Safety and Health at Work

Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency (산업안전보건연구원)
권호 표지
DOI QR코드

DOI QR Code

The Influence of Organizational External Factors on Construction Risk Management among Nigerian Construction Companies

  • Adeleke, A.Q. (Faculty of Industrial Management, Universiti Malaysia Pahang) ;
  • Bahaudin, A.Y. (School of Technology Management and Logistics, Universiti Utara Malaysia) ;
  • Kamaruddeen, A.M. (Quantity Surveying Programme, School of Built Environment, University College of Technology Sarawak) ;
  • Bamgbade, J.A. (Faculty of Engineering, Computing and Science, Swinburne University of Technology) ;
  • Salimon, Maruf Gbadebo (Department of Marketing, School of Business Management, Universiti Utara Malaysia) ;
  • Khan, Muhammad Waris Ali (Faculty of Industrial Management, Universiti Malaysia Pahang) ;
  • Sorooshian, Shahryar (Faculty of Industrial Management, Universiti Malaysia Pahang)
  • Received : 2016.10.25
  • Accepted : 2017.05.22
  • Published : 2018.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

Background: Substantial empirical research has shown conflicting results regarding the influence of organizational external factors on construction risk management, suggesting the necessity to introduce a moderator into the study. The present research confirmed whether rules and regulations matter on the relationships between organizational external factors and construction risk management. Methods: Based on discouragement and organizational control theory, this research examined the effects of organizational external factors and rules and regulations on construction risk management among 238 employees operating in construction companies in Abuja and Lagos, Nigeria. A personally administered questionnaire was used to acquire the data. The data were analyzed using partial least squares structural equation modeling. Results: A significant positive relationship between organizational external factors and construction risk management was asserted. This study also found a significant positive relationship between rules and regulations and construction risk management. As anticipated, rules and regulations were found to moderate the relationship between organizational external factors and construction risk management, with a significant positive result. Similarly, a significant interaction effect was also found between rules and regulations and organizational external factors. Implications of the research from a Nigerian point of view have also been discussed. Conclusion: Political, economy, and technology factors helped the construction companies to reduce the chance of risk occurrence during the construction activities. Rules and regulations also helped to lessen the rate of accidents involving construction workers as well as the duration of the projects. Similarly, the influence of the organizational external factors with rules and regulations on construction risk management has proven that most of the construction companies that implement the aforementioned factors have the chance to deliver their projects within the stipulated time, cost, and qualities, which can be used as a yardstick to measure a good project.

Keywords

References

  1. Project Management Body of Knowledge (PMBOK). A guide to the project management body of knowledge. Newtown Square (PA): Project Management Institute; 2000.
  2. Baloi D, Price AD. Modelling global risk factors affecting construction cost performance. Int J Proj Manag 2003;214:261-9.
  3. Zikmund WG, Babin BJ, Carr JC, Griffin M. Business research methods. 8th ed. Mason (OH): South Western College Publishing; 2009.
  4. Aibinu A, Jagboro G. The effects of construction delays on project delivery in Nigerian construction company. Int J Proj Manag 2002;208:593-9.
  5. Ojo G. An assessment of the construction site risk-related factors. Proceedings of The 40th Annual General Meeting/Conference of the Nigerian Institute of Building. Asaba (Delta State); 2010.
  6. Adeleke AQ, Bahaudin AY, Kamaruddeen AM. Rules and regulations as potential moderator on the relationship between organizational internal and external factors with effective construction risk management in Nigerian construction companies: a proposed framework. Proceedings of The International Conference on Applied Science and Technology 2016 (ICAST'16). Melville, (NY): AIP Publishing; 2016. 020008 p.
  7. Niu Y. The performance and problems of affordable housing policy in China: the estimations of benefits, costs and affordability. Int J Hous Mark Anal 2008;12:125-46.
  8. Iroegbu AN. Housing in Nigeria: a role of the construction company. In: Kalu AI, Chima GN, editors. Housing development in Nigeria: concepts, issues and strategies. Abakaliki (Nigeria): Pauliton Press; 2005. p. 225-34.
  9. Algahtany M, Alhammadi Y, Kashiwagi D. Introducing a new risk management model to the SaudiArabian construction industry. Procedia Eng 2016;145:940-7. https://doi.org/10.1016/j.proeng.2016.04.122
  10. Alaghbari WE, Kadir MRA. Salim. The significant factors causing delay of building construction projects in Malaysia. Eng Constr Archit Manag 2007;142:192-206.
  11. Karim Jallow A, Demian PN, Baldwi A, Anumba C. An empirical study of the complexity of requirements management in construction projects. Eng Constr Archit Manag 2014;215:505-31.
  12. Geraldi JG, Lee-Kelley L, Kutsch E. The Titanic sunk, so what? Project manager response to unexpected events. Int J Proj Manag 2010;286:547-58.
  13. Doloi H. Relational partnerships: the importance of communication, trust and confidence and joint risk management in achieving project success. Constr Manag Econ 2009;2711:1099-109.
  14. Moe TL, Pathranarakul P. An integrated approach to natural disaster management: public project management and its critical success factors. Disaster Prev Manag 2006;153:396-413.
  15. Abu Hassan BAB, Ali K, Onyeizu EN, Yusof MN. Evaluating risk management practices in construction company: evidence from Oman. Int J Acad Res 2012;42:32-7.
  16. Simpkins RA. How great leaders avoid disaster: the value of contingency planning. Bus Strategy Ser 2009;102:104-8.
  17. Ho SS, Pike RH. The use of risk analysis techniques in capital investment appraisal. In: Ansell J, Wharton F, editors. Risk analysis assessment and management. Hoboken (NJ): John Wiley & Sons; 1992. p. 71-94.
  18. Kangari R, Riggs LS. Construction risk assessment by linguistics. IEEE Trans Eng Manag 1989;362:126-31.
  19. Israelsson N, Hansson B. Factors influencing flexibility in buildings. Struct Surv 2009;272:138-47.
  20. Scupola A. The adoption of Internet commerce by SMEs in the south of Italy: An environmental, technological and organizational perspective. J Glob Inf Technol Manag 2003;61:52-71.
  21. Lewis W, Agarwal R, Sambamurthy V. Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Q 2003:657-78.
  22. Gibb K. Delivering new affordable housing in the age of austerity: housing policy in Scotland. Int J Hous Mark Anal 2011;44:357-68.
  23. Ismail E. Industrialized building system for housing in Malaysia. Proceedings of The 6th Asia Pacific Science and Technology Management Seminar. Tokyo (Japan); 2001.
  24. Kumaraswamy MM, Chan DW. Contributors to construction delays. Constr Manag Econ 1998;161:17-29.
  25. Jabnoun N, Sedrani K. TQM, culture, and performance in UAE manufacturing firms. Qual Manag J 2005;124:8 [Abstract].
  26. Aniekwu A. The business environment of the construction company in Nigeria. Constr Manag Econ 1995;136:445-55.
  27. Blalock HM. The presidential address: measurement and conceptualization problems: the major obstacle to integrating theory and research. Am Sociol Rev 1979;44:881-94. https://doi.org/10.2307/2094714
  28. Flamholtz EG, Das TK, Tsui AS. Toward an integrative framework of organizational control. Account Organ Soc 1985;101:35-50.
  29. Karim N, Rahman I, Memmon A, Jamil N, Azis A. Significant risk factors in constructionprojects: contractor'sperception.Proceedingsof TheHumanities, Science and Engineering (CHUSER), 2012 IEEE Colloquium. Piscataway (NJ): IEEE; 2012.
  30. Adams O. Contractor development in Nigeria: perceptions of contractors and professionals. Constr Manag Econ 1997;151:95-108.
  31. Ugochukwu SC, Onyekwena T. Participation of indigenous contractors in Nigerian public sector, construction projects and their challenges in managing working capital. Int J Civil Eng 2014;1:1-21.
  32. Ukoha OM, Beamish JO. Predictors of housing satisfaction in Abuja, Nigeria. Hous Soc 1996;233:26-46.
  33. Chin WW, Marcolin BL, Newsted PR. A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo Simulation study and an electronic-mail emotion/adoption study. Inf Syst Res 2003;14:189-217.
  34. Ringle CM, Wende S, Will S. SmartPLS 2.0 beta. Hamburg (Germany): University of Hamburg; 2005.
  35. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 7th ed. Upper Saddle River (NJ): Prentice Hall; 2010.
  36. Wold H. Causal flows with latent variables: partings of the ways in the light of NIPALS modelling. Eur Econ Rev 1974;5:67-86. https://doi.org/10.1016/0014-2921(74)90008-7
  37. Wold H. Partial least squares. In: Kotz S, Johnson NL, editors. Encyclopedia of statistical sciences. New York: Wiley; 1985. p. 581-91.
  38. Vinzi V, Trinchera L, Amato S. PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H, editors. Handbook of partial least squares. Berlin (Germany): Springer; 2010. p. 47-82.
  39. Duarte P, Raposo M. A PLS model to study brand preference: an application to the mobile phone market. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H, editors. Handbook of partial least squares. Berlin (Germany): Springer; 2010. p. 449-85.
  40. Gerlach RW, Kowalski BR, Wold HOA. Partial least-squares path modelling with latent variables. Anal Chim Acta 1979;112:417-21. https://doi.org/10.1016/S0003-2670(01)85039-X
  41. Lohmoller JB. Latent variable path modeling with partial least squares. Heidelberg (Germany): Physica-Verlag Heidelberg; 1989.
  42. Fornell C, Bookstein FL. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. J Mark Res 1982;19:440-52.
  43. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: indeed a silver bullet. J Mark Theory Pract 2011;18:139-52.
  44. Hulland J. Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strateg Manag J 1999;20:195-204.
  45. Ringle CM, Sarstedt M, Straub DW. A critical look at the use of PLS-SEM in MIS Q 2012;361:314 [Abstract].
  46. Hair JF, Sarstedt M, Pieper TM, Ringle CM. The use of partial least squares structural equation modelling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plann 2012;45:320-40. https://doi.org/10.1016/j.lrp.2012.09.008
  47. Hair JF, Ringle CM, Sarstedt M. Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Plann 2013;4612:1-12.
  48. Peng DX, Lai F. Using partial least squares in operations management research: a practical guideline and summary of past research. J Oper Manag 2012;30:467-80. https://doi.org/10.1016/j.jom.2012.06.002
  49. Hair JF, Hult GTM, Ringle CM, Sarstedt M. A primer on partial least squares structural equation modelling (PLS-SEM). Thousand Oaks (CA): Sage; 2014.
  50. Barclay D, Thompson R, Higgins C. The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration. Technol Stud 1995;2:285-309.
  51. Chin WW. The partial least squares approach to structural equation modeling. In: Marcoulides GA, editor. Modern methods for business research. Oxon (UK): Psychology Press; 1998. p. 295-336.
  52. Bagozzi R, Yi Y. On the evaluation of structural equation models. J Acad Mark Sci 1998;16:74-94.
  53. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 1981;18:39-50. https://doi.org/10.2307/3151312
  54. Kimm HD. Three criteria for the use of one-tailed tests. Psychol Bull 1957;54:351-3. https://doi.org/10.1037/h0046737
  55. Cho HC, Abe S. Is two-tailed testing for directional research hypotheses tests legitimate? J Bus Res 2013;66:1261-6.
  56. Falk RF, Miller NB. A primer for soft modeling. Akron (OH): The University of Akron Press; 1992.
  57. Cohen J. Statistical power analysis for the behavioural sciences. Hillsdale (NJ): Lawrence Erlbaum; 1988.
  58. Selya AS, Rose JS, Dierker LC, Hedeker D, Mermelstein RJ. A practical guide to calculating Cohen's $f^{2}$, a measure of local effect size, from PROC MIXED. Front Psychol 2012;3:111-6.
  59. Wilson B, Callaghan W, Ringle C, Henseler J. Exploring causal path directionality for a marketing model using Cohen's path method. Paper presented at the PLS'07 International Symposium on PLS and Related Methods-Causalities Explored by Indirect Observation; 2007.
  60. Geisser S. A predictive approach to the random effect model. Biometrika 1974;61:101-7.
  61. Stone M. Cross-validatory choiceand assessment of statistical predictions. J R Stat Soc Series B Stat Methodol 1974;36:111-47.
  62. Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. In: Sinkovics RR, Ghauri PN, editors. Advances in international marketing. Bingley (UK): Emerald; 2009. p. 277-320.
  63. Helm S, Eggert A, Garnefeld I. Modeling the impact of corporate reputation on customer satisfaction and loyalty using partial least squares. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H, editors. Handbook of partial least squares. Berlin (Germany): Springer; 2010. p. 515-34.
  64. Greenberg J, Baron RA. Behavior in organizations. 9th ed. Upper Saddle River (NJ): Pearson Prentice Hall; 2008.
  65. Jaworski BJ. Toward a theory of marketing control: environmental context, control types, and consequences. J Mark 1988;52:23-39. https://doi.org/10.2307/1251447
  66. Ahmed SM, Ahmad R, Saram D, Darshi D. Risk management trends in the Hong Kong construction company: a comparison of contractors and owners perceptions. Eng Constr Architect Manag 1999;63:225-34.
  67. Alinaitwe HM. An assessment of clients' performance in having an efficient building process in Uganda. J Civil Eng Manag 2008;142:73-8.
  68. Bresnen M, Marshall N. Building partnerships: case studies of cliente contractor collaboration in the UK construction company. Constr Manag Econ 2000;187:819-32.

Cited by

  1. Analysis of Complexity of Unsafe Behavior in Construction Teams and a Multiagent Simulation vol.2018, pp.None, 2018, https://doi.org/10.1155/2018/6568719
  2. Corporate sustainability and environmental disasters: A case of the 2011 Thai floods vol.15, pp.3, 2018, https://doi.org/10.22495/cocv15i3art11
  3. Risk Assessment of Ex-Post Transaction Cost in Construction Projects Using Structural Equation Modeling vol.10, pp.11, 2018, https://doi.org/10.3390/su10114017
  4. Development of knowledge management in integration management systems in order to increase the organisational performance of construction companies vol.276, pp.None, 2019, https://doi.org/10.1051/matecconf/201927602015
  5. Assessment of Risk Management Maturity of Construction Organisations in Joint Venture Projects vol.9, pp.1, 2019, https://doi.org/10.2478/jeppm-2019-0004
  6. Development of information systems in integrated management systems in order to increase organisational performance in a construction company vol.258, pp.None, 2018, https://doi.org/10.1088/1755-1315/258/1/012012
  7. A Life-Cycle Framework for Managing Risks in Public Private Partnership Housing Projects vol.10, pp.1, 2018, https://doi.org/10.2478/jeppm-2020-0004
  8. Value Generation in Front-End Design of Social Housing with QFD and Multiattribute Utility Theory vol.146, pp.4, 2018, https://doi.org/10.1061/(asce)co.1943-7862.0001787
  9. Sustainable Risk Assessment through the Analysis of Financial Losses from Third-Party Damage in Bridge Construction vol.12, pp.8, 2018, https://doi.org/10.3390/su12083435
  10. Risk Factors Affecting the Performance of Construction Projects in Gaza Strip vol.14, pp.1, 2020, https://doi.org/10.2174/1874149502014010094
  11. Does government policy matter? Factors influencing contractors' risk attitudes in the Malaysian construction industry: A structural equation modelling analysis vol.10, pp.1, 2018, https://doi.org/10.14424/ijcscm100120-01-29
  12. Investigating the underlying attributes of a reliability-focused improvement and culture in manufacturing and product development organizations vol.9, pp.3, 2018, https://doi.org/10.1007/s41872-020-00123-2
  13. Multi-objective mathematical model based on fuzzy hybrid multi-criteria decision-making and FMEA approach for the risks of oil and gas projects vol.18, pp.6, 2018, https://doi.org/10.1108/jedt-01-2020-0020
  14. Government policy as a key moderator to contractors’ risk attitudes among Malaysian construction companies vol.18, pp.6, 2018, https://doi.org/10.1108/jedt-08-2019-0192
  15. AHP-Systems Thinking Analyses for Kaizen Costing Implementation in the Construction Industry vol.10, pp.12, 2018, https://doi.org/10.3390/buildings10120230
  16. A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies : Cost overrun in construction projects vol.14, pp.2, 2018, https://doi.org/10.1108/ijmpb-02-2019-0047
  17. Research for Risk Management of Construction Projects in Taiwan vol.13, pp.4, 2018, https://doi.org/10.3390/su13042034
  18. “I Want to Serve, but the Public Does Not Understand”-An Approach to Employees’ Intention to Stay in the Malaysian Construction Companies vol.4, pp.1, 2018, https://doi.org/10.3390/asi4010012
  19. Coercive pressure as a moderator of organizational structure and risk management: Empirical evidence from Malaysian construction industry vol.77, pp.None, 2018, https://doi.org/10.1016/j.jsr.2021.02.011
  20. An investigation of the influence of economic cycles on safety performance in Western Australia vol.138, pp.None, 2018, https://doi.org/10.1016/j.ssci.2021.105230
  21. Qualitative Analysis of Risks Affecting the Delivery of Land Surveying Project Activities vol.13, pp.22, 2018, https://doi.org/10.3390/su132212645
  22. Managing External Risk Factors on Oil and Gas Project Success: A Dream for All Firms vol.7, pp.4, 2018, https://doi.org/10.1061/ajrua6.0001187