Events

Ms. Lin CHEN
PhD Candidate in Technology and Operations Management
IINSEAD, France

In many gamified services, users receive individual performance feedback upon service completion, which shapes their perception of goal achievement (through prospect theory) and relative status (when users are ahead-seeking or behind-averse). This feedback can vary in degrees of transparency, from disclosing individual scores to revealing only ranges of scores (e.g., top 5%). How transparent should service providers be in their individual performance feedback to maximize the utility users derive from their service? We employ a Bayesian persuasion framework to determine the optimal information disclosure policy, depending on whether a goal has been specified and whether the other users’ scores are communicated. Without a goal, if the other users’ scores are not available, any information policy is optimal; if they are available, the provider should be fully transparent (resp., opaque) when users are ahead-seeking (resp., behind-averse). With a goal, if the other users’ scores are not available, the users who have exceeded or just fallen short of the goal should only be told that they lie in that range, whereas the lowest-performing users should be told their exact scores; if the other users’ scores are available, the range of opaque disclosure may not extend to the top performers when users are ahead-seeking. Comparing the aggregate user utility across conditions, we also characterize when setting a goal or sharing the score of the other users leads to a higher aggregate utility. Our paper offers guidelines to service providers to enhance user utility by engineering the design of their relative performance feedback.