Can Digital Transformation Promote Innovation Performance in Manufacturing Enterprises? The Mediating Role of R&D Capability
Abstract
:1. Introduction
2. Theoretical Review and Research Hypotheses
2.1. Digital Transformation and Enterprise Innovation Performance
2.2. Digital Transformation and R&D Capability
2.3. The Mediating Effect of R&D Capability
3. Research Design Variable Description
3.1. Model Design
- (1)
- The direct impact of digital transformation on process innovation performance and product innovation performance:
- (2)
- The direct impact of digital transformation on exploratory R&D capability and exploitative R&D capability:
- (3)
- The mediating effect of exploratory R&D capability and exploitative R&D capability:
3.2. Variable Description
- (1)
- Explanatory variable: Digital transformation degree of digital transformation of manufacturing enterprises. The annual report of an enterprise is an essential presentation of the enterprise’s development progress and strategic orientation. Therefore, this paper selects the frequency of keywords for digital transformation in annual reports of listed companies as the measurement index of this variable. Based on the research of Wu [53] and other scholars, the word frequency counting method identifies the keywords of the text of the annual report of enterprises from the 80 subdivision indicators in five aspects of artificial intelligence technology (artificial intelligence, machine learning, image understanding…), blockchain technology (digital currency, smart contracts, distributed computing…), cloud computing technology (memory computing, cloud computing, stream computing…), big data technology (big data, data mining, text mining…), and digital technology application (mobile internet, industrial internet, mobile payment…). Subsequently, the proxy indicators related to the digital transformation of enterprises are obtained by accumulating the word frequency of these keywords.
- (2)
- Intermediary variables: Exploratory R&D capability and exploitative R&D capability of manufacturing enterprises. Using Ning [54] for reference, this study selects the proportion of exploratory R&D patents and exploitative R&D patents in all patents of enterprises as the measurement indicators of the two types of R&D capabilities. According to the proposal of Sørensen and Stuart [55], Benner and Tushman [56], and Custodio et al. [57], whether a patent is an exploratory patent or an exploitative patent is mainly defined by the degree to which the patent uses existing knowledge or new knowledge. If more than 60% of the IPC4 classification numbers cited by a patent are different from the IPC4 classification numbers of existing company patents, it can be considered that the patent uses at least 60% of the new knowledge, and the patent is identified as an exploratory patent. If more than 60% of the IPC4 classification numbers cited by a patent are the same as the IPC4 classification numbers of existing company patents, it can be considered that the patent uses at least 60% of the existing knowledge, and the patent is considered to be an exploitative patent. Therefore, this study uses 60% of new knowledge as the critical value of exploratory R&D patents and exploitative R&D patents to obtain the measurement indicators of two types of R&D capabilities of each enterprise.
- (3)
- Explained variables. The explained variables in this study are the process innovation performance and product innovation performance of manufacturing enterprises. Considering that most scholars measure the innovation performance of enterprises by using the number of patent applications [58,59,60], this study selects the number of process innovation patents and product innovation patents of enterprises as the measurement indicators of the two types of innovation performance. Bena and Simintzi [61] proposed the measurement standard for enterprise process innovation patents and product innovation patents. Therefore, this paper mainly distinguishes process patents and product patents according to the patent name, which is based on the standardized language format used by enterprises when applying for patent names. For example, process innovation patents are usually described as “a method of…” or “a process of…”. In March 2013, Shenzhen Municipal Engineering Corporation of Shenzhen, China applied for a patent entitled “method for determination of coarse aggregate interstitial ratio”; in April 2019, Hunan Zhongke Shinzoom Technology Co., Ltd. of Changsha, China applied for a patent named “an elastic carbon material coating structure and coating process”. These are examples of typical process innovation patents. Compared with process innovation patents, the language formats used for the names of product innovation patents have the performance of variable language formats and a lower degree of standardization. Generally, on the basis of fixed language formats such as “a kind of … equipment” or “a kind of … device”, the name or attribute of the product will be directly used as the patent name. In October 2017, Shenzhen Municipal Engineering Corporation of Shenzhen, China applied for a patent named “a hammer-type gravel equipment”; in April 2021, Anhui Guoxing biochemistry Co., Ltd. of Maanshan, China applied for a patent entitled “a treatment device for acetaldehyde containing wastewater”; in March 2018, Shenzhen Skyworth Digital technology Co., Ltd. of Shenzhen, China applied for a patent named “a lighting control device and set-top box”, which are all typical examples of product innovation patents. Since process innovation patents are easier to judge than product innovation patents, this study first extracts the process innovation patents of Chinese manufacturing enterprises from the text information of Chinese invention and utility model patents, according to the method proposed by Bena and Molina [62]. After that, the remaining invention and utility model patents after extraction are taken as the product innovation patents of the enterprise.
- (4)
- Control variables. Referring to the practices of Chi et al. [63], the control variables chosen in this study include ownership concentration, chair–CEO duality, ownership type, debt–asset ratio, stock turnover ratio, audit opinion, and executive gender. The definitions of relevant variables are shown in Table 1.
3.3. Data Source
4. Empirical Analysis
4.1. Results of Regression Analysis
4.2. Indirect Mediation Effect Test Based on the Bootstrap Sampling Method
4.3. Sectoral Heterogeneity Analysis
4.4. Robustness Test
5. Conclusions and Implications
5.1. Results of Regression Analysis
5.2. Management Implications and Policy Recommendations
- (1)
- Manufacturing enterprises should continue to promote the digital transformation strategy. On the one hand, enterprises should focus on the in-depth integration of digital technology and enterprise manufacturing, especially processes and techniques. Enterprises can use digital technology to collect production data in real time, and strengthen data analysis and value mining. They can also rely on digital technology to achieve accurate demand prediction, equipment remote monitoring, energy consumption management, and finely manage production processes. On the other hand, enterprises should increase the construction and investment of the digital platform. Enterprises should further construct digital platforms on the basis of the current informatization construction achievements, and introduce emerging digital technologies such as artificial intelligence, big data, and cloud computing to upgrade enterprise information systems. Improving the efficiency of enterprise information collection, information mining, and information sharing will be conducive to the output of new products.
- (2)
- R&D is the source of enterprises innovation. Chinese manufacturing enterprises should accelerate the deep integration of digital technology and the R&D department, reform and enlarge the R&D capability of enterprises through digital transformation, and adjust the R&D mode of the R&D department to make it more compatible with the digital transformation strategy. This paper suggests that the digital transformation of China’s manufacturing enterprises at this stage should concentrate on improving the exploitative R&D capability of enterprises. Enterprises need to know the advantages and disadvantages of their production processes, the market positioning of the products they produce, and their current growth stage. Although digital technology can accelerate the transformation and upgrading of manufacturing enterprises, it cannot be denied that the cost of digital transformation is expensive. Enterprises should fully utilize the existing digital infrastructure and steadily advance through exploitative R&D on the basis of the current level of enterprise digitalization.
- (3)
- Enterprises should aim for balanced investment in R&D to avoid sinking into the digital paradox. Practical experience shows that the digital transformation of enterprises is a long-term and onerous task, and failures in the transformation process are inevitable [70,71]. Therefore, while taking the improvement of exploitative R&D capability as the key focus in the initial stage of digital transformation, manufacturing enterprises should also focus on continuously accumulating experience in the practice of exploratory R&D activities. Enterprises should use digital technology to expand their R&D advantages in basic research and industrial generic technology, and help enterprises achieve technological breakthroughs in key areas. This not only establishes a long-term competitive advantage for enterprises, but also makes an important contribution to China’s in-depth implementation of innovation-driven development strategy.
- (4)
- The government should formulate differentiated policies that are suitable for different types of manufacturing enterprises to carry out digital transformation. The empirical results of this paper show that high-tech manufacturing enterprises have the ability to carry out digital transformation independently, and such enterprises are often the biggest beneficiaries of digital transformation. Therefore, such enterprises should be mainly subsidized secretly, that is, the government needs to issue more precise policies to guide them to further deepen the digital transformation and avoid the waste of enterprise resources. On the other hand, for enterprises with low technology content, the road to digital transformation is often more difficult. Therefore, for such enterprises, both explicit and implicit subsidies should be applied. The government not only needs to give them more powerful and looser policy support, but also needs to directly grant special subsidies for digital transformation to enterprises that meet the conditions.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable Type | Variable Name | Variable Symbol | Variable Definition |
---|---|---|---|
Explanatory variable | Digital transformation | Dig | The natural logarithm of word frequency count of keywords related to digital transformation in the annual report of the enterprise in the current year |
Mediating variable | Exploratory R&D Capability | Explor | The proportion of patents that use at least 60% new knowledge in all patents applied for by the enterprise this year |
Exploitative R&D Capabilities | Exploi | The proportion of patents that use at least 60% existing knowledge in the patents applied for by the enterprise this year | |
Explained variable | Process innovation | Proc | The natural logarithm of the total number of process innovation patent applications of the enterprise in the current year |
Product Innovation | Prod | The natural logarithm of the total number of product innovation patent applications of the enterprise in the current year | |
Control variable | Ownership concentration | OC | Shareholding ratio of the largest shareholder of the enterprise in the current year |
Chair–CEO duality | Dual | It is 1 when the chairman and general manager of the company are concurrently held; otherwise, it is 0 | |
Ownership type | OW | The nature of the enterprise’s equity, i.e., state-owned enterprise, private enterprise, foreign investment, or other | |
Debt–asset ratio | DAR | Total liabilities at the end of the period/total assets at the end of the period | |
Stock turnover ratio | TR | The average daily turnover rate of tradable shares of the enterprise in the current year | |
Audit opinion | Audit | It is 0 when the audit department issues the standard without reservation; otherwise, it is 1 | |
Executive gender | Gender | The proportion of men in the enterprise’s management |
Type | Variable Symbol | Variable Name | Mean | Sd | Min | Max |
---|---|---|---|---|---|---|
Explanatory variable | Dig | Digital transformation | 13.14 | 23.41 | 1 | 388 |
Mediating variable | Explor | Exploratory R&D capability | 0.23 | 0.39 | 0 | 1 |
Exploi | Exploitative R&D capability | 0.58 | 0.40 | 0 | 1 | |
Explained variable | Proc | Process innovation | 33.95 | 196.21 | 0 | 4458 |
Prod | Product Innovation | 69.46 | 342.88 | 0 | 9588 | |
Control variable | OC | Ownership concentration | 0.35 | 0.15 | 0.03 | 0.89 |
Dual | Chair–CEO duality | 0.34 | 0.47 | 0 | 1 | |
DAR | Ownership type | 0.41 | 0.20 | 0.01 | 3.17 | |
TR | Debt–asset ratio | 6.72 | 5.66 | 0.00038 | 43.08 | |
Audit | Stock turnover ratio | 0.02 | 0.13 | 0 | 1 | |
Gender | Audit opinion | 0.81 | 0.11 | 0.36 | 1 |
(1) | (2) | (3) | (4) | (5) | (6) | |
---|---|---|---|---|---|---|
Proc | Prod | Explor | Exploit | Proc | Prod | |
Dig | 0.103 *** | 0.050 ** | −0.047 *** | 0.050 *** | 0.095 *** | 0.048 ** |
(5.01) | (2.37) | (−6.63) | (6.85) | (4.77) | (2.31) | |
OC | −1.146 *** | −0.738 * | 0.905 *** | −0.824 *** | −1.192 *** | −0.932 ** |
(−3.00) | (−1.93) | (7.01) | (−6.15) | (−3.18) | (−2.48) | |
DAR | 0.309 * | 0.360 ** | −0.185 *** | 0.173 *** | 0.290 * | 0.352 ** |
(1.85) | (2.12) | (−3.19) | (2.87) | (1.79) | (2.12) | |
Dual | 0.019 | 0.028 | −0.032 | 0.010 | 0.0004 | 0.008 |
(0.32) | (0.45) | (−1.58) | (0.44) | (0.01) | (0.14) | |
Gender | −0.248 | 0.380 | 0.378 *** | −0.302 *** | −0.295 | 0.293 |
(−0.81) | (1.21) | (3.53) | (−2.72) | (−0.99) | (0.96) | |
Audit | −0.390 *** | 0.081 | −0.034 | 0.043 | −0.388 *** | 0.094 |
(−3.10) | (0.59) | (−0.76) | (0.94) | (−3.19) | (0.71) | |
TR | −0.007 ** | −0.010 *** | 0.003 ** | −0.002 * | −0.008 ** | −0.011 *** |
(−1.96) | (−2.74) | (2.15) | (−1.74) | (−2.30) | (−3.15) | |
Explor | −0.164 *** | −0.175 *** | ||||
(−12.00) | (−12.24) | |||||
Exploi | 0.270 *** | 0.122 ** | ||||
(5.26) | (2.37) | |||||
Cons | 2.443 *** | 2.743 *** | −0.210 | 1.085 *** | 2.209 *** | 2.683 *** |
(5.76) | (6.28) | (−1.43) | (7.11) | (5.32) | (6.26) | |
N | 3624 | 3760 | 3963 | 3963 | 3624 | 3760 |
Type of Effect | Effect Value | Boot Standard Error | Boot CI | ||
---|---|---|---|---|---|
Upper Limit | Lower Limit | ||||
Process innovation | Direct effect | 0.189 | 0.014 | 0.161 | 0.217 |
Indirect effect (Digital transformation→Exploratory R&D→Process innovation) | 0.034 | 0.007 | 0.020 | 0.048 | |
Indirect effect (Digital transformation→Exploitative R&D→Process innovation) | 0.015 | 0.004 | 0.006 | 0.023 | |
Total indirect effect | 0.048 | 0.009 | 0.031 | 0.066 | |
Total effect | 0.237 | 0.017 | 0.204 | 0.270 | |
Product Innovation | Direct effect | 0.119 | 0.016 | 0.087 | 0.151 |
Indirect effect (Digital transformation→Exploratory R&D→Product Innovation) | 0.030 | 0.006 | 0.018 | 0.043 | |
Indirect effect (Digital transformation→Exploitative R&D→Product Innovation) | 0.009 | 0.003 | 0.005 | 0.014 | |
Total indirect effect | 0.040 | 0.007 | 0.030 | 0.053 | |
Total effect | 0.159 | 0.018 | 0.124 | 0.193 |
Textile Manufacturing Sector | Resource Processing Sector | Machinery and Equipment Manufacturing Sector | ||||
---|---|---|---|---|---|---|
(1) | (7) | (2) | (8) | (3) | (9) | |
Dig | 0.009 | −0.018 | 0.145 *** | 0.118 *** | 0.092 *** | 0.089 *** |
(0.14) | (−0.27) | (3.21) | (2.67) | (3.60) | (3.58) | |
Explor | −0.1381 ** | −0.1956 *** | −0.161 *** | |||
(−2.55) | (−6.10) | (−10.06) | ||||
Exploi | 0.4501 *** | 0.2604 ** | 0.231 *** | |||
(3.33) | (2.29) | (3.62) | ||||
Cons | 1.411 | 2.222 *** | 3.399 *** | 2.222 *** | 2.410 *** | 2.222 *** |
(1.09) | (4.30) | (2.91) | (4.30) | (4.56) | (4.30) | |
Control variable | Yes | Yes | Yes | Yes | Yes | Yes |
Direct effect | - | 0.134 | 0.222 | |||
(0.064, 0.203) | (0.192, 0.253) | |||||
Indirect effect (Dig→Explor→Proc) | - | 0.023 | 0.007 | |||
(0.0039, 0.042) | (0.003, 0.010) | |||||
Indirect effect (Dig→Exploi→Proc) | - | 0.056 | 0.019 | |||
(0.019, 0.093) | (0.002, 0.036) | |||||
Total effect | - | 0.212 | 0.248 | |||
(0.134, 0.29) | (0.213, 0.283) | |||||
N | 398 | 398 | 896 | 896 | 2612 | 2612 |
Textile Manufacturing Sector | Resource Processing Sector | Machinery and Equipment Manufacturing Sector | ||||
---|---|---|---|---|---|---|
(4) | (10) | (5) | (11) | (6) | (12) | |
Dig | 0.050 | 0.049 | 0.054 | 0.047 | 0.050 ** | 0.048 ** |
(0.56) | (0.54) | (0.94) | (0.84) | (2.08) | (2.05) | |
Exploi | 0.102 | −0.166 | 0.175 *** | |||
(0.58) | (−1.18) | (2.96) | ||||
Explor | −0.120 | −0.185 *** | −0.176 *** | |||
(−1.65) | (−4.86) | (−11.07) | ||||
Cons | 2.930 *** | 2.656 ** | 2.026 | 2.428 * | 2.720 *** | 2.605 *** |
(2.76) | (2.44) | (1.41) | (1.72) | (5.43) | (5.33) | |
Control variable | Yes | Yes | Yes | Yes | Yes | Yes |
Direct effect | - | - | 0.036 | |||
(0.005, 0.067) | ||||||
Indirect effect (Dig→Explor→Prod) | - | - | 0.002 | |||
(0.0006, 0.005) | ||||||
Indirect effect (Dig→Exploi→Prod) | - | - | 0.238 | |||
(0.010, 0.037) | ||||||
Total effect | - | - | 0.062 | |||
(0.028, 0.095) | ||||||
N | 403 | 403 | 814 | 814 | 2828 | 2828 |
(1) | (2) | |||||||
---|---|---|---|---|---|---|---|---|
Proc | Prod | Proc | Prod | Proc | Prod | Proc | Prod | |
Dig | 0.102 *** | 0.060 *** | 0.094 *** | 0.059 *** | 0.103 *** | 0.047 * | 0.098 *** | 0.046 * |
(5.04) | (2.95) | (4.76) | (2.92) | (4.27) | (1.91) | (4.17) | (1.90) | |
Explor | −0.166 *** | −0.173 *** | −0.165 *** | −0.166 *** | ||||
(−12.28) | (−12.19) | (−9.92) | (−9.40) | |||||
Exploit | 0.279 *** | 0.144 *** | 0.288 *** | 0.197 *** | ||||
(5.51) | (2.84) | (4.81) | (3.29) | |||||
Cons | 2.364 *** | 2.363 *** | 2.119 *** | 2.226 *** | 2.379 *** | 2.467 *** | 2.185 *** | 2.387 *** |
(5.59) | (5.58) | (5.14) | (5.38) | (4.20) | (4.04) | (3.95) | (4.00) | |
Control variable | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
N | 4048 | 4195 | 4048 | 4195 | 2661 | 2777 | 2661 | 2777 |
(1) | (2) | (4) | (3) | (5) | (6) | |
---|---|---|---|---|---|---|
Proc | Prod | Explor | Exploi | Proc | Prod | |
Dig | 0.256 *** | 0.674 ** | −0.086 *** | 0.103 *** | 0.293 *** | 0.791 *** |
(2.58) | (2.57) | (−2.83) | (3.10) | (2.95) | (2.75) | |
Explor | −0.140 *** | −0.183 *** | ||||
(−8.46) | (−10.35) | |||||
Exploi | 0.142 * | −0.109 | ||||
(1.79) | (−0.99) | |||||
Control variable | Yes | Yes | Yes | Yes | Yes | Yes |
LM statistic p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Cragg-Donald Wald | 144.11 | 23.27 | 165.81 | 150.54 | 138.40 | 20.81 |
F statistic | 16.38 | 16.38 | 16.38 | 16.38 | 16.38 | 16.38 |
N | 1909 | 3699 | 2084 | 2084 | 1909 | 3699 |
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Liang, S.; Li, T. Can Digital Transformation Promote Innovation Performance in Manufacturing Enterprises? The Mediating Role of R&D Capability. Sustainability 2022, 14, 10939. https://doi.org/10.3390/su141710939
Liang S, Li T. Can Digital Transformation Promote Innovation Performance in Manufacturing Enterprises? The Mediating Role of R&D Capability. Sustainability. 2022; 14(17):10939. https://doi.org/10.3390/su141710939
Chicago/Turabian StyleLiang, Shuhao, and Tingting Li. 2022. "Can Digital Transformation Promote Innovation Performance in Manufacturing Enterprises? The Mediating Role of R&D Capability" Sustainability 14, no. 17: 10939. https://doi.org/10.3390/su141710939