Level Measurement and Obstacle Diagnosis of High-Quality Development of The Construction Industry Empowered by Digital Intelligence

Authors

  • Yuning Zhu

DOI:

https://doi.org/10.54097/hrh71m75

Keywords:

Digital Intelligence, High Quality Development in Construction Industry, Level Measurement, Barrier Degree Analysis

Abstract

As a fusion application of digital technology and intelligence, digital intellectualization has a great role in promoting economic development and social life change and realizing high-quality development. Based on the panel data of 31 provinces in China from 2012 to 2021, this paper selects indicators from three dimensions: infrastructure conditions of empowerment, subjects of empowerment, and subjects of empowerment, and constructs an evaluation system to measure the level of high-quality development of the construction empowered by digital intelligence and analyze the obstacle factors. It is found that the overall trend of high-quality development level of the digital intelligence-enabled construction industry from 2012 to 2021 is upward, and there is a wide gap in the level of regions, with the south, north, and east areas having a high level of high-quality development of the construction industry empowered by digital intelligence, the center and southwest area having a low to medium level, the northeast and northwest area having a low level. The results of the obstacle degree show that input indicators, output indicators, industrial efficiency, broadband penetration rate, and facility sharing are the main obstacle factors restricting the high-quality development of the construction industry empowered by digital intelligence. In the future, accelerating the high-quality development of the construction industry empowered by digital intelligence should start with strengthening regional collaborative development, providing a good policy environment and resource protection, accelerating the absorption and proliferation of digital intelligence industry technology, and strengthening the sharing of facilities and resources among relevant participants in the construction industry.

Downloads

Download data is not yet available.

References

[1] J. D. Sun, M. Zheng, J. W. Fu. Connotation and policy suggestions for high-quality development of the construction industry in the new era [J]. Construction Economy, 2019, 40(5): 5-9.

[2] BARBOSA F, WOETZEL J, MISCHKE. Reinventing Construction: a Route of Higher Productivity [J]. McKinsey Global Institute, 2017, 32(4): 12-18.

[3] J. Chen, Y. H. Liu. Digital intelligence enables operations management transformation: from supply chain to supply chain ecosystem [J]. Management World, 2021, 37(11): 227-240+14.

[4] WU B, HU Y, GU X. Achieving greater educational impact through data intelligence: practice, challenges and expectations of education [M]. Singapore: World Scientific, 2022, 10(3): 8-9.

[5] B. Wang. What is digital intelligence: A study of the multiple meanings of the concept of digital intelligence [J]. Journal of Intelligence, 2023, 42(7): 71-76.

[6] MITHAS S, MCFARLAN F W. What is digital intelligence? [J]. IT Professional, 2017, 19(4): 3-6.

[7] MUNIR A, BLASCH E, KWON J, et al. Artificial intelligence and data fusion at the edge [J]. IEEE Aerospace and Electronic Systems Magazine, 2021, 36(7): 62-78.

[8] VIAL G. Understanding digital transformation: a review and a research agenda [J]. The Journal of Strategic Information Systems, 2019, 28(2): 118-144.

[9] J. F. Zhang, L. H. Xiao, S. J. Xu. Digitalization: the great convergence of digital government, digital economy and digital society [J]. Management of State-owned Enterprises, 2022(5): 18.

[10] G. Q. Chen, M. Ren, Q. Wei, G. X. Hua, C. Yi. Digital intelligence empowerment: a new leap in information systems research [J]. Management World, 2022, 38(1): 180-196.

[11] S. C. Li. Analysis of the realization path of high-quality development of state-owned enterprises in the new era - research based on the construction industry [J]. Academic Research, 2020(3): 88-94.

[12] Y. Xiang, M. Zheng, T. H. Dai. Research on motivating factors and influencing mechanism of high-quality development of China's construction industry [J]. Construction Economy, 2019, 40(12): 15-20.

[13] C. Q. Yang, H. P. Xiong, M. Z. Li. Research on the evaluation of high-quality development of construction industry in Hubei Province [J]. Construction Economy, 2020, 41(12): 15-20.

[14] D. Q. Yang, R. J. Yang, A. B. Yue. A preliminary study on the evaluation framework of construction industry development quality based on SSM [J]. Construction Economy, 2020, 41(7): 106-111.

[15] CRAWFORD P, VOGL B. Measuring productivity in the construction industry [J]. Building Research & Information, 2006, 15(11): 32-39.

[16] LES R, STEVEN R. Evaluation of trends in the UK construction industry using growth and productivity accounts [J]. Construction Management and Economics, 2011, 33(12): 40-47.

[17] LOW E, JOHN G. The measurement of productivity in the construction industry [J]. Construction Management & Economics, 1987, 5(2): 101-113.

[18] Y. N. Yang, Z. C. Zhang, Y. D. Ma, X. Y. Sun. Analysis of digital transformation path of construction industry from the perspective of techno-logic [J]. Science and Technology Management Research, 2022, 42(24): 137-142.

[19] FORCAEL E, FERRARI I, OPAZO-VEGA A, ET AL. Construction 4.0: A literature review [J]. Sustainability, 2020, 12(22): 1-28.

[20] FLÁVIO C, JOSÉ P, HELENA B, PAULO J. Additive manufacturing as an enabling technology for digital construction: A perspective on Construction 4.0 [J]. Automation in Construction, 2019, 103(8): 251–267.

[21] Y. Pan, L. M. Zhang. Roles of artificial intelligence in construction engineering and management: a critical review and future trends. [J]. Automation in Construction, 2021, 122(3): 89-95.

[22] B. J. Su, F. Y. Lu, F. Zhu. Y. L. Li. Development level of China's digital economy: spatio-temporal characteristics, dynamic evolution and influencing factors [J]. Operations Research and Management, 2022, 31(9): 161-168.

[23] G. Y. Wu, G. G. Pang, J. H. Tang, J. D. Liu. Measurement, regional differences, and spatial and temporal evolution of the development level of rural digital economy in China [J]. Journal of Hunan Agricultural University (Social Science Edition), 2022, 23(4): 15-27.

[24] W. Z. Wang, Z. F. Zi, L. T. Zhang. The construction and evaluation of high-quality development measurement system of construction industry in the new era [J]. Construction Economy, 2019, 40(12): 21-26.

[25] X. H. Wu, L. T. Zhang. Research on comprehensive evaluation of high-quality development of construction industry--Taking Jiangsu Province as an example [J]. Construction Economy, 2021, 42(12): 20-26.

[26] L. Wang, H. M. Li. Measurement of high-quality development level and path selection of construction industry--Taking Shaanxi Province as an example [J]. Construction Economy, 2020, 41(9): 24-28.

[27] LINDA K, INETA G, JĀNIS V, NATALIJA L. Environmental Aspects of the Construction Industry Development in Latvia [J]. Baltic Journal of Real Estate Economics and Construction Management, 2017, 5(1): 7-12.

[28] M. Xu, C. Dang, D. Y. Liu. Digital economy leads economic high-quality development: mechanism and research outlook [J]. Contemporary Economic Management, 2023, 45(02): 66-72.

[29] J. Wang. The improvement of resource allocation efficiency in China: conditions, changes and suggestions [J]. Southern Economy, 2018(9): 1-9.

[30] H. D. Liu, R. Ji. Research on the mechanism and effect of digital economy to promote industrial structure upgrading [J]. Science and Technology Progress and Countermeasures, 2023, 40(1): 61-70.

[31] D. X. Wang, Z. Q. Peng, L. Y. Li. Measuring and evaluating the level of integrated development of digital economy and agriculture in China [J]. China Rural Economy, 2023(6): 48.

Downloads

Published

21-02-2025

Issue

Section

Articles

How to Cite

Zhu, Y. (2025). Level Measurement and Obstacle Diagnosis of High-Quality Development of The Construction Industry Empowered by Digital Intelligence. Frontiers in Business, Economics and Management, 18(2), 6-16. https://doi.org/10.54097/hrh71m75