Foundational Research & Feature Prioritization
Security barriers to wearables:
Foundational qualitative research with users across the technology adoption curve
Our client’s innovative wearables product was catching on with Innovators and Early Adopters but struggling to reach Majority users. As one potential barrier to explore, they asked Amplinate to help identify privacy and security concerns that could be limiting trust and engagement.
Amplinate gathered insights from users across the adoption spectrum to develop a security strategy that helps them “cross the chasm.”
Amplinate conducted foundational qualitative research focusing on user participant perceptions of security risks and assurances for users across the adoption spectrum. One-on-one interviews and a mini-focus group were used to explore both individual perspectives and the durability of stances when confronted with differing opinions.
The findings revealed that security features can move the needle on Majority users’ comfort and purchase considerations. They also provided strategic insights on use cases with perceived security risk and security education and marketing for this transition.
Research Goal & Questions
The research goal was to understand user perceptions (across the adoption curve) of security risks and what assurances would make them feel comfortable adopting the wearables product.
What are the key differences in user participant perceptions, experiences, and expectations regarding product security features across the adoption curve?
What are common privacy and security barriers that limit use or adoption of the product?
What are potential “must have” or “delighter” security features that could enhance user trust and engagement with the product?
How do users categorize AI use cases in terms of trust, security, and perceived intrusion, and what are their concerns regarding AI integration in the product?
Our Approach
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N=8 participants from the USA
N=2 Early Adopters
N=3 Majority Adopters
N=3 Late Adopters
Mix of genders, ages, and household incomes
Some experienced issues with data privacy and security in wearable devices
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We conducted N=8 individual interviews with users across the adoption curve. Then held a triad discussion with 3 participants (1 Early Adopter, 1 Mainstream, 1 Late Adopter) to explore differing perspectives.
In the interviews:
Discussed current tech habits and preferences, reactions to proposed security features, and potential AI-powered use cases for wearables.
Categorization activities were used to sort security features and use cases and discuss needs and impact on user trust and comfort.
In the triad discussion:
Explored group dynamics, opinions, and attitudes towards security barriers, desired features, AI integration, and trust in the wearables product.
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9 weeks
Impact
Developed security user needs framework that will guide feature prioritization and design.
Identified high-value and high-comfort use cases for the design team to focus on and raised red flags about potential use case ideas that cross security boundaries.