Next Article in Journal
Fuzzy Logic Model for Informed Decision-Making in Risk Assessment During Software Design
Previous Article in Journal
Service Quality Evaluation and Analysis of Autonomous-Rail Rapid Transit in Yibin City of China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Systems
Volume 13
Issue 9
10.3390/systems13090824
systems-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Systemic Pathway for Empowering Urban Digital Transformation Through the Industrial Internet

by
Xuefei Liu
1,
Zhe Li
1,2,*,
Zhitong Liu
3,
Wei Sun
4 and
Jun Yang
2,5,6,*
1
School of Economics, Liaoning University of International Business and Economics, Dalian 116029, China
2
Human Settlements Research Center, Liaoning Normal University, Dalian 116029, China
3
School of Geographical Sciences, Liaoning Normal University, Dalian 116029, China
4
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
5
Jangho Architecture College, Northeastern University, Shenyang 110169, China
6
School of Humanities and Law, Northeastern University, Shenyang 110169, China
*
Authors to whom correspondence should be addressed.
Systems 2025, 13(9), 824; https://doi.org/10.3390/systems13090824
Submission received: 18 August 2025 / Revised: 10 September 2025 / Accepted: 18 September 2025 / Published: 19 September 2025
(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas Under the Guidance of Ecological Wisdom—2nd Edition)
Browse Figures
Versions Notes

Abstract

As an integrated socio-technical system linking information technology with industrial infrastructure, the Industrial Internet is increasingly central to urban digital transformation. However, current research largely centers on national or sectoral scales, lacking systematic analysis at the city level—particularly regarding system structure, enabling mechanisms, and region-specific pathways. This study takes Dalian, a city with a strong industrial base and urgent digital transformation needs, leveraging the Industrial Internet Development Index (IIDI), employing a “system structure–mechanism–pathway” analytical framework, we conducted a comprehensive assessment of the spatiotemporal relationship between industrial structure and Industrial Internet performance in Dalian from 2020 to 2022. The study finds that, during the research period, Dalian’s Composite IIDI increased from 0.31 to 0.65, with substantial improvements in platform infrastructure, resource coordination, and data application capacity—providing key support for enterprise digitalization and intelligent consumption. A strong correlation (R2 = 0.85) between industrial structure and Industrial Internet performance underscores the structural foundation’s critical role. However, comparative analysis reveals that Dalian still faces structural deficiencies in platform openness, international interface integration, and ecosystem synergy. The study introduces a systemic pathway for empowering Industrial Internet capabilities and offers actionable insights for policymakers seeking to foster regionally adapted digital transformation.

1. Introduction

As global technological shifts and industrial restructuring deepen, digitalization, intelligence, and connectivity are reshaping urban industrial systems and governance frameworks [1]. Worldwide, digital transformation has become a common development agenda, profoundly influencing economic structures, industrial upgrading, and governance paradigms. According to reports by the OECD, World Bank, and United Nations, digital technologies such as the Industrial Internet, artificial intelligence, big data analytics, and cloud computing are accelerating the transition toward smarter, more sustainable, and more resilient cities [2]. International organizations, including the World Economic Forum and the United Nations, also highlight digital transformation as a critical pathway to achieving Sustainable Development Goals (SDGs), particularly for promoting inclusive growth and reducing regional disparities. The Industrial Internet, defined as a socio-technical system that bridges digital technologies with the physical economy, has emerged as a core enabler of urban digital transformation [3,4]. By integrating devices, platforms, data, and human operators, the Industrial Internet facilitates intelligent coordination across industrial chains, supply networks, and value creation systems [5], thereby improving overall operational efficiency while enhancing the institutional mobility and systemic allocation of data as a key production factor.
Strategically, the Industrial Internet is positioned to activate local assets, boost systemic resilience, and connect domestic and global markets. Under China’s “dual circulation” strategy, cities play a pivotal role as nodes linking internal industrial ecosystems with global networks. The maturity, operation, and trajectory of their Industrial Internet systems now serve as key indicators of digital capacity and collaborative potential [6]. Urban Industrial Internet systems often exhibit cross-sector coupling, multi-agent dynamics, and structural heterogeneity. Their development hinges not only on supply side infrastructure and platform capabilities but also on demand-side conditions such as data application and regulatory environment. These interdependencies create challenges—fragmented infrastructure, dispersed data resources, and low industrial coordination efficiency.
Amid this complexity, cities—especially within China’s urbanization context—are vital platforms for absorbing industrial shifts, reallocating resources, and enhancing systemic resilience. Yet, compared to advanced cases like Shenzhen or Chongqing, many prefecture-level cities face persistent bottlenecks: uneven resource distribution, weak platform integration, and unclear development paths. Under limited policy support and decentralized resource environments, identifying a context-specific, scalable pathway for Industrial Internet development is critical to advancing urban digital transformation [7].

2. Literature Review

Since the early 21st century, the Industrial Internet—emerging from the deep integration of next-generation information technology and industrial systems—has attracted increasing scholarly and policy attention. Existing research mainly focuses on three thematic directions: (1) Integration with 5G Technology [8,9]. Studies have examined how 5G enhances the Industrial Internet’s network architecture, data transmission efficiency, and device connectivity stability, thereby expanding its applications in industrial scenarios and smart urban services [10]. (2) Platform-Based Industrial Internet and Big Data Analytics [11,12,13]. A large body of literature highlights platforms as core hubs for data aggregation and sharing across enterprises. These platforms enable intelligent decision-making, process optimization, and improved efficiency, data quality, and cybersecurity in manufacturing systems [14,15]. (3) Sector-Specific Applications [16,17,18]. Research on the application of the Industrial Internet across the manufacturing, services, and energy sectors has identified key technologies and digital transformation pathways within specific industry contexts [19].
On the urban scale, emerging studies have started to explore the role of digital infrastructure in enhancing urban operations and governance capacity. Empirical analyses using city-level panel data suggest that digital infrastructure improves urban digitalization by enhancing innovation resilience and systemic robustness [20,21]. Other studies emphasize the synergy between smart city initiatives and Industrial Internet platforms in improving governance efficiency and the delivery of digital public services [22]. National policy programs such as “Broadband China” have also demonstrated the Industrial Internet’s contribution to strengthening industrial supply chains, particularly by boosting the resilience of local enterprises and the fluidity of regional resources [23].
In addition to these technology-centered perspectives, recent studies have highlighted the importance of social and organizational dimensions in shaping digital transformation outcomes. For example, collaborative governance mechanisms, organizational adaptability, and multi-stakeholder engagement are increasingly recognized as essential to integrating Industrial Internet technologies into urban ecosystems and achieving sustainable digital transitions [24]. Such approaches stress that technological infrastructure alone is insufficient; the success of digital transformation also depends on institutional innovations, organizational learning, and inclusive governance frameworks that align diverse actors and resources across government, industry, and society.
Despite these contributions, most existing studies still focus on macro-level (national or sectoral) analyses. Few systematically investigate the internal structure, enabling mechanisms, or regionally adapted pathways of urban-level Industrial Internet systems. Consequently, existing research offers limited direct guidance for local governments seeking actionable policies or optimization strategies tailored to their specific urban contexts.
As a representative city with a robust industrial base, Dalian has actively positioned the Industrial Internet as a strategic lever for post-pandemic economic recovery and digital transformation. Efforts include local platform development, optimization of data flows, and the deployment of intelligent service scenarios [25]. However, despite increased investment, Dalian still faces systemic bottlenecks—such as limited platform depth, inefficient data circulation, weak supply–demand coordination, and slow implementation of intelligent applications. These challenges underscore the need for a systematic evaluation of its development path. Furthermore, this study also refers to several benchmark cities such as Suzhou, Shenzhen, Chongqing, and Qingdao. These cities represent the leading edge of China’s Industrial Internet development, having established advanced digital infrastructure, mature platform ecosystems, and diversified application scenarios. They are widely recognized as national demonstration zones and innovation hubs, and their policy practices and technological achievements offer valuable references for other cities seeking to accelerate digital transformation.
To address this gap, this study takes Dalian as a case to construct a city-scale evaluation framework for Industrial Internet development, grounded in a “system structure–mechanism–pathway” perspective. We identify systemic empowerment paths across both supply and demand sides, quantify the coupling between development level and industrial characteristics, and offer regionally adapted policy recommendations. This study contributes in two key ways: (1) it provides a localized analysis of how the Industrial Internet empowers urban digital transformation, using Dalian as a prefecture-level case to fill the research gap in subnational implementation; (2) through a comparative approach, it benchmarks Dalian against more advanced cities—Suzhou, Qingdao, Shenzhen, and Chongqing—along dimensions of development speed and system quality, extracting transferable insights to support the formation of region-specific development models and promote coordinated regional digital transformation.

3. Theoretical Framework and Research Hypotheses

3.1. Mechanisms of the Industrial Internet in Empowering Urban Digital Transformation

3.1.1. Supply Side Mechanism

The Industrial Internet can be conceptualized as a multi-layered, nested complex system comprising network infrastructure, platform resources, algorithmic logic, terminal devices, and human–machine interfaces. It enables the integration of information and value flows across organizations, industries, and regions. Through digitalization, networking, and intelligent transformation, it facilitates the shift in traditional manufacturing toward greater efficiency, flexibility, and intelligence [26]. On the supply side, the Industrial Internet enhances enterprise collaboration and optimizes production processes, enabling the efficient scheduling and flexible allocation of production resources. Real-time monitoring and management of supply chains improves transparency and responsiveness, reduces inventory costs, and accelerates market adaptation [27]. Moreover, industrial platforms allow upstream and downstream enterprises to share information and optimize resource distribution, thereby improving operational efficiency and enhancing the overall synergy of industrial chains [28].
In the context of China’s new development paradigm, the stability of industrial and supply chains is vital. The Industrial Internet plays a key role in supply chain digitalization, which is essential for enterprise-level transformation [29]. Its capabilities—system-wide coordination, rapid response, intelligent decision-making, and dynamic optimization—are central to enabling supply chain upgrades and industrial resilience. These mechanisms operate through pathways such as technology spillovers from advanced industrial platforms, industry linkages across upstream and downstream enterprises, and cross-platform data integration, collectively enhancing production efficiency and industrial resilience.

3.1.2. Demand-Side Mechanism

The Industrial Internet also positively impacts the integration of supply and value chains [30], thereby promoting industrial convergence and expanding consumer participation in production and innovation. This increased engagement enhances user satisfaction and drives personalized demand. As consumer expectations evolve, traditional manufacturing faces growing challenges. The Industrial Internet allows enterprises to respond quickly to personalized demands [31], supporting customized product design and flexible manufacturing [32]. These capabilities enrich consumer experiences by enabling more intelligent, convenient, and tailored services, thereby stimulating purchasing behavior and decision-making. On the demand side, empowerment occurs through industrial convergence, user-driven innovation, and platform-enabled personalized services, which stimulate new consumption patterns and accelerate the integration of digital technologies into everyday urban life.
Overall, by connecting its system structure with dual mechanisms and multiple empowerment pathways, the Industrial Internet drives urban digital transformation through technology spillovers, industry linkages, and cross-sector integration. Based on the above analysis, the following hypotheses are proposed:
H1. The Industrial Internet significantly facilitates urban digital transformation through both supply side and demand-side mechanisms (Figure 1).

3.2. Heterogeneity in the Empowering Effects of the Industrial Internet

Due to China’s vast geographic scope and uneven regional development, significant disparities exist in the infrastructure and maturity of Industrial Internet systems across cities. Economically advanced regions typically have greater investment capacity and better-developed 5G networks and data centers, providing stronger physical foundations for Industrial Internet deployment [20]. In addition, cities differ in their industrial bases and structures, which shape both their demand for and modes of applying Industrial Internet technologies. Cities with highly developed manufacturing or high-tech sectors often face greater pressure for digital upgrading and thus exhibit stronger demand for Industrial Internet solutions. Government policy and investment environments also play a decisive role [33]. Public-sector support—through policies, funding, and infrastructure—greatly influences the pace and quality of Industrial Internet’s development. Furthermore, a city’s position in global value chains, degree of openness, and exposure to international trade policies all affect its industrial digitization capacity and competitiveness. Given these regional differences, the impact of the Industrial Internet on urban digital transformation is likely to vary. Therefore, we propose:
H2. Cities with higher Industrial Internet Development Index (IIDI) values experience stronger effects in promoting urban digital transformation.

4. Materials and Methods

4.1. Construction of Indicator Systems

4.1.1. Evaluation System for Industrial Internet Development

To assess the level of Industrial Internet development, this study draws on the White Paper on the Industrial Internet Development and Application Index (https://www.china-aii.com/yjbg/index.jhtml) (accessed on 10 September 2025) and relevant research findings [34]. Three primary dimensions—platform infrastructure, development foundation, and market environment—are selected, comprising 11 secondary indicators. Since these indicators differ in units, scales, and variability, an objective weighting method is required to ensure comparability. The entropy method objectively evaluates indicator weights by measuring the degree of information dispersion, avoiding subjective biases associated with expert scoring [35]. Therefore, we adopted the entropy weight method to determine the weights of individual indicators based on their degree of variation across cities and years. The basic principle is that indicators with greater variability across observations provide more information and thus receive higher weights, whereas indicators with less variability are assigned lower weights. This method avoids subjective bias and is suitable for handling multidimensional evaluation systems. Using the entropy method combined with a linear weighting approach, we calculate city-level Industrial Internet scores to reflect systemic efficiency and coordination [36]. To ensure applicability at the prefecture-level, certain indicators were adjusted. Specifically, the primary indicators include: (1) platform infrastructure, reflecting the construction and access capacity of urban internet systems; (2) development foundation, measuring supporting factors such as postal and telecommunications services, software and information technology, and energy development; (3) market environment, capturing the vitality and openness of the urban economy. This framework is applied to measure the Industrial Internet development level of Dalian from 2020 to 2022 (Table 1).

4.1.2. Evaluation System for Industrial Internet Industrial Characteristics

In June 2024, the China Academy of Industrial Internet released the IIDI Report of Major Cities in China (https://www.china-aii.com/xyzx/7140304.jhtml) (accessed on 10 September 2025), which established an index system for regional Industrial Internet development and evaluated industrial characteristics nationwide. Building on this framework, this study examines industrial characteristics from dimensions such as dual-cross platforms, big data projects, and 5G factories. We constructed a regression model to analyze the relationship between these characteristics and the IIDI, enabling comparative analysis between Dalian and leading cities such as Suzhou, Qingdao, Shenzhen, and Chongqing. The evaluation system is shown in Table 2.

4.2. Data Sources

4.2.1. Industrial Internet Development Data

Data on Dalian’s Industrial Internet development are mainly derived from the Dalian Statistical Yearbook (https://stats.dl.gov.cn/col/col3811/) (accessed on 10 September 2025), the Report on Industrial and Information Technology Development (http://www.cfie.org.cn/index/information/show/id/2058.html) (accessed on 10 September 2025), and city-level index reports issued by the China Academy of Industrial Internet (https://www.china-aii.com/xyzx/7140304.jhtml) (accessed on 10 September 2025). The dataset covers the years 2020–2022 (Table 3). Comparative samples include Suzhou, Shenzhen, Qingdao, and Chongqing.

4.2.2. Industrial Internet Industrial Characteristics Data

Data on industrial development characteristics are drawn from the IIDI Report of Major Cities in China (https://www.china-aii.com/xyzx/7140304.jhtml) (accessed on 10 September 2025) published by the China Academy of Industrial Internet, as well as lists released by the Ministry of Industry and Information Technology (MIIT), including the 2023 Cross-Industry and Cross-Field Industrial Internet Platforms (https://www.miit.gov.cn/jgsj/xxjsfzs/wjfb/art/2023/art_6ec0b30b5d384abb98dab4ed46273647.html) (accessed on 10 September 2025), the 2023 Big Data Industry Demonstration Projects (https://www.cnii.com.cn/tx/202401/t20240129_540935.html) (accessed on 10 September 2025), the 2023 5G Factory Catalog (https://www.cnii.com.cn/gy/202311/t20231121_523287.html) (accessed on 10 September 2025), the 2023 Data Security Typical Cases in Industry and Information Technology (https://www.cnii.com.cn/gxxww/tx/202401/t20240108_535592.html) (accessed on 10 September 2025), and the 2023 Intelligent Manufacturing Demonstration Factories (https://www.cnii.com.cn/rmydb/202312/t20231208_527663.html) (accessed on 10 September 2025). These data are used to compare Dalian with Suzhou, Chongqing, Qingdao, and Shenzhen, all of which exhibit higher levels of Industrial Internet development, in order to explore Dalian’s development features and pathways (Table 4).

4.3. Comparative System and Regression Method

We calculated the industrial development characteristic scores of each city using the comprehensive evaluation method and established a linear regression equation between these scores and the IIDI:
Zi = aSi2 + bSi + c
where Zi denotes the IIDI of city i, Si represents the industrial characteristic score, and a, b, and c are constants. This model is applied to test the explanatory power of industrial characteristic indicators on development levels and serves as a quantitative validation of systemic structure effectiveness.
The regression analysis employs the ordinary least squares (OLS) method to estimate parameters, with the coefficient of determination (R2) used to evaluate model fit. When R2 > 0.8, the results indicate a strong correlation between industrial characteristics and development levels. Cross-city comparisons further assess differences in systemic effectiveness.

5. Results

5.1. Development Trends of Dalian’s Industrial Internet System

Using the entropy weight method and linear weighting approach, we calculated a composite score for Dalian’s Industrial Internet development from 2020 to 2022 (Figure 2). The index increased from 0.31 in 2020 to 0.65 in 2022, indicating a notable upward trend and reflecting growing attention from both the government and enterprises. Among the three primary dimensions, platform infrastructure contributed the most to the overall score, while market environment exhibited the fastest improvement. The continuous rise in platform infrastructure suggests a substantial increase in internet user scale and broader integration of the Industrial Internet into enterprise production and sales processes.
Although the development foundation fluctuated during the period—declining in 2021 but recovering in 2022—it demonstrates resilience in system fundamentals. The marked improvement in market environment in 2021 likely stems from favorable policy conditions and post-COVID economic recovery efforts.
Further analysis based on Table 3 reveals the following: (1) Platform infrastructure steadily improved from 2020 to 2022, with the largest increase observed in mobile internet users, reflecting widespread internet adoption and expanded application of Industrial Internet platforms. Although the number of large-scale internet and related service enterprises declined, the number of Industrial Internet platforms grew from 9 to 22, highlighting increased institutional focus and continual enhancement of foundational systems. (2) Development foundation showed significant year-on-year volatility, with a dip in 2021—likely influenced by the pandemic and policy shifts—but rebounded steadily in 2022. (3) The market environment improved significantly, providing positive support for the development of the Industrial Internet.
Overall, from 2020 to 2022, Dalian’s Industrial Internet experienced steady growth, expanded its application scope, and facilitated both supply side digital transformation and consumer participation in production and innovation.

5.2. Evaluation of Dalian’s Industrial Internet’s Industrial Characteristics

According to the IIDI Report of Major Cities in China, a higher index value corresponds to greater industrial added value. Based on the index scores, cities were categorized into four tiers: Shenzhen (68.7), Chongqing (61.5), and Suzhou (61.0) ranked in the first tier; Qingdao (44.5) in the second; and Dalian (33.8) led the third tier.
As shown in Table 4, Dalian lags behind leading cities in several dimensions. Suzhou stands out with a large number of big data demonstration projects and 5G factories—particularly in the latter, where it far exceeds other cities. Chongqing leads in intelligent manufacturing demonstration projects (nine in total) and shows balanced development across indicators. Shenzhen hosts five dual-cross platforms, suggesting strong cross-industry Industrial Internet integration. Qingdao performs relatively well in the number of 5G factories.
The linear regression yielded an R2 of 0.8503 (Figure 3), indicating a strong positive correlation between industrial development characteristics and the IIDI. As R2 > 0.8, we conclude that cities with more pronounced industrial development characteristics tend to achieve higher Industrial Internet development scores. This finding underscores the importance of cities aligning their development strategies with local strengths to reinforce Industrial Internet advantages and drive digital transformation.

6. Discussion

6.1. Systemic Empowerment Mechanisms of the Industrial Internet in Urban Digital Transformation

Using Dalian as a case study, this research systematically evaluates the development status of the Industrial Internet and its functional role in driving urban digital transformation. The results indicate that the Industrial Internet, as a critical technological infrastructure embedded within the urban production–consumption system, demonstrates systemic empowerment on both the supply and demand sides, supporting the evolutionary transformation of urban systems.
The systemic advantages of the Industrial Internet are reflected in its multi-level coupling and cross-domain collaborative capabilities [37]. Alsagr found that investment in city-level digital infrastructure significantly promotes financial development and industrial coordination [38]. On the supply side, the Industrial Internet enhances upstream and downstream collaboration and responsiveness through platform integration, resource coordination, and data interoperability. The openness and agglomerative properties of platform systems enable enterprises to achieve “digital-intelligent transformation”, establishing real-time demand-responsive production systems and strengthening resilience and adaptability in urban manufacturing.
Meanwhile, consumer-side intelligence is increasingly visible in urban governance. Citizens experience improved digital services in energy, mobility, and healthcare, fostering more dynamic and diversified digital consumption [39]. This finding aligns with Zhang et al.’s conclusion that “one-stop smart governance platforms enhance user engagement and urban operational efficiency” [40]. Moreover, data-driven flexible manufacturing and precision marketing mechanisms boost consumer engagement and satisfaction, advancing a shift toward personalized and intelligent consumption. Such precision in capturing and responding to demand not only activates local market potential but also compels enterprises to integrate products, services, and data—injecting sustained momentum into urban digital transformation.
Cross-city comparisons reveal that the Industrial Internet’s enabling effects are influenced not only by platform scale but also by local industrial structures, technological capacity, and institutional contexts [41]. Based on the evaluation results, Dalian’s lower IIDI score compared with Shenzhen, Chongqing, and Suzhou reflects its weaker foundation in several key dimensions. At the industrial level, leading cities demonstrate stronger innovation capacity and broader application scenarios. For example, Suzhou has a higher number of big data demonstration projects and 5G-enabled factories, while Chongqing hosts nine intelligent manufacturing demonstration projects and shows balanced development across multiple indicators. By contrast, Dalian’s industrial base remains concentrated in traditional manufacturing sectors, limiting endogenous demand for large-scale digital upgrading and cross-industry integration. From a governance perspective, benchmark cities achieve deeper cross-industry collaboration through multi-level governance models. Shenzhen’s five dual-cross platforms indicate effective cross-sector coordination and robust institutional support for platform integration. Similarly, Chongqing leverages cross-departmental coordination mechanisms to jointly invest in infrastructure and accelerate the implementation of intelligent applications. In contrast, Dalian’s limited number of cross-industry platforms and fragmented institutional responsibilities suggest weaker inter-agency collaboration and lower efficiency in resource integration. At the institutional level, advanced cities benefit from strong policy incentives, central government pilot programs, and unified regulatory frameworks that prioritize the Industrial Internet as a strategic national agenda. The scarcity of nationally oriented demonstration projects in Dalian constrains its participation in higher-level initiatives and limits its integration into global value chain restructuring. Overall, the observed gaps are shaped by institutional-, industrial-, and governance-related factors rather than technological limitations alone [42,43]. To narrow these gaps, Dalian should leverage its existing industrial strengths to develop localized platform ecosystems, expand 5G-enabled demonstration projects, and improve cross-industry integration. In addition, establishing regional demonstration zones, enhancing data governance frameworks, fostering public–private partnerships, and supporting SME digitalization could further stimulate ecosystem vitality.
Accordingly, cities should not only expand the number of platforms but also enhance system integration, cross-sector coupling, and governance adaptability. Institutional coordination is equally vital, including fiscal support mechanisms, enterprise data-sharing frameworks, and alignment with national-level platforms [44], ensuring a high-quality development path of “platform + scenario + governance”.

6.2. Policy Recommendations

As a core node connecting the Bohai Rim economic zone with Northeast Asia, Dalian serves as a critical hub for industrial collaboration, technological diffusion, and cross-regional resource allocation. Strengthening its Industrial Internet ecosystem not only accelerates local digital transformation but also generates spillover effects that can benefit surrounding cities within the cluster. A major challenge is designing locally adapted Industrial Internet systems that balance high synergy with local specificity. Based on the above findings, we propose the following recommendations: (1) Leverage industrial strengths to build customized platform tailored to Dalian’s leading sectors, such as equipment manufacturing, shipbuilding, and modern services. For example, pilot programs could focus on smart shipyards or precision machinery clusters, facilitating deep integration of digital technologies with the local industrial base. (2) Enhance technological integration and infrastructure connectivity. Promote the convergence between big data, 5G technologies, and industrial sectors. Localized data hubs can be established to support full-chain collaboration within key industries, taking advantage of existing port infrastructure and logistics networks to optimize supply chain digitization. (3) Establish demonstration mechanisms to create regional exemplars. Develop pilot demonstration zones and cultivate leading enterprises to serve as benchmarks and diffuse best practices across regions. (4) Strengthen policy frameworks and collaborative governance. Dalian should align with national strategies while formulating policies reflecting local needs, such as supporting maritime and machinery industries. Collaborative efforts should address both intrinsic Industrial Internet development and external market integration, including partnerships with neighboring cities in the Bohai Rim region. (5) Support SMEs and foster innovation ecosystems. Provide targeted incentives to encourage cloud adoption and digital upgrades among Dalian’s SMEs, particularly in manufacturing and logistics. Promote industry–academia collaboration with Dalian universities and research institutes, and support talent development through localized funding and institutional programs.

6.3. Limitations and Future Research

Despite providing a systematic analysis of Dalian’s Industrial Internet development and its role in digital transformation, this study has several limitations: (1) The evaluation system was constrained by the availability of city-level data. Future research could explore mathematical modeling techniques to estimate values using provincial and sectoral data. (2) The study period was relatively short and included a limited set of comparative indicators. Further in-depth, longitudinal studies are needed to expand these insights. (3) This study used the entropy method for indicator weighting due to its objectivity, but it assumes that higher variability implies greater importance, which may not fully capture interdependencies among indicators. Although methods like XGBoost (2.1.3) could better reveal indicator contributions, their application requires larger datasets and longer time series. Given the limited sample size and short study period, future research could integrate such techniques when broader data are available. (4) Finally, while the proposed evaluation model offers a replicable approach, its transferability to non-Chinese urban contexts may be limited by differences in institutional frameworks, industrial structures, and governance capacities. Future studies should extend the analysis to international cases and incorporate longer time series to improve the robustness and applicability of the model.

7. Conclusions

From a systems perspective, this study takes Dalian as a representative case to evaluate how the Industrial Internet enables urban digital transformation. The findings show that from 2020 to 2022, Dalian’s Industrial Internet development level steadily improved, with the composite index rising from 0.31 to 0.65. Notable advancements were observed in platform infrastructure, data interoperability, and cybersecurity, laying a solid foundation for the digital economy. The Industrial Internet exhibited systemic empowerment effects on both the supply and demand sides. On the supply side, platform integration and data coordination enhanced production efficiency and industrial chain resilience. On the demand side, intelligent interactions and customized services increased consumer participation and decision-making, jointly propelling deep urban digital transformation. Moreover, the regression model results indicate a strong linear correlation between industrial internet development features and the overall IIDI (R2 = 0.85), suggesting that cities with more prominent Industrial Internet features tend to exhibit higher development levels. This confirms the intrinsic coupling between system structure and urban digital capabilities, affirming the feasibility of Dalian’s development pathway. However, comparative analysis with leading cities such as Suzhou, Chongqing, and Shenzhen reveals that Dalian still lags in areas such as cross-industry platforms, big data application, and the construction of 5G-enabled factories. To fully unlock the potential of the system, it is essential to enhance platform openness, international interface connectivity, and ecosystem coordination capacity, thereby strengthening both external integration and internal synergy.
Overall, this study provides a structured and quantifiable framework for assessing the role of the Industrial Internet in urban digital transformation. By integrating the comparative findings, it identifies transferable pathways for cities like Dalian to enhance platform ecosystems, promote demonstration projects, and strengthen cross-industry integration. In particular, our findings highlight the value of targeted interventions such as establishing pilot demonstration zones, improving data governance frameworks, and fostering public–private partnerships to stimulate innovation ecosystems and accelerate digital upgrading. Practically, the results provide actionable insights for local governments and policymakers, especially for cities seeking to strengthen their Industrial Internet systems and promote regionally adapted digital transformation strategies. In summary, as a core infrastructure for urban digital transformation, the Industrial Internet’s pathway of “systemic construction—mechanistic empowerment—localized implementation” forms a critical foundation for regional digital upgrading. This study offers a replicable and quantifiable framework for building regionally distinctive Industrial Internet systems, supporting sustainable, coordinated, and high-quality urban development.

Author Contributions

Conceptualization, X.L. and Z.L. (Zhe Li); methodology, X.L. and Z.L. (Zhe Li); software, Z.L. (Zhitong Liu); validation, Z.L. (Zhitong Liu) and W.S.; formal analysis, X.L. and Z.L. (Zhe Li); data curation, X.L. and Z.L. (Zhitong Liu); writing—original draft preparation, X.L.; writing—review and editing, W.S. and J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Liaoning Revitalization Talents Program (grant no. XLYC2202024), the Basic Scientific Research Project (Key Project) of the Education Department of Liaoning Province (grant no. LJ212410165084), and the National Natural Science Foundation of China (grant no. 41771178).

Data Availability Statement

Data will be made available on request.

Acknowledgments

The authors would like to acknowledge all colleagues and friends who have voluntarily reviewed the translation of the survey and the manuscript of this study.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Brandt, T.; Ketter, W.; Kolbe, L.M.; Neumann, D.; Watson, R.T. Smart Cities and Digitized Urban Management. Bus. Inf. Syst. Eng. 2018, 60, 193–195. [Google Scholar] [CrossRef]
  2. OECD. Smart City Data Governance: Challenges and the Way Forward. In OECD Urban Studies; OECD Publishing: Paris, France, 2023. [Google Scholar] [CrossRef]
  3. Boyes, H.; Hallaq, B.; Cunningham, J.; Watson, T. The Industrial Internet of Things (IIoT): An Analysis Framework. Comput. Ind. 2018, 101, 1–12. [Google Scholar] [CrossRef]
  4. Matin, M.A.; Sai, V. Emerging Communications Technologies for Smart Cities and Smart Villages. IEEE Commun. Mag. 2023, 61, 138. [Google Scholar] [CrossRef]
  5. Wu, W.; Lu, J.; Zhang, H. A Fractal-Theory-Based Multi-Agent Model of the Cyber Physical Production System for Customized Products. J. Manuf. Syst. 2023, 67, 143–154. [Google Scholar] [CrossRef]
  6. Ur Rehman, M.H.; Yaqoob, I.; Salah, K.; Imran, M.; Jayaraman, P.P.; Perera, C. The Role of Big Data Analytics in Industrial Internet of Things. Future Gener. Comput. Syst. 2019, 99, 247–259. [Google Scholar] [CrossRef]
  7. Vărzaru, A.A.; Bocean, C.G. Digital Transformation and Innovation: The Influence of Digital Technologies on Turnover from Innovation Activities and Types of Innovation. Systems 2024, 12, 359. [Google Scholar] [CrossRef]
  8. Mahmood, A.; Beltramelli, L.; Abedin, S.F.; Zeb, S.; Mowla, N.I.; Hassan, S.A.; Sisinni, E.; Gidlund, M. Industrial IoT in 5G-and-Beyond Networks: Vision, Architecture, and Design Trends. IEEE Trans. Ind. Inform. 2022, 18, 4122–4137. [Google Scholar] [CrossRef]
  9. Siriwardhana, Y.; Porambage, P.; Ylianttila, M.; Liyanage, M. Performance Analysis of Local 5G Operator Architectures for Industrial Internet. IEEE Internet Things J. 2020, 7, 11559–11575. [Google Scholar] [CrossRef]
  10. Aijaz, A. Private 5G: The Future of Industrial Wireless. IEEE Ind. Electron. Mag. 2020, 14, 136–145. [Google Scholar] [CrossRef]
  11. Ahmed, E.; Yaqoob, I.; Hashem, I.A.T.; Khan, I.; Ahmed, A.I.A.; Imran, M.; Vasilakos, A.V. The Role of Big Data Analytics in Internet of Things. Comput. Netw. 2017, 129, 459–471. [Google Scholar] [CrossRef]
  12. Faheem, M.; Shah, S.B.H.; Butt, R.A.; Raza, B.; Anwar, M.; Ashraf, M.W.; Ngadi, M.A.; Gungor, V.C. Smart Grid Communication and Information Technologies in the Perspective of Industry 4.0: Opportunities and Challenges. Comput. Sci. Rev. 2018, 30, 1–30. [Google Scholar] [CrossRef]
  13. Osterrieder, P.; Budde, L.; Friedli, T. The Smart Factory as a Key Construct of Industry 4.0: A Systematic Literature Review. Int. J. Prod. Econ. 2020, 221, 107476. [Google Scholar] [CrossRef]
  14. Rai, R.; Tiwari, M.K.; Ivanov, D.; Dolgui, A. Machine Learning in Manufacturing and Industry 4.0 Applications. Int. J. Prod. Res. 2021, 59, 4773–4778. [Google Scholar] [CrossRef]
  15. Shang, M.; Lee, C.; Cao, J.; Liu, Y. A Construction and Empirical Study of Quality Management Evaluation Index System in the Internet of Things Industry. Systems 2022, 10, 231. [Google Scholar] [CrossRef]
  16. Wollschlaeger, M.; Sauter, T.; Jasperneite, J. The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0. IEEE Ind. Electron. Mag. 2017, 11, 17–27. [Google Scholar] [CrossRef]
  17. Hassoun, A.; Jagtap, S.; Trollman, H.; Garcia-Garcia, G.; Abdullah, N.A.; Goksen, G.; Bader, F.; Ozogul, F.; Barba, F.J.; Cropotova, J.; et al. Food Processing 4.0: Current and Future Developments Spurred by the Fourth Industrial Revolution. Food Control 2023, 145, 109507. [Google Scholar] [CrossRef]
  18. Grau, A.; Indri, M.; Bello, L.L.; Sauter, T. Robots in Industry: The Past, Present, and Future of a Growing Collaboration with Humans. IEEE Ind. Electron. Mag. 2021, 15, 50–61. [Google Scholar] [CrossRef]
  19. Jiao, R.; Commuri, S.; Panchal, J.; Milisavljevic-Syed, J.; Allen, J.K.; Mistree, F.; Schaefer, D. Design Engineering in the Age of Industry 4.0. J. Mech. Des. 2021, 143, 070801. [Google Scholar] [CrossRef]
  20. Manny, L.; Angst, M.; Rieckermann, J.; Fischer, M. Socio-Technical Networks of Infrastructure Management: Network Concepts and Motifs for Studying Digitalization, Decentralization, and Integrated Management. J. Environ. Manag. 2022, 318, 115596. [Google Scholar] [CrossRef]
  21. Aleca, O.E.; Mihai, F. The Role of Digital Infrastructure and Skills in Enhancing Labor Productivity: Insights from Industry 4.0 in the European Union. Systems 2025, 13, 113. [Google Scholar] [CrossRef]
  22. Mehmood, Y.; Ahmad, F.; Yaqoob, I.; Adnane, A.; Imran, M.; Guizani, S. Internet-of-Things-Based Smart Cities: Recent Advances and Challenges. IEEE Commun. Mag. 2017, 55, 16–24. [Google Scholar] [CrossRef]
  23. Jum’a, L.; Ikram, M.; Jose Chiappetta Jabbour, C. Towards Circular Economy: A IoT Enabled Framework for Circular Supply Chain Integration. Comput. Ind. Eng. 2024, 192, 110194. [Google Scholar] [CrossRef]
  24. Fantozzi, I.C.; Olhager, J.; Johnsson, C.; Schiraldi, M.M. Guiding Organizations in the Digital Era: Tools and Metrics for Success. Int. J. Eng. Bus. Manag. 2025, 17, 18479790241312804. [Google Scholar] [CrossRef]
  25. Curry, E.; Dustdar, S.; Sheng, Q.Z.; Sheth, A. Smart Cities—Enabling Services and Applications. J. Internet Serv. Appl. 2016, 7, 6. [Google Scholar] [CrossRef]
  26. Taleb, T.; Afolabi, I.; Bagaa, M. Orchestrating 5G Network Slices to Support Industrial Internet and to Shape Next-Generation Smart Factories. IEEE Netw. 2019, 33, 146–154. [Google Scholar] [CrossRef]
  27. Binder, C.; Neureiter, C.; Lüder, A. Towards a Domain-Specific Approach Enabling Tool-Supported Model-Based Systems Engineering of Complex Industrial Internet-of-Things Applications. Systems 2021, 9, 21. [Google Scholar] [CrossRef]
  28. Zhou, Y.; Jia, J.; Lai, Y.; Li, L. Key Resource Sharing and Sustainable Innovation in Innovation Consortium: A Multiagent Collaboration and Multihelix Perspective. Manag. Decis. Econ. 2025, 46, 3935–3947. [Google Scholar] [CrossRef]
  29. Ben-Daya, M.; Hassini, E.; Bahroun, Z. Internet of Things and Supply Chain Management: A Literature Review. Int. J. Prod. Res. 2017, 57, 4719–4742. [Google Scholar] [CrossRef]
  30. Al-Khatib, A.W. The Impact of Industrial Internet of Things on Sustainable Performance: The Indirect Effect of Supply Chain Visibility. Bus. Process Manag. J. 2023, 29, 1607–1629. [Google Scholar] [CrossRef]
  31. Tao, F.; Zhang, Y.; Cheng, Y.; Ren, J.; Wang, D.; Qi, Q.; Li, P. Digital Twin and Blockchain Enhanced Smart Manufacturing Service Collaboration and Management. J. Manuf. Syst. 2022, 62, 903–914. [Google Scholar] [CrossRef]
  32. Kusiak, A. Smart Manufacturing. Int. J. Prod. Res. 2017, 56, 508–517. [Google Scholar] [CrossRef]
  33. Wang, B.; Ma, M.; Zhang, Z.; Li, C. How Do the Key Capabilities of the Industrial Internet Platform Support Its Growth? A Longitudinal Case Study Based on the Resource Orchestration Perspective. Technol. Forecast. Soc. Change 2024, 200, 123186. [Google Scholar] [CrossRef]
  34. Jiang, C.; Cong, Y.; Chen, J.; Wang, C.; Wu, G.; Zhao, R.; Wang, Z.; Xiao, B.; Chen, T. Rethinking Development and Major Research Plans of Industrial Internet in China. Fundam. Res. 2024, 4, 3–7. [Google Scholar] [CrossRef]
  35. Chen, M.; Lu, D.; Zha, L. The Comprehensive Evaluation of China’s Urbanization and Effects on Resources and Environment. J. Geogr. Sci. 2010, 20, 17–30. [Google Scholar] [CrossRef]
  36. Prajapati, D.K.; Mathiyazhagan, K.; Agarwal, V.; Khorana, S.; Gunasekaran, A. Enabling Industry 4.0: Assessing Technologies and Prioritization Framework for Agile Manufacturing in India. J. Clean. Prod. 2024, 447, 141488. [Google Scholar] [CrossRef]
  37. Zhang, Y.; Zhang, P.; Tao, F.; Liu, Y.; Zuo, Y. Consensus Aware Manufacturing Service Collaboration Optimization under Blockchain Based Industrial Internet Platform. Comput. Ind. Eng. 2019, 135, 1025–1035. [Google Scholar] [CrossRef]
  38. Alsagr, N. Are Natural Resources a Blessing or a Curse for Digital Infrastructure Development? The Role of Financial Sector Development. J. Clean. Prod. 2025, 491, 144828. [Google Scholar] [CrossRef]
  39. Marchesani, F.; Ceci, F. A Quadruple Helix View on Smart City: Exploring the Effect of Internal and External Open Innovation on Public Services Digitalization. Technovation 2025, 139, 103141. [Google Scholar] [CrossRef]
  40. Zhang, B.; Peng, G.; Xing, F.; Liang, X.; Gao, Q. One-Stop Smart Urban Apps and Determinants of Their Continuance Usage. J. Glob. Inf. Manag. 2021, 29, 1–21. [Google Scholar] [CrossRef]
  41. Wang, R.; Wang, X.; Zhang, Z.; Zhang, S.; Li, K. Research on the Nonlinear and Interactive Effects of Multidimensional Influencing Factors on Urban Innovation Cooperation: A Method Based on an Explainable Machine Learning Model. Systems 2025, 13, 187. [Google Scholar] [CrossRef]
  42. Chen, Y.; Jiang, C.; Peng, L.; Zhao, S.; Chen, C. Digital Infrastructure Construction and Urban Industrial Chain Resilience: Evidence from the “Broadband China” Strategy. Sustain. Cities Soc. 2025, 121, 106228. [Google Scholar] [CrossRef]
  43. Bai, T.; Qi, Y.; Li, Z.; Xu, D. Digital Economy, Industrial Transformation and Upgrading, and Spatial Transfer of Carbon Emissions: The Paths for Low-Carbon Transformation of Chinese Cities. J. Environ. Manag. 2023, 344, 118528. [Google Scholar] [CrossRef] [PubMed]
  44. Kraus, S.; Durst, S.; Ferreira, J.J.; Veiga, P.; Kailer, N.; Weinmann, A. Digital Transformation in Business and Management Research: An Overview of the Current Status Quo. Int. J. Inf. Manag. 2022, 63, 102466. [Google Scholar] [CrossRef]
Systems 13 00824 g001
Figure 1. Logic framework of the industrial internet empowering urban digital transformation.
Figure 1. Logic framework of the industrial internet empowering urban digital transformation.
Systems 13 00824 g001
Systems 13 00824 g002
Figure 2. The score of the development level of Industrial Internet in Dalian City.
Figure 2. The score of the development level of Industrial Internet in Dalian City.
Systems 13 00824 g002
Systems 13 00824 g003
Figure 3. The relationship between the Industrial Internet’s Industrial Characteristics and the IIDI.
Figure 3. The relationship between the Industrial Internet’s Industrial Characteristics and the IIDI.
Systems 13 00824 g003
Table 1. Evaluation system of the Industrial Internet development level.
Table 1. Evaluation system of the Industrial Internet development level.
Composite IndicatorPrimary IndicatorSecondary Indicator
Industrial Internet Development LevelInfrastructure Level of Industrial Internet PlatformsNumber of Internet Enterprises above Designated Size (units)
Number of Fixed Broadband Internet Access Users (10,000 households)
Number of Mobile Internet Users (10,000 users)
Internet Electricity Consumption (million kilowatt-hours)
Development Foundation Level of the Industrial InternetTotal Telecommunications Business Volume (billion yuan)
Total Postal Business Volume (billion yuan)
Number of Urban Employees in Information Transmission, Software and Information Technology Services (10,000 persons)
Total Energy Consumption of Industrial Enterprises above Designated Size (10,000 tons of standard coal)
Market Environment Level of the Industrial InternetPer Capita GDP (CNY)
Total Import and Export Volume in Foreign Trade (billion CNY)
Number of Industrial Enterprises above Designated Size (units)
Table 2. Evaluation system of industrial development characteristics.
Table 2. Evaluation system of industrial development characteristics.
Evaluation FrameworkIndicators
Industrial Development Characteristics Evaluation FrameworkNumber of Dual-Cross Platforms (units)
Number of Big Data Industry Development Demonstration Projects (units)
Number of 5G Factories (units)
Number of Typical Cases of Data Security in the Field of Industry and Information Technology (units)
Number of Unveiled Projects for Intelligent Manufacturing Demonstration Factories (units)
Table 3. Relevant indicators of Industrial Internet in Dalian.
Table 3. Relevant indicators of Industrial Internet in Dalian.
DatasetIndicators202020212022
Infrastructure Level of Industrial Internet PlatformsNumber of Internet Enterprises above Designated Size (units)10108
Number of Fixed Broadband Internet Access Users (10,000 households)224.70278.00295.50
Number of Mobile Internet Users (10,000 users)760.00770.00848.60
Internet Electricity Consumption (million kilowatt-hours)19,78523,11224,670
Development Foundation Level of the Industrial InternetTotal Telecommunications Business Volume (billion yuan)68.9081.6078.40
Total Postal Business Volume (billion CNY)52.3040.3042.10
Number of Urban Employees in Information Transmission, Software and Information Technology Services (persons)80,59778,47782,348
Total Energy Consumption of Industrial Enterprises above Designated Size (10,000 tons of standard coal)4006.903647.003853.90
Market Environment Level of the Industrial InternetPer Capita Regional GDP (CNY)94,685105,046112,270
Total Import and Export Volume in Foreign Trade (billion CNY)3854.204248.504792.10
Growth Rate of Industrial Added Value above Designated Size (%)3.8015.005.10
Table 4. Evaluation of the development characteristics of the Industrial Internet industry.
Table 4. Evaluation of the development characteristics of the Industrial Internet industry.
CityNumber of Cross-Industry and Cross-Domain PlatformsNumber of Big Data Industry Development Demonstration ProjectsNumber of 5G FactoriesTypical Cases of Data Security in the Field of Industry and Information TechnologyAwarded Projects of Intelligent Manufacturing Demonstration Factories
Dalian10400
Suzhou154512
Chongqing34649
Qingdao21700
Shenzhen55200
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liu, X.; Li, Z.; Liu, Z.; Sun, W.; Yang, J. A Systemic Pathway for Empowering Urban Digital Transformation Through the Industrial Internet. Systems 2025, 13, 824. https://doi.org/10.3390/systems13090824

AMA Style

Liu X, Li Z, Liu Z, Sun W, Yang J. A Systemic Pathway for Empowering Urban Digital Transformation Through the Industrial Internet. Systems. 2025; 13(9):824. https://doi.org/10.3390/systems13090824

Chicago/Turabian Style

Liu, Xuefei, Zhe Li, Zhitong Liu, Wei Sun, and Jun Yang. 2025. "A Systemic Pathway for Empowering Urban Digital Transformation Through the Industrial Internet" Systems 13, no. 9: 824. https://doi.org/10.3390/systems13090824

APA Style

Liu, X., Li, Z., Liu, Z., Sun, W., & Yang, J. (2025). A Systemic Pathway for Empowering Urban Digital Transformation Through the Industrial Internet. Systems, 13(9), 824. https://doi.org/10.3390/systems13090824

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop