Designing Operations to Inspire Trust

Abstract

In this dissertation I study trustworthy operations across three chapters. These span two streams. In the first – corresponding to Chapters 1 and 2 – I seek to understand how to inspire consumer trust in companies through building socially responsible operations. In Chapter 1 we examine when organizations should make statements on sociopolitical issues to best appeal to consumers. We find that consumers express more positive sentiment and greater purchasing intentions toward firms that react more quickly to sociopolitical issues. In Chapter 2 we examine how consumers perceive transparency into an operation’s workforce diversity and we find that consumers perceive firms that disclose their workforce diversity data to be more committed to DEI initiatives, view disclosing firms more positively, and are more likely to choose their offerings over those of non-disclosing firms. In my second stream of research – corresponding to Chapter 3 – I study the calibration of employee trust in algorithms for more effective human-algorithm collaboration. In Chapter 3 we hypothesize that people are biased towards following a naïve advice weighting (NAW) heuristic when overriding algorithms: they take a weighted average between their own prediction and the algorithm’s, with a constant weight across prediction instances, regardless of whether they have valuable private information. This leads to humans over-adhering to the algorithm’s predictions when their private information is valuable and under-adhering when it is not. We further design interventions to get users to move away from NAW, leading to improved human-algorithm collaboration in predictions.