The proposed Pursuits App combines behavioral insight from psychology and behavioral studies with user analysis insights to offer a solution that prompts awareness to build traction towards pursuits. User responses demonstrate a high level of desirability towards adopting an aid for self-regulating; all proposed features are attributed to “attractive” or “performance” qualities based on a Kano model analysis. While this suggests that all proposed features contribute positively toward high customer satisfaction, the pilot questionnaire study revealed that users use many alternate tools and methods to self-regulate their traction; therefore, the proposed features may be biased with positive perception due to novelty in a market without dedicated tools.
While the user persona was presented as age 28, working full-time, the users with the highest overall rated importance for features include those aged 23-26, not-working, and primarily culturally identified with East or Southeast Asia. This suggests an opportunity to investigate user groups outside of the persona to uncover new respective motivations. Finally, the importance rating for weekly-prompts declined as notification fatigue increased, while the end-of-commitment-cycle prompt importance rating remained consistent across user groups.
The Pursuits App demonstrates potential in creating new categories for applications that contribute to user well-being by steering users towards their intended behavior. By gathering inspiration from concepts from behavioral psychology, this research hopes to contribute to conversations on how technology can be positioned to allow humans to achieve intended behaviors in pursuit of overall well-being.
The limitation of results for the “indifferent” and “reverse” categories is likely a result of the systematic UCD method, which proposes features based directly on user needs instead of biased speculation. Further insight into which features belong to these categories can be obtained by diversifying participants outside the target user profile to understand how other demographic characteristics affect feature prioritization. This can uncover new user groups, including a clear distinction between primary and secondary user profiles and a “non-persona” to describe a user who does not want to use a product or service (Augustin et al., 2021), which is insightful for market implementation.
Creating a high-fidelity prototype of the product concept will provide rich insight for longitudinal studies on how feature attitudes change over time. This would be insightful for that prompt weekly to determine the change in notification fatigue based on the increase of value and relevance for personalized prompts. This would also be insightful when understanding how other behavioral factors may affect user perception, leveraging models from behavioral psychology to evaluate the success of proposed functions through actual usage.
Finally, the proposed features of this research study have been presented conservatively to target specific user needs; therefore, further studies to investigate a broader range of experimental features can reveal a larger spectrum of usage scenarios and how users respond. This would provide insight combined with a longitudinal study to determine how a large variety of divisive features transform through time based on different user segments.