The Transformation Efficiency and Influencing Factors of High-Tech Enterprises’ Technological Achievements

Considering the role of science and technology intermediary institutions in the transformation of technological achievements, this paper studies the transformation efficiency and influencing factors of high-tech enterprises’ technological achievements in various provinces of China. Firstly, the DEA-BCC model is used to analyze the transformation efficiency of technological achievements of high-tech enterprises in 27 provinces of China. Then, the T test tests the significance of the factors affecting the efficiency of technological achievements. The study found that the transformation efficiency of high-tech industrial technology achievements in 5 provinces including Beijing at the frontier of efficiency; the main factors affecting the transformation efficiency of high-tech enterprises in different provinces are not the same, but the science and technology intermediary institution is the key factors affecting the efficiency. Comprehensively, the cost of technological transformation and the impact of new product development costs on the efficiency of technological achievements transformation are not significant.


Introduction
With innovation-driving national background, Chinese enterprises have shifted from extensive growth led by expansion to sustainable development led by quality and efficiency. The key to this change lies in the improvement of the company's inherent technological innovation capabilities. High-tech enterprises, tech-nology and knowledge intensive industries, are the main carriers and typical enterprises of technological innovation, and their development will become a strategic center in the next stage of practice [1]. According to data from the National Bureau Statistics, in 2016, the main business income of high-tech enterprises nationwide was 15.4 trillion CNY, and the total profit reached 10.3 trillion CNY. At the same time, the state and enterprises have invested a lot of resources to promote the development of high-tech enterprises. In fact, if you increase the input and neglect the improvement of the innovation efficiency of the transformation of technological achievements, it will lead to enormous waste of scientific research results and the ineffectiveness of scientific research inputs into economic output [2]. Therefore, in the high-speed development stage of high-tech enterprises' technology research and development, the input-output efficiency of factors in the transformation of technological achievements and the optimization of resource allocation have also become important issues in the academic community.
The technological achievements are transformed into an important part of technological innovation. The research in China's academic area is mainly focused on the technological innovation process, and the research is mainly divided into three stages. In the first stage, scholars regarded the process of technological innovation as a "Technical Black Box", and directly evaluated the efficiency of technological innovation on the input and output of the "Black Box". Liu and Guan [3], Guo and Zhang [4] used DEA to evaluate the innovation performance of various regions in China. Wu [5] constructed a knowledge production function to study the impact of factors such as R & D capital stock on the efficiency of knowledge production, and innovatively proposed the concept of R & D capital stock. Based on the perspective of efficiency, Bai Junhong [6] used the stochastic frontier model to study the efficiency of government R & D funding and other input factors, finding R & D funding of government is remarkably positive to technical innovation efficiency. In the second stage, scholars further dug out the "Black Box" of technological innovation, and divided the process of technological innovation into two stages: technological research and development and technological transformation, and studied the performance of China's technological innovation by stages. Liu and Lee [7] used a three-stage DEA model to study the innovation efficiency of China's high-tech districts in 2012. Guan [8], Han [9], Yu [10], and Zheng [11] evaluated the innovation performance of China based on two-stage DEA. In the third stage, scholars used the stochastic frontier model and DEA to analyze the technological innovation efficiency of each subject in China, and at the same time analyzed the factors affecting efficiency from different angles [12] [13] [14] [15] [16]. However, the previous research mainly focused on the technology research and development stage, and lacked in-depth research on the technological achievement transformation stage.
Based on above, this study considers the role of technology intermediaries in the transformation of technological achievements, uses the DEA-BCC model to evaluate the efficiency of the technological transformation process of Chinese high-tech enterprises, and then combines T-test to analyze the factors that affect the transformation efficiency of technological achievements, providing a reference for enterprises to improve the efficiency of the transformation of technological achievements, and also providing a basis for the country to formulate policies for the development of technology markets and optimize resource allocation.
The paper is organized as follows. After the introduction, we introduce the methodology the paper use, which includes data envelopment analysis and T-test. Then describes the data collection method and explains the measurement we choose based on the previous research, show the summary statistics as well to verify validity of samples. Next we do data analysis to explore the efficient status of 27 provinces and select inefficient provinces to do T-test so that digs out the reason why these provinces are not efficient enough. Finally, the article concludes 4 main findings, corresponding practical implications and future research the word has opened up.

Research Framework
The research in this paper is divided into two parts. The first part uses DEA to evaluate and analyze the technological transformation efficiency of 27 provinces in the country according to the input and output indicators. The second part uses the paired sample T test to extract the internal key indicators that affect the efficiency of technological achievement transformation, analyzes the influencing factors of technological achievement transformation efficiency of high-tech enterprises in different provinces, and proposes corresponding efficiency improvement paths.  [20], which is a non-parametric efficiency estimation method for "departments" or "units" (DMU) with multiple inputs, used as determine whether the DMU is located on the production frontier of the production possible set [21] [22] [23] [24] [25]. Commonly used DEA models are CCR and BCC models, which are used to deal with the efficiency evaluation problems under "constant returns to scale (CRS)" and "variable returns to scale (VRS)", respectively. Because the transformation of technological achievements has the characteristics of knowledge economy, it has caused the variability of scale returns of high-tech enterprises in different provinces, so this paper uses the BCC model. The model assumes that there are n decision-making units, the decision-making units have input data m and output data s, and the scale return of the first decision-making unit k depends on the parameters 0 µ , the efficiency model is,

Research Method
Modeling into dual form and adding slack variables S − , S + , then the dual model is: In addition, BCC model and CCR model together yield technical efficiency (TE), scale efficiency (SE), and pure technical efficiency (PTE) [26]. TE (comprehensive efficiency under the condition of constant returns to scale) represents the ability to achieve the maximum output under a given input or the minimum input under a given output. SE represents the extent to which economies of scale are exerted compared to scale effective units. PTE (Pure technical efficiency obtained with variable returns to scale) expresses the efficiency of eliminating scale factors. Relationship among them, TE = SE × PTE. TRS is the indicator judged production scale returns,

2) T-Test
T test is also called Student-T test, which suits for a normal distribution with a small sample size and an unknown overall standard deviation σ . In this paper, the paired sample T test is used to determine whether there is a significant dif-Open Journal of Business and Management ference in the mean of the paired sample populations from which the paired samples come. Employing the notation 0 1 , X X are samples, S is the standard deviation of 0 1 X X − , then the statistic Since the statistic t follows a distribution with a degree of freedom of 1 n − , it can be judged according to the value of the t statistic and the corresponding value 0 t in the t statistical distribution table. If 0 t t > , then reject the null hypothesis and consider that there is a significant difference between the two populations; if 0 t t < then accept the null hypothesis, consider that there is no significant difference between the two populations. In this paper, the conversion efficiency value of the technical results obtained by the model with the index removed is the control group ( 0 X ), and the efficiency value obtained by the model with the index removed is the experimental group ( 1 X ). The paper constructs T statistics and judge whether there is a significant difference in the pairing group. If there is no significant difference, the elimination index is not a key indicator that affects the conversion efficiency of technical results. If there is a significant difference, this index is a key indicator that affects the conversion efficiency of technical results.

Data Collection
The data in this article comes from the 2012-2016 Statistical Yearbook of China's High-Tech Industry and the National Technology Market Statistics Annual Report issued by the National Bureau of Statistics and the Torch High-Tech Industry Development Center of the Ministry of Science and Technology. Because the statistical data of the four provinces of Qinghai, Hainan, Inner Mongolia, and Tibet are missing in the data statistics, this article only selects 27 high-tech enterprises in the country.

Measures
Based on the references and the National Innovation Enterprise Report [27]- [32], combined with the characteristics of high-tech industries, the reliability, availability of data and the requirements of the DEA model comprehensively. The paper selects 6 input indicators and an output indicator from 4 aspects including technology, capital, labour and technology intermediary service agency, as shown in Table 1. The state of innovation investment, reflecting the orientation of corporate resource allocation, is an important indicator of corporate innovation awareness [33] [34]. The input indicators selected in this paper includes the number of domestic patent application grants, the number of valid invention patents, full-time equivalent of R & D personnel by performance, new product development expense and technical transformation expense.
Since the time lag in the transformation of technical results is one year [13] [35] [36], the input indicators for this article start from 2012, and the output in-Open Journal of Business and Management dicators start from 2013. In addition, the prerequisite for the use of DEA is that the number of DMUs should be at least two times the number of variables to ensure that the estimated efficiency value of the model is close to the true efficiency value [37]. The number of DMUs in this paper exceeds three times the number of variables. Therefore, the estimation result is reliable. The descriptive statistics of the original data in this paper are shown in Table 2.

Correlation Analysis
First, the input-output index was analyzed by correlation coefficients, and the results are shown in Table 3. It can be seen from Table 3 that each index is significantly related and all passed the 1% significance level. Therefore, the index selected in this paper can be used for DEA in the stage of technical achievement transformation.

DEA Efficiency Analysis of Technical Transformation
The paper uses DEAP 2.1 to analyze the technological transformation efficiency of high-tech enterprises in 27 provinces from 2012 to 2015, the results are shown in Table 4.    According to Table 4 Besides, the transformation efficiency of Chongqing's technological achievements is in a state of rapid growth.  Table 5.

T-test of Key Influence Factors to Technical Transformation
The 6 models are further paired with TE0 in order to perform a sample T test.
If the model corresponding to an indicator is excluded and the test result is significant, the indicator is a key indicator that affects the efficiency of technical achievement conversion. Otherwise, it is a non-key indicator. As shown in Table 6. In the 6 pairs of samples T test, pair 1 (p = 0.005), pair 3 (p = 0.001) were significant at the 1% significance level, and group 2 (p = 0.050) was at the 5% level. Group 4 (p = 0.077) is significant at a significance level of 10%, but not significant for Group 5 and Group 6, namely new product sales revenue, domestic patent application authorizations, number of valid invention patents, R & D institutions the full-time equivalent of personnel and the number of national technology    Table 6 and Table 7, the paper will analyze the reasons for the low efficiency in the transformation of technological achievements at the current stage except 3 provinces that are effective next.
According to Table 7  inefficient invention patent technology redundancy that leads to inefficient transformation of technological achievements. This is due to the emphasis on the input-output performance of the technology research and development stage in technology innovation, and the technology is ignored after the technology patent is authorized, resulting in the accumulation of effective invention patents, but cannot be applied to new products.
Second, the role of R & D personnel has not been brought into full play. For Hebei, Shanxi, Heilongjiang, one of the main reason for the low technical efficiency of high-tech enterprises is that the size of R & D personnel is too large.
The rate of underutilization of R & D personnel in Shanxi is as high as 37.92%.
The main reason may be that the definition of R & D personnel is ambiguous, including scientific researchers and technical achievement transformation per-sonnel, which work together in the entire process of technological innovation, rather than precise in the process of technological achievement transformation. Besides, there is a large demand for talents who are familiar with the high-end of technological transformation in high-tech enterprises, but the imbalance between the supply and demand of such talents in the market causes the supply of talents in the market to be unable to meet the needs, leading to mismatches in academic qualifications, talent structure, technical capabilities, and positions [38] [39]. Finally, insufficient incentive mechanism for technical talents and their insufficient incentives also lead to low motivation.
Third, the effective activity of technology intermediary service agencies is rel-

Main Findings
Based on the DEA and paired sample T test, this paper evaluates the technological transformation efficiency of 27 provinces in China, analyzes the factors that affect the efficiency, and draws the following conclusions: First of all, although China has made some improvements in the efficiency of technology research and development in recent years, the research and development achievements have been remarkable, but the efficiency of technology achievement transformation in our country is still relatively low.

Implication
Based on the above conclusions, this article makes the following suggestions.

Limitation and Future Research
The study has certain limitations below. 1) Indicators of intermediary are not comprehensive enough. Because intermediary is a new indicator of State Statistics Bureau since 2016, and the institution constantly improves accuracy of the indicators so the measurement of the intermediary is under further exploration.
2) Based on the results of DEA, the article did a t-test to decide key factors of technological transformation efficiency, while T-teat can only research whether an indicator is a key factor and the degree of the indicator influencing the efficiency, which is not deep enough.
3) The article studies the efficiency of the Chinese technological achievement transformation and finds there is a huge gap between the development of technological innovation and transformation, which improve the need to do further and systematic research of the market.
According to the limitation and the results of the article, we shall also continue to do a systematic and further research, which combines the two stages of technological innovation, and accurate improving path on the basis of different innovative status.

Conflicts of Interest
The author declares no conflicts of interest regarding the publication of this paper.