Clocking in or Not? Optimal Design of a Novel Gamified Business Model in Online Learning

Clocking-in cash-back (CIC), an emerging gamified business model in online learning, has recently garnered significant attention. CIC allows users to secure a full refund of the course fee through consecutive completion of specific tasks within a required time window. These tasks, known as clocking in, encompass activities such as daily assignments and sharing progress updates on social media. By employing this gamification system, the firm effectively monitors user efforts, categorizing them as winners or quitters based on clocking-in completion. Despite its growing popularity, this new business model has yet to be systematically analyzed in the literature. This paper fills this critical gap by examining how an online learning firm should set the optimal time window for its course and how the time window is affected by context-specific factors. We identify two opposing effects associated with extending the time window on users’ quitting time: the psychological-disutility increasing effect (negative) and the effort-cost decreasing effect (positive). Our results indicate that as quitters’ positive word-of-mouth (WOM) effects increase, there are cases in which the firm should opt for shortening the time window, primarily because of the psychological-disutility increasing effect outweighing the effort-cost decreasing effect. Furthermore, we find that it is not always beneficial for the firm to extend the time window when there is an increased presence of high-ability users. Additionally, we find that as the marginal content creation cost rises, instead of reducing the difficulty level of each task, the firm may find it more advantageous to raise the difficulty level by shortening the time window. Our findings provide valuable insights that online learning firms can utilize to enhance their design of the CIC mechanism.

History: Anandasivam Gopal, Senior Editor; Yan Huang, Associate Editor.

Funding: D. Liu’s research was supported in part by the National Natural Science Foundation of China [Grant NSFC-72071118] and WU Jiapei Award for Information Economics in 2019 [Grant M19100295].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0138.