Implementing positive reinforcement.

Designers should provide immediate positive feedback in various formats to reinforce sustainable energy actions and boost intrinsic motivation.

About this paper

The author argues that existing energy feedback technologies are ineffective because they use a universal approach, failing to account for individual differences in attitudes and motivational stages.

They propose leveraging motivational psychology, specifically the Transtheoretical Model, to develop more personalized and effective strategies for promoting sustainable energy behaviors.

Here are some methods used in this study:

Transtheoretical Model Motivational Interviewing

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

“‘Ubigreen’[24] (Figure 3, top right) employs these techniques. It is a mobile phone visualization that uses semi-automatic sensing technologies to provide feedback of transportation behaviors. It uses a series of emotionally persuasive icons [24] (i.e. a polar bear standing on an iceberg) as positive reinforcement. The more “green” one’s transportation behaviors, the further in the progression of icons one gets (i.e. the iceberg grows and the ecosystem improves) until one reaches the final stage (...)” (‘Positive Reinforcement, Emotional Persuasion (through the ELM) & Values’ section)

He, H. A., Greenberg, S., & Huang, E. M. (2010). One size does not fit all. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.

Inspiration and scope

In this paper, the authors focused on characteristics of designers of energy feedback technologies to motivate sustainable energy behaviors.

You are designing for conscious shoppers who care about sustainability. Your design and the paper's context differ: energy feedback tech needs long-term engagement for users not initially motivated, while a daycare product analyzer targets conscious shoppers needing immediate, actionable info. At the same time, both designs necessitate educating about sustainability. Insights from papers on communicating complex data can help in conveying product sustainability details to shoppers.

Also, they differ as energy feedback tech handles complex, large-scale data needing analytics and visualizations, whereas the analyzer handles simpler, product-specific data accessible at purchase. At the same time, both require ensuring the trustworthiness of provided info. Methodologies for verifying energy data can ensure the ingredient analyzer offers accurate, trusted information, boosting user confidence.

By leveraging these similarities, you might design an analyzer giving immediate, positive feedback when users choose sustainable products. This reinforces the joy of responsible choices, fostering sustained behavior towards sustainability.

Your input

  • What: a daycare product ingredient analyzer
  • Who: conscious shoppers who care about sustainability
  • Design stage: Research

Understanding users

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

Effective Communication of Complex Data

Designers need to present ingredient information in a clear and engaging way. Using visualizations or tailored messaging can help users better understand the sustainability impacts of daycare products.

Building Trust and Reliability

Ensuring the accuracy and trustworthiness of ingredient data is crucial for user confidence. Implementing verification methods and transparent data sources can enhance the reliability of the information provided by the analyzer.

Methods for you

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

Transtheoretical Model (TTM)

The Transtheoretical Model helps in understanding the various stages users go through before making a behavior change. Designers should assess which stage their target users are in to tailor the ingredient analyzer's features to effectively encourage sustainable choices.

Motivational Interviewing (MI)

Motivational Interviewing can uncover the intrinsic motivations of conscious shoppers, helping designers create more persuasive engagement strategies for the ingredient analyzer. Keep MI principles in mind to build user self-efficacy and align product benefits with user values.

[Table 2] From this figure, you can understand the motivational goals at different stages of user adoption, which is crucial for effectively addressing the problem space in designing a product ingredient analyzer for conscious shoppers.