杠杆(统计)
监督人
生产力
集合(抽象数据类型)
建筑业
知识管理
计算机科学
工程类
工程管理
过程管理
管理
人工智能
经济
建筑工程
宏观经济学
程序设计语言
作者
Awad S. Hanna,Michael W. Ibrahim,Wafik Boulos Lotfallah,Karim A. Iskandar,Jeffrey S. Russell
出处
期刊:Journal of the Construction Division and Management
[American Society of Civil Engineers]
日期:2016-08-01
卷期号:142 (8)
被引量:38
标识
DOI:10.1061/(asce)co.1943-7862.0001141
摘要
Although the construction industry is a major component of the U.S. economy, it has been suffering from declining productivity for decades. The human element, project managers (PMs) in particular, are key in solving these persisting problems. Measurement of a PM’s overall performance is important to identify training needs and enables executives to better match competent PMs with the appropriate projects. This paper provides the construction industry with a generic mathematical formulation to reliably weigh different PM competencies. The developed data-driven mathematical model reflects the relative importance that industry practitioners place on different PM competencies while distinguishing exceptional PMs from average ones. This developed model is applied to a data set of 124 PM assessments filled by 62 PM supervisors so that each PM supervisor selected and rated an exceptional PM and an average PM. The results presented in the paper suggest that PMs should focus on developing their cognitive side, rather than settling only for possessing adequate knowledge and experience, managerial skills, and leadership capabilities. Also, these quantitative results illustrate that having business and financial acumens, disciplinary understanding of all the phases of construction projects and their interrelationships, continuous monitoring of similar construction projects, and consistent awareness of the available information technologies are among the most distinguishing competencies between exceptional and average PMs. Such results can assist the construction industry in directing its efforts toward accurately identified leverage development areas through pinpointing actual training and educational needs. Additionally, this paper compares the results of the developed data-driven mathematical model to an existing expert evaluation, presenting a key step in revealing and, in turn, reducing experts’ subjectivity.
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