Project control system (PCS) implementation in engineering and construction projects: an empirical study in Saudi’s petroleum and chemical industry
TL;DR: In this paper , a survey of 400 project managers in Saudi Arabia's petroleum and chemical industry revealed that successful project control systems are the key to achieving all project outcomes, but they are particularly critical for meeting project cost objectives.
read more
Abstract: PurposeThere is growing recognition that effective project control systems (PCS) are critical to the success of projects. The relationship between the individual elements of PCS and successfully achieving project objectives has yet to be explored. This research investigates the enablers and barriers that influence the elements of PCS success and drive project objectives.Design/methodology/approachThis study adopts a mixed approach of descriptive analysis and regression models to explore the impact of six PCS elements on project outcomes. Petroleum and chemical projects in Saudi Arabia were selected as a case study to validate the research model.FindingsData from a survey of 400 project managers in Saudi’s petroleum and chemical industry reveal that successful PCS are the key to achieving all project outcomes, but they are particularly critical for meeting project cost objectives. Project Governance was identified as the most important of the six PCS elements for meeting project objectives. A lack of standard processes emerged as the most significant barrier to achieving effective project governance, while having skilled and experienced project team members was the most significant enabler for implementing earned value.Practical implicationsThe study offers a direction for implementing and developing PCS as a strategic tool and focuses on the PCS elements that can improve project outcomes.Originality/valueThis research contributes to project management knowledge and differs from previous attempts in two ways. Firstly, it investigates the elements of PCS that are critical to achieving project scope, schedule and cost objectives; secondly, enablers and barriers of PCS success are examined to see how they influence each element independently.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Project cost control system and enabling-factors model: PLS-SEM approach and importance-performance map analysis
An Thi Hoai Le,Monty Sutrisna +1 more
TL;DR: In this paper , a mixed approach of descriptive analysis and partial least squares structural equation modelling (PLS-SEM) is adopted to measure the current maturity level of PCCS and evaluate the relationships between elements within PCSS to identify improvement areas.
9
A novel approach for measuring the accuracy of front end engineering design
TL;DR: In this article , the authors focus on measuring front-end engineering design (FEED) accuracy and quantifying its impact on project performance in terms of cost change, schedule change, change performance, financial performance and customer satisfaction.
1
Practices and techniques for construction projects cost control- a critical review
Montasser Elserougy,L. M. Khodeir,Fatema Fathy +2 more
TL;DR: This study critically reviews cost control practices in construction projects, identifying 15 successful techniques, including budgeting, resource monitoring, and cost variance analysis, and proposes a control framework integrating proactive and corrective measures to reduce project overruns and ensure profit margins.
References
Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis.
TL;DR: Practical information on making decisions regarding (a) extraction, (b) rotation, (c) the number of factors to interpret, and (d) sample size is provided.
Determining Sample Size for Research Activities
Halim Ahmad,Hasnita Halim +1 more
- 01 Dec 2017
TL;DR: In this article, the importance and procedure of determining sample size for continuous and categorical variables using Cochran's (1977) formula is described and the usage of sample sizes formula, including the formula for adjusting for the Cochran (1977), correction when the sample size exceeds 5% of the population.
5.1K
Calculating, Interpreting, And Reporting Cronbach’s Alpha Reliability Coefficient For Likert-Type Scales
Joseph A. Gliem,Rosemary R. Gliem +1 more
- 01 Jan 2003
TL;DR: This paper showed that single-item questions pertaining to a construct are not reliable and should not be used in drawing conclusions, and compared the reliability of a summated, multi-item scale versus a single item question.
Starting at the beginning: An introduction to coefficient alpha and internal consistency
TL;DR: The historical development of a from other indexes of internal consistency (split-half reliability and Kuder-Richardson 20) and four myths associated with a are discussed, including that it is a fixed property of the scale and that higher values are always preferred over lower ones.