Operational Risk and Safety Management in the Nigerian Residential Real Estate Sector Using Artificial Intelligence

References

[1] Adelekan IO (2016). Flood risk management in the coastal city of Lagos, Nigeria. International Journal of Urban Sustainable Development, 8(2), 240–248.

[2] Aibinu AA, Jagboro GO (2002). The effects of construction delays on project delivery in Nigerian construction industry. International Journal of Project Management, 20(8), 593–599. https://doi.org/10.1016/S0263-7863(02)00028-5

[3] Aigbavboa C, Thwala W (2016). Occupational Health and Safety Implementation Barriers and Safety Performance in the Construction Industry. University of Johannesburg.

[4] Aven T (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1–13. https://doi.org/10.1016/j.ejor.2015.12.023

[5] Awoyera PO, Alfa J, Odetoyan A, Akinwumi II (2021). Building collapse in Nigeria during recent years: Causes, effects and way forward. IOP Conference Series: Materials Science and Engineering, 1036, 012021. https://doi.org/10.1088/1757-899X/1036/1/012021

[6] Basel Committee on Banking Supervision (2011). Principles for the Sound Management of Operational Risk. Bank for International Settlements.

[7] Boje C, Guerriero A, Kubicki S, Rezgui Y (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114, 103179. https://doi.org/10.1016/j.autcon.2020.103179

[8] Bryman A (2016). Social Research Methods. 5th ed. Oxford University Press.

[9] Burke MJ, Sarpy SA, Tesluk PE, Smith-Crowe K (2006). Relative effectiveness of worker safety and health training methods. American Journal of Public Health, 96(2), 315–324. https://doi.org/10.2105/AJPH.2004.059840

[10] Cheng MY, Kusoemo D, Gosno RA (2020). Text mining-based construction site accident classification using hybrid supervised machine learning. Automation in Construction, 118, 103265. https://doi.org/10.1016/j.autcon.2020.103265

[11] Choudhry RM, Fang D, Mohamed S (2007). The nature of safety culture: A survey of the state-of-the-art. Safety Science, 45(10), 993–1012. https://doi.org/10.1016/j.ssci.2006.09.003

[12] Committee of Sponsoring Organizations of the Treadway Commission (COSO) (2017). Enterprise Risk Management: Integrating with Strategy and Performance. COSO.

[13] Cooper MD (2000). Towards a model of safety culture. Safety Science, 36(2), 111–136. https://doi.org/10.1016/S0925-7535(00)00035-7

[14] Council for the Regulation of Engineering in Nigeria (COREN) (2021). Regulation of Engineering Practice and Enforcement in Nigeria. COREN.

[15] Davis FD (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

[16] Ebekozien A (2020). Corrupt acts in the Nigerian construction industry: Is the ruling party fighting corruption? Journal of Contemporary African Studies, 38(3), 348–365. https://doi.org/10.1080/02589001.2020.1773420

[17] El-Sayegh SM (2008). Risk assessment and allocation in the UAE construction industry. International Journal of Project Management, 26(4), 431–438. https://doi.org/10.1016/j.ijproman.2007.07.004

[18] Fadamiro JA, Atolagbe AMO (2006). Urban design and quality of housing delivery in Nigeria. Journal of Environmental Technology, 5(1), 26–35.

[19] Fang W, Ding L, Zhong B, Love PED, Luo H (2018). Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach. Advanced Engineering Informatics, 37, 139–149. https://doi.org/10.1016/j.aei.2018.05.003

[20] Fellows RF, Liu AMM (2015). Research Methods for Construction. 4th ed. Wiley-Blackwell.

[21] Field A (2018). Discovering Statistics Using IBM SPSS Statistics. 5th ed. Sage.

[22] Hair JF, Black WC, Babin BJ, Anderson RE (2014). Multivariate Data Analysis. 7th ed. Pearson.

[23] Hamdan A, Alshibani A (2022). Computer vision-based safety compliance monitoring for construction sites. Journal of Building Engineering, 52, 104468.

[24] Hillson D (2017). Managing Risk in Projects. 2nd ed. Routledge.

[25] Hinze JW (2011). Construction Safety. 2nd ed. Pearson Prentice Hall.

[26] Hughes P, Ferrett E (2020). Introduction to Health and Safety in Construction. 6th ed. Routledge.

[27] International Labour Organization (2001). Safety and Health in Construction (ILO Code of Practice). ILO.

[28] International Labour Organization (2020). Global Trends in Occupational Safety and Health in Construction. ILO.

[29] International Organization for Standardization (ISO) (2018a). Risk Management: Guidelines (ISO 31000:2018).

[30] International Organization for Standardization (ISO) (2018b). Occupational Health and Safety Management Systems: Requirements with Guidance for Use (ISO 45001:2018).

[31] Juran JM, Godfrey AB (1999). Juran's Quality Handbook. 5th ed. McGraw-Hill.

[32] Kasim OF, Alabi AM, Wusu S (2020). Risk assessment for hazard exposure and its consequences on housing construction sites in Lagos, Nigeria. Acta Structilia, 27(1), 59–84. https://doi.org/10.18820/24150487/as27i1.3

[33] Kheni NA, Dainty ARJ, Gibb AGF (2008). Health and safety management within small- and medium-sized enterprises (SMEs) in developing countries: Study of contextual influences. Journal of Construction Engineering and Management, 134(7), 1104–1115. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:7(1104)

[34] Lagos State Building Control Agency (LASBCA) (2022). Building Control and Enforcement Report. LASBCA.

[35] Li CZ, Xue F, Li X, Hong J, Shen GQ (2019). 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

[36] Love PED, Ahiaga-Dagbui DD, Irani Z (2018). Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation. Transportation Research Part A: Policy and Practice, 118, 782–795.

[37] Lynn MR (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–385.

[38] Marzouk M, Hisham M (2014). Applications of building information modeling in cost estimation of infrastructure bridges. International Journal of 3-D Information Modeling, 3(3), 1–18.

[39] National Bureau of Statistics (2023). Nigerian Gross Domestic Product Report. National Bureau of Statistics.

[40] Neal A, Griffin MA (2006). A study of the lagged relationships among safety climate, safety motivation, safety behavior, and accidents at the individual and group levels. Journal of Applied Psychology, 91(4), 946–953. https://doi.org/10.1037/0021-9010.91.4.946

[41] Odeh AM, Battaineh HT (2002). Causes of construction delay: Traditional contracts. International Journal of Project Management, 20(1), 67–73. https://doi.org/10.1016/S0263-7863(00)00037-5

[42] Oke AE, Aigbavboa CO, Semenya K (2021). Regulatory and institutional constraints to construction safety compliance in developing countries. Journal of Engineering, Design and Technology, 19(1), 45–60.

[43] Oloyede SA (2010). Building collapse in Nigeria: The trend of casualties in the last decade. International Journal of Civil and Environmental Engineering, 10(6), 32–38.

[44] Pan Y, Zhang L (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, 103517. https://doi.org/10.1016/j.autcon.2020.103517

[45] Project Management Institute (2021). A Guide to the Project Management Body of Knowledge (PMBOK Guide). 7th ed. Project Management Institute.

[46] Reason J (1997). Managing the Risks of Organizational Accidents. Ashgate.

[47] Russell S, Norvig P (2021). Artificial Intelligence: A Modern Approach. 4th ed. Pearson.

[48] Saunders M, Lewis P, Thornhill A (2019). Research Methods for Business Students. 8th ed. Pearson.

[49] Schein EH (2010). Organizational Culture and Leadership. 4th ed. Jossey-Bass.

[50] Succar B (2009). Building information modelling framework: A research and delivery foundation for industry stakeholders. Automation in Construction, 18(3), 357–375. https://doi.org/10.1016/j.autcon.2008.10.003

[51] UN-Habitat (2022). World Cities Report 2022: Envisaging the Future of Cities. United Nations Human Settlements Programme.

[52] Venkatesh V, Davis FD (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

[53] Windapo AO, Rotimi JO (2012). Contemporary issues in building collapse and its implications for sustainable development. Buildings, 2(3), 283–299. https://doi.org/10.3390/buildings2030283

[54] World Bank (2021). Nigeria Development Update: Rising to the Challenge. World Bank Group.

[55] Yamane T (1967). Statistics: An Introductory Analysis. 2nd ed. Harper and Row.

[56] Zikmund WG, Babin BJ, Carr JC, Griffin M (2013). Business Research Methods. 9th ed. Cengage Learning.

[57] Zou PXW, Zhang G, Wang J (2007). Understanding the key risks in construction projects in China. International Journal of Project Management, 25(6), 601–614. https://doi.org/10.1016/j.ijproman.2007.03.001

@article{1724358,
      author = {Sunkanmi Bamisaye, Sunday Olufemi Adesogan},
      title = {Operational Risk and Safety Management in the Nigerian Residential Real Estate Sector Using Artificial Intelligence},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {3800-3816},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724358.pdf},
      abstract = {Operational failures and weak safety management continue to undermine residential real estate development in Nigeria, while the potential of artificial intelligence (AI) to address them remains little examined. This study assessed operational risks and safety management practices, their association with safety compliance and project performance, and the potential of AI in residential real estate development in Lagos and Ibadan. A cross-sectional quantitative survey, guided by Risk Management Theory, Safety Culture Theory and the Technology Acceptance Model, used a structured five-point Likert questionnaire; 141 of 180 questionnaires distributed were valid (78.3%). Data were analyzed with means, standard deviations, rankings and Pearson correlation. All sixteen operational risks scored 4.04-4.46, led by inadequate worker skills and training (4.46) and poor supervision (4.45); human-related and technical/structural risks ranked first and second. PPE training (4.01) and provision (3.91) were the highest-rated safety practices, whereas risk registers and non-punitive near-miss reporting (2.98) were lowest. Management commitment (4.50) and safety culture (4.49) were rated the strongest influences on compliance. Operational risk was positively and significantly correlated with safety compliance (r = 0.879) and project performance (r = 0.840), both p < 0.001. Respondents rated AI's potential highly (means 4.09-4.48), with predictive risk assessment ranked highest in impact, but endorsed inadequate digital infrastructure (4.48) as the main barrier. Visible protection appears to outpace preventive safety management, and AI is a conditional rather than demonstrated solution. Associations do not establish causation.},
      keywords = {operational risk management; construction safety; residential real estate; artificial intelligence; safety compliance; construction project performance; Nigeria},
      month = {October},
  }