Designing salient and supportive systems for proximal behaviors.

Designers should create salient reminders and support mechanisms to help users stick to near-term plans by reducing participation costs and increasing belief in the ease of tasks.

About this paper

The author conducted two studies to understand how temporal distance affects planned behavior, finding that attitudes become more important for distant events while perceived behavior control influences intentions regardless of timing.

These findings advance the Theory of Planned Behavior and provide strategies for designers and event organizers to motivate behaviors over different timeframes.

Here are some methods used in this study:

Theory Of Planned Behavior Construal Level Theory

Which part of the paper did the design guideline come from?

“We hypothesized that people tend to have a higher intention to perform the behavior in the far future compared to near future (H5). Results of the paired-samples t-test show that the mean of willingness to attend the yoga class differs a month before the event (M=.80, SD=.41) and a few days before the event (M=.60, SD=.49) at the .01 level of significance (t=2.70, df=29, p<.01, 95% CI, for a mean difference .05 to .35, r=.62). We should point out that in the end, only 6 participants actually (...)” (‘Change in Intention Over Time’ section)

Suh, M. (Mia), & Hsieh, G. (2016). Designing for Future Behaviors. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems.

Inspiration and scope

In this paper, authors focused on designers' and organizers' characteristics to encourage participation in proximal behaviors.

Your design is for experienced bug reporters identifying digital security vulnerabilities. The context differs as academic papers design for designers and organizers encouraging behavioral engagement over time, while you focus on bug reporters with immediate, transactional tasks. At the same time, both rely heavily on clear, effective communication. Ensuring designers and organizers understand behavior-enforcing strategies is crucial, just as it is for your AI to communicate potential duplicate issues clearly. Clarity and simplicity can benefit both.

Also, they differ as academic engagement seeks behavior influence over time, while yours is more transactional, focusing on immediate bug reporting and duplicate prevention. At the same time, both rely on feedback mechanisms to enhance user experience. Academic designs use feedback to encourage behavior over time, while your design gives real-time feedback to avoid duplicate reports. Effective, timely, and relevant feedback can refine the AI-driven bug reporting.

Leveraging these similarities, consider designing an AI feature in the bug reporting form to detect duplicates and provide concise, clear, actionable feedback. Users can quickly identify duplicates and report more effectively, enhancing their experience and confidence.

Your input

  • What: I'm adding AI-driven functionality into an existing bug reporting form that would alert users if the content they are inputting matches existing content that other people have filed.
  • Who: The user is a bug reporter, experienced in reporting digital security vulnerabilities.
  • Design stage: Research

Understanding users

The following user needs and pain points may apply to your design target as well:

Clarity In Communication

Design an interface that conveys alerts about duplicate bug reports clearly and concisely. Clarity in communication will help users quickly understand and act on the AI-driven feedback, reducing redundancy in bug reporting.

Timely Feedback

Implement real-time feedback mechanisms to alert users as they enter information into the bug reporting form. This can help users quickly identify and correct potential duplicates, thereby enhancing the user experience and system efficiency.

Methods for you

Consider the following method(s) used in this paper for your design work:

Theory of Planned Behavior (TPB)

Using the TPB may help designers understand the key factors influencing a bug reporter's intention to use the AI-driven alert feature. Keep in mind to evaluate attitudes, social norms, and perceived behavioral control concerning the new functionality.

Construal Level Theory (CLT)

Applying CLT can help designers understand how a bug reporter's perception of time affects their focus on the alert feature's desirability (why) and feasibility (how). Keep in mind that focusing on the 'why' might be more effective for distant future intentions, while the 'how' is crucial for near-term adoption.

[Figure 2] From this figure, you can draw insights on how different levels of cognitive abstraction can impact user behavior, which is useful for refining the AI-driven functionality to better alert users matching existing content.