 44.1 and 42.1, respectively.

The prioritization of SDGs was also calculated based on economic, social, environmental, and institutional dimensions of sustainable development. The results at the regional level are presented in Figure 6, while

Table 4. Descriptive statistics for the SDGs prioritization score: sustainability dimensions across countries.

Country	Economic		Social		Environmental		Institutional
	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Qatar	73.0	12.331	77.8	14.578	73.1	13.283	71.2	5.158
UAE	70.2	13.212	75.7	15.056	73.5	13.381	69.2	7.409
Saudi Arabia	66.5	14.620	72.2	15.708	70.3	11.051	66.1	3.145
Oman	66.1	13.800	71.7	16.159	68.4	12.128	68.0	6.932
Tunisia	63.0	16.321	69.5	18.681	63.5	16.971	63.1	3.839
Jordan	60.9	14.317	68.4	18.295	59.8	14.120	64.1	7.857
Kuwait	64.8	13.152	67.3	18.991	62.8	7.913	65.0	0.000
Morocco	61.4	14.932	67.5	20.944	62.8	14.940	63.0	1.291
Algeria	59.5	15.858	64.4	19.484	58.5	13.492	59.8	1.040
Egypt	56.6	15.723	63.8	19.320	58.7	16.227	59.3	1.993
Iraq	49.5	21.394	57.5	18.418	52.2	17.956	54.7	8.935
Syria	37.5	17.037	51.1	16.495	46.5	15.914	50.3	7.329
Sudan	35.7	14.820	48.1	15.811	44.9	14.553	48.2	4.806
Yemen	33.9	17.465	47.0	20.385	41.3	15.906	48.7	6.031
All	57.0	14.388	64.4	16.013	59.7	13.446	60.8	0.730

displays the results across countries. Overall, the findings indicate that the environmental dimension received the highest priority in all three sub-regions. At the country level, the results show that universities in Qatar and the United Arab Emirates placed high priority on all sustainability dimensions. In contrast, universities in Yemen, Sudan, and Syria showed the lowest priority across all dimensions. The prioritization of SDGs can guide universities, policymakers, and stakeholders in aligning their efforts and resources with the most pressing sustainable development challenges. Addressing disparities in prioritization can foster collaboration and coordination among stakeholders, leading to a more balanced and comprehensive approach to achieving the SDGs. Continuous monitoring and reassessment of SDG priorities are essential to adapt strategies in response to evolving challenges and opportunities.

4.2. The Ordinal Logistic Regression Results

4.2.1. Descriptive Analysis

As indicated earlier, the OLR model in this study incorporates “the contribution of universities to SDGs” as the dependent or response variable with three ordered categories (low, medium, and high), and four independent variables: institutional governance, partnership with community, research productivity, and teaching and learning. Descriptive statistics were utilized to summarize the data for variables in the OLR model, including the mean, standard deviation, minimum, maximum, frequency, and percentage. Results were categorized based on country classification: low-income countries (LICs), middle-income countries (MICs), and high-income countries (HICs). Out of all 428 faculty responses for each of the three contribution levels, 48 (11.2%), 226 (52.8%), and 154 (36.0%) were at low, medium, and high levels, respectively. At the sub-regional level, the distribution of responses show that 62.2% of faculty responses from universities in high-income countries (HICs) were at the higher level of university contribution to the sustainable development goals compared to 19.1% reported for universities from middle-income countries (MICs) and 32.3% for universities from low-income countries (LICs). Additionally

Table 5. Descriptive statistics for the dependent and independent variables.

Variable	Mean			SD	Min.	Max.
	Possible Range	Value	(%)
A. Dependent variable
University contribution to SDGs
Low-income countries	(1–3)	1.98	66.0%	0.820	1	3
Middle-income countries	(1–3)	2.09	69.7%	0.530	1	3
High-income countries	(1–3)	2.59	86.3%	0.559	1	3
All countries	(1–3)	2.25	75.0%	0.642	1	3
B. Independent variables
Low-income countries
1. Institutional governance	(3–15)	10.22	68.1%	1.566	4	12
2. Partnership with community	(4–20)	10.91	54.6%	2.448	7	18
3. Research productivity	(4–20)	12.38	61.9%	2.956	4	16
4. Teaching and learning	(4–20)	13.29	66.5%	3.035	6	20
Middle-income countries
1. Institutional governance	(3–15)	10.75	71.7%	3.180	3	15
2. Partnership with community	(4–20)	10.97	54.9%	3.892	4	20
3. Research productivity	(4–20)	12.72	63.6%	3.832	4	20
4. Teaching and learning	(4–20)	14.05	70.3%	4.589	4	20
High-income countries
1. Institutional governance	(3–15)	13.07	87.1%	1.954	6	15
2. Partnership with community	(4–20)	17.66	88.3%	2.861	8	20
3. Research productivity	(4–20)	17.73	88.7%	2.599	8	20
4. Teaching and learning	(4–20)	18.30	91.5%	2.762	6	20
All countries
1. Institutional governance	(3–15)	11.47	76.5%	2.852	3	15
2. Partnership with community	(4–20)	13.27	66.4%	4.636	4	20
3. Research productivity	(4–20)	14.40	72.0%	4.109	4	20
4. Teaching and learning	(4–20)	15.40	77.0%	4.369	4	20

shows that the average of all responses (as a percentage) is 75%, with the lowest value being 66% for universities in LICs and the highest value being 83.3% for universities in HICs.

Regarding the independent variables, it is important to note that a new combined variable was created for each variable by summing faculty responses across all Likert-scale items relate to that variable. The resulting variables can have the following possible ranges: 3 to 15 for institutional governance variable and 4 to 20 for other independent variables. Table 5 displays essential descriptive statistics for both the dependent and independent variables. Overall, the results indicate that universities in high-income countries received higher average scores for all independent variables compared to universities in middle- and low-income countries. The results suggest that institutional governance is a key factor for universities in low- and middle-income countries to effectively support the implementation of the SDGs, followed by teaching and learning practices. In high-income countries, all independent variables received higher score values, ranging from 87.1% for institutional governance to 91.5% for teaching and learning. These results indicate that universities in these countries are effective in supporting the implementation of the SDGs through these four functional areas of universities.

4.2.2. Regression Results

The estimation results of the OLR model are presented in three steps: first, checking the appropriateness of the model; second, interpreting the results; and third, evaluating the significance and goodness of fit of the estimated model. To assess the appropriateness of the OLR model, the assumption of proportional odds was tested through the parallel lines test. The null hypothesis is that the location parameters (slope coefficients) for any independent variables are constant across different cut points (thresholds) in the response variable. Rejection of this hypothesis suggests that another logistic model could be used as an alternative to OLR. In this study, the chi-square results for the parallel lines test in Table 5 show that the proportional odds assumption is satisfied, as all p-values are greater than 5%. This indicates that the OLR model was appropriate for the analysis.

As indicated in the methodological part, the interpretation of the OLR results is presented in the form of odds ratios (OR) and their 95% confidence intervals (CIs). The main objective is to discover which independent variable(s) have a statistically significant impact on the universities contribution to the SDGs implementation. The OLR model was estimated for each sub-region separately (

Table 6. Ordinal logistic regression estimation results.

Parameters		Estimate $\left(\mathsf{\beta }\right)$	$\mathbf{S}.\mathbf{E}\left(\mathsf{\beta }\right)$	$\mathbf{W}\mathbf{a}\mathbf{l}\mathbf{d}{\mathsf{\chi }}^2$	Df	p-Value	Exp$(\mathsf{\beta }$)	95% CI for Exp($\mathsf{\beta }$)
Low-Income Countries
Threshold	Constant 1	29.254	6.11	22.87	1	0.000	-	17.265	41.243
	Constant 1	34.277	7.18	22.76	1	0.000	-	20.195	48.359
Location	Governance	1.319	0.397	11.06	1	0.001	3.741	0.542	2.097
	Partnership	0.373	0.204	3.334	1	0.068	1.452	−0.027	0.773
	Research	0.689	0.272	6.389	1	0.011	1.991	0.155	1.223
	Learning	0.394	0.236	2.801	1	0.094	1.483	−0.067	0.856
Model Fit
Chi-square (df = 4) = 78.703 (p-value = 0.000)
Deviance (df = 54) = 48.094 (p-value = 0.700)
Nagelkerke Pseudo R-square = 0.790
McFadden Pseudo R-square = 0.551
Test of Parallel Lines
−2 Log Likelihood		Chi-Square	df	p-value
45.956		6.496	4	0.165
Middle-Income Countries
Threshold	Constant 1	10.804	1.64	43.08	1	0.000	-	7.578	14.03
	Constant 1	19.766	2.46	64.38	1	0.000	-	14.938	24.59
Location	Governance	0.379	0.089	18.30	1	0.000	1.461	0.205	0.552
	Partnership	0.396	0.084	22.32	1	0.000	1.486	0.232	0.560
	Research	0.473	0.093	25.72	1	0.000	1.605	0.290	0.656
	Learning	0.143	0.074	3.724	1	0.054	1.154	−0.002	0.288
Model Fit
Chi-square (df = 4) = 200.193 (p-value = 0.000)
Deviance (df = 250) = 87.598 (p-value = 1.000)
Nagelkerke Pseudo R-square = 0.765
McFadden Pseudo R-square = 0.593
Test of Parallel Lines
−2 Log Likelihood		Chi-Square	df	p-value
102.958		0.872	4	0.929
High-Income Countries
Threshold	Constant 1	28.272	4.64	37.01	1	0.000	-	19.16	37.38
	Constant 1	38.608	6.17	39.06	1	0.000	-	26.50	50.71
Location	Governance	0.355	0.150	5.571	1	0.018	1.426	1.062	1.916
	Partnership	0.388	0.117	11.03	1	0.001	1.474	1.172	1.853
	Research	0.283	0.125	5.133	1	0.023	1.328	1.039	1.697
	Learning	1.234	0.208	35.18	1	0.000	3.435	2.284	5.165
Model Fit
Chi-square (df = 4) = 160.240 (p-value = 0.000)
Deviance (df = 218) = 69.785 (p-value = 1.000)
Nagelkerke Pseudo R-square = 0.839
McFadden Pseudo R-square = 0.697
Test of Parallel Lines
−2 Log Likelihood		Chi-Square	df	p-value
63.780		6.006	4	0.199

). The estimation results revealed that the strength and significance of the independent variables vary across the three sub-regions. In high-income countries, all independent variables showed positive and statistically significant regression coefficients. For universities in middle-income countries, three independent variables were statistically significant, while universities in low-income countries had two significant factors. It is important to note that university research productivity and institutional governance operations had statistically significant impacts on accelerating the implementation of the sustainable development goals in all three sub-regions.

Among the predictor variables for universities in HICs, the teaching and learning factor had the most significant impact on supporting the implementation of the SDGs with an estimated odds ratio of 3.435 (95% CI, 2.284–5.165). This finding demonstrates that teaching and learning practices were 3.435 times more likely to enhance the universities’ contribution to the implementation of the SDGs, holding all other independent variables constant.

Research productivity was found to be the most significant predictor for universities in the MICs. The estimated odds ratio (OR = 1.605, CI, 0.290–0.656) indicates that for a one-unit increase in research productivity, the odds of moving from a low contribution level to moderate or higher levels are 1.605 times greater, assuming all other explanatory (predictor) variables in the OLR model are held constant. In other words, this suggests that the higher the level of university research productivity, the greater the university contribution to the implementation of the SDGs. Partnership and institutional governance were also found to be significant predictors that influence the implementation of the SDGs in MICs. However, the results showed no significant impact of the teaching and learning factor at a 5% level of significance.

Similar to universities in middle-income countries, teaching and learning practices in universities from low-income countries had no significant impact on the implementation of the SDGs in these countries. Similarly, the contribution of university partnership with community was found to be insignificant. The most significant predictor was institutional governance. The estimated odd ratio (OR = 3.741, CI, 0.542–2.097) indicated that the partnership factor was 3.741 times more likely to enhance universities’ contribution towards the implementation of the SDGs, with all other predictor variables remaining constant. Research productivity was another statistically significant predictor (OR = 1.991, CI, 0.155–1.223).

The goodness-of-fit and predictive performance of the estimated OLR model were evaluated using chi-square, Nagelkerke pseudo R-square, and McFadden pseudo R-square. The results showed that the chi-square goodness-of-fit test statistic was highly statistically significant in all three cases (p-value < 0.001), demonstrating the overall significant of the OLR model. These results indicate strong evidence that the OLR model with predictors (i.e., the full model) fits significantly better than the initial model when only the intercept is included (i.e., the null model). Furthermore, the larger p-values of the deviance goodness-of-fit test provided additional evidence that the specified OLR model fits the data well in all three cases.

Finally, Nagelkerke pseudo R-square and McFadden pseudo R-square measures are used to evaluate how well the estimated OLR model explains the overall variation in the response variable (i.e., variation explained by the explanatory variables). The results of the Nagelkerke pseudo R-square showed that 79.0% of the variation among response variable was explained by existing explanatory variables for universities in LICs, 76.5% for MICs, and 83.9% for HICs. Furthermore, the values of the McFadden pseudo R-square ranged from 0.551 for universities in LICs to 0.697 for universities in HICs. This provides further evidence that the estimated OLR model more accurately predicts the outcomes of the response variable based on existing explanatory variables.

5. Discussion and Implications

The aim of this study was two-fold: to evaluate the prioritization of the Sustainable Development Goals (SDGs) within the institutional framework of Arab universities and to explore the key factors that drive their involvement with these goals. In the first part, an SDG prioritization score was calculated to gain insights into the relative importance assigned by universities in the Arab region to each SDG. In the second part, ordinal logistic regression analysis was performed to evaluate universities’ performance in four key functional areas: institutional capacity and governance, university–community partnerships and collaboration, research productivity, and teaching and learning. For analytical purposes, we considered the World Bank’s classification of Arab countries into three categories: low-income countries, middle-income countries, and high-income countries [46].

5.1. Prioritization of the Sustainable Development Goals

The study on the prioritization of Sustainable Development Goals (SDGs) by universities in the Arab region yielded insightful results concerning the focus areas of Sustainable Development Goals (SDGs) at different regional levels. At the regional level, the results of the study identified SDG4 (Quality Education) and SDG8 (Decent Work and Economic Growth) as the most prioritized goals at the regional level. This suggests that universities in our sample are placing significant emphasis on improving educational quality and fostering economic growth through decent employment opportunities. As for the least-prioritized SDGs, the results shows that the SDGs with the lowest priorities are SDG15 (Life on Land), SDG2 (No Hunger), and SDG5 (Gender Equality). This indicates a lesser focus on environmental sustainability, eradicating hunger, and promoting gender equality within these institutions. At the sub-regional level, the results revealed universities in high-income countries (HICs) prioritize SDGs more highly than those in middle-income countries (MICs) and low-income countries (LICs). This disparity may be due to differences in resources, infrastructure, and policy frameworks that support SDG initiatives.

These finding have important implications for policy and practice. Policymakers and universities in the region should consider providing targeted support for lower-priority SDGs and addressing disparities between universities. There is a need for targeted interventions and resource allocation to boost the prioritization of SDG15, SDG2, and SDG5. Universities need to incorporate the less-prioritized SDGs such as Life on Land (SDG15), No Hunger (SDG2), and Gender Equality (SDG5) into their core missions. This can be achieved through curriculum development, research initiatives, and community engagement programs that emphasize these critical areas. Universities and policymakers should develop strategies to address these gaps, potentially through partnerships, funding, and awareness campaigns. The lower prioritization of SDGs in MICs and LICs highlights the need for increased support and capacity building in these regions. Regional cooperation and funding can help bridge the gap and ensure a more balanced approach to achieving the SDGs across the Arab region. Inter-university collaboration and the sharing of best practices are crucial in this regard [47]. Universities in high-income countries (HICs) can play a pivotal role in sharing best practices, knowledge, and resources with those in middle-income countries (MICs) and low-income countries (LICs). Establishing collaborative networks and partnerships can enhance the overall capacity of universities to prioritize and achieve the SDGs.

5.2. Factors Driving Arab Universities Contribution to Advancing Sustainable Development Goals

According to the ordinal logistic regression analysis, the study found that universities in the three sub-regions play a crucial role in supporting the Sustainable Development Goals through their institutional capacity and research productivity. Similar results are provided in several studies, e.g., [18,48,49,50]. The significance of the institutional governance factor in our study suggests that universities with strong institutional capacity and governance structures are more likely to integrate Sustainable Development Goals into their policies and operations. These universities tend to have comprehensive policies that integrate sustainability principles into the university’s strategic plans, operations, and mission and vision statements. A strong leadership commitment is crucial factor in this regard. Universities with top management actively supporting sustainability initiatives tend to have better integration of sustainable development goals into their governance structures [35].

This involvement includes university presidents, deans, and department heads in sustainability efforts. This finding has an important implication for policy and practice. University leaders, especially those at universities with a low impact of institutional governance, should consider enhancing governance capacities. This includes providing resources for building robust governance frameworks that can support sustainability initiatives. Capacity-building programs addressing sustainable development issues should also be considered. Offering professional development opportunities and training workshops for university administrators and staff on sustainability issues can enhance their capacity [51,52,53]. This, in turn, can promote a more integrated approach to strengthen universities’ role in accelerating the implementation of the SDGs. By addressing these institutional aspects, universities can enhance their governance structures and practices, leading to a more effective integration of Sustainable Development Goals and a greater contribution to national and regional sustainability efforts.

Similarly, the significance of the research productivity factor, as indicated by a large number of publications, ensures that new knowledge on sustainability issues is widely shared and accessible. This dissemination of knowledge is vital for informing policy and practice. Arab universities with higher publication rates in sustainability issues are more influential in policy-making processes related to sustainable development. This finding has several important implications for policymakers, university administrators, and other higher education stakeholders. For example, university leaders should recognize the importance of research productivity in universities as a key driver for sustainable development. This involves prioritizing research projects that align sustainability goals with universities’ institutional policies and strategic initiatives. Collaboration and partnerships also play a significant role in this context. Collaborative efforts between universities, industry, and government can enhance the impact of research on sustainable development [54,55]. Likewise, partnerships can facilitate the transfer of knowledge from academia to practical applications, thereby accelerating the achievement of the SDGs. Additionally, universities should consider establishing clear metrics for evaluating research productivity and its impact on sustainable development goals. This will help universities track their sustainability-related research outputs, demonstrate their contributions to the SDGs, and identify areas for improvement.

It is worth noting that the research productivity impact of universities from high-income countries, such as those in the GCC region, is significantly higher than that of universities from low- and middle-income countries. According to the perspectives of the faculty members in our sample, universities in these countries provide sufficient funding and resources to support high research productivity. They also make substantial investments in research infrastructure, grants, and scholarships for sustainability-focused research projects. This has resulted in an increase in research output from GCC universities and has enhanced their ability to contribute to the SDGs.

The partnership between Arab universities and the community was identified as a significant factor in the contribution of universities from high- and middle-income countries towards the SDGs. This finding aligns with existing research that has highlighted the significant role of university–community partnerships in accelerating the achievement of the SDGs [56,57,58]. Consistent with these studies, our study revealed that Arab universities actively engaging in partnerships and collaborative projects with local communities, governments, NGOs, and industry partners tend to demonstrate higher success rates in advancing the implementation of the SDGs. These collaborations and partnerships enable resource sharing, knowledge exchange, and the co-creation of solutions, which are crucial for effectively addressing local sustainable development challenges.

This finding has important practical implications for policy and practice. Policymakers at the national level and university leaders should encourage and support initiatives that promote stronger connections between universities and their surrounding communities. Universities in low-income countries, in particular, should actively seek out and cultivate partnerships with various stakeholders, including local governments, NGOs, and industry partners, to enhance their contributions to the SDGs. This could involve providing funding and incentives for collaborative projects, prioritizing those that demonstrate clear potential in addressing the challenges related to the implementation of the SDGs. By strengthening these partnership aspects, promoting resource sharing, and fostering co-creation of solutions, Arab universities can significantly contribute to the implementation of sustainable development in their communities and beyond.

The results of the regression analysis revealed a significant impact of teaching and learning practices at universities in high-income countries on accelerating the achievement of the SDGs. This finding aligns with previous studies that have emphasized the significance of academic programs in higher education institutions for advancing the implementation of the SDGs [27,59]. The significance of this factor in our study emphasizes that universities are successful in aligning their curricula with the needs of sustainable development. This alignment may reflect that academic programs are adopting interdisciplinary approaches that integrate various fields of study to provide a comprehensive understanding of sustainability issues. This integration helps students acquire a unique set of skills and specialized knowledge that enables them to think critically about global challenges and understand the interconnectedness of social, economic, and environmental dimensions of sustainability.

This finding has practical implications for policy and curriculum development. Higher education institutions should continue to emphasize and invest in curriculum development that aligns with the SDGs [60]. This includes fostering interdisciplinary programs and encouraging innovative teaching practices that integrate sustainability concepts across various disciplines [61]. Universities should prioritize regional collaboration to enhance educational efforts aimed at achieving the SDGs. Universities in high-income countries should share their best teaching and learning practices and collaborate with those in low- and middle-income countries. This collaboration could involve exchange programs, joint research initiatives, and collaborative curriculum development projects. Additionally, universities should adopt continuous improvement mechanisms to assess and improve their teaching and learning practices in order to stay aligned with the evolving nature of the SDGs. This includes conducting regular curriculum reviews, incorporating feedback from students and industry partners, and staying updated with the latest sustainability research and trends.

However, despite the significant benefits of integrating SDGs into teaching and learning practices, several challenges need to be addressed in order to fully realize these benefits. Some challenges include resource constraints, insufficient training of faculty in sustainability issues, and a lack of strong institutional commitment to sustainability. In this regard, it is worth noting that many universities in the region, especially those in low-income countries, face resource constraints that limit their ability to implement comprehensive sustainability education programs. This limitation can hinder the effectiveness of such programs, preventing students from gaining the full breadth of knowledge and skills necessary to effectively address sustainability issues. To overcome these challenges, strong institutional support is required. This includes not only financial support but also administrative backing and a commitment to sustainability from the highest levels of the institution.

Our study provides a comprehensive analysis of the role of Arab universities in advancing the Sustainable Development Goals (SDGs). While our findings are robust, it is important to contextualize these results by comparing them with similar studies conducted in other regions. For example, many studies conducted in Europe have demonstrated that universities play a crucial role in promoting sustainability through research, education, and community engagement. For instance, ref. [62] found that European universities are increasingly integrating SDGs into their curricula and operational strategies, emphasizing the importance of multi-disciplinary approaches and partnerships with local communities. In comparison, our study indicates that Arab universities are also making significant strides in these areas but face unique challenges such as limited funding and political instability. Similarly, Asian universities have shown a strong commitment to SDGs through large-scale research projects and international collaborations. For example, ref. [63] found that Indian universities are leveraging community engagement and public–private partnerships to advance sustainability initiatives. In contrast, our research indicates that Arab universities are beginning to adopt similar technological innovations but at a different pace and scale due to varying resource availability and socio-economic contexts. In Africa, universities are increasingly recognized as key players in promoting sustainable development. For instance, a study by [64] found that universities are actively involved in community-based sustainability projects despite facing challenges such as limited funding and political instability. Our findings resonate with these efforts but also reveal that Arab universities are focusing more on specific regional priorities such as water scarcity and food security.

6. Conclusions

In conclusion, this study explores the fundamental role of Arab universities in driving forward the Sustainable Development Goals (SDGs) within the region. Through a multi-dimensional analysis focusing on institutional capacity, university–community partnerships, research productivity, and teaching and learning, the research underscores the potential contributions of universities towards achieving the SDGs. By examining these functional domains, the study not only sheds light on the diverse ways in which universities can support sustainable development but also emphasizes the importance of fostering effective multi-sectoral partnerships at the national level. Through collaborative efforts encompassing monitoring, evaluation, and reporting progress, universities can enhance their effectiveness in advancing the SDGs, thus contributing significantly to the broader sustainable development agenda in the Arab region. Furthermore, the research highlights the need for a careful and meticulous understanding of the similarities and disparities among universities in their contributions to the SDGs. By recognizing variations in institutional capacities, collaboration frameworks, and research priorities, policymakers and stakeholders can tailor strategies and interventions to leverage the strengths of each university towards sustainable development. Ultimately, the findings of this study serve as a call to action for universities, policymakers, and civil society actors to prioritize sustainable development within higher education agendas, foster cross-sectoral partnerships, and empower universities to fulfill their potential as engines of social change and progress in the Arab region. Based on the findings of the study, it is clear that Arab universities have the potential to significantly advance the Sustainable Development Goals (SDGs) through their institutional capacities, partnerships, research endeavors, and educational practices. However, disparities among universities in their contributions to the SDGs underscore the importance of considering this result. Recommendations include fostering a culture of sustainability across university campuses, enhancing interdisciplinary collaboration within academia and with external stakeholders, and providing resources and incentives to support research and teaching focused on sustainable development. Additionally, promoting inclusivity and gender equality within university structures and initiatives can enhance the impact of universities in addressing the SDGs. Furthermore, strengthening monitoring and evaluation mechanisms and promoting knowledge sharing among universities can facilitate the exchange of best practices and accelerate progress towards the SDGs in the Arab region. Overall, concerted efforts are needed to harness the full potential of Arab universities in driving sustainable development and ensuring a prosperous future for the region.

Finally, we acknowledge that the governance structure at universities plays a crucial role in shaping policies, strategies, and the overall environment for advancing Sustainable Development Goals (SDGs). The diversity in governance structures across universities can influence the effectiveness and implementation of SDG initiatives, thereby impacting the outcomes. This study considers that governance structure is crucial and needs future in-depth investigation in future research. Future research could focus on a comparative analysis of governance models in different universities and their effectiveness in promoting SDGs.