3.3.1. Construction of Absorptive Capacity
Based on the existing literature, this paper adjusted the seven-dimensional index evaluation system of absorptive capacity as follows:
R & D capabilities. The carrier of knowledge generation is people. The research and development capabilities are the core of new knowledge generation. In addition, they are closely related to the ability of companies in the region to digest and apply existing knowledge. In other words, improving R & D capabilities will help the region acquire external knowledge and increase positive externalities.
Economic level. This reflects the prosperity of a region. For most people, wages and benefits are among the important considerations in career choice and planning. Generally, economically developed regions (such as Beijing, Shanghai, and Shenzhen) offer higher wages and more benefits and employment opportunities. Therefore, the economic level is one of the key factors for encouraging innovative talents; it is also an important component of absorbing capacity.
Human capital. This directly affects a region’s ability to imitate and absorb new knowledge, especially tacit knowledge. It is one of the important channels for knowledge spillovers. Abundant human capital can enhance the frequency and efficiency of knowledge exchange. It can expand the breadth and depth of knowledge exchange, and it is also an integral part of absorptive capacity.
Openness. This mainly refers to the degree of opening to the outside world, that is, the degree of interaction between a region and foreign enterprises. It is related to the ability of enterprises to learn advanced foreign knowledge. Trading with excellent foreign manufacturers is more conducive to accumulating experience for regional companies. In this way, companies can absorb advanced knowledge and technology, and learn from each other. Therefore, the degree of openness is one of the important dimensions of absorptive capacity.
Infrastructure. Economically developed regions will attract more talent. Similarly, a beautiful environment and sound infrastructure are also important considerations when choosing a career. In particular, when a person’s salary rises to a certain level, his or her requirements for the quality of life will obviously increase. In reality, such high-wage people are often the main force of innovation. Therefore, infrastructure construction is of great significance to innovation and is an important part of absorptive capacity.
Way of spreading. Early research shows that the spillover effect of knowledge is limited by geographic distance. The farther apart two places are, the more difficult it is for them to communicate knowledge. Rich and perfect transmission channels can greatly weaken the constraints of geographical distance. They can enhance the effective range of spillover effects and promote the absorption of external knowledge in various regions. Therefore, the transmission route is an important component of absorptive capacity.
Government governance. Wang and Hu found that the venture capital investment of state-owned enterprises to promote innovation is significantly weaker than that of non-state-owned ones [
41]. In China, we found that government R & D investment is negatively related to the technology market transaction value. There are “offside,” “absence,” and “dislocation” in government support measures. That is to say, it is difficult for the government’s innovation investment to effectively flow into small and medium-sized enterprises, which lack sufficient funds. In a freely competitive market, funds will flow automatically toward companies with investment value under the guidance of “invisible acceptance.” This promotes exchanges between enterprises and enhances their innovation capabilities. Therefore, this study treats government governance as part of absorptive capacity.
3.3.2. Description of Secondary Indicators of Absorptive Capacity
Based on the seven dimensions of the absorptive capacity index evaluation system, this paper selects 14 representative secondary indicators from the
China Statistical Yearbook, China Statistical Yearbook on Science and Technology, Provincial Statistical Yearbook, etc., as shown in
Table 1. They are as follows:
Dimensions of R & D intensity, using traditional indicators, namely R & D input intensity (unit: %) and scientific and technological expenditure input intensity (unit: %). These are calculated by dividing the regional R & D expenditure and the general public budget expenditure of science and technology by the regional GDP.
The economic level is measured by the two most direct and generally accepted indicators: regional GDP (unit: billion yuan) and per capita GDP (unit: yuan). Regional GDP measures the overall economic level of the region. GDP per capita includes regional size and general economic level.
Human capital dimension, measured by R & D personnel full-time equivalent (unit: person-year) and average education level (unit: year/person). The R & D personnel’s full-time equivalent is obtained from the existing data in the China Science and Technology Statistics Yearbook. This study calculates the average education level according to the current Chinese academic year system. The specific calculation formula is as follows: Average education level = (proportion of junior college or above * 16 + proportion of high school * 12 + proportion of junior high school * 9 + proportion of primary school * 6)/number of persons over 6 years of age.
The degree of openness is measured by the proportion of total imports in GDP (unit: %) and the proportion of high-tech product exports in GDP (unit: %). Imported goods and high-tech products often have advantages that are worth considering. Through long-term contact, companies will subtly improve their own understanding of the product and then enhance their absorption capacity.
In terms of infrastructure, Zhao and Bi found that high-speed rail can promote labor mobility. This in turn promotes knowledge spillover effects [
42]. Therefore, we use the road density (unit: km/km
2) and railway density (unit: km/km
2) to reflect the level of regional transportation facilities. Moreover, we use the per capita power generation (kWh/person or degree/person) to reflect the quality of life of people in the region. The two density indicators are the corresponding mileage and the proportion of the land area. The per capita power generation is calculated by dividing the regional power generation by the total population at the end of the year.
Dimensions of transmission channels. These are measured by the mobile phone penetration rate (unit: %) and Internet penetration rate (unit: %). Mobile phones and the Internet have facilitated exchanges between innovation subjects. Both are calculated by dividing the corresponding number of local users by the total population at the end of the year.
The dimension of government governance is measured by the denationalization rate (unit: %), that is, the denationalization rate = 1 − total industrial output value of state-owned enterprises/industrial output value of enterprises above a designated size.
3.3.3. Entropy Weight Method and Weight Determination Method
“Entropy” is derived from thermodynamics, indicating the uncertainty (degree of dispersion) of things. The greater the degree of dispersion of each secondary indicator in the indicator system, the greater the amount of effective information it contains, and the higher the weight. The entropy method has the advantages of alleviating the consistency of indicators, giving weight objectively, and reducing the overlap of multi-index information. Therefore, this paper uses the entropy method to give weight to each secondary index of absorption capacity. The basic principle of the entropy method is as follows:
First, we establish an index system matrix. Assuming that there are m samples in each of k periods, and the indicator system consists of n secondary indicators, the indicator system matrix is:
In Equation (8), the factor of the sample i in the t-th period is expressed. In order to eliminate the influence of the dimensions between the indicators, matrix At is standardized, and the standardized matrix of each indicator is obtained.
Secondly, due to the dimensional problem in
, this paper uses the “0‒1 normalization method” to process the data. Considering that the “0‒1 normalization method” will lead to 0 in the standardized data and each secondary index is a positive indicator, we use an improved positive “0‒1 normalization method.” Let the normalized matrix be
and the coefficient U be 1 to obtain the result before the improvement method. The constant D is 1 to solve the situation where the value is 0. The specific formula is:
After normalizing the statistical values, the information entropy can be calculated:
where
.
Finally, the weight of each indicator is calculated by the information entropy of each indicator. The formula is as follows: