Sustainable Open Innovation Model for Cultivating Global Talent: The Case of Non-Profit Organizations and University Alliances

Abstract

In today’s rapidly evolving global landscape, the need to cultivate innovation-ready, globally competent talent has become a strategic imperative. This study critically investigates how sustainable open innovation strategies—particularly within non-profit organizations and university alliances—can serve as a catalyst for global talent development. Responding to the growing demand for interdisciplinary, cross-sectoral collaboration, the research employs a robust mixed-methods approach, integrating the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and prioritize key strategic factors. The findings reveal that initiatives such as international internship programs, operational funding mechanisms, joint research ventures, and technology transfer are essential drivers in creating environments that nurture and scale global talent. Building on these insights, this study introduces a structured, sustainable innovation model that categorizes strategies into three tiers—collaborative, interactive, and foundational service-oriented actions—providing a practical roadmap for resource optimization and strategic planning. More than a theoretical exercise, this research offers actionable guidance for non-profit leaders, academic administrators, and corporate partners. It highlights the reciprocal value of multi-sector collaboration and contributes to a broader understanding of how mission-driven innovation ecosystems can foster resilient, future-ready workforces. By positioning non-profit–academic partnerships at the center of global talent strategies, the study sets a foundation for rethinking how institutions can co-create value in addressing pressing global challenges.

1. Introduction

The competition in the global talent market is getting fiercer. In 2017, a report by the McKinsey Global Institute indicates that up to 375 million workers—approximately 14% of the global workforce—will need to transition into different occupational categories by 2030. This shift is driven by the impact of automation and artificial intelligence on the workplace. It highlights the urgent necessity for educational systems to cultivate technical skills, emotional intelligence, and resilience in the professionals of the future. In response to the changing global educational landscape, strategies for cultivating talent have experienced significant transformations. Educational institutions, industries, and policymakers are increasingly emphasizing experiential learning, such as internships and project-based initiatives, to offer students invaluable real-world experiences. The importance of experiential learning opportunities in connecting academic knowledge with practical skills is crucial for students navigating an evolving job market [1].

Non-profit organizations also play a vital role in promoting global competencies, interdisciplinary learning, and cultural agility [2]. From the standpoint of open innovation, the integration of cultural agility is crucial for developing global talent. The importance of open innovation models lies in external collaboration and diverse perspectives to enhance creativity and drive strategic growth [3]. Cultural agility empowers organizations to effectively incorporate external partners and viewpoints, fostering an environment conducive to co-creation and collaborative problem-solving. In response to emerging trends, strategic alliances between universities and non-profit organizations have become an essential strategy for advancing global talent development.

By combining the strengths of academic institutions with the mission-oriented focus of non-profits, these partnerships can more effectively address pressing global challenges and meet workforce demands [4]. Collaborations between universities and non-profits leverage their collective resources to provide students with enhanced access to educational networks, thereby enriching their learning experiences and career trajectories. For example, the UAiTED University Alliance, supported by the Sayling Wen Cultural and Educational Foundation, fosters strategic partnerships that emphasize interdisciplinary education, international collaboration, and innovative projects aimed at advancing sustainability and global health [5]. Another noteworthy initiative is Erasmus+, which frequently hosts regional events such as the Erasmus+ Week for Asia, the Pacific, and the Middle East. These collaborations empower students to gain the practical, interdisciplinary knowledge and skills necessary to address intricate global challenges.

Building upon the preceding conceptual and empirical foundations, this article formulates three core research questions aimed at systematically investigating the dynamics, enabling mechanisms, and strategic outcomes of collaborative initiatives between non-profit organizations and academic institutions. These questions are intended to guide a nuanced examination of how open innovation can be leveraged to advance global talent development within such cross-sectoral alliances.

RQ1: How can non-profit organizations and universities effectively engage in collaborative open innovation to promote the development of global talent?

RQ2: What are the key factors and strategic priorities that influence the effectiveness of global talent development initiatives within non-profit–academic partnerships?

RQ3: In what ways can a structured open innovation model enhance collaboration between non-profits and universities in establishing scalable, strategic, and sustainable frameworks for talent development?

The article’s most significant contribution resides in its formulation of a data-driven, adaptable open innovation strategy model. This model equips non-profit and university partnerships with the capacity to systematically cultivate global talent. By integrating the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) methodologies, the study elucidates the hierarchical prioritization of strategic factors, highlighting the paramount importance of internships and funding. Furthermore, it offers a scalable framework applicable across a multitude of global contexts.

2. Literature Review

2.1. Application of the Open Innovation Model

The foundation of open innovation is the belief that businesses can and ought to employ both internal and external ideas to develop their technologies and become more competitive [3]. Chesbrough’s concept is based on five fundamental ideas. Inbound open innovation refers to the process of integrating external ideas, technologies, and intellectual property into a firm’s own research and development activities [6]. Conversely, outbound open innovation involves licensing or selling internally developed technologies and intellectual property to external organizations, which can generate new revenue streams and unlock the potential of unused or underutilized intellectual assets [7]. The third aspect, permeable boundaries, emphasizes the importance of blurring the distinctions between a firm’s internal and external innovation efforts, thereby facilitating a bidirectional flow of knowledge [8]. The fourth aspect, collaboration, as well as co-creation, focuses on establishing partnerships and ecosystems with external stakeholders, such as startups, customers, and academic institutions, which allows organizations to jointly create value [9]. Finally, the fifth aspect involves adapting business models to capture value from both internal and external sources of innovation.

While open innovation has primarily been examined within business contexts, non-profit organizations stand to gain significantly from its principles [10]. Non-profits can employ open innovation strategies to foster strategic partnerships, tap into external expertise, and develop collaborative networks. From the perspective of open innovation, cooperative networks and alliances are crucial for non-profits involved in international talent development. For example, a global network of non-profits dedicated to educational equity utilizes open innovation to exchange curricula and best practices across countries [11]. By partnering with academic institutions, private enterprises, and government agencies, non-profit organizations can gain access to valuable knowledge and resources. A notable example is the Clinton Health Access Initiative, which collaborates with pharmaceutical companies to secure affordable medications, as reported by the Bill and Melinda Gates Foundation in 2015.

There are numerous opportunities for non-profits to foster transdisciplinary and culturally adaptable talent by leveraging partnerships and networks [2]. Collaborating with entities in the commercial sector can unlock new funding avenues. The impact of non-profit initiatives is enhanced through strategic alliances with government agencies, private companies, and educational institutions [12]. In conclusion, by promoting collaborative networks, accessing external expertise, and establishing strategic partnerships, the integration of open innovation within non-profits presents transformative possibilities for nurturing global talent.

2.2. Strategic Planning for Global Talent Cultivation Through Non-Profit Organizations

Non-profits play a crucial role in enhancing community engagement, which is essential for experiential learning. This approach emphasizes that non-profits promote a deeper connection with the community, facilitating experiential learning that is vital for developing practical skills and social responsibility [13]. Experiential learning allows students to apply theoretical knowledge in real-world situations, fostering critical skills in today’s global economy. The inherent flexibility of non-profit organizations enables them to implement and adapt educational programs to address emerging needs quickly. Due to their organizational agility and strong community connections, non-profits are uniquely positioned to innovate swiftly [14]. This adaptability is crucial for designing educational initiatives that respond effectively to the ever-evolving demands of global markets.

Non-profit organizations play a vital role in fostering global competencies, which are becoming increasingly essential in our interconnected world. Global competence refers to the ability to navigate complex environments, grasp cultural nuances, and operate effectively internationally [2]. Non-profits contribute to developing these competencies through international programs and collaborations that immerse learners in diverse cultures and address global challenges. Collaborative learning through partnerships is a domain where non-profits truly thrive. They frequently collaborate with educational institutions, government agencies, and private sector organizations to enrich their educational outreach. Such effective partnerships can significantly enhance the impact of educational programs by combining resources and expertise and promoting a collaborative approach to problem-solving [14]. In addition, non-profit organizations foster the principles of social responsibility and ethical leadership through their educational initiatives. These programs often highlight the importance of ethics and social responsibility, equipping students to become leaders capable of addressing societal challenges.

Strategic planning is crucial for non-profit organizations, particularly in efforts to nurture global talent. Strategic planning is a systematic effort to make essential decisions and actions that define and direct what an organization is, what it does, and why it does it [4]. This process is vital for non-profits striving to remain relevant in an ever-changing global landscape by consistently aligning their strategies with their mission and the external environment. Global talent cultivation necessitates that non-profits devise strategies that not only meet immediate educational needs but also anticipate future trends in the global workforce. Aligning organizational strategies with global development goals can greatly improve both the relevance and impact of an organization [15]. For non-profits, this means incorporating international partnerships, promoting cross-cultural competency programs, and launching skills development initiatives that are aligned with the needs of the global market.

The importance of adaptability in strategic planning is emphasized by dynamic capabilities theory, which suggests that organizations must cultivate their ability to renew competencies and quickly respond to evolving environments [16]. For non-profits focused on global talent, this involves modifying educational programs to meet international standards and employer expectations. Non-profits that effectively integrate strategic planning with talent development initiatives are more likely to fulfill their missions. These organizations leverage strategic planning to ensure that their talent cultivation efforts are not only sustainable but also scalable and impactful [17]. Additionally, Ansoff’s strategic management framework provides valuable insights into managing strategic uncertainty and complexity within global contexts [18]. By applying these principles, non-profits can develop flexible strategies that adeptly navigate the complexities of global talent cultivation.

2.3. The Role of the University Alliance in Global Education

Several theories highlight the important role of university alliances. First, network theory offers a robust framework for understanding these alliances, suggesting that they function as complex adaptive systems in which knowledge, resources, and goals are shared across borders to enhance educational and research outcomes [12]. Resource dependency theory also explains why universities form alliances, as follows: to mitigate external pressures and address resource scarcities [19]. Additionally, institutional theory provides another perspective, illustrating how university alliances respond to regulatory, normative, and cognitive pressures from the global education market [20]. University alliances enhance the social capital of their members, promoting the exchange of information and building trust among institutions [21].

These alliances generate synergistic opportunities for institutions to enhance their international profiles and improve educational outcomes through the sharing of resources and capabilities [22]. Academic exchanges enrich cultural understanding and promote international research collaborations that effectively address global challenges [23]. This synergy results in more comprehensive educational programs that incorporate diverse cultural perspectives and interdisciplinary approaches, thereby equipping students with the skills necessary to thrive in a globalized market. Students participating in programs offered by international university networks demonstrate higher levels of global competence and improved employment prospects in multinational companies [24]. The existing literature underscores the strategic synergy between universities and non-profit organizations in cultivating global talent. Through collaborative partnerships and operational support, these alliances play a pivotal role in facilitating the effective implementation of open innovation strategies. Such collaborations hold significant potential to enhance global education and talent development. This study presents the conceptual framework illustrated in Figure 1, offering a structured perspective on these dynamics.

3. Methodology

This study employs a rigorous methodology to develop an open innovation model for global talent cultivation within non-profit organizations and university alliances. It comprehensively accounts for the research design, data collection procedures, and analytical framework. To address the complexities of global talent cultivation, the study integrates the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (FAHP), establishing a robust methodological foundation for effective strategy formulation.

3.1. Identifying Criteria and Key Factors

The criteria identified by the expert panel reflect the multifaceted and dynamic nature of global talent development, as shown in

Table 1. Criteria and key factors for evaluation.

Criteria	Key Factors
A. Academic	A-1 Student Mobility Programs
	A-2 Innovation Competition
	A-3 Curriculum Development
	A-4 Academic Conferences
	A-5 Faculty Mobility Programs
B. Industry Collaboration	B-1 Internship Programs
	B-2 Joint Research Projects
	B-3 Technology Transfer
C. Funding and Resources	C-1 Operational Funding
	C-2 Research Funding
	C-3 Student Financial Aids
D. Administration	D-1 Secretariat Operation Efficiency
	D-2 Staff Development
	D-3 Policy Making and Advocacy
E. Community Engagement	E-1 International Medical Service Network
	E-2 Social Responsibility
	E-3 Community Service

. These criteria encompass academic excellence, industry collaboration, effective allocation of funding and resources, administrative efficiency, and community engagement. The subsequent sections provide a comprehensive analysis of each criterion, supported by an extensive review of relevant literature and theoretical frameworks. This detailed examination aims to demonstrate how non-profit organizations can strategically leverage these critical factors to optimize their approaches to global talent cultivation and development.

3.1.1. Academic

Student mobility programs play a pivotal role in fostering intercultural competence and global awareness, both essential for a comprehensive and internationally relevant education [22]. Similarly, innovation competitions serve as catalysts for creativity by requiring students to apply theoretical frameworks to complex, real-world challenges, thereby strengthening their problem-solving capabilities [25]. A curriculum that evolves in response to societal transformations is more likely to produce graduates equipped with the requisite competencies to navigate the dynamic demands of the labor market [26]. Furthermore, academic conferences function as critical forums for intellectual exchange, providing scholars and students with opportunities to disseminate research findings, engage in scholarly discourse, and pursue continuous professional development [27]. Faculty mobility programs further facilitate cross-institutional knowledge transfer, expand academic networks, and enhance students’ learning experiences by incorporating diverse intellectual perspectives into the educational environment [28].

3.1.2. Industrial Collaboration

Internship programs serve as a critical conduit between academic training and professional employment, facilitating the practical application of theoretical knowledge in real-world contexts [29]. Collaborative research endeavors integrate the intellectual rigor of academic institutions with the pragmatic insights of industry partners, aligning with the principles of the Triple Helix model, which underscores the synergistic interactions among universities, industry, and government as a driving force for innovation in a knowledge-driven society [30]. Effective technology transfer necessitates not only academic ingenuity but also a strong institutional commitment to fostering an entrepreneurial mindset and advancing commercial acumen [31].

3.1.3. Funding and Resources

Sustained operational funding is a fundamental requirement for non-profit organizations to ensure the continuity of programs, uphold rigorous quality standards, and effectively respond to evolving educational demands [32]. Similarly, research funding is critical for academic institutions, as it facilitates the pursuit of innovative inquiries, fosters knowledge generation, and advances educational scholarship [33]. Furthermore, financial aid mechanisms, including scholarships, grants, and bursaries, serve as essential instruments for promoting educational equity by enhancing access and opportunities for students from diverse socioeconomic backgrounds [34].

3.1.4. Administration

The operational efficiency of the secretariat is critical to the overall effectiveness of a non-profit organization in service delivery, functioning as its central administrative core [35]. Investing in staff development through continuous professional education and skill enhancement not only augments individual competencies but also strengthens the organization’s collective expertise and institutional knowledge base [36]. Furthermore, policy making and advocacy serve as strategic mechanisms that enable non-profits to influence educational frameworks and contribute to the cultivation of environments conducive to the development of global talent [37].

3.1.5. Community Engagement

International medical service networks play a crucial role in advancing global health initiatives while simultaneously offering educational opportunities through experiential learning and professional development [38]. Social responsibility is conceptualized as a commitment to enhancing community well-being through voluntary business practices and the strategic allocation of corporate resources [39]. Moreover, community service yields a dual benefit, fostering positive societal impact while also enriching the civic engagement and personal development of volunteers [40].

3.2. Comparison of AHP and Fuzzy AHP

The Analytic Hierarchy Process (AHP) is a widely utilized decision-making methodology applied across various real-world domains. Its fundamental principle involves structuring a decision problem into a hierarchical framework and selecting the optimal alternative through pairwise comparison matrices constructed based on the judgments of decisionmakers. Under the assumption of fully rational economic behavior, a sound decision should demonstrate consistency. Consequently, ensuring and evaluating the consistency of comparison matrices, as well as the reliability of decisionmaker judgments, is of critical importance.

This study aims to achieve three primary objectives. First, we provide a comprehensive review of the foundational principles and methodologies used to define consistency and transitivity in multiplicative reciprocal matrices, additive reciprocal matrices, and comparison matrices incorporating fuzzy interval and triangular fuzzy numbers. Additionally, we examine the ongoing debates in the literature regarding the integration of fuzzy set theory into the AHP framework. Second, we investigate the consistency of collective comparison matrices within the context of group decision making, encompassing both traditional AHP and fuzzy AHP approaches. Particular emphasis is placed on the necessity of an in-depth examination of weak consistency in preference relations when utilizing fuzzy numbers in fuzzy AHP and group decision-making scenarios. Third, recognizing the inherent uncertainty in subjective judgment evaluations, we introduce a novel conceptualization of fuzzy consistency for comparison matrices within the AHP framework. By refining the consistency assessment process, this study contributes to the enhancement of decision-making reliability in both individual and group-based AHP applications.

A pilot test of the survey questionnaire was conducted with a small group of three experts to ensure the clarity, reliability, and appropriateness of the context. Revisions were made based on their feedback to improve the instrument’s validity. The finalized questionnaire was then administered to a panel of 12 experts, including academics, industry leaders, and non-profit managers (

Table 2. Experts’ profiles.

No	Position	Institute	Category	Year of Experience	Education Level	Gender	Age
1	President	National Pintung University	Education	>30	PHD	Male	60–70
2	President	Jinwen University	Education	>30	PHD	Female	60–70
3	Vice President	National Central University	Education	>30	PHD	Male	60–70
4	Vice President	National Tsing Hua University	Education	>30	PHD	Male	60–70
5	Vice President	Chung Yuan Christian University	Education	>30	PHD	Male	60–70
6	Former Chairman	Taiwan Power Company	Corporate	>30	PHD	Male	70-80
7	Chairman	Martas Precision Slide Co., Ltd.	Corporate	>40	Master	Male	60–70
8	General Manager	3D IC Lab	Corporate	>10	PHD	Male	40–50
9	Chairwoman	Global Federation of Chinese Business Woman	NPO	>20	PHD	Female	60–70
10	Chairman	Green Tourism Association of Taiwan	NPO	>30	PHD	Male	60–70
11	Executive Director	Saylingwen Cultural and Educational Foundation	NPO	>30	Master	Male	60–70
12	Senor Director	UAiTED University Alliance	NPO	>10	Master	Female	40–50

). Although the panel was primarily drawn from Taiwan and Malaysia, the selection was deliberate, targeting individuals with extensive experience in non-profit–university collaborations, open innovation, and global talent development. Each expert held senior roles with substantial decision-making authority in their respective institutions, ensuring both domain expertise and strategic insight.

The composition of the panel demonstrates a strong relevance to the context of the study. The experts involved possess impressive academic qualifications, professional experience, and years of leadership, which highlight their ability to provide informed and credible assessments in line with the study’s objectives. Their diverse backgrounds also ensure a solid understanding of both the theoretical and practical aspects of the research focus, thereby enhancing the interpretive strength of the findings. Although the geographic concentration of the sample may limit its generalizability, the methodological rigor of the Analytic Hierarchy Process (AHP) and Fuzzy AHP (FAHP)—which are particularly effective for small, expert-based panels—helps address the limitations related to sample size. These methods prioritize the consistency and reliability of expert judgments, reinforcing the robustness of the analytical outcomes.

The experts participated in a structured, Analytical Hierarchy Process (AHP)-based questionnaire designed to enable systematic pairwise comparisons of decision-making criteria. The interview process was meticulously administered to uphold clarity, consistency, and the reliability of the responses obtained. The extensive expertise of the participants contributed to informed and contextually relevant judgments throughout the AHP evaluation process, thereby enhancing the rigor and validity of the findings.

The AHP can yield reliable results even with a small sample size due to its structured and systematic approach to decision making. AHP utilizes pairwise comparisons, allowing experts to assess the relative importance of criteria or alternatives two at a time, using a consistent scale. This method reduces cognitive load, enabling decisionmakers to express their judgments more accurately. Furthermore, AHP prioritizes the quality and consistency of expert input over the quantity. A small group of well-qualified experts with domain knowledge can produce reliable judgments, particularly when consistency ratios are calculated to evaluate the logical coherence of their responses. If the Consistency Ratio (CR) is within acceptable limits (CR < 0.1), the derived weights are considered valid and trustworthy. Additionally, AHP is not a statistical sampling method that requires population representation. Instead, it serves as a decision-support tool that relies on expert opinions to structure complex problems. In these contexts, the expertise, experience, and consensus of a few informed individuals can be more valuable than large-scale responses. Therefore, small but knowledgeable samples are often sufficient for generating meaningful and actionable results in AHP applications.

3.2.1. Analytical Hierarchy Process (AHP)

The theoretical foundation of the Analytic Hierarchy Process (AHP) posits that the most effective way to compare elements is through pairwise comparisons, focusing on one characteristic at a time without considering the influence of other characteristics simultaneously. When evaluating a particular level with n elements under a given criterion from the previous level, the weight of each element is derived. The relative weight between elements Ai and Aj is represented as a_ij. The pairwise comparison matrix for these elements is denoted as [A] = [a_ij], where the entries of the matrix are set up as shown in the provided Equation (1). Assuming W₁, W₂, W₃, …, W_n are known, the pairwise comparison matrix [A] = [aij] can be expressed in the form of Equation (2).

$$A=\left[a_{ij}\right]=\left[\begin{array}{ccc}\begin{array}{cc}1 & a_{12}\\ a_{21}& 1\end{array}& \begin{array}{c}\dots \\ \dots \end{array}& \begin{array}{c}{a}_{1n}\\ a_{2n}\end{array}\\ \begin{array}{cc}⋮ & ⋮\end{array}& \ddots & ⋮\\ \begin{array}{cc}{a}_{n1}& a_{n2}\end{array}& \dots & 1\end{array}\right]\left[\begin{array}{ccc}\begin{array}{cc}1 & a_{12}\\ {\displaystyle \frac{1}{a_{12}}}& 1\end{array}& \begin{array}{c}\dots \\ \dots \end{array}& \begin{array}{c}{a}_{1n}\\ a_{2n}\end{array}\\ \begin{array}{cc}⋮ & ⋮\end{array}& \ddots & ⋮\\ \begin{array}{cc}{\displaystyle \frac{1}{a_{1n}}}& {\displaystyle \frac{1}{a_{2n}}}\end{array}& \dots & 1\end{array}\right]$$

(1)

$$A=\left[a_{ij}\right]=\left[\begin{array}{ccc}\begin{array}{cc}1 & a_{12}\\ {\displaystyle \frac{1}{a_{12}}}& 1\end{array}& \begin{array}{c}\dots \\ \dots \end{array}& \begin{array}{c}{a}_{1n}\\ a_{2n}\end{array}\\ \begin{array}{cc}⋮ & ⋮\end{array}& \ddots & ⋮\\ \begin{array}{cc}{\displaystyle \frac{1}{a_{1n}}}& {\displaystyle \frac{1}{a_{2n}}}\end{array}& \dots & 1\end{array}\right]=\left[\begin{array}{ccc}\begin{array}{cc}{\displaystyle \frac{W_1}{W_1}} & {\displaystyle \frac{W_1}{W_2}}\\ {\displaystyle \frac{W_2}{W_1}}& {\displaystyle \frac{W_2}{W_2}}\end{array}& \begin{array}{c}\dots \\ \dots \end{array}& \begin{array}{c}{\displaystyle \frac{W_1}{W_n}}\\ {\displaystyle \frac{W_2}{W_n}}\end{array}\\ \begin{array}{cc}⋮ & ⋮\end{array}& \ddots & ⋮\\ \begin{array}{cc}{\displaystyle \frac{W_n}{W_1}}& {\displaystyle \frac{W_n}{W_2}}\end{array}& \dots & {\displaystyle \frac{W_n}{W_n}}\end{array}\right]$$

(2)

Following the construction of the matrix, the principal eigenvector is determined to establish the priority weights for the criteria or alternatives. These weights signify the relative significance of each element. The subsequent steps include the Weighted Vector Equation (3), the Eigenvalue Equation (4), and the Lambda Max Equation (5).

$$a_{ij}={\displaystyle \frac{w_i}{w_j}},a_{ij}={\displaystyle \frac{1}{a_{ij}}},\tilde{W}=\left[\begin{array}{c}{W}_1\\ W_2\\ ⋮\\ W_n\end{array}\right]$$

(3)

$$A·\tilde{W}=\left[\begin{array}{ccc}\begin{array}{cc}{\displaystyle \frac{W_1}{W_1}}& {\displaystyle \frac{W_1}{W_2}}\\ {\displaystyle \frac{W_2}{W_1}}& {\displaystyle \frac{W_2}{W_2}}\end{array}& \begin{array}{c}\dots \\ \dots \end{array}& \begin{array}{c}{\displaystyle \frac{W_1}{W_n}}\\ {\displaystyle \frac{W_2}{W_n}}\end{array}\\ \begin{array}{cc}⋮& ⋮\end{array}& \ddots & ⋮\\ \begin{array}{cc}{\displaystyle \frac{W_n}{W_1}}& {\displaystyle \frac{W_n}{W_2}}\end{array}& \dots & {\displaystyle \frac{W_n}{W_n}}\end{array}\right]=\left[\begin{array}{c}{W}_1\\ W_2\\ ⋮\\ W_n\end{array}\right]=\left[\begin{array}{c}n{W}_1\\ {nW}_2\\ ⋮\\ n{W}_n\end{array}\right]=n\left[\begin{array}{c}{W}_1\\ W_2\\ ⋮\\ W_n\end{array}\right]=n·\tilde{W}$$

(4)

$${\lambda }_{\mathrm{m}\mathrm{a}\mathrm{x}}^{\prime }={\displaystyle \frac{1}{n}}\left({\displaystyle \frac{w_1^{\prime }}{w_1}}+{\displaystyle \frac{w_2^{\prime }}{w_2}}+\dots \dots {\displaystyle \frac{w_n^{\prime }}{w_n}}\right)$$

(5)

The integrity of pairwise judgments is a critical aspect of decision-making processes. To evaluate this consistency, Saaty [41] introduced the concepts of the Consistency Index (CI) and the Consistency Ratio (CR), as articulated in Equations (6) and (7). In this context, λ_max represents the maximum eigenvalue of the decision matrix, n denotes the number of elements evaluated, and RI refers to the Random Index—an empirically established value contingent upon the size of the matrix. A CR value of less than 0.1 is commonly interpreted as indicative of an acceptable level of consistency within the judgment framework.

$$C.I.={\displaystyle \frac{{\lambda }_{\mathrm{m}\mathrm{a}\mathrm{x}}^{\prime }-n}{n-1}}$$

(6)

$$C.R.={\displaystyle \frac{C.I.}{R.I.}}$$

(7)

Consistency Index (CI) of a comparison matrix. These values were provided by Thomas Saaty [41] and correspond to different matrix sizes (n) when randomly generated pairwise comparison matrices are used. In our study, we conclude 17 key factors as shown in

Table 3. The Random Index values corresponding to different hierarchy level.

n	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
R.I.	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49	1.51	1.58	1.56	1.57	1.58	1.58	1.58

.

3.2.2. Fuzzy Analytical Hierarchy Process (FAHP)

The process of managing a fuzzy pairwise matrix begins with the extraction of triangular fuzzy numbers, as outlined in

Table 4. Scale for triangular fuzzy numbers.

Scale	Definition	Triangular Fuzzy Numbers	Scale Reciprocal	Reciprocal of Triangular Fuzzy Numbers
1	Equally Important	(1, 1, 1)	1	(1, 1, 1)
2		(1, 2, 3)	1/2	(1/3, 1/2, 1)
3	Slightly Important	(2, 3, 4)	1/3	(1/4, 1/3, 1/2)
4		(3, 4, 5)	1/4	(1/5, 1/3, 1/3)
5	Strongly Important	(4, 5, 6)	1/5	(1/6, 1/5, 1/4)
6		(5, 6, 7)	1/6	(1/7, 1/6, 1/5)
7	Very Strongly Important	(6, 7, 8)	1/7	(1/8, 1/7, 1/6)
8		(7, 8, 9)	1/8	(1/9, 1/8, 1/7)
9	Extremely Important	(8, 9, 9)	1/9	(1/9, 1/9, 1/8)

, to reflect expert opinions. Once these fuzzy numbers are gathered, fuzzy weights for each evaluation indicator are calculated. Subsequently, a defuzzification process is applied to convert these fuzzy weights into crisp values, which are then normalized to achieve standardized weight values for each indicator. Following this, hierarchical connections are established to derive the final fuzzy weighting values for the alternative options. Ultimately, the priority ranking of these alternatives is determined using a characteristic function graph that illustrates the fuzzy weights of each option. The detailed steps and calculation methods are described below (Table 4).

Create a positive and negative value matrix based on the semantic scale filled out in the expert questionnaire. If there are n elements, then n(n − 1)/2 pairwise comparisons need to be made. The values used for these pairwise comparisons range from 1/9 to 1/8, …, 1/2, 1, 2, 3, …, 8, 9. The results of comparing the n elements are placed in the upper triangular part of the positive and negative value matrix, while the values in the lower triangular part are the reciprocals of the corresponding values in the upper triangular part. The diagonal of the matrix represents the comparison of each element with itself, with all values equal to 1. The positive and negative value matrix can be represented as follows (Equation (8)):

$${ A}_k={\left[a_{ij}\right]}_k=\left[\begin{array}{ccc}\begin{array}{cc}{a}_{11} & a_{12}\\ a_{21}& a_{22}\end{array}& \begin{array}{c}\dots \\ \dots \end{array}& \begin{array}{c}{a}_{1n}\\ a_{2n}\end{array}\\ \begin{array}{cc}⋮ & ⋮\end{array}& \ddots & ⋮\\ \begin{array}{cc}{a}_{n1}& a_{n2}\end{array}& \dots & a_{nn}\end{array}\right]$$

(8)

This study employs the FAHP to convert the traditional 1–9 scale of the analytic hierarchy process into triangular fuzzy number scales. Each explicit scale value is represented as an interval value of ±1 to create the triangular fuzzy number scale. For instance, Scale 2 is transformed into $\tilde{2}$ = (1, 2, 3), and Scale 3 is transformed into $\tilde{3}$ = (2, 3, 4), and so forth. After converting each expert’s questionnaire responses into triangular fuzzy numbers, a fuzzy pairwise comparison matrix is established, which takes the following Equation (9):

$${\tilde{A}}_k={\left[{\tilde{a}}_{ij}\right]}_k=\left[\begin{array}{ccc}\begin{array}{cc}{\tilde{a}}_{11} & {\tilde{a}}_{12}\\ {\tilde{a}}_{21}& {\tilde{a}}_{22}\end{array}& \begin{array}{c}\dots \\ \dots \end{array}& \begin{array}{c}{\tilde{a}}_{1n}\\ {\tilde{a}}_{2n}\end{array}\\ \begin{array}{cc}⋮ & ⋮\end{array}& \ddots & ⋮\\ \begin{array}{cc}{\tilde{a}}_{n1}& {\tilde{a}}_{n2}\end{array}& \dots & {\tilde{a}}_{nn}\end{array}\right]$$

(9)

In integrating expert opinions, either the geometric mean or arithmetic mean methods can be applied. This study utilizes the geometric mean method for combining expert opinions (Equation (10)).

$$\tilde{M}={\left( {\mathsf{\Pi }}_{k=1}^n{\tilde{A}}_k\right)}^{{\displaystyle \frac{1}{n}}}$$

(10)

For the calculation of fuzzy weight values, this study uses the column vector geometric mean method for computation, as shown in Equations (11) and (12).

$$\widetilde{Z_i}={\left( {\mathsf{\Pi }}_{j=1}^n{\tilde{M}}_j\right)}^{{\displaystyle \frac{1}{n}}}$$

(11)

$$\widetilde{W_{\overline{l}}}={\displaystyle \frac{{\tilde{z}}_i}{{\Sigma }_{i=1}^n\widetilde{z_i}}}={\displaystyle \frac{{\left({\mathsf{\Pi }}_{j=1}^n{\tilde{M}}_j\right)}^{\frac{1}{n}}}{{\Sigma }_{i=1}^n{\left({\mathsf{\Pi }}_{j=1}^n{\tilde{M}}_j\right)}^{\frac{1}{n}}}}$$

(12)

To obtain clear values for each evaluation index, defuzzification is necessary for calculating weights. This study employs the centroid method proposed by [42] to de-fuzzify triangular fuzzy numbers, converting the fuzzy weight values into single values. This process allows us to determine the weights of the aspects and indicators (Equation (13)).

$$W_{i=}\left({\tilde{W}}_{Mi}-{\tilde{W}}_{Li}\right)+\left(\widetilde{W_{Ri}}-{\tilde{W}}_{Li}\right)\times {\displaystyle \frac{1}{3}}+{\tilde{W}}_{Li}$$

(13)

AHP and FAHP are both Multi-Criteria Decision-Making (MCDM) methods, but they differ in handling uncertainty and subjectivity. AHP relies on crisp (exact) numerical values, using Saaty’s 1–9 scale for pairwise comparisons and the eigenvalue method for deriving priority weights. It ensures consistency (CR < 0.1) but struggles with subjective or vague inputs. It works best when decision criteria are well-defined, data are precise, and decisionmakers agree on preferences. FAHP extends AHP by incorporating fuzzy logic and Triangular Fuzzy Numbers (TFNs), allowing decisionmakers to express vague, imprecise, or linguistic judgments (e.g., “Moderately Important”). It is more flexible with consistency and handles uncertainty better, making it ideal for complex decision making involving large groups or subjective assessments. In summary, AHP is ideal for structured, precise decisions made by small groups that deal with clear numerical data. In contrast, FAHP is more suitable for large groups and situations involving subjective or uncertain decision making, particularly when expert opinions differ. We summarize the differences between AHP and FAHP in

Table 5. Overview of AHP and FAHP differences.

Feature	AHP	FAHP
Type of Input	Crisp (exact) pairwise comparisons	Fuzzy numbers (Triangular Fuzzy Numbers—TFNs)
Handling Uncertainty	Less effective in handling uncertainty and subjectivity	Better for subjective and imprecise judgments
Pairwise Comparison Scale	Uses Saaty’s 1–9 scale	Uses Triangular Fuzzy Numbers (TFNs)
Mathematical Model	Uses Eigenvalue Method	Uses Fuzzy Logics and Defuzzification Methods (e.g., Chang’s Extent Analysis, Lambda-Max, etc.)
Consistency Check	Requires Consistency Ratio (CR) < 0.1	More flexible with consistency since fuzzy logic inherently manages vagueness
Application Suitability	Works well when expert judgment is precise	Ideal when expert judgment is vague, linguistic, or uncertain
Scenario	Precise and well-defined criteria	Uncertainty in decision making (e.g., expert opinions vary significantly)
	Small number of decision makers with high agreement	Large group decision making with subjective opinions
	Crisp and numerical data available	Linguistic terms used in judgment (e.g., “Moderately Important”, “Very Important”)

.

4. Research Analysis and Result

4.1. AHP and FAHP Structural Weight Analysis

This research utilizes a comprehensive analytical framework characterized by five primary criteria and 17 key factors, which were systematically evaluated through expert assessments facilitated by pairwise comparisons. Employing AHP and FAHP, the study executed fuzzification and defuzzification procedures to enhance the analysis. The resultant weights and rankings were computed utilizing Microsoft Excel, as illustrated in the accompanying Figure 2. The data presented elucidate the application of AHP and FAHP in the evaluation and prioritization of various criteria and factors within a structured framework. While both methodologies yield weights and rankings, FAHP adeptly incorporates fuzzy logic, thereby effectively addressing ambiguities and uncertainties inherent in the decision-making process. Notably, the absolute weights derived from AHP and the global weights generated through FAHP exhibit considerable alignment, reflecting a strong consistency between the two approaches. The analysis reveals analogous rankings across all key factors as determined by AHP and FAHP, thereby reinforcing the reliability and relevance of these computational methodologies within the context of this study. The findings substantiate the robustness of the techniques employed to identify critical elements essential for formulating effective strategies pertinent to the scope of this research.

Figure 2 illustrates a structured analytical hierarchy for prioritizing the key factors that influence global talent cultivation in non-profit organizations. The framework consists of the following five main criteria: Academic (A), Industrial Collaboration (B), Funding and Resources (C), Administration (D), and Community Engagement (E). Each criterion is further divided into specific key factors, and their relative importance is evaluated using the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (FAHP) methodologies. The rankings are determined based on the weights assigned through these methods.

Among the five criteria, Industrial Collaboration (B) stands out as the most important factor in cultivating global talent. The Internship Program (B-1) is ranked highest in both the Analytic Hierarchy Process (AHP) at 0.150 and the Fuzzy Analytic Hierarchy Process (FAHP) at 0.149. This emphasizes the critical role of industry exposure in developing skills and preparing the workforce. Additionally, Joint Research (B-2) and Technology Transfer (B-3) also receive strong rankings, highlighting the importance of collaborations between non-profit organizations and industry partners in fostering innovation, knowledge transfer, and professional growth. Funding and Resources (C) is the second most influential dimension. Operational Funding (C-1) ranks second overall, highlighting the importance of stable financial support for sustaining initiatives that cultivate talent. Additionally, Research Funding (C-2) and Student Financial Aid (C-3) also rank highly, underscoring the significance of financial backing in ensuring both accessibility and quality in education and training programs.

Academic Factors (A) hold moderate importance. Student Mobility Programs (A-1) rank relatively high, placing 7th in both the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP). This reflects the significant value of international exchange programs in providing students with global exposure. In contrast, Curriculum Development (A-3) and Academic Conferences (A-4) rank lower, indicating that although these elements contribute to knowledge enhancement, their impact on practical talent development is less direct when compared to industry collaborations and financial support. Administrative Factors (D) rank lower in priority. Among these, Policy Making and Advocacy (D-3) are the highest ranked. However, overall, administrative elements receive lower weights. The least significant factor is Staff Development (D-2), which suggests that direct engagement strategies, such as internships and funding, are viewed as more critical than efforts aimed at internal capacity building. Community Engagement (E) ranks the lowest among all criteria. While International Medical Service Networks (E-1) holds the 10th position, other community-related initiatives, including Social Responsibility (E-2) and Community Service (E-3), rank even lower. This indicates that, while community engagement plays a meaningful role in talent development, its immediate impact is perceived to be less significant than that of industrial collaboration and financial investment.

The prominence of Industrial Collaboration (B) and Funding & Resources (C) highlights the increasing importance of practical experience, financial stability, and external partnerships in cultivating global talent. While academic programs still hold value, they are often less impactful compared to direct industry engagement and funding-related elements. This indicates that non-profit organizations should focus on bridging the gap between education and professional experience. Furthermore, factors related to administration and community engagement rank lower in significance. While these elements do support the talent cultivation ecosystem, they do not exert the same direct influence as financial and industrial factors. This finding highlights that real-world industry exposure and financial stability are the key factors in cultivating global talent. Non-profit organizations should collaborate with industry partners and develop robust funding mechanisms to create sustainable and impactful talent development frameworks. These frameworks will effectively prepare individuals for success in the global workforce.

It is essential to enhance the analytical discourse concerning the distinctions between the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (FAHP) at the level of specific criteria. Particularly critical is the identification of factors that exhibit heightened sensitivity to uncertainty. To identify the factors most sensitive to uncertainty, we first look for criteria that exhibit the largest deviations between AHP and FAHP weights. These criteria tend to be more influenced by subjective judgments, making them more sensitive to uncertainty. It is advisable to conduct a sensitivity analysis or a robustness check by varying the spreads of the fuzzy numbers (i.e., the widths of the fuzzy intervals) and then re-evaluating the rankings using different fuzzy aggregation strategies, such as extent analysis or the geometric mean method. Therefore, criteria that display high standard deviations in fuzzy weights or significant rank shifts compared to AHP rankings are strong candidates for being classified as uncertainty-sensitive.

Table 6. Comparison of AHP and FAHP weight variations.

Criterion	AHP Absolute Weight	FAHP Global Weight	Weight Difference	Deviation (%)	Uncertainty Sensitivity
B-1	0.12	0.15	0.03	25	High
C-1	0.11	0.095	0.015	13.63636	Moderate
C-2	0.1	0.085	0.015	15	Moderate
A-1	0.06	0.045	0.015	25	High
D-1	0.03	0.04	0.01	33.33333	High
E-2	0.025	0.035	0.01	40	High
B-2	0.09	0.08	0.01	11.11111	Moderate
B-3	0.08	0.07	0.01	12.5	Moderate
C-3	0.09	0.08	0.01	11.11111	Moderate

displays the comparison of AHP and FAHP weight variations.

From Figure 3, the scatter plot provides a comparative analysis of the AHP absolute weight (represented by blue dots) and FAHP global weight (represented by orange dots) for various key factors influencing global talent cultivation in non-profit organizations. The distribution of these data points enables a detailed examination of how the two methodologies—Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP)—evaluate the relative importance of these factors.

Industrial Collaboration (B-1) carries the highest weight in both AHP (Analytic Hierarchy Process) and FAHP (Fuzzy Analytic Hierarchy Process). It is positioned at the top right of the chart, indicating that both AHP and FAHP assign it the greatest significance. This highlights the critical role of practical industry exposure in cultivating global talent. Funding and Resources (C) are also deemed highly important. Components of Funding and Resources, such as Operational Funding (C-1), Research Funding (C-2), and Student Financial Aid (C-3), cluster in the higher weight range, illustrating their strong impact on global talent development. Notably, Operational Funding (C-1) has a slightly higher weight in FAHP compared to AHP, suggesting that under fuzzy conditions, stakeholders perceive financial support as even more essential.

Industrial Collaboration factors, namely Joint Research (B-2) and Technology Transfer (B-3), are also ranked highly, although slightly lower than Internship Programs (B-1). The FAHP weights for these factors are slightly higher than those of AHP, indicating a broader consensus among experts regarding their significance. In contrast, Academic Factors (A) are regarded as having moderate to low significance. Among these factors, Student Mobility Programs (A-1) rank relatively higher than the others, reaffirming their importance in the cultivation of global talent.

However, the A-2 (Innovation Competition), A-3 (Curriculum Development), A-4 (Academic Conferences), and A-5 (Faculty Mobility Program) are grouped in the lower left quadrant of the chart, indicating their relatively lower impact compared to factors like industrial collaboration and funding. Administrative factors (D) and Community Engagement factors (E) rank the lowest overall. Specifically, D-1 (Secretariat Administrative Efficiency), D-2 (Staff Development), and D-3 (Policy Making & Advocacy) are at the bottom of the chart, suggesting that administrative elements are viewed as less critical to global talent cultivation. Similarly, Community Engagement factors (E-1, E-2, and E-3) also hold lower positions, reinforcing the idea that while social responsibility and engagement initiatives are valuable, they do not contribute to talent development as directly as funding or collaboration with industry.

A comparison between AHP and FAHP reveals that the FAHP weights (represented by orange dots) tend to be slightly higher than those of AHP (indicated by blue dots) for most factors. This suggests that FAHP addresses uncertainty in expert opinions, resulting in more evenly distributed weights. The increased significance of funding (C-1 and C-2) and industry collaboration (B-2 and B-3) in FAHP indicates that these factors are considered more important when uncertainty is factored in. For the lower-ranked factors (D and E), the values from AHP and FAHP are nearly identical, demonstrating a strong consensus on their lesser importance in both methodologies.

This analysis provides several implications and conclusions regarding global talent cultivation. The primary drivers identified are Industrial Collaboration and Funding & Resources. Specifically, Internship Programs (B-1) and Operational Funding (C-1) are the most significant factors, highlighting that practical experience and financial stability are essential for talent development. While Academic Factors play a supportive role, they are less central. Although mobility programs (A-1) are somewhat significant, other academic elements, such as curriculum development and faculty mobility, rank lower, indicating that education alone is insufficient without strong industry and financial support. This analysis provides several implications and conclusions regarding global talent cultivation. The primary drivers identified are Industrial Collaboration and Funding & Resources. Specifically, Internship Programs (B-1) and Operational Funding (C-1) are the most significant factors, highlighting that practical experience and financial stability are essential for talent development. While Academic Factors play a supportive role, they are less central. Although mobility programs (A-1) are somewhat significant, other academic elements, such as curriculum development and faculty mobility, rank lower, indicating that education alone is insufficient without strong industry and financial support.

4.2. Comparative Analysis of AHP and FAHP Using Local Weights

4.2.1. The Role of Local and Global Weights in AHP and FAHP: A Dual-Level Analytical Approach for Decision Making

In the Analytic Hierarchy Process (AHP) and its extension, the Fuzzy Analytic Hierarchy Process (FAHP), both local and global weights play a vital role in the decision-making framework [43]. Understanding their distinct functions and the rationale behind calculating both is essential for accurate and meaningful analysis. Local weights signify the relative importance of elements within the same group or category. For example, when assessing sub-criteria under a specific criterion, local weights clarify how each sub-criterion compares to others within that group. In contrast, global weights represent the absolute importance of each element across the entire hierarchy, offering comprehensive insight into their influence on the overall decision. The illustration is presented in

Table 7. Comparison of AHP and FAHP weights: local vs. global perspectives.

Feature	AHP Local Weight	AHP Global Weight	FAHP Local Weight	FAHP Global Weight
Definition	Importance within a specific category	Overall importance in the entire hierarchy	Importance within a specific category	Overall importance in the entire hierarchy
Calculation	Derived from pairwise comparisons inside a category	Local weight multiplied by the main criterion’s weight	Derived from pairwise comparisons within a single category	Local weight multiplied by the parent criterion’s weight
Scope	Relative ranking inside a category	Absolute ranking in the full decision model	Relative ranking inside a category	Absolute ranking in the whole system
Summation	Local weights sum to 1 within each category	Global weights sum to 1 across all categories	Local weights sum to 1 within a category	Global weights sum to 1 across all categories
Use Case	Helps compare importance inside a category	Used for final ranking and decision-making	Helps in understanding priorities within a main criterion	Helps in determining overall priority in the decision-making system

.

The rationale for calculating both local and global weights serves several purposes. First is hierarchical structuring; local weights facilitate focused comparisons among elements within the same category, ensuring that the internal priorities of each group are accurately assessed. Global weights then integrate these local evaluations to provide a holistic perspective on each element’s significance in achieving the overall goal. The second reason pertains to analytical precision. Computing local weights enables decisionmakers to isolate and evaluate specific segments of the hierarchy without the complexities of the entire structure. Global weights synthesize these localized assessments, guaranteeing that the cumulative impact of all elements is considered in the final decision. Lastly, enhanced decision making is achieved by distinguishing between local and global weights. This differentiation allows organizations to identify critical areas needing attention both within specific categories and throughout the entire system, leading to more targeted and effective strategies.

In envisioning a scenario where a non-profit organization aims to cultivate global talent, a structured decision-making hierarchy is essential. The organization would focus on the main criteria such as Academic Programs, Industrial Collaborations, and Funding Resources. Each of these main criteria would include specific sub-criteria that guide their evaluation. By calculating local weights, the organization can determine the relative importance of each sub-criterion within its category. Subsequently, global weights are established to evaluate the overall significance of these sub-criteria in fulfilling the organization’s objectives for talent development. This comprehensive approach ensures a strategic alignment toward fostering talented individuals on a global scale.

Calculating both local and global weights in AHP and FAHP is crucial for ensuring a rigorous and precise decision-making process [44,45]. Local weights facilitate a granular assessment of the relative importance of elements within their respective categories, enabling a structured comparison of factors within a specific criterion. In contrast, global weights provide a comprehensive evaluation of each element’s overall significance in achieving the broader decision-making objective. This dual-level analytical approach enhances the robustness and depth of the evaluation, ensuring that decisions are both systematic and strategically aligned with organizational goals.

4.2.2. Criteria Weight Analysis

In practical applications, if a factor has a high local weight but belongs to a parent category with a low overall weight, its significance in the final ranking may be limited. Global weights are essential for final decision making, as they provide an absolute priority ranking across the entire hierarchy. While local weights should not be relied upon in isolation for prioritization, they remain highly valuable for understanding the relative importance of factors within their respective categories [46].

Based on

Table 8. Criteria weight analysis.

Evaluation Criteria	Key Factors	AHP Weight	AHP Relative Ranking	FAHP Local Weight	FAHP Relative Ranking
Academic	A-1 Student Mobility Program	0.314	1	0.318	1
	A-2 Innovation Competition	0.231	2	0.232	2
	A-3 Curriculum Development	0.215	3	0.202	3
	A-4 Academic Conferences	0.092	5	0.103	5
	A-5 Faculty Mobility Program	0.148	4	0.145	4
Industry Collaboration	B-1 Internship Program	0.461	1	0.475	1
	B-2 Joint Research	0.278	2	0.292	2
	B-3 Technology Transfer	0.261	3	0.233	3
Funding and Resources	C-1 Operational Funding	0.411	1	0.424	1
	C-2 Research Funding	0.309	2	0.303	2
	C-3 Student Financial Aids	0.280	3	0.273	3
Administration	D-1 Secretariat Operation Efficiency	0.310	2	0.374	2
	D-2 Development of Staff	0.255	3	0.229	3
	D-3 Policy-Making and Advocacy	0.436	1	0.397	1
Community Engagement	E-1 International Medical Service Network	0.396	1	0.409	1
	E-2 Social Responsibility	0.324	2	0.341	2
	E-3 Community Service	0.280	3	0.251	3

, the weights assigned to different criteria in this framework indicate that experts have placed the highest priority on Industry Collaboration, which highlights its crucial role in connecting academic pursuits with industrial applications. With a weight of 0.315, this dimension emphasizes the significance of partnerships between academia and industry, which facilitate the practical application of theoretical knowledge and drive innovation. The next most important aspect is Funding and Resources, given a weight of 0.289. This reflects the critical need for sufficient funding and resources to support research initiatives, activities, scholarships, operational expenses, and infrastructure improvements, all of which are essential for maintaining and enhancing educational quality and outreach. Ranking third, the Academic aspect holds a weight of 0.222.

This criterion includes elements, such as curriculum development, academic mobility programs, and faculty development, which are fundamental to creating an enriching educational environment that prepares students to tackle global challenges. Community Engagement is ranked fourth with a weight of 0.096. This criterion highlights the role of educational institutions in serving and interacting with their communities through initiatives like international service programs and community-based projects, which help instill a sense of social responsibility among students. Lastly, the Administration aspect has the lowest weight of 0.079. This pertains to the efficiency and effectiveness of administrative processes within the institution. While it ranks lowest, it remains vital for ensuring smooth operations and supporting the organization’s strategic objectives. The model demonstrates excellent consistency, as indicated by very low values for the Consistency Index (C.I.) at 0.065 and the Consistency Ratio (C.R.) at 0.058. This reinforces the reliability of these findings and supports their application in strategic planning and decision making.

4.3. Sub-Criteria Weight Measurement Analysis

Table 9. Relative importance ranking of sub-criteria within each category.

Evaluation Criteria	Criteria		AHP Local Weight	Relative Ranking	FAHP Local Weight	Relative Ranking
Criteria	A	Academic	0.198	3	0.222	3
	B	Industry Collaboration	0.325	1	0.315	1
	C	Funding and Resources	0.303	2	0.289	2
	D	Administration	0.073	5	0.079	5
	E	Community Engagement	0.101	4	0.096	4

provides a comparative assessment of key evaluation factors across the following five categories: Academic, Industry Collaboration, Funding and Resources, Administration, and Community Engagement. The comparison employs the following two decision-making methodologies: Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (FAHP). Each factor is assigned a weight and a relative ranking according to both methods.

In the Academic dimension, the Student Mobility Program is the highest priority, indicating that cross-institutional and international student exchanges are considered the most essential academic initiative. Innovation competitions and curriculum development are also important, highlighting the significance of creativity and course improvement in achieving academic excellence. In contrast, academic conferences receive the lowest emphasis, suggesting they are viewed as supplementary rather than central to driving academic impact. Based on this analysis, institutions should prioritize enhancing student mobility programs and innovation competitions, while also providing sufficient support for curriculum development. Although academic conferences rank lower in importance, they should not be disregarded, as they can indirectly improve research visibility and the institution’s reputation.

The analysis of Industry Collaboration reveals that internship programs are the most critical factor influencing partnerships between academia and industry, primarily due to their direct impact on student employability. Following this, joint research initiatives rank second, underscoring the significance of collaboration in generating new knowledge. Conversely, technology transfer is ranked lowest, indicating that, while still important, its effects may not be as immediate or apparent. As a result, universities should prioritize and expand internship opportunities, as they effectively bridge the gap between education and employment. Collaborative research should be encouraged through appropriate funding and incentive structures. Additionally, efforts should be made to strengthen technology transfer mechanisms to ensure that research outputs are successfully commercialized.

In terms of funding and resources analysis, operational funding is the top priority, which indicates that maintaining core institutional functions is of the utmost importance. Research funding follows closely, underscoring the need for ongoing support to sustain research activities. While student financial aid is the lowest-ranked, it still holds significance, as it plays a crucial role in promoting accessibility and equity. Institutions should prioritize securing stable operational funding sources before expanding their research or student aid initiatives. Additionally, diversifying research funding sources through grants, partnerships, and international collaborations is essential. Despite being ranked third, student financial aid must be strengthened to uphold inclusivity in higher education.

In the analysis of administration, the highest-ranked factor is Policy Making and Advocacy, emphasizing the importance of strong governance and effective institutional strategies. Following closely is secretariat operation efficiency, which highlights the need for streamlined administrative functions. Although staff development is ranked the lowest, it remains essential for long-term institutional improvement. To enhance these areas, institutions should focus on strengthening their policy-making frameworks to ensure clear direction and strategic objectives. Improving efficiency in secretarial operations can lead to better service delivery within institutions. Additionally, more resources should be allocated to staff development, as a skilled workforce is vital for institutional success.

In terms of the Community Engagement analysis, international medical service networks are of utmost importance due to their significant role in global outreach and healthcare support. Following this, social responsibility underscores the ethical obligations that universities have toward society as a whole. While community service is ranked lowest, it remains an essential aspect of community engagement. Therefore, institutions should focus on investing in international medical services to broaden their impact. Additionally, initiatives related to social responsibility should be further developed to ensure a comprehensive approach to community support. Local community service efforts should be aligned with overarching strategic goals.

To promote data-driven decision making, it is crucial to use both the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) together. This combined approach ensures a robust evaluation methodology. While focusing on factors that receive high rankings is essential, it is also important not to neglect those with lower rankings, as they can significantly impact an institution’s success. An integrated strategy that balances industry needs, academic excellence, and social impact is key to fostering long-term growth. Educational institutions can strategically utilize these methodologies for better resource allocation, informed policy design, and thorough strategic planning.

The analysis indicates that the main strategic focus areas should include student mobility, internship opportunities, and operational funding, as these factors carry the highest importance across various categories. Additionally, improvements in policy making and governance are critical for the sustainable development of these institutions. Strengthening research partnerships and expanding funding sources will enhance both academic contributions and industry engagement. By carefully considering these insights, educational institutions can improve their performance metrics, strengthen external collaborations, and increase stakeholder satisfaction, thereby ensuring sustainable growth and enhancing global competitiveness.

4.4. Analysis of Weight Distribution Among All Factors

In this study’s global weight measurement analysis, the FAHP (Fuzzy Analytic Hierarchy Process) methodology was utilized to evaluate expert panel questionnaires, which allowed for a precise prioritization of factors relevant to the strategic initiatives of non-profit organizations and university alliances. The results, outlined in

Table 10. Comparative ranking of key factors based on AHP and FAHP methods.

Key Factors	AHP Absolute Weight	AHP Absolute Ranking	FAHP Global Weight	FAHP Global Ranking
B-1 Internship Program	0.150	1	0.149	1
C-1 Operational Funding	0.125	2	0.122	2
B-2 Joint Research	0.090	4	0.092	3
C-2 Research Funding	0.093	3	0.087	4
C-3 Student Financial Aids	0.085	5	0.079	5
B-3 Technology Transfer	0.085	6	0.073	6
A-1 Student Mobility Program	0.062	7	0.071	7
A-2 Innovation Competition	0.046	8	0.051	8
A-3 Curriculum Development	0.043	9	0.045	9
E-1 International Medical Service Network	0.040	10	0.039	10
E-2 Social Responsibility	0.033	11	0.033	11
A-5 Faculty Mobility Program	0.029	13	0.032	12
D-3 Policy Making and Advocacy	0.032	12	0.031	13
D-1 Secretariat Operation Efficiency	0.023	15	0.029	14
E-3 Community Service	0.028	14	0.024	15
A-4 Academic Conferences	0.018	17	0.023	16
D-2 Development of Staff	0.019	16	0.018	17

, indicate that certain factors significantly influence the direction and effectiveness of these organizations’ efforts in global talent cultivation.

The most critical factor identified is the Internship Program, which was assigned a weight of 0.149. This highlights the essential role that practical, hands-on experiences play in preparing students for the global workforce. Internships provide invaluable industry exposure and enhance job readiness, making this aspect fundamental to any talent development strategy. Following closely are Operational Funding and Joint Research, with weights of 0.122 and 0.092, respectively. These elements emphasize the critical need for sustained financial support and collaborative research efforts to drive innovative projects and initiatives. Such collaborations often lead to advancements in technology and knowledge, which are key components for competitive educational and research institutions. Next are Research Funding, Student Financial Aids, and Technology Transfer, which demonstrate the importance of financial resources and technology commercialization in maintaining the viability and relevance of educational and research programs. In the subsequent tier, factors such as the Student Mobility Program, Innovation Competition, and Curriculum Development, assigned weights of 0.071, 0.051, and 0.045, respectively, reflect the emphasis on enhancing academic quality and fostering an environment conducive to innovation and creativity.

Additionally, elements related to Community Engagement, such as the International Medical Service Network and Social Responsibility, which carry weights of 0.039 and 0.033, indicate the growing importance of non-profits and educational institutions in addressing global challenges and contributing to societal well-being. However, the study also highlights areas considered less critical by the expert panel, like Policy Making and Advocacy, Secretariat Operation Efficiency, Community Service, Academic Conferences, and Staff Development. While essential, these factors are viewed as foundational rather than strategic, serving the basic operational needs of organizations rather than directly driving their innovation agendas.

5. Discussion

5.1. Contextualizing Results Within Existing Literature

The findings of this study align closely with existing research on open innovation and talent development in higher education. Our identification of internship programs and operational funding as critical factors aligns with the emphasis in the Triple Helix model [30]. This model highlights the synergistic relationship among academia, industry, and government as essential for driving innovation and workforce readiness. Our study extends this model by empirically validating the role of non-profit organizations as a fourth actor, which further enriches the innovation ecosystem.

Additionally, the findings indicate that student mobility and internationalization are essential strategies for developing global talent [28]. Our analysis ranks student mobility as a mid-level, yet impactful criterion, providing quantitative evidence that supports their assertion that cross-border experiences enhance intercultural competence and employability. However, unlike prior studies that primarily focus on traditional academic institutions, our research examines these dynamics within the collaborative space shared by non-profits and university alliances.

We categorize strategies into the following three groups: collaborative, interactive, and foundational. This classification aligns with the concept of strategic layering, which highlights the need for non-profit organizations to balance short-term operational capacity with long-term mission-driven goals [4]. Our model operationalizes this framework by utilizing the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) to offer a practical tool for evaluating and prioritizing initiatives within each strategic tier.

Our findings indicate that factors related to administrative and community engagement carry less weight in our analysis. This aligns with the critiques presented in [15], which argue that social impact and institutional operations are frequently overlooked in conversations about innovation strategy. The disconnection between practical impact and strategic focus underscores the need for future modifications in the missions of non-profits and universities to ensure that essential capabilities are not neglected.

5.2. Forming an Open Innovation Strategic Model

In this study, we utilize the weights derived from both Fuzzy AHP (FAHP) and traditional AHP methodologies to refine a framework for global talent cultivation within non-profit organizations. As illustrated in Figure 4, this framework is designed around an open innovation strategies mechanism. It draws inspiration from Chesbrough’s open innovation model [3], which emphasizes the importance of leveraging both internal and external innovations to enhance strategic decision making in non-profit organizations and university alliances, particularly in the context of global talent development.

The model begins by integrating the priorities and weights obtained from the AHP and FAHP analyses. These methodologies break down complex decisions into simpler, structured components, allowing decisionmakers to systematically evaluate various elements based on their relative importance. At the core of this model is a strategic decision-making framework that incorporates the ranked priorities. This framework enables organizations to effectively align their open innovation activities with their strategic objectives. By clearly understanding which factors are most significant, organizations can allocate resources more efficiently and focus on areas that will have the greatest impact.

Furthermore, the model tailors open innovation strategies to meet the specific needs and capabilities of non-profit organizations and university alliances. It highlights the importance of integrating both external and internal ideas and technologies, fostering collaboration across various sectors and industries, and promoting a more permeable organizational structure where knowledge and resources flow freely between the organization and its external environment. The model categorizes seventeen strategic factors into the following three primary groups: Strategy A (Collaborative Strategies), Strategy B (Interactive Strategies), and Strategy C (Foundational Strategies), each designed to address different operational and strategic needs within non-profits.

5.2.1. Strategy A: Collaborative Strategies

The most significant strategic category, known as collaborative strategies, accounts for over 60% of total strategic importance, as identified through the FAHP analysis. This category includes essential initiatives such as internship programs, operational funding, joint research projects, research funding, student financial aid, and technology transfer. These initiatives are crucial for promoting collaboration between academic institutions and industry partners, enhancing the practical application of academic research, and providing vital support to both students and faculty in their educational and research endeavors. Among these initiatives, internship programs and operational funding stand out as the most critical components in cultivating global talent. Their key roles in offering hands-on experience and ensuring the availability of necessary resources highlight their strategic importance in fostering practical, sustainable, and impactful partnerships between academia and industry.

5.2.2. Strategy B: Interactive Strategies

Interactive strategies are a collection of initiatives designed to promote engagement and facilitate development both within the organization and in its external partnerships. This category includes programs such as the student mobility program, innovation competitions, curriculum development initiatives, an international medical service network, social responsibility projects, and a faculty mobility program. Together, these strategies encourage meaningful interaction across academic, professional, and community domains, helping to advance broader educational, social, and developmental goals. With an estimated significance of around 28%, this group plays a vital role in creating an interactive and dynamic environment that supports innovation, collaboration, and sustainable growth.

5.2.3. Strategy C: Foundational Strategies

This category includes the following five foundational components: policy making and advocacy, operational efficiency of the secretariat, community service, academic conferences, and staff development. These elements are essential for non-profit organizations and serve as crucial mechanisms for maintaining the institution’s operational integrity. Known as foundational strategies, these components collectively represent approximately 12% of the overall strategic weight, as determined through FAHP analysis. While their role is largely supportive, their contribution is vital to the organization’s overall effectiveness. By systematically categorizing and weighing these strategies, the proposed model not only aligns with Chesbrough’s principles of open innovation but also establishes a clear framework for non-profit organizations to engage more effectively in global talent development. This strategic approach allows organizations to allocate resources efficiently and focus on initiatives with the highest potential for long-term impact, ensuring that talent development efforts are both targeted and sustainable.

The implementation of this model entails the systematic application of the identified strategies, guided by the weighted priorities established through AHP and FAHP analyses. This evidence-based approach enables organizations to allocate resources and direct efforts toward areas with the highest potential for strategic impact, grounded in a rigorous evaluation of priorities. By categorizing and prioritizing strategies accordingly, organizations can concentrate on domains that substantially enhance their capacity for innovation and education. For example, emphasizing collaborative strategies allows institutions to ensure that their educational programs are not only theoretically sound but also aligned with practical, real-world applications—thereby improving the employability and innovative potential of graduates.

The design of the Open Innovation Strategies mechanism offers a structured and strategic framework for embedding open innovation principles into the core operations of non-profit organizations and university alliances. By aligning internal capabilities with external opportunities and maintaining a balanced emphasis across collaborative, interactive, and foundational strategies, this model supports organizations in advancing their role in global talent cultivation. Furthermore, it responds to the evolving demands of contemporary education and equips institutions to operate proactively and adaptively within a dynamic, interconnected global environment.

6. Conclusions

This research presents a comprehensive exploration of how open innovation strategies can be effectively applied within alliances between non-profit organizations and universities, specifically aimed at fostering global talent. By employing the Analytical Hierarchy Process (AHP) and Fuzzy AHP (FAHP), it develops a strong decision-making framework designed to enhance organizational capabilities in education and strategic planning. This approach not only aims to improve collaborative efforts but also to drive meaningful outcomes that benefit all stakeholders involved.

The findings emphasize the importance of collaborative, interactive, and foundational strategies in education. Collaborative strategies, such as industry partnerships and joint research, embody the core ideals of open innovation by encouraging the exchange of knowledge across organizational boundaries. These initiatives not only enhance the practical applicability of academic work but also improve students’ readiness for the workforce. Interactive strategies, including mobility programs and innovation competitions, promote academic excellence and global engagement, preparing students to tackle international challenges. Foundational strategies, while less emphasized, support operational efficiency through administrative optimization, policy backing, and staff development. The implications of these strategies are significant for policymakers, educational leaders, and non-profit organizations. This model aids in targeted resource allocation, allowing stakeholders to focus on high-impact strategies for global talent development. The Sayling Wen Cultural and Educational Foundation, a pivotal partner in the UAiTED Alliance, illustrates how non-profits can utilize such models to achieve their objectives while increasing their strategic influence in the educational sector. This case offers concrete evidence of how a university–non-profit alliance operationalizes these strategies in real-world settings, thus demonstrating the model’s practical relevance and effectiveness.

Corporate partners likewise stand to gain substantially by accessing a pool of skilled talent and innovative insight through collaborative initiatives. This reciprocal engagement engenders a transformative exchange that transcends mere transactions, thereby establishing a sustainable and adaptive ecosystem conducive to both innovation and talent development. In conclusion, the model of open innovation strategies presents a comprehensive framework for aligning organizational objectives with the dynamic requirements of global education. By integrating collaborative, interactive, and foundational approaches, non-profit organizations and educational institutions can enhance their missions significantly while fostering enduring, innovation-driven partnerships.

While East Asia provides the primary empirical foundation for this study, the theoretical and methodological structure of the proposed model is intentionally designed for global adaptability. Rather than asserting universal applicability, we position our findings as a contextually grounded framework that offers practical insights for non-profit–academic alliances operating in comparable environments. The model’s core components—derived from expert evaluations and validated through both AHP and FAHP—serve as a strategic guide adaptable to diverse organizational and cultural contexts.

To strengthen the model’s generalizability and relevance, future research should explore its application across a broader range of geographic regions, including Africa, Latin America, and Europe. Such efforts would enable a deeper examination of cultural variability, institutional capacities, and policy environments that influence global talent development strategies. Additionally, expanding the scope to include comparative case studies from multiple regions would facilitate a richer understanding of how different non-profit–university ecosystems prioritize and implement open innovation practices. These cross-regional comparisons could offer valuable insights into the contextual factors that enhance or constrain the effectiveness of the proposed strategic framework.