We examine how bonus deferral and bonus recovery affect performance in an effort-sensitive task, where improving overall performance requires effort towards output quantity and output quality. Deferred bonuses and bonus recovery are important elements in contemporary incentive schemes designed to motivate employees to act in the best interest of the firm. We propose that bonus deferral improves performance by encouraging employees to adopt an abstract mindset that focuses on behavior for the good of the firm (“to do the right thing”) and thus to exert more effort. In contrast, bonus recovery serves an effort-directing role by indicating undesirable behavior that should be avoided. Bonus recovery activates a mindset that focuses on the monetary implications of their behavior and thus supersedes the abstract mindset of bonus deferral and its positive effects. Our experimental results show that bonus deferral leads to higher performance quantity; in contrast, bonus recovery increases performance quality, the performance domain related to bonus recovery, but at the expense of performance quantity. Furthermore, we find that bonus recovery counteracts the positive effects of bonus deferral and leads to a reduction in overall performance. Two supplemental experiments provide direct process evidence supporting the proposed theoretical mechanism. Our study contributes to the debate on effective compensation by showing that combining bonus deferral and bonus recovery may have undesirable consequences on effort provision and allocation.

Despite the potential of algorithms to improve judgment quality, recent research suggests that individuals may be averse to algorithmic use. We experimentally examine whether and how managers’ use of an algorithm-advised performance rating is influenced by rating valence and the decision rights managers have to adjust the algorithm. We find that managers are less willing to use an algorithm to evaluate subordinate performance when it advises a low, rather than high, rating. We further show that when the algorithm-advised rating is low, allowing managers to adjust how the algorithm computes the rating, compared with adjusting the rating itself or not allowing any adjustment, increases algorithmic use. Further analyses show this effect to be consistent with managers’ increased understanding of an algorithm when involved in its computation. Our findings inform organizations’ implementation of performance evaluation algorithms by showing how rating valence and decision rights jointly influence managers’ use of the algorithms.