團隊

隨著科技和網路發展，拼車服務勢必向自動化及智慧化方向邁進。 筆者透過研究不同定價模式和匹配機制，為網約車平台建議提升拼車盈利的策略。

On-demand service platforms are interested in having gig workers use self-set, nonbinding performance goals to improve efforts and performance. To examine the effects of such self-set goal mechanisms, we build a behavioral model, derive theoretical results and testable hypotheses, and conduct a field experiment using a large gig platform for food delivery. Our model analysis finds that individual workers’ optimal self-set goals may exhibit a spectrum of difficulty levels, ranging from trivial to impossible, depending on workers’ reference-dependent utility coefficients and self-control cost. Moreover, workers’ efforts are higher with properly set goals rather than no-goals. Consistently, our experimental data show significant treatment effects of self-goal setting, and a causal tree algorithm identifies subgroups who are mostly motivated by self-set goals. Furthermore, our study compares two common types of performance metrics for goal setting: the number of completed orders and total revenue. Our model suggests different cases of effort and performance improvement for the two goal types. The experimental data suggests that both goal types improve efforts equally but lead to different attainment rates. Specifically, the goal attainment rate is lower for the revenue-goal treatment than for the order-quantity-goal treatment. Further analysis reveals that this disparity is due to workers setting excessively high revenue goals. Our study demonstrates the efficacy and limitations of self-goal-setting mechanisms and yields two important managerial implications. First, the implementation of self-goal-setting mechanisms could improve gig workers’ efforts and performance. Second, encouraging order-quantity goals instead of revenue goals could help gig workers achieve higher attainment rates.