Strategic shirking in competitive labor markets: A general model of multi‐task promotion tournaments with employer learning

Abstract

In a multitask, market-based promotion tournament model, under different environments concerning employer learning about worker ability, it is shown that: (a) asymmetric learning in multitask jobs is a necessary condition for "strategic shirking" (i.e., underperforming on certain tasks to increase the promotion probability); (b) when learning becomes increasingly symmetric on one task, the effort allocated to that task could increase or decrease, but effort on the other task increases; (c) strategic shirking does not occur in equilibrium in single-task models; and (d) promotions signal worker ability even when there is symmetric learning on one task, if learning is asymmetric on another.

Exploring the Continuous Motivation of Algorithm Engineers under Multiple Objectives: A Mixed-Methods Study

“…The literature studied the interaction between different tasks, as well as how to optimize the assignment of tasks among different professional agents [9,10]. The dynamic model proposed to study how to handle multi-task situations when employees have a certain specialty was also investigated [11,12]. However, the above studies did not consider the case that the incentive object can be difficult to replace among different tasks, and the incentive object itself is relatively homogenous.…”