Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Eng    2022, Vol. 9 Issue (2) : 281-296    https://doi.org/10.1007/s42524-020-0122-4
RESEARCH ARTICLE
Identifying critical factors that affect the application of information technology in construction management: A case study of China
Meishan JIA1(), Youquan XU1, Pengwang HE1, Lingmin ZHAO2
1. School of Management Engineering, Shandong Jianzhu University, Jinan 250101, China
2. School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China
 Download: PDF(495 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

The new mode for managing construction projects with information technology (IT) has attracted worldwide attention because it can help managers and workers perform tasks and bring potential benefits, such as high-quality products and accident-free production, effectively. However, the application of IT in site have not achieved expected results because it is faced with many constraints caused by internal factors from enterprises and projects and external factors from the government and environment. Although many relevant studies have discussed the constraints of implementing different IT and devices in the construction industry or site, few articles have specifically focused on identifying and analyzing the indicator system. In this work, we took China as the background, scientifically identified 23 influential factors that affect the implementation of IT in construction management through literature review and expert interviews. Subsequently, questionnaires were issued, and Delphi method was used to obtain empirical data that aimed at four different management fields. Then, an efficient and convenient method called DEMATEL was used to deal with these data. Afterward, the factors were divided into four categories, namely, core, diving, independent, and impact factors. Finally, the similarities and differences of the analysis results from the four fields were compared, and the key factors were identified. Results show that the cross-domain talent ability, concept and value cognition, and organization structure are core factors in all management fields that should be managed first along with the IT innovation ability in the enterprise. The formulation of technical standards and related device and training input are also critical in specific fields. Strategic planning plays a role in macro control and promotion. Data management and application, platform construction, solution, and collaboration have direct impacts on information management. The research results provide suggestions not only for the government to formulate effective policies for IT application and promotion in construction industry, but also for enterprises to take measures in improving management efficiency in the construction site and realizing its substantial benefits.
Keywords information technology      construction manage-ment      influential factors      empirical study      DEMATEL     
Corresponding Author(s): Meishan JIA   
Just Accepted Date: 28 June 2020   Online First Date: 28 July 2020    Issue Date: 25 May 2022
 Cite this article:   
Meishan JIA,Youquan XU,Pengwang HE, et al. Identifying critical factors that affect the application of information technology in construction management: A case study of China[J]. Front. Eng, 2022, 9(2): 281-296.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-020-0122-4
https://academic.hep.com.cn/fem/EN/Y2022/V9/I2/281
Fig.1  Delphi execution process.
Fig.2  Research method and technical route.
No. Work unit Education background Position/Title Years of working experience Number of projects involved
1 Shandong Museum of Science and Technology Bachelor Project Manager 17 3
2 Shandong Museum of Science and Technology Master Expert Engineer 8 3
3 Jinan Hanyujingu High-rise Project Bachelor Project Manager 13 2
4 Jinan Hanyujingu High-rise Project Bachelor Expert Engineer 7 1
5 Jinan Metro Line 3 Project Master Project Manager 11 2
6 Shandong Jianzhu University Doctor Professor 22 4
7 Huazhong University of Science and Technology Doctor Professor 25 6
8 Huazhong University of Science and Technology Doctor Associate Professor 12 5
9 Inter Construction Project Management Co., Ltd. Doctor Project Director 18 3
10 Ministry of Housing and Urban-Rural Development Master Division Chief 14 6
Tab.1  Expert profiles
Fig.3  Causal diagram.
Number Factor Paper Policy
A B C D E F G H I J K L M N O
F1 Abilities of cross-domain talents √ √ √ √ √ √ √ √
F2 Product functional maturity √ √ √ √ √ √
F3 Hardware, software and system integration √ √ √ √ √ √ √
F4 Data interaction √ √ √ √ √
F5 Information platform integrity √ √ √ √ √ √
F6 Ability of data application √ √ √ √
F7 Improper implementation √ √ √ √
F8 System and equipment input √ √ √ √ √ √ √ √
F9 Training and management input √ √ √ √
F10 Participants’ cooperate willingness √ √ √ √ √
F11 Adaptability of organization structure √ √ √ √
F12 Solution and process suitability √ √ √ √ √
F13 Organization and profession synergy √ √ √ √ √
F14 Concept and value cognition √ √ √ √ √ √
F15 Vanity project √ √ √
F16 Personnel incentive and performance appraisal √ √ √ √ √
F17 Enterprise IT innovation ability √ √ √ √
F18 Knowledge management √ √ √ √ √
F19 Enterprise ITSP √ √
F20 Government supervision √ √ √ √ √
F21 Formulation of regulations and technical standards √ √ √ √ √ √ √ √ √
F22 Formulation of incentive policy √ √ √ √ √ √ √
Tab.2  Initial index system
Classification Number Influencing factor Factor specification
Internal Technology F1 Abilities of cross-domain talents The abilities of technicians and managers to apply IT into engineering practice
F2 Level of site network facilities The level of Internet of Things and Internet such as Wi-Fi and mobile communication networks
F3 Product function maturity The practicability of technical products, which means whether the product meets the actual demand of users
F4 Degree of system integration The ability to integrate various data acquisition equipment, application software and subsystems into an unified and coordinated system
F5 Degree of data interaction The unify of data standards and the compatibility and interoperability of system
F6 Level of information platform The functionality level of integrated supervision platform, and the perfection of each module
F7 Ability to analyze and apply data The ability to process, analyze, and apply data so as to manage construction and integrate site data with business operation
Economy F8 System and equipment input The input of purchasing, researching, and maintaining information system and equipment
F9 Training and management input The input of organization, coordination and management, as well as technical personnel training
F10 Enterprise financial capacity The investment strength of enterprise information construction
Organization F11 Cooperative willingness of participants The willingness of owners, supervisors, builders, regulatory authorities and suppliers to participate in information management process
F12 Adaptability of organization structure The adaptability of enterprise and project organization structure to information management
F13 Solution and process suitability The applicability of integral solution and implementation process to the project
F14 Organization and profession synergy Level of horizontal information sharing and vertical information transmission among organizations and professions
Culture F15 Concept and value cognition The attitude of leaders toward changing traditional construction and management mode though new technology
F16 Enterprise IT innovation capacity Enterprise information product R&D and secondary development ability, organization structure and implementation process innovation ability
F17 Enterprise knowledge management The enterprise management of monitoring data, project information, business data, documents and policies for further use
F18 Enterprise strategic planning The enterprise long-term strategic planning for information development
External Government F19 The government supervision The strength of supervision, inspection and acceptance projects by relevant departments
F20 Regulation and technical standards formulation The policies and technical standards issued by the government to guide the implementation of IT
F21 Stimulating and supporting policy formulation The incentive policies such as technical assistance and financial support adopted by the government to promote IT implementation in CM
Environment F22 Demonstration projects The number of pilot and demonstration projects for reference
F23 Project scale and nature The size, duration, nature and surroundings of project
Tab.3  Final index system
F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14 F15 F16 F17 F18 F19 F20 F21 F22 F23
F1 0 0 2 1 1 2 3 0 0 0 1 2 3 2 1 3 2 3 0 0 0 0 0
F2 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0
F3 0 0 0 0 0 2 2 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0
F4 0 0 1 0 1 3 3 0 0 0 1 0 0 2 0 0 1 0 0 0 0 0 0
F5 0 0 1 0 1 2 2 0 0 0 2 0 0 3 0 0 2 0 0 0 0 0 0
F6 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0
F7 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 3 0 0 0 0 0 0
F8 0 0 3 1 1 3 1 0 1 0 0 0 0 1 0 2 1 0 0 0 0 0 0
F9 3 0 1 0 0 1 3 0 0 0 0 2 2 3 0 0 1 0 0 0 0 0 0
F10 2 1 2 1 1 1 0 3 3 0 1 0 0 0 0 1 0 0 0 0 0 0 0
F11 0 0 0 0 0 0 0 0 0 0 0 1 1 3 0 0 1 0 0 0 0 0 0
F12 1 0 0 0 0 2 1 0 1 0 1 1 2 3 0 2 1 2 0 0 0 0 0
F13 0 0 0 0 0 0 1 0 0 0 1 0 0 2 0 0 1 0 0 0 0 0 0
F14 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
F15 2 1 1 0 0 1 1 2 2 0 3 0 0 0 0 2 0 2 0 0 0 0 0
F16 0 0 2 1 1 2 1 1 1 0 0 2 3 2 0 0 0 2 0 0 0 0 0
F17 0 0 0 0 0 0 3 0 0 0 0 0 2 0 0 1 0 1 0 0 0 0 0
F18 3 0 1 0 0 1 1 2 2 0 0 1 1 1 1 2 0 0 0 0 0 0 0
F19 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 0 1 0
F20 1 3 3 2 1 3 1 3 1 0 1 0 3 1 1 1 0 2 2 0 1 3 0
F21 1 1 0 0 0 0 0 1 1 2 3 0 0 0 2 0 0 1 1 0 0 2 0
F22 1 2 1 0 0 2 1 1 1 0 0 1 1 1 2 1 0 1 0 0 1 0 0
F23 2 3 0 0 0 0 0 2 0 2 0 2 1 1 1 0 0 0 1 0 0 0 0
Tab.4  Direct impact matrix
Factor R C R + C R - C Factor R C R + C R - C
F1 0.9862 0.6269 1.6131 0.3593 F13 0.1760 0.8859 1.0619 -0.7099
F2 0.1056 0.3821 0.4877 -0.2765 F14 0.1764 1.3824 1.5588 -1.2060
F3 0.2453 0.7262 0.9715 -0.4809 F15 0.8343 0.3195 1.1538 0.5148
F4 0.4877 0.2877 0.7754 0.2000 F16 0.7588 0.6553 1.4141 0.1035
F5 0.5260 0.2583 0.7843 0.2677 F17 0.2755 1.0317 1.3072 -0.7562
F6 0.2119 0.9339 1.1458 -0.7220 F18 0.8129 0.6228 1.4357 0.1901
F7 0.1472 1.4475 1.5947 -1.3003 F19 0.4305 0.1196 0.5501 0.3109
F8 0.5925 0.5725 1.1650 0.0200 F20 1.1328 0 1.1328 1.1328
F9 0.6469 0.5881 1.2350 0.0588 F21 0.7437 0.0643 0.8080 0.6794
F10 0.8203 0.1214 0.9417 0.6989 F22 0.7730 0.1839 0.9569 0.5891
F11 0.2198 0.5555 0.7753 -0.3357 F23 0.7387 0 0.7387 0.7387
F12 0.7903 0.4734 1.2637 0.3169
Tab.5  Comprehensive influence relationship
Fig.4  Causal diagram of cost management.
Fig.5  Causal diagram of schedule management.
Fig.6  Causal diagram of quality management.
Fig.7  Causal diagram of safety management.
1 Z Asgari, F P Rahimian (2017). Advanced virtual reality applications and intelligent agents for construction process optimisation and defect prevention. Procedia Engineering, 196: 1130–1137
https://doi.org/10.1016/j.proeng.2017.08.070
2 S Azhar (2011). Building Information Modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadership and Management in Engineering, 11(3): 241–252
https://doi.org/10.1061/(ASCE)LM.1943-5630.0000127
3 BIM Committee (2012). AEC (UK) BIM Protocol, version 2.0
4 A M Blayse, K Manley (2004). Key influences on construction innovation. Construction Innovation, 4(3): 143–154
https://doi.org/10.1108/14714170410815060
5 C Cheng, K C Shen, X D Li, Z H Zhang (2017). Major barriers to different kinds of prefabricated public housing in China: The developers’ perspective. In: ICCREM—Prefabricated Buildings, Industrialized Construction, and Public-Private Partnerships, 79–88
https://doi.org/10.1061/9780784481059.008
6 K F Chien, Z H Wu, S C Huang (2014). Identifying and assessing critical risk factors for BIM projects: Empirical study. Automation in Construction, 45: 1–15
https://doi.org/10.1016/j.autcon.2014.04.012
7 I Cinite, L E Duxbury (2018). Measuring the behavioral properties of commitment and resistance to organizational change. Journal of Applied Behavioral Science, 54(2): 113–139
https://doi.org/10.1177/0021886318757997
8 X H Ding, J G Feng (2019). Construction and application of intelligent safety supervision system. Building Economy, 40(7): 12–15 (in Chinese)
9 X Ding, Z Li, B Lu (2019a). Study on the problems and countermeasures in the government’s promotion of intelligent construction site. Construction Safety, 34(2): 58–62 (in Chinese)
10 Z K Ding, S Liu, L H Liao, L Zhang (2019b). A digital construction framework integrating building information modeling and reverse engineering technologies for renovation projects. Automation in Construction, 102: 45–58
https://doi.org/10.1016/j.autcon.2019.02.012
11 R Eadie, M Browne, H Odeyinka, C McKeown, S McNiff (2013). BIM implementation throughout the UK construction project lifecycle: An analysis. Automation in Construction, 36: 145–151
https://doi.org/10.1016/j.autcon.2013.09.001
12 Y Y Gan, L Y Shen, J D Chen, V Tam, Y Tan, I Illankoon (2017). Critical factors affecting the quality of industrialized building system projects in China. Sustainability, 9(2): 216–228
https://doi.org/10.3390/su9020216
13 General Office of the State Council (2017). Opinions on promoting the sustainable and healthy development of the construction industry (in Chinese)
14 GOV.UK: Department for Business, Energy & Industrial Strategy (2013). Construction 2025. Industrial Strategy: Government and industry in partnership
15 S C Guan, M R Zhu (2010). Identification of the influence factors of enterprise informatization construction and empirical study basing on grey relational theory. In: Proceedings of 17th International Conference on Management Science & Engineering. Melbourne: IEEE, 240–246
16 A Hammad, F Vahdatikhaki, C Zhang, M Mawlana, A Doriani (2012). Towards the smart construction site: Improving productivity and safety of construction projects using multi-agent systems, real-time simulation and automated machine control. In: Proceedings of the Winter Simulation Conference (WSC). Berlin: IEEE, 1–12
17 M Hardie, G Newell (2011). Factors influencing technical innovation in construction SMEs: An Australian perspective. Engineering, Construction, and Architectural Management, 18(6): 618–636
https://doi.org/10.1108/09699981111180926
18 Y Jung, M Joo (2011). Building information modelling (BIM) framework for practical implementation. Automation in Construction, 20(2): 126–133
https://doi.org/10.1016/j.autcon.2010.09.010
19 T W Kang, C H Hong (2015). A study on software architecture for effective BIM/GIS-based facility management data integration. Automation in Construction, 54: 25–38
https://doi.org/10.1016/j.autcon.2015.03.019
20 J S J Koolwijk, C J Vanoel, J W F Waelink, R Vrijhoef (2018). Collaboration and integration in project-based supply chains in the construction industry. Journal of Management Engineering, 34(3): 34–42
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000592
21 R Kuenzel, J Teizer, M Mueller, A Blickle (2016). SmartSite: Intelligent and autonomous environments, machinery, and processes to realize smart road construction projects. Automation in Construction, 71: 21–33
https://doi.org/10.1016/j.autcon.2016.03.012
22 P T Lam, E H Chan, C S Poon, C K Chau, K P Chun (2010). Factors affecting the implementation of green specifications in construction. Journal of Environmental Management, 91(3): 654–661
https://doi.org/10.1016/j.jenvman.2009.09.029 pmid: 19853367
23 S Lee, J Yu, D Jeong (2015). BIM acceptance model in construction organizations. Journal of Management in Engineering, 31(3): 04014048
24 C Z D Li, F Xue, X Li, J Hong, G Q Shen (2018). An Internet of Things-enabled BIM platform for on-site assembly services in prefabricated construction. Automation in Construction, 89: 146–161
https://doi.org/10.1016/j.autcon.2018.01.001
25 R Y M Li (2015). Construction safety knowledge sharing via smart phone Apps and technologies. In: Zhang Y, Cristol D, eds. Handbook of Mobile Teaching and Learning. Heidelberg: Springer, 261–273
26 H Liu, M J Wang, M J Skibniewski, J S He, Z S Zhang (2014). Identification of critical success factors for construction innovation: From the perspective of strategic cooperation. Frontiers of Engineering Management, 1(2): 202–209
27 J Liu, G C Li, B B Zhang (2019). Research on intelligent site management system based on general integration methodology. Journal of Luoyang Institute of Science and Technology (Natural Science Edition), 29(1): 25–29 (in Chinese)
28 Z B Mao (2017). Promote the construction of smart sites to facilitate the sustainable and healthy development of the construction industry. Journal of Engineering Management, 31(5): 80–84 (in Chinese)
29 S McClary (2011). Two go at RICS after “improper behaviour”. Estates Gazette, 11/26/2011-1147-33
30 MOHURD (2016). Informatization development outline in construction industry (2016–2020) (in Chinese)
31 MOHURD (2017a). The 13th Five-Year Plan for scientific and technological innovation in urban and rural housing development(in Chinese)
32 MOHURD (2017b). 10 new technologies in the construction industry (in Chinese)
33 MOHURD (2018). Technical standard for supervision information system in construction site (in Chinese)
34 P T Nguyen, K D Vo, P T, Phan T A Nguyen, T M Cao, V D B Huynh, Q L H T T Nguyen (2018). Construction project quality management using Building Information Modeling 360 Field. International Journal of Advanced Computer Science and Applications, 9(10): 228–233
https://doi.org/10.14569/IJACSA.2018.091028
35 P Nitithamyong, M J Skibniewski (2006). Success/Failure factors and performance measures of web-based construction project management systems: Professionals’ viewpoint. Journal of Construction Engineering and Management, 132(1): 80–87
https://doi.org/10.1061/(ASCE)0733-9364(2006)132:1(80)
36 Y H Niu, W S Lu, K Chen, G G Huang, C Anumba (2016). Smart construction objects. Journal of Computing in Civil Engineering, 30(4): 04015070
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000550
37 J Ren, M Lützen, H B Rasmussen (2018). Identification of success factors for green shipping with measurement of greenness based on ANP and ISM. In: Lee P W, Yang Z, eds. Multi-Criteria Decision Making in Maritime Studies and Logistics, International Series in Operations Research & Management Science. Cham: Springer, 260: 79–103
https://doi.org/10.1007/978-3-319-62338-2_4
38 A Rossi, Y Vila, F Lusiani, L Barsotti, L Sani, P Ceccarelli, M Lanzetta (2019). Embedded smart sensor device in construction site machinery. Computers in Industry, 108: 12–20
https://doi.org/10.1016/j.compind.2019.02.008
39 G, Wright G Rowe (2013). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15(4): 353–375
40 K, Hyland P, Sawang S Sargent (2012). Factors influencing the adoption of information technology in a construction business. Australasian Journal of Construction Economics and Building, 12(2): 72–86
41 T A Saurin (2016). Safety inspections in construction sites: A systems thinking perspective. Accident Analysis & Prevention, 93: 240–250
https://doi.org/10.1016/j.aap.2015.10.032 pmid: 26554499
42 S M Seyed-Hosseini, N Safaei, M J Asgharpour (2006). Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique. Reliability Engineering & System Safety, 91(8): 872–881
https://doi.org/10.1016/j.ress.2005.09.005
43 J H Shi, L Liang, T L Shi (2019). The discussion of application of intelligent site system in construction process. China Southern Agricultural Machinery, 50(11): 203–253 (in Chinese)
44 M Silverio-Fernández, S Renukappa, S Suresh (2019).Evaluating critical success factors for implementing smart devices in the construction industry: An empirical study in the Dominican Republic. Engineering, Construction and Architectural Management, 7(1): 15–29
45 J Teizer (2015). Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites. Advanced Engineering Informatics, 29(2): 225–238
https://doi.org/10.1016/j.aei.2015.03.006
46 The Japanese Cabinet (2013). Japan Revitalisation Strategy
47 J Wang, H Yuan (2011). Factors affecting contractors’ risk attitudes in construction projects: Case study from China. International Journal of Project Management, 29(2): 209–219
https://doi.org/10.1016/j.ijproman.2010.02.006
48 G Xu, Z T Dai, X J Cui (2019). Research and implementation of whole-process quality and safety supervision platform for intelligent construction sites. Urban Geotechnical Investigation & Surveying, (1): 37–40 (in Chinese)
49 L Zhang, X Sun, H Xue (2019). Identifying critical risks in Sponge City PPP projects using DEMATEL method: A case study of China. Journal of Cleaner Production, 226: 949–958
https://doi.org/10.1016/j.jclepro.2019.04.067
50 Y C Zhang (2018). Discussion on construction demand and information integration application on smart site. Intelligent Building and Smart City, (5): 86–88 (in Chinese)
51 D Zhou, L Zhang, H Li (2006). A study of the system’s hierarchical structure through integration of DEMATEL and ISM. In: International Conference on Machine Learning and Cybernetics. Dalian: IEEE, 1449–1453
52 H T Zhou, H W Wang, W Zeng (2018). Smart construction site in mega construction projects: A case study on island tunneling project of Hong Kong–Zhuhai–Macao Bridge. Frontiers of Engineering Management, 5(1): 78–87
53 J P Ziarnik, G S Bernstein (1982). A critical examination of the effect of inservice training on staff performance. Mental Retardation, 20(3): 109–114
pmid: 7132718
[1] Conrad BOTON, Éric ST-PIERRE, Gabriel LEFEBVRE. Should medium-sized contractors still implement home-made information technologies on construction sites?[J]. Front. Eng, 2020, 7(1): 142-158.
[2] Shanlin YANG, Jianmin WANG, Leyuan SHI, Yuejin TAN, Fei QIAO. Engineering management for high-end equipment intelligent manufacturing[J]. Front. Eng, 2018, 5(4): 420-450.
[3] Guang-she Jia,Song-yu Yan,Wen-jun Wang,Ralf Müller,Chen Lin. An Empirical Study on the Generation Mechanism of NIMBY Conflicts of Construction Projects[J]. Front. Eng, 2016, 3(1): 39-49.
[4] Lin-xiu Wang,Mu-xi Yu,Si-jia Wang. Driving Factors of Green Mining in Coal Mining Enterprises in China[J]. Front. Eng, 2015, 2(3): 211-223.
[5] Mirosɬaw J. Skibniewski. Research Trends in Information Technology Applications in Construction Safety Engineering and Management[J]. Front. Eng, 2014, 1(3): 246-259.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed