Deep Integration and Innovation Development in the Logistics and Manufacturing Industries and Their Performances: A Case Study of Anhui Province, China
Abstract
:1. Introduction
1.1. Research Background
1.2. Research Review
- (1)
- The current degrees of LMDIIs are still unclear, and there is a lack of assessment of their coupling levels. Therefore, building a scientific and precise evaluation index system to assess the coupling level of LMDIIs has become an important issue.
- (2)
- Analyzing the comprehensive performance of LMDIIs not only allows for an examination of its impact on the development of the two industries but also helps identify weaknesses in its effectiveness. However, this area has been the subject of few related studies.
- (1)
- Theoretical value: This study constructs an evaluation index system for LMDIIs based on its essential meaning to thereby enable its scientific measurement. Research on the comprehensive performance of LMDIIs offers a new perspective for studying the integration and innovation of the two industries.
- (2)
- Practical value: This study can assist the government and practitioners in scientifically measuring and evaluating the development level of LMDIIs, providing decision-making support for promoting high-quality economic development, upgrading the industrial structure, and formulating related policies.
2. Materials and Methods
2.1. Methodology Flow
2.2. Data on Evaluation Indicators for the LMDIIs
Subsystems | Level 1 Indicators | Secondary Indicators | Interpretation of Indicators | Sources |
---|---|---|---|---|
Logistics industry (L) | Asset investment (L1) | Investment in fixed assets | Investment in fixed assets | Reference [33] |
Number of employed persons | Number of employed persons | |||
Average wage of employed persons | Average wage of employed persons | |||
Civilian goods vehicles ownership | Civilian goods vehicles ownership | |||
Number of legal entities | Number of legal entities | |||
Process optimization (L2) | Service level | Average population served per outlet | Reference [33] | |
Transportation structure | Ratio of railroad to road freight traffic | |||
Cargo turnover | Cargo turnover | |||
Technology upgrades (L3) | Patent scale of LU | Total number of patent applications of the LU | Reference [34] | |
Logistics patent quality | Number of patent applications for inventions in the LU/ Total number of patent applications in the LU | |||
Logistics patent growth rate | Growth rate of total patent applications of the LU | |||
Organizational synergy (L4) | Logistics capital productivity | Value added for logistics/investment in fixed assets of the LU | Reference [33] | |
Logistics labor productivity | Value added for logistics/number of people working in logistics | |||
Manufacturing Industry (M) | Asset investment (M1) | Total assets of industrial enterprises above designated size | Total assets of industrial enterprises above designated size | Reference [33] |
Investment in fixed assets | Investment in fixed assets | |||
Average wage of employed persons | Average wage of employed persons | |||
Number of legal entities | Number of legal entities | |||
Process optimization (M2) | Product quality | Qualified rate of product quality | Reference [33] | |
Production costs | Cost per USD 100 of operating income in manufacturing | |||
Technology upgrade (M3) | Number of patent applications | Number of patent applications by industrial enterprises above designated size | Reference [33] | |
Number of new product development projects | Number of new product development projects | |||
Innovation input level | Manufacturing R&D expenditures/main business revenue | Reference [33] | ||
Innovation output level | Number of active invention patents/R&D expenditure | |||
Organizational synergy (M4) | Manufacturing capital productivity | Manufacturing value added/investment in fixed assets | Reference [33] | |
Manufacturing labor productivity | Manufacturing value added/number of people employed in manufacturing |
2.3. Performance Data on the LMDIIs
2.4. Data Pre-Processing
2.5. Methodology and Modeling
2.5.1. CRITIC Weighting Method
- (1)
- Solve for indicator variability. Let Sj denote the standard deviation of the jth indicator.In the CRITIC method, the standard deviation measures the fluctuation in indicator values. A larger standard deviation indicates greater variation in the indicator values, which provides more information and strengthens the evaluation of that indicator, thereby warranting a higher weight.
- (2)
- The indicator conflict is represented by the correlation coefficient. Let rij denote the correlation between evaluation indicators i and j. Then,The correlation coefficient assesses the degree of correlation between indicators. A stronger correlation with other indicators means that the indicator shares more information and has reduced conflict with others. This repetition reduces the unique contribution of the indicator to the evaluation, suggesting that its weight should be lowered.
- (3)
- Information, Cj. The greater the value of Cj is, the greater the role of the jth evaluation indicator in the evaluation indicator system, and the more weight should be assigned to it.
- (4)
- Find the weights. The objective weight of the jth indicator is denoted by Wj.
2.5.2. Superior and Inferior Solution Distance Method for TOPSIS
- (1)
- Build an original matrix. Assuming that there are m objects to be evaluated and that each object has n evaluation indicators, we construct the original matrix:In Equation (7), i represents the object to be evaluated (i = 1, 2, …, m), j represents the different evaluation indicators (j = 1, 2, …, n), and xij is the original value of the jth evaluation indicator for the ith evaluation object.
- (2)
- Normalize the data. Refer to Equations (1) and (2).
- (3)
- Determine the indicator weights. The CRITIC method is used to determine the weights.
- (4)
- Construct a weighted decision matrix. After determining the indicator weight wj via the CRITIC method, construct a weighted decision matrix = = , where = . Determine the optimal and worst solutions ( based on the weighted decision matrix.
- (5)
- Calculate the gap between each solution and the potential optimal solution.The smaller the value of is, the closer the comprehensive level of the ith evaluation unit is to the positive ideal solution, and the smaller the value is, the closer the comprehensive level of the ith evaluation unit is to the negative ideal solution.
- (6)
- Measure the proximity of the evaluation object to the optimal program.The strengths and weaknesses of each program are assessed based on their closeness values, which range from 0 to 1. A higher closeness value indicates a better program score and greater desirability.
2.5.3. Coupled Coordination Degree Models
2.5.4. Ridge Regression Modeling
3. Case Study
3.1. Case Study Background
3.2. Empirical Results
3.2.1. Comprehensive Level of LMDIIs in Anhui Province
3.2.2. Coupling Level of LMDIIs in Anhui Province
3.2.3. Comprehensive Performance of LMDIIs in Anhui Province
4. Discussion
- (1)
- Strengthen the greening effect of LMDIIs.
- (2)
- Upgrade digitization and stimulate scientific and technological innovation.
- (3)
- Strengthen regional industrial clusters.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Variant | Level 1 Indicators | Secondary Indicators | Notation |
---|---|---|---|
Independent variable | Level of coupling between the two industries’ integration and innovative development | Degree of coupling coordination | X1 |
Dependent variable | Level of technological development of the two industries in terms of digital intelligence | Number of patent applications for inventions | Y1 |
Number of active patents | Y2 | ||
Levels of low-carbon transition in the two sectors | Carbon intensity of the LU | Y3 | |
Carbon intensity of the MU | Y4 | ||
Profit levels of the two industries | Value added for the LU | Y5 | |
Manufacturing value added | Y6 | ||
Control variable | Level of economic development | Regional GDP | K1 |
Level of information technology | Internet broadband access port | K2 | |
Environmental protection | Expenditures on environmental protection in local finances | K3 | |
Market size | Total retail sales of consumer goods | K4 |
Degree of Coupling Coordination | Level of Coordination |
---|---|
0.0000–0.0999 | Extreme disorder |
0.1000–0.1999 | High degree of disproportion |
0.2000–0.2999 | Moderate disorder |
0.3000–0.3999 | Low-grade disorder |
0.4000–0.4999 | Slightly out of tune |
0.5000–0.5999 | Weakly coordinated |
0.6000–0.6999 | Low level of coordination |
0.7000–0.7999 | Moderate coordination |
0.8000–0.8999 | High degree of coordination |
0.9000–0.1000 | Strongest coordination |
Year | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
L-Score | 0.4514 | 0.4349 | 0.4033 | 0.4746 | 0.4240 | 0.4736 | 0.4310 | 0.4853 | 0.4438 | 0.4804 |
M-Score | 0.4197 | 0.5002 | 0.4837 | 0.4676 | 0.4816 | 0.4467 | 0.4345 | 0.5260 | 0.5395 | 0.4968 |
Year | C | T | D | Level of Coordination | Degree of Coupling Coordination |
---|---|---|---|---|---|
2013 | 0.2175 | 0.4356 | 0.3078 | 3 | Mild disorder |
2014 | 0.2326 | 0.4675 | 0.3298 | 3 | Mild disorder |
2015 | 0.2199 | 0.4435 | 0.3123 | 3 | Mild disorder |
2016 | 0.2356 | 0.4711 | 0.3331 | 3 | Mild disorder |
2017 | 0.2255 | 0.4528 | 0.3195 | 3 | Mild disorder |
2018 | 0.2299 | 0.4601 | 0.3252 | 3 | Mild disorder |
2019 | 0.2164 | 0.4327 | 0.3060 | 3 | Mild disorder |
2020 | 0.2524 | 0.5057 | 0.3573 | 3 | Mild disorder |
2021 | 0.2435 | 0.4916 | 0.3460 | 3 | Mild disorder |
2022 | 0.2442 | 0.4886 | 0.3454 | 3 | Mild disorder |
Non-Standardized Coefficient | ||||||
---|---|---|---|---|---|---|
Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | |
Constant | −2.606 | −0.984 | 1.695 | 3.591 | −0.603 | −0.775 |
X1 | 8.615 | 3.223 | −1.954 | −8.59 | 1.74 | 2.648 |
K1 | −0.849 | 0.254 | −0.96 | 0.852 | 0.833 | 0.048 |
K2 | 0.943 | 0.41 | 0.464 | −0.759 | −0.292 | 0.382 |
K3 | 0.523 | −0.018 | −0.254 | −0.794 | 0.218 | 0.257 |
K4 | −0.158 | 0.132 | −0.377 | 0.488 | 0.319 | 0.207 |
R2 | 0.778 | 0.98 | 0.916 | 0.557 | 0.94 | 0.937 |
F | 2.81 | 40.206 | 8.722 | 1.007 | 12.591 | 11.949 |
(0.169 *) | (0.002 ***) | (0.028 **) | (0.512 *) | (0.015 **) | (0.016 **) |
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Ding, H.; Gao, Y.; Hu, F.; Guo, Y.; Liu, C. Deep Integration and Innovation Development in the Logistics and Manufacturing Industries and Their Performances: A Case Study of Anhui Province, China. Processes 2024, 12, 1867. https://doi.org/10.3390/pr12091867
Ding H, Gao Y, Hu F, Guo Y, Liu C. Deep Integration and Innovation Development in the Logistics and Manufacturing Industries and Their Performances: A Case Study of Anhui Province, China. Processes. 2024; 12(9):1867. https://doi.org/10.3390/pr12091867
Chicago/Turabian StyleDing, Heping, Yuchang Gao, Fagang Hu, Yuxia Guo, and Conghu Liu. 2024. "Deep Integration and Innovation Development in the Logistics and Manufacturing Industries and Their Performances: A Case Study of Anhui Province, China" Processes 12, no. 9: 1867. https://doi.org/10.3390/pr12091867