Research: How One Bad Employee Can Corrupt a Whole Team

Stephen Dimmock & William Gerken

Harvard Business Review

Originally posted 5 March 2018

Here is an excerpt:

In our research, we wanted to understand just how contagious bad behavior is. To do so, we examined peer effects in misconduct by financial advisors, focusing on mergers between financial advisory firms that each have multiple branches. In these mergers, financial advisors meet new co-workers from one of the branches of the other firm, exposing them to new ideas and behaviors.

We collected an extensive data set using the detailed regulatory filings available for financial advisors. We defined misconduct as customer complaints for which the financial advisor either paid a settlement of at least $10,000 or lost an arbitration decision. We observed when complaints occurred for each financial advisor, as well as for the advisor’s co-workers.

We found that financial advisors are 37% more likely to commit misconduct if they encounter a new co-worker with a history of misconduct. This result implies that misconduct has a social multiplier of 1.59 — meaning that, on average, each case of misconduct results in an additional 0.59 cases of misconduct through peer effects.

However, observing similar behavior among co-workers does not explain why this similarity occurs. Co-workers could behave similarly because of peer effects – in which workers learn behaviors or social norms from each other — but similar behavior could arise because co-workers face the same incentives or because individuals prone to making similar choices naturally choose to work together.

In our research, we wanted to understand how peer effects contribute to the spread of misconduct. We compared financial advisors across different branches of the same firm, because this allowed us to control for the effect of the incentive structure faced by all advisors in the firm. We also focused on changes in co-workers caused by mergers, because this allowed us to remove the effect of advisors choosing their co-workers. As a result, we were able to isolate peer effects.

The info is here.

Here is my summary: 

The article discusses a study that found that even the most honest employees are more likely to commit misconduct if they work alongside a dishonest individual. The study, which was conducted by researchers at the University of California, Irvine, found that financial advisors were 37% more likely to commit misconduct if they encountered a new co-worker with a history of misconduct.

The researchers believe that this is because people are more likely to learn bad behavior than good behavior. When we see someone else getting away with misconduct, it can make us think that it's okay to do the same thing. Additionally, when we're surrounded by people who are behaving badly, it can create a culture of acceptance for misconduct.

Immoral actors’ meta-perceptions are accurate but overly positive

Lees, J. M., Young, L., & Waytz, A.

(2021, August 16).

https://doi.org/10.31234/osf.io/j24tn

Abstract

We examine how actors think others perceive their immoral behavior (moral meta-perception) across a diverse set of real-world moral violations. Utilizing a novel methodology, we solicit written instances of actors’ immoral behavior (N_total=135), measure motives and meta-perceptions, then provide these accounts to separate samples of third-party observers (N_total=933), using US convenience and representative samples (N_actor-observer pairs=4,615). We find that immoral actors can accurately predict how they are perceived, how they are uniquely perceived relative to the average immoral actor, and how they are misperceived. Actors who are better at judging the motives of other immoral actors also have more accurate meta-perceptions. Yet accuracy is accompanied by two distinct biases: overestimating the positive perceptions others’ hold, and believing one’s motives are more clearly perceived than they are. These results contribute to a detailed account of the multiple components underlying both accuracy and bias in moral meta-perception.

From the General Discussion

These results collectively suggest that individuals who have engaged in immoral behavior can accurately forecast how others will react to their moral violations.  

Studies 1-4 also found similar evidence for accuracy in observers’ judgments of the unique motives of immoral actors, suggesting that individuals are able to successfully perspective-take with those who have committed moral violations. Observers higher in cognitive ability (Studies 2-3) and empathic concern (Studies 2-4) were consistently more accurate in these judgments, while observers higher in Machiavellianism (Studies 2-4) and the propensity to engage in unethical workplace behaviors (Studies 3-4) were consistently less accurate. This latter result suggests that more frequently engaging in immoral behavior does not grant one insight into the moral minds of others, and in fact is associated with less ability to understand the motives behind others’ immoral behavior.

Despite strong evidence for meta-accuracy (and observer accuracy) across studies, actors’ accuracy in judging how they would be perceived was accompanied by two judgment biases.  Studies 1-4 found evidence for a transparency bias among immoral actors (Gilovich et al., 1998), meaning that actors overestimated how accurately observers would perceive their self-reported moral motives. Similarly, in Study 4 an examination of actors’ meta-perception point estimates found evidence for a positivity bias. Actors systematically overestimate the positive attributions, and underestimate the negative attributions, made of them and their motives. In fact, the single meta-perception found to be the most inaccurate in its average point estimate was the meta-perception of harm caused, which was significantly underestimated.

The research is here.

Using Machine Learning to Generate Novel Hypotheses: Increasing Optimism About COVID-19 Makes People Less Willing to Justify Unethical Behaviors

Sheetal A, Feng Z, Savani K. 

Psychological Science. 2020;31(10):

1222-1235. 

doi:10.1177/0956797620959594

Abstract

How can we nudge people to not engage in unethical behaviors, such as hoarding and violating social-distancing guidelines, during the COVID-19 pandemic? Because past research on antecedents of unethical behavior has not provided a clear answer, we turned to machine learning to generate novel hypotheses. We trained a deep-learning model to predict whether or not World Values Survey respondents perceived unethical behaviors as justifiable, on the basis of their responses to 708 other items. The model identified optimism about the future of humanity as one of the top predictors of unethicality. A preregistered correlational study (N = 218 U.S. residents) conceptually replicated this finding. A preregistered experiment (N = 294 U.S. residents) provided causal support: Participants who read a scenario conveying optimism about the COVID-19 pandemic were less willing to justify hoarding and violating social-distancing guidelines than participants who read a scenario conveying pessimism. The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.

Here is how the research article begins:

Unethical behaviors can have substantial consequences in times of crisis. For example, in the midst of the COVID-19 pandemic, many people hoarded face masks and hand sanitizers; this hoarding deprived those who needed protective supplies most (e.g., medical workers and the elderly) and, therefore, put them at risk. Despite escalating deaths, more than 50,000 people were caught violating quarantine orders in Italy, putting themselves and others at risk. Governments covered up the scale of the pandemic in that country, thereby allowing the infection to spread in an uncontrolled manner. Thus, understanding antecedents of unethical behavior and identifying nudges to reduce unethical behaviors are particularly important in times of crisis.

Here is part of the Discussion

We formulated a novel hypothesis—that optimism reduces unethicality—on the basis of the deep-learning model’s finding that whether people think that the future of humanity is bleak or bright is a strong predictor of unethicality. This variable was not flagged as a top predictor either by the correlational analysis or by the lasso regression. Consistent with this idea, the results of a correlational study showed that people higher on dispositional optimism were less willing to engage in unethical behaviors. A following experiment found that increasing participants’ optimism about the COVID-19 epidemic reduced the extent to which they justified unethical behaviors related to the epidemic. The behavioral studies were conducted with U.S. American participants; thus, the cultural generalizability of the present findings is unclear. Future research needs to test whether optimism reduces unethical behavior in other cultural contexts.