Analytic hierarchy process evaluation model for the introduction of talents among university teachers

In this paper, a quantitative evaluation model based on the Analytic Hierarchy Process (AHP) is constructed to addresses the issues of ambiguous evaluation criteria and strong subjectivity in the current talent recruitment process in universities. Firstly, a visible ability evaluation system consisting of 2 first-level indicators and 9 second-level indicators was established. Secondly, the AHP method was used to calculate the weights of each indicator, among which academic papers (0.303) had the highest weight. Finally, an empirical analysis was conducted by using the talents recruited from 2019 to 2024 as samples, verifying the validity of the model. This provides a scientific and systematic evaluation method for talent recruitment in universities and helps improve the quality of talent recruitment.

1 Introduction

In the context of intensifying global competition in higher education, the recruitment of talents by universities has gradually become an indispensable strategy to enhance their core competitiveness. According to the “National Education Development Statistical Bulletin” issued by the Ministry of Education in 2023, the total number of full-time teachers in China's universities has reached 1.983 million; however, there remains a significant shortage of high-level talents. OECD education indicators reveal that the proportion of university teachers with overseas doctoral degrees in China is only 8.3%, markedly lower than that of the United States (32.7%) and the United Kingdom (28.5%) (Jiao et al., 2023). This structural talent deficit constrains the further development of university construction (Jiao et al., 2023). Simultaneously, universities face numerous challenges in talent recruitment. A survey conducted by the Personnel System Reform Research Group of Peking University (2021) indicates that ~65% of universities report the phenomenon of “overemphasizing recruitment while neglecting training,” and 42% of respondents believe that current evaluation standards struggle to accurately identify the potential of talents. Such circumstances lead some universities to excessively focus on selecting talents based on “talent titles,” potentially undermining the original state of school education. Overemphasis on the research capabilities of young talents exacerbates the trend of “neglecting teaching.” Excessive differentiated treatment in attracting talents is detrimental to the sustainable development of higher education. Abnormal “preference for foreign talents” inevitably disrupts the order of the talent flow market (Guo and Lu, 2019). From a policy perspective, the “Overall Plan for Deepening the Reform of Education Evaluation in the New Era” issued by the Central Committee of the Communist Party of China and the State Council explicitly emphasizes the need to “enhance the evaluation of university teachers' scientific research with a quality-oriented approach,” providing a policy foundation for constructing a new talent evaluation system (Fan, 2024). In practice, universities such as Tsinghua University and Zhejiang University have begun exploring the “representative work” evaluation system. However, issues like insufficient indicator quantification and unreasonable weight distribution are still faced in practical operations—specifically, excessively high weights for research-related indicators, excessively low ones for teaching and social service indicators, higher weights on paper quantity than quality, and far higher weights on vertical than horizontal projects.

Scholars both domestically and internationally have conducted multi-faceted research on the evaluation of university talents. Cheng (2010) pioneered the construction of an explicit/implicit ability evaluation framework, establishing a comprehensive evaluation index system for introduced talents through expert interviews and practical investigations. Yu and Li (2017) developed an evaluation system for innovation and entrepreneurship quality and ability, utilizing hierarchical fuzzy mathematics to construct an evaluation model for innovation and entrepreneurship quality and ability. Li et al. (2024) constructed 98 quantitative and qualitative characteristic indicators from four dimensions—academic integrity, scientific research output, scientific research activities, and academic reputation—and uncovered the implicit relationship between sample characteristic indicators and evaluation results via machine learning, offering a novel perspective and method for evaluating scientific and technological talents. Therefore, establishing a scientific and systematic talent introduction evaluation system holds significant theoretical and practical importance for enhancing the quality of talent introduction in universities and optimizing the structure of the teaching staff. Sun (2020) designed an evaluation index system for talent introduction in private universities based on basic qualities, knowledge structure, comprehensive qualities, and turnover risk using the AHP model, constructing an evaluation model for talent introduction in private universities. Zhou and Dong (2025) identified measures for improving talent introduction in local engineering colleges through qualitative analysis of their talent introduction policies. Liu and Li (2025) focused on the imbalance problem in enterprise talent team construction, revealing the multidimensional challenges faced by enterprise talent teams through in-depth analysis of the management and operation talent team structure, professional and technical talent team strength, and skill talent team construction. Shi and Zheng (2025) emphasized the primary task of high-quality development, centered on the core and serving the overall situation, taking assessment as a handle to optimize and improve the assessment mechanism, continuously stimulating the vitality of talent work, and providing solid talent support for the high-quality development of the college.

This article is divided into four parts: The first part of the introduction mainly introduces the research background, research status at home and abroad and the structure of the article. The second part constructs the evaluation index model, introduces the design of the evaluation index system in detail, determines the judgment matrix and weight coefficient, and scores the quantitative criteria. The third part of the empirical analysis, through the actual case to verify the effect of the model. The fourth part is the conclusion, summarizing the research results.

1.1 Construction of the evaluation index model

The Analytic Hierarchy Process (AHP), developed by Professor T.L. Saaty of the University of Pittsburgh in the 1970s for the U.S. Department of Defense in response to contingency planning issues, is a multi-criteria decision-making process theory primarily applied to decision-making in uncertain situations with multiple evaluation factors. The AHP method (Saaty, 2008) can be used to evaluate and select the optimal option or determine the relative importance of various factors. Using the Analytic Hierarchy Process, the judgment matrix of each level of the explicit ability index system is established, and the weight coefficients W of each index are determined by calculating the eigenvalues and eigenvectors of the judgment matrix. AHP model was applied in Li (2018) to investigate college students' learning motivation. It was found that the influencing factors of postgraduate entrance examination motivation, ranked from strongest to weakest, were: individual self, social opinion, external support, and higher education institutions. According to the connotation and specific content of employment and entrepreneurship work, an evaluation index system for the quality of employment and entrepreneurship work was established from four aspects: Work effectiveness, management system, guidance services and job satisfaction (Cheng, 2021). The AHP model has also been applied in educational research, such as the teaching competence of young university faculty (Liu et al., 2010), the cultivation models of teaching staff (Wang, 2020), and the evaluation of scientific research capability (Wu, 2021).

The index system (Xuewang, 2025) is the core content of building a multi-dimensional evaluation system for attracting talents and serves as the specific implementation point of fundamental concepts and principles. The scientific of the index setting directly determines the success or failure of the evaluation system. The evaluation indicators for attracting talents under exploration are primarily based on the discipline evaluation index system of the Ministry of Education and the monitoring index system for “Double First-Class” construction, combined with the school's connotation development indicators. Based on the principle of overall consideration (Sun, 2018) and in conjunction with relevant literature, the comprehensive ability of attracting talents in universities is comprehensively evaluated into two major categories: academic research and introduced teachers. Academic research is described by four assessment factors: academic papers, research projects, awards, and patents and monographs; the assessment factors for introduced teachers include educational background, final title, age of entry, dual-teacher dual-ability, and academic origin structure. The specific composition of the index system is illustrated in Figure 1.

Figure 1

Figure 1. Evaluation index.

The scientific validity of talent evaluation indicators is critical to the quality of the evaluation system. Below, we elaborate on the connotations of the nine secondary indicators.

1.2 Academic research

Academic papers (Yu et al., 2023) serve as a key indicator for evaluating talents. This encompasses not only the quantity and quality of published papers but also their impact factors. The academic papers authored by university science and technology talents reflect their innovation capabilities, influence, and academic standing within the academic community.

Research projects can be categorized into vertical and horizontal projects. Vertical projects are classified into national, provincial/ministerial, and municipal/university levels, primarily reflecting the research contributions of the talents. Horizontal projects, which involve collaborations with enterprises or institutions, highlight the income-generating potential of the talents.

Awards not only signify the academic level and innovation capacity of talents but also underscore the social value and disciplinary influence of their research outcomes. Awards are classified into national, provincial/ministerial, municipal, and university levels.

Patents and monographs comprehensively reflect the innovation capabilities and academic influence of talents. Patents directly demonstrate the scientific and technological innovation level and the ability to transform research findings into practical applications, showcasing the originality in solving real-world problems. Monographs systematically reflect the theoretical depth and academic accumulation of researchers in specific fields, representing their comprehensive ability to construct knowledge systems.

1.3 Introduced teachers

Introduced teachers typically are faculty members whom universities actively recruit or appoint from outside the institution, such as from other domestic or international universities, research institutes, or industry.

Educational qualifications and degrees reflect the academic competence and professional development potential of teachers, indicating the depth and breadth of systematic professional education received by the talents. Postdoctoral experience, in particular, highlights the independent research capabilities of the talents.

Professional titles serve as an important measure of the professional development level and academic accumulation of university talents, reflecting the maturity of their teaching and research capabilities. Professional titles are classified into senior, associate senior, intermediate, junior, and no title categories.

The academic background structure reflects the diversity of academic backgrounds and innovative development potential of university talents. The dual dimensions of “first degree - highest degree” demonstrate the diversity of academic genes and the integrated innovation capability of knowledge systems.

Dual-teacher and dual-ability qualifications reflect the integration of industry and education capabilities among university talents, highlighting the organic combination of theoretical foundations and practical experiences. The age of entry reflects the developmental stage and career potential of talents. Young teachers under 35 years old typically exhibit stronger innovation vitality and plasticity, while introduced talents over 35 years old often bring mature academic accumulation and industry resources (Liu, 2010; Zhang, 2008; Jia and Zhang, 2013).

2 Construction of the AHP Model

2.1 Construction of the introduced talent judgment matrix

After establishing the evaluation index system, pairwise comparisons of the relative importance of each factor are conducted according to Thomas Saaty's consistency matrix method to determine the weight values of each influencing factor (Xu, 2022). In AHP (Analytic Hierarchy Process), the judgment matrix is primarily used to compare the importance of elements at the same level relative to a certain element at the upper level (Zhou, 2008). By constructing the judgment matrix, these comparison results can be quantified, and the relative weights of each element can then be calculated. Its basic form is a symmetric matrix, where the element Cij represents the importance of indicator i relative to indicator j. The degree of importance is typically expressed using integers from 1 to 9 or their reciprocals, with the specific meanings as follows:

The judgment matrix A, B1, B2 were constructed with reference to literature (Zhang, 2006; Wang and Chen, 2012), among others. Taking the first-level indicators and second-level indicators in this article as an example, the specific steps are as follows: The first-level indicators include academic research (B1) and introduced teachers (B2) (see Table 1). Assuming that academic research is slightly more important than introduced teachers, the following first-level indicator judgment matrix is constructed:

Table 1

Table 1. A-B judgment matrix.

The secondary indicators under Academic Research (B1) include academic papers (C11), research projects (C12), awards received (C13), and patents and monographs (C14) (see Table 2). Assuming that academic papers are the most important, followed by research projects, and that awards received and patents and monographs are of equal importance, the following judgment matrix for the secondary indicators of Academic Research (B1) is constructed.

Table 2

Table 2. B1-C_1j judgment matrix.

The secondary indicators under the category of “Introduction of Teachers (B2)” include academic qualifications and degrees (C21), the last professional title (C22), age of entry (C23), dual-qualified and dual-capable (C24), and academic background structure (C25) (see Table 3). Assuming that academic qualifications and degrees (C21) and academic background structure (C25) are the most important, followed by dual-qualified and dual-capable (C24), while the last professional title (C22) and age of entry (C23) are of relatively lower importance, the judgment matrix for “Introduction of Teachers (B2)” is constructed as follows:

Table 3

Table 3. B2-C2j judgment matrix.

2.2 Determination of weight values for the talent recruitment evaluation index system

The weight value serves as a quantitative representation of the relative importance of each evaluation dimension within the index system, reflecting the contribution of a specific indicator to the overall evaluation objective. The weight value of each evaluation indicator represents its relative proportion in relation to the upper-level element (Lin and Wang, 2013; Yin et al., 2012). In this study, the Analytic Hierarchy Process (AHP) is employed to determine the weights of the evaluation index system. Its mathematical definition is as follows:

$\begin{array}{l}{W}_i=\frac{v_i}{{\sum }_{i=1}^n{v}_i}\end{array}$

where v_i is the eigenvector value of the judgment matrix in the analytic hierarchy process (AHP), and W_i is the weight of the i th index (range 0-1, sum 1). We take the judgment matrix shown in Table 4 as an example to illustrate the process:

Table 4

Table 4. Meaning of scale values.

Normalize the matrix by columns so that the sum of each column is 1:

$\begin{array}{l}{b}_{ij}=a_{ij}/{\displaystyle \sum _{i=1}^n}{a}_{ij},B=\left[\begin{array}{cc}\frac{1}{1.5}& \frac{2}{3}\\ \frac{0.5}{1.5}& \frac{1}{3}\end{array}\right]\approx \left[\begin{array}{cc}0.6667& 0.6667\\ 0.3333& 0.3333\end{array}\right].\end{array}$

Calculate the sum of each row:

$\begin{array}{l}{v}_i={\displaystyle \sum _{j=1}^2}{b_i}_j,v={(1.3334,0.6666)}^T.\end{array}$

Normalize to obtain the weight vector:

$\begin{array}{l}{w}_i=v_i/{\displaystyle \sum _{j=1}^2}{v_i}_j,w={(0.6667,0.3333)}^T.\end{array}$

To determine whether the weights derived from the judgment matrix pass the test, we use the Consistency Ratio (CR) as the evaluation criterion:

$\begin{array}{l}CR=CI/RI,\end{array}$

where, $CI=\frac{{\lambda }_{max}-n}{n-1},{\lambda }_{max}=\frac{1}{n}{\displaystyle \sum _i}\frac{{(AW)}_i}{w_i}.$

For the consistency test, the Average Random Consistency Index (RI) is retrieved. The RI values for judgment matrices of order 1 to 9 are:

The specific weight and CR values of each evaluation indicator are presented in Table 5, while the weight coefficients of each evaluation indicator are shown in Table 6. Based on the weighting scores calculated in Table 7, it can be observed that among academic research indicators, academic papers hold the highest proportion at 0.303; among introduced teachers, educational background and academic origin structure share higher proportion at 0.087 and 0.114.

Table 5

Table 5. Weight calculation table.

Table 6

Table 6. Weight coefficient.

Table 7

Table 7. Quantitative criteria for scoring academic papers.

2.3 Formulation of scoring quantification standards for each indicator

The scoring quantification standards constitute the core operational guidelines of the evaluation model for the introduction of talents in higher education institutions. These standards transform abstract indicator requirements into measurable and specific scores, ensuring the objectivity and operability of the evaluation process. This section systematically elaborates on the scoring standards for the nine secondary indicators, including design principles and specific quantification rules.

The design principles of the scoring standards include:

(1) Scientificity: Based on the discipline evaluation indicators established by the Ministry of Education and aligned with the actual needs of universities.

(2) Operability: Each indicator has clear scoring rules and well-defined grading criteria.

(3) Discrimination: The score gradient effectively distinguishes talents across different levels.

(4) Dynamism: A flexible space of 10%−15% is reserved for the special evaluation of exceptional talents.

The dynamic adjustment mechanism of the scoring standards includes:

(1) Annual Revision: Adjust appropriate indicator details annually according to the evolving needs of discipline development.

(2) Exceptional Handling: Establish a “green channel” scoring rule for top-tier talents such as Nobel laureates.

(3) Negative Deduction: Implement appropriate punitive deduction items for cases of academic misconduct.

The scoring standards for each indicator are determined in accordance with the relevant provisions of the “Interim Measures for the Comprehensive Assessment of All Staff in a Certain University,” “Interim Measures for the Calculation of Teaching Workload in a Certain University,” and “Measures for the Calculation of Scientific and Technological Workload in a Certain University.” Specific scoring quantification standards are presented in Tables 7–14. For ordinal data of grade type, such as that in Tables 8, 9, the one-time scoring principle is adopted, i.e., scores are assigned based on the corresponding grades. For accumulable data, such as that in Tables 4, 10, the actual quantity is multiplied by the corresponding score, and the results are summed for scoring.

Table 8

Table 8. Quantitative criteria for scientific research project scoring.

Table 9

Table 9. Awards scoring quantitative criteria.

Table 10

Table 10. Patent monograph score quantization table.

Table 11

Table 11. Quantitative criteria for academic degree scoring.

Table 12

Table 12. The final title score quantitative criteria.

Table 13

Table 13. Quantitative criteria of academic margin structure score.

Table 14

Table 14. Quantitative criteria of academic margin structure score.

3 Empirical analysis

This study selects the teachers recruited by a specific university from 2019 to 2024 as empirical samples is shown in Tables 15, 16. The sample selection adheres to the following principles: First, ensuring comprehensive discipline coverage, with the samples encompassing six major disciplines, including science, engineering, literature, law, economics, and management. Second, emphasizing the diversity of talent types, including recent doctoral graduates, mature talents with work experience, and overseas-recruited talents. Finally, considering the continuity of the time span to ensure a relatively balanced number of recruited talents each year.

Table 15

Table 15. Academic research evaluation score from 2019 to 2024.

Table 16

Table 16. The evaluation scores of faculty recruited between 2019 and 2024.

Through an in-depth analysis of the samples, several significant features have been identified: Academic papers (weight 0.303) and research projects (weight 0.175), as core indicators, exhibit score changes that are highly synchronized with fluctuations in the total score. In 2024, when the academic paper score reached its peak at 31,961 points, it drove the total score up to 10,716.36 points, aligning with the theoretical expectations of the model design. Secondary indicators, such as awards (weight 0.094), show relatively minor impacts on the total score. For instance, in 2022, when the award score increased abruptly by 72 points, the total score only rose by 3.2%. This demonstrates that the weight grading design effectively distinguishes between key and non-key indicators, thereby validating the effectiveness of the model.

Through 6 years of empirical research, it has been demonstrated that this model can effectively identify high-potential talents with development potential.

3.1 Optimization suggestions

3.1.1 Dynamic adjustment of weights

Scores for academic papers and research projects are continuously increasing, while scores for indicators such as educational qualifications and degrees are gradually decreasing. This reflects a shift in talent recruitment standards from emphasizing academic qualifications to focusing on capabilities.

Reduce the weight of educational qualifications and degrees (from 0.087 to 0.05), and increase the weights of teaching ability (e.g., student evaluations, weight 0.1) and technology transfer (weight 0.08). Additionally, dynamically adjust the number of recruits based on the assessment results of each discipline. For example, for A-class disciplines, recruit 8–10 individuals annually (with a focus on top-tier talents, accounting for 20%); for B-class disciplines, recruit 5–7 individuals annually (with a focus on mid-to-young backbone talents, accounting for 60%); for C-class disciplines, recruit 3–5 individuals annually (with a focus on teaching-oriented talents, accounting for 20%).

3.1.2 Categorized evaluation and differentiated recruitment

Classify talents into three categories: research-oriented talents, teaching-oriented talents, and interdisciplinary talents, and assign differentiated weights for each category, including primary and secondary weights. The weights for research-oriented talents should primarily focus on academic papers and research projects, while those for teaching-oriented talents should primarily focus on teaching awards and student evaluations. The weights for interdisciplinary talents should primarily focus on industry practice and cross-disciplinary achievements. For research-oriented talents, their proportion can be set at 40%-50% of the total number, with the number of recruits determined by discipline. For key disciplines (such as science and engineering), the number of recruits can be increased to 6–8 individuals annually. For teaching-oriented talents, their proportion should be around 30%-40%, with an annual recruitment of 4–6 individuals. For interdisciplinary talents, their proportion should be around 10%-20%, with an annual recruitment of 2–3 individuals.

3.1.3 Introduction of dynamic scoring and negative lists

Establish a dynamic scoring and negative list system. Adjust the scores of academic papers based on their annual citation count to avoid overemphasis on quantity. Set up a deduction mechanism for academic misconduct. The number of recruits can be dynamically allocated by discipline. For instance, if research performance increases by more than 20%, the number of recruits for the following year will increase by 10%. If the target is not met for two consecutive years, the number of recruits will decrease by 20%. For individuals who fail to meet contract goals within 3 years or have a score of 0 due to academic misconduct, employment can be terminated or the number of recruits for the discipline can be reduced.

3.1.4 Establishment of a flexible exceptional admission mechanism

Set direct recognition terms for top-tier talents (such as Nobel laureates and academicians), exempting them from some indicator reviews. Provide additional appropriate scores for interdisciplinary achievements. The proportion of top-tier talents recruited should not exceed 15% of the total quota. For mid-to-young backbone talents (aged 35–45), their proportion should be controlled at 50%−60%. For young teachers (under 35 years old), their proportion should be controlled at 25%−35%.

3.1.5 Supporting guarantee measures

Link evaluation results with resource allocation. Provide a 30% increase in research start-up funds for talents in the top 5% of the total score. Set appropriate penalties for individuals with negative scores within the specified period. Regularly release the “White Paper on Talent Recruitment Quality” to disclose the basis for indicator adjustments and enhance transparency. For special talents, such as Nobel laureates and academicians, set separate quotas. For talents in shortage disciplines, add 1–2 additional flexible quotas.

The above five suggestions can be implemented in three phases. In the first stage, a pilot program will be carried out for 1–2 years, with classified introduction in 3-5 advantageous disciplines. A talent introduction database will be established to dynamically monitor the effect. In the second stage, the program will be promoted for 3–5 years, with the implementation of a differentiated quota system throughout the university, forming a closed-loop management of “evaluation—introduction—assessment”. In the third stage, optimization will be conducted after 5 years, with dynamic adjustment of discipline indicators based on big data and the establishment of a talent reserve pool.

4 Conclusion

Talent introduction is an inevitable measure for the development of universities and an essential path to improving teaching quality, research, and sustainable development. While introducing talents, it is necessary to take into account scientificity, fairness, and dynamics. Establishing a talent introduction model for university teachers is of vital importance. This paper analyzes the introduction of talents in universities based on the AHP (Analytic Hierarchy Process) and proposes suggestions such as dynamic adjustment of weights, categorized evaluation and differentiated introduction, introduction of dynamic scoring and negative lists, establishment of flexible exceptional mechanisms, and supporting measures. The aim is to promote the introduction of talents in universities in line with the progress of the times. However, certain limitations still need to be addressed. For instance, external variables such as the key priorities of university discipline construction directly influence the standards and priorities of talent introduction, which may result in insufficient pertinence of the evaluation model. Furthermore, considering that talent introduction is a long-term and dynamic process, it is necessary to construct a dynamic evaluation mechanism oriented toward the time dimension. The aforementioned limitations remain issues worthy of further consideration and resolution.

In the future, universities should continuously optimize the model in accordance with their disciplinary characteristics and development needs, providing a solid talent support for the high-quality development of higher education.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Author contributions

XY: Writing – original draft, Supervision, Visualization, Methodology, Project administration, Data curation, Conceptualization, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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