# GIP Evaluation and Path Improvement of Technological SMEs Based on Digital Information Technology

Authors: Hailin Yang; Fengming Liu; Lihong Zhang
Journal: Mobile Information Systems
 (2022)

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## Abstract

Small and medium-sized technology-based SMEs also play a certain role in promoting the economic development and social progress of a country and can provide relative social resources. However, under the wave of green development, green technology innovation for SMEs is also important, which is of great significance to the sustainable development of enterprises. This paper summarizes the situation of a certain stage of Chinese enterprises’ green technology innovation and builds a GIP evaluation index system suitable for small and medium-sized technology-based enterprises. This paper selectsZ province for analysis and uses data envelopment analysis and Malmquist index analysis to analyze the GI situation of 11 cities in Z province. And this paper proposes a green innovation performance (GIP) evaluation path for technology-based SMEs. The results show that the overall green innovation (GI) level of Z province shows an upward and progressive trend from 2016 to 2020, and the total factor productivity change index (TPF) in the overall period from 2018 to 2020 is 0.913. The overall GIP of enterprises in cities C and D in Z province is insufficient and needs to be further expanded.


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## Body

## 1. Introduction

Many technology-based SMEs have also followed the trend and become an important part of economic development. However, the rapid economic development is also accompanied by a large amount of energy consumption, and the phenomenon of resource waste is very serious. At present, the international community calls on enterprises to develop a more green economy, take green technological innovation as the main support, and incorporate environmental output into technological innovation to promote the structural adjustment and transformation of enterprises.In the era of the digital economy, the digital, networked, and intelligent development of technology-based SMEs needs to aim at sustainable development. The purpose of this study is to analyze the relationship between the development level of various technological SMEs and their GIP. This paper establishes the GIP evaluation index, thus establishes a scientific enterprise GIP evaluation plan, and explores the optimization path of the GI development of SMEs.The research of this paper has a certain guiding role in the introduction of GI strategy by small and medium-sized technology-based enterprises, and it has both theoretical and practical significance. From a theoretical point of view, this paper deepens and expands the theoretical system of GIP from the perspective of the GI process and input-output. From the perspective of practical value, it opens up a new way for the performance evaluation of green innovation of small and medium-sized science and technology enterprises.The main innovations of this paper are as follows. First, from the perspective of input and output, the evaluation indicators of enterprise GIP are selected, taking into account the environmental indicators and benefit indicators. The second is to use the data envelopment analysis method and Malmquist index analysis method to analyze the data in the time dimension. The third is to propose a GI model framework for SMEs that covers the whole process of technology research and development, manufacturing, marketing, and service and takes into account technological progress, government support, and market competition factors. The fourth is to propose a GIP improvement plan based on information technology involving multiple subjects. This paper summarizes the development situation of green technology innovation in Chinese enterprises and puts forward the CNN evaluation index system of green technology innovation suitable for small and medium-sized technology enterprises by using the neural network model. The geographical indications of 11 cities inZ province were analyzed by data envelope analysis and Malmquist index analysis. The performance evaluation path of green innovation of small and medium-sized technology-based enterprises is put forward.


## 2. Related Work

The Albort-MorantG study explores the links between knowledge bases, relational learning, and GIP within the framework of cooperative competition. He believes that GI is directly influenced by a broad and deep knowledge base. He used a sample of 112 companies from the Spanish auto parts manufacturing industry. The study found that the mediating effect of relational learning on the knowledge base-GIP link is positive and significant [1]. Based on the theory of behavior and innovation, Li G studies green innovation in industrial cities [2]. With the development of the knowledge economy, improving the efficiency of dissemination and promotion of knowledge innovation results within an organization has become an important factor affecting the process of organizational knowledge innovation. Guan analyzes the performance evaluation model of enterprise innovation management based on the organizational shared mental model. He concluded that since the organization sharing idea plays an important role in the process of knowledge innovation, enterprises should focus on building a special management structure system for the dissemination and dissemination of knowledge innovation results [3]. Zhou puts forward the connotation of enterprise green management and green management performance evaluation and uses the triple performance method to construct an enterprise green management performance evaluation index system. Taking BH Petrochemical Co. Ltd., a high-efficiency ecological economic zone in the Yellow River Delta as an example, the weight of each index in the evaluation index system of enterprise green management performance is determined by using the analytic hierarchy process (AHP) and MATLAB programming. He used the fuzzy comprehensive evaluation method to evaluate the green management performance of industrial enterprises [4]. He conducted research and analysis on 220 enterprises in the Pearl River Delta. He found through questionnaires that environmental planning would not easily affect GIP [5]. Malaysia encourages many companies from different industries to adopt GI practices. SMEs that quickly adopt GI strategies will surely gain a competitive advantage over their competitors [6]. Nuryakin uses a purposeful sampling method with a sample of owners/managers of batik SMEs in Yogyakarta, Indonesia, with a total of 223 respondents. The hypothesis test results show that there is a significant positive correlation between green product innovation and green product competitiveness and green product success. Green process innovation has a significant impact on green product competitiveness and green product success [7]. The Molinillo considered previous studies investigating digital information and technology adoption in SMEs. Molinillo finds that the digital information and technology used for marketing-related positions make SMEs more competitive. Molinillo theoretical review is one of the few to present research findings reporting on the digital information and technology adoption process among SMEs. In addition, the Molinillo summarizes the issues related to the adoption processes (i.e., drivers, outcomes, risks, and barriers) [8].


## 3. Contents Related to Green Technology Innovation

### 3.1. Green Technology Innovation

The emergence and development of green technology innovation stems from environmental protection. Green technology is a key element to breaking through the constraints of resources and the environment in economic growth. It is based on reducing pollution, reducing consumption, and improving ecology, focusing on the future, and emphasizing the coordinated development of the social environment, economic environment, and ecological environment [9]. Green technology is a technology that achieves the effect of energy-saving and environmental protection through the clean production of products. The data envelopment method is a new field of operation research management science and mathematical economics. It is a quantitative analysis method to evaluate the relative effectiveness of comparable units of the same type by using a linear programming method according to multiple input indexes and multiple output indexes.The cultivation of green technology innovation capability refers to the organized establishment and strengthening of green technology innovation capability through certain methods. It includes two aspects: first, the process of cultivating green technology innovation ability of enterprises without green technology innovation ability, that is, the process of starting from scratch and, second, the process in which the enterprise already has a certain green technology innovation ability and needs to be further strengthened, that is, the process from having to strong or the process of upgrading from basic state and substate to mature state. Life cycle theory shows that the development of green technology generally goes through four stages: lacking stage, embryo stage, construction stage, and filling stage, as shown in Figure1.Figure 1
Development stage of green technology innovation capability.At different stages of development, the GI capabilities of enterprises also have different characteristics. In the period of lack, the old technology is still dominant, and the technical risk is high; the breakthrough is frequent; the research and development is the priority; and the technology is in the experimental stage. In the embryonic stage, the potential of old technologies reaches the limit, and new technologies enter the market, but the maturity is not high. At this time, technical risks are reduced; key technologies have breakthroughs; and multiple technical tracks appear. During the construction period, the stable technical structure standard is basically formed, leading the design and forming, occupying a certain market segment. During the filling period, the techno-economic paradigm is recognized by society and gradually becomes the dominant force in the market [10, 11]. This open green evolution system is a platform system based on modern science and technology and combined with various aspects of information.Open GI is a distributed innovation process that requires the participation of multiple innovation elements across time and space. Through the incentives of monetary or non-monetary mechanisms, which combine the attributes of each organization, the flow of GI resources is purposefully managed [12]. The stage of network convergence is the period of network combination; the stage of intelligent collaboration is the stage of intelligence; and the stage of digital resource allocation is the stage of initialization. The open evolution model of the GI system based on information technology is shown in Figure 2.Figure 2
Open evolution model of GI system.


### 3.2. Status Quo of Green Technology Innovation in Technology-Based SMEs

Enterprise GIP is the efficiency of enterprise GI input to GI output. GI resources include human, financial, and energy resources. Human resources refer to labor resources, while financial resources specifically refer to financial support. Energy consumption is usually used to measure the energy input of GI activities of enterprises. Here, the proportion of equipment manufacturing in the total energy consumption of industrial enterprises is taken as an example, as shown in Table1. The proportion of energy input of equipment manufacturing enterprises in industrial enterprises is generally declining. It can be seen that in the process of GI, the implementation of environmental protection strategies plays an important role in reducing energy consumption. These data are all from the website analysis data. It fully considers environmental factors, reduces energy consumption, and achieves green development of enterprises.Table 1
Energy consumption of equipment manufacturing and industrial enterprises.
Particular yearIndustrial enterpriseProportion of equipment manufacturing industry (%)2017448,5294.772016435,8194.782015429,9054.682014425,8064.792013418,9134.81Figure3 shows the level of some key indicators for the integration of industrialization and industrialization in China in the five-year period from 2016 to 2019. Specifically, although the overall level of intelligence is low, it has a strong growth momentum and is accelerating toward networking. From the perspective of a digital foundation, enterprises vigorously promote the construction of informatization, and R&D, production, and services are all moving toward digitalization. In 2019, the penetration rate of digital R&D design tools and the numerical control rate of key processes reached 69.7% and 49.7%, respectively. More and more enterprises have realized cross-department and cross-link business integration operations. In 2019, the proportion of enterprises that achieved networked collaboration reached 35.3%, with overall steady progress. In terms of intelligent exploration, nearly 8% of Chinese enterprises already have good basic conditions for intelligent manufacturing. However, from the data point of view, there is still a lot of room for improvement. In this context, combined with the concept of green development, to promote the iterative upgrade of green products, it is urgent to update the geographical indication system.Figure 3
Changes in key indicators of China’s integration of industrialization and industrialization from 2016 to 2019.Figure4 shows the changes in the level of integration of industrialization and industrialization of SMEs in China in 2016 and 2020. It can be seen that the gap between SMEs and large enterprises is constantly narrowing. In 2020, the gap between small and micro enterprises and large enterprises has narrowed by 22.2% compared with 2016, and the gap between medium-sized enterprises and large enterprises has narrowed by 2.4% compared with 2016. Faced with increasingly segmented industry demands, SMEs have a broader space for innovation and development [13, 14].Figure 4
Changes in the level of integration of industrialization and industrialization of Chinese SMEs in 2016 and 2020.


### 3.3. Elements of Enterprise GI System in Information Environment

The conceptual model of the influencing factors of GI of SMEs is shown in Figure5, which includes the content of multiple dimensions and multiple subjects. Among them, the innovation process, innovation environment, and organizational factors have an impact on GI in manufacturing from different perspectives. The enterprise technology development strategy tends to be a strategy for enterprise technology development, which is a strategy for the overall long-term fundamental problems of enterprise technology development. It is mainly divided into three aspects, namely environmental factors, process factors, and organizational factors of GI [15].Figure 5
Conceptual model of corporate GI influencing factors.Environmental factors include government support and the intensity of market competition. Government subsidies are conducive to promoting corporate R&D investment and overcoming competition barriers, which can play a very good incentive effect. Because the integration of industrialization and modern technology can promote the iteration and update of technology and make industrial production more green and sustainable development in the region. Regarding the intensity of market competition, companies can have different strategic tendencies in the face of different market environments. The process factors mainly include technological progress and infrastructure construction. The technology market can effectively promote environmental technology innovation and can produce cumulative effects. The level of technological progress has a direct driving effect on the green transformation of enterprises. Infrastructure construction can effectively improve production efficiency, accelerate the development of science and technology, and give birth to more new models and new formats [16]. Organizational factors mainly include talent base and enterprise innovation investment. In the process of digital innovation, the active training and introduction of innovative talents is an important driving force for enterprises to increase innovation and development. Corporate R&D capital investment has the greatest impact on green product innovation and green technology innovation in lightly polluted industries.


## 3.1. Green Technology Innovation

The emergence and development of green technology innovation stems from environmental protection. Green technology is a key element to breaking through the constraints of resources and the environment in economic growth. It is based on reducing pollution, reducing consumption, and improving ecology, focusing on the future, and emphasizing the coordinated development of the social environment, economic environment, and ecological environment [9]. Green technology is a technology that achieves the effect of energy-saving and environmental protection through the clean production of products. The data envelopment method is a new field of operation research management science and mathematical economics. It is a quantitative analysis method to evaluate the relative effectiveness of comparable units of the same type by using a linear programming method according to multiple input indexes and multiple output indexes.The cultivation of green technology innovation capability refers to the organized establishment and strengthening of green technology innovation capability through certain methods. It includes two aspects: first, the process of cultivating green technology innovation ability of enterprises without green technology innovation ability, that is, the process of starting from scratch and, second, the process in which the enterprise already has a certain green technology innovation ability and needs to be further strengthened, that is, the process from having to strong or the process of upgrading from basic state and substate to mature state. Life cycle theory shows that the development of green technology generally goes through four stages: lacking stage, embryo stage, construction stage, and filling stage, as shown in Figure1.Figure 1
Development stage of green technology innovation capability.At different stages of development, the GI capabilities of enterprises also have different characteristics. In the period of lack, the old technology is still dominant, and the technical risk is high; the breakthrough is frequent; the research and development is the priority; and the technology is in the experimental stage. In the embryonic stage, the potential of old technologies reaches the limit, and new technologies enter the market, but the maturity is not high. At this time, technical risks are reduced; key technologies have breakthroughs; and multiple technical tracks appear. During the construction period, the stable technical structure standard is basically formed, leading the design and forming, occupying a certain market segment. During the filling period, the techno-economic paradigm is recognized by society and gradually becomes the dominant force in the market [10, 11]. This open green evolution system is a platform system based on modern science and technology and combined with various aspects of information.Open GI is a distributed innovation process that requires the participation of multiple innovation elements across time and space. Through the incentives of monetary or non-monetary mechanisms, which combine the attributes of each organization, the flow of GI resources is purposefully managed [12]. The stage of network convergence is the period of network combination; the stage of intelligent collaboration is the stage of intelligence; and the stage of digital resource allocation is the stage of initialization. The open evolution model of the GI system based on information technology is shown in Figure 2.Figure 2
Open evolution model of GI system.


## 3.2. Status Quo of Green Technology Innovation in Technology-Based SMEs

Enterprise GIP is the efficiency of enterprise GI input to GI output. GI resources include human, financial, and energy resources. Human resources refer to labor resources, while financial resources specifically refer to financial support. Energy consumption is usually used to measure the energy input of GI activities of enterprises. Here, the proportion of equipment manufacturing in the total energy consumption of industrial enterprises is taken as an example, as shown in Table1. The proportion of energy input of equipment manufacturing enterprises in industrial enterprises is generally declining. It can be seen that in the process of GI, the implementation of environmental protection strategies plays an important role in reducing energy consumption. These data are all from the website analysis data. It fully considers environmental factors, reduces energy consumption, and achieves green development of enterprises.Table 1
Energy consumption of equipment manufacturing and industrial enterprises.
Particular yearIndustrial enterpriseProportion of equipment manufacturing industry (%)2017448,5294.772016435,8194.782015429,9054.682014425,8064.792013418,9134.81Figure3 shows the level of some key indicators for the integration of industrialization and industrialization in China in the five-year period from 2016 to 2019. Specifically, although the overall level of intelligence is low, it has a strong growth momentum and is accelerating toward networking. From the perspective of a digital foundation, enterprises vigorously promote the construction of informatization, and R&D, production, and services are all moving toward digitalization. In 2019, the penetration rate of digital R&D design tools and the numerical control rate of key processes reached 69.7% and 49.7%, respectively. More and more enterprises have realized cross-department and cross-link business integration operations. In 2019, the proportion of enterprises that achieved networked collaboration reached 35.3%, with overall steady progress. In terms of intelligent exploration, nearly 8% of Chinese enterprises already have good basic conditions for intelligent manufacturing. However, from the data point of view, there is still a lot of room for improvement. In this context, combined with the concept of green development, to promote the iterative upgrade of green products, it is urgent to update the geographical indication system.Figure 3
Changes in key indicators of China’s integration of industrialization and industrialization from 2016 to 2019.Figure4 shows the changes in the level of integration of industrialization and industrialization of SMEs in China in 2016 and 2020. It can be seen that the gap between SMEs and large enterprises is constantly narrowing. In 2020, the gap between small and micro enterprises and large enterprises has narrowed by 22.2% compared with 2016, and the gap between medium-sized enterprises and large enterprises has narrowed by 2.4% compared with 2016. Faced with increasingly segmented industry demands, SMEs have a broader space for innovation and development [13, 14].Figure 4
Changes in the level of integration of industrialization and industrialization of Chinese SMEs in 2016 and 2020.


## 3.3. Elements of Enterprise GI System in Information Environment

The conceptual model of the influencing factors of GI of SMEs is shown in Figure5, which includes the content of multiple dimensions and multiple subjects. Among them, the innovation process, innovation environment, and organizational factors have an impact on GI in manufacturing from different perspectives. The enterprise technology development strategy tends to be a strategy for enterprise technology development, which is a strategy for the overall long-term fundamental problems of enterprise technology development. It is mainly divided into three aspects, namely environmental factors, process factors, and organizational factors of GI [15].Figure 5
Conceptual model of corporate GI influencing factors.Environmental factors include government support and the intensity of market competition. Government subsidies are conducive to promoting corporate R&D investment and overcoming competition barriers, which can play a very good incentive effect. Because the integration of industrialization and modern technology can promote the iteration and update of technology and make industrial production more green and sustainable development in the region. Regarding the intensity of market competition, companies can have different strategic tendencies in the face of different market environments. The process factors mainly include technological progress and infrastructure construction. The technology market can effectively promote environmental technology innovation and can produce cumulative effects. The level of technological progress has a direct driving effect on the green transformation of enterprises. Infrastructure construction can effectively improve production efficiency, accelerate the development of science and technology, and give birth to more new models and new formats [16]. Organizational factors mainly include talent base and enterprise innovation investment. In the process of digital innovation, the active training and introduction of innovative talents is an important driving force for enterprises to increase innovation and development. Corporate R&D capital investment has the greatest impact on green product innovation and green technology innovation in lightly polluted industries.


## 4. Construction of GIP Evaluation Index System Based on Digital Information Technology

### 4.1. Green Technology Innovation Performance Evaluation Method

There are many methods for performance evaluation, such as the commonly used factor analysis, principal component analysis, grey relational analysis, and so on. However, these methods either have high requirements on the quantity and composition of data, are complicated to calculate, or are greatly affected by subjective factors [17, 18]. The process of enterprise GI is a process of converting resource input into innovation output and involves multiple inputs and multiple outputs. Therefore, two more suitable evaluation methods for multiple inputs and outputs are introduced next.


#### 4.1.1. Malmquist Exponential Model

This model was proposed in consumption analysis. Inspired by the Malmquist consumption index, the productivity index was constructed through the ratio of the distance function, and the Malmquist index was applied to the measurement of productivity changes, which further developed the Malmquist index.xt,yt represents the input-output vector of the decision-making unit; Dtxt,yt represents the distance between the decision-making unit and the efficiency frontier interface at t; and the productivity from t to t+1 period is(1)Mt=Dtxt,ytDtxt+1,yt+1.Under the technical conditions att+1, the rate of change in production from t to t+1 is(2)Mt+1=Dt+1xt,ytDt+1xt+1,yt+1.Use the geometric mean ofMt and Mt+1 to calculate the Malmquist exponent as follows:(3)Mxt+1,yt+1,xt,yt=Dtxt,ytDtxt+1,yt+1∗Dt+1xt,ytDt+1xt+1,yt+11/2.Under the corresponding assumptions:(4)Mxt+1,yt+1,xt,yt=Dt+1xt,ytDt+1xt+1,yt+1∗Dt+1xt+1,yt+1Dtxt+1,yt+1∗Dt+1xt+1,yt+1Dt+1xt,yt1/2=EC∗TC.When EC > 1, it indicates that the gap between the decision-making unit and the optimal efficiency frontier is narrowing, and vice versa, it indicates that the gap is increasing; when TC > 1, the production frontier moves upward, indicating technological progress, and vice versa.


#### 4.1.2. Data Envelope Analysis (DEA Analysis)

The evaluation steps of the DEA analysis method are shown in Figure6. The CRR model and the BCC model are the most basic models in the DEA method.Figure 6
DEA method evaluation steps.CCR model assumes that DMU is in the case of fixed return to scale, which is used to measure the total efficiency. Fixed return to scale is the efficiency evaluation of all DMU compared together. The CRR model uses the dual theory of linear programming and the non-Archimedes infinitesimal technique to establish a DEA model based on the input and output data of each DMU and judge the effectiveness of the DMU according to the results. Suppose there arem DUMs denoted as follows:(5)DUMjj=1,2,3,…,m.There arem DUMs with n inputs and r outputs. The input vector is(6)Aj=a1j,a2j,…,anjT.The output vector is denoted as follows:(7)Bj=b1j,b2j,…,brjT.The weight vectors of thei-th input and the s-th output are(8)pi=p1,p2,…,pnTos=o1,o2,…,orT.The efficiency evaluation index ofDUMj is the ratio of output to the sum of input, which is(9)fj=∑s=1rosbsj∑i=1npiaij,j=1,2,…,m.There are certainp and o that can make the efficiency evaluation index less than or equal to 1. Taking the efficiency evaluation index fj0 of DUMj0 as the goal and fj as the constraint, the resulting planning problem is the CCR model, which is given in the following formula:(10)max=∑s=1rosbsj0∑i=1npiaij=f0,s.t.∑s=1rosbsj−∑i=1npiaij≤0,j=1,2,…,m,pi=p1,p2,…,pnT≥0,os=o1,o2,…,orT≥0.The linear programming model of the BCC model is(11)BC2max=∑s=1rosbsj0∑i=1npiaij+Vj0=f0s.t.∑s=1rosbsj−∑i=1npiaij≤1,j=1,2,…,mpi=p1,p2,…,pnT≥0os=o1,o2,…,orT≥0.The DEA method has two perspectives: input-oriented and output-oriented. Input-oriented observes the situation of enterprise input resources with fixed output, and the latter is the fixed input [19, 20]. However, this paper studies the efficiency of enterprises in GI, fully combining the data controllability and method operability of enterprises. This paper chooses to use the input-oriented BCC model to evaluate and analyze the comprehensive technical efficiency value, pure technical efficiency value, and scale efficiency of the GIP of enterprises. This paper introduces Archimedes infinitesimal ε and input slack variables and output variables S− and S+; then the BCC model of the m0-th industry is(12)minϑ−ε∑r=1RSr++∑n=1NSn−,∑m=1MλmAnm+Sn−=ϑAn0,∑m=1MλmBrm−Sr+=Br0,∑m=1Mλm=1,λm≥0;m=1,2,3…,M,Sn−≥0,Sr+≥0.


### 4.2. Selection of Evaluation Indicators

Summarize previous researches on green technology innovation performance and select appropriate input, output, and environmental indicators based on the principles of indicator selection and data availability and according to the research object of science and technology SMEs. The indicators are measured in two stages. The secondary indicators in the R&D stage include R&D input indicators and intermediate output indicators. The secondary indicators in the achievement transformation stage include non-R&D input indicators, expected output indicators, and unexpected output indicators. These three dimensions restrict and influence each other. The specific three-level indicators are given in Table2.Table 2
Indicator system for GIP evaluation of technology-based SMEs.
StageIndex classificationIndicator nameScientific and technological R&DInput indexA1: Full-time equivalent of R&D personnelA2: R&D capital stockIntermediate output indexB1: Number of new product development projectsB2: Number of patent applicationsB3: Number of valid invention patentsAchievement transformationNon-R&D investment indexC1: Average number of all employeesC2: Original price of fixed assetsC3: Technology introduction and transformation fundsExpected output indexD1: New product sales revenueD2: Total profitUnexpected output indexD3: Environmental pollution indexIn the R&D innovation stage, the full-time equivalent of R&D personnel is selected to represent the human input index, and the R&D capital stock converted from the internal expenditure of R&D funds is selected to measure the capital investment. The intermediate output is measured by the number of new product development projects and the intellectual output of effective patented inventions [21]. It selects the average number of employees and the original price of fixed assets as the indicators of human and material investment. In addition, the capital investment index is measured by the investment in the introduction and transformation of selected technologies. GIP should consider not only the level of innovation but also the economic benefits after commercialization. It selects the sales revenue and total profit of new products to measure the economic benefits brought by technological innovation to the enterprise. The production and operation of enterprises will have certain environmental benefits, and the environmental pollution index is used as an undesired output index, which is mainly the environmental pollution index of each enterprise.


### 4.3. Performance Analysis of Green Technology Innovation of Science and Technology SMEs

#### 4.3.1. Statistical Analysis

In the experiment, the 2016–2020 technology-based SMEs inZ province of China were selected as the basic decision-making unit for GIP calculation and evaluation. There are a total of 11 cities in Z province, which are represented by A–K. Because the R&D input resources are not able to obtain results and outputs in the current period, there is a lag, and the lag period is 2 years. That is, the R&D investment uses the data of 2016–2018; the intermediate output and non-technological R&D investment use the data of 2017–2019; and the expected output and unexpected output use the data of 2018–2020.Descriptive statistical analysis was performed on the selected indicators, including minimum, maximum, mean, standard deviation, and variance, as shown in Table3. Among them, the unit of C2 is 100 million yuan, and the unit of C3, D1, and D2 is 10,000 yuan. As can be seen from the table, except for the unexpected output indicators, the standard deviation values of all indicators are larger. This shows that the degree of dispersion between the indicators is large, and the content and scope of the selected data are more suitable for further analysis.Table 3
Descriptive statistical analysis.
IndexMinimumMaximumAverageStandard deviationA1272,7461,256.16602.57A2417.49154,88524,490.6610,653.84B16816297.19238.76B241,055207.65400.15B310237114.2894.71C11,87527,7329,24443,645.55C26173.6556.37646.13C370.65,319.462,534.1165,576.06D14,236.39710,679.27124,509.7546,537.54D21,412.58236,893.1741,503.1521,456.89D30.624.92.541.26In order to examine the correlation between input and output, Pearson correlation analysis was performed on the data of each indicator, and the correlation coefficient between each indicator was obtained, as shown in Table4. The correlation coefficient between input and output indicators is positive, and most of them reach a significant level of 1%. However, the correlation coefficients between all input and expected output and undesired output indicators environmental pollution index are negative, reaching a significant level of 1%. When the structure of input and the desired output is reasonable, the undesired output will decline. In general, the relationship between input and output indicators is reasonable [22, 23].Table 4
Pearson correlation coefficient analysis.
A1A2B1B2B3C1C2C3D1D2D3A110.711∗∗0.543∗∗0.784∗∗0.747∗∗0.709∗∗0.654∗∗0.502∗∗0.578∗∗0.842∗∗−0.607∗∗A20.711∗∗10.736∗∗0.517∗∗0.586∗∗0.762∗∗0.655∗∗0.655∗∗0.613∗∗0.538∗∗−0.577∗∗B10.543∗∗0.736∗∗10.795∗∗0.816∗∗0.777∗∗0.736∗∗0.623∗∗0.585∗∗0.501∗∗−0.509∗∗B20.784∗∗0.517∗∗0.795∗∗10.678∗∗0.788∗∗0.512∗∗0.1400.677∗∗0.769∗∗−0.536∗∗B30.747∗∗0.586∗∗0.816∗∗0.678∗∗10.831∗∗0.805∗∗0.73∗∗0.77∗∗0.709∗∗−0.59∗∗C10.709∗∗0.762∗∗0.777∗∗0.788∗∗0.831∗∗10.704∗∗0.512∗∗0.543∗∗0.721∗∗−0.531∗∗C20.654∗∗0.655∗∗0.736∗∗0.512∗∗0.805∗∗0.704∗∗10.5∗∗0.691∗∗0.692∗∗−0.632∗∗C30.502∗∗0.655∗∗0.623∗∗0.1400.73∗∗0.512∗∗0.5∗∗10.515∗∗0.164−0.648∗∗D10.578∗∗0.613∗∗0.585∗∗0.677∗∗0.77∗∗0.543∗∗0.691∗∗0.515∗∗10.634∗∗−0.456∗∗D20.842∗∗0.538∗∗0.501∗∗0.769∗∗0.709∗∗0.721∗∗0.692∗∗0.1640.634∗∗1−0.597∗∗D3−0.607∗∗−0.577∗∗−0.509∗∗−0.536∗∗−0.59∗∗−0.531∗∗−0.632∗∗−0.648∗∗−0.456∗∗−0.597∗∗1Next is the single-sampleT-test analysis, which mainly tests whether the difference between a sample mean and a known overall mean is significant. The test results show that the P values of all indicators are less than 0.01, reaching a 1% significance level. This shows that the mean of the indicators is significantly different from the mean of the overall sample, and it also shows that in different years and regions, the differences between input and output indicators are relatively large, and the selection of indicators is more reasonable.


#### 4.3.2. DEA Analysis of GIP of Technology-Based SMEs

According to the data, with the help of MaxDEA Ultra, the efficiency value of green technology innovation of technology-based SMEs in all cities inZ province is calculated and subdivided from the dimensions of pure technical efficiency and scale efficiency. Firstly, the average GI efficiency value of technology-based SMEs in 11 cities in Z province from 2016 to 2020 at each stage is analyzed. The results are shown in Figure 7. The overall GI efficiency of technology-based SMEs in province Z is 0.65, indicating that the overall green technology innovation performance is relatively reasonable and not too low. And it can be seen that the GI technology performance of the three regions of A, G, and J city has reached 1, and the GI technology in the scientific research stage and the achievement transformation stage has reached 1. It shows that from 2016 to 2020, the GIP of these cities has reached the DEA effectiveness and achieved reasonable input and output. The overall GIP of C and D cities is relatively low, below 0.2, and the scale efficiency values in other stages are lower than pure technical efficiency, indicating that the scale should be expanded as soon as possible [24].Figure 7
Comparative analysis of GIP in different urban areas inZ province: (a) overall stage, (b) scientific research stage, and (c) achievement stage.
(a)(b)(c)


#### 4.3.3. Malmquist Analysis of GIP of Technology-Based SMEs

The Malmquist index dynamic analysis was carried out on the GIP from 2018 to 2020 and got the corresponding index. The Malmquist exponent and its decomposition values are shown in Figure8.Figure 8
Malmquist dynamic efficiency analysis 2018–2020: (a) overall stage, (b) scientific research stage, and (c) achievement stage.
(a)(b)(c)From the perspective of the overall stage, the TPF index of the overall stage from 2018 to 2020 was 0.913, an increase of 2.7% compared with 2016–2017, indicating that the overall GIP of technology-based SMEs in the province is on the rise. The TPF index in 2018–2019 is relatively high. Data suggest that technological progress has boosted overall growth in the TFP change index. From the perspective of staged efficiency, the average TPF index in the scientific research stage is 1.13, which is higher than 0.8625 in the achievement transformation stage. It shows that the overall scientific research efficiency of these enterprises in the scientific research stage has a relatively fast growth rate, the achievement transformation stage is relatively slow, and the level of scientific and technological achievement transformation needs to be further improved. EC refers to the level of technological stagnation, while TC refers to the level of continuous progress and update, indicating that the enterprise’s green production refers to great progress.


## 4.1. Green Technology Innovation Performance Evaluation Method

There are many methods for performance evaluation, such as the commonly used factor analysis, principal component analysis, grey relational analysis, and so on. However, these methods either have high requirements on the quantity and composition of data, are complicated to calculate, or are greatly affected by subjective factors [17, 18]. The process of enterprise GI is a process of converting resource input into innovation output and involves multiple inputs and multiple outputs. Therefore, two more suitable evaluation methods for multiple inputs and outputs are introduced next.


### 4.1.1. Malmquist Exponential Model

This model was proposed in consumption analysis. Inspired by the Malmquist consumption index, the productivity index was constructed through the ratio of the distance function, and the Malmquist index was applied to the measurement of productivity changes, which further developed the Malmquist index.xt,yt represents the input-output vector of the decision-making unit; Dtxt,yt represents the distance between the decision-making unit and the efficiency frontier interface at t; and the productivity from t to t+1 period is(1)Mt=Dtxt,ytDtxt+1,yt+1.Under the technical conditions att+1, the rate of change in production from t to t+1 is(2)Mt+1=Dt+1xt,ytDt+1xt+1,yt+1.Use the geometric mean ofMt and Mt+1 to calculate the Malmquist exponent as follows:(3)Mxt+1,yt+1,xt,yt=Dtxt,ytDtxt+1,yt+1∗Dt+1xt,ytDt+1xt+1,yt+11/2.Under the corresponding assumptions:(4)Mxt+1,yt+1,xt,yt=Dt+1xt,ytDt+1xt+1,yt+1∗Dt+1xt+1,yt+1Dtxt+1,yt+1∗Dt+1xt+1,yt+1Dt+1xt,yt1/2=EC∗TC.When EC > 1, it indicates that the gap between the decision-making unit and the optimal efficiency frontier is narrowing, and vice versa, it indicates that the gap is increasing; when TC > 1, the production frontier moves upward, indicating technological progress, and vice versa.


### 4.1.2. Data Envelope Analysis (DEA Analysis)

The evaluation steps of the DEA analysis method are shown in Figure6. The CRR model and the BCC model are the most basic models in the DEA method.Figure 6
DEA method evaluation steps.CCR model assumes that DMU is in the case of fixed return to scale, which is used to measure the total efficiency. Fixed return to scale is the efficiency evaluation of all DMU compared together. The CRR model uses the dual theory of linear programming and the non-Archimedes infinitesimal technique to establish a DEA model based on the input and output data of each DMU and judge the effectiveness of the DMU according to the results. Suppose there arem DUMs denoted as follows:(5)DUMjj=1,2,3,…,m.There arem DUMs with n inputs and r outputs. The input vector is(6)Aj=a1j,a2j,…,anjT.The output vector is denoted as follows:(7)Bj=b1j,b2j,…,brjT.The weight vectors of thei-th input and the s-th output are(8)pi=p1,p2,…,pnTos=o1,o2,…,orT.The efficiency evaluation index ofDUMj is the ratio of output to the sum of input, which is(9)fj=∑s=1rosbsj∑i=1npiaij,j=1,2,…,m.There are certainp and o that can make the efficiency evaluation index less than or equal to 1. Taking the efficiency evaluation index fj0 of DUMj0 as the goal and fj as the constraint, the resulting planning problem is the CCR model, which is given in the following formula:(10)max=∑s=1rosbsj0∑i=1npiaij=f0,s.t.∑s=1rosbsj−∑i=1npiaij≤0,j=1,2,…,m,pi=p1,p2,…,pnT≥0,os=o1,o2,…,orT≥0.The linear programming model of the BCC model is(11)BC2max=∑s=1rosbsj0∑i=1npiaij+Vj0=f0s.t.∑s=1rosbsj−∑i=1npiaij≤1,j=1,2,…,mpi=p1,p2,…,pnT≥0os=o1,o2,…,orT≥0.The DEA method has two perspectives: input-oriented and output-oriented. Input-oriented observes the situation of enterprise input resources with fixed output, and the latter is the fixed input [19, 20]. However, this paper studies the efficiency of enterprises in GI, fully combining the data controllability and method operability of enterprises. This paper chooses to use the input-oriented BCC model to evaluate and analyze the comprehensive technical efficiency value, pure technical efficiency value, and scale efficiency of the GIP of enterprises. This paper introduces Archimedes infinitesimal ε and input slack variables and output variables S− and S+; then the BCC model of the m0-th industry is(12)minϑ−ε∑r=1RSr++∑n=1NSn−,∑m=1MλmAnm+Sn−=ϑAn0,∑m=1MλmBrm−Sr+=Br0,∑m=1Mλm=1,λm≥0;m=1,2,3…,M,Sn−≥0,Sr+≥0.


## 4.1.1. Malmquist Exponential Model

This model was proposed in consumption analysis. Inspired by the Malmquist consumption index, the productivity index was constructed through the ratio of the distance function, and the Malmquist index was applied to the measurement of productivity changes, which further developed the Malmquist index.xt,yt represents the input-output vector of the decision-making unit; Dtxt,yt represents the distance between the decision-making unit and the efficiency frontier interface at t; and the productivity from t to t+1 period is(1)Mt=Dtxt,ytDtxt+1,yt+1.Under the technical conditions att+1, the rate of change in production from t to t+1 is(2)Mt+1=Dt+1xt,ytDt+1xt+1,yt+1.Use the geometric mean ofMt and Mt+1 to calculate the Malmquist exponent as follows:(3)Mxt+1,yt+1,xt,yt=Dtxt,ytDtxt+1,yt+1∗Dt+1xt,ytDt+1xt+1,yt+11/2.Under the corresponding assumptions:(4)Mxt+1,yt+1,xt,yt=Dt+1xt,ytDt+1xt+1,yt+1∗Dt+1xt+1,yt+1Dtxt+1,yt+1∗Dt+1xt+1,yt+1Dt+1xt,yt1/2=EC∗TC.When EC > 1, it indicates that the gap between the decision-making unit and the optimal efficiency frontier is narrowing, and vice versa, it indicates that the gap is increasing; when TC > 1, the production frontier moves upward, indicating technological progress, and vice versa.


## 4.1.2. Data Envelope Analysis (DEA Analysis)

The evaluation steps of the DEA analysis method are shown in Figure6. The CRR model and the BCC model are the most basic models in the DEA method.Figure 6
DEA method evaluation steps.CCR model assumes that DMU is in the case of fixed return to scale, which is used to measure the total efficiency. Fixed return to scale is the efficiency evaluation of all DMU compared together. The CRR model uses the dual theory of linear programming and the non-Archimedes infinitesimal technique to establish a DEA model based on the input and output data of each DMU and judge the effectiveness of the DMU according to the results. Suppose there arem DUMs denoted as follows:(5)DUMjj=1,2,3,…,m.There arem DUMs with n inputs and r outputs. The input vector is(6)Aj=a1j,a2j,…,anjT.The output vector is denoted as follows:(7)Bj=b1j,b2j,…,brjT.The weight vectors of thei-th input and the s-th output are(8)pi=p1,p2,…,pnTos=o1,o2,…,orT.The efficiency evaluation index ofDUMj is the ratio of output to the sum of input, which is(9)fj=∑s=1rosbsj∑i=1npiaij,j=1,2,…,m.There are certainp and o that can make the efficiency evaluation index less than or equal to 1. Taking the efficiency evaluation index fj0 of DUMj0 as the goal and fj as the constraint, the resulting planning problem is the CCR model, which is given in the following formula:(10)max=∑s=1rosbsj0∑i=1npiaij=f0,s.t.∑s=1rosbsj−∑i=1npiaij≤0,j=1,2,…,m,pi=p1,p2,…,pnT≥0,os=o1,o2,…,orT≥0.The linear programming model of the BCC model is(11)BC2max=∑s=1rosbsj0∑i=1npiaij+Vj0=f0s.t.∑s=1rosbsj−∑i=1npiaij≤1,j=1,2,…,mpi=p1,p2,…,pnT≥0os=o1,o2,…,orT≥0.The DEA method has two perspectives: input-oriented and output-oriented. Input-oriented observes the situation of enterprise input resources with fixed output, and the latter is the fixed input [19, 20]. However, this paper studies the efficiency of enterprises in GI, fully combining the data controllability and method operability of enterprises. This paper chooses to use the input-oriented BCC model to evaluate and analyze the comprehensive technical efficiency value, pure technical efficiency value, and scale efficiency of the GIP of enterprises. This paper introduces Archimedes infinitesimal ε and input slack variables and output variables S− and S+; then the BCC model of the m0-th industry is(12)minϑ−ε∑r=1RSr++∑n=1NSn−,∑m=1MλmAnm+Sn−=ϑAn0,∑m=1MλmBrm−Sr+=Br0,∑m=1Mλm=1,λm≥0;m=1,2,3…,M,Sn−≥0,Sr+≥0.


## 4.2. Selection of Evaluation Indicators

Summarize previous researches on green technology innovation performance and select appropriate input, output, and environmental indicators based on the principles of indicator selection and data availability and according to the research object of science and technology SMEs. The indicators are measured in two stages. The secondary indicators in the R&D stage include R&D input indicators and intermediate output indicators. The secondary indicators in the achievement transformation stage include non-R&D input indicators, expected output indicators, and unexpected output indicators. These three dimensions restrict and influence each other. The specific three-level indicators are given in Table2.Table 2
Indicator system for GIP evaluation of technology-based SMEs.
StageIndex classificationIndicator nameScientific and technological R&DInput indexA1: Full-time equivalent of R&D personnelA2: R&D capital stockIntermediate output indexB1: Number of new product development projectsB2: Number of patent applicationsB3: Number of valid invention patentsAchievement transformationNon-R&D investment indexC1: Average number of all employeesC2: Original price of fixed assetsC3: Technology introduction and transformation fundsExpected output indexD1: New product sales revenueD2: Total profitUnexpected output indexD3: Environmental pollution indexIn the R&D innovation stage, the full-time equivalent of R&D personnel is selected to represent the human input index, and the R&D capital stock converted from the internal expenditure of R&D funds is selected to measure the capital investment. The intermediate output is measured by the number of new product development projects and the intellectual output of effective patented inventions [21]. It selects the average number of employees and the original price of fixed assets as the indicators of human and material investment. In addition, the capital investment index is measured by the investment in the introduction and transformation of selected technologies. GIP should consider not only the level of innovation but also the economic benefits after commercialization. It selects the sales revenue and total profit of new products to measure the economic benefits brought by technological innovation to the enterprise. The production and operation of enterprises will have certain environmental benefits, and the environmental pollution index is used as an undesired output index, which is mainly the environmental pollution index of each enterprise.


## 4.3. Performance Analysis of Green Technology Innovation of Science and Technology SMEs

### 4.3.1. Statistical Analysis

In the experiment, the 2016–2020 technology-based SMEs inZ province of China were selected as the basic decision-making unit for GIP calculation and evaluation. There are a total of 11 cities in Z province, which are represented by A–K. Because the R&D input resources are not able to obtain results and outputs in the current period, there is a lag, and the lag period is 2 years. That is, the R&D investment uses the data of 2016–2018; the intermediate output and non-technological R&D investment use the data of 2017–2019; and the expected output and unexpected output use the data of 2018–2020.Descriptive statistical analysis was performed on the selected indicators, including minimum, maximum, mean, standard deviation, and variance, as shown in Table3. Among them, the unit of C2 is 100 million yuan, and the unit of C3, D1, and D2 is 10,000 yuan. As can be seen from the table, except for the unexpected output indicators, the standard deviation values of all indicators are larger. This shows that the degree of dispersion between the indicators is large, and the content and scope of the selected data are more suitable for further analysis.Table 3
Descriptive statistical analysis.
IndexMinimumMaximumAverageStandard deviationA1272,7461,256.16602.57A2417.49154,88524,490.6610,653.84B16816297.19238.76B241,055207.65400.15B310237114.2894.71C11,87527,7329,24443,645.55C26173.6556.37646.13C370.65,319.462,534.1165,576.06D14,236.39710,679.27124,509.7546,537.54D21,412.58236,893.1741,503.1521,456.89D30.624.92.541.26In order to examine the correlation between input and output, Pearson correlation analysis was performed on the data of each indicator, and the correlation coefficient between each indicator was obtained, as shown in Table4. The correlation coefficient between input and output indicators is positive, and most of them reach a significant level of 1%. However, the correlation coefficients between all input and expected output and undesired output indicators environmental pollution index are negative, reaching a significant level of 1%. When the structure of input and the desired output is reasonable, the undesired output will decline. In general, the relationship between input and output indicators is reasonable [22, 23].Table 4
Pearson correlation coefficient analysis.
A1A2B1B2B3C1C2C3D1D2D3A110.711∗∗0.543∗∗0.784∗∗0.747∗∗0.709∗∗0.654∗∗0.502∗∗0.578∗∗0.842∗∗−0.607∗∗A20.711∗∗10.736∗∗0.517∗∗0.586∗∗0.762∗∗0.655∗∗0.655∗∗0.613∗∗0.538∗∗−0.577∗∗B10.543∗∗0.736∗∗10.795∗∗0.816∗∗0.777∗∗0.736∗∗0.623∗∗0.585∗∗0.501∗∗−0.509∗∗B20.784∗∗0.517∗∗0.795∗∗10.678∗∗0.788∗∗0.512∗∗0.1400.677∗∗0.769∗∗−0.536∗∗B30.747∗∗0.586∗∗0.816∗∗0.678∗∗10.831∗∗0.805∗∗0.73∗∗0.77∗∗0.709∗∗−0.59∗∗C10.709∗∗0.762∗∗0.777∗∗0.788∗∗0.831∗∗10.704∗∗0.512∗∗0.543∗∗0.721∗∗−0.531∗∗C20.654∗∗0.655∗∗0.736∗∗0.512∗∗0.805∗∗0.704∗∗10.5∗∗0.691∗∗0.692∗∗−0.632∗∗C30.502∗∗0.655∗∗0.623∗∗0.1400.73∗∗0.512∗∗0.5∗∗10.515∗∗0.164−0.648∗∗D10.578∗∗0.613∗∗0.585∗∗0.677∗∗0.77∗∗0.543∗∗0.691∗∗0.515∗∗10.634∗∗−0.456∗∗D20.842∗∗0.538∗∗0.501∗∗0.769∗∗0.709∗∗0.721∗∗0.692∗∗0.1640.634∗∗1−0.597∗∗D3−0.607∗∗−0.577∗∗−0.509∗∗−0.536∗∗−0.59∗∗−0.531∗∗−0.632∗∗−0.648∗∗−0.456∗∗−0.597∗∗1Next is the single-sampleT-test analysis, which mainly tests whether the difference between a sample mean and a known overall mean is significant. The test results show that the P values of all indicators are less than 0.01, reaching a 1% significance level. This shows that the mean of the indicators is significantly different from the mean of the overall sample, and it also shows that in different years and regions, the differences between input and output indicators are relatively large, and the selection of indicators is more reasonable.


### 4.3.2. DEA Analysis of GIP of Technology-Based SMEs

According to the data, with the help of MaxDEA Ultra, the efficiency value of green technology innovation of technology-based SMEs in all cities inZ province is calculated and subdivided from the dimensions of pure technical efficiency and scale efficiency. Firstly, the average GI efficiency value of technology-based SMEs in 11 cities in Z province from 2016 to 2020 at each stage is analyzed. The results are shown in Figure 7. The overall GI efficiency of technology-based SMEs in province Z is 0.65, indicating that the overall green technology innovation performance is relatively reasonable and not too low. And it can be seen that the GI technology performance of the three regions of A, G, and J city has reached 1, and the GI technology in the scientific research stage and the achievement transformation stage has reached 1. It shows that from 2016 to 2020, the GIP of these cities has reached the DEA effectiveness and achieved reasonable input and output. The overall GIP of C and D cities is relatively low, below 0.2, and the scale efficiency values in other stages are lower than pure technical efficiency, indicating that the scale should be expanded as soon as possible [24].Figure 7
Comparative analysis of GIP in different urban areas inZ province: (a) overall stage, (b) scientific research stage, and (c) achievement stage.
(a)(b)(c)


### 4.3.3. Malmquist Analysis of GIP of Technology-Based SMEs

The Malmquist index dynamic analysis was carried out on the GIP from 2018 to 2020 and got the corresponding index. The Malmquist exponent and its decomposition values are shown in Figure8.Figure 8
Malmquist dynamic efficiency analysis 2018–2020: (a) overall stage, (b) scientific research stage, and (c) achievement stage.
(a)(b)(c)From the perspective of the overall stage, the TPF index of the overall stage from 2018 to 2020 was 0.913, an increase of 2.7% compared with 2016–2017, indicating that the overall GIP of technology-based SMEs in the province is on the rise. The TPF index in 2018–2019 is relatively high. Data suggest that technological progress has boosted overall growth in the TFP change index. From the perspective of staged efficiency, the average TPF index in the scientific research stage is 1.13, which is higher than 0.8625 in the achievement transformation stage. It shows that the overall scientific research efficiency of these enterprises in the scientific research stage has a relatively fast growth rate, the achievement transformation stage is relatively slow, and the level of scientific and technological achievement transformation needs to be further improved. EC refers to the level of technological stagnation, while TC refers to the level of continuous progress and update, indicating that the enterprise’s green production refers to great progress.


## 4.3.1. Statistical Analysis

In the experiment, the 2016–2020 technology-based SMEs inZ province of China were selected as the basic decision-making unit for GIP calculation and evaluation. There are a total of 11 cities in Z province, which are represented by A–K. Because the R&D input resources are not able to obtain results and outputs in the current period, there is a lag, and the lag period is 2 years. That is, the R&D investment uses the data of 2016–2018; the intermediate output and non-technological R&D investment use the data of 2017–2019; and the expected output and unexpected output use the data of 2018–2020.Descriptive statistical analysis was performed on the selected indicators, including minimum, maximum, mean, standard deviation, and variance, as shown in Table3. Among them, the unit of C2 is 100 million yuan, and the unit of C3, D1, and D2 is 10,000 yuan. As can be seen from the table, except for the unexpected output indicators, the standard deviation values of all indicators are larger. This shows that the degree of dispersion between the indicators is large, and the content and scope of the selected data are more suitable for further analysis.Table 3
Descriptive statistical analysis.
IndexMinimumMaximumAverageStandard deviationA1272,7461,256.16602.57A2417.49154,88524,490.6610,653.84B16816297.19238.76B241,055207.65400.15B310237114.2894.71C11,87527,7329,24443,645.55C26173.6556.37646.13C370.65,319.462,534.1165,576.06D14,236.39710,679.27124,509.7546,537.54D21,412.58236,893.1741,503.1521,456.89D30.624.92.541.26In order to examine the correlation between input and output, Pearson correlation analysis was performed on the data of each indicator, and the correlation coefficient between each indicator was obtained, as shown in Table4. The correlation coefficient between input and output indicators is positive, and most of them reach a significant level of 1%. However, the correlation coefficients between all input and expected output and undesired output indicators environmental pollution index are negative, reaching a significant level of 1%. When the structure of input and the desired output is reasonable, the undesired output will decline. In general, the relationship between input and output indicators is reasonable [22, 23].Table 4
Pearson correlation coefficient analysis.
A1A2B1B2B3C1C2C3D1D2D3A110.711∗∗0.543∗∗0.784∗∗0.747∗∗0.709∗∗0.654∗∗0.502∗∗0.578∗∗0.842∗∗−0.607∗∗A20.711∗∗10.736∗∗0.517∗∗0.586∗∗0.762∗∗0.655∗∗0.655∗∗0.613∗∗0.538∗∗−0.577∗∗B10.543∗∗0.736∗∗10.795∗∗0.816∗∗0.777∗∗0.736∗∗0.623∗∗0.585∗∗0.501∗∗−0.509∗∗B20.784∗∗0.517∗∗0.795∗∗10.678∗∗0.788∗∗0.512∗∗0.1400.677∗∗0.769∗∗−0.536∗∗B30.747∗∗0.586∗∗0.816∗∗0.678∗∗10.831∗∗0.805∗∗0.73∗∗0.77∗∗0.709∗∗−0.59∗∗C10.709∗∗0.762∗∗0.777∗∗0.788∗∗0.831∗∗10.704∗∗0.512∗∗0.543∗∗0.721∗∗−0.531∗∗C20.654∗∗0.655∗∗0.736∗∗0.512∗∗0.805∗∗0.704∗∗10.5∗∗0.691∗∗0.692∗∗−0.632∗∗C30.502∗∗0.655∗∗0.623∗∗0.1400.73∗∗0.512∗∗0.5∗∗10.515∗∗0.164−0.648∗∗D10.578∗∗0.613∗∗0.585∗∗0.677∗∗0.77∗∗0.543∗∗0.691∗∗0.515∗∗10.634∗∗−0.456∗∗D20.842∗∗0.538∗∗0.501∗∗0.769∗∗0.709∗∗0.721∗∗0.692∗∗0.1640.634∗∗1−0.597∗∗D3−0.607∗∗−0.577∗∗−0.509∗∗−0.536∗∗−0.59∗∗−0.531∗∗−0.632∗∗−0.648∗∗−0.456∗∗−0.597∗∗1Next is the single-sampleT-test analysis, which mainly tests whether the difference between a sample mean and a known overall mean is significant. The test results show that the P values of all indicators are less than 0.01, reaching a 1% significance level. This shows that the mean of the indicators is significantly different from the mean of the overall sample, and it also shows that in different years and regions, the differences between input and output indicators are relatively large, and the selection of indicators is more reasonable.


## 4.3.2. DEA Analysis of GIP of Technology-Based SMEs

According to the data, with the help of MaxDEA Ultra, the efficiency value of green technology innovation of technology-based SMEs in all cities inZ province is calculated and subdivided from the dimensions of pure technical efficiency and scale efficiency. Firstly, the average GI efficiency value of technology-based SMEs in 11 cities in Z province from 2016 to 2020 at each stage is analyzed. The results are shown in Figure 7. The overall GI efficiency of technology-based SMEs in province Z is 0.65, indicating that the overall green technology innovation performance is relatively reasonable and not too low. And it can be seen that the GI technology performance of the three regions of A, G, and J city has reached 1, and the GI technology in the scientific research stage and the achievement transformation stage has reached 1. It shows that from 2016 to 2020, the GIP of these cities has reached the DEA effectiveness and achieved reasonable input and output. The overall GIP of C and D cities is relatively low, below 0.2, and the scale efficiency values in other stages are lower than pure technical efficiency, indicating that the scale should be expanded as soon as possible [24].Figure 7
Comparative analysis of GIP in different urban areas inZ province: (a) overall stage, (b) scientific research stage, and (c) achievement stage.
(a)(b)(c)


## 4.3.3. Malmquist Analysis of GIP of Technology-Based SMEs

The Malmquist index dynamic analysis was carried out on the GIP from 2018 to 2020 and got the corresponding index. The Malmquist exponent and its decomposition values are shown in Figure8.Figure 8
Malmquist dynamic efficiency analysis 2018–2020: (a) overall stage, (b) scientific research stage, and (c) achievement stage.
(a)(b)(c)From the perspective of the overall stage, the TPF index of the overall stage from 2018 to 2020 was 0.913, an increase of 2.7% compared with 2016–2017, indicating that the overall GIP of technology-based SMEs in the province is on the rise. The TPF index in 2018–2019 is relatively high. Data suggest that technological progress has boosted overall growth in the TFP change index. From the perspective of staged efficiency, the average TPF index in the scientific research stage is 1.13, which is higher than 0.8625 in the achievement transformation stage. It shows that the overall scientific research efficiency of these enterprises in the scientific research stage has a relatively fast growth rate, the achievement transformation stage is relatively slow, and the level of scientific and technological achievement transformation needs to be further improved. EC refers to the level of technological stagnation, while TC refers to the level of continuous progress and update, indicating that the enterprise’s green production refers to great progress.


## 5. Improve the GI Path of Technology-Based SMEs

### 5.1. GI Model Architecture

Technology-based SMEs are an operation mode involving a variety of subjects. Through GIP evaluation, key shortcomings in the operation process of the enterprise can be found, and a series of indicators can be used to reflect the overall and decomposition level of the enterprise’s GI development. Starting from the idea of synchronously improving the performance of scientific research and achievement transformation, we propose a model framework for improving the level of GI, as shown in Figure9. The main bodies of innovation involved in the structure mainly include universities, enterprises, governments, and scientific research institutions. First, in the GI system of technology-based SMEs, the main body of innovation feels external stimuli, such as market demand, technology status, government support and regulation, and so on, to generate innovation momentum and to select and analyze GI resources. Secondly, the perception and feedback process of each innovation subject to the environment is also a process of absorbing and transforming GI resources. This also requires enterprises to analyze resource requirements and possible risks and timely adjust innovation plans. The last part is the output of GI achievements, which transforms STA, and the scale effect after transformation will stimulate enterprises to carry out new GI activities [25].Figure 9
GI model architecture.


### 5.2. GIP Improvement Strategy

For GI, technical R&D personnel are more important subjects, so improving the quality of innovation can promote technological progress. This requires a good innovation atmosphere, and enterprises must consciously guide and encourage R&D personnel to participate in innovation activities to stimulate their innovation inspiration. The number of R&D people owned by the company and the number of R&D activities can be used as the assessment index to perform performance quantization evaluation. Vigorously introducing external experts to conduct skill training for R&D personnel, and cultivating compound personnel who are not only suitable for enterprise development, possess management skills, and have the technology.Scientific allocation of innovation resources is an important measure to solve low innovation efficiency. It is necessary to scientifically allocate the number of R&D personnel , optimize the input and output structure and increase the proportion of green technology research and development funds. At the same time, it is led by leading enterprises to guide funds from different channels to invest in small and medium-sized technology-based enterprises. The improvement of the green level is also inseparable from the support and regulation of the government. It is necessary to strengthen the assessment and evaluation of optimization policies, increase support for SMEs, and establish a scientific assessment system for GI involving technology, economy, and ecological environment.The transformation of scientific and technological achievements (STA) is also an important part of innovation. Previous studies have also found that compared with technological innovation, the insufficient transformation of achievements is a more obvious problem. This requires introducing and absorbing advanced technology and effective investment, improving the incentive mechanism for the transformation of STA, and giving full play to the marketing role of new product salesmen. At the same time, it establishes and improves a platform for the transformation and exchange of STA, promotes and applies technologies, enables enterprises to achieve innovation-driven development, and transforms green STA that meets their needs into real productivity.


## 5.1. GI Model Architecture

Technology-based SMEs are an operation mode involving a variety of subjects. Through GIP evaluation, key shortcomings in the operation process of the enterprise can be found, and a series of indicators can be used to reflect the overall and decomposition level of the enterprise’s GI development. Starting from the idea of synchronously improving the performance of scientific research and achievement transformation, we propose a model framework for improving the level of GI, as shown in Figure9. The main bodies of innovation involved in the structure mainly include universities, enterprises, governments, and scientific research institutions. First, in the GI system of technology-based SMEs, the main body of innovation feels external stimuli, such as market demand, technology status, government support and regulation, and so on, to generate innovation momentum and to select and analyze GI resources. Secondly, the perception and feedback process of each innovation subject to the environment is also a process of absorbing and transforming GI resources. This also requires enterprises to analyze resource requirements and possible risks and timely adjust innovation plans. The last part is the output of GI achievements, which transforms STA, and the scale effect after transformation will stimulate enterprises to carry out new GI activities [25].Figure 9
GI model architecture.


## 5.2. GIP Improvement Strategy

For GI, technical R&D personnel are more important subjects, so improving the quality of innovation can promote technological progress. This requires a good innovation atmosphere, and enterprises must consciously guide and encourage R&D personnel to participate in innovation activities to stimulate their innovation inspiration. The number of R&D people owned by the company and the number of R&D activities can be used as the assessment index to perform performance quantization evaluation. Vigorously introducing external experts to conduct skill training for R&D personnel, and cultivating compound personnel who are not only suitable for enterprise development, possess management skills, and have the technology.Scientific allocation of innovation resources is an important measure to solve low innovation efficiency. It is necessary to scientifically allocate the number of R&D personnel , optimize the input and output structure and increase the proportion of green technology research and development funds. At the same time, it is led by leading enterprises to guide funds from different channels to invest in small and medium-sized technology-based enterprises. The improvement of the green level is also inseparable from the support and regulation of the government. It is necessary to strengthen the assessment and evaluation of optimization policies, increase support for SMEs, and establish a scientific assessment system for GI involving technology, economy, and ecological environment.The transformation of scientific and technological achievements (STA) is also an important part of innovation. Previous studies have also found that compared with technological innovation, the insufficient transformation of achievements is a more obvious problem. This requires introducing and absorbing advanced technology and effective investment, improving the incentive mechanism for the transformation of STA, and giving full play to the marketing role of new product salesmen. At the same time, it establishes and improves a platform for the transformation and exchange of STA, promotes and applies technologies, enables enterprises to achieve innovation-driven development, and transforms green STA that meets their needs into real productivity.


## 6. Discussion

The main research direction of this paper is the GIP evaluation and path improvement of technology-based SMEs, with digital information technology and statistics as the main technical theoretical support. This paper constructs the GIP evaluation index of enterprises and uses the Malmquist index model and data envelopment analysis method to analyze the GI evaluation results. The article starts with the relevant research and summarizes the relevant research contents related to the evaluation and improvement of enterprise GIP. Secondly, with the GI model and influencing factors as the main content, this paper describes the development stage of green technology innovation capability, the open evolution model of the GI system, and other related content. Then there is an overview of the current status of GI technology for small and medium-sized technology-based SMEs. Next is the evaluation method of green technology innovation performance, data envelopment analysis method, and Malmquist index model analysis method. This paper introduces the calculation process and development ideas of the two methods and determines the evaluation index system. It is selected from input, output, and environmental indicators and includes the stage of scientific and technological research and development and the stage of achievement transformation. Then this paper evaluates the green technology innovation inZ province with the index system of this paper. In this paper, the results are analyzed using the Malmquist exponential model and data envelopment analysis. Finally, this paper puts forward the GI model framework and GI promotion strategy of SMEs based on information technology around the GI promotion path of scientific and technological SMEs.


## 7. Conclusion

In the stage of digital GI management, this paper collects a large amount of data and information, which provides a digital innovation resource base for the further development of an open GI system. This paper first lists some research cases to understand the current situation of GIP evaluation of digital information technology; then the theory introduces the status of green technology innovation and its development, then introduces the elements of enterprise geographic information system in the information environment, puts forward Malmquist index model and DEA analysis method, and finally introduces the selection of evaluation index and innovation performance statistical analysis in the experiment section. The conclusions drawn are of certain reliability. With the circulation of information flow in the innovation network, the core innovation entities and equipment of enterprises have been transferred to the cloud, forming a series of collaborative innovation activities. GI cannot meet the market demand for GI. The relationship between innovation subjects has changed from a simple bilateral transaction to a strategic cooperation network between organizations. At this stage, building a green open innovation network model can further improve the level of green open innovation and promote the development of the GI system for SMEs.



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*Source: 1574267-2022-05-25.xml*