Factors That Influence The Appointment Of Service Providers In Gauteng Department Of Roads And Transport
DOI:
https://doi.org/10.14738/abr.89.9131Abstract
Many South African roads experience inadequate construction and maintenance. The road construction scantiness is evidenced by the Moloto Road, Gauteng, Mpumalanga and Limpopo. This was dubbed the “notorious road” for the high number of fatal accidents. An assessment of factors influencing the appointment of service providers might be a profound strategy to ensure efficiency and effectiveness in the construction and maintenance of South African roads. The factors under study included financial stability and resources indicators, quality, environmental and safety indicators, management ability, technical capacity and past experience and performance in appointing service providers.
This study sought to determine the importance and influence of the factors affecting the successful appointment of service providers within the Gauteng Department of Roads and Transport.
A quantitative descriptive approach, with the use of a structured questionnaire was utilised to collect data from a sample of 155 employees of Gauteng Department of Roads and Transport within the City of Tshwane. Descriptive and multivariate analyses including factor correlations and regression analyses were conducted.
Some important factors identified when choosing service providers were financial stability and resources indicators, quality, environmental and safety indicators, management ability and experience and performance. Technical capacity was found to be less important. A significant positive relationship was found between factors for choosing service providers and the selection of competent service providers.
The results of this study greatly corresponded with the literature review. The same factors were identified as influencing the successful appointment of service providers in this department.
References
Alzahrani, J.I. & Emsley, M.W. (2012). The impact of contractors attributes on construction project success: A post construction evaluation. International Journal of Project Management, 3. Doi: https://doi.org/10.1016/j.ijproman.2020.06.005.
Alzober, W. & Yaakub, A.R. (2014). Integrated model for selection the prequalification criteria of contractor. Lecture Notes on Software Engineering, 2, 233-237.
Blumberg, B., Cooper, D.R., & Schindler, P.S. (2011). Business research methods, McGraw-Hill, London.
Constitution of South Africa. (1996). The Constitution of the Republic of South Africa, Government Printers, Pretoria, South Africa.
Construction Industry Development Board (CIDB). (2007). Construction procurement practice guidelines: Applying the procurement prescripts of the CIDB in Public Sector, 5th ed., Pretoria, South Africa.
Department of Public Works. (1999). White paper: Creating an enabling environment for reconstruction, growth and development in the construction industry in South Africa. Government Printers, Pretoria, South Africa.
Doloi, H., Iyer, K. & Sawhney, A. (2011). Structural equation model for assessing impacts of contractor’s performance on project success. International Journal of Project Management, 29, 687-695. Doi: https://doi.org/10.1016/j.ijproman.2020.06.005.
Field, A. (2009). Discovering statistics using SPSS. 3rd ed., Sage, Thousand Oaks, CA.
Ginevicius, R. & Podvezko, V. (2008). Multi-criteria graphical analytical evaluation of the financial state of construction enterprises. Technological & Economic Development of Economy, 14, 452-461. Doi: 10.3846/tede
Huang, X. (2011). An analysis of the selection of project contractor in the construction management process. International Journal of Business & Management, 6, 184-189. 176
Lam, K. & Yu, C. (2011). A multiple kernel learning based decision support model for contractor pre-qualification. Automation in Construction, 20, 531-536.
Lo, W. & Yan, M.R. (2009). Evaluating qualifications based selection system: A simulation approach. Journal of Construction Engineering & Management, 135, 458-465.
Lo, W., Lin, C. & Yan, M. (2007). Contractor’s opportunistic bidding behavior and equilibrium price level in the construction market. Journal of Construction Engineering & anagement, 133, 409-416.
Loannou, P.G. & Awwad, R.E. (2010). Below average bidding method. Journal of Construction Engineering & Management, 136, 936-946.
Mathonsi, M.D. & Thwala, W.D. (2012). Factors influencing the selection of procurement systems in South African construction industry. African Journal of Business Management, 6, 3583-3594. doi: 10.5897/AJBM
Mitkus, S. & Trinkuniene, E. (2007). Analysis of criteria system model for construction contract evaluation. Technological & Economic Development of Economy, 13, 244-252.
Mitkus, S. & Trinkuniene, E. (2008). Reasoned decisions in construction contracts evaluation. Technological & Economic Development of Economy, 14, 402-416.
Mohemad, R., Hamdan, A.R., Othman, Z.A. & Noor, N.M.M. (2010). Decision support systems in construction tendering processes. Journal of Engineering & Management, 12, 14-27.
Plebankiewicz, E. (2008). Criteria of contractor selection used by polish investors. Journal of Civil Engineering & Management, 15, 377-385.
Plebankiewicz, E. (2010). Construction contractor prequalification from Polish clients’ perspective. Journal of Civil Engineering & Management, 16, 57-64.
Plebankiewicz, E. (2012). A fuzzy sets based contractor prequalification procedure. Automation in Construction, 22, 433-443.
Puri, D. & Tiwari, S. (2014). Evaluating the criteria for contractors’ selection and bid evaluation. International Journal of Engineering Science Invention, 3, 44-48.
Sawalhi, E.L., Eaton, D. & Rustom, R. (2008). bForecasting contractor performance using a neural network and genetic algorithm in a pre-qualification model. Construction Innovation: Information Process and Management, 8, 280-298.
Sipahi, S. & Timor, M. (2010). The analytic hierarchy process and analytic network process: an overview of applications. Management Decision, 48, 775-808.
Subramanian, N. & Ramanathan, R. (2012). A review of applications of analytical hierarchy process in operations management. International Journal of Production Economics, 138, 215-241.
Trivedi, M., Pandey, M. & Bhadoria, S. (2011). Prequalification of construction contractor using a FAHP. International Journal of Computer Applications, 28(1), 39-45.
Turban, E., Sharda, R. & Delen, D. (2011). Decision support and business intelligence systems. Pearson International, Upper Saddle River, New Jersey.
Turskis, Z. (2008). Multi-attribute contractors ranking method by applying ordering of feasible alternatives of solutions in terms of preferability technique. Technological & Economic Development of Economy, 14, 224-239.
Wang, J.J., Jing, Y.Y., Zhang, C.F. & Zhao, J.H. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision making. Renewable & Sustainable Energy Reviews, 13, 2263-2278.
Zavadskas, E.K., Liias, R. & Turskis, Z. (2008). Multi-attribute decision making methods for assessment of quality in bridges and roads construction: State of the art surveys. The Baltic Journal of Road & Bridge Engineering, 3, 152-160.
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