Goals as Reference Points: Empirical Evidence from a Virtual Reward System
H eath et al. [Heath C, Larrick RP, Wu G (1999) Goals as reference points. Cognitive Psych. 38(1):79–109] propose a prospect theory model for goal behavior. Their analytical model is based on the assumption that goals inherit the main properties of the prospect theory value function, i.e., reference point dependence, loss aversion, and diminishing sensitivity. We investigate whether these main properties transfer to goal behavior in the field. We take user activity data from a gamified question and answer (Q&A) community and analyze how users adjust their contribution behavior in the days surrounding goal achievement, where goals are represented by badges. We find that users gradually increase their performance in the days prior to earning a badge, with performance peaking on the day of the promotion. In subsequent days, user performance gradually diminishes again, with the decline being strongest on the day immediately following the badge achievement. These findings reflect the characteristic S-shape of the prospect theory value function which is convex below the reference point and concave above it. Employing the target-based approach, we can interpret the value function as a cumulative density function of a unimodal probability distribution. Our results suggest that it is more likely that active members of the community focus on the next badge relative to the status already achieved, as their next goal and are less likely to focus on more remote (higher-ranked) badges. Our results thus support the transferability of the main properties of the prospect theory value function to goal behavior in the field and suggest a distinct shape of the value function around goals.
|Keywords||goal-setting theory, prospect theory, value function, gamification, badges|
|Persistent URL||dx.doi.org/10.1287/deca.2016.0331, hdl.handle.net/1765/119965|
von Rechenberg, T., Gutt, D., & Kundisch, D. (2016). Goals as Reference Points: Empirical Evidence from a Virtual Reward System. Decision Analysis, 13(2), 153–171. doi:10.1287/deca.2016.0331