#A thumbs up how to

Rotterdam School of Management, Erasmus University (RSM) is one of Europe’s top-ranked business schools. Thumbs Up or Down: Consumer Reactions to Decisions by Algorithms Versus Humans is published in the Journal of Marketing Research. Averting the negative consequences of algorithmic decision making by making algorithms more human-like, for example by using a more conversational format, a human name, or a human-like photo, can help companies to stay in their customers’ good graces. This research validates these efforts and offers an important insight to policymakers: one of the studies reveals that neglecting to mention who made the decision leads customers to assume that the decision was made by a human. In other words, researchers warn managers that passive human oversight will not necessarily improve customer responses, and customers still react less positively to the favourable decisions the algorithms make.Ĭompanies are increasingly required to disclose how they use algorithms to make decisions that affect people and society. One of the researchers’ studies shows that as long as algorithm is making the decision, having a human working somewhere in the decision-making process isn’t enough to offset the effect. If you tell customers that decisions affecting them are overseen by a real person, that should be enough to overcome less positive reactions to algorithmic acceptances – right? Today, many automated decision processes are actually monitored by a human. Just the presence of a human is not enough Researchers find that customers react more positively toward companies when a human-like algorithm accepts them rather than a regular algorithm. One of the studies provides managers with an easy-to-implement solution: humanising the algorithms, for example by giving it a name or an avatar that looks human.

#A thumbs up how to

The researchers advise managers how to limit the likelihood of less positive reactions towards acceptances made by algorithms, how to design processes to avoid the effects on customers’ evaluations of the company, and how best to communicate about how decisions are taken. Accordingly, they find it similarly easy to blame others for an unfavourable decision regardless of whether it was a human or an algorithm that made it.” “For unfavourable decisions, however, customers are motivated to protect their self-worth and blame others. That would reduce you to a number just like everyone else. Customers find it easier to take credit for an acceptance when the decision is made by a human employee – “ my request was accepted because I am special, and I deserve it” than when an algorithm is responsible for the acceptance. Gizem Yalcin said: “It’s easier for customers to attribute a favourable decision to themselves when it is rendered by a human rather than an algorithm. The effect of customers being less positive about algorithms decisions that go their way than decision by humans that go their way is driven by ‘distinct attribution’ processes. In our studies, we covered various contexts such loan and membership applications.” We then asked participants to tell us what they think about the company. The participants were randomly told that the decision about their application was either made by an algorithm or a human employee. “We tested customer reactions to favourable and unfavourable decision outcomes, like an acceptance or rejection. Ten studies revealed that customers defy managers’ predictions by reacting less positively when a favourable decision is made by an algorithm rather than a human employee, whereas this difference is less for an unfavourable decision.

“Specifically, this paper tests whether and how customers evaluate a company differently depending on whether they are accepted or rejected by an algorithm or a human employee.” “However, there’s little research that examines how customers react to different types of decisions about themselves made by algorithms, and how their reactions differ if they know the decisions are made by humans. Researcher Gizem Yalcin said: “Companies increasingly adopt algorithms to make business decisions that directly affect their potential and existing customers: algorithms decide which applicants to admit to companies’ platforms or which customers’ applications to approve or deny.