The Impact of Market-Oriented Allocation of Data Elements on Enterprises’ New Quality Productive Forces
Abstract
1. Introduction
2. Policies, Mechanisms, and Hypotheses
2.1. Introduction to China’s NBDCPZ Policies
2.2. Mechanisms and Hypotheses
2.2.1. Mediation Effect of Digital Transformation on NBDCPZs Promoting NQPF
2.2.2. Moderation Effect of Digital Talent on NBDCPZs Promoting Digital Transformation
3. Model and Data
3.1. Model Specification
3.2. Description of Variables
3.2.1. Explained Variable
3.2.2. Explanatory Variable
3.2.3. Controlled Variables
3.3. Description of Data Sources and Processing
4. Empirical Results and Discussion
4.1. Parallel Trend Test
4.2. Correlation Analysis
4.3. Baseline Regression
4.4. Robustness Test
4.4.1. Placebo Test (Combination of Virtual Experimental Group Method and Virtual Experimental Period Method)
4.4.2. Replacement of Identification Strategy (Stacked DID)
4.4.3. Controlling for the Impact of Other Policy Factors
4.4.4. Controlling for Expected Effect
4.4.5. Adjusting the Sample
4.5. Endogeneity Discussion and Model Limitations
4.6. Mechanism Test
4.6.1. The Mediation Effect of Digital Transformation on the NBDCPZ Promoting NQPF
4.6.2. The Moderation Effect of Digital Talent on the NBDCPZ Promoting Digitalization
4.7. Heterogeneity Analysis
4.7.1. The Impact of the NBDCPZ on the NQPF of Enterprises of Varying Ownership Structures
4.7.2. The Impact of the NBDCPZ on the NQPF of Enterprises of Different Sizes
4.7.3. The Impact of the NBDCPZ on the NQPF of Enterprises of Various Industries
4.7.4. The Impact of the NBDCPZ on the NQPF of Enterprises Located in Cities with Different Levels of Digital Infrastructure
4.7.5. The Impact of the NBDCPZ on the NQPF of Enterprises Located in Cities with Different Levels of Financial Development
5. Research Conclusions and Policy Implications
5.1. Research Conclusions
5.2. Policy Implications
5.2.1. Establish a Special Fund for Enterprises’ Digital Transformation to Alleviate the Financing Constraints of Enterprises
5.2.2. Utilize the Agglomeration Effect of the NBDCPZ to Foster the Concentration of Digital Industries and Digital Talent
5.2.3. Implementing Varying Policies for Enterprises of Different Sizes or Ownership Structures
5.2.4. Strengthen the Building of Digital Infrastructure, Integrate and Share Data Elements, and Promote the Utilization and Popularization of Digital Technology
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
NBDCPZ | National Big Data Comprehensive Pilot Zone |
NQPF | New Quality Productive Forces |
MWFE Staggered DID | the multi-way fixed effects staggered difference-in-differences |
POEs | privately owned enterprises |
FIEs | foreign-invested enterprises |
SOEs | state-owned enterprises |
Appendix A. Entropy Weighting Method for NQPF Index Construction
Appendix A.1. Raw Indicators and Units
Indicator Code | Sub-Indicators | Raw Indicators | Raw Data Unit |
---|---|---|---|
A1 | Percentage of R&D salary | (Salaries and wages in R&D expenses)/Operational revenues | Chinese Yuan (CNY) |
A2 | Percentage of R&D staff | Number of R&D staff/Number of employees | Headcount |
A3 | Percentage of educated staff | Number of people with master’s degree or higher/Number of employees | Headcount |
B1 | Percentage of fixed assets | Fixed assets/Total assets | Chinese Yuan (CNY) |
B2 | Percentage of manufacturing costs | (Subtotal of cash outflows from operating activities + Depreciation of fixed assets + Amortization of intangible assets + Provision for impairment—Cash paid for purchases of goods and labor—Wages paid to employees)/(Subtotal of cash outflows from operating activities + Depreciation of fixed assets + Amortization of intangible assets + Provision for impairment) | Chinese Yuan (CNY) |
C1 | Percentage of R&D depreciation and amortization | (Depreciation and amortization in R&D expenses)/Operational revenues | Chinese Yuan (CNY) |
C2 | Percentage of R&D lease expenses | (Lease expenses in R&D expenses)/Operational revenues | Chinese Yuan (CNY) |
C3 | Percentage of R&D direct investment | (Direct investment in R&D expenses)/Operational revenues | Chinese Yuan (CNY) |
C4 | Percentage of intangible assets | Intangible assets/Total assets | Chinese Yuan (CNY) |
D1 | Turnover rate of total assets | Operational revenues/Average total assets | Chinese Yuan (CNY) |
D2 | Inverse of equity multiplier | Owners’ equity/Total assets | Chinese Yuan (CNY) |
Appendix A.2. Monotonicity and Directionality
Appendix A.3. Normalization Steps
Appendix A.4. Entropy and Weight Formulas
Appendix A.4.1. Calculate Proportion Pij
Appendix A.4.2. Calculate Entropy Value ej
Appendix A.4.3. Calculate Degree of Divergence dj
Appendix A.4.4. Calculate Entropy Weight wj
Appendix A.5. Final Weights
Indicator | Weight |
---|---|
A1 | 0.2565175 |
A2 | 0.0217321 |
A3 | 0.0263404 |
B1 | 0.0087363 |
B2 | 0.0000116 |
C1 | 0.2492229 |
C2 | 0.1387839 |
C3 | 0.2775407 |
C4 | 0.0148563 |
D1 | 0.0062573 |
D2 | 0.00000087 |
Appendix A.6. Sensitivity Analysis to Alternative Normalization Methods
Method 1 (Min–Max) | Method 2 (Z-Score) | Method 3 (Vector) | |
---|---|---|---|
Method 1 | 1.000 | 1.000 ** | 0.900 |
Method 2 | 1.000 ** | 1.000 | 0.900 |
Method 3 | 0.900 | 0.900 | 1.000 |
Appendix A.7. A Sensitivity Check in Which Missing Values Are Left Missing
Method 1 (Min–Max) | Method 2 (Z-Score) | Method 3 (Vector) | |
---|---|---|---|
Method 1 | 1.000 | 1.000 ** | 0.836 |
Method 2 | 1.000 ** | 1.000 | 0.836 |
Method 3 | 0.836 | 0.836 | 1.000 |
Appendix B. Digital Transformation Keywords List and Its Frequency Analysis Details
Appendix B.1. Keyword List
人工智能 | 异构数据 | 数字货币 | 智能穿戴 |
Artificial Intelligence | Heterogeneous Data | Digital Currency | Smart Wearables |
商业智能 | 征信 | 分布式计算 | 智慧农业 |
Business Intelligence | Credit Reporting | Distributed Computing | Smart Agriculture |
图像理解 | 增强现实 | 差分隐私技术 | 智能交通 |
Image Understanding | Augmented Reality | Differential Privacy Technology | Intelligent Transportation |
投资决策辅助系统 | 混合现实 | 智能金融合约 | 智能医疗 |
Investment Decision Support System | Mixed Reality | Smart Financial Contracts | Smart Healthcare |
智能数据分析 | 虚拟现实 | 移动互联网 | 智能客服 |
Intelligent Data Analytics | Virtual Reality | Mobile Internet | Intelligent Customer Service |
智能机器人 | 云计算 | 工业互联网 | 智能家居 |
Intelligent Robots | Cloud Computing | Industrial Internet | Smart Home |
机器学习 | 流计算 | 移动互联 | 智能投顾 |
Machine Learning | Stream Computing | Mobile Connectivity | Intelligent Investment Advisory |
深度学习 | 图计算 | 互联网医疗 | 智能文旅 |
Deep Learning | Graph Computing | Internet Healthcare | Smart Culture and Tourism |
语义搜索 | 内存计算 | 电子商务 | 智能环保 |
Semantic Search | In-Memory Computing | E-Commerce | Smart Environmental Protection |
生物识别技术 | 多方安全计算 | 移动支付 | 智能电网 |
Biometric Technology | Secure Multi-Party Computation | Mobile Payment | Smart Grid |
人脸识别 | 类脑计算 | 第三方支付 | 智能营销 |
Facial Recognition | Neuromorphic Computing | Third-Party Payment | Smart Marketing |
语音识别 | 绿色计算 | NFC支付 | 数字营销 |
Speech Recognition | Green Computing | NFC Payment | Digital Marketing |
身份验证 | 认知计算 | 智能能源 | 无人零售 |
Identity Authentication | Cognitive Computing | Smart Energy | Unmanned Retail |
自动驾驶 | 融合架构 | B2B | 互联网金融 |
Autonomous Driving | Converged Architecture | Business-to-Business | Internet Finance |
自然语言处理 | 亿级并发 | B2C | 数字金融 |
Natural Language Processing | Hundreds of Millions-Level Concurrency | Business-to-Consumer | Digital Finance |
大数据 | EB级存储 | C2B | Fintech |
Big Data | Exabyte-Level Storage | Customer-to-Business | Financial Technology |
数据挖掘 | 物联网 | C2C | 金融科技 |
Data Mining | Internet of Things | Customer-to-Customer | Fintech |
文本挖掘 | 信息物理系统 | O2O | 量化金融 |
Text Mining | Cyber-Physical Systems | Online-to-Offline | Quantitative Finance |
数据可视化 | 区块链 | 网联 | 开放银行 |
Data Visualization | Blockchain | Networked Connectivity | Open Banking |
Appendix B.2. Analysis Steps
Appendix B.3. Technical Details
- For Chinese text segmentation, we apply the jieba tokenizer, with an additional procedure to protect digital transformation keywords. Specifically, each keyword is temporarily replaced by a unique placeholder of the form “_DT_WORD {a digital transformation word}__”, ensuring that jieba does not break multi-word entries during tokenization. After the jieba tokenization, the original keywords were restored by putting back the placeholders.
- Regarding text cleaning, we remove common stop-words based on the widely accepted Harbin Institute of Technology (HIT) stop-word list.
- Unlike some studies that restrict analysis to particular sections (e.g., MD&A), we have not applied section filters to more comprehensively capture digital transformation characteristics throughout the annual reports.
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Labor force | Living labor | Percentage of R&D salary | (Salaries and wages in R&D expenses)/Operational revenues |
Percentage of R&D staff | Number of R&D staff/ Number of employees | ||
Percentage of educated staff | Number of people with master’s degree or higher/Number of employees | ||
Materialized labor | Percentage of fixed assets | Fixed Assets/Total assets | |
Percentage of manufacturing costs | (Subtotal of cash outflows from operating activities + Depreciation of fixed assets + Amortization of intangible assets + Provision for impairment—Cash paid for purchases of goods and labor—Wages paid to employees)/(Subtotal of cash outflows from operating activities + Depreciation of fixed assets + Amortization of intangible assets + Provision for impairment) | ||
Production tools | Hard technology | Percentage of R&D depreciation and amortization | (Depreciation and amortization in R&D expenses)/Operational revenues |
Percentage of R&D lease expenses | (Lease expenses in R&D expenses)/ Operational revenues | ||
Percentage of R&D direct investment | (Direct investment in R&D expenses)/Operational revenues | ||
Percentage of intangible assets | Intangible assets/Total assets | ||
Soft technology | Turnover rate of total assets | Operational revenues/ Average total assets | |
Inverse of equity multiplier | Owners’ equity/Total assets |
Variable Type | Variable Name | Symbol | Measurement Method |
---|---|---|---|
Explained variable | NQPF | NQPF | Natural logarithmic value of the result plus one measured by the entropy method |
Explanatory variable | NBDCPZ dummy variable | DID | The value of 1 is taken in the year of the establishment of the NBDCPZ in the city where the enterprise in the experimental group is located and in the following years, otherwise, it is 0 |
Controlled variable | Enterprise size | SIZE | Ln(Total assets of enterprise) |
Enterprise age | AGE | Ln(Sample year—year of enterprise’s establishment + 1) | |
Leverage | LEV | Ln(Total liabilities/total assets + 1) | |
Revenue growth | RG | Ln((Current year’s operational revenues—previous year’s operational revenues)/previous year’s operational revenues + 1) | |
Ownership concentration | OC | Ln(Sum of the shareholdings of the enterprise’s top ten public shareholders + 1) | |
Size of the board of directors | SD | Ln(Number of on-the-job directors at the end of the year + 1) | |
Ratio of independent directors | RID | Ln(Number of on-the-job independent directors at the end of the year/Number of on-the-job directors at the end of the year + 1) | |
Management capabilities | MC | Ln(Cash inflow from operating activities—cash outflow from operating activities + 1) | |
Separating the extent of ownership and controlling rights | SEOC | Ln((Total number of shares - number of shares held by the Board of Directors)/Total number of shares + 1) |
DID | SIZE | AGE | LEV | RG | OC | SD | RID | MC | SEOC | |
---|---|---|---|---|---|---|---|---|---|---|
DID | 1 | |||||||||
SIZE | 0.1759 | 1 | ||||||||
AGE | 0.2201 | 0.1784 | 1 | |||||||
LEV | 0.0252 | 0.4286 | 0.1779 | 1 | ||||||
RG | −0.0185 | 0.0527 | −0.0891 | −0.0152 | 1 | |||||
OC | −0.0353 | 0.2257 | −0.2005 | −0.0596 | 0.1114 | 1 | ||||
SD | −0.0526 | 0.2476 | 0.0207 | 0.1273 | 0.0038 | 0.1061 | 1 | |||
RID | 0.0843 | 0.0193 | −0.0069 | 0.0021 | −0.0111 | 0.002 | −0.5174 | 1 | ||
MC | 0.0409 | 0.204 | 0.0493 | −0.0523 | 0.0423 | 0.0942 | 0.0686 | −0.0051 | 1 | |
SEOC | −0.0783 | 0.0491 | 0.0516 | 0.0593 | 0.008 | 0.0771 | 0.0437 | −0.0616 | 0.0245 | 1 |
Variable | (1) | (2) | (3) |
---|---|---|---|
DID | 0.1294 *** (0.0057) | 0.0301 *** (0.0064) | 0.0200 *** (0.0063) |
SIZE | - | - | 0.0281 *** (0.0049) |
AGE | - | - | 0.2203 *** (0.0293) |
LEV | - | - | 0.2510 *** (0.0274) |
RG | - | - | 0.0052 (0.0070) |
OC | - | - | −0.1220 *** (0.0125) |
SD | - | - | −0.0466 ** (0.0188) |
RID | - | - | −0.1884 *** (0.0714) |
MC | - | - | 0.0009 *** (0.0001) |
SEOC | - | - | −0.0027 (0.0024) |
Individual fixed effect | No | Yes | Yes |
Time fixed effect | No | Yes | Yes |
City fixed effect | No | Yes | Yes |
Sample | 23,295 | 23,294 | 23,294 |
R-squared | 0.0213 | 0.7062 | 0.7172 |
Variable | (1) | (2) |
---|---|---|
DID | 0.0200 *** (0.0063) | 0.0212 * (0.0127) |
Controlled variable | Yes | Yes |
Individual fixed effect | Yes | Yes |
Time fixed effect | Yes | Yes |
City fixed effect | Yes | Yes |
Sample | 23,294 | 36,768 |
R-squared | 0.7172 | 0.7176 |
Variable | (1) | (2) |
---|---|---|
DID | 0.0200 *** (0.0063) | 0.0144 ** (0.0064) |
IPZCE | - | 0.0247 *** (0.0050) |
Controlled variable | Yes | Yes |
Individual fixed effect | Yes | Yes |
Time fixed effect | Yes | Yes |
City fixed effect | Yes | Yes |
Sample | 23,294 | 23,294 |
R-squared | 0.7172 | 0.7174 |
Variable | (1) | (2) |
---|---|---|
DID | 0.0200 *** (0.0063) | 0.0252 *** (0.0071) |
EXPECT | - | 0.0238 ** (0.0108) |
Controlled variable | Yes | Yes |
Individual fixed effect | Yes | Yes |
Time fixed effect | Yes | Yes |
City fixed effect | Yes | Yes |
Sample | 23,294 | 23,294 |
R-squared | 0.7172 | 0.7172 |
Variable | (1) | (2) |
---|---|---|
DID | 0.0200 *** (0.0063) | 0.0185 *** (0.0066) |
Controlled variable | Yes | Yes |
Individual fixed effect | Yes | Yes |
Time fixed effect | Yes | Yes |
City fixed effect | Yes | Yes |
Sample | 23,294 | 17,359 |
R-squared | 0.7172 | 0.7352 |
Variable | (1) | (2) | (3) | (4) |
---|---|---|---|---|
NQPF | DIG | NQPF | DIG | |
DID | 0.0200 (0.0063) | 1.9489 *** (0.2660) | 0.0158 ** (0.0062) | 1.2342 *** (0.3804) |
DIG | - | - | 0.0021 *** (0.0002) | - |
TAL | - | - | - | 1.3400 (7.5515) |
- | - | - | 16.6951 ** (7.3561) | |
Controlled variable | Yes | Yes | Yes | Yes |
Individual fixed effect | Yes | Yes | Yes | Yes |
Time fixed effect | Yes | Yes | Yes | Yes |
City fixed effect | Yes | Yes | Yes | Yes |
Sample | 23,294 | 23,294 | 23,294 | 23,294 |
R-squared | 0.7172 | 0.6945 | 0.7198 | 0.6947 |
Variable | (1) | (2) | (3) |
---|---|---|---|
POEs | FIEs | SOEs | |
DID | 0.0620 *** (0.0097) | 0.0774 * (0.0427) | −0.0244 *** (0.0084) |
Controlled variable | Yes | Yes | Yes |
Individual fixed effect | Yes | Yes | Yes |
Time fixed effect | Yes | Yes | Yes |
City fixed effect | Yes | Yes | Yes |
Sample | 10,820 | 655 | 10,798 |
R-squared | 0.6903 | 0.6624 | 0.7481 |
Variable | (1) | (2) |
---|---|---|
Large-Scale Enterprises | Small-Scale Enterprises | |
DID | −0.0156 * (0.0084) | 0.0463 *** (0.0090) |
Controlled variable | Yes | Yes |
Individual fixed effect | Yes | Yes |
Time fixed effect | Yes | Yes |
City fixed effect | Yes | Yes |
Sample | 9953 | 13,340 |
R-squared | 0.7784 | 0.6716 |
Variable | (1) | (2) | (3) | (4) |
---|---|---|---|---|
Primary | Secondary | Manufacturing | Tertiary | |
DID | −0.0984 ** (0.0474) | 0.0240 *** (0.0066) | 0.0330 *** (0.0070) | 0.0025 (0.0155) |
Controlled variable | Yes | Yes | Yes | Yes |
Individual fixed effect | Yes | Yes | Yes | Yes |
Time fixed effect | Yes | Yes | Yes | Yes |
City fixed effect | Yes | Yes | Yes | Yes |
Sample | 296 | 17,261 | 14,974 | 5735 |
R-squared | 0.8486 | 0.7285 | 0.7210 | 0.7070 |
Variable | (1) | (2) |
---|---|---|
High-Level Digital Infrastructure | Low-Level Digital Infrastructure | |
DID | 0.0199 *** (0.0067) | −0.0237 (0.0178) |
Controlled variable | Yes | Yes |
Individual fixed effect | Yes | Yes |
Time fixed effect | Yes | Yes |
City fixed effect | Yes | Yes |
Sample | 20,561 | 2726 |
R-squared | 0.7253 | 0.6454 |
Variable | (1) | (2) |
---|---|---|
High-Level Financial Development | Low-Level Financial Development | |
DID | 0.0198 *** (0.0069) | 0.0115 (0.0145) |
Controlled variable | Yes | Yes |
Individual fixed effect | Yes | Yes |
Time fixed effect | Yes | Yes |
City fixed effect | Yes | Yes |
Sample | 19,287 | 3999 |
R-squared | 0.7211 | 0.6992 |
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Zhou, Y.; Li, G.; Sun, T.; Huo, W. The Impact of Market-Oriented Allocation of Data Elements on Enterprises’ New Quality Productive Forces. Sustainability 2025, 17, 8262. https://doi.org/10.3390/su17188262
Zhou Y, Li G, Sun T, Huo W. The Impact of Market-Oriented Allocation of Data Elements on Enterprises’ New Quality Productive Forces. Sustainability. 2025; 17(18):8262. https://doi.org/10.3390/su17188262
Chicago/Turabian StyleZhou, Yacheng, Guang Li, Tong Sun, and Weidong Huo. 2025. "The Impact of Market-Oriented Allocation of Data Elements on Enterprises’ New Quality Productive Forces" Sustainability 17, no. 18: 8262. https://doi.org/10.3390/su17188262
APA StyleZhou, Y., Li, G., Sun, T., & Huo, W. (2025). The Impact of Market-Oriented Allocation of Data Elements on Enterprises’ New Quality Productive Forces. Sustainability, 17(18), 8262. https://doi.org/10.3390/su17188262