Let algorithms choose workers’ incentive schemes to increase performance | by Jamie Hose | Mar, 2024

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Targeting workers with different incentive schemes based on their individual characteristics leads to greater performance and is much more effective than a one size fits all approach, according to new research from Frankfurt School of Finance & Management.

To study the impact of targeted incentive schemes on performance, Timo Vogelsang, Professor of Accounting at Frankfurt School of Finance & Management, alongside colleagues from University of Cologne, ran two large-scale experiments with more than 12,000 participants on Amazon MTurk.

The first experiment surveyed participant demographics, personality traits, and social and economic preferences before performing a task. Participants were randomly assigned to one of six different incentive schemes and a control group. The schemes included a fixed wage, a piece-rate scheme, two target bonus schemes where the bonus is either gained or lost based on performance, a competitive scheme where the bonus is dependent on comparisons to prior participant performance, and a social incentive scheme where a portion of money is donated to Doctors Without Borders whenever the participant receives a bonus.

Results showed that highest average performance is achieved through the scheme in which a bonus can be lost if a target isn’t met (bonus loss). However, when considering workers’ distinct characteristics, different individually assigned schemes would lead to significantly higher performance.

This was validated by the second experiment where participants were assigned to one of three conditions: 1) an untreated control group, 2) bonus loss scheme, or 3) assigned by an algorithm to the scheme predicted to yield the highest performance based on specific characteristics of participants.

Assigning individuals to targeted incentive schemes based on their characteristics led to greater performance than the bonus loss scheme, which achieved the highest average performance when applied over all participants (as in experiment 1). While the bonus loss condition raised performance by 23.9% compared to the control group, targeted assignment raised performance by 29.3%.

Specifically, older workers and women were significantly more likely to be assigned to the bonus loss scheme. The latter is in line with previous findings that women tend to be more loss averse than men, implying they exert more effort to avoid a loss. Women are also less likely to be assigned to the competitive scheme, in line with research that suggests women don’t perform as well under competitive incentives.

Also, participants with more altruistic tendencies are significantly more likely to be assigned to the social scheme and less likely to be assigned to competitive bonus scheme. High levels of reciprocity, agreeableness, and extraversion, all associated with prosocial traits, also increase the chance of an individual being assigned to social incentive scheme.

If using individual characteristics to assign targeted incentive schemes, caution would need to be taken. Professor Vogelsang says, “Individually targeting different incentives within a team may lead to pay inequity and potential adverse effects. However, the rise of alternative work arrangements, especially the gig economy, opens a particularly suitable field for the assignment of different schemes to different workers due to the independent work environment typical for gig work.”

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