Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises
Abstract
1. Introduction
2. Literature Review
2.1. Research on the Pathways of Enterprise Digital Transformation
2.2. Research on the Measurement of Enterprise Operational Efficiency
2.3. Research on the Relationship Between Digital Transformation and Enterprise Operational Efficiency
2.4. Research on the Relationship Between Logistics Enterprise Operational Efficiency and High-Quality Development of E-Commerce
3. Study Area, Methodology, and Variable Selection
3.1. Research Object
3.2. Research Method
3.2.1. Super-Efficiency SBM Model
3.2.2. Coupling Coordination Degree Model
3.2.3. Random Forest Model
3.3. Variable Selection
3.3.1. Enterprise Digital Transformation Index System
3.3.2. Enterprise Operational Efficiency Evaluation Index System
3.3.3. Data Sources
4. Empirical Analysis
4.1. Analysis of Operational Efficiency of Logistics Enterprises
4.2. Analysis of the Coupling and Coordination Degree Between Digital Transformation and Operational Efficiency
4.3. Trend Forecasting
5. Conclusions and Recommendations
5.1. Conclusions
- The overall operational efficiency of logistics enterprises remains low but has shown gradual improvements with fluctuations. From 2014 to 2023, the efficiency values ranged from 0.520 to 0.585, reflecting modest but upward progress. The road transport sector consistently recorded the highest efficiency but was notably impacted by the pandemic. The air transport sector experienced severe volatility, with efficiency plunging in 2020, highlighting its relatively weak risk resilience. In contrast, the water transport sector exhibited relatively stable but slower growth. This pattern of “low-level growth” reflects the incomplete integration of digital technology investments with traditional operational models. Technical applications are mostly concentrated in isolated segments of the logistics chain, and full-chain collaborative efficiency has not been fully realized. As a result, achieving substantial improvements in overall operational efficiency remains a challenge.
- The coupling and coordination between digital transformation and operational efficiency remain at the primary coordination stage, though pronounced sectoral differences are observed. From 2014 to 2023, the overall coupling coordination degree ranged from 0.642 to 0.677. Road transport performed best, reaching 0.718 in 2021, entering intermediate coordination with a generally upward trend. Water transport improved steadily from 0.615 in 2014 to 0.651 in 2023, while air transport, affected by external environmental shocks, fell below 0.6 in 2020, indicating instability. These findings suggest that the synergy between digital transformation and operational efficiency is still in a developmental phase, and a stronger alignment between digital initiatives and performance outcomes is needed. Moreover, the significant fluctuations observed in the air transport industry indicate that the external environment exerts considerable influence on the coupling and coordination between digital transformation and operational efficiency. Emergencies such as the COVID-19 pandemic and international supply chain disruptions have had a significant impact on the transport sector. In contrast, the road transportation sector is likely to recover more rapidly due to its mature infrastructure. This highlights the need to integrate digital transformation with risk prevention mechanisms to enhance the resilience of logistics enterprises.
- Forecasts indicate steady growth in coupling coordination, although the projected values vary across sectors. From 2024 to 2026, coupling coordination is expected to grow at an average annual rate of 0.31%. Road transport is projected to maintain steady gains, air transport is expected to grow from a lower base but with stable momentum, and water transport is anticipated to exhibit gradual improvements. Technological progress and increasingly mature digital applications are facilitating the smooth digital transformation of enterprises, thereby enhancing operational efficiency. In turn, improvements in operational efficiency reinforce the degree of digital transformation, encouraging enterprises to further invest in digital advancements. This mutual reinforcement is expected to drive the continued growth of coupling and coordination between digital transformation and operational efficiency in logistics enterprises. Moving forward, greater emphasis should be placed on improving the resilience of air transport and advancing technological innovation in road transport.
5.2. Recommendations
- To accelerate digital transformation, it is essential to sustain investment in digital logistics technologies and build a fully integrated digital chain. Logistics companies should invest in research and development and prioritize eliminating information silos between warehousing, transportation, and distribution. This includes the deep integration of artificial intelligence, big data, blockchain, and other digital technologies into core operations. For instance, road transport enterprises can optimize vehicle routing using intelligent logistics scheduling systems to reduce operating costs and energy waste, while blockchain technology can improve cargo tracking transparency in the air transport sector.
- Companies should strengthen talent development to improve resource allocation efficiency. Cultivating hybrid professionals skilled in both digital technology and logistics management is essential. Strategic partnerships with research institutes and technology centers should be established to accelerate the supply of digital talent. Enterprises should also monitor the input–output ratio, adjust digital capital structures dynamically, and prioritize investments in high-yield digital application areas.
- Risk management capabilities must be enhanced to improve shock resilience. Establishing digital risk early-warning systems is essential, particularly for the air transport sector, which is more vulnerable to external disruptions. Road transport companies can also employ real-time traffic data to anticipate extreme weather impacts and adjust fleet routes accordingly. In addition, companies should use big data analytics and digital technology to build decision simulation systems, enabling the evaluation of multiple response strategies and reducing operational delays and losses.
- The government should promote enterprise-level innovation and competition while ensuring the accurate allocation of digital transformation subsidies. Incentives such as tax incentives, dedicated funds for digital transformation, and fiscal subsidies can motivate logistics companies to achieve digital upgrading and enhance efficiency. Additionally, an improved evaluation system is needed to promote and guide the coordinated development of digital transformation and operational efficiency. Regular assessments and policy oversight will help ensure effectiveness and prevent the misallocation of public resources. For industries with persistently low or unstable coordination degrees, such as air transport, government support should focus on improving foundational infrastructure, ensuring its practical application, extending subsidy benefits, and encouraging sector-specific digital transformation to improve operational efficiency.
- Building a synergistic system between logistics infrastructure and high-quality e-commerce development to form a self-reinforcing “Logistics–E-commerce Cycle”. Leveraging the highly symbiotic, interdependent, and mutually reinforcing relationship between the logistics industry and e-commerce, this ultimately achieves a dynamic where the pursuit of high-quality e-commerce development compels logistics operational efficiency improvements, and enhanced enterprise operational efficiency propels high-quality e-commerce development in turn.
5.3. Discussion
- The issue of model assumptions: The coupling coordination degree model assumes equal weighting between digital transformation and operational efficiency. In practice, however, logistics enterprises at different stages of development may emphasize one dimension more than the other. This suggests a need to explore dynamic weighting mechanisms to better reflect actual enterprise conditions.
- The issue of the construction of the evaluation index system: The output indicators in the operational efficiency evaluation system only consider operating income, which may not fully capture the distinctive dimensions of digital transformation across different logistics enterprises. Due to data limitations and reference to previous studies, non-financial indicators such as customer satisfaction and environmental benefits were excluded, potentially leading to an incomplete assessment of operational efficiency. In future research, incorporating output indicators such as delivery time, customer satisfaction, and environmental benefits could enable a more comprehensive evaluation.
- The issue of data timeliness: The data set used in this study extends only to 2023 and does not account for the potential influence of emerging technologies by logistics enterprises in 2024 and beyond. Rapid technological iteration and sudden policy changes may significantly affect the coupling and coordination relationship between digital transformation and operational efficiency, but these are not reflected in the current data. As a result, there may be deviations between the model’s projected values and actual future developments.
- The issue of regional heterogeneity: The article focuses on the registered addresses of the selected sample logistics companies, which operate both domestically and internationally. It does not conduct a regional heterogeneity analysis. Further analysis will be conducted using provincial panel data from the logistics industry.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Li, W.; Yun, K.; Li, F. The Dynamic Driving Path of Organizational Resilience to Digital Transformation—An Empirical Study Based on TJ-QCA. J. Knowl. Econ. 2024, 1–23. [Google Scholar] [CrossRef]
- Wang, J.; Lim, M.K.; Zhan, Y.; Wang, X. An intelligent logistics service system for enhancing dispatching operations in an IoT environment. Transp. Res. Part E 2020, 135, 101886. [Google Scholar] [CrossRef]
- Parpala, R.C.; Iacob, R. Application of IoT concept on predictive maintenance of industrial equipment. MATEC Web Conf. 2017, 121, 02008. [Google Scholar] [CrossRef]
- Bokolo, A.J. Artificial intelligence of things and distributed technologies as enablers for intelligent mobility services in smart cities-A survey. Internet Things 2024, 28, 101399. [Google Scholar] [CrossRef]
- Du, Q.; Kong, D.; Li, Y.; Ye, K. Customer ratings and firm value: Evidence from big data analysis of online consumption in China. Financ. Res. Lett. 2025, 75, 106867. [Google Scholar] [CrossRef]
- Chen, Y. Awareness of green logistics technology, certification, and standards by logistics practitioners at Chinese e-commerce company, Jing Dong. Asian J. Shipp. Logist. 2023, 39, 37–46. [Google Scholar] [CrossRef]
- Yang, Z.; Hui, L.; Zeliang, Y. Intellectual capital, digital transformation and firm performance: Evidence based on listed companies in the Chinese construction industry. Eng. Constr. Archit. Manag. 2025, 32, 2128–2159. [Google Scholar] [CrossRef]
- Feng, Q.; Hu, C.; Zhang, X. Hierarchical Probabilistic Decision-making for Intelligent Heavy Vehicles in Freight Transportation Networks. IFAC Pap. 2024, 58, 273–278. [Google Scholar] [CrossRef]
- Zhang, Q.; Liu, Z.; Yang, S. Enhancing construction workers’ health and safety: Mechanisms for implementing Construction 4.0 technologies in construction organizations. Eng. Constr. Archit. Manag. 2025, 32, 68–103. [Google Scholar] [CrossRef]
- Liu, M.; Yu, W.; Liu, Z.; Guo, X. Exact and approximation algorithms for the multi-depot data mule scheduling with handling time and time span constraints. J. Comb. Optim. 2025, 49, 39. [Google Scholar] [CrossRef]
- Erly, S.J.; Hill, L.O.; Gavigan, K.B.; Haecker, K.L.; Amiya, R.M. Real-Time Linkage of Public-Facing Jail Registers and Syphilis Testing and Treatment Monitoring Data, Washington State, 2023–2024. Am. J. Public Health 2025, 115, 282–286. [Google Scholar] [CrossRef]
- Friesz, L.T. Differential Multiplant Monopoly on a Freight Network. Netw. Spat. Econ. 2024, 24, 763–788. [Google Scholar] [CrossRef]
- Wu, J.; Chen, L.; Fang, S.; Wu, C. An Application Programming Interface (API) Sensitive Data Identification Method Based on the Federated Large Language Model. Appl. Sci. 2024, 14, 10162. [Google Scholar] [CrossRef]
- Xia, Z.; Yi, D. Evaluating Green Environmental Performance Through Multi-Stakeholder Governance: A Comparative Analysis of NCA and fsQCA in the New Energy Vehicle Industry. J. Manag. Sustain. 2024, 14, 36. [Google Scholar] [CrossRef]
- Xia, X.; Zhuang, H.; Wang, Z.; Chen, Z. Two-stage heuristic algorithm with pseudo node-based model for electric vehicle routing problem. Appl. Soft Comput. 2024, 165, 112102. [Google Scholar] [CrossRef]
- Fan, M.; Tang, Y.; Qalati, S.A.; Ibrahim, B. Can logistics enterprises improve their competitiveness through ESG in the context of digitalization? Evidence from China. Int. J. Logist. Manag. 2025, 36, 196–224. [Google Scholar] [CrossRef]
- Wang, F.; Jiang, J.; Cosenz, F. Understanding data-driven business model innovation in complexity: A system dynamics approach. J. Bus. Res. 2025, 186, 114967. [Google Scholar] [CrossRef]
- Prasanna, V.K.; Sujata, K. Measuring the efficiency of Indian public and private banks using the two-stage network DEA model. Benchmarking Int. J. 2023, 30, 382–406. [Google Scholar] [CrossRef]
- Lan, Q. Profitability Analysis of OPPEIN Based on DuPont Analysis System. Stud. Soc. Sci. Res. 2024, 5, 210. [Google Scholar] [CrossRef]
- Holst-Jæger, E.; Barstad, M.; Salvesen, Ø.; Torp, H.; Seternes, A.; Pettersen, E.M. Assessment of volume flow rate in arteriovenous fistulas with a novel ultrasound Doppler device (earlybird): Trend analysis, comparison of methods, and inter- and intra-rater reliability. J. Vasc. Access 2024, 26, 862–870. [Google Scholar] [CrossRef]
- Boutyour, Y.; Idrissi, A. Dynamic confidence-based constraint adjustment in distributional constrained policy optimization: Enhancing supply chain management through adaptive reinforcement learning. J. Intell. Manuf. 2024, 1–17. [Google Scholar] [CrossRef]
- Xu, C.; Zhuang, J.; Shen, J.; Sun, H.; Cai, J.; Wei, X. Cost-utility analysis of olaparib assisted targeted therapy for BRCA mutation HER2-negative early breast cancer in China and in the United States. Cost Eff. Resour. Alloc. 2025, 23, 16. [Google Scholar] [CrossRef]
- Guan, Y.; Shi, Y.; Gao, X.; Chen, Y. Research on the Curriculum Reform of “Corporate Strategy and Risk Management” under the Concept of Research-Oriented Teaching. Front. Educ. Res. 2024, 7. [Google Scholar] [CrossRef]
- Lin, Z.; Gu, H.; Gillani, K.Z.; Fahlevi, M. Impact of Green Work–Life Balance and Green Human Resource Management Practices on Corporate Sustainability Performance and Employee Retention: Mediation of Green Innovation and Organisational Culture. Sustainability 2024, 16, 6621. [Google Scholar] [CrossRef]
- Guo, F.; Huang, S.Y.; Nemoto, M. Reimagining hospital management: The balanced scorecard as a catalyst for employee retention and organizational excellence. Front. Public Health 2024, 12, 1485683. [Google Scholar] [CrossRef]
- Han, B. Research on the structure of corporate financial management objective system based on BRP method. Appl. Math. Nonlinear Sci. 2024, 9. [Google Scholar] [CrossRef]
- Hu, W.; Shao, C.; Zhang, W. Predicting U.S. bank failures and stress testing with machine learning algorithms. Financ. Res. Lett. 2025, 75, 106802. [Google Scholar] [CrossRef]
- Qiao, Z. Research on the Impact of Artificial Intelligence, Enterprise Production Efficiency and Enterprise Innovation Performance. Adv. Soc. Sci. Cult. 2025, 7, 140–152. [Google Scholar] [CrossRef]
- Zhao, Y. Research on Computer Aided Information Analysis Technology Based on Data Mining and Social Network Analysis. Inf. Knowl. Manag. 2025, 6, 8–13. [Google Scholar] [CrossRef]
- Cheng, H.; Qi, S.; Qiu, L. Career Incentives of Political Leaders and Corporate Operational Efficiency. Prod. Oper. Manag. 2024, 33, 1931–1952. [Google Scholar] [CrossRef]
- Zhou, Q.; Yang, Y.; Ma, F. A Stackelberg-based deep reinforcement learning approach for dynamic cooperative advertising in a two-echelon supply chain. Comput. Chem. Eng. 2025, 196, 109048. [Google Scholar] [CrossRef]
- Du, X. Cross Cultural Communication Strategy of Enterprise Brand under the Background of New Media. Acad. J. Humanit. Soc. Sci. 2022, 5. [Google Scholar] [CrossRef]
- Chen, X.; Lv, J.; Wang, Z.; Qin, G.; Zhou, Z. Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis. Comput. Biol. Med. 2024, 183, 109299. [Google Scholar] [CrossRef]
- Wang, J.; Huang, Q. The Impact of Digital Transformation on the Export Technology Complexity of Manufacturing Enterprises: Based on Empirical Evidence from China. Sustainability 2025, 17, 2596. [Google Scholar] [CrossRef]
- Wang, T.; Feng, X.; Jia, J.; Li, J.; Xu, S. Research on the Application of Resource Virtualization Management Soft for Spaceborne Edge Computing Node. J. Big Data Comput. 2024, 2, 120–128. [Google Scholar] [CrossRef]
- Brovko, L.G.; Kozhukhov, V.V.; Martynova, D.E. Proper Motions of the Flat Structure of Cosserat Type. Mech. Solids 2024, 59, 1237–1248. [Google Scholar] [CrossRef]
- Liu, X.; Zhao, W.; Gao, B.; Yu, L.; Yuan, J.; Liang, T.; Wang, C.; Zhou, Q.; Huan, Y.; Zhou, G.; et al. Threshold effects of vegetation increase on ecosystem services based on the constraint line method in the Loess Plateau gully Zone. Ecol. Indic. 2025, 174, 113470. [Google Scholar] [CrossRef]
- Wang, N.; Wan, J.; Ma, Z.; Zhou, Y.; Chen, J. How digital platform capabilities improve sustainable innovation performance of firms: The mediating role of open innovation. J. Bus. Res. 2023, 167, 114080. [Google Scholar] [CrossRef]
- Liu, P.; Zhang, F.; Liu, Y.; Liu, S.; Huo, C. Enabling or burdening?—The double-edged sword impact of digital transformation on employee resilience. Comput. Hum. Behav. 2024, 157, 108220. [Google Scholar] [CrossRef]
- Liu, Y.; Zhang, J.Z.; Jasimuddin, S.; Babai, Z.D. Exploring servitization and digital transformation of manufacturing enterprises: Evidence from an industrial internet platform in China. Int. J. Prod. Res. 2024, 62, 2812–2831. [Google Scholar] [CrossRef]
- Khatami, F.; Vilamová, Š.; Cagno, E.; De Bernardi, P.; Neri, A.; Cantino, V. Efficiency of consumer behaviour and digital ecosystem in the generation of the plastic waste toward the circular economy. J. Environ. Manag. 2022, 325 Pt B, 116555. [Google Scholar] [CrossRef]
- Liao, J.; Pang, J.; Dong, X. More gain, more give? The impact of brand community value on users’ value co-creation. J. Retail. Consum. Serv. 2023, 74, 103389. [Google Scholar] [CrossRef]
- Schallmo, D.; Kolb, J.; Schuater, T.; Athanassopoulou, N.; Sepetis, A. Twin Transformation: Understanding the Nature and Combination of Digital and Sustainability Transformation. Int. J. Innov. Manag. 2025, 29, 2501001. [Google Scholar] [CrossRef]
- Vibhav, S.; Kumar, N.V.; Vinod, K. Profit over principles: Unveiling the motivating factors behind dark patterns in e-commerce through the lens of agency theory. J. Enterp. Inf. Manag. 2025, 38, 821–848. [Google Scholar] [CrossRef]
- Kolesnikov, V.M.; Lyabakh, N.N.; Mamaev, E.A.; Bakalov, M.V. Efficient and secure logistics transportation system. IOPConference Ser. Mater. Sci. Eng. 2020, 918, 012031. [Google Scholar] [CrossRef]
- Mutemi, A.; Bacao, F. Balancing act: Tackling organized retail fraud on e-commerce platforms with imbalanced learning text models. Int. J. Inf. Manag. Data Insights 2024, 4, 100256. [Google Scholar] [CrossRef]
- Jabeen, R.; Khan, U.K.; Zain, F.; Atlas, F.; Khan, F. Investigating the impact of social media advertising and risk factors on customer online buying behavior: A trust-based perspective. Future Bus. J. 2024, 10, 123. [Google Scholar] [CrossRef]
- Yousaf, A.; Muhammad, S.; Abdullah, A.; Hayder, S.; Syed, B.A. Improving inland freight logistic efficiencies: Is there any ideal modal spilt? Case Stud. Transp. Policy 2022, 10, 777–784. [Google Scholar] [CrossRef]
- Pecherskaya, E.A.; Golubkov, P.E.; Novichkov, M.D.; Gurin, S.A.; Metal’nikov, A.M. Intelligent Information-Measurement System for Measuring the Parameters of Oxide Coatings in the Process of Microarc Oxidation. Meas. Tech. 2023, 66, 420–429. [Google Scholar] [CrossRef]
- Catherine, M.O.; Maria, B.H.; Jonas, A.; Jenny, K. Exploring green logistics practices in freight transport and logistics: A study of biomethane use in Sweden. Int. J. Logist. Res. Appl. 2023, 26, 548–567. [Google Scholar] [CrossRef]
- Yan, M.-R.; Yan, H.; Chen, Y.-R.; Zhang, Y.; Yan, X.; Zhao, Y. Integrated green supply chain system development with digital transformation. Int. J. Logist. Res. Appl. 2025, 1–22. [Google Scholar] [CrossRef]
- Jeenanunta, C.; Lan, L.T.N.; Rittippant, N.; Chongphaisal, P.; Machikita, T.; Uek, Y.; Tsuji, M. Examining the role of top management leadership style on transportation efficiency and profitability of logistics firms. Songklanakarin J. Sci. Technol. 2018, 40, 1306–1314. [Google Scholar] [CrossRef]
- He, Y.; Huang, X. Coupling and Coordination of Technological Innovation and the Efficiency of Green Supply Chain of Logistics Enterprises and the Influencing Factors. J. Chang. Univ. 2025, 39, 74–84. [Google Scholar]
- Meng, Y.; Wang, P. Random Forest Financial Early Warning Model Based on Zipf’s Law. J. Shanxi Univ. Nat. Sci. Ed. 2023, 46, 821–829. [Google Scholar] [CrossRef]
- Wang, J.; Xiong, B.; Mo, Y.; Huang, J.; Li, X.; Zhao, P. Recognition method of helicopter flight condition based on random forest. Computer Engineering and Applications. Comput. Eng. Appl. 2017, 53, 149–152. [Google Scholar] [CrossRef]
Primary Indicators | Weight of the First-Level Index | Secondary Indicators | Secondary Indicator Weights |
---|---|---|---|
Strategic guidance | 34.72% | Establish digital positions for management | 23.82% |
The management is oriented towards digital innovation and forward-looking | 27.88% | ||
The management layer is oriented towards digital innovation and sustainability | 18.79% | ||
The number of management layers is oriented towards the breadth of innovation | 12.83% | ||
Management layer digital innovation orientation intensity | 16.68% | ||
Technology-driven | 16.20% | artificial intelligence technology | 55.04% |
Blockchain technology | 12.98% | ||
Cloud computing technology | 18.32% | ||
Big data technology | 13.66% | ||
Organizational empowerment | 9.69% | Digital capital investment program | 50.22% |
Digital human input plan | 25.53% | ||
Digital infrastructure construction | 12.06% | ||
Construction of scientific and technological innovation bases | 12.19% | ||
Environmental support | 3.42% | Number of invention patents in the industry | 19.23% |
R&D activities in the industry | 17.79% | ||
New product development and sales in the industry | 14.98% | ||
The intensity of digital technology in the industry | 11.57% | ||
Digital capital input intensity in the industry | 11.4% | ||
The intensity of human capital input in the same industry | 7.89% | ||
Cable density in the city | 4.77% | ||
Capacity of mobile switches in the city | 4.03% | ||
The number of Internet broadband access users in the city | 4.00% | ||
The number of mobile Internet users in the city | 4.34% | ||
Digital achievements | 27.13% | Digital innovation standards | 36.68% |
Digital innovation paper | 11.74% | ||
Digital invention patent | 23.54% | ||
Digital innovation credentials | 14.73% | ||
Digital national awards | 13.31% | ||
Digital applications | 8.84% | technical innovation | 63.42% |
Process innovation | 23.78% | ||
Business innovation | 12.80% |
Secondary Indicators | Interpretation |
---|---|
Establish digital positions for management | Positions specifically set up within the enterprise management layer to be responsible for digital-related work |
The management is oriented towards digital innovation and forward-looking | The management’s awareness of predicting and making advanced arrangements for future trends, technology applications, and industry changes in digital innovation |
The management layer is oriented towards digital innovation and sustainability | The management’s determination and actions to maintain long-term investment and promotion in digital innovation, and to continuously integrate digital concepts into enterprise operations |
The number of management layers is oriented towards the breadth of innovation | The scope of the management layer’s promotion of digital innovation across enterprise business and management, covering digital exploration in links like production, logistics, sales, and management |
Management layer digital innovation orientation intensity | The level of resources the management layer invests in digital innovation and the resolve to drive innovation implementation |
Artificial intelligence technology | Technology that uses computers to simulate, extend, and expand human intelligence |
Blockchain technology | A type of distributed ledger technology |
Cloud computing technology | Technology that provides computing resources via network “clouds” (remote server clusters) |
Big data technology | Technology for collecting, storing, analyzing, and mining vast and diverse data |
Digital capital investment program | Enterprises’ capital investment plans for digital transformation, including digital technology equipment procurement, digital platform building, and digital R&D |
Digital human input plan | Enterprises’ personnel allocation plans for digital talent recruitment, cultivation, and introduction |
Digital infrastructure construction | Digital hardware, network facilities, and digital platform construction by enterprises as basic support for digital operations |
Construction of scientific and technological innovation bases | Physical platforms in logistics and related industries for digital and other sci-tech innovation activities |
Number of invention patents in the industry | The count of invention patents related to digitalization and logistics technology obtained by enterprises or institutions in logistics and related industries, reflecting industry tech innovation achievements |
R&D activities in the industry | The frequency, investment, and scope of Research and Development (R&D) activities by industry enterprises |
New product development and sales in the industry | The launch and market sales of new logistics service products and new transportation tools developed by industry enterprises based on digital and other innovations |
The intensity of digital technology in the industry | The proportion and significance of digital technology in overall technology application and business operations within the industry |
Digital capital input intensity in the industry | The proportion of industry enterprises’ digital capital investment to total investment |
The intensity of human capital input in the same industry | The proportion of industry enterprises’ investment in digital talent and other human capital to total human capital investment |
Cable density in the city | The laying density of optical cables within the city, i.e., the length of optical cables per unit area |
Capacity of mobile switches in the city | The maximum number of mobile calls, data services, etc., that mobile switches in the city can handle |
The number of Internet broadband access users in the city | The number of users accessing Internet broadband services in the city |
The number of mobile Internet users in the city | The number of users accessing mobile Internet in the city |
Digital innovation standards | Specifications and criteria for technology, processes, quality, etc., formulated in the digital innovation field |
Digital innovation paper | The number of academic papers published in digital innovation-related research |
Digital invention patent | The number of invention patents related to digital innovation |
Digital innovation credentials | Qualifications obtained by enterprises or institutions in digital innovation |
Digital national awards | Awards recognized at the national level for enterprises or individuals in the digital innovation field |
Technical innovation | Adopting new digital and logistics technologies in logistics and other businesses |
Process innovation | Digitally transforming and optimizing logistics business processes, reshaping process links via digital technology for efficiency |
Business innovation | Developing new logistics business models and service forms based on digital technology |
Indicator Categories | Name of Index | Measures of Achievement |
---|---|---|
Input indicators | capital input | Net value of fixed assets/10,000 yuan |
Labor input | Number of employees/10,000 | |
Costs | Operating cost/10,000 yuan | |
Output indicators | Expected outputs | Operating income/10,000 yuan |
Sector | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
---|---|---|---|---|---|---|---|---|---|---|
Road transport | 0.653 | 0.641 | 0.661 | 0.698 | 0.677 | 0.681 | 0.599 | 0.675 | 0.623 | 0.681 |
Water transport | 0.514 | 0.498 | 0.486 | 0.495 | 0.502 | 0.500 | 0.507 | 0.535 | 0.549 | 0.541 |
Air transport | 0.504 | 0.505 | 0.498 | 0.509 | 0.504 | 0.515 | 0.362 | 0.355 | 0.287 | 0.452 |
Logistics industry | 0.572 | 0.561 | 0.562 | 0.583 | 0.577 | 0.579 | 0.520 | 0.559 | 0.527 | 0.585 |
Scale Division | Degree of Coordination | Scale Division | Degree of Coordination |
---|---|---|---|
0 ≤ D < 0.1 | Extreme dislocation | 0.5 ≤ D < 0.6 | Basic coordination |
0.1 ≤ D < 0.2 | major maladjustment | 0.6 ≤ D < 0.7 | Primary coordination |
0.2 ≤ D < 0.3 | Moderate disorientation | 0.7 ≤ D < 0.8 | Intermediate coordination |
0.3 ≤ D < 0.4 | Mild dislocation | 0.8 ≤ D < 0.9 | Good coordination |
0.4 ≤ D < 0.5 | Near to dislocation | 0.9 ≤ D ≤ 1 | Quality coordination |
Iterms | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
---|---|---|---|---|---|---|---|---|---|---|
The road transport industry | 0.673 | 0.689 | 0.688 | 0.711 | 0.710 | 0.716 | 0.699 | 0.718 | 0.700 | 0.715 |
The water transport industry | 0.615 | 0.622 | 0.610 | 0.619 | 0.627 | 0.633 | 0.639 | 0.656 | 0.651 | 0.651 |
The aviation transport industry | 0.619 | 0.637 | 0.623 | 0.637 | 0.637 | 0.642 | 0.593 | 0.596 | 0.560 | 0.632 |
Logistics industry | 0.642 | 0.654 | 0.647 | 0.663 | 0.666 | 0.671 | 0.658 | 0.674 | 0.658 | 0.677 |
Year | Logistics Industry Forecast | The Road Transport Industry Prediction Values | The Water Transport Industry Forecast Values | The Aviation Transport Industry Forecast |
---|---|---|---|---|
2024 | 0.6789 | 0.7195 | 0.6532 | 0.6350 |
2025 | 0.6821 | 0.7238 | 0.6567 | 0.6389 |
2026 | 0.6852 | 0.7269 | 0.6595 | 0.6420 |
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Zhang, P.; Fu, Y.; Lu, B. Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises. J. Theor. Appl. Electron. Commer. Res. 2025, 20, 211. https://doi.org/10.3390/jtaer20030211
Zhang P, Fu Y, Lu B. Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises. Journal of Theoretical and Applied Electronic Commerce Research. 2025; 20(3):211. https://doi.org/10.3390/jtaer20030211
Chicago/Turabian StyleZhang, Pengcheng, Yaoyao Fu, and Boliang Lu. 2025. "Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises" Journal of Theoretical and Applied Electronic Commerce Research 20, no. 3: 211. https://doi.org/10.3390/jtaer20030211
APA StyleZhang, P., Fu, Y., & Lu, B. (2025). Analyzing the Coupling Coordination and Forecast Trends of Digital Transformation and Operational Efficiency in Logistics Enterprises. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 211. https://doi.org/10.3390/jtaer20030211