User Interviews

Participant Demographics

Demographics Overview

Age Distribution

24

Average Age

Range:20-35 years

Gender Distribution

8
4
MaleFemale

Location Distribution

Waterloo6
Toronto1
Montreal2
Seattle2
San Francisco1

Mock Interviews

Participant 1
  • Age:22
  • Gender:M
  • Profession:Computer Science Student at UW
  • Location:Waterloo
Participant 2
  • Age:22
  • Gender:F
  • Profession:Student
  • Location:Waterloo

Informational Interviews

Participant 1
  • Age:20
  • Gender:M
  • Profession:Exchange Student from Singapore
  • Location:Waterloo
Participant 2
  • Age:21
  • Gender:M
  • Profession:Data Science Co-op/Student
  • Location:Toronto
Participant 3
  • Age:22
  • Gender:M
  • Profession:Young Adult (Full-time)
  • Location:San Francisco
Participant 4
  • Age:35
  • Gender:M
  • Profession:Software Professional with Young Family
  • Location:Seattle
Participant 5
  • Age:23
  • Gender:M
  • Profession:Post-grad Student
  • Location:Waterloo
Participant 6
  • Age:23
  • Gender:F
  • Profession:Software Engineering Intern
  • Location:Montreal

Prototype Evaluation

Participant 1
  • Age:22
  • Gender:M
  • Profession:Product Designer/Student
  • Location:Waterloo
Participant 2
  • Age:35
  • Gender:M
  • Profession:Software Professional with Young Family
  • Location:Seattle
Participant 3
  • Age:23
  • Gender:F
  • Profession:Student
  • Location:Waterloo
Participant 4
  • Age:23
  • Gender:F
  • Profession:Software Engineering Intern
  • Location:Montreal

Research Methods

Interview Process

Question Development

  • Open-ended questions focused on transportation habits
  • Probing questions about social connection experiences
  • Scenarios exploring safety concerns and preferences
  • Questions about local exploration methods

Interview Format

  • Semi-structured interviews allowing natural conversation flow
  • Mix of in-person and virtual sessions
  • 60-minute sessions with follow-up questions
  • Recording and note-taking for detailed analysis

Research Analysis

Work Model

A work model visualizes how users currently navigate their transportation choices, highlighting key decision points, pain points, and environmental factors that influence their behavior.

Work model visualization of user research findings

Affinity Diagram

An affinity diagram organizes interview insights into related groups, helping us identify patterns and common themes across different user experiences and perspectives.

Affinity diagram showing interview data analysis

Key Findings

Transportation Choice Factors

  • Convenience and time prioritization
  • Weather dependency affects choices
  • Physical effort considerations

Social Connection

  • Selective social engagement during commutes
  • Interest in community events
  • Word-of-mouth and app-based discovery

Safety Concerns

  • Well-lit roads importance
  • City-specific safety issues
  • Route modification for safety

Exploration Patterns

  • Limited motivation for new routes
  • Interest in landmarks
  • Established route preferences

Identified Problems

Limited Information Access

Difficulty finding reliable, centralized sources for local events and safe routes

Safety Concerns

Inadequate infrastructure and lighting affecting active transportation choices

Low Engagement

Limited motivation to explore new routes or engage with community during commutes

Passed the squint test... barely 👀

© 2024 Motion