Determinants of Financial Performance in China’s Intelligent Manufacturing Industry: Innovation and Liquidity
Abstract
:1. Introduction
2. Theoretical Background
2.1. The Fourth Industrial Revolution and China’s Intelligent Manufacturing
2.2. Literature Review
3. Research Model and Analysis Methods
3.1. Methodology of SEM
3.2. Research Model and Hypotheses
3.3. Data Collection and Survey Overview
4. Results of Empirical Analysis
4.1. General Characteristics of the Samples
4.2. Analysis of Measurement Model
4.2.1. Reliability Analysis
4.2.2. Validity Analysis
4.3. Analysis of Structural Model
4.3.1. Structural Model Fit
4.3.2. Verification of Hypotheses
4.4. Confirmation of Mediating Effects
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No. | Hypotheses | |
---|---|---|
H1-1 | Urban economic development has a significant and positive impact on an enterprise’s internal R&D investment. | |
H1 | H1-2 | Government’s support policy has a significant and positive impact on an enterprise’s internal R&D investment. |
H1-3 | Regional innovation capability has a significant and positive impact on an enterprise’s internal R&D investment. | |
H4 | There is a significant positive relationship between an enterprise’s internal R&D investment and the enterprise’s liquidity. | |
H5 | There is a significant positive relationship between an enterprise’s internal R&D investment and the enterprise’s financial performance. | |
H6 | There is a significant positive relationship between an enterprise’s liquidity and the enterprise’s financial performance. |
Types | Variables | Items |
---|---|---|
Exogenous Variables | Economic Development | City’s GRDP |
Urbanization Rate | ||
Government’s Support Policy | Government Subsidies for Enterprise R&D | |
Financial Support for Science Park Constructions | ||
Regional Innovation Capability | Human and Resource Environment Index of Regional Innovation | |
Institutional Service Index of Regional Innovation | ||
Endogenous Variables | Internal R&D Investment | Ratio of R&D Investment to Operating Income |
High-level Scientific Research Personnel | ||
University-enterprise Cooperation Projects for Technological Innovation | ||
Enterprise’s Liquidity | Current Ratio (Working Capital Ratio) | |
Cash Ratio | ||
Owner’s Equity Ratio | ||
Financial Performance | Profit Margin Ratio | |
Return on Assets | ||
Social Contribution Rate | ||
General Characteristics | Attributes | |
Industry Classifications | ||
Location by Economic Circles | ||
Applied Technologies |
Categories | Frequency (N = 317) | Percentage (%) | |
---|---|---|---|
Attributes | Listed Enterprise | 317 | 100 |
Unlisted Enterprise | 0 | 0 | |
Industry Classifications | Machinery Manufacturing | 143 | 45.11 |
Information and Communications Technology | 92 | 29.02 | |
Biomedicine and Instruments | 20 | 6.31 | |
New Material | 34 | 10.73 | |
Transportation and Logistics | 20 | 6.31 | |
Others | 8 | 2.52 | |
Locations by Economic Circles | Bohai Rim Economic Circle | 60 | 18.93 |
Midlands Economic Circle | 45 | 14.20 | |
Yangtze River Delta Economic Circle | 116 | 36.60 | |
Pearl River Delta Economic Circle | 78 | 24.60 | |
Southwest Economic Circle | 13 | 4.10 | |
Others | 5 | 1.58 | |
Applied Technologies | Internet of Things | 214 | 25.91 |
Artificial Intelligence | 152 | 18.40 | |
3D Printing | 50 | 6.05 | |
Big Data | 310 | 37.53 | |
Robotics | 100 | 12.10 | |
Post-listing Operation Period | 1~5 years | 32 | 10.07 |
5~10 years | 217 | 68.50 | |
over 10 years | 68 | 21.43 |
Variables | Observed Variables | Factor Loading | Cronbach’s Alpha | rho_A (ρA) |
---|---|---|---|---|
Economic Development | Eco1 | 0.90 | 0.969 | 0.971 |
Eco2 | 0.96 | |||
Government’s Support Policy | Gov1 | 0.78 | 0.877 | 0.931 |
Gov2 | 0.97 | |||
Regional Innovation Capability | Tech1 | 0.96 | 0.858 | 0.930 |
Tech2 | 0.71 | |||
Internal R&D Investment | Rnd1 | 0.84 | 0.801 | 0.814 |
Rnd2 | 0.76 | |||
Rnd3 | 0.78 | |||
Enterprise’s Liquidity | Liqui1 | 0.95 | 0.850 | 0.894 |
Liqui2 | 0.82 | |||
Financial Performance | Perf1 | 0.71 | 0.820 | 0.946 |
Perf2 | 0.80 | |||
Perf3 | 0.81 |
Latent Variables | Items | Factor Loading | Std.Err | z-Value | SMC | Std.all | p-Value | CR | AVE |
---|---|---|---|---|---|---|---|---|---|
Economic Development | Eco1 | 1.000 | - | - | 0.850 | 0.922 | - | 0.975 | 0.950 |
Eco2 | 1.165 | 0.040 | 29.139 | 0.787 | 0.870 | 0.000 | |||
Government’s Support Policy | Gov1 | 1.000 | - | - | 0.692 | 0.701 | - | 0.941 | 0.895 |
Gov2 | 1.708 | 0.267 | 13.407 | 0.769 | 0.812 | 0.000 | |||
Regional Innovation Capability | Tech1 | 1.000 | - | - | 0.627 | 0.726 | - | 0.956 | 0.841 |
Tech2 | 1.045 | 0.195 | 26.521 | 0.725 | 0.869 | 0.000 | |||
Internal R&D Investment | Rnd1 | 1.000 | - | - | 0.714 | 0.905 | - | 0.827 | 0.674 |
Rnd2 | 0.885 | 0.070 | 7.624 | 0.619 | 0.746 | 0.000 | |||
Rnd3 | 0.914 | 0.057 | 6.308 | 0.703 | 0.724 | 0.000 | |||
Enterprise’s Liquidity | Liqui1 | 1.000 | - | - | 0.803 | 0.896 | - | 0.873 | 0.675 |
Liqui2 | 0.806 | 0.095 | 10.824 | 0.711 | 0.760 | 0.000 | |||
Financial Performance | Perf1 | 1.000 | - | - | 0.695 | 0.761 | - | 0.898 | 0.723 |
Perf2 | 0.762 | 0.058 | 11.858 | 0.701 | 0.713 | 0.000 | |||
Perf3 | 0.946 | 0.079 | 29.949 | 0.837 | 0.874 | 0.000 |
Goodness of Fit Index | Structural Model | Confirmation Criteria | ||
---|---|---|---|---|
Absolute Fit Measures | Overall Fitness of the Model | x2/df | 1.893 | 1.0 ≤ x2/df ≤ 2.0~3.0 |
RMSEA | 0.067 | ≤0.05~0.08 | ||
RMR | 0.045 | ≤0.05~0.08 | ||
Explanatory Power of the Model | GFI | 0.914 | ≥0.90 | |
AGFI | 0.867 | ≥0.80~0.90 | ||
Incremental Fit Measures | NFI | 0.908 | ≥0.90 | |
CFI | 0.939 | ≥0.90 | ||
IFI | 0.940 | ≥0.90 |
Hypotheses | Est. Std | S.E. | z-Value | p-Value | Results | ||
---|---|---|---|---|---|---|---|
H1-1 | Economic Development → Internal R&D Investment | 0.026 | 0.075 | 0.342 | 0.732 | Rejected | |
H1 | H1-2 | Government’s Support Policy → Internal R&D Investment | 0.375 | 0.061 | 4.641 | 0.009 | Accepted |
H1-3 | Regional Innovation Capability → Internal R&D Investment | 0.186 | 0.064 | 2.509 | 0.012 | Accepted | |
H4 | Internal R&D Investment → Enterprise’s Liquidity | 0.559 | 0.077 | 10.846 | 0.000 | Accepted | |
H5 | Internal R&D Investment → Financial Performance | −0.047 | 0.084 | −0.840 | 0.625 | Rejected | |
H6 | Enterprise’s Liquidity → Financial Performance | 0.789 | 0.090 | 18.416 | 0.000 | Accepted |
Impact Path | Mediating Effect | Path Coef. | Std. Err | t-Value |
---|---|---|---|---|
1 | ECO → RND → LIQUI | 0.010 | 0.020 | 0.340 |
2 | GOV → RND → LIQUI | 0.170 | 0.020 | 2.680 ** |
3 | TECH → RND → LIQUI | 0.150 | 0.030 | 2.230 * |
4 | ECO → RND → PERF | 0.000 | 0.010 | 0.340 |
5 | GOV → RND → PERF | 0.030 | 0.010 | 2.010 * |
6 | TECH → RND→ PERF | 0.040 | 0.020 | 1.920 |
7 | RND → LIQUI → PERF | 0.280 | 0.060 | 3.910 *** |
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Zhang, G.; Lee, Y. Determinants of Financial Performance in China’s Intelligent Manufacturing Industry: Innovation and Liquidity. Int. J. Financial Stud. 2021, 9, 15. https://doi.org/10.3390/ijfs9010015
Zhang G, Lee Y. Determinants of Financial Performance in China’s Intelligent Manufacturing Industry: Innovation and Liquidity. International Journal of Financial Studies. 2021; 9(1):15. https://doi.org/10.3390/ijfs9010015
Chicago/Turabian StyleZhang, Guanghong, and Yune Lee. 2021. "Determinants of Financial Performance in China’s Intelligent Manufacturing Industry: Innovation and Liquidity" International Journal of Financial Studies 9, no. 1: 15. https://doi.org/10.3390/ijfs9010015
APA StyleZhang, G., & Lee, Y. (2021). Determinants of Financial Performance in China’s Intelligent Manufacturing Industry: Innovation and Liquidity. International Journal of Financial Studies, 9(1), 15. https://doi.org/10.3390/ijfs9010015