Emerging Challenges in the Completion of Construction Projects through Public Private Partnerships: Empirical Literature Review

• 作者: Pamela Akinyi Oyieyo
• 来源期刊: 房屋建造与规划研究(英文)
• 学科: 经济
• 关键词: Construction Projects Completion of Construction Projects Public Private Partnership Challenges in Construction Projects
• 年，卷（期）: 2020,（4）
• 页码范围: 263-272
• 页数: 10页
• 分类号: F42

摘要

The process of engaging private sector in the provision of public infrastructure can improve economic value in service delivery and enable the government to utilize the capabilities and expertise held by private sector. This has a positive impact on the public in terms of improving their well-being. This literature review attempts to compare the challenges encountered in implementation of PPP projects across the world, with a view to determining cross cutting challenges and recommendations for developing countries. A literature review was conducted for empirical studies focusing on construction projects. The studies are reviewed by analyzing the methodology used and conclusions made to identify the geographical study area, Public Private Partnership project financing model, emerging challenges and recommendation for future project management. The literature search was based on four keywords: construction projects, completion of construction projects, public private partnership, challenges in construction projects. Thus, a total of 11 empirical studies were identified for review in Europe, Asia and Africa. The review revealed that most risk factors affecting project completion can be categorized as human and organizational factors and unknown geotechnical conditions that were not known beforehand. Specifically, project owners’ interference was also attributable to delays with the delays having significant implications for project execution. Risks have great impact on the performance of construction projects because they affect their quality, time and cost. The general remedy to such potential risk factors is adoption of reliable prediction mechanisms which would estimate those factors using a probabilistic model that does not rely on expert judgments as they tend to be unreliable.