The algorithm-augmented business model promises service companies the benefits of both algorithms and humans. But companies will only realize this promise if their workers rely on algorithms, and there is conflicting evidence about workers’ willingness to do this. We design a laboratory experiment to resolve this conflict, and find that workers are generally unwilling to rely on algorithms but that they become more willing to do so in response to high customer loads and learning interventions.

We conduct a series of online experiments to isolate the pure effects of three types of Relative Performance Feedback, RPF, on middle-ranked workers' performance. We find that providing any type of feedback reduces performance compared to no feedback. Aligned with theory, delivering feedback increases the focal employee's shame and social comparison involvement (SCI), which measures the focal individual’s level of engagement in social comparison while performing the task.

Many human-machine interactions focus on the optimization of the system output yet tend to overlook human behavior. Using an intervention-based field experiment in a semi-automated warehouse, we study the impact of a behavioral intervention that provides humans with more information about the picking process and enables them to choose out of a set of pre-defined goals. We find that human performance is enhanced by 6%. Our insights enrich the discussion on human-machine interactions.