Abstract
Cultivating and building world-class advanced manufacturing clusters is a crucial national strategic plan in the new era. The overall production capacity of China’s power battery industry has occupied a significant market share globally, making it one of the national strategic emerging industries. Based on the concept of open and collaborative innovation within innovation management, this dissertation delves into the construction and operation of the innovation ecosystem of the power battery industry cluster. Utilizing social network analysis, system dynamics, and other analytical tools, it explores the internal influencing factors and operational pathways of this system. The research provides valuable insights for regions aiming to build or improve their power battery industry cluster innovation ecosystems, thereby enhancing their innovation momentum.
1. Introduction
In recent years, with the surge in popularity of new energy vehicles, the power battery industry has undergone rapid development. However, research on internal collaborative innovation within this field is relatively scarce. This dissertation aims to address this gap by examining the innovation ecosystem of the power battery industry cluster from an innovation management perspective.
2. Literature Review
Scholars have mainly focused on how traditional industrial clusters can transform and upgrade their development. For traditional industrial clusters, resource, technological, and ecological constraints often hinder their collaborative innovation capabilities, performance levels, and risk resistance. Transformation and upgrading thus become essential for enhancing their core competitiveness. With the advent of the new era, constructing a more robust innovation ecosystem is not only crucial for the transformation and upgrading of industrial clusters but also vital for accelerating the industrialization of innovative outcomes and improving international competitiveness.
3. Conceptual Framework and Methodology
3.1 Definition and Characteristics of Power Battery Industry Cluster
Based on Michael Porter’s definition of industrial clusters and the Ministry of Industry and Information Technology’s classification of power batteries, this dissertation defines the power battery industry cluster and briefly analyzes its characteristics.
3.2 Methodology
This research adopts a multi-method approach combining literature review, social network analysis, system dynamics modeling, and case studies. Gephi software is used to visualize the innovation cooperation network within the power battery industry cluster, while Vensim PLE software is employed to simulate and validate the system dynamics model.
4. Analysis of the Innovation Ecosystem of Power Battery Industry Cluster
4.1 Innovation Cooperation Network
Taking Changzhou as an example, this section uses Gephi software to draw an intuitive representation of the innovation cooperation network within the power battery industry cluster. The network diagram reveals the connections and collaborations among various innovation entities, forming the basic framework of the innovation ecosystem.
Table 1: List of Enterprises in Changzhou Power Battery Cluster
| No. | Company Name | Main Products & R&D Directions |
|---|---|---|
| 1 | Zhongchuang Airlines Technology Co., Ltd. | High-performance power batteries |
| … | … | … |
4.2 Overall Framework of the Innovation Ecosystem
Through analysis of the innovation cooperation network and past research, this section outlines the general framework of the power battery industry cluster innovation ecosystem. The framework includes supply, demand, and support modules, encapsulating various entities and their interactions.
4.3 Influencing Factors and SD Model Construction
Based on the established framework, this section divides it into three modules: supply, demand, and support. It then identifies relevant influencing factors through a combination of framework analysis, survey interviews, and literature review (Table 4.1).
Table 4.1: Influencing Factors of Innovation Ecosystem of Power Battery Industry Cluster
| System Module | First-Level Indicator | Second-Level Indicator | Third-Level Indicator |
|---|---|---|---|
| Supply Module | Innovation Subject | Research Entities | Number of universities, enterprise technology research centers, engineering research centers |
| Innovation Input | R&D Investment | Number of new product projects, cluster R&D investment, new product development expenditure | |
| Demand Module | Innovation Supply | Scenario Demand | Number of new energy vehicle enterprises, power battery market demand, consumer purchase intention |
| Innovation Output | Product Output | Total output value, main business income, new product sales revenue of power battery clusters | |
| Support Module | Innovation Service | Innovation Environment | Government support, funding assistance, public service platforms, research platforms |
Utilizing these factors, a preliminary system operation pathway is formed for each module. Following extensive consultations, Vensim PLE software is used to draw the overall causal loop diagram of the system, reflecting its operational pathways.
5. SD Model Simulation and Validation
5.1 Causal Loop Diagram and Cause Tree Analysis
This section presents the causal loop diagrams for the supply and demand modules. The demand module focuses on the application of innovative outcomes by entities such as new energy vehicle companies. It highlights that the innovation drive of power battery enterprises primarily stems from expanded market demand.
5.2 Model Validation
Model validation is conducted by comparing the simulated results with historical data. The patent application totals of the Changzhou power battery cluster from 2019 to 2021 are used as direct output values. The relative error rates between the simulated and actual values are within 15%, indicating the model’s validity (Table 5.1).
Table 5.1: Historical Data Test Results
| Year | Actual Value | Simulated Value | Relative Error Rate (%) |
|---|---|---|---|
| 2019 | 584 | 596 | 2.05 |
| 2020 | 613 | 634 | 3.43 |
| 2021 | 671 | 693 | 3.58 |
6. Discussion and Conclusions
6.1 Key Findings
This research reveals that government support and market demand are pivotal in driving innovation activities within the power battery industry cluster. Government initiatives, such as support policies, R&D investment, talent introduction, and public service platform construction, significantly influence the cluster’s innovation ecosystem.
6.2 Limitations and Future Research Directions
Despite constructing the overall framework and operational pathways of the innovation ecosystem, this study has limitations. First, the case studies may not be exhaustive, potentially missing other influencing factors and pathways in clusters like Ningde. Second, some difficult-to-quantify factors are summarized as ‘other factors,’ and their impact on the system remains unclear. Future research could clarify these factors’ feedback relationships, quantify them, and incorporate time delay processes to make the model more realistic.
Conclusion
This dissertation contributes to the literature by exploring the innovation ecosystem of the power battery industry cluster, constructing its framework, identifying internal influencing factors, and mapping operational pathways. It provides practical insights for regions seeking to enhance their power battery industry cluster innovation ecosystems, ultimately boosting innovation momentum.