A Novel Hybrid Approach for Prioritizing Investment Initiatives to Achieve Financial Sustainability in Higher Education Institutions Using MEREC-G and RATMI

Abstract

Financial sustainability is a crucial challenge for higher education institutions due to various challenges and constraints. This necessitates determining their investment priorities accurately based on multiple criteria to ensure a sustainable income. This study puts forward a novel, hybrid approach for prioritizing the financial sustainability plan investment initiatives in higher education institutions using an updated method for the removal effects of criteria with a geometric mean (MEREC-G) and ranking the alternatives based on the trace-to-median index (RATMI) techniques. The developed approach is applied to the strategic financial sustainability plan investment initiatives at King Abdulaziz University (KAU). The study’s results prioritized the investment initiatives based on quantitative and qualitative criteria with different weights. Results also revealed the potential initiatives that can be considered quick-winning initiatives. Furthermore, results determined one high-potential initiative for each of KAU’s financial sustainability plan pillars. Based on the results, the study recommended four steps that assist KAU in ranking the initiatives effectively. Implications of the novel approach include assisting decision makers in higher education institutions to evaluate investment initiative priorities based on objective and subjective criteria to ensure the financial sustainability of their institutions.

1. Introduction

Sustainability and financial sustainability are both essential and interrelated aspects of Higher Education Institutions (HEIs). Sustainability refers to the ability of HEIs to contribute to social, environmental, and economic transformation by incorporating sustainability as a transversal concept in instruction, research, operations, and knowledge transfer [1,2]. Financial sustainability refers to the capacity of HEIs to produce and manage their resources efficiently and effectively to achieve their mission and goals [3]. The relationship between sustainability and financial sustainability at HEIs can be seen from different perspectives. One perspective is that sustainability can be a driver for financial sustainability, as HEIs can adopt innovative practices that can lower costs, increase revenues, and enhance reputation, for example, by reducing energy consumption, waste management, circular economy models (which aim to minimize waste and maximize resource efficiency), and social well-being programs. Another perspective is that financial sustainability can be a precondition for sustainability, as HEIs need adequate funding and resources to integrate sustainability into all their activities. A third perspective is that sustainability and financial sustainability can be mutually reinforced. HEIs can align their strategies and governance structures with both objectives and create synergies and partnerships with other societal actors [4,5]. These perspectives align with the 2030 Agenda for Sustainable Development, a global framework that sets 17 goals (SDGs) to address the most pressing challenges of our time. Higher education is vital in advancing the SDGs by generating and disseminating knowledge, developing skills and competencies, fostering values and attitudes, and engaging with local and global communities [6,7].

Financial sustainability is a crucial challenge for higher education institutions (HEIs) in Saudi Arabia, especially in the context of rapid economic changes and increasing competition. According to Abubakar et al. [8], some of the factors influencing the financial sustainability of Saudi HEIs are their dependence on government funding, which may fluctuate due to oil price fluctuations and budget cuts; the lack of diversification of income sources, such as tuition fees, donations, endowments, research grants, and commercial activities; the high operational costs, such as salaries, infrastructure, and utilities; and the transparency in financial management and reporting. Universities in Saudi Arabia have a big challenge: how can they stay financially stable without relying only on the government? This challenge aligns with the vision of KSA for 2030, which invites higher education to seek diverse and inventive ways to fund itself and handle its finances prudently and effectively [9,10,11,12]. Therefore, Saudi HEIs need to adopt innovative strategies to enhance their financial sustainability, such as developing new educational programs that meet market demand, fostering research and innovation that are linked to the industry, building partnerships and networks with external stakeholders, promoting a culture of entrepreneurship and innovation among students and faculty, and improving their financial governance [13]. To solve this issue, the Saudi Ministry of Education launched the New Universities Law, which aims to organize Saudi universities, guide their policies, and establish executive rules and regulations [14,15].

In November 2019, the Council of Ministers approved the new university law in Saudi Arabia to improve the quality and efficiency of higher education in the Kingdom. The law aims to enhance public universities’ education quality and make them more competitive and independent globally. Some of the benefits of this law are as follows: (1) it gives universities more freedom and responsibility to design their own curricula, policies, and strategies according to the public policies and the needs of the labor market; (2) it reduces the operating costs of universities and encourages them to identify new sources of funding through endowment programs and investment companies; (3) it fosters cooperation and partnership between universities and the private sector, as well as foreign institutions, to enhance the quality and diversity of education; and (4) it supports the vision of privatization and innovation in higher education, by allowing universities to adopt new operating models such as corporatization or public–private partnerships [15]. The law is expected to improve the performance and efficiency of public universities and contribute to the realization of Saudi Vision 2030.

Three of the largest public Saudi universities presently operate under the New Universities Law, with plans to gradually apply it to the other 27 universities by cabinet decree. The three universities that are piloting the new law are King Abdulaziz University (KAU), King Saud University (KSU), and Imam Abdulrahman bin Faisal University (IAU).

KAU is one of the leading universities in the Arab world and ranks among the best 200 universities in the world according to various international classifications. KAU offers a variety of academic programs and research activities that serve the region’s development needs and employment opportunities [16]. To maintain its financial sustainability and optimize its investment sources, KAU has developed an innovative strategy plan called the Green Ocean Strategy for Financial Sustainability (GOSFS) [17]. The GOSFS is a visionary roadmap that consists of 18 steps across three key performance areas: resource development (KPA1), good governance (KPA2), and regulations and legislation (KPA3). The GOSFS sets ambitious targets for 2025 and 2045 by employing innovative tools such as the Balance Scorecard, the Business Model Canvas, and the Strategy Map to align with the GOSFS concept [17]. The Financial Sustainability Office (FSO) at KAU is responsible for implementing the GOSFS to enable KAU to achieve financial sustainability by effectively using, allocating, and investing its human and technical resources, scientific programs, facilities, and infrastructure at the highest international standards. The KPA1 comprises eight pillars that encompass 33 initiatives (treated as alternatives) and 43 performance indicators. This paper aims to rank the alternatives, corresponding to each KPA1’s pillar, in a way that accelerates achieving the target income based on their weighted criteria. This situation entails multi-criteria decision making (MCDM) as the criteria for selecting the initiatives that may conflict with their objectives. Therefore, the main objective of this paper is to apply a new hybrid MCDM approach to evaluate and rank initiatives of the KAU financial sustainability plan. Consequently, the paper will address the following questions:

• What is the ranking of the KAU’s financial sustainability plan initiatives based on a set of quantitative and qualitative criteria with different weights?
• What are the potential alternatives that the KAU’s investment administrator has to consider for generating quick income by the year 2025?
• How can the decision makers and planners at KAU obtain revenues by implementing at least one initiative in each of the eight KPA1 pillars of the financial sustainability plan?

This paper is organized as follows: Section 2 explores the literature on financial sustainability in higher education and MCDM tools. Section 3 proposes a novel integrated approach to evaluating and ranking the financial sustainability plan initiatives. Section 4 applies the approach to a case study from KAU and presents the results. Section 5 discusses the findings, and Section 6 concludes with recommendations for future research.

2. Literature Review

Diversifying revenue streams, reducing costs as much as possible, boosting productivity, raising quality, encouraging innovation, and fortifying relationships with other stakeholders are all ways to attain financial sustainability. In contrast, MCDM techniques help evaluate and rank alternatives based on multiple and often-conflicting criteria. MCDM tools can be applied to various aspects of financial sustainability, such as investment appraisal, risk management, portfolio optimization, and performance measurement [18,19]. This literature review will first examine the financial sustainability of HEIs. Then, it will introduce different MCDM tools and their applications in various sustainability fields.

2.1. Financial Sustainability in Higher Education Institutions (HEIs)

How do universities maintain their financial sustainability? This question has sparked many studies in the past decades. Some scholars have mapped the trends and theories in this field by analyzing hundreds of papers and their citations [20]. Others have proposed and tested various indicators and benchmarks to measure and compare the financial health of different institutions [21]. Some have focused on the specific challenges and opportunities faced by universities in certain regions or countries, such as the UK [22], OECD members [23], Latvia [24], and Zimbabwe [25]. Some have explored how universities can balance their autonomy, accountability, and innovation in financial reporting and management [26]. Also, some have surveyed university leaders’ and staff’s opinions and strategies for enhancing their income and reducing expenses [27]. These studies provide valuable insights and recommendations for improving the financial sustainability of higher education in a changing world.

The financial performance of income-generating units (IGUs) in Puntland State universities was explored by Mohamed and Muturi [28]. They suggested universities should enhance their market research, management skills, and financial planning. Mahmud et al. [29] conducted a case study on a public university in Indonesia. They proposed innovative ways to increase revenue, such as renting buildings, offering laboratory services, and providing scientific consulting services. Sakhiyya and Rata [30] emphasized the importance of marketing the knowledge produced by human capital through research and innovation to boost the income of HEIs. Alstete [31] examined and evaluated various novel methods for funding higher education, such as income-contingent loans, social-impact bonds, endowment funds, securitization of future earnings, and alumni donations. Using a structural equation model and survey data from 111 respondents across various units, faculties, and universities. Mahmud et al. [32] examined the influence of staff awareness, staff participation, and top management support on income generation. They found that staff awareness affects income-generating performance indirectly through top management support. Liu and Gao [33] investigated how public universities in China finance their campus sustainability initiatives, including employing modern technology, such as energy and water conservation, renewable energy, landscape design, and environmental project training, to enhance the physical environment and lower carbon footprints. They found that strong leadership and cooperation among university stakeholders are essential for achieving financial sustainability.

2.2. Multi-Criteria Decision Making (MCDM) Tools and Applications

MCDM is a process of evaluating and choosing among different alternatives based on multiple conflicting criteria in decision making. The purpose of MCDM is to support decision makers facing such problems where there is no unique optimal solution, and preferences are needed to differentiate between solutions [34,35]. MCDM consists of two main parts: weighting the criteria and ranking the alternatives. Weighting the criteria involves assigning relative importance or preference values to each criterion, reflecting the decision maker’s goals and priorities. Ranking the alternatives involves applying a mathematical method or algorithm to combine each alternative’s weights and performance scores on each criterion, resulting in a final score or rank for each alternative. The alternative with the highest score or rank is then selected as the best option.

There are different methods of weighting criteria, depending on whether the weights are derived from the decision maker’s judgments (subjective methods) [36,37,38], from the alternative data (objective methods) [39,40,41,42,43], or a combination of both (hybrid methods) [44,45,46]. Some examples of subjective methods are the analytic hierarchy process (AHP), the analytic network process (ANP), and the best–worst method (BWM) [37,38]. These methods involve pairwise comparisons of criteria or alternatives based on a scale of preferences. Some examples of objective methods are the entropy, CRITIC, and MEREC methods [47,48,49,50]. These methods use mathematical formulas to calculate the weights based on the information entropy, the correlation coefficients, or the compromise ranking of the alternatives. Some examples of hybrid methods are fuzzy AHP, fuzzy ANP, and fuzzy BWM. These methods incorporate fuzzy logic to handle the uncertainty and vagueness in the decision maker’s judgments [51].

After weighing the criteria, the next step is to rank the alternatives based on their performance on each criterion. There are different methods of ranking alternatives, depending on whether they use a single criterion or multiple criteria to compare the alternatives. Simple additive weighting (SAW), the weighted product method (WPM), and technique for order preference by similarity to ideal solution (TOPSIS) are a few examples of single-criterion approaches [52]. These methods use a linear or nonlinear aggregation function to combine the weighted scores of each alternative on each criterion into a single value. Some examples of multiple criteria methods are outranking methods, such as ELECTRE and PROMETHEE [53,54,55]. These methods use pairwise comparisons of alternatives based on concordance and discordance indices, which measure the degree of agreement and disagreement between two alternatives on each criterion.

A literature review showed various ways to achieve financial sustainability and generate income for HEIs. Moreover, MCDM has different tools and methods that can be used in various fields, such as finance, engineering, and education, where decision makers compare different options based on multiple factors. For instance, in finance, MCDM can help portfolio managers balance risk and return [18,19,56]; in engineering, MCDM can help design engineers choose the best manufacturing contractors [47]; and in education, MCDM can help educators in assessing education quality standards for HEIs based on multiple criteria [57,58,59]. Therefore, based on the importance of the financial sustainability plan in HEIs, especially for answering this research question, two recent MCDM tools will be integrated for the case study at KAU. The updated method for removal effects of criteria with a geometric mean (MEREC-G) tool [49] will be used for weighting the criteria since some data are objective and others are subjective. Moreover, ranking the alternatives based on the trace-to-median index (RATMI) tool [60] will be used to rank the alternatives since it demonstrated its effectiveness in several real-world fields.

3. The Proposed Novel Approach

This paper aims to evaluate and rank the initiatives of the financial sustainability plan at KAU using a novel strategy that consists of five main phases, as shown in Figure 1. The first phase identifies various financial resources as the plan’s first key performance area (KPA1). The second phase determines the initiatives (alternatives) and the criteria for evaluating each financial resource. The third phase organizes the alternatives and criteria in a decision matrix. The fourth phase assigns weights to each criterion using MEREC-G [49]. The fifth phase ranks the alternatives of the financial sustainability plan using the ranking of the RATMI technique [60], a recent MCDM tool based on the weighted criteria. The following sections explain these five phases in more detail.

3.1. Phase 1: Identifying Financial Resources

Step 1.1: To align with the fourth KAU strategic plan (2022–2025) and the Kingdom’s Vision for 2030 [9] and to optimize KAU’s financial resources in compliance with the New Universities Law [14], the KAU administration leaders embarked on the GOSFS plan’s roadmap [17]. Through a series of meetups and brainstorming sessions inspired by the “Fiscal Sustainability Program” [10] and the “Privatization Program” [11], they will establish a long-term goal for the year 2045 and a general goal for the four years, from 2022 to 2025.

Step 1.2: This step is crucial for reaching the general goal. It hinges on the first key performance area (KPA1) called “Resource Development,” which consists of eight pillars representing various financial resources. Each pillar has its objectives, initiatives, and performance indicators. Then, A SWOT analysis will be applied based on the approach given by Al-Filali et al. [17] to ensure the success and visibility of each objective.

3.2. Phase 2: Determining the Alternatives and Their Criteria

Step 2.1: The FSO at KAU will collaborate with a team of experts from various administrative and faculty members to apply the strategic planning framework of Al-Filali et al. [17] to define the specific objectives for each of the eight pillars of KPA1. Next, they will design initiatives for each objective to generate revenue via performance indicators. These initiatives will be evaluated as potential alternatives using three sequential strategic models: the balanced scorecard, the business model canvas, and the strategy map. These models help assess the feasibility and achievability of each alternative.

Step 2.2: In this step, the KAU’s top leaders will collaborate with the FSO and the scientific experts to devise a set of criteria for screening the initiatives. These criteria will reflect the values, principles, factors, and constraints the KAU will apply to assess and select the most promising initiatives. The team’s expertise will inform the criteria, a comprehensive analysis of the KAU’s internal and external context, the human resources needed for each initiative, the initial cost required, the expected revenue generated by each initiative, and so on. The criteria are essential for ensuring the quality and fairness of the KAU’s decisions.

3.3. Phase 3: Constructing the Decision Matrix

This step involves constructing a decision matrix that maps the initiatives (possible alternatives) derived from Step 2.1 and the criteria derived from Step 2.2. The matrix enables the assessment and prioritization of the alternatives based on a set of criteria with assigned weights. This is crucial for the KAU decision maker because it contrasts different alternatives logically and clearly, and pinpoints the best one according to the decision maker’s preferences. Hence, two techniques are needed: the first is to assign weights to the criteria, and the second is to rank the alternatives based on the weighted criteria.

3.4. Phase 4: Calculating the Criteria Weights

As the data from the initiatives are objective, and others are subjective, the method based on the removal effects of criteria (MEREC) is suitable for calculating the criteria weights. This method measures the impact of each criterion on the ranking of alternatives by removing it. Keleş [49] proposed two modified versions of MEREC using the geometric mean (MEREC-G) and the harmonic mean (MEREC-H) from the multiplicative function of the criteria. He found that MEREC-G was more effective in determining the objective weights of the criteria with a lower standard deviation than the MEREC and MEREC-H methods. Hence, this phase will use MEREC-G. The following steps summarize the MEREC-G methodology:

Step 4.1: Construct the decision matrix $\left(X\right)$. The value of each alternative $\left(i\right)$ for each criterion $\left(j\right)$ is $\left(x_{ij}\right)$. The number of alternatives is $\left(n\right)$ and the number of criteria is $\left(m\right)$. All values must be greater than zero. The form of the decision matrix is as follows:

$$X={\left[x_{ij}\right]}_{nm}$$

(1)

Step 4.2: Normalize the decision matrix by elements $N_{ij}$. If the beneficial $\left(B\right)$ represents the maximum set of criteria and nonbeneficial $\left(NB\right)$ represents the minimum set of criteria, the normalization equations are as follows:

$$N_{ij}=\left\{\frac{\begin{array}{c}mi{n}_{kj}\\ k\end{array}}{x_{ij}}\right\} if j\in B \mathrm{for} \mathrm{a} \mathrm{maximum} \mathrm{set} \mathrm{of} \mathrm{criteria}$$

(2)

$$N_{ij}=\left\{\frac{x_{ij}}{\begin{array}{c}ma{x}_{kj}\\ k\end{array}}\right\} if j\in NB \mathrm{for} \mathrm{a} \mathrm{minimum} \mathrm{set} \mathrm{of} \mathrm{criteria}$$

(3)

Step 4.3: Calculate the overall performance value $S_i$ of the alternatives using the geometric mean of the normalized matrix. The following equation shows how to calculate the overall performance values from the normalized values in the previous step:

$$S_i=\sqrt[m]{{{\displaystyle \prod }}_{j=1}^m{N}_{ij}}$$

(4)

Step 4.4: Calculate the discrete overall performance value of the alternatives by removing the value of each criterion. Consider $S_{ij}^{\prime }$ as the overall performance of the $i$th alternative concerning the removal of the $j$th criterion. The following equation is used for the calculations of this step:

$$S_{ij}^{\prime }=\sqrt[m]{{{\displaystyle \prod }}_{k,k\ne j}^m{N}_{ij}}$$

(5)

where $k$ is the number of remaining criteria in the calculation made by removing any criteria.

Step 4.5: Compute the removal effect of the $j$th criterion based on the values obtained from the previous two steps. Let $Y_j$ denote the effect of removing the $j$th criterion by adding up the absolute deviations. This effect can be expressed using the following equation:

$$Y_j={\displaystyle \sum }_i\left|S_{ij}^{\prime }-S_i\right|$$

(6)

Step 4.6: Determine the final objective weights of the criteria using the removal effects $\left(E_j\right)$ of the previous step. Let $w_j$ stands for the weight of the $j$th criterion. Then, the following equation is used for the calculation of $w_j$:

$$w_j=\frac{Y_j}{{{\displaystyle \sum }}_k{Y}_k}$$

(7)

3.5. Phase 5: Ranking the Initiatives (Alternatives)

There are various MCDM tools for ranking the alternatives. One of these tools is ranking the alternatives based on the trace-to-median index (RATMI). This technique was applied to 15 real-world problems and proved its effectiveness compared to other well-known MCDM tools [60]. Hence, RATMI will be used in this phase. The following steps summarize the RATMI methodology:

Step 5.1: Construct the problem data in the form of a decision-making matrix $X_{ij}$:

$$D={\left[x_{ij}\right]}_{mxn}=\left[\begin{array}{ccccc}A/C& C_1& C_2& \dots & C_n\\ A_1& x_{11}& x_{12}& \dots & x_{1n}\\ A_2& x_{21}& x_{22}& \dots & x_{2n}\\ ⋮& ⋮& ⋮& \ddots & ⋮\\ A_m& x_{m1}& x_{m2}& \dots & x_{mn}\end{array}\right],$$

(8)

where $A=\left[A_1, A_2, \dots , A_m\right]$ is a given set of initiatives (alternatives), and m is the total number of alternatives.

$C=\left[C_1, C_2, \dots , C_n\right]$ is a predetermined set of criteria, and n is the total number of criteria. As the set includes conflicting criteria (i.e., beneficial and cost criteria), the objective is to maximize the beneficial and minimize the cost criteria.

${\left[x_{ij}\right]}_{mxn}$ is an assessment of alternative $A_i$ with respect to a set of criteria.

Step 5.2: Normalize the problem data. The problem data are multidimensional since each criterion is described by its associated dimension. Making decisions in this circumstance is challenging. Therefore, the multidimensional decision space must be transformed into a nondimensional space. In this step, normalization is determined in the following manner for the maximization criteria:

$$r_{ij}=\frac{x_{ij}}{ma{x}_i\left(x_{ij}\right)}, \forall i\in \left[1, 2, \dots , m\right] \wedge j\in S_{max}$$

(9)

while for the minimization criteria,

$$r_{ij}=\frac{mi{n}_i\left(x_{ij}\right)}{x_{ij}}, \forall i\in \left[1, 2, \dots , m\right] \wedge j\in S_{min}$$

(10)

where:

• $S_{max}$ is a set of criteria that should be maximized.
• $S_{min}$ is a set of criteria that should be minimized.

As a result, the normalized decision matrix will have the following form:

$$R={\left[r_{ij}\right]}_{mxn}=\left[\begin{array}{ccccc}A/C& C_1& C_2& \dots & C_n\\ A_1& r_{11}& r_{12}& \dots & r_{1n}\\ A_2& r_{21}& r_{22}& \dots & r_{2n}\\ ⋮& ⋮& ⋮& \ddots & ⋮\\ A_m& r_{m1}& r_{m2}& \dots & r_{mn}\end{array}\right]$$

(11)

Step 5.3: Perform the weighted normalization as follows for the normalized assessment matrix $r_{ij}$:

$$u_{ij}=w_j{r}_{ij}, \forall i\in \left[1, 2, \dots , m\right], \forall j\in \left[1, 2, \dots , n\right]$$

(12)

where

• $w_j$ is a weight of criterion $j$ that is calculated in phase 4. The sum of the weights must equal one: ${{\displaystyle \sum }}_{j=1}^n{w}_j=1$.

Then, the weighted normalization matrix can be formed as follows:

$$U={\left[u_{ij}\right]}_{mxn}=\left[\begin{array}{ccccc}A/C& C_1& C_2& \dots & C_n\\ A_1& u_{11}& u_{12}& \dots & u_{1n}\\ A_2& u_{21}& u_{22}& \dots & u_{2n}\\ ⋮& ⋮& ⋮& \ddots & ⋮\\ A_m& u_{m1}& u_{m2}& \dots & u_{mn}\end{array}\right]$$

(13)

Step 5.4: Determine each component of the optimal alternative as follows:

$$q_j=max\left(u_{ij}|1\le j\le n\right), \forall i\in \left[1, 2, \dots , m\right]$$

(14)

The following set represents the optimal alternative:

$$Q=\left\{q_1, q_2, \dots , q_j\right\}, j=1, 2, \dots , n$$

(15)

Step 5.5: Decompose the optimal alternatives into two sets or two components.

$$Q=Q^{max}\cup Q^{min},$$

(16)

$$Q=\left\{q_1, q_2, \dots , q_k\right\}\cup \left\{q_1, q_2, \dots , q_h\right\}; k+h=j$$

(17)

where:

• k: denotes the total number of criteria that should be maximized.
• h: denotes the total number of criteria that should be minimized.

Step 5.6: Similar to Step 5.5, decompose each alternative.

$$U_i=U_i^{max}\cup U_i^{min}, \forall i\in \left[1, 2, \dots , m\right],$$

(18)

$$U_i=\left\{u_{i1}, u_{i2}, \dots , u_{ik}\right\}\cup \left\{u_{i1}, u_{i2}, \dots , u_{ih}\right\}; \forall i=\left[1, 2, \dots , m\right]$$

(19)

Step 5.7: For each component of the optimal alternative, calculate the magnitude defined by

$$Q_k=\sqrt{q_1^2+q_2^2+\dots q_k^2}$$

(20)

$$Q_h=\sqrt{q_1^2+q_2^2+\dots q_h^2}$$

(21)

The same approach is applied to each alternative.

$$U_{ik}=\sqrt{u_{i1}^2+u_{i2}^2+\dots u_{ik}^2} , \forall i=\left[1, 2, \dots , m\right],$$

(22)

$$U_{ih}=\sqrt{u_{i1}^2+u_{i2}^2+\dots u_{ih}^2} , \forall i=\left[1, 2, \dots , m\right]$$

(23)

From this point, the following two methods were developed to create the rank of alternatives:

Step 5.7a: Rank by alternative trace. Create the matrix F composed of optimal alternative components:

$$F=\left[\begin{array}{cc}{Q}_k& 0\\ 0& Q_h\end{array}\right]$$

(24)

Create the matrix $G_j$ composed of alternative components:

$$G_j=\left[\begin{array}{cc}{U}_{ik}& 0\\ 0& U_{ih}\end{array}\right], \forall i=\left[1, 2, \dots , m\right]$$

(25)

Create the matrix $T_i$ as follows:

$$T_i=F\times G_j=\left[\begin{array}{cc}{t}_{11;i}& 0\\ 0& t_{22;i}\end{array}\right], \forall i=\left[1, 2, \dots , m\right]$$

(26)

Then, the trace of the matrix $T_i$ is as follows:

$$tr\left(T_i\right)=t_{11;i}+t_{22;i}, \forall i=\left[1, 2, \dots , m\right]$$

(27)

Alternatives are ranked at this stage according to the descending order of $tr\left(T_i\right)$.

Step 5.7b: Rank by alternative median similarity. The median of the optimal alternative is represented by the median of the right angle. Components $Q_k$ and $Q_h$ represent the base and perpendicular side of this triangle.

$$M=\left(\sqrt{Q_k^2+Q_h^2}\right)/2$$

(28)

The median of each alternative is calculated in the same way.

$$M_i=\left(\sqrt{U_{ik}^2+U_{ih}^2}\right)/2$$

(29)

The ratio between the perimeter of each alternative and the optimal alternative is represented by the median similarity in the following form:

$$M{S}_i=\frac{M_i}{M}, \forall i=\left[1, 2, \dots , m\right]$$

(30)

Alternatives are now ranked according to the descending order of $M{S}_i$.

Step 5.8: Rank the alternatives based on the RATMI technique. The median of the right angle represents the median of the optimal alternative. If $v$ is the weight of the Multiple Criteria Ranking by Alternative Trace (MCRAT) approach and $\left(1-v\right)$ is the weight of Ranks Alternatives based on the Median Similarity (RAMS) approach, then the majority index $Z_i$ between the two strategies is as follows:

$$Z_i=v\frac{\left(t{r}_i-t{r}^{*}\right)}{\left(t{r}^{-}-t{r}^{*}\right)}+\left(1-v\right)\frac{\left(M{S}_i-M{S}^{*}\right)}{\left(M{S}^{-}-M{S}^{*}\right)}$$

(31)

where:

• $t{r}_i=tr\left(T_i\right), \forall i=\left[1, 2, \dots , m\right]$.
• $t{r}^{-}=\max \left(t{r}_i, \forall i=\left[1, 2, \dots , m\right]\right)$.
• $M{S}^{-}=\max \left(M{S}_i, \forall i=\left[1, 2, \dots , m\right]\right)$.
• $t{r}^{*}=\min \left(t{r}_i, \forall i=\left[1, 2, \dots , m\right]\right)$.
• $M{S}^{*}=\min \left(M{S}_i, \forall i=\left[1, 2, \dots , m\right]\right)$.
• $v$ is a value from 0 to 1. Here, $v$ = 0.5.

4. Application and Results

The following sections apply the five phases that were mentioned earlier to rank the initiatives of the financial sustainability plan at KAU according to their potential to accelerate the desired income by 2025.

4.1. Identifying Financial Resources

In accordance with Step 1.1 and the approach of Al-Filali et al. [17], the long-term goal for KAU’s financial sustainability plan for 2045 stated the following: “Ranking among the top 200 international universities in terms of applying the standards of financial sustainability” and the general goal for 2025 stated the following: “Fulfilling the enabling elements of financial sustainability and achieving at least one successful case in each of the eight work pillars”. Based on Step 1.2, the process of reaching this general goal depends on the first key performance area (KPA1) called “Resource Development” and eight pillars of financial resources (Education & Learning; Research & Development; Community Development; Digital Facilities & Infrastructure; Health Services; Strategic Mega Projects; Human Capital; and Endowments & Donations).

4.2. Determining the Alternatives and Their Criteria

Based on Step 2.1, a group of experienced KAU faculty members were selected to create, manage, and supervise the implementation of the financial sustainability plan’s initiatives (alternatives). This group is known as the “initiatives’ owners”. In Step 2.2, the KAU’s top administration (including the university president, vice presidents, financial staff, investment staff, legal staff, and stakeholders from the private sector), the FSO, and the initiatives’ owners held brainstorming sessions to determine nine criteria for evaluating the initiatives.

Table 1. List of KAU Financial sustainability plan initiatives.

Pillar Code	Financial Resources	Initiative Code	Initiatives
L	Learning & Education	L1	Paid distance education
		L2	Paid parallel education programs
		L3	Paid quality graduate and postgraduate programs
R	Research & Development	R1	New research products
		R2	Investing in preclinical drug trials
C	Community Development	C1	Community courses and diploma
		C2	Contracts with government agencies
		C3	Graduate training and qualification programs
D	Digital Facilities & Infrastructure	D1	Investment in the commercial land on Abdullah Sulayman Street
		D2	Investment in a multi-story parking structure
		D3	Digital advertising and billboards investment initiative
		D4	Investment in KAU’s sports facilities
		D5	Renting sites to provide fast food services
		D6	Allocation of the auxiliary facilities
H	Healthcare Services	H1	Allocation of medical diagnostic and treatment services
		H2	Privatizing the university hospital into a private hospital of 300 beds
		H3	Allocation of 150 clinics in the dental hospital
		H4	Jeddah knowledge medical village project
		H5	Strategic medical partnerships
S	Strategic Mega Projects	S1	Polymer membranes manufacturing and approval for water desalination
		S2	Production of activated carbon and non-activated carbon micro- and nanoparticles
		S3	Establishing a UAV services company
		S4	Establishing a Saudi IOT company
		S5	Integrated Research and Services Center for investigational new drugs
		S6	A Company to manufacture ultrasound imaging equipment
		S7	Prototyping Company
		S8	Technology valley
P	Human Capital	P1	Part-time employment to prepare students for the labor market
		P2	Establishment of the KAU Expert Company
E	Endowments & Donations	E1	Five new endowments
		E2	Scientific endowment
		E3	Qur’an endowment
		E4	Strategic mega project endowment

lists the 33 initiatives created and considered. The criteria are as follows:

• Target income (C1): This criterion refers to the expected target profit that KAU desires for the initiative by the end of 2025.
• Venture capital (C2): This criterion refers to the amount of money KAU needs to spend before launching the initiative. It includes, for example, marketing costs, equipment, research expenses, etc.
• Degree of the required approval and procedures (C3): This criterion indicates the number of required approvals and the steps to implement the initiative. For example, official approval either from KAU sectors or other stakeholders such as the Ministry of Education.
• Harmonization of present regulations (C4): This criterion refers to the compliance level of the initiative with the current university statutes and regulations.
• Availability of human resources (C5): This means the availability and accessibility level of required staff for the intuitive. This includes the type and number of personnel (skilled, trained faculty or admin members).
• Technical and infrastructure capacity (C6): This means the availability level of the necessary technical and infrastructure requirements or the intuitive, such as the type, number, and capacity of the systems, platforms, networks, devices, data centers, etc.
• Material and equipment availability (C7): This criterion refers to the degree to which the suppliers can provide the material and equipment needed for the initiative reliably and conveniently.
• Time to start the initiative (C8): This criterion refers to the required preparation time for the initiative to be officially launched.
• Risk management (C9): This criterion means the level of threats or risks for the initiative, such as financial uncertainty, legal liabilities, and circumstances that could prohibit the initiative from being carried out.

4.3. Constructing the Decision Matrix

After determining the initiatives (alternatives) and their criteria, a decision matrix was constructed as a table where each row corresponds to an initiative (alternative), and each column corresponds to a criterion. The table’s cells contain the values or scores of the alternatives concerning the criteria. In this phase, two objective criteria (C1 and C2) are measured in Saudi Riyal, and seven subjective criteria (C3 to C9) are measured on the Likert scale. The criteria C1, C4, C5, C6, and C7 are beneficial (i.e., the higher the value/score, the better), and the criteria C2, C3, C8, and C9 are nonbeneficial (i.e., the lower the value/score, the better). As defined before, C1 measures the expected income from the initiatives by 2025. However, some initiatives will not generate any income by that year; so, they are assigned a zero value for C1 in the decision matrix. To avoid having zero values in the decision matrix, which must have positive values, a tiny value (0.0001) is used instead of zero for C1 for those initiatives. Most of the initiatives in the “Strategic Mega Projects” category, such as S2, S3, S5, S6, S7, and S8, belong to this group of delayed income generators.

4.4. Calculating the Criteria Weights

The MEREC-G method was applied to calculate the criteria weights. Steps 4.2–4.6 are presented together in

Table 2. Calculations of the MEREC-G method.

Step 4.2			C1			C2			C3			C4			C5			C6			C7			C8			C9
			Max.			Min.			Min.			Max.			Max.			Max.			Max.			Min.			Min.
L1			0.0000			0.3214			0.7500			0.2500			1.0000			0.2500			0.3333			0.7500			1.0000
L2			0.0000			0.1607			1.0000			0.5000			0.5000			0.2500			0.2500			0.5000			0.5000
…			…			…			…			…			…			…			…			…			…
E3			0.0000			0.0071			0.5000			0.5000			0.5000			0.3333			0.3333			0.5000			0.2500
E4			0.0000			0.0143			0.7500			0.5000			0.5000			0.3333			0.2500			1.0000			0.7500
Step 4.3				Step 4.4			C1			C2			C3		C4		C5			C6			C7			C8			C9
							Max.			Min.			Min.		Max.		Max.			Max.			Max.			Min.			Min.
S1	0.0158			L1			0.4977			0.0109			0.0098		0.0112		0.0094			0.0112			0.0108			0.0098			0.0094
S2	0.0140			L2			0.3979			0.0103			0.0082		0.0090		0.0090			0.0098			0.0098			0.0090			0.0090
…	…			…			…			…			…		…		…			…			…			…			…
S32	0.0132			E3			0.2436			0.0143			0.0084		0.0084		0.0084			0.0088			0.0088			0.0084			0.0092
S33	0.0164			E4			0.3373			0.0166			0.0101		0.0107		0.0107			0.0112			0.0116			0.0098			0.0101
Step 4.5		Y1			Y2			Y3			Y4			Y5		Y6			Y7			Y8			Y9			Total
		8.8757			0.9622			0.5247			0.3583			0.3836		0.3904			0.3051			0.5272			0.4781			12.8052
Step 4.6		w1			w2			w3			w4			w5		w6			w7			w8			w9
		0.6931			0.0751			0.0410			0.0280			0.0300		0.0305			0.0238			0.0412			0.0373

.

4.5. Ranking the Initiatives (Alternatives)

The RATMI method was applied to rank the initiatives. Steps 5.2–5.7b are presented together in

Table 3. Calculations of the RATMI method.

Step 5.2	C1	C2	C3	C4	C5	C6	C7	C8	C9
	Max.	Min.	Min.	Max.	Max.	Max.	Max.	Min.	Min.
L1	1.0000	0.0222	0.3333	1.0000	0.5000	1.0000	0.7500	0.3333	0.2500
L2	0.5000	0.0444	0.2500	0.5000	1.0000	1.0000	1.0000	0.5000	0.5000
…	…	…	…	…	…	…	…	…	…
E3	0.0167	1.0000	0.5000	0.5000	1.0000	0.7500	0.7500	0.5000	1.0000
E4	0.0333	0.5000	0.3333	0.5000	1.0000	0.7500	1.0000	0.2500	0.3333
Step 5.3	C1	C2	C3	C4	C5	C6	C7	C8	C9
	Max.	Min.	Min.	Max.	Max.	Max.	Max.	Min.	Min.
L1	0.6931	0.0017	0.0137	0.0280	0.0150	0.0305	0.0179	0.0137	0.0093
L2	0.3466	0.0033	0.0102	0.0140	0.0300	0.0305	0.0238	0.0206	0.0187
…	…	…	…	…	…	…	…	…	…
E3	0.0116	0.0751	0.0205	0.0140	0.0300	0.0229	0.0179	0.0206	0.0373
E4	0.0231	0.0376	0.0137	0.0140	0.0300	0.0229	0.0238	0.0103	0.0124
Step 5.4	C1	C2	C3	C4	C5	C6	C7	C8	C9
&	Max.	Min.	Min.	Max.	Max.	Max.	Max.	Min.	Min.
Step 5.5	$q_1$	$q_2$	$q_3$	$q_4$	$q_5$	$q_6$	$q_7$	$q_8$	$q_9$
Qmax	0.4804	…	…	0.0008	0.0009	0.0009	0.0006	-…	…
Qmin	…	0.0056	0.0017	…	-…	…	…	0.0017	0.0014
Step 5.6	C1	C2	C3	C4	C5	C6	C7	C8	C9
	Max.	Min.	Min.	Max.	Max.	Max.	Max.	Min.	Min.
	$u_1$	$u_2$	$u_3$	$u_4$	$u_5$	$u_6$	$u_7$	$u_8$	$u_9$
L1Umax	0.4804	…	…	0.0008	0.0002	0.0009	0.0003	0.4804	…
L1Umin	…	0.0000	0.0002	…	…	…	…	0.0002	0.0001
L2Umax	0.1201	…	…	0.0002	0.0009	0.0009	0.0006	0.1201	…
L2Umin	…	0.0000	0.0001	…	…	…	…	0.0004	0.0003
…	…	…	…	…	…	…	…	…	…
E3Umax	0.0001	…	…	0.0002	0.0009	0.0005	0.0003	0.0001	…
E3Umin	…	0.0056	0.0004	…	…	…	…	0.0004	0.0014
E4Umax	0.0005	…	…	0.0002	0.0009	0.0005	0.0006	0.0005	…
E4Umin	…	0.0014	0.0002	…	…	…	…	0.0001	0.0002
Step 5.7	Max.	Min.	Step 5.7a	Trace	Value	Rank	Step 5.7b	Perimeter	Rank
	$Q_k$	$Q_h$						Similarity
	0.6954	0.1020						$M{S}_i=M_i/M$
	$U_{ik}$	$U_{ih}$
L1	0.6948	0.0216	L1	$tr\left(T_1\right)$	0.4854	1	L1	0.9889	1
L2	0.3503	0.0298	L2	$tr\left(T_2\right)$	0.2466	2	L2	0.5002	2
…	…	…	…	…	…	…	…	…	…
E3	0.0455	0.0888	E3	$tr\left(T_{32}\right)$	0.0407	16	E3	0.1419	8
E4	0.0521	0.0431	E4	$tr\left(T_{33}\right)$	0.0407	17	E4	0.0962	18

. The proposed initiative ranking (Step 5.8) is given in

Table 4. Ranked initiatives using the RATMI method.

Step 5.8	Financial Resource	Alternative Trace	Median Similarity	Majority Index	Rank
		$t{r}^{*}=$ 0.0175	$M{S}^{*}=$ 0.0405
		$t{r}^{-}=$ 0.4854	$M{S}^{-}=$ 0.9889
		$t{r}_i$	$M{S}_i$	$Z_i$
L1	Learning & Education	0.4854	0.9889	1.0000	1
L2		0.2466	0.5002	0.4872	2
L3		0.0790	0.1626	0.1301	7
R1	Research & Development	0.0469	0.0989	0.0622	16
R2		0.2457	0.4987	0.4855	3
C1	Community Development	0.0504	0.1120	0.0728	11
C2		0.0920	0.1871	0.1568	5
C3		0.0401	0.1326	0.0726	12
D1	Digital Facilities & Infrastructure	0.0847	0.1720	0.1412	6
D2		0.0435	0.1102	0.0645	15
D3		0.0453	0.1198	0.0714	13
D4		0.0404	0.0875	0.0492	19
D5		0.0415	0.1073	0.0608	17
D6		0.0356	0.0945	0.0478	20
H1	Healthcare Services	0.0300	0.0673	0.0275	27
H2		0.0344	0.0711	0.0341	24
H3		0.0345	0.0716	0.0345	23
H4		0.0313	0.0652	0.0277	26
H5		0.0504	0.1066	0.0700	14
S1	Strategic Mega Projects	0.0324	0.0680	0.0303	25
S2		0.0253	0.0581	0.0175	29
S3		0.0233	0.0520	0.0123	31
S4		0.0349	0.0749	0.0366	22
S5		0.0267	0.0580	0.0191	28
S6		0.0248	0.0547	0.0153	30
S7		0.0176	0.0414	0.0006	32
S8		0.0175	0.0405	0.0000	33
P1	Human Capital	0.0343	0.0867	0.0423	21
P2		0.0577	0.1206	0.0852	8
E1	Endowments & Donations	0.0534	0.1107	0.0753	10
E2		0.1699	0.3474	0.3247	4
E3		0.0407	0.1419	0.0782	9
E4		0.0407	0.0962	0.0541	18

.

5. Discussion

This research study aimed to address three questions related to the financial sustainability plan of KAU based on the GOSFS framework [17]. The questions were as follows: (1) How can the KAU’s financial sustainability plan initiatives be ranked according to a set of quantitative and qualitative criteria with different weights? (2) What are the potential alternatives that the KAU’s investment administrator has to consider for generating quick income by the year 2025? and (3) How can the KAU’s decision makers and planners obtain revenues by implementing at least one initiative under each of the eight pillars of the KPA1’s financial sustainability plan? A hybrid MCDM approach was adopted to answer these questions, consisting of two phases. The first phase applied the MEREC-G technique to determine the weights of the criteria for evaluating the initiatives. The second phase used the RATMI technique to rank the alternatives based on the weighted criteria. The data for both phases were collected from various sources at KAU and validated by an expert group comprising the university’s top administrators, the FSO, the owners of the initiatives, and the stakeholders from the private sector.

Table 2 shows the weighted criteria for evaluating the initiatives. The first criterion (C1: total income) has the highest weight of 0.6931, followed by the second criterion (C2: venture capital), with a weight of 0.0751, compared to the other seven criteria. This is because C1 and C2 are objective criteria that use monetary value in millions of Saudi Riyals to evaluate the initiatives, while the other criteria are subjective and use a Likert scale from 1 to 5. Table 3 shows the calculations using the RATMI technique.

Table 4 shows the ranked 33 initiatives in descending order based on the nine criteria for eight pillars of resource development at KAU for its financial sustainability plan from 2022 to 2025. The initiatives related to the “Strategic Mega Projects” were at the bottom of the rank, as they will not generate income by 2025, while the initiatives related to “Learning & Education” were at the top of the rank, as they are easy to implement. The first ten initiatives in the rank are called potential initiatives, as they can generate quick income during the KAU’s financial sustainability plan. Also, 60% of the potential initiatives (L1, L2, L3, E1, E2, and E3) belong to the “Learning and Education” and “Endowments & Donations” pillars. The remaining 40% of the potential initiatives (R2, C1, C2, and P2) are distributed among the “Research & Development”, “Community Development”, and “Human Capital” pillars. On the other hand, the initiatives L1, R2, C2, D1, H5, S4, P2, and E2 are the first ones to generate income in each pillar, regardless of the KAU’s financial sustainability plan period.

The university’s investment administration assigns each initiative to the relevant entity that will carry out the initiative. Each entity owns its initiative and is responsible for its implementation and outcomes. The financial performance and generated income from executing the initiatives are also monitored, assessed, and evaluated.

6. Conclusions and Recommendations

Achieving financial sustainability in higher education is not easy, as HEIs face various challenges and constraints. Some of the common challenges are (1) reduced public funding due to fiscal pressures, (2) increased competition with other providers of education and training, (3) changing demands of students, employers, and society at large, (4) complex regulations imposed by governments, accreditation bodies, or international organizations that increase administrative costs, and (5) global challenges such as climate change, migration, inequality, or health crises. Many scholars approached these challenges from different perspectives [61,62,63] and initiated financial sustainability plans to overcome these challenges and constraints [17].

This paper presented a novel approach to financial sustainability at HEIs from a new perspective. It proposed a financial sustainability strategy plan for KAU, which involves identifying and prioritizing beneficial initiatives to achieve the desired income. A set of criteria was chosen to evaluate the initiatives, some of which were objective, and some of which were subjective. Therefore, the paper applied a recent MCDM tool suitable for this case, the MEREC-G technique, to assign weights to the criteria. Then, it ranked the initiatives based on the weighted criteria using the RATMI technique. This is the first study to integrate MEREC-G and RATMI in this context. The results showed that the top 10 potential initiatives can swiftly generate income during the KAU’s financial sustainability plan from 2022 to 2025. Currently, KAU is already generating income from L1, L2, C1, C2, E2, and E3 initiatives. The other initiatives are in the initiating stage.

In order to rank the initiatives effectively, it is recommended that the university should follow these steps: (1) collect comprehensive data on the requirements of implementing each initiative, as the criteria will be weighted and the initiatives will be ranked based on these data; (2) secure appropriate venture capital funding for each initiative to support its launch; (3) monitor any changes in the education laws and regulations that may require removing or adding initiatives to align with the goals of KAU’s financial sustainability plan; and (4) evaluate the operation of each launched initiative regularly and ensure that the income goals are met.

This study’s theoretical and practical implications lie in the proposed novel approach using the MEREC-G and RATMI methods. Theoretically, this is the first study to integrate MEREC-G and RATMI, providing a valuable reference for the hybrid approach procedures. Practically, this study suggests a general and flexible procedure for other HEIs or institutions in different industries to implement using their alternatives and criteria aligned with their requirements.

Restrictions and Future Directions

Like any other research study, this study has some limitations. The main limitation is the reliability and accuracy of the initiative’s data in the decision matrix, which significantly influences the selection of the most urgent and profitable initiatives. Another limitation pertains to Articles 49 and 50 of the New Universities Law as well as the privatization and financial sustainability programs in the Kingdom’s Vision 2030. Any changes in these articles or programs may affect the suitability of the university’s initiatives for its financial sustainability plan. Moreover, another limitation comes from the availability of venture capital for the initiatives, especially those with substantial financial gains, such as healthcare and strategic initiatives. One more limitation is the initiatives’ owner commitment to manage and supervise the implementation of their initiatives effectively with their concern to achieving the target income. Moreover, another limitation comes from fostering a legislative and regulatory environment that supports investment.

MCDM tools have gained much interest from different scholars who developed mathematical techniques and applied them in various fields [64]. So, a possible direction for future research is to apply other ranking methods, such as TOPSIS, VIKOR, or COPRAS, in addition to RATMI, to verify the validity of prioritizing the initiatives of the financial sustainability plan. Future research may also employ fuzzy MCDM methods, as most of the data collected are subjective opinions from a selected group of experts in the initiatives.