Research Study: Measuring Trust
Our Team
My Role - Lead UX Researcher
Yuting Liao - Researcher
Varun Kharti - Product Designer
Project Brief:
Our product teams wanted to know how we could create trust in our personalized AI-driven experiences. We knew from previous research that trust is hard to measure and context often plays a large role in the various methods companies use to build trust. We set out to do various experiments to answer one question -
HMW build trust in data usage to personalize experiences?
Our Approach
We created two experiments to see what factors affect customer perceptions of data use.
The Experiments
Part one experimental attribute - transparency on how data is used
Research methodology: A/B testing
Part two experimental attribute - how using the data will benefit the customer
Research methodology: Scenario-based survey experiment
Study participants included two segments: Consumers (n=200) + Business owners (n=100)
Our Hypothesis:
Customers who are shown how their data will be used will increase trust and their likeliness to share data.
Customers, who aren’t shown how their data will be used, will be hesitant to complete the flow.
Our Hypothesis:
If customers believe that data collection and use is beneficial and appropriate, their trust will increase and they will be more willing to share their data. We measured this trust by evaluating two types of customer perceptions:
Appropriateness: measured based on level of agreement with statement “The use of my data was appropriate.”
Benefit: measured based on level of agreement with statement “The use of my data was beneficial.”
