Richins, Greg2018-12-202018-12-202018-12-202018-11-30http://hdl.handle.net/10012/14269In a high-complexity task environment individual productivity can be improved through exerting more effort (i.e., working harder) as well as by learning improved task strategies. I examine the productivity effects of both learning goals and performance goals in such an environment. I argue that in a high-complexity task environment learning can often be an important predictor of task performance. As such, focusing on learning may be at least as important as working harder. Using an experiment with graduate and undergraduate accounting student participants, I predict and find that learning goals alone lead to increased learning relative to performance goals alone and that directing effort away from conventional performance toward learning does not impair task performance. I further predict that productivity can be enhanced by combining learning and performance goals. I predict that when assigning both goal types simultaneously, the presence of a performance goal will impair learning. However, I find that combining the two goal types simultaneously does not harm learning and improves performance. I further predict and find that assigning both goal types sequentially such that performance goals are assigned only after learning goals have induced learning leads to better performance than using learning goals in isolation. My results provide an understanding of the relationships among goal type, learning, and performance. This understanding contributes to the extant academic literature on goal setting and will be relevant to managers when designing and implementing management control systems.enlearninglearning goalperformance goalproductivitytask complexityDoctoral Thesis