A Game Strategy Study on Innovation Efficiency of China’s Listed Charging Pile Companies: Based on Generalized Fuzzy DEA Method

Abstract

As a necessary infrastructure for new energy vehicles (NEV), charging piles have become a hot topic of social concern. Studying the innovation efficiency of charging pile companies can help to solve the development dilemma of the charging pile industry. This paper uses the generalized fuzzy data envelopment analysis (DEA) method based on game theory to study 23 Chinese listed charging pile manufacturing companies’ innovation efficiency from 2019 to 2021. On this basis, 15 charging pile technological interrelatedness companies are joined to establish a new game environment and to discuss the possibility of charging pile product manufacturing companies shifting to a new business model led by value-added service provision. The results show that the innovation efficiency of each company is significantly improved in the cooperative state, while the opposite is true in the competitive state, so cooperation is the company’s priority choice. Charging pile product manufacturing companies are more inclined to cooperate with technologically interrelated companies. The business model dominated by value-added service provision is conducive to improving the level of industrial innovation. The competition will easily lead to the decline of their innovation efficiency. Hence, companies should carefully choose their competition strategy.

1. Introduction

As we all know, the charging pile, as a necessary infrastructure for the operation of new energy vehicles, is the key to developing new energy vehicles. Studying the innovative efficiency of charging pile companies helps the development of the charging pile industry, thus promoting the further improvement of the development level of the upstream charging pile industry, i.e., the new energy vehicle industry. With the deteriorating global environment and significant problems, such as energy shortages, people’s awareness of environmental protection is gradually awakening. Electric vehicles (EVs) are considered a low-carbon and environmentally friendly travel tool widely concerned by the public [1,2]. Taking China as an example: the Chinese government has proposed a “double carbon” strategic goal to promote the transformation of traditional energy sources into new energy sources. It is also committed to building a clean, low-carbon, safe, and efficient energy system during the 14th Five-Year Plan period and a new power system with new energy as the mainstay. Data show that China’s NEV fleet will reach 7.84 million by the end of 2021, which brings enormous charging demand and stimulates the entire deployment and rapid growth of charging infrastructure [3,4]. According to the data of ownership, the number of public charging piles reaches 1.14 million units, and the number of private charging piles reaches 1.47 million units in 2021, totaling 2.61 million units, with a total vehicle-pile ratio of 3:1 [5]. Although this ratio is decreasing in an orderly manner year by year, the existing charging infrastructure (CI) is compared to the market demand, and the quantity and quality of charging piles need to be further improved.

However, some problems have emerged along with the development of the charging pile industry. On the one hand, due to the need for more land resources, especially in large and medium-sized cities, it is unrealistic to meet the growing charging demand by expanding the scale of CIs [6]. On the other hand, the charging pile industry is in the growth stage with high technical barriers, and it is challenging to achieve breakthroughs in core key technologies [7]. Moreover, the unbalanced utilization of charging resources and low utilization of charging facilities are, to some extent, caused by the poor management of CI [8]. This presents a significant challenge to the charging pile company, which must determine how to allocate various resources scientifically and rationally with minimum input, improve resource utilization efficiency, enhance competitiveness, and obtain maximum benefits.

In the ‘New Energy Vehicle Industry Development Plan (2021–2035)’, the Chinese government states that the future development of the charging pile industry revolves around three levels: charging infrastructure construction, service quality, and economic model innovation. Among them, innovation, as a critical driver of technological development, is a crucial source of competitive advantage [9]. Moreover, companies that are more efficient in innovation and invest more in innovation activities find more growth opportunities [10]. Therefore, studying the innovation efficiency of charging pile companies can be of great help to improve competitiveness and the level of company development. In addition, Duan et al. (2022) found that the evolution of collaborative innovation networks can facilitate inter-firm collaboration through industry-academia-research collaboration and that the technology diffusion and knowledge spillover phenomena in this model can promote collaborative innovation among firms [11]. Huang (2023) found that competition forces firms to improve the utilization of limited R&D resources and reduce waste [12]. Moreover, under conditions of intense competition, firms can more effectively convert R&D investments into innovations [13]. Moreover, since companies manufacturing charging pile products are in the same market environment, there will inevitably be a gaming relationship among them. Especially in recent years, the competition in the charging pile market has gradually become fierce, and the willingness to cooperate among charging pile companies has been increasing with the support of policies. Therefore, it is economically and socially efficient to study the innovation efficiency of charging pile companies in conjunction with game theory. By searching the literature, it was found that scholars have yet to conduct relevant research on the game behavior among charging pile companies to improve innovation efficiency and competitiveness.

This paper will explore the innovation efficiency of charging pile companies from a game perspective. Considering the fact that manufacturing companies, such as charging pile product manufacturers, are not able to establish a sustainable competitive advantage for their companies by purely relying on the competition of tangible products with the rapid development of the global economy [14], traditional manufacturing companies have started to consider the transformation to a business model dominated by value-added service provision [15]. Thus, this paper selects 23 charging pile product manufacturing companies listed in China as the main research subjects and 15 technologically interrelated charging pile companies as the value-added service research subjects, based on the generalized fuzzy data envelopment analysis method based on game theory, and researches the following issues:

• (1). When relying solely on the tangible product game, what is the game behavior based on innovation efficiency among charging pile product manufacturing companies of different size categories, and what is the willingness to choose inter-firm cooperation and competition?

• (2). When does the game among charging pile product manufacturing companies relying solely on tangible products not bring effective improvement to the company’s innovation efficiency or the possibility of the company turning to a business model led by value-added service provision?

• (3). What are the strategies for collaboration between charging pile product manufacturing companies of different size categories and technologically interrelated charging pile companies to improve innovation efficiency and competitiveness?

The rest of the article is organized as follows. Section 2 reviews the literature in the relevant research fields. Section 3 describes the methodology and model. Section 4 gives the indicator data sources as well as the empirical analysis. Finally, Section 5 summarizes the study, gives relevant recommendations, and presents a discussion.

2. Literature Review

To emphasize this paper’s research value and relevance, we review relevant studies on the efficiency of charging pile innovation and games in Section 2.1 and then introduce the concept of technological interrelatedness and its contribution to inter-firm collaborative innovation in Section 2.2. Finally, we discuss the generalized fuzzy DEA method based on game theory in Section 2.3.

2.1. Charging Pile-Related Research

Currently, research on the charging pile industry focuses on the R&D innovation of charging pile products [16,17,18], user charging behavior, charging pile deployment [19,20,21], NEV charging and distribution [22,23,24]. However, the research on the innovation efficiency of the charging pile industry is relatively scarce.

There has been much research on innovation efficiency at home and abroad, such as the research on innovation efficiency in the NEV, the upstream charging pile industry. Wu et al. (2020) examined how firm-level technological capabilities affect their eco-innovation performance and the moderating role of ownership and government support. Further, they explored the relationship between firms’ technological capabilities and their R&D strategies [25]. Chen et al. (2022) embedded the two-stage network relaxation measurement method into the three-level meta-frontier framework to explore the innovation efficiency and technology heterogeneity within the NEV industry, which is of great significance for guiding the growth and healthy development of the industry [26]. Fang et al. (2020) studied the innovation efficiency of 23 listed new energy vehicle companies from 2013 to 2018 using the DEA model and Malmquist productivity index and established the Tobit model to analyze the factors influencing the innovation efficiency of the companies [27]. These studies on the innovation efficiency of the NEV industry bring inspiration to the subsequent research on the charging pile industry’s innovation efficiency, which has a specific reference effect.

The game is more about macro discussions, such as the research on charging pricing strategies [28,29,30], charging and discharging scheduling and bidding strategies [31,32], and the lack of micro-level research on charging pile company gaming behavior.

Overall, a significant amount of effort has been devoted to studying the technological development and infrastructure of charging piles. Few studies have focused on innovation efficiency and charging pile companies’ gaming behavior.

2.2. Technological Interrelatedness and Synergistic Innovation

Technological interrelatedness refers to the manifestation of cognitive proximity at the product level. It is usually defined as the similarity between two industries or products regarding production technologies, management mechanisms, production factors, and infrastructure [33,34,35]. Since manufacturing different products or providing different services requires different technological knowledge, different technological fields may apply the same basic technology or basic disciplinary knowledge, which makes different technological fields technologically related [34].

Existing studies have mainly linked technological interrelatedness to economic indicators such as firm survival, innovation, and economic growth [36,37]. Wernerfelt (1984) points out that technological interrelatedness can significantly impact firm technological innovation, that technologies in different technology areas may create “cross-border” synergies, and that innovation is more likely to occur in interrelated knowledge areas [38]. Specifically, talent exchange, research cooperation, business investment, and commodity trade between technologically interrelated industries can lead to more efficient knowledge spillovers [39]. For example, due to the similar knowledge base, exchanging talents or R&D cooperation among interrelated industries is easier, and collaboration is more efficient [40]. Due to the similar industry background, business investment among interrelated industries is more likely to promote the export of business experience [41]. Since the close product production technology, the commodity trade among interrelated industries is more likely to imitate each other [42]. The technological innovation diffusion network formed by this knowledge spillover and technological interrelatedness will promote the technological innovation capability of the industrial chain and form the synergistic innovation of related industries [43].

In this paper, technological interrelatedness is mainly used to explore the objects of value-added services of charging pile product manufacturing companies. This paper aims to investigate the impact of charging pile product manufacturing companies’ shift to a new business model dominated by value-added service provision on innovation efficiency. To investigate further the game choice of charging pile product manufacturing companies, innovation efficiency and game decision changes are compared under two different game environments.

2.3. Generalized Fuzzy DEA Method Based on Game Theory

The most commonly used methods to assess efficiency are ratio analysis, balanced scorecard (BSC), total factor productivity (TFP), regression analysis, production frontier approach (PFA), and data envelope analysis (DEA). Ratio analysis can only evaluate a single input-output factor, while the balanced scorecard, TFP, and PFA apply to multiple input-single output problems. Whereas the DEA method does not require the construction of production functions, weights are not affected by human subjective factors. They can solve multiple input and output problems and are widely used [44,45,46]. DEA is an evaluation method pioneered by Charnes et al. (1978) for calculating the relative efficiency of decision units with the same properties [47]. The method has been developed over four decades and has made breakthroughs in all aspects [48]. Banker et al. (1984) extended the model to develop a variation of returns to scale [49]. Ma (2002) proposed the generalized data envelopment analysis (G-DEA) method, which breaks through the limitation of decision unit set selection in the traditional DEA method [50]. In addition, since the traditional DEA method requires the input-output data to be exact, the situation where the input-output data are fuzzy cannot be handled. Muren et al. (2012) proposed a generalized fuzzy DEA method, which further improved the selection of particular points in fuzzy decision units [51]. These studies have greatly enriched the evaluation methods of fuzzy DEA methods and promoted the application fields of fuzzy DEA methods.

However, with the advent of big data and information technology, competition among companies has never been more intense. Although the generalized fuzzy DEA method has dramatically expanded the application area of DEA methods, it still needs to be highly applicable in evaluating those problems where cooperation and competition exist between decision units [52]. Scholars have researched the complex cooperation and competition of decision units in the DEA method [53,54,55]. Mu et al. (2020) proposed a generalized fuzzy data envelopment analysis method based on game theory and further illustrated how to determine the optimal partner and the strongest competitor of a decision unit by introducing an evaluation method based on generalized fuzzy decision units in optimal and worst forms, and applied this method to the cooperation and competition decision problem of a tertiary hospital in the Inner Mongolia Autonomous Region [56]. The DEA method based on game theory mainly studies the tough competition and cooperation relationship between decision units. These complex cooperation and competition relationships are real and widespread in practical business. For example, to enhance their competitiveness, charging pile companies can improve their innovation efficiency not only through internal investment in human, material, and financial resources, but also through cooperation with outstanding companies in the industry to reach synergistic innovation and improve their overall innovation capability. Moreover, competition is universal. Even the leading companies will tend to suppress their rivals through competitive means, while the lagging companies are more willing to cooperate than to compete [56]. In conclusion, no matter how strong a company is, it can’t guarantee that everything will be done and that it could continue to thrive. Therefore, moderate gaming behavior is necessary for leading and backward companies when the benefit distribution mechanism is reasonable.

In summary, although there have been many studies on charging piles, almost no scholars have applied the generalized fuzzy DEA method based on game theory to study the innovation efficiency of charging pile companies. The contributions of this paper are as follows: (1) Using a generalized fuzzy DEA model based on game theory to study the innovation efficiency of charging pile product manufacturing companies fills the research gap in the innovation efficiency of charging piles and enriches the application of the generalized fuzzy DEA method based on game theory. (2) The study of value-added services is conducted by adding companies with technological interrelatedness with the manufacturing of charging pile products, comparing and analyzing the changes in the decision-making of charging pile product manufacturing companies under different gaming environments, and providing ideas for a new business model led by value-added service provision for charging pile product manufacturing companies in the future. (3) A comprehensive analysis of the game choices of charging pile product manufacturing companies for improving innovation efficiency and competitiveness is presented. Thus, this study provides more comprehensive and valuable insights into the innovation development of companies, offering new perspectives to solve the current development “dilemma” of the charging pile industry.

3. Methods

Firstly, the generalized fuzzy DEA model is briefly introduced, and then the generalized fuzzy DEA model and its related definition are introduced under the guidance of cooperation and competition.

3.1. Generalized Fuzzy DEA Model

According to the literature analysis, the generalized fuzzy DEA model is obtained by further promoting the selection method of the evaluation decision unit set and the evaluated decision unit based on the traditional fuzzy DEA model. The specific generalized fuzzy CCR model is shown in Model (1).

(1)$GFCC{R}_P\left\{\begin{array}{c}\max {\mu }^T{\tilde{Y}}_{S0},\hfill \\ s.t.{\omega }^T{\tilde{X}}_{Si} - {\mu }^T{\tilde{Y}}_{Si} \ge 0,i = 1,2,\dots ,k,\hfill \\ \hspace{1em}\hspace{1em}{\omega }^T{\tilde{X}}_{S0} = 1,\hfill \\ \hspace{1em}\hspace{1em}\omega \ge 0,\mu \ge 0\hfill \end{array}\right.$

where μ is the weight of the output indicator to be computed, ω is the weight of the input indicator with computation. k is the number of decision units, in this paper refers to the number of charging pile companies. In addition, $({\tilde{X}}_{S0},{\tilde{Y}}_{S0})$ is an arbitrary sample of decision units to be evaluated, which may take decision units consistent with the traditional DEA method or may be other decision units constructed or selected, and in this paper refers to any charging pile company.$({\tilde{X}}_{Si},{\tilde{Y}}_{Si})$ is the selected reference system, which may be the original decision unit, or may be part of the original decision unit, and of course, may be other alternative decision units with the selection of alternative decision unit on a scientific basis, and in this paper refers to the set of all charging pile companies.

3.2. Cooperative-Oriented Generalized Fuzzy DEA Model

Suppose that $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$ and $({\tilde{X}}_{S2},{\tilde{Y}}_{S2})$ are two sample fuzzy decision units, which in this paper refer to any two charging pile companies. Note that the decision units after the cooperation of these two decision units are as follows:

$({\tilde{X}}_{S\mathrm{C}},{\tilde{Y}}_{S\mathrm{C}}) = ({\tilde{X}}_{S1},{\tilde{Y}}_{S1})\vee ({\tilde{X}}_{S2},{\tilde{Y}}_{S2}) = ({\tilde{X}}_{S1}\vee {\tilde{X}}_{S2},{\tilde{Y}}_{S1}\vee {\tilde{Y}}_{S2})$

where $\vee$ denotes the change operator of the input-output data after cooperation of the decision units. In the usual case, the inputs become smaller and the outputs become greater after cooperation between two sample decision units.

Definition 1 (Generalized fuzzy sample decision unit cooperation efficiency).

The cooperation efficiency of two generalized fuzzy sample decision units based on the CCR model can be calculated by model (2).

(2) $GFCC{R}_{PC}\left\{\begin{array}{c}\max {\mu }^T{\tilde{Y}}_{SC},\hfill \\ s.t.{\omega }^T{\tilde{X}}_{Si} - {\mu }^T{\tilde{Y}}_{Si} \ge 0,i = 1,2,\dots ,k,\hfill \\ \hspace{1em}\hspace{1em}{\omega }^T{\tilde{X}}_{SC} = 1,\hfill \\ \hspace{1em}\hspace{1em}\omega \ge 0,\mu \ge 0\hfill \end{array}\right.$

Definition 2 (optimal partner of generalized fuzzy sample decision unit).

$({\tilde{\mathrm{X}}}_{\mathrm{S}1},{\tilde{\mathrm{Y}}}_{\mathrm{S}1})$is a generalized fuzzy sample decision unit for a partner seeking. $({\tilde{X}}_{S{H}_i},{\tilde{Y}}_{S{H}_i}),i=1,2,\dots ,t$are potential partners of $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$and $({\tilde{X}}_{Si},{\tilde{Y}}_{Si})$is the selected reference system. The decision unit $({\tilde{X}}_{SC1},{\tilde{Y}}_{SC1})$in $({\tilde{X}}_{S{H}_i},{\tilde{Y}}_{S{H}_i})$that makes the optimal efficiency improvement of$({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$after cooperation with $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$is called the optimal partner of $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$. One of the optimal partner selection models based on the CCR model is shown in model (3).

(3) $GFCC{R}_{PCopt}\left\{\begin{array}{l}\underset{1 \le i \le t}{\max }\max {\mu }^T({\tilde{Y}}_{S1}\vee {\tilde{Y}}_{S{H}_i}),\\ s.t.{\omega }^T{\tilde{X}}_{Si} - {\mu }^T{\tilde{Y}}_{Si} \ge 0,i = 1,2,\dots ,k,\\ {\omega }^T({\tilde{X}}_{S1}\vee {\tilde{X}}_{S{H}_i}) = 1,\\ \omega \ge 0,\mu \ge 0\end{array}\right.$

Definition 3 (Optimal cooperative efficiency of generalized fuzzy sample decision units).

The optimal cooperative efficiency of two generalized fuzzy sample decision units$({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$and $({\tilde{X}}_{S2},{\tilde{Y}}_{S2})$is defined as the efficiency value of selecting the decision unit with the smallest of all inputs and the largest of all outputs among all possible decision units in$({\tilde{X}}_{SC},{\tilde{Y}}_{SC})$.

Definition 4 (Generalized fuzzy sample decision unit worst cooperative efficiency).

The worst cooperative efficiency of two generalized fuzzy sample decision units $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$and $({\tilde{X}}_{S2},{\tilde{Y}}_{S2})$is defined as the efficiency value of selecting the decision unit with the largest of all inputs and the smallest of all outputs among all possible decision units in$({\tilde{X}}_{SC},{\tilde{Y}}_{SC})$.

3.3. Competition-Oriented Generalized Fuzzy DEA Model

The decision units $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$ and $({\tilde{X}}_{S2},{\tilde{Y}}_{S2})$ after competition are as follows:

$({\tilde{X}}_{S\mathrm{F}},{\tilde{Y}}_{S\mathrm{F}}) = ({\tilde{X}}_{S1},{\tilde{Y}}_{S1})\wedge ({\tilde{X}}_{S2},{\tilde{Y}}_{S2}) = ({\tilde{X}}_{S1}\wedge {\tilde{X}}_{S2},{\tilde{Y}}_{S1}\wedge {\tilde{Y}}_{S2})$

where ⋀ denotes the change operator of the input-output data after competition between the decision units. In the usual case, the inputs become larger and the outputs become smaller after competition between two sample decision units.

Definition 5 (Generalized fuzzy sample decision unit competition efficiency).

The competition efficiency of two generalized fuzzy sample decision units based on the CCR model can be calculated using model (4).

(4) $GFCC{R}_{PF}\left\{\begin{array}{c}\max {\mu }^T{\tilde{Y}}_{SF},\hfill \\ s.t.{\omega }^T{\tilde{X}}_{Si} - {\mu }^T{\tilde{Y}}_{Si} \ge 0,i = 1,2,\dots ,k,\hfill \\ \hspace{1em}\hspace{1em}{\omega }^T{\tilde{X}}_{SF} = 1,\hfill \\ \hspace{1em}\hspace{1em}\omega \ge 0,\mu \ge 0\hfill \end{array}\right.$

Definition 6 (Strongest competitor of a generalized fuzzy sample decision unit).

$({\tilde{X}}_{S{H}_i},{\tilde{Y}}_{S{H}_i}),i=1,2,\dots ,t$is a certain generalized fuzzy sample decision unit in a competitive position. $({\tilde{X}}_{S1},{\tilde{Y}}_{SS1}),i=1,2,\dots ,t$are the potential competitors of $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$and $({\tilde{X}}_{Si},{\tilde{Y}}_{Si})$is the selected reference system. The decision unit $({\tilde{X}}_{SF1},{\tilde{Y}}_{SF1})$that reduces the efficiency of$({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$the most after competing with $({\tilde{X}}_{Si},{\tilde{Y}}_{Si})$is called the strongest competitor of $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$. The selection model of the strongest competitor based on the CCR model is shown in model (5).

(5) $GFCC{R}_{PCom}\left\{\begin{array}{l}\underset{1 \le i \le t}{\min }\max {\mu }^T({\tilde{Y}}_{S1}\wedge {\tilde{Y}}_{S{H}_i}),\\ s.t. {\omega }^T{\tilde{X}}_{Si} - {\mu }^T{\tilde{Y}}_{Si} \ge 0,i = 1,2,\dots ,k,\\ {\omega }^T({\tilde{X}}_{S1}\wedge {\tilde{X}}_{S{H}_i}) = 1,\\ \omega \ge 0,\mu \ge 0\end{array}\right.$

Definition 7 (Optimal competitive efficiency of generalized fuzzy sample decision units).

The optimal competitive efficiency of two generalized fuzzy sample decision units $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$and $({\tilde{X}}_{S2},{\tilde{Y}}_{S2})$is defined as the efficiency value of selecting the decision unit with the smallest of all inputs and the largest of all outputs among all possible decision units in $({\tilde{X}}_{SF},{\tilde{Y}}_{SF})$.

Definition 8 (Generalized fuzzy sample decision unit worst competitive efficiency).

The worst competitive efficiency of two generalized fuzzy sample decision units $({\tilde{X}}_{S1},{\tilde{Y}}_{S1})$and $({\tilde{X}}_{S2},{\tilde{Y}}_{S2})$is defined as the efficiency value of choosing the decision unit with the largest of all inputs and the smallest of all outputs among all possible decision units in $({\tilde{X}}_{SF},{\tilde{Y}}_{SF})$.

4. Results

4.1. Indicators and Data

In the existing research on the evaluation of the innovation efficiency of companies, the commonly used input indicators can be divided into two categories: human resource input and financial resource input. The output index is divided into two parts, namely R&D output and economic benefit output [57]. Through research, Griliches (1979) found that the knowledge production function, which considers R&D and patent number as the most critical input and output variables, is a good statistical model for studying scientific and technological innovation [58]. In addition, based on the finding of Fang et al. (2019) that intangible assets generated by a company’s innovation activities are the key to reflecting the company’s core competitiveness, end-of-period intangible assets can be used as an output variable to study innovation efficiency [59]. In this paper, we mainly study innovation efficiency in the technology development stage, focusing on the innovation inputs and outputs within the company. Therefore, to ensure the scientific nature of the index system, the innovation efficiency evaluation index system of charging pile companies is constructed based on the current research results, as shown in Table 1.

The data for each indicator of a company are inherently ambiguous. For example, the number of R&D personnel working for a charging pile company in a given year is a fixed value. In contrast, over the whole year, personnel may fluctuate [60]. Therefore, this state of R&D personnel is viewed as fuzzy data and analyzed using the generalized fuzzy DEA method. Considering that the input and output of the company in a particular year may have outliers, it is not objective to describe a company’s state using only a particular year’s data. So, it is more realistic to describe a company’s state using data from three years, 2019–2021.

In this paper, according to the charging pile section of Dongfang Fortune Network, 23 charging pile companies that manufacture, develop, and sell charging pile products were selected from all China listed charging pile companies as the primary research objects. In addition, 15 listed companies belonging to the charging pile sector with complete data, which do not manufacture and produce charging pile products but have technological interrelatedness with the charging pile industry, are selected as the research subjects of value-added services. The financial data of the companies for the study are obtained from the companies’ financial statements in the China Juchao Information Database, and the patent data of the companies are gained from the search platform of the official website of the State Intellectual Property Office.

4.2. Gaming Analysis of Charging Pile Product Manufacturing Companies

The generalized fuzzy DEA model has been used to measure the minimum and maximum innovation efficiency of listed 23 charging pile product manufacturing companies in China. The specific data are shown in Table 2. The maximum value of the input index measures the minimum innovation efficiency as input data and the minimum value of the output index as output data. The minimum value of the input index measures the maximum innovation efficiency as input data and the maximum value of the output index as output data [52]. Overall, the innovation efficiency of charging pile product manufacturing companies is low, and nowadays, there is a greater demand for the development of charging pile products. Companies must improve their innovation efficiency to provide better charging pile products to society.

Considering that the size of each company is different and the competition and willingness to cooperate between companies of different sizes are not equal, the size difference is quantified by the average value of the companies’ total assets at the end of the three years from 2019 to 2021. The systematic clustering method is applied to classify 23 charging pile product manufacturing companies and draw a spectrum chart, as shown in Figure 1. For the sake of simplicity in the subsequent analysis, the following descriptions use the company abbreviations.

According to the size difference, the 23 companies are divided into four categories: The first category includes ST, KE, SOJO, CSG, INVT, Cp., DE, AEPP, Wm., CE, KST, Zyd.E, Zh.E, TET, SE, HIT, and HE, for a total of 17 companies. The second category includes TGOOD, EG, PE, XJ, for a total of 4 companies. The third is a company called SPS. The fourth category contains NARI, one company.

Cooperation and competition between companies of the same size are more representative. This is because it is difficult for large-scale companies to think of cooperation between them and smaller companies in terms of willingness to cooperate, and it is difficult for smaller companies to constitute competitors of larger companies [56]. Therefore, the game analysis of charging pile companies of the same size will be conducted next based on the classification results. Since the third and fourth categories contain only one company, a separate game analysis is not conducted for these two categories.

4.2.1. First Category Companies Game Analysis

The innovation efficiency change intervals after cooperation among 17 charging pile product manufacturing companies in the first category are calculated using a generalized fuzzy DEA model based on game theory, as shown in Table 3, with abbreviations referring to the companies for ease of presentation.

According to Table 3, the macroscopic analysis shows that the diagonal value is the innovation efficiency of the original company in Table 2, the innovation efficiency of each company after cooperation is higher than the original efficiency, which indicates that cooperation among companies is desirable and meets the actual economic significance. Microscopic analysis of individual company cooperation will be conducted from two perspectives, pessimistic and optimistic, to find out the optimal partners of the company under the pessimistic and optimistic perspectives. Companies can choose different perspectives to plan their decisions according to their decision-making needs. When the pessimistic and optimistic perspectives are the same, the company can directly take the current decision plan. When the pessimistic and optimistic perspectives are different, if the company decides to seek stability, it can choose the pessimistic perspective to plan the game behavior. If the company seeks progress, it can choose an optimistic perspective to plan the game’s behavior. The following is an example of ST company to start the analysis.

ST’s innovation efficiency in the cooperative state is more significant than its innovation efficiency in the non-cooperative state (0.101,0.530), indicating that its cooperation with the other 16 charging pile product manufacturing companies is worthwhile. From the pessimistic perspective, the highest innovation efficiency of ST and CE can reach 1.226, and the increase is 0.125. So, CE is the optimal partner of ST under the pessimistic perspective. From the optimistic perspective, the highest innovation efficiency of ST and SE can reach 6.409, and the increase is 5.879, so SE is the optimal partner of ST under the optimistic perspective. Similarly, the optimal partners of the remaining 16 companies are summarized as shown in Table 4.

According to Table 3, it is found that some companies such as KE, have HE as their optimal partner from both pessimistic and optimistic perspectives, so HE is said to be the absolute partner of KE. In addition, it is observed that CE, SE, and HE are the optimal choices for most companies to cooperate. The optimal partner for CE and SE is HE, with innovation efficiency intervals of (3.362,9.700) and (2.001,25.928) after cooperation, respectively. Compared to the original three innovation efficiency intervals of (0.314,0.782), (0.402,4.003), and (0.132,0.746), the cooperation state can promote the innovation efficiency of each company. Moreover, the optimal partners of HE in the pessimistic and optimistic perspectives are CE and SE, respectively. This indicates that the three companies are expected to reach a “win-win” or even a “multi-win”.

Then, according to the calculation results of the generalized fuzzy DEA model based on the game theory, the lowest and highest innovation efficiency values of the first type of 17 companies after the competition are obtained, and the thermodynamic diagram is drawn, as shown in Figure 2.

From Figure 2, in general, the innovation efficiency of most companies in the competitive state has decreased, which is in line with the actual economic significance. Individually, the selection of the optimal competitor should conform to the criterion that its loss is less, unchanged, or even increased after the competition, and the rival loses more. In comparison, the strongest competitor that should be avoided is judged based on whether it will cause a significant loss to itself after the competition [52]. Given this, taking ST as an example for analysis, in terms of minimum innovation efficiency, ST’s innovation efficiency does not decrease after competing with Cp. and Zyd.E, but significantly reduces Cp.’s innovation efficiency by 0.316 in comparison. So, considering that ST’s optimal competitor in a pessimistic view is Cp. In contrast, ST’s innovation efficiency value after competing with Wm. decreases from 0.101 to 0.017. It takes the biggest hit, claiming that Wm. is the strongest competitor of ST from a pessimistic perspective. Regarding the highest innovation efficiency, after ST competes with TET, the former’s efficiency remains unchanged, while the latter’s efficiency decreases significantly by 2.076. So, TET is ST’s optimal competitive partner with an optimistic perspective. After ST competes with DE, its innovation efficiency value decreases from 0.5303 to 0.1076, which is the most significant decrease, and DE is said to be ST’s optimistic perspective strongest competitive partner. Similarly, the optimal and strongest competitors of the remaining 16 companies are summarized, and the details are shown in Table 5.

According to Table 5, DE, Wm., HIT, and HE are strong competitors that most companies want to avoid. Moreover, it is also found that HE is a more popular choice in both cooperation and competition states, indicating that it has a strong innovative ability and operational strength among charging pile product manufacturing companies at the same scale level. Furthermore, other companies can focus on considering cooperation with HE. In addition, it is also found that some companies’ optimal competitors do not exist, such as KE. Then, all the companies except the strongest competitors may form a competitive situation with them and choose the appropriate competitive strategy on merit. For example, according to Figure 2, it is found that KE does not significantly reduce its own highest innovation efficiency after competing with SE. However, it significantly reduces the innovation efficiency of its rival by 0.148. Hence, KE may form a competitive situation with SE.

Although the optimal competition strategy is given, some companies, such as KST and TET, whose innovation efficiency is already low, are not recommended to enter a competition. It is better to avoid the strongest competitors for a while, enhance innovation efficiency mainly by cooperation, and continuously improve the company’s competitiveness. Companies whose original innovation efficiency is high or whose efficiency remains the same or even increases after the competition, such as Wm. and INVT, can consider suppressing their rivals through competition. Improve their position in the field of charging pile product manufacturing. Each company needs to develop the most suitable competition strategy for its situation.

4.2.2. Second Category Companies Game Analysis

According to the classification results, the second category of charging pile product manufacturing companies includes TGOOD, EG, PE, and XJ, four companies. Their innovation efficiency change intervals after cooperation are calculated using the generalized fuzzy DEA model based on game theory in Table 6.

According to the analysis in Table 6, the optimal partner of the pessimistic perspective of TGOOD is EG, and the optimal partner of the optimistic perspective is XJ. The optimal partner of the pessimistic perspective of EG is PE, and the optimal partner of the optimistic perspective is XJ. The optimal partner of the pessimistic perspective of PE is EG, and the optimal partner of the optimistic perspective is XJ. The optimal partner of the pessimistic perspective of XJ is PE, and the optimal partner of the optimistic perspective is EG. It can be found that XJ is the most popular partner among the second category of companies, followed by PE and EG. This indicates that choosing XJ as a partner can significantly improve the innovation efficiency of other companies, and XJ can achieve a “win-win” effect by choosing PE or EG as a partner.

Similarly, the calculated innovation efficiency change intervals for the four charging pile product manufacturing companies in the second category after the competition are shown in Table 7.

From Table 7, we can see that TGOOD’s pessimistic perspective optimal competitor is EG, and the optimistic perspective optimal competitor is PE; the pessimistic perspective strongest competitor is XJ, and the optimistic perspective strongest competitor is EG. However, TGOOD’s pessimistic perspective of optimal competitor and the optimistic perspective of the strongest competitor is the same as EG currently, which is contradictory, indicating that the competition with EG is risky and not the best. Thus, it is not the best choice. TGOOD’s lowest and highest innovation efficiency remain unchanged after competing with PE, and the latter’s innovation efficiency decreases significantly, which means PE and TGOOD are more likely to form a competitive situation. XJ’s best competitor is PE, and since its minimum efficiency remains the same in the competitive state, XJ’s strongest competitor is EG in terms of maximum innovation efficiency. It needs to avoid forming a competitive situation with it.

4.3. Game Analysis after Value-Added Services

This paper adds 15 technically related companies to the charging pile manufacturing industry as the research object of value-added services. This way, companies can carry out R&D, information, technical support, financing, logistics, marketing, and other services to expand the original game environment. The original game environment is expanded to study the changes in cooperation and competition strategies of charging pile product manufacturing companies to improve the company’s innovation level under the business model dominated by value-added service provision. The new game environment is shown schematically in Figure 3.

After the expansion of the game environment, the original 23 charging pile product manufacturing companies are impacted by the new 15 companies, and the innovation efficiency in the non-game state has changed, with little change in the highest efficiency and a slight decrease in the lowest efficiency, as shown in Figure 4. In the more complex game environment, the original charging pile product manufacturing companies need to improve their innovation efficiency to strengthen their product competitiveness and take root in the general environment.

4.3.1. First Category Companies Game Analysis after Value-Added Services

In the new game environment, the generalized fuzzy DEA model based on game theory is used again to calculate the innovation efficiency interval of each company after the game. Firstly, 17 companies in the first category are analyzed, and their cooperation object ranking after adding 15 new companies is shown in Table 8.

According to Table 8, the analysis is as follows: In general, the innovation efficiency of each company after cooperation in the new game environment has improved compared with the innovation decreasing after cooperation in the original game environment, and the top three cooperation partners are mainly newly joined, technologically interrelated companies, indicating that the technological innovation diffusion network formed by charging pile product manufacturing companies and the technological innovation diffusion network formed by technologically interrelatedness companies in the cooperative state will promote the technological innovation capability of the industry chain as a whole and accelerate the innovation efficiency of charging pile companies [43]. A closer look reveals that SAIC, as an old automotive company, has tremendous advantages in scale and innovation resources and is a famous company for cooperation. GH and SEP are also popular as value-added service cooperation targets. HE, which belongs to the first category of charging pile product manufacturing companies, also has certain advantages in cooperation under the new game environment and can help in the technological innovation level of product manufacturing.

In addition, observing the competitive status of the first category of charging pile product manufacturing companies in the new game environment, the optimal and strong competitors of 17 companies are summarized in order, as shown in Table 9. Since the 15 companies that are the target of value-added service research do not manufacture and sell charging pile products, there is a competitive business inequality if they are considered to compete with the original charging pile product manufacturing companies. This paper only analyzes the competitive game among charging pile product manufacturing companies from the perspective of charging pile product competition.

Comparing Table 9 with Table 5, it is found that there are fewer choices of optimal competitors for the 17 companies in the first category in the new game environment. This is because the innovation efficiency of companies in the non-game state has decreased under the new game environment, which is not conducive to individual competition in the case of low efficiency. Although the change of the strongest competitors is more significant in a one-to-one view, the overall strong competitors change among individual companies, such as DE, HE, and HIT, still maintain a strong competitive advantage. Wm. and AEPP are also still in the upper circle of competitors, indicating that the game environment change has little impact on the general direction of the competitive game strategy of the first-category companies.

4.3.2. Second Category Companies Game Analysis after Value-Added Services

The game analysis is conducted for the second category’s four charging pile product manufacturing companies. The results from the calculation of the generalized fuzzy DEA model based on game theory in the new game environment are summarized to obtain the partner’s ranking in the cooperation state of the second category companies, as shown in Table 10.

According to Table 1, it can be found that the second category of charging pile product manufacturing companies can significantly improve their innovation efficiency by cooperating with newly joined technological interrelatedness companies. SAIC and DIE are the more popular value-added service cooperation targets, which is also similar to the conclusion of the analysis of the cooperation targets of the first category of charging pile product manufacturing companies in the new game environment.

Next, the optimal and strongest competitors in the competitive state are summarized. Four companies are in the second category, so strong competitors are not ranked, and the specific information is shown in Table 11.

According to Table 11 and the previous analysis of the competitors of the second category of charging pile product manufacturing companies in the original game environment, it is found that the competitive situation is more stable in the new environment, and no contradictory situation occurs. EG becomes the most competitive company in the second category and is the strongest competitor with XJ, which is similar to the previous analysis and confirms that the change of game environment has little effect on the strategy of the competitive game among charging pile product manufacturing companies. This is also like the previous analysis, which confirms the conclusion that changes in the game environment have little effect on the strategy of the competitive game among charging pile product manufacturers.

5. Conclusions and Discussion

In this paper, 38 listed charging pile companies in China are selected as samples, among which 23 charging pile product manufacturing companies are the main research subjects and 15 technologically interrelated charging pile companies are the research subjects of value-added services, to study the innovation efficiency of charging pile companies from a game perspective. The game decisions of the companies to improve their innovation efficiency are explored, and the cooperation choices of charging pile manufacturing companies and the changes in competitive strategies under the new business model dominated by value-added service provision are discussed. Three research questions are answered:

For research question 1 (When relying solely on the tangible product game, what is the game behavior based on innovation efficiency among charging pile product manufacturing companies of different size categories, and what is the willingness to choose inter-firm cooperation and competition?): According to the model measurement results, in the case of considering only the game among charging pile product manufacturing companies, the choice of cooperation leads to an increase in the innovation efficiency of each company, while competition leads to a general decrease in the innovation efficiency of each company. Among them, CE, SE, and HE represent the optimal choices for most companies to cooperate, and the three are the most likely to reach cooperation with each other as the optimal partners. DE, HIT, HE, Wm., and AEPP are strong competitors that most companies want to avoid. Especially, whether in cooperation or competition, HE is a more popular choice, indicating that at the same level, as HE has a strong innovation capability and operational strength, other companies had better avoid competition and choose to cooperate with it. In addition, although the optimal competition strategy is given in the paper, some companies, such as Wm. and INVT, can attack their opponents through competition, and their efficiency remains the same or can even improve. However, the improvement is far less than that in cooperation. Therefore, it is suggested that charging pile product manufacturing companies should prioritize cooperation in the current stage of strategy selection, improve innovation efficiency and enhance the company’s competitiveness by complementing the advantages of innovative technologies and resources with other companies. In terms of competitive choice, it is suggested that for strong competitors, companies should avoid positive conflicts with them and consider cooperating with them first. For the best competitors, the company should carefully consider the advantages and disadvantages of competition to identify their positioning clear advantages and disadvantages, as far as possible, to reduce their losses.

For research question 2 (When does the game among charging pile product manufacturing companies relying solely on tangible products not bring effective improvement to the company’s innovation efficiency or the possibility of the company turning to a business model led by value-added service provision?): After the expansion of 15 companies with technological interrelatedness with the charging pile product manufacturing industry, the efficiency of cooperation is greatly improved, and the cooperation targets are mostly new entrants. This indicates that the technological innovation diffusion network formed under the new game environment will promote the technological innovation capability of the industrial chain and form collaborative innovation of related industries. The change of charging pile product manufacturing companies to a business model dominated by value-added services is conducive to improving industrial innovation. At the same time, the competition efficiency dropped sharply. However, the strong competitive partners on the macro change are small, showing that the game environment changes based on the charging pile product manufacturing company competition game strategy present little effect. The company can basically follow the original competition perspective planning scheme to continue execution. Therefore, it is suggested that charging pile product manufacturing companies consider turning to a business model dominated by value-added service provision. Expand the scope of cooperation, promote the construction of the charging pile industry chain, enhance the innovation ability of the whole industry, and achieve a ‘win-win‘ or even ‘multi-win‘ situation.

For research question 3 (What are the strategies for collaboration between charging pile product manufacturing companies of different size categories and technologically interrelated charging pile companies to improve innovation efficiency and competitiveness?): As an old automobile company SAIC is a popular partner for choosing cooperation. It has more significant advantages in company scale and innovative resources and can provide information and financing services for charging pile product manufacturing companies. GH and SEP are also popular as value-added service cooperation objects that provide marketing and information services. HE, which belongs to the first category of charging pile product manufacturing companies, has certain cooperation advantages in the new gaming environment and can provide services, such as R&D and technical support. Therefore, it is suggested that the charging pile product manufacturing company strengthen its cooperation with the technologically interrelated charging pile company to provide research and development services, information services, technical support services, and other services to improve the commercial value of charging pile products and make full use of technological interrelatedness to improve the performance of technological innovation, so as to obtain more significant benefits.

Although the research has obtained relatively ideal results, there are still some limitations in the current research. First, due to space constraints, the article cannot provide a more detailed analysis of the charging pile company’s partners and competitors and give general conclusions in the form of examples. Second, in the study of the game behavior of each company, only inter-firm interactions are considered, and other external influences are not regarded. Last, due to data limitations, data are only available for listed companies, so whether these findings apply to a broader context is yet to be discovered and may require further research studies to verify.

Author Contributions

Conceptualization, J.L. and S.L.; methodology, J.L. and S.L.; software, J.D. and M.S.; validation, J.L. and C.C.; formal analysis, S.L. and J.D.; investigation, M.S. and C.C.; resources, S.L.; data curation, M.S.; writing—original draft preparation, S.L.; writing—review and editing, J.L.; visualization, C.C.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. 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

The data comes from the China Juchao Information Database “http://www.cninfo.com.cn/new/index (accessed on 10 July 2022)” and the State Intellectual Property Office “http://pss-system.cnipa.gov.cn/sipopublicsearch/portal/uilogin-forwardLogin.shtml (accessed on 15 July 2022)”.

Acknowledgments

We thank all the participants of the study for their availability and contribution to this research. We would also like to express our gratitude to the reviewers of the study, who provided important feedback for further improvement of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Charging Pile manufacturing companies Spectrograms using average linkage (between groups).

Figure 2. First category companies’ post-competitive innovation efficiency heat map.

Figure 2. First category companies’ post-competitive innovation efficiency heat map.

Figure 3. Schematic diagram of the new game environment.

Figure 4. Change in innovation efficiency after value-added services.

Table 1

Evaluation indicators of innovation efficiency of charging pile companies.

Variable Type	Variable Name	Unit	Indicator Description
Input variable	R&D personnel	People	R&D Personnel of Charging Pile Company
	R&D investment	Million yuan	R&D investment of the company
Output variable	End-of-period intangible asset value	Million yuan	Intangible asset values disclosed by charging pile companies at year-end
	Patent Applications	Item	Number of patent applications by charging pile companies

Table 2

Innovation efficiency values for charging pile product manufacturing companies.

NO.	Company Abbreviation	DMU (Co.)	Minimum Innovation Efficiency	Highest Innovation Efficiency
1	ST	Sunrise Technology Co., Ltd.	0.101	0.530
2	KE	Kelin Electric Co., Ltd.	0.331	0.918
3	SOJO	SOJO Electric Co., Ltd.	0.346	0.898
4	CSG	CSG Smart Science and Technology Co., Ltd.	0.113	0.456
5	INVT	INVT Electric Co., Ltd.	0.112	0.622
6	Cp.	Ceepower Co., Ltd.	0.417	0.876
7	DE	Dongfang Electronics Co., Ltd.	0.051	0.174
8	NARI	NARI Technology Co., Ltd.	0.239	0.747
9	AEPP	Auto Electric Power Plant Co., Ltd.	0.171	0.396
10	Wm.	Wanma Co., Ltd.	0.118	0.567
11	TGOOD	TGOOD Electric Co., Ltd.	0.378	0.697
12	CE	Clou Electronics Co., Ltd.	0.314	0.782
13	EG	East Group Co., Ltd.	0.191	0.764
14	KST	Kstar Science and Technology Co., Ltd.	0.078	0.524
15	PE	Pinggao Electric Co., Ltd.	0.763	1.988
16	Zyd.E	Zhongyeda Electric Co., Ltd.	0.402	1.100
17	Zh.E	Zhongheng Electric Co., Ltd.	0.079	0.303
18	TET	Tonhe Electronics Technologies Co., Ltd.	0.129	2.606
19	SPS	Sungrow Power Supply Co., Ltd.	0.230	2.166
20	SE	Senyuan Electric Co., Ltd.	0.402	4.003
21	HIT	Hifuture Information Technology Co., Ltd.	0.037	0.403
22	XJ	XJ Electric Co., Ltd.	0.270	1.256
23	HE	Heshun Electric Co., Ltd.	0.132	0.746

Table 3

Innovation efficiency intervals after collaboration between any two firms in the first category.

Co.	ST	KE	SOJO	CSG	INVT	Cp.	DE	AEPP	Wm.	CE	KST	Zyd.E	Zh.E	TET	SE	HIT	HE
ST	(0.101,0.530)	(0.497,1.430)	(0.457,1.140)	(0.333,1.607)	(0.494,2.859)	(0.417,1.060)	(0.249,0.839)	(0.232,0.935)	(0.522,2.559)	(1.226,2.983)	(0.169,1.329)	(0.402,1.170)	(0.159,0.553)	(0.202,2.606)	(0.549,6.409)	(0.102,0.842)	(0.278,2.145)
KE	(0.497,1.430)	(0.331,0.918)	(0.375,1.128)	(0.331,1.081)	(0.494,1.614)	(1.166,2.489)	(0.331,0.918)	(1.137,2.698)	(0.366,1.520)	(0.817,1.914)	(0.331,0.918)	(0.931,2.351)	(0.331,0.918)	(0.865,2.606)	(0.494,4.003)	(0.501,1.977)	(1.364,5.036)
SOJO	(0.459,1.140)	(0.375,1.128)	(0.346,0.898)	(0.347,1.235)	(0.435,2.591)	(1.076,1.982)	(0.346,0.898)	(1.049,2.150)	(0.394,2.319)	(0.924,2.292)	(0.346,1.024)	(0.859,1.872)	(0.346,0.898)	(0.798,2.606)	(0.485,5.809)	(0.462,1.574)	(1.258,4.010)
CSG	(0.333,1.607)	(0.331,1.081)	(0.347,1.235)	(0.113,0.456)	(0.152,0.910)	(0.780,2.753)	(0.113,0.456)	(0.760,3.032)	(0.177,0.823)	(0.417,0.846)	(0.154,0.572)	(0.623,2.576)	(0.165,0.833)	(0.578,2.999)	(0.402,4.003)	(0.335,2.218)	(0.912,4.884)
INVT	(0.494,2.859)	(0.402,1.614)	(0.435,2.591)	(0.152,0.910)	(0.112,0.623)	(1.025,5.716)	(0.112,0.623)	(0.995,4.918)	(0.186,0.787)	(0.317,0.863)	(0.368,1.327)	(1.032,5.400)	(0.303,1.563)	(0.757,4.616)	(0.402,4.003)	(0.464,4.540)	(1.803,11.565)
Cp.	(0.417,1.060)	(1.166,2.489)	(1.076,1.982)	(0.780,2.753)	(1.025,5.716)	(0.417,0.876)	(0.583,1.567)	(0.417,0.929)	(1.223,5.117)	(2.875,5.109)	(0.417,2.277)	(0.503,1.176)	(0.425,1.106)	(0.425,3.227)	(1.193,12.816)	(0.417,0.876)	(0.488,1.772)
DE	(0.249,0.839)	(0.331,0.918)	(0.346,0.898)	(0.113,0.456)	(0.112,0.623)	(0.583,1.567)	(0.051,0.174)	(0.569,1.583)	(0.127,0.675)	(0.314,0.782)	(0.115,0.524)	(0.466,1.481)	(0.123,0.435)	(0.433,2.606)	(0.402,4.003)	(0.251,1.245)	(0.682,3.171)
AEPP	(0.232,0.935)	(1.137,2.698)	(1.049,2.150)	(0.760,3.032)	(0.995,4.918)	(0.417,0.929)	(0.569,1.583)	(0.171,0.396)	(1.193,4.469)	(2.803,5.627)	(0.387,2.507)	(0.491,1.295)	(0.363,1.027)	(0.215,2.889)	(1.163,11.026)	(0.172,0.484)	(0.213,0.746)
Wm.	(0.522,2.559)	(0.366,1.520)	(0.394,2.319)	(0.177,0.823)	(0.186,0.787)	(1.223,5.117)	(0.127,0.675)	(1.193,4.469)	(0.123,0.567)	(0.314,0.810)	(0.256,1.188)	(0.977,4.834)	(0.259,1.399)	(0.907,4.132)	(0.472,4.003)	(0.525,4.064)	(1.431,10.353)
CE	(1.226,2.983)	(0.817,1.914)	(0.924,2.292)	(0.417,0.846)	(0.317,0.863)	(2.875,5.109)	(0.314,0.782)	(2.803,5.627)	(0.314,0.810)	(0.314,0.782)	(0.566,1.113)	(2.296,4.781)	(0.608,1.546)	(2.132,4.629)	(0.990,4.244)	(1.234,4.116)	(3.362,9.700)
KST	(0.169,1.329)	(0.331,0.918)	(0.346,1.024)	(0.154,0.572)	(0.368,1.327)	(0.417,2.277)	(0.115,0.524)	(0.387,2.507)	(0.256,1.188)	(0.566,1.113)	(0.078,0.524)	(0.402,2.134)	(0.086,0.689)	(0.303,2.606)	(0.430,4.668)	(0.171,1.834)	(0.465,4.570)
Zyd.E	(0.402,1.170)	(0.931,2.351)	(0.859,1.872)	(0.623,2.576)	(1.032,5.400)	(0.503,1.176)	(0.466,1.481)	(0.491,1.295)	(0.977,4.834)	(2.296,4.781)	(0.402,2.134)	(0.402,1.100)	(0.402,1.195)	(0.402,3.048)	(1.145,12.106)	(0.402,1.100)	(0.589,2.086)
Zh.E	(0.159,0.553)	(0.331,0.918)	(0.346,0.898)	(0.165,0.833)	(0.303,1.563)	(0.425,1.106)	(0.123,0.435)	(0.363,1.027)	(0.259,1.399)	(0.608,1.546)	(0.086,0.689)	(0.402,1.195)	(0.079,0.303)	(0.276,2.606)	(0.402,4.231)	(0.160,0.879)	(0.436,2.238)
TET	(0.202,2.606)	(0.865,2.606)	(0.798,2.606)	(0.578,2.999)	(0.757,4.616)	(0.425,3.227)	(0.433,2.606)	(0.215,2.889)	(0.907,4.132)	(2.132,4.629)	(0.303,2.606)	(0.402,3.048)	(0.276,2.606)	(0.129,2.606)	(0.884,10.348)	(0.129,2.749)	(0.286,6.529)
SE	(0.549,6.409)	(0.494,4.003)	(0.486,5.809)	(0.402,4.003)	(0.402,4.003)	(1.193,12.816)	(0.402,4.003)	(1.163,11.026)	(0.472,4.003)	(0.990,4.244)	(0.430,4.668)	(1.145,12.106)	(0.402,4.231)	(0.884,10.348)	(0.402,4.003)	(0.515,10.178)	(2.001,25.928)
HIT	(0.102,0.842)	(0.501,1.977)	(0.462,1.574)	(0.335,2.218)	(0.464,4.540)	(0.417,0.876)	(0.251,1.245)	(0.172,0.484)	(0.525,4.064)	(1.234,4.116)	(0.171,1.834)	(0.402,1.100)	(0.160,0.879)	(0.129,2.749)	(0.515,10.178)	(0.037,0.403)	(0.132,1.026)
HE	(0.278,2.145)	(1.364,5.036)	(1.258,4.010)	(0.912,4.884)	(1.803,11.565)	(0.488,1.772)	(0.682,3.171)	(0.213,0.746)	(1.431,10.353)	(3.362,9.700)	(0.465,4.570)	(0.589,2.086)	(0.436,2.238)	(0.286,6.529)	(2.001,25.928)	(0.132,1.026)	(0.132,0.746)

Table 4

Optimal partners in two perspectives for the first group of companies.

Co.	Pessimistic Perspective Optimal Partner	Optimistic Perspective Optimal Partner
ST(0.101,0.530)	CE(1.226,2.983)	SE(0.549,6.409)
KE(0.331,0.918)	HE(1.364,5.036)	HE(1.364,5.036)
SOJO(0.346,0.898)	HE(1.258,4.010)	SE(0.486,5.809)
CSG(0.113,0.456)	HE(0.912,4.884)	HE(0.912,4.884)
INVT(0.112,0.623)	HE(1.803,11.565)	HE(1.803,11.565)
Cp.(0.417,0.876)	CE(2.875,5.109)	SE(1.193,12.816)
DE(0.051,0.174)	HE(0.682,3.171)	SE(0.402,4.003)
AEPP(0.171,0.396)	CE(2.803,5.627)	SE(1.163,11.026)
Wm.(0.123,0.567)	HE(1.431,10.353)	HE(1.431,10.353)
CE(0.314,0.782)	HE(3.362,9.700)	HE(3.362,9.700)
KST(0.078,0.524)	CE(0.566,1.113)	SE(0.430,4.668)
Zyd.E(0.402,1.100)	CE(2.296,4.781)	SE(1.145,12.106)
Zh.E(0.079,0.303)	CE(0.608,1.546)	SE(0.402,4.231)
TET(0.129,2.606)	CE(2.132,4.629)	SE(0.884,10.348)
SE(0.402,4.003)	HE(2.001,25.928)	HE(2.001,25.928)
HIT(0.037,0.403)	CE(1.234,4.116)	SE(0.515,10.178)
HE(0.132,0.746)	CE(3.362,9.700)	SE(2.001,25.928)

Table 5

First category companies’ strongest competitors in two perspectives.

Co.	Pessimistic Perspective Optimal Competitor	Optimistic Perspective Optimal Competitor	Pessimistic Perspective of the Strongest Competitor	Optimistic Perspective of the Strongest Competitor
ST(0.101,0.530)	Cp.(0.101,0.438)	TET(0.058,0.53)	Wm.(0.017,0.117)	DE(0.021,0.108)
KE(0.331,0.918)	null	null	HE(0.015,0.099)	HE(0.015,0.099)
SOJO(0.346,0.898)	null	TET(0.069,0.898)	HIT(0.028,0.23)	HE(0.032,0.167)
CSG(0.113,0.456)	SE(0.113,0.36)	null	HE(0.009,0.059)	HE(0.009,0.059)
INVT(0.112,0.623)	SE(0.112,0.623)	SE(0.112,0.623)	HE(0.008,0.04)	HE(0.008,0.04)
Cp.(0.417,0.876)	null	null	HE(0.014,0.369)	Wm.(0.029,0.097)
DE(0.051,0.174)	SE(0.051,0.174)	SE(0.051,0.174)	HIT(0.008,0.051)	HE(0.008,0.037)
AEPP(0.171,0.396)	Cp.(0.171,0.36)	null	Wm.(0.012,0.041)	Wm.(0.012,0.041)
Wm.(0.123,0.567)	null	SE(0.126,0.567)	HIT(0.011,0.056)	AEPP, HE(0.012,0.041)
CE(0.314,0.782)	null	null	HIT(0.009,0.064)	HE(0.01,0.047)
KST(0.078,0.524)	Cp.(0.078,0.203)	TET(0.026,0.524)	HE(0.022,0.086)	HE(0.022,0.086)
Zyd.E(0.402,1.100)	null	null	HIT(0.035,0.293)	Wm.(0.035,0.111)
Zh.E(0.079,0.303)	SE(0.079,0.282)	TET(0.048,0.303)	HIT(0.018,0.139)	HE(0.022,0.101)
TET(0.129,2.606)	null	null	DE(0.017,0.159)	DE(0.017,0.159)
SE(0.402,4.003)	null	null	HE(0.018,0.115)	HE(0.018,0.115)
HIT(0.037,0.403)	TET(0.037,0.383)	Cp.(0.03,0.403)	DE(0.008,0.051)	DE(0.008,0.051)
HE(0.132,0.746)	null	null	DE(0.008,0.037)	DE(0.008,0.037)

Table 6

Innovation efficiency interval after the cooperation of the second category of companies.

Co.	TGOOD	EG	PE	XJ
TGOOD	(0.378,0.697)	(1.038,1.660)	(0.763,1.988)	(0.605,2.461)
EG	(1.038,1.660)	(0.191,0.764)	(1.574,2.907)	(1.094,4.809)
PE	(0.763,1.988)	(1.574,2.907)	(0.763,1.988)	(1.099,4.046)
XJ	(0.605,2.461)	(1.094,4.809)	(1.099,4.046)	(0.270,1.256)

Table 7

Innovation efficiency intervals for the second category of companies after competition.

Co.	TGOOD	EG	PE	XJ
TGOOD	(0.019,0.697)	(0.019,0.349)	(0.019,0.697)	(0.013,0.386)
EG	(0.019,0.349)	(0.053,0.764)	(0.026,0.482)	(0.013,0.2)
PE	(0.019,0.697)	(0.026,0.482)	(0.026,1.988)	(0.013,0.617)
XJ	(0.013,0.386)	(0.013,0.2)	(0.013,0.617)	(0.013,1.256)

Table 8

First category charging pile product manufacturing companies in the new game environment cooperation partner ranking.

Co.	Pessimistic Perspective Partner Ranking			Optimistic Perspective Partner Ranking
	1	2	3	1	2	3
ST(0.042,0.529)	SAIC(8.369,74.778)	GH(3.578,19.367)	SEP(1.421,19.721)	SAIC(8.369,74.778)	SEP(1.421,19.721)	GH(3.578,19.367)
KE(0.138,0.918)	SAIC(5.573,47.977)	GH(1.592,11.384)	SEP(1.008,12.653)	SAIC(5.573,47.977)	SEP(1.008,12.653)	GH(1.592,11.384)
SOJO(0.183,0.898)	SAIC(6.302,57.473)	GH(3.064,17.468)	SEP(1.128,15.157)	SAIC(6.302,57.473)	GH(3.064,17.468)	SEP(1.128,15.157)
CSG(0.055,0.456)	SAIC(2.845,21.223)	GH(0.946,6.143)	DIE(0.728,4.712)	SAIC(2.845,21.223)	SEP(0.51,8.306)	GH(0.946,6.143)
INVT(0.105,0.62)	SAIC(2.157,16.576)	DIE(1.672,9.341)	HE(1.645,11.406)	SAIC(2.157,16.576)	HE(1.645,11.406)	DIE(1.672,9.341)
Cp.(0.083,0.874)	SAIC(19.616,128.097)	GH(7.575,38.55)	SEP(3.332,33.782)	SAIC(19.616,128.097)	GH(7.575,38.55)	SEP(3.332,33.782)
DE(0.028,0.174)	SAIC(1.7,15.544)	GH(0.751,4.088)	DIE(0.477,2.578)	SAIC(1.7,15.544)	SEP(0.297,8.306)	GH(0.751,4.088)
AEPP(0.053,0.396)	SAIC(19.124,141.066)	GH(5.946,33.472)	SEP(3.248,37.202)	SAIC(19.124,141.066)	SEP(3.248,37.202)	GH(5.946,33.472)
Wm.(0.109,0.56)	SAIC(1.648,12.116)	DIE(1.275,8.385)	HE(1.244,10.24)	SAIC(1.648,12.116)	HE(1.244,10.24)	DIE(1.275,8.385)
CE(0.117,0.782)	SAIC(2.143,19.6)	DIE(1.558,8.745)	HE(1.519,9.677)	SAIC(2.143,19.6)	HE(1.519,9.677)	DIE(1.558,8.745)
KST(0.028,0.523)	SAIC(3.865,26.616)	GH(2.392,8.918)	SEP(0.656,8.306)	SAIC(3.865,26.616)	GH(2.392,8.918)	SEP(0.656,8.306)
Zyd.E(0.097,1.073)	SAIC(15.666,119.868)	GH(7.334,36.404)	SEP(2.661,31.612)	SAIC(15.666,119.868)	GH(7.334,36.404)	SEP(2.661,31.612)
Zh.E(0.049,0.302)	SAIC(4.149,38.751)	GH(2.086,10.574)	SEP(0.712,10.332)	SAIC(4.149,38.751)	GH(2.086,10.574)	SEP(0.712,10.332)
TET(0.117,2.591)	SAIC(14.548,116.05)	GH(5.555,31.235)	SEP(2.497,30.605)	SAIC(14.548,116.05)	GH(5.555,31.235)	SEP(2.497,30.605)
SE(0.282,4.003)	SAIC(6.614,55.886)	GH(1.916,13.261)	DIE(1.856,20.943)	SAIC(6.614,55.886)	HE(1.825,25.572)	DIE(1.856,20.943)
HIT(0.028,0.399)	SAIC(8.422,103.2)	GH(3.401,30.631)	SEP(1.43,27.216)	SAIC(8.422,103.2)	GH(3.401,30.631)	SEP(1.43,27.216)
HE(0.12,0.736)	SAIC(22.944,227.273)	GH(12.494,77.65)	SEP(3.897,59.937)	SAIC(22.944,227.273)	GH(12.494,77.65)	SEP(3.897,59.937)

Table 9

Competitors of the first category charging pile product manufacturing companies in the new gaming environment.

Co.	Pessimistic Perspective Optimal Competitor	Optimistic Perspective Optimal Competitor	Pessimistic Perspective Strong Competitors Ranking			Pessimistic Perspective Strong Competitors Ranking
			1	2	3	1	2	3
ST(0.042,0.529)	null	null	DE(0.009,0.107)	INVT(0.01,0.115)	CE(0.011,0.135)	DE(0.009,0.107)	INVT(0.01,0.115)	Wm.(0.012,0.116)
KE(0.138,0.918)	null	null	HE(0.014,0.099)	HIT(0.014,0.136)	AEPP(0.015,0.135)	HE(0.014,0.099)	AEPP(0.015,0.135)	HIT(0.014,0.136)
SOJO(0.183,0.898)	null	null	AEPP(0.02,0.167)	HIT(0.025,0.228)	Cp.(0.027,0.396)	HE(0.03,0.166)	AEPP(0.02,0.167)	DE(0.028,0.174)
CSG(0.055,0.456)	KE(0.055,0.384)	null	AEPP(0.008,0.06)	HIT(0.008,0.08)	HE(0.009,0.058)	HE(0.009,0.058)	AEPP(0.008,0.06)	HIT(0.008,0.08)
INVT(0.105,0.62)	SE(0.105,0.62)	SE(0.105,0.62)	AEPP(0.006,0.047)	HIT(0.007,0.055)	HE(0.008,0.04)	HE(0.008,0.04)	AEPP(0.006,0.047)	HIT(0.007,0.055)
Cp.(0.083,0.874)	null	null	Wm.(0.006,0.096)	DE(0.007,0.1)	INVT(0.009,0.107)	Wm.(0.006,0.096)	DE(0.007,0.1)	INVT(0.009,0.107)
DE(0.028,0.174)	SE(0.028,0.174)	SE(0.028,0.174)	AEPP(0.005,0.044)	HIT(0.006,0.051)	HE(0.007,0.037)	HE(0.007,0.037)	AEPP(0.005,0.044)	HIT(0.006,0.051)
AEPP(0.053,0.396)	null	null	DE(0.005,0.044)	INVT(0.006,0.047)	CE(0.006,0.055)	Wm.(0.007,0.041)	DE(0.005,0.044)	INVT(0.006,0.047)
Wm.(0.109,0.56)	SE(0.109,0.558)	null	Cp.(0.006,0.096)	AEPP(0.007,0.041)	HIT(0.009,0.055)	HE(0.011,0.04)	AEPP(0.007,0.041)	HIT(0.009,0.055)
CE(0.117,0.782)	null	null	AEPP(0.006,0.055)	HIT(0.008,0.064)	HE(0.009,0.047)	HE(0.009,0.047)	AEPP(0.006,0.055)	HIT(0.008,0.064)
KST(0.028,0.523)	SOJO(0.028,0.458)	null	DE(0.01,0.174)	INVT(0.012,0.246)	Wm.(0.012,0.249)	AEPP(0.015,0.085)	HE(0.019,0.085)	HIT(0.019,0.116)
Zyd.E(0.097,1.073)	null	null	DE(0.011,0.117)	Wm.(0.012,0.108)	INVT(0.013,0.124)	Wm.(0.012,0.108)	DE(0.011,0.117)	INVT(0.013,0.124)
Zh.E(0.049,0.302)	SOJO(0.049,0.302)	SOJO(0.049,0.302)	AEPP(0.013,0.109)	Cp.(0.015,0.242)	HIT(0.017,0.138)	HE(0.02,0.1)	AEPP(0.013,0.109)	INVT(0.019,0.121)
TET(0.117,2.591)	null	null	DE(0.016,0.158)	INVT(0.017,0.35)	CE(0.02,0.408)	DE(0.016,0.158)	HE(0.053,0.296)	Zh.E(0.044,0.301)
SE(0.282,4.003)	null	null	HIT(0.017,0.158)	HE(0.018,0.115)	AEPP(0.018,0.157)	HE(0.018,0.115)	AEPP(0.018,0.157)	HIT(0.017,0.158)
HIT(0.028,0.399)	TET(0.028,0.38)	Cp.(0.009,0.399)	DE(0.006,0.051)	INVT(0.007,0.055)	CE(0.008,0.064)	DE(0.006,0.051)	INVT(0.007,0.055)	Wm.(0.009,0.055)
HE(0.12,0.736)	null	null	DE(0.007,0.037)	INVT(0.008,0.04)	CE(0.009,0.047)	DE(0.007,0.037)	INVT(0.008,0.04)	Wm.(0.011,0.04)

Table 10

Partner ranking of the second category companies in the new gaming environment.

Co.	Pessimistic Perspective Partner Ranking			Optimistic Perspective Partner Ranking
	1	2	3	1	2	3
TGOOD(0.086,0.685)	SAIC(1.583,12.996)	DIE(1.363,11.551)	AST(0.922,5.878)	SAIC(1.583,12.996)	DIE(1.363,11.551)	SEP(0.279,8.306)
EG(0.124,0.759)	SAIC(4.349,30.945)	GH(1.915,8.65)	XJ(1.037,4.78)	SAIC(4.349,30.945)	GH(1.915,8.65)	SEP(0.787,8.417)
PE(0.557,1.983)	DIE(4.064,20.599)	AST(3.316,10.605)	SAIC(2.699,19.608)	DIE(4.064,20.599)	SAIC(2.699,19.608)	AST(3.316,10.605)
XJ(0.26,1.248)	DIE(6.827,35.135)	AST(5.646,21.307)	JG(3.637,16.413)	DIE(6.827,35.135)	AST(5.646,21.307)	JG(3.637,16.413)

Table 11

Competitors of the second category charging pile product manufacturing companies in the new gaming environment.

Co.	Pessimistic Perspective Optimal Competitor	Optimistic Perspective Optimal Competitor	Pessimistic Perspective of The Strongest Competitor	Optimistic Perspective of The Strongest Competitor
TGOOD(0.086,0.685)	PE(0.086,0.685)	PE(0.086,0.685)	EG(0.046,0.345)	EG(0.046,0.345)
EG(0.124,0.759)	null	null	XJ(0.031,0.198)	XJ(0.031,0.198)
PE(0.557,1.983)	null	null	TGOOD(0.086,0.685)	EG(0.096,0.475)
XJ(0.26,1.248)	null	null	EG(0.031,0.198)	EG(0.031,0.198)

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