Start Date

11-12-2016 12:00 AM

Description

Data analytics has been accused of contributing to discriminatory managerial decisions in organizations’ marketing strategies. To date, most studies have focused on the technical antecedents of such discriminations and, therefore, little is known about the role of human factors in making these discriminatory decisions. This work-in-progress study aims at addressing this gap by opening the black box between data analytics use in organizations and making discriminatory decisions. Drawing upon the theory of moral disengagement, we posit that four dimensions of moral disengagement, namely, dehumanization, euphemistic labeling, displacement of responsibility, and disregard of consequences are the mechanisms through which the use of data analytics tools in organizations could bring about discriminatory decisions. Moreover, data size and employees’ competency are discussed as having moderating impacts on some of these mechanisms. A survey-based methodology to empirically validate the proposed model is outlined. Potential contributions to theory and practice are delineated.

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Dec 11th, 12:00 AM

Understanding the Role of Data Analytics in Driving Discriminatory Managerial Decisions

Data analytics has been accused of contributing to discriminatory managerial decisions in organizations’ marketing strategies. To date, most studies have focused on the technical antecedents of such discriminations and, therefore, little is known about the role of human factors in making these discriminatory decisions. This work-in-progress study aims at addressing this gap by opening the black box between data analytics use in organizations and making discriminatory decisions. Drawing upon the theory of moral disengagement, we posit that four dimensions of moral disengagement, namely, dehumanization, euphemistic labeling, displacement of responsibility, and disregard of consequences are the mechanisms through which the use of data analytics tools in organizations could bring about discriminatory decisions. Moreover, data size and employees’ competency are discussed as having moderating impacts on some of these mechanisms. A survey-based methodology to empirically validate the proposed model is outlined. Potential contributions to theory and practice are delineated.