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
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by/3.0/
Poster
Included in
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.
Department
University of Tennessee at Chattanooga. Dept. of Psychology