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    Mitigating Common Method Bias in Construction Engineering and Management Research

    Source: Journal of Construction Engineering and Management:;2022:;Volume ( 148 ):;issue: 009::page 04022089
    Author:
    Wei Zhang
    ,
    Guodong Yuan
    ,
    Rui Xue
    ,
    Yilong Han
    ,
    John E. Taylor
    DOI: 10.1061/(ASCE)CO.1943-7862.0002364
    Publisher: ASCE
    Abstract: The importance of reliability in conducting empirical research has been emphasized by construction engineering and management (CEM) scholars. Survey data commonly are used in empirical CEM studies. However, the use of such data in empirical analysis is susceptible to common method bias (CMB) issues, which can generate false correlations between dependent and explanatory variables, leading to spurious and unreliable results. To facilitate recent discussion about the rigorous application of research methodologies in CEM research, we provide a comprehensive overview of major CMB sources and examine articles published in the last 5 years to identify potential CMB issues in the Journal of Construction Engineering and Management. We found that approximately 90% of articles that used survey data in the empirical analysis were likely to be affected by CMB. The major source is the common rater effect, followed by the item characteristics effect and the measurement context effect. Because only a handful of studies tested or controlled for CMB, we propose effective and targeted ex ante procedural controls and ex post statistical controls to mitigate its negative impact. This study sheds light on an important and pervasive methodological issue in empirical CEM studies, and provides guidelines for improving the reliability of future empirical research in the CEM field.
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      Mitigating Common Method Bias in Construction Engineering and Management Research

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4286166
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    contributor authorWei Zhang
    contributor authorGuodong Yuan
    contributor authorRui Xue
    contributor authorYilong Han
    contributor authorJohn E. Taylor
    date accessioned2022-08-18T12:11:23Z
    date available2022-08-18T12:11:23Z
    date issued2022/07/05
    identifier other%28ASCE%29CO.1943-7862.0002364.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4286166
    description abstractThe importance of reliability in conducting empirical research has been emphasized by construction engineering and management (CEM) scholars. Survey data commonly are used in empirical CEM studies. However, the use of such data in empirical analysis is susceptible to common method bias (CMB) issues, which can generate false correlations between dependent and explanatory variables, leading to spurious and unreliable results. To facilitate recent discussion about the rigorous application of research methodologies in CEM research, we provide a comprehensive overview of major CMB sources and examine articles published in the last 5 years to identify potential CMB issues in the Journal of Construction Engineering and Management. We found that approximately 90% of articles that used survey data in the empirical analysis were likely to be affected by CMB. The major source is the common rater effect, followed by the item characteristics effect and the measurement context effect. Because only a handful of studies tested or controlled for CMB, we propose effective and targeted ex ante procedural controls and ex post statistical controls to mitigate its negative impact. This study sheds light on an important and pervasive methodological issue in empirical CEM studies, and provides guidelines for improving the reliability of future empirical research in the CEM field.
    publisherASCE
    titleMitigating Common Method Bias in Construction Engineering and Management Research
    typeJournal Article
    journal volume148
    journal issue9
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0002364
    journal fristpage04022089
    journal lastpage04022089-11
    page11
    treeJournal of Construction Engineering and Management:;2022:;Volume ( 148 ):;issue: 009
    contenttypeFulltext
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