We develop and empirically test a parsimonious model of how specific knowledge and the value of knowledge sharing influence manufacturing plants' incentive design choices. Our results confirm the prediction that increases in the extent of agents' specific knowledge and the value of knowledge sharing are associated with greater (less) reliance on output (input) performance measures. Moreover, consistent with our model's prediction, we find that plants rely more on group-based (as opposed to individual-based) output performance measures when the value of knowledge sharing is higher, or the extent of agents' specific knowledge is lower. Finally, consistent with previous research, we find that as output performance measures become noisier, firms rely less on these measures in incentive contracts.

Firms commonly employ leaderboards within their peer-to-peer recognition programs. We experimentally investigate how ranking basis—variation in the measure firms use to determine leaderboard rankings—affects employees’ proactive helping behavior. We find that leaderboards ranking employees based on the number of times peer-to-peer recognition is received decrease proactive helping compared with when no leaderboard is provided. Conversely, leaderboards ranking employees based on the number of times peer-to-peer recognition is given increase proactive helping compared with when no leaderboard is provided. These findings underscore the influence of ranking basis on shaping motives linked to proactive helping behavior. Furthermore, these findings highlight for firms the importance of judiciously selecting a ranking basis when utilizing peer-to-peer recognition leaderboards.