Research on the Efficiency Measurement and Improvement Paths of the Logistics Industry Innovation Ecosystem

Qiongbei Shi

doi:10.63313/EBM.9101

Authors

Qiongbei Shi Henan Polytechnic University, Jiaozuo, China Author

DOI:

https://doi.org/10.63313/EBM.9101

Keywords:

logistics industry, efficiency, configurational analysis, innovation ecosystem

Abstract

The logistics industry, as a rapidly developing composite service industry in China, provides a solid foundation for successfully building a "strong logistics nation," optimizing the operation of related industries, and enhancing competitiveness. According to statistics from the National Development and Reform Commission, the total social logistics volume in China has maintained steady growth in recent years, ranking among the top globally. Despite this, the development model of China's logistics industry still suffers from a series of drawbacks such as "high input, high energy consumption, and low efficiency," which adversely affect the future high-quality economic development. Therefore, the state has successively issued several plans to reduce costs and increase efficiency in the logistics industry, emphasizing that the logistics industry should gradually transition from a "quantity-oriented" model to an "efficiency-oriented" one, promoting the transformation and upgrading of the logistics industry's development model. Against this backdrop, measuring the efficiency of the logistics industry, identifying the formation paths of high logistics industry efficiency, and recognizing the key factors influencing logistics industry efficiency have practical significance for promoting the healthy and high-quality development of China's logistics industry. This paper, based on a holistic perspective and configurational thinking, uses the fuzzy-set Qualitative Comparative Analysis (fsQCA) method to explore the multiple concurrent causal relationships and diverse paths of factors influencing high logistics industry efficiency from both internal and external industry dimensions.

References

[1] Mei Guoping, Gong Yaling, Wan Jianxiang, Ji Kaiwen. Research on the Efficiency Measure-ment of the Logistics Industry in East China Based on the Three-Stage DEA Model [J]. Man-agement Review, 2019, 31(10): 234-241.

[2] Liu Chengliang, Guan Mingming. Spatial Evolution and Influencing Factors of China's Logis-tics Industry Efficiency under Low-Carbon Constraints [J]. Scientia Geographica Sinica, 2017, 37(12): 1805-1814.

[3] Maersk. Maersk Group's Impact Report in China [R]. Copenhagen, Denmark: Maersk Group, 2015.

[4] State Bureau of Quality and Technical Supervision, National Standardization Management Committee. GB/T18354-2006 National Standard of the People's Republic of China Logis-tics Terms [S]. Beijing: China Standard Press, 2006.

[5] Zheng Wenling. Research on Cultivating the Core Competitiveness of Regional Logistics Industry Based on Sustainable Development. Logistics Technology, 2014, 33(3): 50-52.

[6] Hamdan A, Rogers K J. Evaluating the efficiency of 3PL logistics operations [J]. Interna-tional Journal of Production Economics, 2008, 113(1): 235-244.

[7] Fan Gang, et al. Outline of Macroeconomic Theory of Public Ownership [M]. Shanghai: Shanghai People's Publishing House, 1995: 14-28.

[8] Hai Feng, Zhang Lili, An Jin. How to Understand Regional Logistics China Logistics and Procurement, 2003(10): 30-31.

[9] Mcade L, Sarkis J. Strategic analysis of logistics and supply chain management systems us-ing the analytical network process [J]. Transportation Research Part E: Logistics and Transportation Review, 1998, 34(3): 201-215.

[10] Dai Q, Yang J. Input-output Analysis on the Contribution of Logistics Park Construction to Regional Economic Development [J]. Procedia Social and Behavioral Sciences, 2013, 96: 599-608.

[11] K. Boulding. General Systems Theory: The Skeleton of Science [J]. Management Science, 1956, 2: 197-208.

[12] J.E. Delery, D.H. Doty. Modes of Theorizing in Strategic Human Resource Management: Tests of Universalistic, Contingency, and Configurational Performance Predictions [J]. Academy of Management Journal, 1996, 39: 802-835.