Evaluation Study on the Ecosystem Governance of Industry–Education Integration Platform in China

Abstract

The industry–education integration platform itself is an ecosystem composed of multiple subjects, and its effective governance cannot be separated from contract management. Platform operation requires efforts to overcome institutional transaction costs and clarify the distribution of benefits among cooperative subjects. Each cooperative subject displays cooperative behavior with consistent organizational goals, which contributes to effective platform regulation reaching a steady state. Therefore, the industry–education integration platform ecosystem involves key elements such as contracts, transaction costs, property rights definition, cooperative behavior, and stability within the ecosystem. Thus, this paper takes the context of the integration of industry and education in China and innovatively proposes an evaluation index system for the governance of the ecosystem of the industry–education integration platform. Then, this paper takes the 14 industry–education integration platforms in China as an example. The grey correlation method was applied to the obtained sample data, which overcame the deficiency of sample evaluation with little data and poor information. Subjective and objective index weight assignment methods are used to improve the scientific and rational nature of the model evaluation. Finally, to improve the governance level of the ecosystem of the industry–education integration platform, new references are provided in four aspects: establishing a flawless mechanism of the platform contract, protecting the rights and interests of platform-related subjects, improving the protection and development mechanism of resources, and exploring the outsourcing of educational services.

1. Introduction

The integration of industry and education necessitates deep cooperation between education and industry, which is also a deep cooperation between institutions and industry enterprises with the goal to improve the quality of talent training [1]. Industry–education integration platforms promote the convergence and integration of talent, intelligence, technology, capital, management, and other resource elements of education and industry systems. Consequently, an ecosystem forms that integrates talent training, industry cultivation, and technological innovation [2]. This ecosystem of industry–education integration platforms can realize the integration and utilization of heterogeneous resources from multiple subjects. This not only helps to promote talent reform, but can also provide new momentum for industrial transformation and upgrading [3]. However, the contractual and property rights relationships of current industry–education integration platforms are relatively unclear, and platforms face institutional transaction costs. The cooperative behavior of platform members affects both the depth and breadth of platform cooperation. Therefore, there is a need to improve the governance of the industry–education integration platform ecosystem and promote its stability, regulation, innovation, and cooperation.

The efficient and smooth operation of the ecosystem of industry–education integration platforms is related to the promotion of industry–education integration, as well as the sustainable development and long-term competitive advantage of platforms [4]. The construction of an ecosystem of industry–education integration platforms urgently needs to address the key elements of relationships within this ecosystem. These key elements are contracts, transaction costs, property rights definitions, cooperative behavior, and stability. However, current research lacks an evaluation index system for constructing the governance of industry–education integration platforms from an ecosystem perspective.

The research goal of this paper is to explore the governance model of the ecosystem of China’s industry–education integration platforms, and to promote the overall depth of industry–education integration by improving the governance level. On this basis, in this paper, sample data of 14 integrated industry–education integration platforms in China are selected. Using the grey correlation method and combining subjective and objective index weighting methods, the current situation of the ecosystem governance of the integration of industry–education platforms is evaluated objectively and comprehensively. The shortcomings of previous studies that constructed sample evaluation systems with little data and poor information are addressed, and in turn, a strategy for the governance of the ecosystem of industry–education integration platforms is proposed. The overall design of this study is as follows. The dimensions of the ecosystem governance of industry–education integration platforms are proposed, and an evaluation index system is constructed. Then, a grey correlation method and a combination of subjective and objective indicator weighting assignment methods are used to evaluate the current situation and deficiencies of the ecosystem governance of industry–education integration platforms in China. Finally, strategies for the governance of the ecosystem of the integration platform of industry–education are proposed.

2. Dimensional Analysis of Ecosystem Governance of Industry–Education Integration Platforms

The governance of the ecosystem of industry–education integration platforms covers the processes of collaborative innovation and value creation by each internal innovation subject through the play of their respective heterogeneity. It is also a sustainable process of dynamic change, the embodiment of interdependence among subjects within the system, the maintenance of continuous innovation capacity, and the creation of value advantages [5]. The purpose of the tangible carrier jointly built by industry, academia, research, and other cooperative subjects linked via contract or capital is collaborative innovation and value creation in an open environment within the ecosystem [6]. Collaborative innovation [7] is an open-form complex network structure model with holistic and dynamic characteristics, the core subjects of which include universities, enterprises, and research institutes. By collaborating, each subject can achieve effective convergence of innovation factors as well as cross technical and information barriers. Further, subjects can deeply integrate innovation factors such as human, technology, information, and capital resources, thus realizing ecosystemic nonlinear utility in the process of collaborative innovation.

However, the operation of industry–education integration platforms inevitably causes institutional transaction costs that determine both the synergistic innovation effect and the collectivist dilemma as a result of industry–academia-research cooperation [8]. Industry–education integration platforms exist both as formal organizations with ties through contracts or capital, and as informal organizations with implicit ties through knowledge, technology, relationships, and trust. The nature of property rights within the platform system is of great concern, and there is an urgent need to clarify property rights relationships [9]. An industry–education integration platform has the properties of a network organization. While paying attention to contractual relationships, transaction costs, and the nature of property rights, it is also important to note that there is both competition and strong emphasis on mutual cooperation among members within the organization.

Governance objectives should focus on operational quality, comprehensive effectiveness, synergistic efficiency, and cooperative flexibility. Regarding the operating bodies of industry–education integration platforms, realizing the deep integration of the industry chain, education chain, talent chain, and innovation chain requires stability, which becomes the key to realizing high-quality platforms to promote governance [10]. Based on the above analysis, this paper explores the main dimensions of ecosystem governance of industry–education integration platforms from five aspects: contract, transaction cost, property rights, cooperative behavior, and stability.

As shown in Figure 1, there are five governance dimensions of the industry–education integration platform ecosystem. The first is the contract. Platform ecosystem organization is essentially a contractual relationship within the framework of the property rights system. This relationship determines that both the construction and operation of the platform are inseparable from contractual governance. The second is the transaction cost, which is explored from the six aspects of information asymmetry within the platform ecosystem, limited rationality of subjects, operational uncertainty, opportunistic behavior of subjects, incomplete property rights, and limitations in the level of technology for resource integration and utilization. The third governance dimension is property rights. The incompleteness of property rights of groups within the industry–education integration platform ecosystem explains the property rights characteristics of the platform. This incompleteness directly affects the distribution of interests of the major subjects within groups, which in turn affects the sustainable development of the industry–education integration platform ecosystem. According to new institutional economics, the platform ecosystem has the characteristics of both education and industry, which determines that the governance of property rights is the key to the governance of the platform ecosystem. The fourth dimension is cooperative behavior. The mechanism of the impact of transaction costs on the strategic choices of actors in the ecosystem discloses the cause of the collectivist dilemma. The choice of actors between “cooperative behavior” or “opportunistic behavior” directly affects whether the integration of industry and education can continue to advance to depth and breadth and must be effectively regulated. The fifth dimension is stability. As an innovation ecosystem, the operation of the platform needs to maintain a high level of structural or state resilience as well as a relatively constant direction of development and evolution [11].

3. Construction of an Evaluation Index System for the Governance of the Ecosystem of Industry–Education Integration Platforms

3.1. Evaluation Indicators of Contractual Governance

Contractual governance is juxtaposed with relational governance, with the former usually referring to the use of formal systems and regulations to achieve governance goals, and the latter usually referring to achieving governance goals through reputation, culture, and ethics [12]. Contracts are essential for regulating economic partnerships, where informal relational governance can only play a supporting role, advocating an ecosystem governance model of “contract to contract”. On the one hand, the selection of contractual governance indicators of the industry–education integration platform must refer to the contractual theory; whereas, on the other hand, it must fit the basic characteristics of the platform ecosystem. The content of contractual governance involves core issues such as rights, responsibilities, and benefits. Its functional dimensions include three main aspects: control, coordination, and flexibility [13]. In terms of the contractual governance of the industry–education integration platform [14], on the one hand, the contract is always incomplete, and the completeness of the contract is directly related to the maintenance of subsequent group relations and the achievement of synergistic goals. On the other hand, it is reflected in the control, coordination, and flexibility of the contract. Because coordination and flexibility have similar meanings and are prone to ambiguity in research, they can be combined into the same indicator [15]. Given that the cost of midway exit default is an important factor for stable cooperation in ecological community organization relationships, the cost of midway exit default is included as an important indicator of contract governance. So far, the evaluation indicators of platform ecosystem contract governance can be summarized into the following four aspects: contract control ability, contract coordination ability, contract completeness, and midway default cost.

3.2. Evaluation Indicators of Transaction Cost Governance

Scholars prefer transaction costs over resource base, principal-agent, information processing, property rights, relational networks, and life cycle theories, and most papers use transaction cost theory as a research tool [16]. In the governance of university-enterprise community industrial colleges, transaction cost is the key factor restricting the participation of industry enterprises and research institutions in industrial colleges. Only when the governance of industrial colleges is truly improved in terms of transaction costs, an ecological community of schools and enterprises with true resource integration and benefit sharing can be built [17]. Scholars both in China and internationally have widely used transaction cost theory to study social and economic issues. However, so far, no consensus has been reached on how to evaluate the governance capacity of transaction costs; only countermeasures have been suggested for specific problems [18]. There are two reasons for this: the need to explore transaction cost governance capacity in the context of specific issues, and the fact that transaction cost governance capacity evaluation indicators are generic and comparable. For example, the Chinese education sector is now actively carrying out the integration of industry and education in the field of engineering education. In this integration, four transaction costs, namely partner selection cost, operational transaction cost, asset specificity cost, and quasi-rent capture cost, have a significant impact on the strategic choice of cooperative masterwork behavior. Controlling these four transaction costs naturally becomes the focus of platform governance. Combining the governance dimensions of the above transaction cost theory, the classical transaction costs related to transaction frequency and uncertainty are classified into transaction cost governance dimensions [16]. In summary, the evaluation indexes of transaction cost governance of industry–education integration include the following five indexes: partner selection governance, operational transaction governance, asset specificity governance, transaction frequency governance, and uncertainty governance.

3.3. Evaluation Indicators of Property Rights Governance

The incomplete nature of property rights in the industry–education integration platform ecosystem poses a challenge to the governance of the platform. Firstly, except for equipment, facilities, and venues that can be measured in monetary terms, the common shared resources provided by participating subjects within the platform ecosystem belong to the category of intellectual property rights. It is necessary to strengthen the definition and protection of ex ante and ex post intellectual property rights to effectively manage platform intellectual property infringement [19]. Combined with Bazel’s property rights theory, property rights among participating subjects of the platform ecosystem are always changing. Ex ante and ex post are only points in time of property rights governance, and a dynamic property rights definition is needed according to the needs of property rights governance. Thus, property rights protection and property rights definition are important manifestations of platform property rights governance [20]. Secondly, as rational subjects, platform participants aim to achieve their respective goal demands through the integration of industry and education. Ultimately, this boils down to the distribution of cooperation benefits and the sharing of possible risks [21]. In particular, in the distribution of cooperation benefits among participating parties, it is necessary to distribute cooperation benefits according to the relative value of innovation resources invested by participating parties. The importance and quantity of the resources invested by cooperating parties need to be evaluated, and the dilemma of cooperation caused by incomplete property rights needs to be solved as much as possible. Finally, the risk borne by partners is considered based on the relative value of input resources. The underlying reasoning is that both benefits and risks are components of the subject’s rights, whereby risk sharing should be included in property rights governance [22]. In summary, this study uses property rights protection capability, resource importance definition capability, resource quantity definition capability, and risk sharing definition capability as evaluation indexes of property rights governance of the industry–education integration platform ecosystem.

3.4. Evaluation Indicators of Cooperative Behavior Governance

The benign operation of ecosystem of industry–education integration platforms cannot be separated from the mutual trust and cooperation between groups. In a study of industry–university cooperation in U.S. biotechnology industry universities, it was shown that trust forms the basis of collaborative innovation between enterprises and universities. Without trust, it is not only difficult to conduct collaborative technological research, but the lack of trust also induces considerable opportunism [23]. For example, the opportunism of enterprises has different manifestations in the three stages of university–industry cooperation and collaborative education in the early, middle, and late stages. Opportunism in the middle stage of cooperation can be described by behaviors such as formalism, face-saving, and withdrawal from cooperation. Opportunism in cooperation can either be classified as strong form opportunism or weak form opportunism. The former refers to explicit and easily perceived opportunistic behavior by the cooperative partner, while the latter is relatively hidden and cannot be easily perceived by the cooperative partner [24]. In the operation of the industry–education integration platform, opportunistic behavior can be further divided into explicit and implicit opportunism. Explicit opportunism mainly includes breach of contract and extortion (blackmail), which have similar connotations to strong forms of opportunism. Implicit opportunism mainly includes evasion of responsibility, refusal to adapt, and free-riding, which have similar connotations to the weak form of opportunism [25]. In the mutual cooperation among major players in the industry–education integration ecosystem, withdrawing midway and maintaining cooperation are listed as two behavioral strategies of platform participating players [26]. In summary, formalism governance, project exit governance, knockout governance, evasion of responsibility governance, refusal to adapt governance, and free-rider governance can all be used as the main evaluation indicators for the governance of cooperative behavior among groups in the industry–education integration platform ecosystem.

3.5. Evaluation Indicators for Platform Stability Governance

The stability of the ecosystem of the industry–education integration platform is the focus and difficulty of platform governance, and the stability of the platform must be improved by matching supply and demand resources. A study of bilateral trading platforms showed that expected economic benefits and matching supply and demand consumption are the main factors that attract users to participate and maintain robust platform operation. This shows that expected returns are equally necessary for platform stability [27]. However, this study argues that not the absolute amount of expected benefits affects platform stability, but the difference between expected benefits and transaction costs of the participant. This emphasizes the key to governance of platform ecosystem stability from the perspective of relative expected benefits [28]. In addition, the stability of the platform ecosystem is mainly reflected in the integration of the intrinsic value of the ecosystem’s industry chain, education chain, innovation chain, and talent chain, the direction of high-quality promotion, and the conversion from old to new dynamics in three aspects. Among these aspects, value integration emphasizes the integration of resources between supply and demand [29]. High-quality promotion mainly emphasizes the efficiency of intensive resource utilization. However, the old and new dynamics emphasize the design of an open innovation ecosystem and the introduction of market mechanisms replacing the original government-led model of promoting the integration of industry and education [30]. In summary, the degree of resource integration, the degree of supply and demand matching, expected benefits, and operation mechanism innovation capacity are taken as evaluation indexes for the governance of the stability of the industry–education integration platform ecosystem.

3.6. Platform Governance Capacity Evaluation Index System

In summary, the evaluation index system of the ecosystem governance of the industry–education integration platform is constructed by selecting evaluation indexes in the following five aspects: contract governance, transaction cost governance, property rights governance, cooperation behavior governance, and platform stability governance, as shown in Table 1. The index system of this study is constructed in accordance with the design principles of the index system, which fully embodies the principles of purpose, completeness, operability, and dynamism.

From Table 1, it can be derived that the evaluation index system of the ecosystem governance of industry–education integration platforms includes a total of 5 primary and 23 secondary indicators. The selection of these indicators is predominantly based on transaction cost theory, property rights theory, governance theory, and finally, a comparison with the literature. Both the design of indicators and the screening process reflect the basic attributes and operation rules of the governance of the industry–education integration platform ecosystem.

4. Evaluation Methodology and Result Analysis of Ecosystem Governance of Industry–Education Integration Platforms

4.1. Grey Correlation Combination Weighting Model

4.1.1. Evaluation Model Building Concepts

In China, the integration of industry and education mainly includes the main forms of industry-university alliances, practice bases, and teaching projects. With the in-depth promotion of the integration of industry and education, it is now developing in the direction of “integrated large platform +”. However, so far, only few platforms have truly integrated functional modules, and the only people who really understand the operation of the platform are core senior managers. Therefore, the investigation targets of the comprehensive governance of the industry–education integration platform ecosystem are limited. It is difficult to obtain first-hand data through large-sample questionnaires, and thus, indicators can only be reasonably assigned, and comprehensive evaluation can only be conducted based on small samples. To improve the reliability of survey data, this study takes industry–education integration platforms in China as example. After repeated selection, the following 14 industry–education integration platforms were selected: the National School-Enterprise Alliance for Excellence in Engineering Education in Local Universities (NASE), U + New Engineering Smart Cloud (U + NE), Dekang Electronics (DKET), Integrated Circuit Industry–Education Integration Development Alliance (ICIE), Jiangsu Zhen Yuexin Intelligent Technology Co. Service Platform (JSZY), Wenzhou Industry–Education Integration School-Enterprise Matching Platform (WZIE), National Transportation Industry–Education Integration Service Platform (NTIE), Xinmaier Industry–Education Integration Talent Cloud Platform (XMIE), Juhua Industry–Education Integration Information Service Platform (JHIE), Industry–Education Integration and School-Enterprise Cooperation Service Platform (IEISE), Huike Group (HKGP), Xuan Yuan Public Service Platform for the Integration of Industry and Education (XYPS), Guangdong New Engineering (GDNE), and Wanjiang Smart Manufacturing Industry–Education Integration Alliance (WJSM). The above 14 industry–education integration platforms are labeled in turn as $T_1,T_2,\mathrm{\dots },T_{14}$.

4.1.2. Determination of Evaluation Index Combination Weights

Firstly, a questionnaire on the governance capacity of the education–industry integration platform ecosystem was designed according to Table 1. Fourteen top managers of the platform were invited to participate. The survey was conducted on a 5-point Likert scale [31], where 1 represents very low and 5 represents very high. The survey was completed within November–December 2021 and a total of 14 completed questionnaires were received from the industry–education integration platforms in Jiangsu, Zhejiang, and Shanghai regions by face-to-face paper-based questionnaires. All other regions conducted the survey online. All data were measured using the 5-point Likert scale, which can be used directly for indicator weighting without standardization.

Secondly, indicators were assigned using the product formula $W_i={\displaystyle \prod _{t=1}^2{w}_i}$ proposed by Saaty [32], where $W_i$ is the combination weight, $t$ is the level of the indicator, $t=1,2$, and $w_i$ is the weight of the secondary indicator $i$. The reasons for this are given in the following: first, all 23 secondary indicators use the 5-point Likert asymmetric design for measuring importance, and there is no difference in the scale; second, the measurement data originate from the top managers of 14 platforms, reflecting their subjective willingness. This completes the subjective empowerment of indicators.

The coefficient of variation method was used to objectively assign weights to evaluation indicators, which is a relatively accurate and simple method [33]. Specifically, it consists of the following four steps:

• 1.. Finding the average value of each index;

(1) $\overline{X_j}=\frac{{\displaystyle \sum _{i=1}^n{x}_{ij}}}{n}(j=1,2,\cdot \cdot \cdot ,m)$

• 2.. Finding the standard deviation of each index;

• 3.. Finding the standard deviation coefficient of each index;

(2) ${\sigma }_j=\sqrt[n]{\frac{{\displaystyle \sum _{i=1}^n{(x_{ij}-\overline{x_j})}^2}}{n-1}}(j=1,2,\cdot \cdot \cdot ,m)$

(3) $V_j=\frac{{\sigma }_j}{\overline{X_j}}(j=1,2,\cdot \cdot \cdot ,m)$

• 4.. Normalizing the weights of each indicator.

In general, the combination of empowerment is designed to eliminate bias caused by objective and subjective empowerment methods. This is achieved by denoting the 23 evaluation indicators obtained by the subjective assignment method as the vector of weights $W_1$. The objective evaluation indexes obtained by the coefficient of variation method are denoted as the vector of weights $W_2$. The weighted average method is used to obtain the final weights of each indicator, and the formula is as follows:

$W_i=\alpha W_1+\left(1-\alpha \right)W_2$

where $0<\alpha <1$.

4.1.3. Grey Correlation Model

Because of the limitation of sample availability, in this study, only the current status of governance evaluation of the platform ecosystem is explored through 14 industry–education integration platforms. Grey system theory is a control theory for systems with incomplete or uncertain information, which was founded by the Chinese cybernetics expert Deng Julong in 1982 [34]. After nearly 40 years of development, grey system theory, which originated in the field of cybernetics and the discipline of technical science, has penetrated various fields such as industry, agriculture, economy, society, ecology, water conservancy, and meteorology [35]. The object of this study is a “small sample” and “information-poor” uncertainty system where “some information is known and some is unknown” [36].

Because the grey system theory method is applicable to a “small sample, poor information” research scenario [37], the grey correlation analysis method is used. A virtual set of optimal samples is used to calculate the grey correlation scores of the index value curves of 14 evaluation objects as well as the index value curves of the optimal samples.

(4)$\gamma (x_0(k),x_i(k))=\frac{\underset{i}{\min }\underset{k}{\min }|x_0(k)-x_i(k)|+\xi \underset{i}{\max }\underset{k}{\max }|x_0(k)-x_i(k)|}{|x_0(k)-x_i(k)|+\xi \underset{i}{\max }\underset{k}{\max }|x_0(k)-x_i(k)|}$

(5)$\gamma (X_0,X_i)=\frac{1}{n}{\displaystyle \sum _{k=1}^n\gamma (x_0(k),x_i(k))}$

where, $\gamma (x_0(k),x_i(k))$ is the correlation coefficient between the $i$th indicator and the optimal value of the $j$th evaluated sample. $x_{0}k$ is the optimal value after normalization of the $i$th indicator, and $x_{i}k$ is the value after normalization of the $i$th indicator of the $j$th evaluated sample. $\xi$ is the discrimination coefficient, where $\xi \in [0,1]$. Generally, the value $\xi$ of 0.5 is taken by convention [38].

Using grey modeling software, the relationship coefficient composition matrix $R$ of all evaluated sample indicators is measured as follows:

(6)$R=(r_{ij})=\left[\begin{array}{cccc}{r}_{11}& r_{12}& \mathrm{\dots }& r_{1n}\\ r_{21}& r_{22}& \mathrm{\dots }& r_{2n}\\ \mathrm{\dots }& \mathrm{\dots }& \mathrm{\dots }& \mathrm{\dots }\\ rm1& rm2& \mathrm{\dots }& rmn\end{array}\right]$

The index weights $W$ and index scores $r_{ij}$ of the above combination are linearly weighted to derive the composite score $Z_j$ of the $j$th integration platform, where $Z_j={\displaystyle \sum _{i=1}^m(r_{ij}\times W_i})$.

4.2. Measurement Results and Analysis

4.2.1. Sample and Data

Among these 14 samples, five platforms are organized and operated by universities (such as NASE), two platforms built by the government (such as WZIE), and seven platforms organized and operated by enterprises (such as HKGP). The “questionnaire on the governance capacity of the ecosystem of the integration platform” was designed, and the 5-point Likert design was used for 23 evaluations, where 1 represents “very low”, 2 represents “low”, 3 represents “unclear”, 4 represents “high”, and 5 represents “very high”. To improve the quality of the original data, first, the authors communicated with the 14 industry–education integration platforms before conducting the survey to obtain their cooperation and support. Secondly, the 14 platforms were organized into WORD documents, so that respondents could browse them beforehand and understand their overall situation. Finally, the senior management teams of these platforms were invited to score the comprehensive governance capability of all 14 platforms. Specifically, for the platforms in Jiangsu, Zhejiang, and Shanghai, a door-to-door and face-to-face survey method was used, while the platforms in other regions used the Tencent classroom form for online video research.

4.2.2. Subjective Empowerment of Indicators

Indicators are weighted using the product formula $W_i={\displaystyle \prod _{t=1}^2{w}_i}$ proposed by Saaty [32]. In the first step, the average of 23 evaluation indicators (secondary indicators, such as contract control capacity), is calculated, as shown in Column ⑤ of Table 2. In the second step, the relative weights of each of the 23 secondary indicators are calculated within primary indicator categories, as shown in Column ⑥. In the third step, the averages of primary indicators, such as contractual governance, are calculated separately, as shown in Column ②. In the fourth step, the weights of each of the primary indicators are measured as a whole, as shown in Column ③. In the fifth step, using Saaty’s product formula, the weights of Column ③ are multiplied with the weights of Column ⑥, resulting in the combined weights of all secondary indicators, as shown in Column ⑦.

4.2.3. Combined Weighting of Indicators

Using the values of indicators for evaluating the governance capacity of the industry–education integration platforms shown in Table 2, the variation coefficient method was used to assign weights. The calculation process included the following three steps: Firstly, the mean and standard deviation of the 23 evaluation indicators were obtained. Secondly, the coefficient of variation of each indicator is calculated. Thirdly, the coefficient of variation of each indicator was divided by the sum of the coefficients of variation to obtain the weights of each indicator. Using this formula $W_i=\alpha W_1+\left(1-\alpha \right)W_2$ and assuming $\alpha =0.5$, the combination weights of the index system can be identified, as shown in Table 3.

4.2.4. Grey Correlation of Evaluation Indicators

The software Grey System Theory and Application 7th Edition (GSTAV7.0) was developed by the Institute of Grey Systems, Nanjing University of Aeronautics and Astronautics, China, for simple and fast calculation of grey model results. The grey correlation degree of evaluation indexes was obtained by substituting Dunn’s correlation formula [39]. The results are shown in Table 4.

Using $Z_j={\displaystyle \sum _{i=1}^m(r_{ij}\times W_i})$, the composite scores of the ecosystem governance evaluation of the 14 industry–education integration platforms were obtained. The evaluation score of HKGP is 0.987, ranking 1st, indicating that its platform has the highest comprehensive governance evaluation ability. In addition, the evaluation scores of XYPS, XMIE, and JSZY are 0.805, 0.792, and 0.770, ranking 2nd, 3rd, and 4th, respectively. JHIE has the lowest score, ranking last among all 14 platforms. Moreover, the comprehensive scores of the governance abilities of IEISE, WJSM, and NTIE are also relatively low. For all 14 sample platforms, the governance capacity of the industry–education integration platforms run by enterprises is relatively high, followed by that of government-led platforms. However, the governance capacity of platforms run by universities is relatively low (Table 5).

5. Concluding Remarks and Recommendations

5.1. Concluding Remarks

The governance of the industry–education integration platform ecosystem must pay attention to the goal demands of the participating and operating subjects of the platform. Based on relevant literature, an evaluation index system for the governance of the industry–education integration platform ecosystem was designed, including five primary indicators (such as contract governance, property rights governance, stability governance, transaction cost governance, and cooperation behavior governance) and 23 secondary indicators. A grey correlation combination weighting model was used and survey data were collected from 14 platforms, including NASE, and a mixed subjective and objective method was used to assign weights to indicators. The data showed that the weight of contractual governance was 18.59%, the weight of property rights governance was 16.85%, the weight of stability governance was 19.48%, the weight of transaction cost governance was 19.86%, and the weight of cooperative behavior governance was 25.40%. Then, an empirical study was conducted on the comprehensive governance evaluation of the platform ecosystem. The results show that the setup of the evaluation index system is scientific and reasonable, and can reflect the current situation of the comprehensive governance evaluation of the industry–education integration platform ecosystem. The adopted grey correlation combination assignment method overcomes the shortcomings of sample evaluation with little data and poor information, and the model has good scientificity, reasonableness, and feasibility.

5.2. Discussion

In this study, platform ecosystem governance is explored based on the practice of industry–education integration in China. Informed by the current theoretical and practical research on industry–education integration, four other aspects need to be emphasized to illustrate the limitations of this paper and clarify the room for improvement in future research. First, the difference between industry–education integration and industry-academia cooperation is not clearly defined. The current international mainstream research focuses on industry-academia cooperation, whereas this study considers industry–education integration as a deeper form of industry-academia cooperation. Industry–education integration reflects the in-depth sharing of resources and other industry-academia research assets. Second, this paper mainly focuses on the reform practice of engineering education in China, and the selected samples all belong to the field of higher engineering education. Therefore, whether the conclusions drawn from this study can be extended to other disciplines (such as science, agriculture, and commerce) should be tested. Third, the operation of industry–education integration platforms is inseparable from modern science and technology. The reason is that only through the support of modern information technology, the industry–education integration platform ecosystem can be built in a platform economy mode. Thus, the gap between the main bodies of industry-university-research cooperation can be filled effectively. Therefore, whether the conclusions of this paper can be applied to the field of industry-university-research cooperation in pure education and teaching needs further exploration. Fourth, the industry–education integration platform ecosystem itself is a complex system, spanning education, industry, government, and further fields. This complex system is inevitably influenced by regional socio-economic and government governance, and the influence of the external environment should also be considered in future research.

5.3. Recommendations

• Establish a mechanism to improve the platform contract. The contractual relationship of the industry–education integration platform ecosystem is always incomplete. Improving the completeness of the contract can help to eliminate cooperation disputes of multiple subjects in the ecosystem. For this purpose, a continuous improvement mechanism of the platform contract needs to be established. Firstly, there is a need to clarify (in the contract) how to resolve the division of residual control, thus limiting blackmailing behavior of partners who hold the bargaining power over the other party. This also reduces the occurrence of improper seizure behavior in the case of incomplete contracts. Secondly, the distribution of cooperation benefits among ecological groups of the industry–education integration platform discloses the responsibility of risk sharing. This also affects the cooperation of platform subjects, and therefore, the cooperation risks of each party should be clarified in the contract. Thirdly, it is necessary to establish a contractual improvement mechanism with property rights at the core. In response to the incomplete characteristics of contractual relationships in the industry–education integration platform ecosystem, the property rights of relevant subjects should be dynamically defined through a continuous improvement mechanism.

• Protect the rights and interests of relevant subjects of the platform. Property rights between groups within the organization of the industry–education integration platform ecosystem are incomplete. This characteristic of property rights affects the competition for quasi-rents and the distribution of cooperative benefits of the platform. To improve the governance capacity of the platform, it is necessary to promote the innovation of institutional mechanisms with property rights at the core. Firstly, it is necessary to clarify the quantity of innovation resources invested by platform-related interest subjects as much as possible. Efforts should also be allocated to establish the evaluation mechanism of heterogeneous elements such as knowledge, technology, information, and management. The rights and interests of platform-related subjects should be guaranteed through the form of contract. Secondly, in addition to the quantity of innovation resources, the platform needs to establish a mechanism for evaluating the importance of innovation resources and for improving the discourse of partners holding key resources. Thirdly, to reduce inappropriate benefit-grabbing behaviors such as knocking and wool-gathering, the platform should restrict property rights behaviors in the public domain as much as possible. Doing so can safeguard the rights and interests of subjects with relatively weak property rights status.

• Improve the protection and development mechanism of resources. The industry–education integration platform ecosystem carries the functions of practical teaching, research and development of technology, innovation and entrepreneurship, and industry cultivation. Protection measures should be implemented for the innovation of education and industrial input, with the aim to strengthen the stability of the platform. As the fundamental purpose of the platform is the realization of the synergistic innovation effect of participating subjects in the ecosystem through the sharing of resources, it is necessary to further improve the institutional mechanism for the conversion of scientific and technological achievements, incubation of start-up enterprises, and common technology development. In the next step of promotion, it is necessary to clarify the intellectual property protection and development system among the industry–education integration platform, universities, industry enterprises, and the academy of sciences. On the one hand, doing so insists on governance by law, and on the other hand, doing so actively explores frontier issues such as copyright, intellectual achievement rights, intellectual property pledge, and intellectual property financing by innovating institutional mechanism.

• Explore the outsourcing model of educational services. Educational service outsourcing originally means that universities transfer non-core business such as IT equipment maintenance, post-security, and personnel agency to outside organizations. In recent years, education service outsourcing institutions outside of the system in China have been rapidly improved in both quantity and quality. Not only have higher education service institutions emerged (such as Beijing Boulder Forward and Wisers Education Technology Group), but the emergence of Baidu Education Business Service Platform, Ali New Business Academy, and Tencent Classroom also marks the entry of Internet giants into the field of education service outsourcing. These companies that have adopted education service outsourcing functions have built education service platforms and have penetrated the core areas of education and teaching in universities. To this end, the effectiveness of the ecosystem governance of industry–education integration platforms can be enhanced by the following actions: drawing on the current education service outsourcing model, innovating the cultivation property rights of universities, and encouraging the use of key resources and key capabilities of education service institutions outside of the system. These actions can jointly create a bonanza for the innovative ecological cultivation of industry–education integration talents.

Author Contributions

Conceptualization, Y.L.; data curation, Y.Z.; formal analysis, Y.L.; funding acquisition, Y.L.; investigation, Y.L. and W.-L.H.; methodology, Y.L., W.-L.H. and Y.Z.; project administration, Y.L.; software, Y.Z.; validation, Y.L. and W.-L.H.; writing—original draft, Y.L.; writing—review and editing, W.-L.H. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figure and Tables

Figure 1. Governance dimensions of the industry–education integration platform ecosystem.

Table 1

Evaluation index system of ecosystem governance of industry–education integration platforms.

Primary Indicators	Secondary Indicators	Primary Indicators	Secondary Indicators
Contractual governance	Contract control ability	Transaction cost governance	Partner selection governance
	Contract coordination ability		Operational transaction governance
	Contract completeness		Asset specificity governance
	Midway default cost		Transaction frequency governance
Property rights governance	Property rights protection capability		Uncertainty governance
	Resource importanceDefinition capability	Cooperative behavior governance	Formalism governance
	Resource quantity definition capability		Project exit governance
	Risk sharing definition capability		Knockout governance
Platform stability governance	Resource integration degree		Evasion of responsibility governance
	Supply and demand matching degree		Refusal to adapt governance
	Expected benefits		Free-rider governance
	Operation mechanismInnovation capacity

Table 2

Subjective assignments of governance evaluation indicators of the industry–education integration platform ecosystem.

① Primary Indicator	② Average of Primary Indicator	③ Weight of Primary Indicator	④ Secondary Indicator	⑤ Average of Secondary Indicator	⑥ Weight of Secondary Indicator	⑦ Combined Weights
Contractual governance	3.714	22.7%	Contract control ability	3.643	24.5%	5.57%
			Contract coordination ability	3.571	24.0%	5.46%
			Contract completeness	3.786	25.5%	5.78%
			Midway default cost	3.857	26.0%	5.89%
Property rights governance	3.429	20.9%	Property rights protection capability	3.286	24.0%	5.01%
			Resource importance definition capability	3.643	26.6%	5.55%
			Resource quantity definition capability	3.857	28.1%	5.88%
			Risk sharing definition capability	2.929	21.4%	4.46%
Platform stability governance	3.393	20.7%	Resource integration degree	3.357	24.7%	5.12%
			Supply and demand matching degree	3.500	25.8%	5.34%
			Expected benefits	3.286	24.2%	5.01%
			Operation mechanism innovation capacity	3.429	25.3%	5.23%
Transaction cost governance	3.014	18.4%	Partner selection governance	3.071	20.4%	3.75%
			Operational transaction governance	3.214	21.3%	3.92%
			Asset specificity governance	3.143	20.9%	3.84%
			Transaction frequency governance	2.786	18.5%	3.40%
			Uncertainty governance	2.857	19.0%	3.49%
Cooperative behavior governance	2.833	17.3%	Formalism governance	3.214	18.9%	3.63%
			Project exit governance	3.571	21.0%	3.63%
			Knockout governance	2.857	16.8%	2.91%
			Evasion of responsibility governance	2.571	15.1%	2.62%
			Refusal to adapt governance	2.429	14.3%	2.47%
			Free-rider governance	2.357	13.9%	2.40%

Table 3

Combination empowerment of the evaluation indicators of the ecosystem governance of industry–education integration platforms.

Primary Indicator	Secondary Indicator	Weight of SubjectiveEmpowerment	Assignment of Coefficient Variation	Weight of Indicator Combinations	Weight of Primary Indicator
Contractual governance	Contract control ability	5.57%	3.20%	4.38%	18.59%
	Contract coordination ability	5.46%	3.33%	4.40%
	Contract completeness	5.78%	3.40%	4.59%
	Midway default cost	5.89%	4.53%	5.21%
Property rights governance	Property rights protection capability	5.01%	3.43%	4.22%	16.85%
	Resource importance definition capability	5.55%	3.77%	4.66%
	Resource quantity definition capability	5.88%	1.73%	3.81%
	Risk sharing definition capability	4.46%	3.88%	4.17%
Platform stability governance	Resource integration degree	5.12%	3.48%	4.30%	19.48%
	Supply and demand matching degree	5.34%	3.42%	4.38%
	Expected benefits	5.01%	5.13%	5.07%
	Operation mechanism innovation capacity	5.23%	6.23%	5.73%
Transaction cost governance	Partner selection governance	3.75%	2.85%	3.30%	19.86%
	Operational transaction governance	3.92%	4.01%	3.97%
	Asset specificity governance	3.84%	4.52%	4.18%
	Transaction frequency governance	3.40%	3.83%	3.62%
	Uncertainty governance	3.49%	6.12%	4.80%
Cooperative behavior governance	Formalism governance	3.63%	6.03%	4.83%	25.40%
	Project exit governance	3.63%	5.24%	4.44%
	Knockout governance	2.91%	3.45%	3.18%
	Evasion of responsibility governance	2.62%	5.42%	4.02%
	Refusal to adapt governance	2.47%	5.74%	4.10%
	Free-rider governance	2.40%	7.26%	4.83%

Table 4

Grey correlation scores of evaluation indicators of the ecosystem governance of all 14 industry–education integration platforms.

T1	T2	T3	T4	T5	T6	T7	T8	T9	T10	T11	T12	T13	T14
1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.571	1.000	1.000	1.000	1.000
1.000	1.000	1.000	0.500	1.000	1.000	1.000	1.000	0.500	0.571	1.000	1.000	0.571	1.000
1.000	0.571	0.571	0.500	1.000	1.000	0.500	0.571	0.500	0.571	1.000	1.000	0.571	0.500
0.625	0.870	0.625	0.625	0.625	0.625	0.714	0.625	0.625	0.870	1.000	0.870	0.625	0.625
0.500	0.571	1.000	0.500	0.571	1.000	1.000	1.000	1.000	0.571	1.000	1.000	0.571	1.000
0.385	0.625	0.625	0.385	0.625	0.385	0.625	0.625	0.385	0.625	1.000	0.625	0.870	0.385
0.833	0.690	0.455	0.455	0.690	0.833	0.833	0.690	0.455	0.690	1.000	0.455	0.690	0.455
0.625	0.870	0.625	0.625	0.625	0.714	0.625	0.625	0.625	0.870	1.000	0.625	0.625	0.625
0.625	0.870	0.625	0.625	0.625	0.714	0.625	0.625	0.385	0.870	1.000	0.625	0.625	0.385
1.000	0.571	1.000	1.000	1.000	0.500	0.500	1.000	1.000	0.571	1.000	1.000	1.000	0.500
0.500	0.571	0.571	1.000	1.000	0.500	0.500	1.000	0.500	0.571	1.000	1.000	0.571	0.500
0.625	0.870	0.625	0.625	0.870	0.714	0.625	0.870	0.625	0.870	1.000	0.870	0.870	0.625
0.625	0.870	0.625	0.625	0.870	0.714	0.625	0.870	0.385	0.870	1.000	0.625	0.625	0.714
0.625	0.870	0.625	0.625	0.625	0.714	0.714	0.870	0.625	0.625	1.000	0.625	0.870	0.714
0.625	0.870	0.870	0.714	0.870	0.714	0.714	0.870	0.625	0.870	1.000	0.870	0.870	0.714
1.000	0.571	0.571	0.500	0.571	0.500	0.333	0.571	1.000	0.571	1.000	1.000	0.571	0.500
1.000	0.571	1.000	1.000	0.571	0.500	0.500	0.571	0.500	0.400	1.000	0.571	1.000	0.500
0.500	1.000	1.000	0.500	1.000	0.500	0.500	1.000	0.500	0.571	1.000	0.571	1.000	1.000
0.625	0.870	0.870	0.625	0.870	0.714	0.714	0.870	0.625	0.526	1.000	0.870	0.870	0.625
0.833	0.690	0.690	0.833	0.690	0.556	0.833	0.690	0.455	0.769	0.625	0.690	0.690	0.833
0.714	0.870	0.870	0.714	0.526	0.417	0.714	0.870	0.714	0.526	1.000	0.870	0.870	0.714
0.500	0.571	0.400	0.500	0.400	0.333	0.500	0.400	0.500	0.400	1.000	0.571	0.571	0.500

All 23 evaluation indicators in this table are arranged in the same order as in Table 3.

Table 5

Comprehensive evaluation results of the ecosystem governance of industry–education integration platforms.

Evaluation Object	Overall Score	Overall Score Ranking
T1: NASE	0.738	8
T2: U + NE	0.766	5
T3: DKET	0.750	7
T4: ICIE	0.681	9
T5: JSZY	0.770	4
T6: WZIE	0.680	10
T7: NTIE	0.676	11
T8: XMIE	0.792	3
T9: JHIE	0.637	14
T10: IESE	0.659	13
T11: HKGP	0.987	1
T12: XYPS	0.805	2
T13: GDNE	0.756	6
T14: WJSM	0.668	12

References

1. Li, X.; Wang, F.; Gu, X. Understanding the roles of ICT enterprises in promoting the ICT industry ecosystem in education: Case studies from China. Br. J. Educ. Technol.; 2019; 50, pp. 1151-1172. [DOI: https://dx.doi.org/10.1111/bjet.12776]

2. Hutcheson, W.; Hoagland, P.; Jin, D. Valuing environmental education as a cultural ecosystem service at Hudson River Park. Ecosyst. Serv.; 2018; 31, pp. 387-394. [DOI: https://dx.doi.org/10.1016/j.ecoser.2018.03.005]

3. Low Beer, D. Teaching and learning ecosystem assessment and valuation. Ecol. Econ.; 2018; 146, pp. 425-434. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2017.12.014]

4. Stephenson, K.; Shabman, L. Does ecosystem valuation contribute to ecosystem decision making?: Evidence from hydropower licensing. Ecol. Econ.; 2019; 163, pp. 1-8. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2019.05.003]

5. Torfing, J. Collaborative innovation in the public sector: The argument. Public Manag. Rev.; 2019; 21, pp. 1-11. [DOI: https://dx.doi.org/10.1080/14719037.2018.1430248]

6. Olk, P.; West, J. The relationship of industry structure to open innovation: Cooperative value creation in pharmaceutical consortia. R&D Manag.; 2020; 50, pp. 116-135. [DOI: https://dx.doi.org/10.1111/radm.12364]

7. Fernández Martínez, P.; de Castro-Pardo, M.; Barroso, V.M.; Azevedo, J.C. Assessing sustainable rural development based on ecosystem services vulnerability. Land; 2020; 9, 222. [DOI: https://dx.doi.org/10.3390/land9070222]

8. Zhou, Y.-W.; Lin, X.; Zhong, Y.; Xie, W. Contract selection for a multi-service sharing platform with self-scheduling capacity. Omega; 2019; 86, pp. 198-217. [DOI: https://dx.doi.org/10.1016/j.omega.2018.07.011]

9. Liao, C.-H.; Lin, H.-E.; Yuan, S.-M. Blockchain-Enabled integrated market platform for contract production. IEEE Access; 2020; 8, pp. 211007-211027. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.3039620]

10. Jiang, S.; Li, Y.; Wang, S.; Zhao, L. Blockchain competition: The tradeoff between platform stability and efficiency. Eur. J. Oper. Res.; 2022; 296, pp. 1084-1097. [DOI: https://dx.doi.org/10.1016/j.ejor.2021.05.031]

11. Schäfer, D.; Klaus, V.H.; Kleinebecker, T.; Boeddinghaus, R.S.; Hinderling, J.; Kandeler, E.; Marhan, S.; Nowak, S.; Sonnemann, I.; Wurst, S. et al. Recovery of ecosystem functions after experimental disturbance in 73 grasslands differing in land-use intensity, plant species richness and community composition. J. Ecol.; 2019; 107, pp. 2635-2649. [DOI: https://dx.doi.org/10.1111/1365-2745.13211]

12. Berkel, G. Contract Management. Business Project Management and Marketing; Springer: Berlin/Heidelberg, Germany, 2016.

13. Schepker, D.J.; Oh, W.-Y.; Martynov, A.; Poppo, L. The many futures of contracts: Moving beyond structure and safeguarding to coordination and adaptation. J. Manage.; 2014; 40, pp. 193-225. [DOI: https://dx.doi.org/10.1177/0149206313491289]

14. Yan, L.; Zhang, L. Interplay of contractual governance and trust in improving construction project performance: Dynamic perspective. J. Manage. Eng.; 2020; 36, 791. [DOI: https://dx.doi.org/10.1061/(ASCE)ME.1943-5479.0000791]

15. Zhao, D.; Gu, F.F.; Wang, L. The impact of contractual governance on forms of opportunism. Ind. MKT. Manag.; 2022; 102, pp. 89-103. [DOI: https://dx.doi.org/10.1016/j.indmarman.2022.01.006]

16. Cuypers, I.R.P.; Hennart, J.-F.; Silverman, B.S.; Ertug, G. Transaction cost theory: Past progress, current challenges, and suggestions for the future. Acad. Manage. Ann.; 2021; 15, pp. 111-150. [DOI: https://dx.doi.org/10.5465/annals.2019.0051]

17. Hu, C.; Tao, J.; Zhang, D.; Adams, D. Price signal of tilled land in rural China: An empirically oriented transaction costs study sased on contract theory. Land; 2021; 10, 837. [DOI: https://dx.doi.org/10.3390/land10080837]

18. Marjosola, H. The problem of regulatory arbitrage: A transaction cost economics perspective. Regul. Governance; 2021; 15, pp. 388-407. [DOI: https://dx.doi.org/10.1111/rego.12287]

19. Léger-Bosch, C.; Houdart, M.; Loudiyi, S.; Le Bel, P.-M. Changes in property-use relationships on French farmland: A social innovation perspective. Land Use Policy; 2020; 94, 104545. [DOI: https://dx.doi.org/10.1016/j.landusepol.2020.104545]

20. Qiu, T.; Ma, X.; Luo, B. Are private property rights better? Evidence from the marketization of land rentals in rural China. Econ. Change Restruct.; 2021; 55, pp. 875-902. [DOI: https://dx.doi.org/10.1007/s10644-021-09330-y]

21. Guo, N.; Chan, E.H.W.; Yung, E.H.K. Alternative governance model for historical building conservation in China: From property rights perspective. Sustainability; 2020; 13, 203. [DOI: https://dx.doi.org/10.3390/su13010203]

22. Ling, G.H.T.; Leng, P.C.; Ho, C.S. Effects of diverse property rights on rural neighbourhood public open space (POS) governance: Evidence from Sabah, Malaysia. Economies; 2019; 7, 61. [DOI: https://dx.doi.org/10.3390/economies7020061]

23. Bstieler, L.; Hemmert, M.; Barczak, G. Trust formation in University-Industry collaborations in the U.S. biotechnology industry: IP policies, shared governance, and champions. J. Prod. Innov. Manag.; 2015; 32, pp. 111-121. [DOI: https://dx.doi.org/10.1111/jpim.12242]

24. Shi, C.; Chen, Y.; You, J.; Yao, H. Asset specificity and contractors’ opportunistic behavior: Moderating roles of contract and trust. J. Manag. Eng.; 2018; 34, 04018026. [DOI: https://dx.doi.org/10.1061/(ASCE)ME.1943-5479.0000632]

25. Yang, Y.; Su, Y. Public voice via social media: Role in cooperative governance during public health emergency. Int. J. Environ. Res. Public Health; 2020; 17, 6840. [DOI: https://dx.doi.org/10.3390/ijerph17186840]

26. You, J.; Chen, Y.; Wang, W.; Shi, C. Uncertainty, opportunistic behavior, and governance in construction projects: The efficacy of contracts. Int. J. Proj. Manag.; 2018; 36, pp. 795-807. [DOI: https://dx.doi.org/10.1016/j.ijproman.2018.03.002]

27. Ek Styvén, M.; Mariani, M.M. Understanding the intention to buy secondhand clothing on sharing economy platforms: The influence of sustainability, distance from the consumption system, and economic motivations. Psychol. MKT; 2020; 37, pp. 724-739. [DOI: https://dx.doi.org/10.1002/mar.21334]

28. Loreau, M.; de Mazancourt, C. Biodiversity and ecosystem stability: A synthesis of underlying mechanisms. Ecol. Lett.; 2013; 16, pp. 106-115. [DOI: https://dx.doi.org/10.1111/ele.12073]

29. Meuleman, L. Public administration and governance for the SDGs: Navigating between change and stability. Sustainability; 2021; 13, 5914. [DOI: https://dx.doi.org/10.3390/su13115914]

30. Xu, H.; Hsu, W.-L.; Meen, T.-H.; Zhu, J.H. Can higher education, economic growth and innovation ability improve each other?. Sustainability; 2020; 12, 2515. [DOI: https://dx.doi.org/10.3390/su12062515]

31. Guy, R.F.; Norvell, M. The neutral point on a likert scale. J. Psychol.; 1977; 95, pp. 199-204. [DOI: https://dx.doi.org/10.1080/00223980.1977.9915880]

32. Saaty, T.L. Fundamentals of the analytic network process—Multiple networks with benefits, costs, opportunities and risks. J. Syst. Sci. Syst. Eng.; 2004; 13, pp. 348-379. [DOI: https://dx.doi.org/10.1007/s11518-006-0171-1]

33. Eldridge, S.M.; Ashby, D.; Kerry, S. Sample size for cluster randomized trials: Effect of coefficient of variation of cluster size and analysis method. Int. J. Epidemiol.; 2006; 35, pp. 1292-1300. [DOI: https://dx.doi.org/10.1093/ije/dyl129] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16943232]

34. Julong, D. Introduction to Grey system theory. J. Grey Syst.; 1989; 1, pp. 1-24. [DOI: https://dx.doi.org/10.5555/90757.90758]

35. Yin, M.-S. Fifteen years of grey system theory research: A historical review and bibliometric analysis. Expert Syst. Appl.; 2013; 40, pp. 2767-2775. [DOI: https://dx.doi.org/10.1016/j.eswa.2012.11.002]

36. Mao, S.; Gao, M.; Xiao, X.; Zhu, M. A novel fractional grey system model and its application. Appl. Math. Modell.; 2016; 40, pp. 5063-5076. [DOI: https://dx.doi.org/10.1016/j.apm.2015.12.014]

37. Kuo, Y.; Yang, T.; Huang, G.-W. The use of grey relational analysis in solving multiple attribute decision-making problems. Comput. Ind. Eng.; 2008; 55, pp. 80-93. [DOI: https://dx.doi.org/10.1016/j.cie.2007.12.002]

38. Kazemian, A.; Parcheforosh, A.; Salari, A.; Ma, T. Optimization of a novel photovoltaic thermal module in series with a solar collector using Taguchi based grey relational analysis. Sol. Energy; 2021; 215, pp. 492-507. [DOI: https://dx.doi.org/10.1016/j.solener.2021.01.006]

39. Jin, M.; Zhou, X.; Zhang, Z.M.; Tentzeris, M.M. Short-term power load forecasting using grey correlation contest modeling. Expert Syst. Appl.; 2012; 39, pp. 773-779. [DOI: https://dx.doi.org/10.1016/j.eswa.2011.07.072]