Department

University of Tennessee at Chattanooga. Dept. of Psychology

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

Evidence-based management practices that include big-data mining strategies have become commonplace in many areas of organizational management and have been shown to be effective. However, organizations have yet to fully take advantage of these analytic methods to improve their occupational safety. The proposed study aims to address this gap by developing a strategy to utilize data that organizations are already collecting to describe, diagnose, and predict workplace safety outcomes. The five proposed predictor variable categories are production, procedures, hazards, behaviors, and participation. Data will be collected from a large American Fortune 500 company that specializes in the production of advanced materials, chemicals, and fibers for everyday purposes.

Date

October 2018

Subject

Industrial and organizational psychology

Document Type

posters

Language

English

Rights

Under copyright.

License

http://creativecommons.org/licenses/by/3.0/

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Developing an Analytics Strategy to Describe, Diagnose, and Predict Workplace Safety Outcomes

Evidence-based management practices that include big-data mining strategies have become commonplace in many areas of organizational management and have been shown to be effective. However, organizations have yet to fully take advantage of these analytic methods to improve their occupational safety. The proposed study aims to address this gap by developing a strategy to utilize data that organizations are already collecting to describe, diagnose, and predict workplace safety outcomes. The five proposed predictor variable categories are production, procedures, hazards, behaviors, and participation. Data will be collected from a large American Fortune 500 company that specializes in the production of advanced materials, chemicals, and fibers for everyday purposes.