Journal of Enterprise and Business Intelligence


Analysis of Organizational Internal Factors Influencing Construction Risk Management in Construction Industries

Seow chee Hsin and Heng meng, Nottingham University Business School, University of Nottingham Malaysia, Malaysia.


Journal of Enterprise and Business Intelligence

Received On : 28 June 2021

Revised On : 18 August 2021

Accepted On : 26 October 2021

Published On : 05 January 2022

Volume 02, Issue 01

Pages : 044-055


Abstract


Many previously conducted empirical studies have displayed mixed findings about the influence of internal factors within an organization on the management of risk among construction companies. Hence, there is a need for introducing a moderating variable to this field of study. The aim of this research was to confirm whether coercive pressure plays a significant role in the relationship between the management of risk in construction and factors within the organization. Therefore, this study examined the influence that internal organizational factors and coercive pressure have on the management of construction risk through the lens of discouragement and organizational control theory, and institutional theory. Data were collected through the distribution of questionnaires involving 165 workers working in the Malaysian Peninsular construction companies, and the analysis was performed by means of partial least squares structural equation modelling. Results revealed a positively significant connection between internal organizational factors and the management of construction risk. Also, coercive pressure and the management of construction risk has a positively significant relationship. Coercive pressure mediated the connection that organizational internal factors had with the management of construction risk. A discussion of the research implications was done from the point of view of Malaysia. In conclusion, the reduction of risk incidents in the process of carrying out construction activities is being facilitated by active leadership and organizational culture. In addition, the rate and period at which accidents occur during and after the completion of construction activities are reduced by coercive pressure. In the same way, some influential internal factors of organizations as well as the introduced coercive pressure in the process of managing construction risk have established that many construction companies that apply the necessary internal factors as well as the coercive pressure introduced by the government are able to make delivery of their construction task at the specified cost, time, and qualities, thus establishing them as the correct standard for measuring a well-constructed project.


Keywords


Construction risk management; Organisational internal factors; Coercive pressure; Partial Least Squire (PLS); Construction Industry.


  1. V. Burlov and O. Lepеshkin, “Modeling the Process for Controlling a Road Traffic Safety System Based on Potentially Active Elements of Space and Time,” Transportation Research Procedia, vol. 20, pp. 94–99, 2017.
  2. L. Dimitriou, T. Tsekeris, and A. Stathopoulos, “Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow,” Transportation Research Part C: Emerging Technologies, vol. 16, no. 5, pp. 554–573, Oct. 2008.
  3. S. Ovcharenko, O. Balagin, and D. Balagin, “Modeling of Diagnostic Parameter for Assessing the Technical Condition of Sections of Locomotive Cooling System,” Transportation Research Procedia, vol. 54, pp. 827–833, 2021.
  4. R. Mesa-Arango and S. V. Ukkusuri, “Modeling the Car-Truck Interaction in a System-Optimal Dynamic Traffic Assignment Model,” Journal of Intelligent Transportation Systems, vol. 18, no. 4, pp. 327–338, Jun. 2013.
  5. M. R. Fatmi and M. A. Habib, “Microsimulation of vehicle transactions within a life-oriented integrated urban modeling system,” Transportation Research Part A: Policy and Practice, vol. 116, pp. 497–512, Oct. 2018.
  6. A. Almulhem, “Threat modeling of a multi-UAV system,” Transportation Research Part A: Policy and Practice, vol. 142, pp. 290–295, Dec. 2020.
  7. A. Faghih-Imani and N. Eluru, “A finite mixture modeling approach to examine New York City bicycle sharing system (CitiBike) users’ destination preferences,” Transportation, vol. 47, no. 2, pp. 529–553, Jun. 2018.
  8. G. Yadav and S. D. Ghodmare, “Transportation Planning Using Conventional Four Stage Modeling : An Attempt for Identification of Problems in a Transportation System,” International Journal of Scientific Research in Science and Technology, pp. 556–567, Aug. 2021.
  9. C. Kapuku, S.-Y. Kho, D.-K. Kim, and S.-H. Cho, “Modeling the competitiveness of a bike-sharing system using bicycle GPS and transit smartcard data,” Transportation Letters, pp. 1–5, Apr. 2020.
  10. A. Almulhem, “Threat modeling of a multi-UAV system,” Transportation Research Part A: Policy and Practice, vol. 142, pp. 290–295, Dec. 2020.
  11. K. Orłowski And Ł. Orłowski, “Modeling And Optimization Of The Combined Transportation System,” Systemy Logistyczne Wojsk, vol. 48, no. 1, pp. 216–235, Sep. 2018.
  12. F. Hossain, “Wind energy modeling and its application to the transportation system,” MOJ Civil Engineering, vol. 1, no. 1, Aug. 2016.
  13. C. Dolya, “Modeling of intercity passenger transportation system,” Technology audit and production reserves, vol. 2, no. 2(34), pp. 37–43, Mar. 2017.
  14. S. H. Cheon, C. Lee, and S. Shin, “Data-driven stochastic transit assignment modeling using an automatic fare collection system,” Transportation Research Part C: Emerging Technologies, vol. 98, pp. 239–254, Jan. 2019.
  15. X. (Jeff) Ban, L. Chu, R. Herring, and J. D. Margulici, “Sequential Modeling Framework for Optimal Sensor Placement for Multiple Intelligent Transportation System Applications,” Journal of Transportation Engineering, vol. 137, no. 2, pp. 112–120, Feb. 2011.
  16. X. Yang and W. W. Recker, “Modeling Dynamic Vehicle Navigation in a Self-Organizing, Peer-to-Peer, Distributed Traffic Information System,” Journal of Intelligent Transportation Systems, vol. 10, no. 4, pp. 185–204, Dec. 2006.
  17. T. A. Raianov, “Mathematical modeling of a strain gauge measurement system in MATLAB SIMULINK program,” Transportation Systems and Technology, vol. 6, no. 2, pp. 85–93, Jun. 2020.
  18. H. Fricke, M. Schlosser, M. A. Garcia, and M. Kaliske, “Embedding aircraft system modeling to ATM safety assessment techniques,” Transportation Research Interdisciplinary Perspectives, vol. 3, p. 100026, Dec. 2019.
  19. Z. Bede and T. Péter, “The mathematical modeling of reversible lane system,” Periodica Polytechnica Transportation Engineering, vol. 39, no. 1, p. 7, 2011.
  20. M. Kabir, “Immunization crisis may develop due to economic crisis during COVID-19 pandemic,” Pakistan BioMedical Journal, vol. 4, no. 1, Mar. 2021.
  21. A. V. Rodionov And O. Y. Grishina, “Economic Security Of Russia In The Context Of The Global Financial Crisis Deepening On The Background Of The Covid-19 Pandemic,” International Scientific Journal, no. 6, pp. 62–71, 2020.
  22. T. Kapecki, “Elements of Sustainable Development in the Context of the Environmental and Financial Crisis and the COVID-19 Pandemic,” Sustainability, vol. 12, no. 15, p. 6188, Jul. 2020.

Cite this article


Seow chee Hsin and Heng meng, “Analysis of Organizational Internal Factors Influencing Construction Risk Management in Construction Industries”, Journal of Enterprise and Business Intelligence, vol.2, no.1, pp. 044-055, January 2022. doi: 10.53759/5181/JEBI202202006.


Copyright


© 2022 Seow chee Hsin and Heng meng. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.