Case Studies
Oct 31, 2023

Driving Factors for the Adoption of Digital Twin Technology Implementation for Construction Project Performance in Nigeria

Publication: Journal of Construction Engineering and Management
Volume 150, Issue 1

Abstract

The adoption of digital twin technology (DTT) for enhancing construction project performance, sustainability, and safety of construction workers, among others, is common practice in developed countries. Meanwhile, construction projects in developing nations are suffering from poor outcomes, which could benefit from DTT. Therefore, this research investigates the drivers of the adoption of DTT implementation in the Nigerian construction industry. Close-ended questionnaires were administered to digitally inclined professionals using purposive and snowballing techniques to elicit necessary information on the drivers of DTT implementation. These respondents include architects, engineers, quantity surveyors, and builders in the study area. It was found that technological advancement/trend, reliable data storage, safety, availability of software, customer satisfaction, and accessibility were the top-ranked drivers for implementing DTT. A Shapiro-Wilk test was conducted to know whether the data are normally distributed or not, which led to the use of the Kruskal-Wallis H-test. The Kruskal-Wallis test revealed that government policies have diverging views among the professionals, whereas other factors have converging opinions among the respondents. Factor analysis was conducted to group the key drivers for the implementation of DTT into innovation, operation, quality, performance, and policy drivers. The findings of this study will provide a reference point for researchers and construction organizations on the driving force that brings about the implementation of DTT in a business context and the construction industry. This study realizes that one of the key drivers of the adoption of this technology is a desire for innovation and technological advancement that can be achieved when the technology performs to the expectation of clients, organizations, and the architecture, engineering, and construction (AEC) industry. The study also suggests ways of implementing DTT in the construction industry as well.

Practical Applications

A digital twin (DT) is a virtual replica or representation of a physical object or product. This research proposes practical implications of DT technology adoption in a developing country with a focus on major drivers for enhancing the uptake of the technology in the construction industry. This will give more insight to stakeholders on major drivers to improve its implementation in the construction and other sectors of the country as well as other developing countries. Technological advancement in different countries as shown in the study will expose professionals to the benefits of digital technologies as well as keep the professionals and other stakeholders abreast of the potential benefits. This can be realized when the government is at the frontline in the usage of DT for public construction projects. In addition, there is a need for policy formulation for its implementation in the construction industry to ensure projects are sustainable and delivered to the satisfaction of concerned stakeholders.

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Data Availability Statement

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

References

Adafin, J., S. Wilkinson, J. O. Rotimi, C. MacGregor, J. Tookey, and R. Potangaroa. 2022. “Creating a case for innovation acceleration in the New Zealand building industry.” Constr. Innov. 22 (1): 185–204. https://doi.org/10.1108/CI-10-2018-0081.
Adeniyi, O., L. D. Ojo, O. A. Idowu, and S. B. Kolawole. 2020. “Compliance with the stipulated procurement process in local governments: A case from a developing nation.” Int. J. Procurement Manage. 13 (5): 678–700. https://doi.org/10.1504/IJPM.2020.110083.
Agostinelli, S., F. Cumo, G. Guidi, and C. Tomazzoli. 2021. “Cyber-physical systems improving building energy management: Digital twin and artificial intelligence.” Energies 14 (18): 2338. https://doi.org/10.3390/en14082338.
Agrawala, S. 2019. Using digital technologies to improve the design and enforcement of public policies. Paris: OECD Publishing. https://doi.org/10.1787/99b9ba70-en.
Akanmu, A. A., C. J. Anumba, and O. O. Ogunseiju. 2021. “Towards next generation cyber-physical systems and digital twins for construction.” J. Inf. Technol. Constr. 26 (Jun): 505–525. https://doi.org/10.36680/j.itcon.2021.027.
Alam, K. M., and A. El Saddik. 2017. “C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems.” IEEE Access 5 (Aug): 2050–2062. https://doi.org/10.1109/ACCESS.2017.2657006.
Andrews, D., G. Nicoletti, and C. Timiliotis. 2018. “Digital technology diffusion: A matter of capabilities, incentives or both?” Eur. Econ. Rev. 128 (Sep): 103513. https://doi.org/10.1016/j.euroecorev.2020.103513.
Batty, M. 2018. “Digital twins.” Environ. Plann. B: Urban Anal. City Sci. 45 (5): 817–820. https://doi.org/10.1177/2399808318796416.
Boje, C., A. Guerrierro, S. Kubicki, and Y. Rezgui. 2020. “Towards a semantic construction digital twin: Directions for future.” Autom. Constr. 114 (Jun): 103179. https://doi.org/10.1016/j.autcon.2020.103179.
Brem, A., and K.-I. Voigt. 2019. “Integration of market pull and technology push in the corporate front end and innovation management insights from the German software industry.” Technovation 29 (5): 351–367. https://doi.org/10.1016/j.technovation.2008.06.003.
Corder, G. W., and D. I. Foreman. 2014. Non-parametric Statistics: A step-by-step approach. Hoboken, NJ: Wiley.
De Jong, J. P. J., and O. Marsili. 2016. “The fruit flies of innovations: A taxonomy of innovative small firms.” Res. Policy 35 (2): 213–229. https://doi.org/10.1016/j.respol.2005.09.007.
Del Giudice, M., V. Scuotto, A. Papa, S. Tarba, S. Bresciani, and M. Warkentin. 2021. “A self-tuning model for smart manufacturing SMEs: Effects on digital innovation.” J. Prod. Innov. Manage. 38 (1): 68–89. https://doi.org/10.1111/jpim.12560.
Deng, M., C. C. Menassa, and V. R. Kamat. 2021. “From BIM to digital twins: A systematic review of the evolution of intelligent building representations in the AEC-FM industry.” J. Inf. Technol. Constr. 26 (5): 58–83. https://doi.org/10.36680/j.itcon.2021.005.
Dixit, S., S. N. Mandal, A. Sawhney, and S. Singh. 2017. “Relationship between skill development and productivity in the construction sector: A literature review.” Int. J. Civ. Eng. Technol. 8 (8): 649–665.
Ebekozien, A., and C. Aigbavboa. 2021. “COVID-19 recovery for the Nigerian construction sites: The role of the fourth industrial revolution technologies.” Sustainable Cities Soc. 69 (Sep): 102803. https://doi.org/10.1016/j.scs.2021.102803.
Fachrunnisa, O., A. Adhiatma, M. N. Ab Majid, and N. Lukman. 2020. “Towards SMEs’ digital transformation: The role of agile leadership and strategic flexibility.” J. Small Bus. Strategy 30 (3): 65–85.
Fagbenle, O. I., F. A. Makinde, and A. O. Oluwunmi. 2011. “Factors influencing construction clients’/contractors’ choice of subcontractors in Nigeria.” J. Sustainable Dev. 4 (2): 254–259. https://doi.org/10.5539/jsd.v4n2p254.
Farmer, M. 2016. “The farmer review of the UK construction labour model: Modernise or die.” In Time to decide the industry’s future. London: Construction Leadership Council.
Ford, D. N., and C. M. Wolf. 2020. “Smart cities with digital twin systems for disaster management.” J. Manage. Eng. 36 (4): 04020027. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000779.
Fosnacht, K., S. Sarraf, E. Howe, and L. K. Peck. 2017. “How important are high response rates for college surveys?” Rev. Higher Educ. 40 (2): 245–265. https://doi.org/10.1353/rhe.2017.0003.
Fox, P., and M. Skitmore. 2007. “Factors facilitating construction industry development.” Build. Res. Inf. 35 (2): 178–188. https://doi.org/10.1080/09613210600980192.
Gangwar, H., H. Date, and R. Ramaswamy. 2015. “Understanding determinants of cloud computing adoption using an integrated TAM-TOE model.” J. Enterprise Inf. Manage. 28 (1): 107–130. https://doi.org/10.1108/JEIM-08-2013-0065.
Grieves, M. 2014. “Digital twin: Manufacturing excellence through virtual factory replication.” White Paper 1 (Jun): 1–7.
Hadi, N. U., N. Abdullah, and I. Sentosa. 2016. “An easy approach to exploratory factor analysis: Marketing perspective.” J. Educ. Soc. Res. 6 (1): 215.
Hair, J. F., W. C. Black, B. J. Babin, and R. E. Anderson. 2010. Multivariate data analysis. 7th ed. New York: Prentice-Hall.
Hazarika, N., and X. Zhang. 2019. “Factors that drive and sustain eco-innovation in the construction industry: The case of Hong Kong.” J. Cleaner Prod. 238 (11): 117816. https://doi.org/10.1016/j.jclepro.2019.117816.
Henson, R. K., and J. K. Roberts. 2006. “Use of exploratory factor analysis in published research: Common errors and some comment on improved practice.” Educ. Psychol. Meas. 66 (3): 393–416. https://doi.org/10.1177/0013164405282485.
Hou, L., H. Chen, G. Zhang, and X. Wang. 2021. “Deep learning-based applications for safety management in the AEC industry: A review.” Appl. Sci. 21 (2): 821. https://doi.org/10.3390/app11020821.
Jara, A. J., D. Genoud, and Y. Bocchi. 2014. “Big data in smart cities: From Poisson to human dynamics.” In Proc., 28th Int. Conf. on Advanced Information Networking and Applications Workshops (WAINA), 785–790. New York: IEEE. https://doi.org/10.1109/WAINA.2014.165.
Jo, S.-K., D.-H. Park, H. Park, and S.-H. Kim. 2018. “Smart livestock farms using digital twin: Feasibility study.” In Proc., 2018 Int. Conf. on Information and Communication Technology Convergence (ICTC), 1461–1463. New York: IEEE. https://doi.org/10.1109/ICTC.2018.8539516.
Kaewunruen, S., J. Sresakoolchai, W. Ma, and O. Phil-Ebosie. 2021. “Digital twin-aided vulnerability assessment and risk-based maintenance planning of bridge infrastructures exposed to extreme conditions.” Sustainability 13 (Apr): 2051. https://doi.org/10.3390/su13042051.
Kan, C., and C. Anumba. 2019. “Digital twins as the next phase of cyber-physical systems in construction.” In Proc., ASCE Int. Conf. on Computing in Civil Engineering 2019: Data, Sensing, and Analytics. Reston, VA: ASCE. https://doi.org/10.1061/9780784482438.033.
Kim, J., and S. A. Kim. 2020. “Lifespan prediction technique for digital twin-based noise barrier tunnels.” Sustainability 12 (7): 1–14. https://doi.org/10.3390/su12072940.
Knapp, T., J. Mukherjee, H. Zuback, T. Wei, A. Palmer, T. De, and G. DebRoy. 2017. “Building blocks for a digital twin of additive manufacturing.” Acta Mater. 135 (6): 390–399. https://doi.org/10.1016/j.actamat.2017.06.039.
Kothari, C. R. 2014. Research methodology: Methods and techniques, 3rd ed. New Delhi, India: New Age International.
Kshetri, N. 2021. “The economics of digital twins.” Computer 54 (4): 86–90. https://doi.org/10.1109/MC.2021.3055683.
Lee, D., and S. Lee. 2021. “Digital twin for supply chain coordination in modular construction.” Appl. Sci. 11 (Apr): 5909. https://doi.org/10.3390/app11135909.
Leng, J., H. Zhang, D. Yan, Q. Liu, X. Chen, and D. Zhang. 2019. “Digital twin-driven manufacturing cyber-physical system for parallel controlling of the smart workshop.” J. Ambient Intell. Humanized Comput. 10 (3): 1155–1166. https://doi.org/10.1007/s12652-018-0881-5.
Leung, M. Y., and P. Olomolaiye. 2010. “Risk and construction stakeholder management.” In Construction stakeholder management, 75–98. New York: Wiley.
Liu, Z., X. Meng, Z. Xing, and A. Jiang. 2021. “Digital twin-based safety risk coupling of prefabricated building hoisting.” Sensors 21 (Mar): 3583. https://doi.org/10.3390/s21113583.
Lu, Q., L. Chen, S. Li, and M. Pitt. 2020. “Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawings.” Autom. Constr. 115 (Jul): 103183. https://doi.org/10.1016/j.autcon.2020.103183.
Meža, S., A. Mauko Pranjić, R. Vezočnik, I. Osmokrović, and S. Lenart. 2021. “Digital twins and road construction using secondary raw materials.” J. Adv. Transp. 2021 (Jan): 1–12. https://doi.org/10.1155/2021/8833058.
Mihai, S., W. Davis, D. V. Hung, R. Trestian, M. Karamanoglu, and B. Barn. 2021. “A digital twin framework for predictive maintenance in industry 4.0.” In Proc., 2020 Int. Conf. on High-Performance Computing & Simulation. New York: IEEE.
Morledge, R. 2011. “Colleges as agents for construction innovation.” Constr. Innov. 11 (4): 441–451. https://doi.org/10.1108/14714171111175909.
OECD (Organisation for Economic Co-operation and Development). 2016. “OECD ministerial declaration on the digital economy: Innovation, growth and social prosperity (‘Cancún Declaration’).” Accessed July 29, 2022. https://www.oecd.org/internet/Digital-Economy-Ministerial-Declaration-2016.pdf.
Oke, A. E., and V. A. Arowoiya. 2021a. “Critical barriers to the adoption of augmented reality in developing countries: A case study of Nigeria.” J. Eng. Des. Technol. 20 (5): 1320–1333. https://doi.org/10.1108/JEDT-12-2020-0519.
Oke, A. E., and V. A. Arowoiya. 2021b. “Evaluation of internet of things application areas for sustainable construction.” Smart Sustainable Built Environ. 10 (3): 387–402. https://doi.org/10.1108/SASBE-11-2020-0167.
Oladinrin, O. T., J. W. Mesthrige, L. D. Ojo, J. Alencastro, and M. Rana. 2023. “Smart home technologies to facilitate ageing-in-place: Professionals perception.” Sustainability 15 (8): 6542. https://doi.org/10.3390/su15086542.
Oladinrin, T. O., D. R. Ogunsemi, and I. O. Aje. 2012. “Role of construction sector in economic growth: Empirical evidence from Nigeria.” FUTY J. Environ. 7 (1): 50–60. https://doi.org/10.4314/fje.v7i1.4.
Olawumi, T. O., D. W. Chan, J. K. Wong, and A. P. Chan. 2018. “Barriers to the integration of BIM and sustainability practices in construction projects: A Delphi survey of international experts.” J. Build. Eng. 20 (Oct): 60–71. https://doi.org/10.1016/j.jobe.2018.06.017.
Opoku, D.-G. J., S. Perera, R. Osei-Kyei, and M. Rashidi. 2021. “Digital twin application in the construction industry: A literature review.” J. Build. Eng. 40 (Aug): 102726. https://doi.org/10.1016/j.jobe.2021.102726.
Opoku, D.-G. J., S. Perera, R. Osei-Kyei, M. Rashidi, T. Famakinwa, and K. Bamdad. 2022. “Drivers for digital twin adoption in the construction industry: A systematic literature review.” Buildings 12 (Jul): 113. https://doi.org/10.3390/buildings12020113.
Ozturk, G. B. 2021. “Digital twin research in the AECO-FM industry.” J. Build. Eng. 40 (Aug): 102730. https://doi.org/10.1016/j.jobe.2021.102730.
Pallant, J. 2020. SPSS survival manual: A step by step guide to data analysis using IBM SPSS. 7th ed. New York: Routledge.
Patel, T., and V. Patel. 2020. “Data privacy in construction industry by privacy-preserving data mining (PPDM) approach.” Asian J. Civ. Eng. 21 (3): 505–515. https://doi.org/10.1007/s42107-020-00225-3.
Raz, Z., A. J. Shenhar, and D. Dvir. 2017. “Risk management, project success, and technological uncertainty.” R&D Manage. 32 (2): 101–109. https://doi.org/10.1111/1467-9310.00243.
Saleh, R. M., and A. Al-Swidi. 2019. “The adoption of green building practices in construction projects in Qatar: Preliminary study.” Manage. Environ. Q. Int. J. 30 (6): 1238–1255. https://doi.org/10.1108/MEQ-12-2018-0208.
Sepasgozar, S. M. E. 2021. “Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment.” Buildings. 11 (4): 151. https://doi.org/10.3390/buildings11040151.
Stojanovic, V., M. Trapp, R. Richter, B. Hagedorn, and J. Döllner. 2018. “Towards the generation of digital twins for facility management based on 3D point clouds.” In Proc., 34th Annual ARCOM Conf., edited by C. Gorse, and Neilson C. J., 270–279. Belfast, UK: Association of Researchers in Construction Management.
Tabachnick, B. G., and L. S. Fidell. 2007. “Using multivariate.” In Statistics. 5th ed. New York: Allyn and Bacon.
Taber, K. S. 2018. “The use of Cronbach’s alpha when developing and reporting research instruments in science education.” Res. Sci. Educ. 48 (Apr): 1273–1296. https://doi.org/10.1007/s11165-016-9602-2.
Tah, J. H. M., and V. A. Carr. 2020. “Proposal for construction project risk assessment using fuzzy logic.” Construct. Manage. Econ. 18 (4): 491–500. https://doi.org/10.1080/01446190050024905.
Tao, F., et al. 2019a. “Digital twin-driven product design framework.” Int. J. Prod. Res. 57 (12): 3935–3953. https://doi.org/10.1080/00207543.2018.1443229.
Tao, F., M. Zhang, and A. Y. C. Nee. 2019b. Digital twin-driven smart manufacturing. New York: Elsevier.
Tuegel, E. J., A. R. Ingraffe, T. G. Eason, and S. M. Spottswood. 2011. “Reengineering aircraft structural life prediction using a digital twin.” Int. J. Aerosp. Eng. 2011 (1): 1–14. https://doi.org/10.1155/2011/154798.
Uhlemann, T. H. J., C. Lehmann, and R. Steinhilper. 2017. “The digital twin: Realizing the cyber-physical production system for industry 4.0.” In Proc., 24th CIRP Conf. on Life Cycle Engineering, 335–340. Amsterdam, Netherlands: Elsevier.
Wang, H., X. Meng, and P. Mcgetrick. 2018. “Early contractor and facility management team involvement in the BIM environment.” Period. Polytech. 49 (1): 47–58. https://doi.org/10.3311/PPar.12693.
Wang, S. Q., M. F. Dulaimi, and M. Y. Aguria. 2014. “Risk management framework for construction projects in developing countries.” Construct. Manage. Econ. 22 (3): 237–252. https://doi.org/10.1080/0144619032000124689.
Williams, P., and E. Naumann. 2011. “Customer satisfaction and business performance: A firm-level analysis.” J. Serv. Marketing 25 (1): 20–32. https://doi.org/10.1108/08876041111107032.
Yahya, M. A., and M. N. S. Shafie. 2018. “Level of acceptance towards industrialized building system (IBS) in Malaysia.” Int. J. Sustainable Constr. Eng. Technol. 3 (1): 96–103.
Yap, B. 2013. “The application of principal component analysis in the selection of industry specific financial ratios.” Br. J. Econ. Manag. Trade 3 (3): 242–252. https://doi.org/10.9734/BJEMT/2013/4125.
Yu, G., S. Zhang, M. Hu, and Y. K. Wang. 2020. “Prediction of highway tunnel pavement performance based on digital twin and multiple time series stacking.” Adv. Civ. Eng. 2020 (May): 1–21. https://doi.org/10.1155/2020/8824135.
Zou, P. X. W., G. Zhang, and J. Wang. 2017. “Understanding the key risks in construction projects in China.” Int. J. Project Manage. 25 (6): 601–614. https://doi.org/10.1016/j.ijproman.2007.03.001.

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Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 150Issue 1January 2024

History

Received: Feb 15, 2023
Accepted: Sep 5, 2023
Published online: Oct 31, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 31, 2024

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Ph.D. Student, Dept. of Civil Engineering, Monash Univ., Clayton, VIC 3800, Australia (corresponding author). ORCID: https://orcid.org/0000-0002-7436-9762. Email: [email protected]
Senior Lecturer, Dept. of Quantity Surveying, Federal Univ. of Technology Akure, P.M.B. 704, Ondo 340252, Nigeria. ORCID: https://orcid.org/0000-0001-6551-8634. Email: [email protected]
Ph.D. Student, Dept. of Architecture and Civil Engineering, City Univ. of Hong Kong, Hong Kong Special Administrative Region, China. ORCID: https://orcid.org/0000-0001-9154-8001. Email: [email protected]
Ayomide Oluwafemi Adelusi [email protected]
Research Student, Dept. of Quantity Surveying, Federal Univ. of Technology Akure, P.M.B. 704, Ondo 340252, Nigeria. Email: [email protected]

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