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Sebola. M. P. (Chief Editor) Molokwane, T. S. (Quest Editor) Mnisi, M. L. Maleka, M. J. 2022 2023-04-10T08:05:12Z 2023-04-10T08:05:12Z 2022-09-14 Mnisi, M. L. and M. J. Maleka (2022) Predictors of Project Success at the South African Selected Energy State-Owned Enterprise. Proceedings of the International Conference on Public Administration and Development Alternatives. 165-174.<http://hdl.handle.net/11602/2424>. 9780992197193 (Print) 9780992197186 (e-book) http://hdl.handle.net/11602/2424 Mnisi M L, Maleka M J. Predictors of Project Success at the South African Selected Energy State-Owned Enterprise. 2022; http://hdl.handle.net/11602/2424. Mnisi, M. L., & Maleka, M. J. (2022). Predictors of Project Success at the South African Selected Energy State-Owned Enterprise. http://hdl.handle.net/11602/2424 Mnisi, M. L., and M. J. Maleka "Predictors of Project Success at the South African Selected Energy State-Owned Enterprise." (2022) http://hdl.handle.net/11602/2424 TY - Article AU - Mnisi, M. L. AU - Maleka, M. J. AB - The study aimed to identify which predictor predicts project success the highest at the selected South African Energy state-owned enterprises (SOE). This study is motivated by the highest failure rates of timeously implementing projects in time by SOEs in the South African context. The literature reviewed revealed many predictors of project success, but the common ones entail the governance committee, project manager, governance structures and project team. Hence in this study, the focus was on them. This study was quantitative and deductive, with a positivist paradigm influenced it. There were 130 employees involved in the projects at the business unit, and a census was used as a sampling strategy. Only 82 responded by completing a close-ended questionnaire which was distributed via SurveyMonkey. The response rate was 63.07%. Statistical techniques like Kurtosis and Skewness were used to determine if the data were normally distributed. Normality and other statistical techniques were calculated in Statistical Package for Social Science (SPSS) version 27. Through exploratory factor analysis (EFA), these factors were extracted: governance committee, project manager; governance structures and processes; project team and project success. For all the predictors and the target variable (i.e. project success), Cronbach's alphas ranged from 0.7 to 0.83. The data showed that 65.9% of the respondents were males and the Pearson correlation results showed that predictors positively correlated with the target variable. The regression results showed that project team was the highest predictor (β = 0.62, t = 5.15, p <0.01) and the second-highest predictor was project manager (β = 4.70, t = 4.70, p <0.01). The R-squared (r2) was 0.58, suggesting that the regression model only predicted 58% of project success at the selected energy SOE. Other predictors were not significant predictors of project success. The results imply that the business unit at the energy SOE should foster a teamwork culture and capitate and support project managers to enhance project success. DA - 2022-09-14 DB - ResearchSpace DP - Univen KW - Governance committee KW - Project manager KW - Governance structures and processes KW - Project team and project success LK - https://univendspace.univen.ac.za PY - 2022 SM - 9780992197193 (Print) SM - 9780992197186 (e-book) T1 - Predictors of Project Success at the South African Selected Energy State-Owned Enterprise TI - Predictors of Project Success at the South African Selected Energy State-Owned Enterprise UR - http://hdl.handle.net/11602/2424 ER - Journal articles of The 7th Annual International Conference on Public Administration and Development Alternatives, 14 - 16 September 2022 The study aimed to identify which predictor predicts project success the highest at the selected South African Energy state-owned enterprises (SOE). This study is motivated by the highest failure rates of timeously implementing projects in time by SOEs in the South African context. The literature reviewed revealed many predictors of project success, but the common ones entail the governance committee, project manager, governance structures and project team. Hence in this study, the focus was on them. This study was quantitative and deductive, with a positivist paradigm influenced it. There were 130 employees involved in the projects at the business unit, and a census was used as a sampling strategy. Only 82 responded by completing a close-ended questionnaire which was distributed via SurveyMonkey. The response rate was 63.07%. Statistical techniques like Kurtosis and Skewness were used to determine if the data were normally distributed. Normality and other statistical techniques were calculated in Statistical Package for Social Science (SPSS) version 27. Through exploratory factor analysis (EFA), these factors were extracted: governance committee, project manager; governance structures and processes; project team and project success. For all the predictors and the target variable (i.e. project success), Cronbach's alphas ranged from 0.7 to 0.83. The data showed that 65.9% of the respondents were males and the Pearson correlation results showed that predictors positively correlated with the target variable. The regression results showed that project team was the highest predictor (β = 0.62, t = 5.15, p <0.01) and the second-highest predictor was project manager (β = 4.70, t = 4.70, p <0.01). The R-squared (r2) was 0.58, suggesting that the regression model only predicted 58% of project success at the selected energy SOE. Other predictors were not significant predictors of project success. The results imply that the business unit at the energy SOE should foster a teamwork culture and capitate and support project managers to enhance project success. 1 online resource (9 pages) en PDF Governance committee UCTD Project manager Governance structures and processes Project team and project success Predictors of Project Success at the South African Selected Energy State-Owned Enterprise Article open.access