An Empirical Analysis of the Influence of Project Governance and Information Technology Governance on Project Delay

Abstract

Project delay adversely affects the cost overrun, poor quality and stakeholder’s disputes thus resulting in unsuccessful projects. Project delay has a number of antecedents including the governance of project and information technology. The study provides empirical analysis of determinants of schedule delays in project completion. This will test the hypothesis that assumes that project governance and information technology governance are major predictor of project schedule delays. The study considers the software industry located in twin cities of Islamabad and Rawalpindi, Pakistan. According to the Pakistan Security and Exchange Commission, there are 245 software houses registered in Islamabad while 80 are registered in Rawalpindi. The study has used survey questionnaires to gather required information. Data was collected through simple random sampling techniques from 220 respondents. The data collected was analysed by doing structural equation modelling (SEM) to examine the said relationships. The reliability and validity of the instrument is established by the Cronbach’s α, composite reliability, and average variance extracted having values above threshold of 0.7, 0.7, and 0.5, respectively. The regression weight for first hypothesis (H1) assuming negative relationship between ITG and PD is −0.292. The second hypothesis (H2) assuming negative relationship between PG and PD has regression value of −0.418. The values of both hypotheses are significant. The study empirically tests the relationship between ITG, PG, and PD. The results suggest that better project governance (PG) reduces project delay (PD) and vice versa. Similarly, an appropriate information technology governance (ITG) also reduces the project delay (PD).

Sustainable Project Governance: Scientometric Analysis and Emerging Trends

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“…Sustainability 2023, 15, x FOR PEER REVIEW 22 of 28 tain period of time detected from a large number of subject words based on word frequency, that is, subject words with a strong abrupt trend. The prediction principle lies in the accurate identification of keywords with high-frequency growth in a specific period according to the emergent word monitoring algorithm first proposed by Kleinberg [59].…”