Mission Driven and Data Averse: How Empathy Fosters Resistance to Algorithms and Hard Data

Socially driven organizations aiming to become more data driven often face practical barriers such as difficulties in measuring complex social objectives. We argue that, even when those practical barriers are addressed, social missions make it harder to foster a data-driven culture by exacerbating employee aversion to algorithms and hard data. Our theory is that emphasizing a social rather than profit-oriented mission increases employees’ concern for empathy, and this, in turn, makes them more averse to the cold, impersonal methods associated with using algorithms and hard data. Furthermore, if the aversion to algorithms and hard data in social organizations stems from empathy concerns as we predict, then designing fair data systems should mitigate this effect because fairness appeals to empathy concerns. Results of four experiments support these predictions. These findings suggest that, even when social organizations can accurately measure their objectives and attract employees with requisite data skills, they may still struggle to become data driven because prosocial employees avoid unempathetic decision approaches. However, social organizations can mitigate these effects by incr