The Moderating Effect of Technology Utilization on Project Management Function and Project Performance

Authors

  • Hafnidar Abdul Rani UUM College of Business Universiti Utara Malaysia, Malaysia
  • Che Sobry Abdullah School of Technology Management & Logistics UUM College of Business Universiti Utara Malaysia, Malaysia
  • Shahimi Mohtar School of Technology Management & Logistics UUM College of Business Universiti Utara Malaysia, Malaysia

DOI:

https://doi.org/10.32890/mmj2013.17.6

Keywords:

Project management, project management function, technology utilization, project performance, partial correlation

Abstract

This study investigated the moderating effects of technology utilization on the relationship between project management function and project performance. The project management function element is operationalized by nine constructs and the technology utilization element examined in this study focuses on three factors. Data was collected using the questionnaire survey approach. This study employed the stratified random sampling procedure in selecting businesses and organizations, that included the construction industry in the sample. Four hundred and forty eight Aceh construction organizations participated in this study. Partial correlation analysis was utilized for hypotheses testing. In general, the result of the partial correlation analysis showed that technology utilization moderated the relationship between seven of the nine functions of project management and overall project performance. Individually, these three variables of technology utilization did not moderate the relationship between communication management and cost performance. The outcome of this study provided vital information on the relationship between project management function, technology utilization and project performance in the Aceh construction organizations. This study also provided an insight into further understanding of the issue of interface between project management function, technology utilization and project performance.

 

Downloads

Download data is not yet available.

References

Abednego, M. P., & Ogunlana, S. O. (2006). Good project governance for proper risk allocation in public–private partnerships in Indonesia. International Journal of Project Management, 24, 22-634.

Anantatmula, V. S. (2008). The role of technology in the project manager performance model. Project Management Journal, 39, 34-48.

Archibald, R. D. (1992). Managing hightechnology programs and projects, (2nd ed.). Wiley, Chichester.

Atkinson, R. (1999). Project management: Cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. International Journal of Project Management, 17(6), 337-342.

Baccarini, D. (1999). The logical framework method for defining project success. Project Management Journal, 30(4),25-32.

Back, W. E., & Bell, L. C. (1994). Quantifying benefits of electronic technology applied to bulk materials management. Houston: Construction Industry Action Group.

Baker, B. N., Murphy, D. C., & Fisher, D. (1988). Factors affecting project success-project management handbook. New York: Van Nostrand Reinhold Co.

Bay, F. A., Skitmore, M., & Susilawati. (2005). Maturity level of project management. Dimensi Teknik Sipil. Jurnal Keilmuwan dan Penerapan Teknik Sipil, 7(2), 8189, Petra Christian University Research Centre. http://mmj.uum.edu.my/

Haeckel, S. H., & Nolan, R. (1993). Managing by wire. Harvard Business Review, 71, 122-132.

Hartman, F., & Ashrafi, R. A. (2002). Project management in the information systems and information technologies industries. Project Management Journal, 33, 5-15.

Hatush, Z., & Skitmore, M. (1997). Evaluating contractor prequalification data: Selection criteria and project success factors. Construction Management and Economics Journal, 15(2), 129-147.

Henry. (2008). Metoda estimasi waktu penyelesaian konstruksi bangunan. (Theses from Civil Engineering). Indonesia: Institut Teknologi Bandung. Hoch, D. J., Roeding, C. R., Purkert, G., &

Lindner, S. K. (2000). Secrets of software success. Boston MA: Harvard Business School Press. Hughes, S. W., Tippett, D. D., & Thomas, W.

K. (2004). Measuring project success in the construction industry. Engineering Management Journal, 16, 3.

Jaselskis, E. J., & Ashley, D. B. (1991). Optimal allocation of project management resources for achieving success. Journal of Construction Engineering and Management, 117(2), 321-340.

Jha, K. N., & Iyer, K. C. (2007). Commitment, coordination, competence and the iron triangle. International Journal of Project Management, 2, 527-540.

Jiang, J. J. (2001). Software project risks and development focus. Project Management Journal, 32(1), 4-9.

Jones, C. (2004). Software project management practices: Failure versus success. Software Productivity Research LLC.

Jugdev, K., & Muller, R. (2005). A retrospective look at our evolving understanding of project success. Project Management Institute, 36(4),19-31.

Keen, P. W. (1991). Shaping the future. Boston: Harvard Business School Press.

Kerzner, H. (2000). Applied project management – Best practices on implementation. New York: John Wiley & Sons. analysis utilizing 4D visualization technologies. Journal of Computer-Aided Civil and Infrastructure Engineering, 21, 498-513.

Diallo, A., & Thuillier, D. (2005). The success of international development projects, trust and communication: An African perspective. International Journal of Project Management, 23, 237-252.

Dvir, D., Raz, T., & Shenhar, A. J. (2003). An empirical analysis of the relationship between project planning and project success. International Journal of Project Management, 21(2), 89-95.

Dvir, D., Sadeh, A., & Pines, A. M. (2006). Projects and project managers: The relationship between project managers’ personality, project types, and project success. Project Management Institute, 37(5), 36-48.

Fortune, J., & White, D. (2006). Framing of project critical success factors by a systems model. International Journal of Project Management, 24(1), 53-65.

Freeman, M., & Beale, P. (1992). Measuring project success. Project Management Journal, 23(1), 8-17.

Frese, R. (2003). Project success and failure: What is success, what is failure, and how can you improve your odds for success? Journal of Knowledge Management Practice.

Goodrum, P. M., & Haas, C. T. (2002). Partial factor productivity and equipment technology change at activity level in US construction industry. Journal of Construction Engineering Management, 128(6), 463-472.

Griffis, F. H., Hogan, D. B., & Li, W. (1995). An analysis of the impacts of using three dimensional computer models in the management of construction. Construction Industry Institute (CII), Research Report 106-11, Austin, TX, USA.

Greer, M. (1999). 14 key principles for project management success. San Francisco: Jossey-Bass. http://mmj.uum.edu.my/

Khang, D. B., & Moe, T. L. (2008). Success criteria and factors for international development projects: A life-cycle-based framework. Project Management Journal, 39(1), 72-84.

Landaeta, R. E. (2008). Evaluating benefits and challenges of knowledge transfer across projects. Engineering Management Journal, 20, 1. Lembaga Pengusaha Jasa Konstruksi Indonesia. (2009). Statistik Badan Usaha Tahun 2009. Indonesia: LPJK.

Levy, S. M. (2000). Project management in construction. New York: McGraw-Hill.

Lim, C. S., & Mohamed, M. Z. (1999). Criteria of project success: An exploratory reexamination. International Journal of Project Management, 17(4), 243-248.

Lim, E. H., & Ling, F. Y. Y. (2002). Model for predicting client’s contribution to project success. Journal of Engineering Construction Architect Management, 9 (5/6), 388-395.

Ling, F. Y. Y., Low, S. P., Wang, S. Q., & Lim, H. H. (2009). Key project management practices affecting Singaporean firms’ project performance in China. International Journal of Project Management, 59-71.

Liu, A. N. N., & Walker, A. (1998). Evaluation of project outcomes. Construction Management and Economics, 16, 209-219. Lusthaus, C., Adrien, M. H., Anderson, G., Carden,

F., & Montalván. (2002). Organizational assessment: A framework for improving performance. Inter-American Development Bank, Washington, D. C., Ottawa, Canada: International Development Research Centre.

Meng, X. (2012). The effect of relationship management on project performance in construction. International Journal of Project Management, 30(2), 188-198.

Morris, P. W. G., & Hough, G. H. (1986). The preconditions of success and failures in major projects. Technical Paper, No. 3, Oxford: Major Project Association.

Morris, P. W. G., & Hough, G. H. (1987). The anatomy of major projects: A study of the reality of project management. New York: John Wiley & Sons.

Müller, R., & Turner, R. (2007). The influence of project managers on project success criteria and project success by type of project. European Management Journal, 25(4), 298-309.

Munns, A. K. (1995). Potential influence of trust on the successful completion of a project. International Journal of Project Management, 13(1), 19-24.

Ofori, G. (1991). Programmes for improving the performance of contracting firms in developing countries: A review of approaches and appropriate options. Journal of Construction Management Economy, 9, 19-38. Ogunlana, S. O., Promkuntong, K., & Jearkjirm,

V. (1996). Construction delays in a fastgrowing economy: Comparing Thailand with other economies. International Journal of Project Management, 14(1), 37-45.

Pinto, J. K., & Prescott, J. E. (1988). Variations in critical success factors over stages in the project life cycle. Journal of Management, 14(1), 5-18.

Pinto, J. K., & Slevin, D. P. (1988). Project success definitions and measurement techniques. Project Management Journal, 19(3), 6773.

Pinto, J. K., & Slevin, D. P. (1989). Critical success factors in R & D projects. Technology Management Journal, 31-35.

Pollack, J. B., & Liberatore, M. J. (1998). Project management software usage patterns and suggested research directions for future developments. Project Management Journal, 29(2), 19-28.

Poon, P., & Wagner, C. (2001). Critical success factors revisited: Success and failure cases of information systems for senior executives. Decision Support System, 30(4), 393-418. http://mmj.uum.edu.my/

Project Management Institute. (1996). A guide to the project management body of knowledge. PMBOK Guide, Project Management Institute. Newtown Square, PA: PMI Publishing.

Project Management Institute. (2000). A guide to the project management body of knowledge. PMBOK Guide, 2000 Edition. Newtown Square, PA: PMI Publishing. Qureshi, T. M., Warraich, A. S., & Hijazi,

S. T. (2009). Significance of project management performance assessment (PMPA) model. International Journal of Project Management, 27, 569-574.

Rasli, A., Madjid, M. Z., & Asmi, A. (2004). Factors that influence implitation of knowledge management and information technology infrastructure to support project performance in the construction industry. International Business Management Conference, 62-70. Malaysia: Universiti Tenaga Nasional.

Reich, B. H., & Wee, S. Y. (2006). Searching for knowledge in the PMBOK guide. Project Management Institute, 37(2),11-26.

Reza, F. (2006). Sebuah agenda rakyat? Bantuan pasca tsunami di Aceh. Eye On Aceh. Retrieved from www.acheh-eye.org.

Rose, K. H., & Suhanic, G. (2001). Computer - aided project management. Project Management Journal, 32(2), 60-61.

Sadeh, A., Dvir, D., & Shenhar, A. (2000). The role of contract type in the success of R & D defence projects under increasing uncertainty. Project Management Journal, 31(3), 14-21.

Shenhar, A. J. (2004). Strategic project leadership: Toward a strategic approach to project management. R & D Management Journal, 34(5), 569-578.

Shenhar, A. J., & Dvir, D. (2007). Project management research: The challenge and opportunity. Project Management Institute, 38(2), 93-99.

Shenhar, A. J., Levy, O., & Dvir, D. (1997). Mapping the dimensions of project success. Project Management Journal, 28(2), 5-13.

Soeharto, I. (1998). Manajemen proyek (dari konseptual sampai operasional), Jilid 1. Jakarta, Indonesia: Erlangga. Soemardi, B. W., Wirahadikusumah, R. D., &

Abduh, M. (2007). Construction project planning and control practices in Indonesia. Faculty of Civil and Enviromental Engineering, Institut Teknologi Bandung, Bandung, Indonesia: Ganesha 10.

Thomas, G., & Fernández, W. (2008). Success in IT projects: A matter of definition? I n t e r n a t i o n a l J o u r n a l o f P ro j e c t Management, 26, 733-742.

Thomas, S. R., Tucker, R. L., & Kelly, W. R. (1998). Critical communications variables. Journal of Construction Engineering Management, 124(1), 58-66.

Toor, S. R., & Ogunlana, S. O. (2008). Critical COMs of success in large-scale construction projects: Evidence from Thailand construction industry. International Journal of Project Management, 26, 420430.

Toor, S. R., & Ogunlana, S. O. (2010). Beyond the ‘Iron Triangle’: Stakeholder perception of key performance indicators (KPIs) for large - scale public sector development projects. International Journal of Project Management, 28(3), 228-236.

Turner, J. R. (1993). The handbook of projectbased management. New York: McGrawHill.

Turner, J. R. (2004). Five necessary conditions for project success. International Journal of Project Management, 22, 349-350.

Turner, J. R. (2009). The handbook of projectbased management-leading strategic change in organizations (3rd ed.). New York: McGraw-Hill.

Turner, J. R., & Müller, R. (2003). One the nature of the project as a temporary organization. International Journal of Project Management, 21(1), 1-8.

Walker, D. H. T. (1995). An investigation into construction time performance. Construction Management and Economics, 13(3), 263-274. http://mmj.uum.edu.my/

Walker, D. H. T. (1996). The contribution of the construction management team to good construction time performance – An Australian experience. Journal of Construction Procurement, 2(2), 4-18.

Wang, X., & Huang, J. (2006). The relationships between key stakeholders’ project performance and project success: Perceptions of Chinese construction supervising engineers. International Journal of Project Management, 24, 253260.

Wateridge, J. (1998). How can IT/IS projects be measured for success? International Journal of Project Management, 16(1), 59-63.

Wesli. (2007). Menelusuri kegagalan proyek konstruksi. Hidup bersama risiko bencana, Topik: Rekonstruksi dan rehabilitasi Aceh, Indonesia.

Westerveld, E. (2003). The project excellence model: Linking success criteria and critical success factors. International Journal of Project Management, 21, 411-418.

White, M. A., & Bruton, G. D. (2007). The management of technology and innovation: A strategic approach. Journal of Engineering and Technology Management, 24, 395-399.

Wood, J. R. (2008). Effective project management. Health Facilities Management, 21, 6.

Wueliner, W. W. (1990). Project performance evaluation checklist for consulting engineers. Journal of Management in Engineering, 6(3), 270-281.

Yang, L. R., O’Connor, J. T., & Wang, C. C. (2006). Technology utilization on different sizes of projects and associated impacts on composite project success. International Journal of Project Management, 24, 96105. Yang, J., Shen, G. O., Ho, M., Drew, D. S., &

Xue, X. (2011). Complexities in managing mega construction projects. International Journal of Project Management, 29(7), 900-910..405.421.649.71814.77759 estimate Std. error of the..316 R Square change 25.884 22.269 F change df1 Change statistics df2 c. Dependent Variable: Time Performance b. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope, Facility, Expertise, Software a. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope.302.316 b square R square.563a R Model Adjusted R Model Summaryc.000.000 Sig. F Change Appendixes Analysis of Hierarchical Regression http://mmj.uum.edu.my/.074 HRM.063 -.034 Quality Procurement. Cost..050 Time Risk.181 Scope -.146.270 Integration Communication 1. B.058.061.059.063.054.059.063.063.051.186 Std. error.062. -.139.072 -.036..046.178.276 Beta coefficients coefficients (Constant) Model Standardized Unstandardized 1. 1.607 -2.488 1.189 -.644 1.785.782 2.873 5.345 5.864 t Coefficientsa.278..013.235.520.075.434.004.000.000 Sig..373.390.287.406.319.421.401.464.482 Zero-order.052. -.119.057 -.031..038.137.249 Partial Correlations.071.031.114.212 Part.043.064 -..047 -.026 http://mmj.uum.edu.my/.478.456.503.432.510.446.467.413.592 Tolerance 2. 2.192 1.988 2.317 1.961 2.243 2.142 2.422 1.689 VIF Collinearity statistics.049.051..023.070 -.038.072 -.135..049.011.000.363 Scope Time Cost Quality HRM Communication Risk Procurement Expertise Software Facility.042.054.057.054.058.050.055.059.059.048.203 Integration.183.669 Std. error (Constant) B.361.000.010.049. -.129.070 -.039.070.022..207 Beta coefficients coefficients a. Dependent Variable: Time Performance Model Standardized Unstandardized 8.570.009.228.903 1.909 -2.477 1.244 -.754 1.273.401 1.781 4.271 3.664 t.000.993.820.367.057.014.214.451.204.689.076.000.000 Sig..535.358.332.373.390.287.406.319.421.401.464.482 Zero-order.382.000.011.043. -.119.060 -.036.061.019..202 Partial Correlations.314.000.008.033.070 -..046 -.028.047.015.065.157 Part http://mmj.uum.edu.my/.759.550.650.463.445.495.430.502.439.464.398.571 Tolerance 1.317 1.819 1.538 2.159 2.246 2.021 2.324 1.990 2.279 2.154 2.514 1.750 VIF Collinearity statistics.475a.590b square.209.329 R square.225.348.75865.82385 estimate Std. error of the.122.225 R square change 26.875 13.984 F Change df2 c. Dependent Variable: Cost Performance b. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope, Facility, Expertise, Software df1 Change Statistics a. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope R Model Adjusted R Model Summaryc http://mmj.uum.edu.my/.000.000 Sig. F Change.117 HRM..006 Quality Procurement.073 Cost -.051.067 Time Risk.138 Scope -.228.276 Integration Communication 1.611 B.061.065.062.066.057.062.067.067.054.197 Std. Error. -.049 -.219.114.006.074.062.136.283 Beta coefficients coefficients (Constant) Model Standardized Unstandardized 1.579 -.790 -3.673 1.772. 1.166.994 2.059 5.148 8.174 t.115.430.000..921.244.321.040.000.000 Sig. Coefficientsa.290.244.165.322.246.319.316.358.409 Zero-order.076 -.038 -.174..005.056.048..240 Partial Correlations.067 -.033 -.155.075.004.049.042..218 Part http://mmj.uum.edu.my/.478.456.503.432.510.446.467.413.592 Tolerance 2. 2.192 1.988 2.317 1.961 2.243 2.142 2.422 1.689 VIF Collinearity statistics.052.054.058.035.040.007.115 -.209 -.039..056 -.065.386 Scope Time Cost Quality HRM Communication Risk Procurement Expertise Software Facility a. Dependent Variable: Cost Performance.045.057.061.058.061.053.058.062.063.050.210 Integration.193 Std. error 1.147 B.387 -.064.052. -.038 -.201..008.041.032.057.215 Beta coefficients Coefficients (Constant) Model Standardized Unstandardized 8.646 -1.216 1.073 1.495 -.646 -3.632 1.879.142.698.558.927 4.178 5.945 t.000.225.284.136.519.000.061.887.486.577.354.000.000 Sig..509.245.283.290.244.165.322.246.319.316.358.409 Zero-order.385 -.059.052.072 -.031 -.173..007.034.027.045.198 Partial Correlations.337 -.047.042.058 -.025 -.141.073.006.027.022.036.163 Part http://mmj.uum.edu.my/.759.550.650.463.445.495.430.502.439.464.398.571 Tolerance 1.317 1.819 1.538 2.159 2.246 2.021 2.324 1.990 2.279 2.154 2.514 1.750 VIF Collinearity statistics.502a.550b square.236.283 R square.252.302.66730.68857 estimate Std. error of the.050.252 R square change 10.351 16.210 df2 c. Dependent Variable: Quality Performance b. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope, Facility, Expertise, Software df1 Change Statistics F change a. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope R Model Adjusted R Model Summaryc http://mmj.uum.edu.my/.000.000 Sig. F change.034.010.160 -. Cost Quality HRM Communication.009. Time Procurement.075 Scope -.006.210 Integration Risk 1.991 B.051.054.052.055.047.052.056.056.045.165 Std. error.010 -.007 -..183.013.041...253 Beta coefficients coefficients (Constant) Model Standardized Unstandardized.168 -. -1.616 2.895.220.657 1.716 1.352 4.693 12. t.867.913..004.826.512..177.000.000 Sig..307.314.271.404.303.357.381.394.433 Zero-order.008 -.005 -..138.011.032..065.220 Partial Correlations.007 -.005 -.067.120.009.027.071.056.195 Part http://mmj.uum.edu.my/.478.456.503.432.510.446.467.413.592 Tolerance 2. 2.192 1.988 2.317 1.961 2.243 2.142 2.422 1.689 VIF Collinearity statistics.046.047.037..019.014.157 -.071.007.008.002 -.047.216 Scope Time Cost Quality HRM Communication Risk Procurement Expertise Software Facility.039.050.053.051.054.046.051.054.055.044.176 Integration.170 1.765 Std. Error (Constant) B.254 -.055.002.010.007 -..179.018.023..043.212 Beta coefficients coefficients a. Dependent Variable: Quality Performance Model Standardized Unstandardized 5.499 -1.008.042.165.122 -1.401 2.916.312.379 1.463.676 3.976 10.401 t.000.314.966.869.903.162.004.755.705.144.499.000.000 Sig..414.270.262.307.314.271.404.303.357.381.394.433 Zero-order.256 -.049.002.008.006 -.067.139.015.018.070.033.188 Partial Correlations.221 -.041.002.007.005 -.056.117.013.015.059.027.160 Part http://mmj.uum.edu.my/.759.550.650.463.445.495.430.502.439.464.398.571 Tolerance 1.317 1.819 1.538 2.159 2.246 2.021 2.324 1.990 2.279 2.154 2.514 1.750 VIF Collinearity statistics.443a.610b square.180.354 R square.196.372.78153.88105 estimate Std. error of the.176.196 R square change 40. 11.747 df2 c. Dependent Variable: Stakeholder Satisfaction b. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope, Facility, Expertise, Software df1 Change Statistics F change a. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope R Model Adjusted R Model Summaryc http://mmj.uum.edu.my/.000.000 Sig. F change 1.325.284. -.016.030.029 -.046 -.037 -.005.196 Integration Scope Time Cost Quality HRM Communication Risk Procurement B.065.070.066.071.061.067.072.071.057.211 Std. error.186 -.004 -.034 -.042.029.029 -.014..277 Beta coefficients coefficients (Constant) Model Standardized Unstandardized 2.989 -.069 -.561 -.644.483.444 -.224 1.394 4.955 6.290 t Coefficientsa.003.945.575.520.629.657.823.164.000.000 Sig..342.268.253.264.269.297.279.336.396 Zero-order.142 -.003 -.027 -.031.023.021 -.011.067.232 Partial Correlations.129 -.003 -.024 -.028.021.019 -.010.060.213 Part http://mmj.uum.edu.my/.478.456.503.432.510.446.467.413.592 Tolerance 2. 2.192 1.988 2.317 1.961 2.243 2.142 2.422 1.689 VIF Collinearity statistics.053.055 -.010 -.057 -.018.024 -.045 -.021 -.001.170. -.037.473 Scope Time Cost Quality HRM Communication Risk Procurement Expertise Software Facility.046.059.062.059.063.054.060.064.065.052.197 Integration.199.698 Std. error (Constant) B.451 -.034..161 -.001 -.020 -.042.024 -.017 -.050 -.009.192 Beta coefficients coefficients a. Dependent Variable: Stakeholder Satisfaction Model Standardized Unstandardized 10.271 -.663 2.036 2.875 -.011 -.360 -.713.447 -.295 -.887 -.152 3.802 3.515 t.000.507.042.004.991.719.476.655.768.376.879.000.000 Sig..555.295.337.342.268.253.264.269.297.279.336.396 Zero-order.444 -.032..137 -.001 -.017 -.034.022 -.014 -.043 -.007.180 Partial Correlations.393 -.025...000 -.014 -.027.017 -.011 -.034 -.006.145 Part http://mmj.uum.edu.my/.759.550.650.463.445.495.430.502.439.464.398.571 Tolerance 1.317 1.819 1.538 2.159 2.246 2.021 2.324 1.990 2.279 2.154 2.514 1.750 VIF Collinearity statistics.619a.751b Adjusted R square.370.552 R square.383.564.47386.56164 estimate.181.383 R square change 59.427 29.843 F Change df1 Change Statistics df2 c. Dependent Variable: Project Performance b. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope, Facility, Expertise, Software a. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope R Model Std. error of the Model Summaryc http://mmj.uum.edu.my/.000.000 Sig. F Change 1.504.260.123.049.060.003. -.124.009. Integration Scope Time Cost Quality HRM Communication Risk Procurement B.042.044.042.045.039.043.046.046.037.134 Std. error.119.012 -.156..004..059.159.349 Beta coefficients coefficients (Constant) Model Standardized Unstandardized 2.178.206 -2.923 1.696.071 1.421 1.074 2.711 7.119 11.199 t.030.837.004..944.156.284.007.000.000 Sig..421.387.311.442.362.444.437.495.549 Zero-order..010 -.139..003.068.052.129.324 Partial Correlations..008 -..064.003.054.041..269 Part http://mmj.uum.edu.my/.478.456.503.432.510.446.467.413.592 Tolerance 2. 2.192 1.988 2.317 1.961 2.243 2.142 2.422 1.689 VIF Collinearity statistics.032.033.048.020.028.002.075 -..019..044 -.037.360 Scope Time Cost Quality HRM Communication Risk Procurement Expertise Software Facility.028.036.038.036.038.033.036.039.039.031.196 Integration.120 1.070 Std. error (Constant) B.471 -.048.054..024 -.137..003.037.025.062.264 Beta coefficients coefficients a. Dependent Variable: Project Performance Model Standardized Unstandardized 12.878 -1. 1.370 2.184.503 -3.034 1.956.065.773.525 1.221 6.258 8.879 t.000.267.171.030.615.003.051.948.440.600.223.000.000 Sig..648.374.391.421.387.311.442.362.444.437.495.549 Zero-order.528 -.054.066..024 -.145..003.037.025.059.289 Partial Correlations.410 -.035.044.070.016 -..062.002.025.017.039.199 Part http://mmj.uum.edu.my/.759.550.650.463.445.495.430.502.439.464.398.571 Tolerance 1.317 1.819 1.538 2.159 2.246 2.021 2.324 1.990 2.279 2.154 2.514 1.750 VIF Collinearity statistics.619a.689b square.370.462 R square.383.474.51893.56164 estimate Std. error of the..383 R square change 75.206 29.843 F change df1 Change Statistics df2 c. Dependent Variable: Project Performance b. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope, TechnologyUtilization a. Predictors: (Constant), Procurement, Integration, Quality, Communication, Time, HRM, Cost, Risk, Scope R Model Adjusted R Model Summaryc http://mmj.uum.edu.my/.000.000 Sig. F change. HRM..003 Quality Procurement.060 Cost.009.049 Time Risk.123 Scope -.124.260 Integration Communication 1.504 B.042.044.042.045.039.043.046.046.037.134 Std. error.119.012 -.156..004..059.159.349 Beta coefficients coefficients (Constant) Model Standardized Unstandardized 2.178.206 -2.923 1.696.071 1.421 1.074 2.711 7.119 11.199 t Coefficientsa.030.837.004..944.156.284.007.000.000 Sig..421.387.311.442.362.444.437.495.549 Zero-order..010 -.139..003.068.052.129.324 Partial Correlations..008 -..064.003.054.041..269 Part http://mmj.uum.edu.my/.478.456.503.432.510.446.467.413.592 Tolerance 2. 2.192 1.988 2.317 1.961 2.243 2.142 2.422 1.689 VIF Collinearity statistics 1.138.212.053.034.021 -.023. -.143 -.023.043.430 (Constant) Integration Scope Time Cost Quality HRM Communication Risk Procurement TechnologyUtilization B.050.039.041.039.042.036.040.042.043.034.131 Std. error.422.057 -.029 -.180. -.032.027.042.068.285 Beta coefficients coefficients a. Dependent Variable: Project Performance Model Standardized Unstandardized 8.672 1.114 -.562 -3.648 2.130 -.647.522.815 1.228 6.197 8.679 t.000.266.574.000.034.518.602.416.220.000.000 Sig..613.421.387.311.442.362.444.437.495.549 Zero-order.385.054 -.027 -.173. -.031.025.039.059.286 Partial Correlations.303.039 -.020 -.127.074 -.023.018.028.043.216 Part http://mmj.uum.edu.my/.513.468.453.501.431.507.440.466.398.576 Tolerance 1.949 2.136 2.210 1.994 2.320 1.974 2.274 2.145 2.513 1.735 VIF Collinearity statistics http://mmj.uum.edu.my/

Downloads

Published

01-12-2013

Issue

Vol. 17 (2013): Malaysian Management Journal (MMJ) Vol. 17, 2013

Section

Articles

How to Cite

Abdul Rani, H., Abdullah, C. S., & Mohtar, S. (2013). The Moderating Effect of Technology Utilization on Project Management Function and Project Performance. Malaysian Management Journal, 17, 77-113. https://doi.org/10.32890/mmj2013.17.6

Research impact

Harvested 2026-09-07
0 citations recorded so far

Counts differ between services because each indexes a different body of literature. None of them is the whole picture.

Identifiers DOI 10.32890/mmj2013.17.6