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    Corporate Misconduct Prediction with Support Vector Machine in the Construction Industry

    Source: Journal of Management in Engineering:;2018:;Volume ( 034 ):;issue: 004
    Author:
    Wang Ran;Lee Chia-Jung;Hsu Shu-Chien;Lee Cheng-Yu
    DOI: 10.1061/(ASCE)ME.1943-5479.0000630
    Publisher: American Society of Civil Engineers
    Abstract: Corporate misconduct may lead to severe economic loss and even fatal injuries to workers and residents in the construction industry. Previous studies have proven that board composition in organizations can be related to illegal business behaviors. By analyzing board composition data from 45 publicly listed construction companies in Taiwan, this paper provides a tool for predicting corporate misconduct (CM). A support vector machine (SVM) was used to construct such a prediction model, and a logistic regression model was used as a benchmark to assess the performance of the established SVM model. The established SVM model achieved an accuracy rate of 72.22% for predicting the occurrence of CM when applied to all observations in the sample, with a rate of 9% accuracy in predicting misconduct by companies found guilty of doing so in the sample, thus performing better than the logistic regression model. The developed model yields new insights on previous research and can guide stakeholders to reduce the risk of illegal business acts occurring in the construction industry.
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      Corporate Misconduct Prediction with Support Vector Machine in the Construction Industry

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4249193
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    contributor authorWang Ran;Lee Chia-Jung;Hsu Shu-Chien;Lee Cheng-Yu
    date accessioned2019-02-26T07:45:52Z
    date available2019-02-26T07:45:52Z
    date issued2018
    identifier other%28ASCE%29ME.1943-5479.0000630.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4249193
    description abstractCorporate misconduct may lead to severe economic loss and even fatal injuries to workers and residents in the construction industry. Previous studies have proven that board composition in organizations can be related to illegal business behaviors. By analyzing board composition data from 45 publicly listed construction companies in Taiwan, this paper provides a tool for predicting corporate misconduct (CM). A support vector machine (SVM) was used to construct such a prediction model, and a logistic regression model was used as a benchmark to assess the performance of the established SVM model. The established SVM model achieved an accuracy rate of 72.22% for predicting the occurrence of CM when applied to all observations in the sample, with a rate of 9% accuracy in predicting misconduct by companies found guilty of doing so in the sample, thus performing better than the logistic regression model. The developed model yields new insights on previous research and can guide stakeholders to reduce the risk of illegal business acts occurring in the construction industry.
    publisherAmerican Society of Civil Engineers
    titleCorporate Misconduct Prediction with Support Vector Machine in the Construction Industry
    typeJournal Paper
    journal volume34
    journal issue4
    journal titleJournal of Management in Engineering
    identifier doi10.1061/(ASCE)ME.1943-5479.0000630
    page4018021
    treeJournal of Management in Engineering:;2018:;Volume ( 034 ):;issue: 004
    contenttypeFulltext
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