Journal Publications
Maya Balakrishnan, Jimin Nam, & Ryan Buell, forthcoming at Production and Operations Management.
Many companies are making efforts to diversify their workforces, motivated by documented operational performance benefits and the desire to heed increased pressure to “walk the talk” on diversity, equity, and inclusion (DEI) initiatives. One specific call-to-action from stakeholders is the public disclosure of EEO-1s. Companies with 100+ employees are federally mandated to annually report the intersectional diversity data of their workforce in the EEO-1. Through five online experiments, we examine how consumers perceive transparency into an operation's workforce diversity. We find no evidence that disclosing workforce diversity data undermines customer attitudes or behaviors toward the company, even when the disclosures reveal racial disparities across job categories. Instead, 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, relative to firms that choose not to disclose. We find these attitudinal and behavioral differences to be especially pronounced when the disclosures reveal progress in diversification.
Nam, J., Balakrishnan, M., De Freitas, J., Brooks, A.W. (2023). Speedy Activists: Firm Response Time to Sociopolitical Events Influences Consumer Behavior. Journal of Consumer Psychology. https://doi.org/10.1002/jcpy.1380
Organizations face growing pressure from their consumers and stakeholders to take public stances on sociopolitical issues. However, many are hesitant to do so lest they make missteps, promises they cannot keep, appear inauthentic, or alienate consumers, employees, or other stakeholders. Here we investigate consumers’ impressions of firms that respond quickly or slowly to sociopolitical events. Using data scraped from Instagram and three online experiments (N=2,452), we find that consumers express more positive sentiment and greater purchasing intentions toward firms that react more quickly to sociopolitical issues. Unlike other types of public firm decision making such as product launch, where careful deliberation can be appreciated, consumers treat firm response time to sociopolitical events as an informative cue of the firm’s authentic commitment to the issue. We identify an important boundary condition of this main effect: speedy responses bring limited benefits when the issue is highly divisive along political lines. Our findings bridge extant research on brand activism and communication, and offer practical advice for firms.Â
Manuscripts Under Review
Maya Balakrishnan, Kris Ferreira, & Jordan Tong, major revision at Management Science.Â
[Job Market Paper]Â
Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by algorithms in such situations? When deciding whether and how to override an algorithm's recommendations, we hypothesize that people are biased towards following a naive advice weighting (NAW) heuristic: 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. In an online experiment where participants are tasked with making demand predictions for 20 products while having access to an algorithm’s recommendations, we confirm this bias towards NAW and find that it leads to a 20-61% increase in prediction error. In a follow-up experiment, we find that feature transparency even when the underlying algorithm is a black box - helps users more effectively discriminate when and how to deviate from algorithms, resulting in a 25% reduction in prediction error.