Do Financial Investment, Disciplinary Differences, and Level of Development Impact on the Efficiency of Resource Allocation in Higher Education: Evidence from China

Abstract

Optimizing the allocation of university resources to improve the efficiency of inputs and outputs is an important issue for the high-quality development of universities. In recent years, China has become an important growth pole for the development of global higher education. In particular, Chinese agricultural universities, with their distinctive disciplinary characteristics and outstanding professional advantages, have made important contributions to the sustainable development of agricultural education around the world. In contrast, academic research on the efficiency of resource allocation in Chinese agricultural universities is very limited. To fill this gap, this study was guided by econometrics and took high-level agricultural universities in China as the research object to measure the effects of financial investment, disciplinary differences, and development level on the level of resource allocation efficiency of universities. With the help of a data envelopment model (DEA) and a Malmquist index decomposition model, we found that the overall level of resource allocation efficiency in the sample universities was high, but there were great disparities in resource input–output effectiveness between universities. In many universities, marginal inputs exceeded marginal outputs, resulting in input redundancy and resource wastage. In addition, this study shows that for high-level agricultural universities, the regression of capital input technology is preventing a sustained increase in productivity, which places the total factor productivity of resource allocation in a diminishing state.

1. Introduction

As a core concept in the context of economics, efficiency is broadly defined as maximizing returns by adjusting the ratio between inputs and outputs, where the ratio between production inputs and resultant outputs is defined as “resource efficiency” [1,2,3,4]. With the development of educational practice, scholars began to treat human education activities as a general production activity from the perspective of economics and treat investment in education as a productive investment, and, thus, put forward the concept of “educational efficiency” [5,6,7]. Efficiency in the allocation of educational resources is the pattern of allocation of human, material, and financial resources in different directions of use [8,9]. Current research has been divided into macro-strategy studies focusing on the country as a whole and micro-strategy studies focusing on universities themselves [10,11,12]. The former emphasizes the principle of resource allocation and focuses on the layout and regulation of overall resources among different regions and types of higher education institutions (HEIs) [13,14]; the latter focuses on the essentials of resource utilization and how HEIs use the acquired resources to achieve organizational goals and HEI functions [15,16]. One study found that in the post-pandemic era, the resource allocation management of HEIs is changing from macro-sloppy to micro-refined [17]. Enhancing and improving the efficiency of resource allocation in HEIs has become a trend requirement and a key issue in the management change of global university organizations that emphasize performance accountability [18,19].

Despite the growing number of studies on the measurement of resource allocation efficiency in HEIs, there are still research gaps in terms of research objects and research methods.

First, in terms of research object gaps, existing studies have mainly focused on the responsibilities and practices of Western universities to enhance resource allocation efficiency, with little attention paid to the Asian context [20]. In recent years, China has become an important growth pole for the continued development of global higher education. In particular, Chinese agricultural universities have made important contributions to the sustainable development of agricultural education worldwide, with distinctive disciplinary characteristics and outstanding professional strengths. In contrast, academic research on the efficiency of resource allocation in Chinese agricultural universities is very limited [21]. In particular, it is still unclear whether factors such as financial investment, discipline classification, and development level have an impact on the resource allocation efficiency of agricultural universities. The lack of such research will seriously constrain the sustainability of agricultural education around the world.

Second, in terms of research methodological gaps, academics still mostly rely on data envelopment models (DEA) for efficiency calculations. This is mainly because DEA, as a typical non-parametric analysis method, is more convenient to calculate, as its calculation process is not restricted by the data outline, form, and weights [10]. However, as research continues, scholars gradually find that the traditional DEA model also has many inherent drawbacks. For example, firstly, the DEA model cannot perform a secondary ranking for decision units that are on the same production frontier surface. Secondly, DEA models do not meet the need for continuous comparisons across time. Thirdly, traditional DEA models are no longer able to meet the increased demand for the accurate calculation of resource allocation efficiency in complex “multiple input–multiple output” systems. As a result, existing research methodological failures and methodological gaps have severely limited more in-depth research exploration.

Therefore, in order to effectively fill the current research gaps and promote the sustainable development of agricultural education around the world, this study innovatively proposes updating the existing efficiency measurement research model, measuring the level of resource allocation in agricultural universities and systematically analyzing the factors influencing allocation efficiency. Specifically, we objectively measure the level of resource allocation efficiency in agricultural universities and systematically summarize the patterns of resource allocation efficiency in agricultural universities in the current period. In particular, considering the highly valuable and unique educational policy, organizational culture, and institutional context of Chinese agricultural universities, this study argues that taking Chinese high-level agricultural universities as the research target is more appropriate for filling the gaps in current research and contributing to the sustainable development of agricultural education worldwide.

The contributions of this study to the existing literature are mainly reflected in refining the research object and updating the research methodology. Firstly, this study clarifies for the first time whether financial investment, disciplinary differences, and development levels have an impact on the resource allocation efficiency of agricultural universities. This will effectively fill the gap in the research on the efficiency of resource allocation in universities in the field of sustainable development of agricultural education. This study will provide meaningful references for the management practices of managers of agricultural HEIs around the world. The findings of this study are important for promoting the sustainable development of agricultural education worldwide. Secondly, this study is further based on the traditional data envelopment model (DEA) and the Malmquist exponential decomposition model. The new model is not only able to accurately capture the characteristics of continuous changes in efficiency, but also to sort out the specific causes that trigger changes in efficiency. The super-efficiency DEA–Malmquist exponential decomposition model used in this study has both stability and scientific advantages over the traditional DEA models used in previous studies. This will provide an important methodological reference for research related to the measurement of resource allocation efficiency.

The rest of this paper is structured as follows. Based on Section 1 (Introduction), Section 2 (Literature Review) systematically reviews the existing literature that is closely related to the efficiency of resource allocation in higher education. Section 3 (Materials and Methods) presents information on the main methods, models, indicator systems, and data sources of this study. Section 4 (Results) details the specific results of the efficiency measures from different perspectives. Section 5 (Conclusions) systematically summarizes the theoretical and practical contributions of this study and provides several potential avenues of research for the future (Figure 1).

The specific ideas presented by this study include (Figure 2):

• Constructing an index system for evaluating the resource allocation of agricultural universities that includes financial investment, disciplinary differences, and development levels;
• Accurately measuring the level of efficiency of resource allocation in agricultural universities in the current period;
• Characterizing the development of resource allocation efficiency and optimal scale layout at a static level;
• Analyzing the dynamic evolution of resource allocation efficiency and trends from a dynamic perspective;
• Identifying whether financial investment, disciplinary differences, and level of development have an impact on the efficiency of resource allocation in agricultural universities

2. Literature Review

A large number of studies have been conducted in the academic field on the efficiency of resource allocation in universities. Based on the perspective of core elements, we can divide the existing studies into the following four main aspects: (1) comprehensive research theories, (2) differentiated research objects, (3) diversified research methods, and (4) systematic index systems (Figure 3).

2.1. Efficient Resource Allocation Research Theory

Based on the non-profit perspective of quasi-public goods, some scholars argue that, as non-profit organizations, universities do not carry out their daily education, teaching, and research activities with the ultimate aim of making financial profits. Therefore, the measurement of their inputs and outputs does not entail the same process as that of an ordinary organization. Similarly, higher education administrators should not be concerned with the efficiency of resource allocation. For example, Walker, M. [22] suggested that unlike general organizations, the purpose of operating HEIs is not to obtain excessive economic benefits, there is no clear share of owners’ interests, and the financiers of HEI operations are not the owners of HEI property rights; therefore, the managers of HEIs need not pay attention to the issue of resource utilization efficiency, and it is even more unnecessary to affect the output of schooling in order to save capital. Wang, C. [23] argued that it is difficult to find a scientific and reasonable way to calculate input–output efficiency due to the special nature of education itself and the complexity of the educational services provided and the final results presented. In addition, improving output results is the primary goal of universities, and research itself has trial and error costs, which will inevitably result in a certain degree of unproductive inputs, and, thus, limiting the input of educational resources will affect the overall research contribution of universities and the country.

Other scholars have put forward differentiated concepts of higher education resource allocation based on the actual development of higher education in each country. For example, the Japanese scholar Ikuo, A. and co-workers [24] advocated for a theory of the type of higher education mass system, arguing that the development of higher education and the construction of its resource allocation system in each country should be fully based on the actual development of each country and should not be generalized. The higher education system represented by Europe and the United States is dominated by public universities, and its development is based on strong government financial support, with less attention paid to the efficiency of resource allocation. In contrast, the higher education system of Japan is dominated by private universities, which are less capable of obtaining substantial financial support from the government and should pay full attention to and improve the efficiency of resource allocation in universities. In his study of the development pattern of regional higher education in China, the scholar Zhao, Q. [25] suggested that many regions have entered the stage of mass development of higher education one after another. In this process, there are, again, differences in the development of higher education in different regions. The investment in higher education should take into account both the level of economic development and the effectiveness of investment. Li, X. [26] focused on the development of higher education as a whole and found that the redundancy and deficiency of higher education resources exist simultaneously in China, and in the government investment-led funding model for higher education, attention is paid to the diversification and continuous growth of funding sources, but the efficiency of the use of education investment is neglected and the increment in resources can only play its proper role under the premise of improving the efficiency of resource allocation.

In addition, some scholars, based on the viewpoint of improving the quality of higher education development, believe that different types of higher education institutions are facing the problem of resource demand exceeding resource supply, and that all of them should pay sufficient attention to the efficiency of higher education resource allocation. For example, the Japanese scholar Fumihiro Maruyama pointed out that with the continuous improvement in market mechanisms in the field of higher education and the steep increase in overall financial pressure, the state’s financial allocations have been decreasing, universities are closer to the market, and the accountability of higher education corresponding to it has been extended, meaning that the requirement for the efficiency of higher education resource utilization has been increased [27]. Qin, Q. [28] argued that the traditional research on the efficiency of higher education resource allocation only stays at the stage of qualitative analysis, and should refer to the concept of efficiency in economics and construct relevant quantitative analysis models to accurately represent and measure the technical relationship between the input and comprehensive output of higher education resources so as to show the level of input–output efficiency of education. Zhou, H. et al. [29] further pointed out that the concept of relative efficiency needs to be introduced to analyze the use of educational input resources, to identify problems and the causes of the problems in time, and to adjust the allocation of resources in order to achieve the goal of optimal overall output.

2.2. Evaluation Object of Resource Allocation Efficiency in Universities

Depending on the object of evaluation, this study divides the main existing literature into geospatial and hierarchy categories [30].

Firstly, there is a large body of literature based on the geospatial dimension, analyzing and comparing the efficiency of higher education resource allocation in different countries and regions and generalizing the overall regional characteristics [31,32,33,34]. Some Western scholars found that regional heterogeneity in the allocation of higher education resources in the United States and Europe is obvious, with higher levels of efficiency in the allocation of higher education resources located in affluent regions [35]. Meanwhile, some Chinese scholars continuously measured the resource allocation efficiency in China and found that the overall comprehensive efficiency has steadily improved, with no significant differences between the eastern, central, and western regions, while there are level differences between urban clusters, and some scholars pointed out that the Yangtze River Delta region has a higher overall efficiency level compared to the Beijing–Tianjin–Hebei urban circle [36,37].

Secondly, an increasing number of studies have analyzed the efficiency of resource allocation in different types of universities, such as public, local, and private universities, specifically based on the dimension of institution type [38,39]. For example, Turkan et al. [40] conducted an efficiency analysis of 43 state universities in Turkey and found that only 22% of the universities reached an efficient state. Deng Yuntao et al. [41] conducted a study to evaluate the efficiency of resource allocation in nine universities of science and technology directly under the Ministry of Education in China and found that the lack of resources resulted in poor overall scale effectiveness. In recent years, China’s higher education reform and development has emphasized the policy orientation of “discipline-based”, which has led to a shift from describing the overall profile to analyzing disciplinary clusters [29]. An et al. [42] conducted a study on the evaluation of resource allocation efficiency of Chinese “985 Project” universities. Based on the resource allocation data of 38 agricultural universities in China, Li et al. [43] found that the current resource allocation efficiency and management level of agricultural universities are in a stage of continuous improvement.

2.3. Resource Allocation Efficiency Evaluation Methods

Scholars at home and abroad mainly use the stochastic frontier (SFA) and data envelopment (DEA) models as the main efficiency evaluation methods [10].

First, the stochastic frontier model (SFA) and the Solow residual method (SRA) are used as parametric analysis methods to derive efficiency results primarily through modeling and variable assumptions [44]. For example, Titus et al. [45] calculated the resource allocation of 752 community colleges in the US with the help of the SFA model and found a high level of overall efficiency. Song Zhiyan et al. [46] used the SFA model to calculate the input–output efficiency of general universities in China and concluded that the overall efficiency profile was good, but there were internal gaps and input redundancy. The statistical method of each parameter is mainly used to measure efficiency by constructing a function with each parameter and reflecting the relationship between inputs and outputs. The main advantage of this method is the effective consistency and accuracy of the efficiency estimates. However, it requires high quality of the raw data [47,48].

Second, data envelopment models (DEA), as non-parametric analysis methods, can measure the relative efficiency of multiple decision units (DMUs) with the help of statistical data and mathematical planning together. Namely, the calculation process of the DEA model does not require a specific production function and is not limited by the size, form, and weight of the data [49,50]. The specific model chosen varies depending on the purpose of the study and the dimension of analysis [51]. Since Charnes, Cooper, and Rhodes introduced the DEA model with constant returns to scale (later known as the DEA-CCR model) and Banker, Charnes, and Cooper introduced the DEA model with variable returns to scale (later known as the DEA-BCC model), many studies have applied this technique to different areas [52]. For example, Navickas, V. et al. [53] used the DEA-CCR model to assess the level of efficient allocation using Slovakian universities and confirmed that most universities were allocated more efficiently and that newly established universities had better efficiency levels. Li, Y et al. [54] analyzed the efficiency of higher education resource input and output in each province of China based on the DEA-BCC model, and found that the overall efficiency needs to be improved and there are disparities in the efficiency levels between regions.

However, traditional DEA models treat each DMU as a single entity and operate in a ‘black box’ with input and output data for all decision units. As a result, this approach ignores the variability and interactions within each DMU. This may lead to the inability of traditional DEA methods to accurately measure the efficiency of each decision unit in the overall system [55]. As a result, scholars, represented by Färe, started to try to open the “black box” for efficiency evaluation by decomposing the internal nodes and stages and, thus, proposed the network DEA model [21,56,57,58,59]. In addition, the free disposal hull (FDH) DEA model relaxed the convexity assumptions required for the basic DEA model and innovatively proposed mixed integer programming. This led to academic attempts to allow the construction of production possibility sets by means of free configurations, i.e., any pair of more inputs and fewer outputs for a given input and output is also productive.

The DEA-CCR, DEA-BCC, Network DEA, and FDH-DEA models are all primarily suitable for calculating static efficiency. They struggle to meet the requirements to analyze trends in efficiency changes across time. For this reason, scholars have started to try to supplement them with the introduction of Malmquist index models [60,61]. For example, Klumpp [62] used the DEA–Malmquist model to calculate efficiency data for 70 European universities, noting that overall efficiency is high and that there is a positive relationship between funding inputs and the international ranking of the university. Shi, L. et al. [63] applied the DEA–Malmquist model to analyze panel data on higher education in the Tibetan region of China, with low resource allocation efficiency and diminishing marginal returns.

2.4. Resource Allocation Efficiency Evaluation Index System

Evaluation indicators are behavioral and specific features that are evaluated through observable and measurable means [64]. In the evaluation indicator system in the field of educational resource allocation, influenced by econometrics, scholars are more consistent in their choice of input indicators, which are specifically divided into two categories: static and dynamic perspectives [65].

First, based on a static analysis perspective, inputs are classified into human, material, and financial resources from factor of production theory [66,67]. For example, Laureti, T. et al. [68] used the number of full-time teaching staff, lecture hall seats, computer lab seats, and library collections as input indicators to measure the efficiency of resource allocation in 59 state universities in Italy and found low and unevenly distributed levels of efficiency over the observation period. Jiang et al. [69] selected the number of full-time teachers, the number of schools, and the actual value of budgeted education expenditure as human, material, and financial input indicators to measure the efficiency of higher education resource input and output in China, and analyzed the regional disparity in the efficiency of higher education resource allocation. The results showed that the allocation efficiency in eastern provinces was significantly lower than that in central and western provinces, and there was a significant waste of education input factors.

Second, based on a dynamic analysis perspective, physical inputs (e.g., number of laboratories, library space, etc.) and financial inputs are isotropic and have high short-term stability, and, therefore, more consideration is given to human and financial inputs when selecting indicators [70,71]. For example, Parteka, A. [72] measured the level of resource productivity of 266 public higher education institutions in seven European countries using the number of faculty members as a proxy for staff input and the total income from schooling as a proxy for financial input, and found that overall efficiency was improving, but there were large disparities between countries. Zong, X. et al. [73] selected the number of full-time faculty members as the human input variable and the total expenditure on R&D in the year as the financial input variable, and found that the overall efficiency of research in Chinese universities under “double first-class” construction was low, and the efficiency improved, but at a slow pace.

The above is about the choice of input indicators. In contrast, current research has greater variability in the selection of output indicators. The evaluation systems represented by the Soft Science Top Ranking System, the QS and THE discipline ranking systems, and the Chinese discipline assessment system differ significantly in their conceptions of output evaluation, the selection of indicators, and the setting of weights [66]. In terms of talent training quality, for example, the Soft Science Ranking does not focus on such performance, while the QS and THE discipline ranking systems rely on employer reputation and doctoral to bachelor’s degree conferred ratio, while China’s fourth-round discipline evaluation system is a comprehensive evaluation by means of a series of data such as outstanding current students, outstanding graduates, employer evaluation, quality of dissertations, quality of academic papers, and number of degrees conferred. Influenced by this, Chinese scholars mostly select items related to basic functions such as teaching, research, and social services as important indicators to measure the output of university resource allocation. For example, Zhou, L. et al. [74] comprehensively measured the level of competitiveness of schools in terms of talent cultivation, scientific research, and social services with the help of data such as the number of master’s and doctoral degrees conferred, the number of journal monographs published, and the income from technology transfer. Some scholars have also proposed supplementing other elements. For example, Wang, C.D. [75] argued that higher education has the function of cultural transmission, but considering that the element of cultural resources is difficult to evaluate quantitatively, scholars do not include this indicator in the assessment of higher education efficiency.

To sum up, the existing research results have laid a solid foundation for this study. However, these results still leave some room for further exploration in terms of research theories, research objects, and research methods.

Research theories. Many classical theories and academic perspectives in the field of educational resource allocation provide important theoretical support for the current practice of higher education reform from the perspective of overall development. However, the practice of higher education resource allocation is characterized by periodicity. That is, educational reform in different periods faces its own particular contradictions and problems. After a comprehensive analysis of existing theories, this study finds that the exploration of current theories relatively lags behind the process of practical change. The scope of application of some of the theories is relatively limited, and it is difficult to provide comprehensive guidance to the evolving world practice of higher education.

Research objects. Different universities and disciplines have their own characteristics and development rules, and a unified performance evaluation index system cannot be used to measure the development of all universities and developments. However, most of the current studies take all universities in the target region and target type as samples. Many studies ignore the disciplinary characteristics and development peculiarities of universities, and cannot objectively show the actual development level of the university and its disciplines. This will lead to insufficient guidance from the conclusion recommendations. Similarly, the relatively limited selection of subjects may also result in unfair evaluation results and make it difficult to bring about the expected guiding and enhancing effects.

Research methodologies. Scholars have actively used research methods such as economics and statistics and used quantitative analysis of data to promote the level of efficiency of resource allocation in universities. Specifically, scholars have mostly relied on data envelopment models (DEA) to carry out efficiency calculations. However, as research has progressed, scholars have gradually found that the traditional DEA model also has many inherent drawbacks. For example, firstly, DEA models are unable to perform a quadratic ranking of decision units on the same production frontier. Secondly, DEA models do not meet the need for continuous comparisons across time. Thirdly, traditional DEA models are no longer able to meet the increased demand for accurate calculation of resource allocation efficiency in complex “multiple input–multiple output” systems. As a result, existing research methodological failures and methodological gaps have severely limited more in-depth research exploration.

Indicator system. The current effectiveness evaluation system lacks flexibility and still focuses mainly on quantitative indicators of scientific research such as the number of papers published, lacking reflection of diversified demands such as connotation development and characteristic development and not flexibly reflecting the importance of higher education reform to the new model of diversified and multi-dimensional effectiveness evaluation of talent training, scientific research, social services, cultural heritage and innovation, and faculty building.

Therefore, this study selected eight high-level agricultural universities in China, constructed an evaluation index system for resource allocation efficiency according to the actual development of Chinese agricultural universities, and used the super-efficiency DEA model to portray the comprehensive efficiency development characteristics from the static level. The Malmquist index decomposition model was also used to analyze the dynamic evolution of agriculture and efficiency drivers from a dynamic perspective, and to propose countermeasures for the exposed efficiency shortcomings so as to provide some reference for decision making to promote the innovative development of higher agricultural education.

3. Materials and Methods

3.1. Data Collection and Sample Selection

The data for this study were obtained from the Compilation of Annual Basic Statistics of Universities directly under the Ministry of Education of China, the Web of Science Science Citation Index database (SCIE), the Chinese Social Science Foundation Project Database, and the Statistical Annual Report of Projects Funded by the Natural Science Foundation of China and related statistics.

We selected eight high-level construction universities, China Agricultural University, Northwest Agriculture and Forestry University, Nanjing Agricultural University, Huazhong Agricultural University, Beijing Forestry University, Northeast Forestry University, Zhejiang University, and Southwest University, as research subjects based on the list of pilot universities of the National Agricultural Talent Education and Training Program reform and on the principle of comparability of objects and availability of data. The time span was from 2014 to 2019, essentially covering the overall history of the National Education and Training Programme for Excellence in Agriculture. To eliminate the influence of inflation on the funding factor, this study deflated the funding variable according to the consumer price index (CPI).

3.2. Methodology and Index System

3.2.1. Data Envelopment Analysis (DEA)

Data envelopment analysis (DEA) models are non-parametric analytical tools for examining the relative efficiency of decision units, mainly for evaluating the relative effectiveness of the same type of decision unit in complex systems with multiple inputs and multiple outputs. According to the difference in payoffs to scale, DEA models are divided into DEA models with constant payoffs to scale (DEA-CCR), introduced by Charnes, Cooper, and Rhodes, and DEA models with variable payoffs to scale (DEA-BCC), introduced by Banker, Charnes, and Cooper. Moreover, as efficiency research progressed, scholars began to break through the limits of environmental influences on efficiency values and entered the stage of super-efficiency DEA, which takes slack variables into account. Specifically, super-efficiency DEA can solve some of the inherent drawbacks of traditional DEA models such as DEA-CCR and DEA-BCC. For example, super-efficiency DEA can perform further ranking analysis for decision units that are on the same production frontier surface (Wang et al., 2021 [76]). Based on the production reality of variable returns to scale in university resource allocation, this study uses the DEA-BCC model with variable returns to scale to solve for allocation efficiency and optimal scale projection, and further differentiates with the help of super-efficiency DEA models.

$$\begin{gathered} \mathrm{s}.\mathrm{t}.{{\displaystyle \sum }}_{\mathrm{i}=1,\mathrm{i}\ne \mathrm{j}}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{i}}{\mathrm{x}}_{\mathrm{i}}+{\mathrm{s}}^{-}={\mathsf{\theta }\mathrm{x}}_{\mathrm{j}} \\ \mathrm{s}.\mathrm{t}.{{\displaystyle \sum }}_{\mathrm{i}=1,\mathrm{i}\ne \mathrm{j}}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{i}}{\mathrm{x}}_{\mathrm{i}}-{\mathrm{s}}^{+}={\mathrm{y}}_{\mathrm{j}}\mathsf{\lambda }\ge 0,\mathrm{i}=1,2,\dots ,\mathrm{n} \end{gathered}$$

where θ is the efficiency evaluation value and s⁻ and s⁺ are the slack variables for input surplus and output deficit, respectively. When θ < 1, the decision unit is non-DEA-efficient and does not reach the optimal production allocation; when θ ≥ 1 and s⁺ ≠ 0 or s⁻ ≠ 0, the decision unit is said to be weakly DEA-efficient, implying that there are still output deficiency or redundant inputs in the decision unit; when θ ≥ 1 and s⁺ = 0 or s⁻ = 0, the decision unit is said to be DEA-efficient and the input–output efficiency is in the optimal state without input redundancy or output deficiency.

3.2.2. Malmquist Model

As the DEA model cannot meet the needs of continuous comparative analysis across periods, this study further introduces the Malmquist index decomposition model. The Malmquist index (Mo, also known as the total factor productivity index, TFP) was first proposed by the Swedish scholar Malmquist. This model calculates the ratio of the input distance function to the output distance function to obtain the change in the efficiency index. The model is both stable and scientifically sound, as it not only accurately measures dynamic and continuous variability, but also identifies the specific causes of efficiency changes. The model allows researchers to accurately characterize the trend in efficiency dynamics. Specifically, the formula for the Malmquist index from period t to period t + 1 is as follows:

$${\mathrm{M}}_0\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)={\left[\frac{{\mathrm{D}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{{\mathrm{D}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}\times \frac{{\mathrm{D}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{{\mathrm{D}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}\right]}^{\frac{1}{2}}$$

A Malmquist index (M₀) > 1 indicates that the resource allocation efficiency has increased compared with the base period and is in an upward phase, when M₀ = 1, this indicates that the resource allocation efficiency is flat compared with the base period, and when M₀ < 1, this indicates that the resource allocation efficiency has decreased compared with the base period and is in a downward phase.

For the decomposition of the Malmquist index, scholars have proposed a number of different decomposition methods. Among them, the most popular is the FGLR decomposition method proposed by Fare et al. In this method, for the case of constant returns to scale, the Malmquist index (M₀) can be broken down into technical progress (TEch) and technical efficiency (EFch); when the returns to scale are variable, technical efficiency (EFch) can be further broken down into pure technical efficiency (PTEch) and scale efficiency (SEch). That is, M₀ = TEch × EFch = TEch × (PTEch × SEch)

$$\begin{gathered} {\mathrm{M}}_0\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)={\left[\frac{{\mathrm{D}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{{\mathrm{D}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}\times \frac{{\mathrm{D}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{{\mathrm{D}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}\right]}^{\frac{1}{2}} \\ \mathrm{EFCH}=\frac{{\mathrm{D}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{{\mathrm{D}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)} \\ \mathrm{TECH}={\frac{{\mathrm{D}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{{\mathrm{D}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}^{\frac{1}{2}} \end{gathered}$$

where (x^t, y^t) and (x^t+1, y^t+1) denote inputs and outputs from period t to period t + 1. Technological progress (TEch) mainly measures technological sophistication. When TEch > 1, this means that with the same factor inputs, technological innovation and progress trigger an increase in potential output, i.e., an innovation effect. Technical efficiency (EFch) measures the extent to which resources are fully utilized. When EFch > 1, it means that the potential of the existing level of technology can be released to a greater extent by increasing the coordination between the various resource factors, i.e., there is a catch-up effect. Pure technical efficiency (PTEch) measures the changes in the efficiency of resource allocation in HE caused by policy regimes and management models. Scale efficiency (SEch) captures the change in efficiency caused by improving the structure of educational resources and maintaining a reasonable level (Titus et al., 2021 [45]).

3.3. Evaluation Indicators

The evaluation index system of this study explores the establishment of an input–output evaluation index system for resource allocation in agricultural universities based on the general idea of promoting the innovative development of agricultural higher education on the basis of previous studies. Specifically, this study specifies the input indicators as belonging to three aspects: human, financial, and material, in accordance with the general theoretical framework of efficiency measurement. Meanwhile, based on the tradition and development tasks of agricultural disciplines and following the principles of relevance, development, and comparability, this study specifies the output indicators as the five main functions of talent cultivation, scientific research, international exchange and cooperation, social services, and faculty building. The specific index system is shown in

Table 1. Input–output evaluation index system of resource allocation in agricultural universities.

Indicators	Secondary Indicators	Tertiary Indicators
Inputs	Human Resources	Number of faculty and staff
	Physical Resources	Total fixed assets
	Financial Resources	Expenditure on education business
Outputs	Talent Development	Number of graduates from undergraduate and postgraduate programs
	Scientific Research	Number of academic paper tables (natural science category)
		Number of scientific and technical publications (natural science category)
		Funds allocated for scientific research activities (natural science category)
	International Exchange and Cooperation	Number of international cooperation papers
	Social Services	Actual income of the year of technology transfer
	Faculty Development	National Outstanding Young Scientists Fund
		National Outstanding Young Scientists Fund Recipient

Note: China has established the National Outstanding Young Scientists Fund and the National Outstanding Young Scientists Fund by the Natural Science Foundation of China to accelerate the training of a group of outstanding academic leaders who will enter the fields of science and technology around the world. The awards are focused on high-level national talents in science and technology. The award fully reflects the achievements of universities in the construction of a high-level faculty. Therefore, this study takes the number of professors who have won this award as an important indicator of the quality of faculty construction in agricultural universities.

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4. Results

4.1. Static Analysis Results

In this study, we referred to the software tool selection scheme of existing studies and carried out efficiency data calculations with the help of DEAP software and DEA-SOLVER software. Specifically, we used DEAP 2.1 to calculate the results of the DEA-BCC model and DEA-SOLVER Pro 5.0 software to calculate the results of the super-efficiency model. This study employs the authoritative software to calculate the decomposition values of the resource allocation efficiency of Chinese agricultural universities from 2014 to 2019. Moreover, this study further uses the efficiency decomposition values to carve out the allocation efficiency time series characteristics as well. The results obtained are shown in

Table 2. Decomposition values of resource allocation efficiency of agricultural universities.

Time	EFch	PTEch	SEch	Super-Efficiency
2014	0.990	0.997	0.993	1.285
2015	1.000	1.000	1.000	1.437
2016	1.000	1.000	1.000	1.443
2017	0.990	0.990	1.000	1.469
2018	1.000	1.000	1.000	1.401
2019	1.000	1.000	1.000	1.245
MEAN	0.997	0.998	0.999	1.380

.

In terms of technical efficiency, the average value of technical efficiency of resource allocation in agricultural universities from 2014 to 2019 is in the range of 0.990–1, and the overall average value is 0.997, which is close to 1, indicating that the overall level of resource allocation efficiency of these eight high-level agricultural universities is high. From the efficiency decomposition relationship, technological innovation plays an important leading role. The pure technical efficiency and scale efficiency of agricultural universities in the period 2014–2019 are not lower than the technical efficiency of the same period, implying that improving the system, upgrading the management level, and expanding the scale of allocation jointly enhance the efficiency of resource allocation. Among these factors, pure technical efficiency and technical efficiency have essentially the same trend of change and appear to develop in the same direction, indicating that the effect of technological innovation and its driving factors have a central leading role in improving resource allocation efficiency.

Comparing the super-efficiency results reveals that the super-efficiency of resource allocation in agricultural colleges and universities fluctuates significantly, with an overall upward and then downward trend. Taking 2017 as the boundary, from 2014 to 2017, there was a surge and then a slow rise, with a maximum increase of 12%, and from 2017 to 2019, there was a slow decline and then a rapid decline, with a maximum decrease of 11%. The course of super-efficiency during the observation period is closely related to the process of “double first-class” construction in China. In fact, in 2017, in order to effectively improve the overall strength of higher education, the Chinese government began to promote a major strategic decision called “building world-class universities and world-class disciplines” (i.e., “double first-class” construction). In 2017, according to the requirements of national resource allocation mechanism reform and path optimization, universities abandoned the original resource allocation concept and mechanism of exchanging high input for output and started to explore new resource allocation paths. However, as the overall reform practice is still in the exploration stage, the new efficient allocation model is still unclear, and the sustainable growth point of efficiency has not yet emerged, which inevitably causes a frictional decline in efficiency.

In order to further compare the resource allocation efficiency levels of the eight agricultural universities precisely, this study uses the super-efficiency model to calculate the efficiency decomposition values of each university in the observation period based on the perspective of inter-school comparison, as shown in

Table 3. Decomposition values of resource allocation efficiency of high-level agricultural universities.

DMU	2014					2015
	EFch	PTEch	SEch	Super-Efficiency	Rank	EFch	PTEch	SEch	Super-Efficiency	Rank
A	1.000	1.000	1.000	2.151	1	1.000	1.000	1.000	1.486	3
B	1.000	1.000	1.000	1.243	3	1.000	1.000	1.000	1.250	5
C	1.000	1.000	1.000	1.000	5	1.000	1.000	1.000	1.811	2
D	1.000	1.000	1.000	1.810	2	1.000	1.000	1.000	1.360	4
E	1.000	1.000	1.000	1.000	5	1.000	1.000	1.000	1.000	7
F	1.000	1.000	1.000	1.195	4	1.000	1.000	1.000	2.456	1
G	1.000	1.000	1.000	1.000	5	1.000	1.000	1.000	1.000	7
H	0.920	0.979	0.940	0.879	8	1.000	1.000	1.000	1.133	6
MEAN	0.990	0.997	0.993	1.285	--	1.000	1.000	1.000	1.437	--
DMU	2016					2017
	EFch	PTEch	SEch	Super-Efficiency	Rank	EFch	PTEch	SEch	Super-Efficiency	Rank
A	1.000	1.000	1.000	1.184	5	1.000	1.000	1.000	1.184	4
B	1.000	1.000	1.000	1.445	2	1.000	1.000	1.000	1.332	3
C	1.000	1.000	1.000	3.243	1	1.000	1.000	1.000	3.488	1
D	1.000	1.000	1.000	1.346	3	1.000	1.000	1.000	1.502	2
E	1.000	1.000	1.000	1.000	7	1.000	1.000	1.000	1.000	7
F	1.000	1.000	1.000	1.206	4	1.000	1.000	1.000	1.175	5
G	1.000	1.000	1.000	1.000	7	1.000	1.000	1.000	1.000	7
H	1.000	1.000	1.000	1.122	6	0.919	0.919	1.000	1.073	6
MEAN	1.000	1.000	1.000	1.443	--	0.990	0.990	1.000	1.469	--
DMU	2018					2019
	EFch	PTEch	SEch	Super-Efficiency	Rank	EFch	PTEch	SEch	Super-Efficiency	Rank
A	1.000	1.000	1.000	1.105	6	1.000	1.000	1.000	1.598	1
B	1.000	1.000	1.000	1.288	4	1.000	1.000	1.000	1.310	4
C	1.000	1.000	1.000	1.732	2	1.000	1.000	1.000	1.440	2
D	1.000	1.000	1.000	2.529	1	1.000	1.000	1.000	1.348	3
E	1.000	1.000	1.000	1.000	7	1.000	1.000	1.000	1.000	7
F	1.000	1.000	1.000	1.435	3	1.000	1.000	1.000	1.086	6
G	1.000	1.000	1.000	1.000	7	1.000	1.000	1.000	1.000	7
H	1.000	1.000	1.000	1.120	5	1.000	1.000	1.000	1.175	5
MEAN	1.000	1.000	1.000	1.401	--	1.000	1.000	1.000	1.245	--

.

Seven agricultural universities, including A and B, had technical efficiency values of 1 for six consecutive years from 2014 to 2019, achieving effective resource allocation, while the percentage of technical efficiency DEA-effective years in University H was 67%, which was lower than the overall average, indicating that there is internal variability in the input–output effectiveness of educational resources in agricultural universities. In terms of the mean value of super-efficiency, University G (2.12), which has the highest mean level of super-efficiency, exceeds the frontier by 122% in resource allocation super-efficiency, while Universities E and G have a mean value of super-efficiency of 1, not exceeding the frontier. On the one hand, this shows that the resource allocation inefficiency of agricultural colleges and universities is obvious, and the unbalanced state of effectiveness is common; on the other hand, it also shows that the educational development environment and the stage of each agricultural college and university are different, so they should seek their own characteristic development path based on the actual development of the school, fully stimulate the potential of advantageous disciplines, and achieve internal development.

In order to further clarify the constraints of resource allocation development in agricultural universities and to optimize the “input–output” structure, this paper uses projection analysis to calculate the target values of resource allocation and adjust the radial values, and the results are shown in

Table 4. Scale benefits of resource allocation for eight high-level agricultural universities.

DMU	Compensation for Size	Input Optimal Scale Projection			Output Optimal Scale Projection
		S1+	S2+	S3+	S1−	S2−	S3−	S4−	S5−	S6−	S7−	S8−
A	Unchanged	1858	129,484	109,261	0	0	0	0	0	0	0	0
B	Unchanged	825	55,091	28,845	0	0	0	0	0	0	0	0
C	Unchanged	1262	207,806	241,487	0	0	0	0	0	0	0	0
D	Unchanged	890	221,459	143,777	0	0	0	0	0	0	0	0
E	Unchanged	0	0	0	0	0	0	0	0	0	0	0
F	Unchanged	769	118,605	99,569	0	0	0	0	0	0	0	0
G	Unchanged	0	0	0	0	0	0	0	0	0	0	0
H	Decreasing	165	31,911	68,814	0	0	122	3528	0	179	0	0
MEAN	-	721	95,545	86,469	0	0	15	441	0	22	0	0

Note: S1+, S2+, S3+, S1−, S2−, S3−, S4−, S5−, S6−, S7−, and S8− represent the slack variables of the number of teaching staff, the total fixed assets, the amount of education funding, the number of undergraduate degree graduates, the number of academic paper tables, the number of scientific and technical publications, the amount of funding allocated to research activities, the number of international cooperation papers, the actual income from technology transfer in the year, the number of nationally outstanding young people, and the number of nationally excellent young people, respectively.

.

Among the eight high-level agricultural universities, only Universities E and G have zero shaded values for both inputs and outputs and constant returns to scale, i.e., the actual values match the target values and the resource use is optimal. The actual input value exceeds the optimal target value, i.e., the resource potential is not fully released and there is input redundancy. The shaded values of both the input and output sides of University H are greater than 0 and the scale payoff is in the decreasing stage, which means that there is both input redundancy and an output shortage.

Comparing the results of the optimal projection analysis on the input side and the output side, it is found that the misalignment between the actual value and the target value mostly occurs on the input side, which indicates that the main body of agricultural education resource input needs to optimize the mode and proportion of resource allocation, pay full attention to the specificity of the cluster development of agricultural colleges and universities, and optimize the resource input structure; agricultural colleges and universities themselves should also pay close attention to the effectiveness of resource allocation, break through the bottleneck of effectiveness, and effectively improve the quality of resource allocation and output.

4.2. Dynamic Analysis Results

In order to explore the dynamic evolution of resource allocation in agricultural universities, this paper breaks down the total factor productivity of resources of eight high-level agricultural universities based on the Malmquist model, and the specific decomposition results are shown in

Table 5. Productivity of resource allocation and decomposition index of agricultural universities.

DMU	EFch	TEch	PTEch	SEch	${\mathbf{M}}_0$
A	1.000	1.026	1.000	1.000	1.026
B	1.000	0.976	1.000	1.000	0.976
C	1.000	0.870	1.000	1.000	0.870
D	1.000	0.913	1.000	1.000	0.913
E	1.000	1.030	1.000	1.000	1.030
F	1.000	0.991	1.000	1.000	0.991
G	1.000	0.956	1.000	1.000	0.956
H	1.017	0.911	1.005	1.011	0.926
MEAN	1.002	0.958	1.001	1.001	0.960

.

From an overall perspective, the average value of total factor productivity of resource allocation in agricultural colleges and universities from 2014 to 2019 is 0.96, with diminishing productive efficiency and wasted resources. Specifically, the total factor productivity of six agricultural colleges and universities, including B and C, is less than 1, and the potential of educational resources has not been released to the greatest extent.

In terms of efficiency decomposition, all eight of the agricultural universities are in a state of unbalanced development between technical efficiency and technical progress efficiency in resource allocation. The technical efficiency-led universities represented by B and C have a higher average annual growth rate of technical efficiency than technical progress efficiency, driving resource allocation efficiency by improving the management level and expanding scale efficiency. The technical progress-led universities represented by A and E, whose average annual growth rate of technical progress efficiency is higher than the growth rate of technical efficiency, mainly rely on the innovation and upgrading of allocation technology to enhance resource allocation efficiency. As can be seen from Table 5, all universities with increasing total factor productivity are technical progress-led universities, and all decreasing universities are technical efficiency-led universities, indicating that the efficiency of technical progress plays an important role in the improvement of total factor production efficiency.

To further determine the level of total factor productivity and its decomposition relationship in different periods, this paper breaks down the total factor productivity of resource allocation in agricultural universities from 2014 to 2019, and the specific results are shown in

Table 6. Productivity and decomposition index of resource allocation in agricultural universities.

Year	EFch	TEch	PTEch	SEch	${\mathbf{M}}_0$
2014–2015	1.010	0.930	1.003	1.007	0.940
2015–2016	1.000	0.949	1.000	1.000	0.949
2016–2017	0.990	1.561	0.991	0.999	1.545
2017–2018	1.011	0.509	1.009	1.001	0.514
2018–2019	1.000	1.149	1.000	1.000	1.149
MEAN	1.002	0.958	1.001	1.001	0.960

.

Total factor productivity (TFP) has been changing significantly, with an overall “N”-shaped trend of rising, falling, and then rising again. In the period 2014–2016, TFP was in the rising stage, with the rate of increase increasing year by year, and the peak of efficiency was reached in 2016; then, it quickly fell back to the trough of efficiency in the interval from 2017 to 2018, and then rebounded rapidly from 2018 to 2019. The TFP for 2019 shows a rapid rebound trend. The change in total factor productivity is closely related to the process of promoting the construction of world-class universities and disciplines, which confirms the static analysis of the frictional decline in efficiency at the beginning of the reform, and also indicates that as the policy continues to advance, the “input–output” structure is further optimized, and the efficiency of investment and resource allocation is improved.

In terms of changes in technological progress, the overall average value of the efficiency of technological progress in agricultural universities from 2014 to 2019 was 0.958, showing the phenomenon of technological regression. Technical progress efficiency fluctuates significantly, with an overall “N”-shaped trend of rising, falling, and then rising again, which is consistent with the trend of total factor productivity change, indicating that with similar levels of technical efficiency, productivity declines due to the slow progress of technical progress in promoting the expansion of the efficiency frontier. Agricultural universities should focus on bridging the shortcomings of technological innovation to fully stimulate resource potential. In terms of technical efficiency changes, the mean values of both pure technical efficiency (1.001) and scale efficiency (1.001) of resource allocation in agricultural universities from 2014 to 2019 are greater than 1. Pure technical efficiency and scale efficiency jointly drive technical efficiency progress. The overall mean value of technical efficiency over the observation period is 1.002, indicating that the existing level of production technology has led to an average annual increase of 0.2% in resource allocation production efficiency by increasing the coordination between resource factors, with a technical catch-up effect. Agricultural universities are unlocking their technical efficiency potential and increasing their total factor productivity by improving management and increasing resource allocation.

In order to better grasp the development trend of the input–output efficiency of resource allocation in agricultural universities, the characteristics of efficiency changes in each time period were divided and summarized based on the level of technical efficiency and technical progress efficiency, using 1 as the cut-off point for high and low values, as shown in

Table 7. Classification of resource allocation efficiency in agricultural universities.

Year	EFch	TEch	Stage
2014–2015	1.010	0.930	High-Low
2015–2016	1.000	0.949	High-Low
2016–2017	0.990	1.561	Low-High
2017–2018	1.011	0.509	High-Low
2018–2019	1.000	1.149	High-High

.

From 2014 to 2018, technical efficiency and technological progress did not achieve balanced and comprehensive development, and only a single efficiency indicator reached a superior level, while comprehensive allocation efficiency did not achieve DEA-effective status. In other words, at the existing production technology level, agricultural universities rely on the incremental advantages of resource endowment and supporting facilities to release the potential of resources by improving the level of resource allocation management, ignoring the important role of technological innovation and progress in resource allocation for the improvement of allocation efficiency. After 2018, they entered a new stage of balanced development, with more emphasis on the level of technical efficiency and the quality of technical progress to make up for the shortcomings of resource allocation efficiency, and the overall level of input and output increased. With the reform of higher agricultural education in the new era, agricultural universities have gradually changed the traditional unbalanced resource allocation mode of “exchanging input scale for output performance”, and are gradually transitioning to a comprehensive and balanced “high technical efficiency–high technical progress” mode.

5. Conclusions

Guided by the theoretical approaches of organizational psychology and econometrics, this study uses a super-efficiency DEA–Malmquist index decomposition model to calculate and measure the effect of financial inputs, disciplinary differences, and development levels on the efficiency level of resource allocation in universities in China, taking high-level agricultural and forestry universities as the research target. Furthermore, this study further examines and identifies the drivers and influences of resource allocation on the overall efficiency of agricultural colleges and universities. In addition, this paper also analyzes the layout of the optimal-size allocation structure, which provides meaningful guidance for Chinese universities, especially for managers of universities with disciplinary characteristics represented by agriculture, to help them understand the value and effect of resource allocation efficiency improvement in universities.

5.1. Theoretical Contributions

This study provides two major contributions to the previous literature on the theory and research on the efficiency of resource allocation in higher education.

Firstly, this study is an empirical study of the efficiency of resource allocation in high-level agricultural universities in China. This study found that the technical efficiency of resource allocation in the eight Chinese high-level agricultural universities was in a steady state of growth. This finding is consistent with the results of previous scholarly studies on the evaluation of resource allocation efficiency in comprehensive universities, suggesting that financial investment can indeed promote efficiency (Li et al., 2023 [43]; Kempkes et al., 2010 [77]). At the same time, it is further clarified that for high-level agricultural universities, higher levels of efficiency are due to the technological innovation of financial inputs playing a leading role. This finding reveals a potential mediating role between resource allocation and efficiency improvement, and provides new empirical evidence for research on the optimal improvement of resource allocation based on technological innovation.

Secondly, this study identifies a trend of increasing and then decreasing super-efficiency over the observed cycle. The overall course of change is closely related to the reform process of China’s higher education policy. At the early stage of policy implementation, the traditional “high input–high output” resource allocation mechanism was abandoned as a result of the reform and path optimization of the resource allocation mechanism of universities, but the overall reform practice is still in the exploratory stage, and new growth points of allocation efficiency have not yet emerged, which inevitably leads to frictional declines in efficiency. This finding sheds further light on the impact of policy implementation on resource allocation efficiency. In view of this, this study contributes to a better understanding of the pathways and impact mechanisms of policy factors on the efficiency of resource allocation in universities.

5.2. Actual Impact

This study provides four general recommendations for improving the efficiency of resource allocation in high-level specialty universities represented by agriculture.

Firstly, a static cross-sectional comparison study found that the efficiency of resource allocation in agricultural universities is individually heterogeneous, with large inter-university disparities. Unlike other science and engineering disciplines, these universities have to build up large-scale basic teaching and research conditions such as experimental fields and experimental forests, as well as agricultural science and technology infrastructures of a world-leading level. Therefore, high-level agricultural universities should implement special development paths and adopt differentiated resource allocation schemes based on the existing disciplinary advantages and characteristics. In particular, universities should strengthen inter-university cooperation by relying on the resources and clustering advantages of the same disciplinary platform in conjunction with the orientation of the construction of new agricultural science. On the one hand, through the method of complementary advantages, universities should improve the quality of agricultural talent training and scientific research output and enhance the efficiency of resource allocation input and output. On the other hand, they should give full attention to the role of radiation, improve the effectiveness of the university resource allocation system, and stimulate the vitality of resource utilization. At the same time, the resource input side, mainly the government, should also take into full consideration the difference in demand according to the actual development of each agricultural university, improve the top-level resource allocation design while combining policy support and resource support, implement dynamic differentiated classification guidance strategies, and adopt a differentiated input scheme for educational resources of agricultural colleges and universities.

Secondly, the static projection analysis revealed that some agricultural colleges and universities encounter the problems of mismatch between inputs and outputs, government inputs not being effectively transformed into outputs, some input elements being redundant, and resources not being effectively used. Therefore, agricultural colleges and universities should accurately project the scale of inputs and establish a multi-level and diversified input mechanism. On the one hand, a scientific and accurate education resource input budget system should be established to accurately forecast and adjust the resource input and control the redundant resources. On the other hand, a multi-level and diversified funding input mechanism should be established to appropriately expand the financial input of agricultural colleges and universities with scarce resources and strengthen the financial guarantee for the construction of new agricultural science facilities, while comprehensively considering the stock of existing educational resources and changes in market demand and prioritizing the investment of human, financial, and material resources in agricultural colleges and universities with a high conversion rate of resource output so as to maximize the efficiency of use and contribute to the national agricultural development strategy of high-level agricultural colleges and universities

Again, the dynamic analysis found that the total factor productivity of resource allocation in the sample universities was in a diminishing state due to the restrictive influence of technological regression during the observation cycle. This study concludes that high-level agricultural universities should improve their technology digestion capacity and make up for the shortcomings of technological innovation in resource allocation. Facing the strategic needs of agriculture, agricultural universities should take the development of agriculture as their guide, pay attention to the development of informatization and the intelligence of production technology, learn from the experience of the resource allocation management of first-class agricultural universities at home and abroad, strengthen their technology digestion capacity, and enhance the depth of learning and intensity of using advanced resource allocation technology. Universities should continue to update their own research and production methods and technologies, making up for the shortcomings of technological innovation, so that the introduction of technology and innovation can become an internal driving force for the optimization of resource allocation in agricultural universities and release the potential of educational resources.

Finally, the trend of fractal change in resource allocation efficiency reveals that relying only on traditional methods such as improving management models, expanding the scale of resource services, and increasing investment in equipment, while neglecting technological innovation and progress in production, cannot comprehensively improve resource allocation efficiency. This is because the sample universities are all high-level Chinese universities whose mission is to serve the needs of national development strategies, and the level of university construction is closely related to national agricultural development; therefore, high-level agricultural universities should break through the single-driven model and comprehensively improve the transformation efficiency of resource allocation. In addition, they should break through the restrictions of the traditional agricultural education development concept and system, actively adapt to and understand the law of cultivating agricultural talents required by national economic and social development, continuously deepen the reform of agricultural universities’ allocation field, optimize the management system, improve the management level, make up for the shortcomings of agricultural technology, and realize the change from a single unbalanced resource allocation mode to a “high technical efficiency–high technical progress” balanced mode. The reform of the allocation of resources in agricultural colleges and universities has been deepened, the management system has been optimized, the management level has been improved, and the shortcomings of agricultural technology have been compensated for.

5.3. Limitations and Future Research Directions

While this study has worked tirelessly to effectively fill the current research gap and promote the sustainability of agricultural education worldwide, it is clear to us that there are indeed still some limitations at present. We have carefully analyzed and summarized the shortcomings of the current study in terms of research coverage, data sources, and research perspectives in order to facilitate continuous improvement in future research.

Firstly, this study focuses on resource allocation efficiency calculations for Chinese agricultural universities. Therefore, in the process of data collection, this study only focused on the resource input and output data of some high-level agricultural universities. The clear scope of this study and the data sources effectively improve the relevance and guiding nature of the findings on the one hand, but may undermine the generalizability of the findings on the other. Based on the current research process and findings, it is unclear whether the findings and conclusions of this study can be generalized to other types of HEIs or to a wider region. In future research, we will build on the current study and gradually expand its coverage and examine the comprehensive guidance implications of the findings.

Secondly, the data used in this study were mainly derived from the official data published in China each year. Specifically, we obtained the relevant data by consulting government statistical yearbooks. However, it is clear from our research that the overall official statistics do, on the one hand, ensure the authenticity and accuracy of the data, and on the other hand, inevitably limit the variability of the data, i.e., the individual data and development characteristics of some universities are obscured. In our future research, we will address the contradiction between comprehensiveness and variability by focusing on supplementing with indicator systems and data sources that can help reflect the heterogeneity of universities—that is, the comprehensiveness of data sources will contribute to the solution of the problem of homogeneous bias.

Thirdly, this study focuses on the allocation efficiency of educational resources from an econometric perspective as the allocation pattern of educational inputs such as human, material, and financial resources in different directions of use. The study as a whole sees HEIs as ‘brokers’, i.e., we place more emphasis and attention on the central role of the rational allocation of resources in enhancing the productivity and the value of HEIs. However, while the principle of efficiency does help HEIs to focus fully on the efficiency of HE resource allocation, it also inevitably ignores the non-quantifiable and comprehensive contribution of HEIs to some extent. It is undeniable that HEIs are different in nature from general organizations. HEIs do not operate for the purpose of obtaining excess financial gain, and the excessive pursuit of efficiency may result in a reduction in overall contribution. Therefore, in our future research, we will comprehensively examine the factors affecting the efficiency of resource allocation and the mechanisms of action of HEIs based on a comprehensive analytical framework and indicator system.