Albert P.C. Chan

Bio: Albert P.C. Chan is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Public–private partnership & Procurement. The author has an hindex of 71, co-authored 489 publications receiving 18652 citations. Previous affiliations of Albert P.C. Chan include University of Hong Kong & City University of Hong Kong.

Key performance indicators for measuring construction success

Abstract: The construction industry is dynamic in nature. The concept of project success has remained ambiguously defined in the construction industry. Project success is almost the ultimate goal for every project. However, it means different things to different people. While some writers consider time, cost and quality as predominant criteria, others suggest that success is something more complex. The aim of this paper is to develop a framework for measuring success of construction projects. In this paper, a set of key performance indicators (KPIs), measured both objectively and subjectively are developed through a comprehensive literature review. The validity of the proposed KPIs is also tested by three case studies. Then, the limitations of the suggested KPIs are discussed. With the development of KPIs, a benchmark for measuring the performance of a construction project can be set. It also provides significant insights into developing a general and comprehensive base for further research.

Factors Affecting the Success of a Construction Project

Abstract: Different researchers have tried to determine the factors for a successful project for a long time. Lists of variables have been abounded in the literature, however, no general agreement can be made. The aim of this paper is to develop a conceptual framework on critical success factors (CSFs). Seven major journals in the construction field are chosen to review the previous works on project success. Five major groups of independent variables, namely project-related factors, project procedures, project management actions, human-related factors, and external environment are identified as crucial to project success. Further study on the key performance indicators (KPIs) is needed to identify the causal relationships between CSFs and KPIs. The causal relationships, once identified, will be a useful piece of information to implement a project successfully.

Exploring Critical Success Factors for Partnering in Construction Projects

Abstract: The construction industry is a very competitive high-risk business. Many problems, such as little cooperation, lack of trust, and ineffective communication resulting in adversarial relationships between contracting parties, are facing the construction industry. Partnering is perhaps one of the most innovative developments in delivering a project efficiently and reducing construction disputes. It provides a sound basis for a “win-win” climate and synergistic teamwork. Project partnering in the Hong Kong construction industry has gained in popularity since 1994. A number of potential factors contributing to partnering success have emerged and deserve further study. This paper presents a review of the development of the partnering concept in general and identifies critical success factors for partnering projects from the Hong Kong perspective in particular. Through a postal questionnaire survey geared toward project participants with hands-on partnering experience, the opinions of various parties—clients, co...

Educational Research: Competencies for Analysis and Application:

Abstract: to many, however, it constantly appears as an instructional problem. The final two chapters serve as excellent summary statements. Chapter 13 emphasizes the application of previous text material to the classroom situation. Of great significance is the author's discussion of commonly defined independent, dependent, and moderator variables. The final chapter focuses on evaluation in the overall sense, particularly as it relates to programs of study. The principles of measurement and research design are applied to the evaluation process through several ex-

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.