would recommend
background
Finding suitable dining options on-the-go is challenging. Finding suitable dining options on-the go when you have food allergies seems near impossible. Our users – namely tech savvy Millennials – want to try new restaurants and food items but feel limited in their choices due to anxieties that come with eating in social settings, concerns about cross contamination, and extensive information gathering processes. From browsing through ratings and reviews to scanning menus, searching for a place to eat was time-consuming and tedious.
objective
Our tech savvy Millennials had anything from food intolerances to severe food allergies and wanted a solution that satisfied their dietary restrictions and taste preferences. With this in mind, we wondered: how might we improve, simplify, and even eliminate part of our users’ research process when finding food options on-the-go?
contributions
I helped develop research objectives after conducting secondary research, as well a survey that yielded 28 responses. I conducted 2 interviews (via phone), 1 contextual inquiry, and used affinity mapping to organize data we collected. I helped with drafting usability testing protocol and created the paper prototype for evaluative research. I also conducted 1 expert-based evaluation and 2 user-based evaluations.
role
Research Lead
team
Tiffany Chau | Research Lead
Joshua Galang | Design Lead
Titilayo Funso | Design Lead
purpose
CS6755: Foundations in Human-Computer Interaction
duration
Fall 2019 Semester
(4-5 months)
Timeline
Generative Research
Secondary Research
Surveys
Interviews
Contextual Inquiries
Affinity Mapping
Design - Concepting
Brainstorming
Concept Critique
Sketching and Mockups
Internal Team Analysis
Prototyping
Paper Prototyping
Wireframing
Evaluative Research
Usability Testing
Expert Evaluations
Design - Iterations
Digital Prototyping
Final Design
generative research
Secondary Research
We first consulted community forums to orient to the problem space. Subreddit thread r/FoodAllergies (3,500 members) revealed potential behaviors, anxieties, and preferences. Users share recipes, ask questions, and post about their personal experiences.
Other online resources – Food Allergy Research and Education and American College of Allergy, Asthma, and Immunology documented common strategies those with food allergies have when dining out. Articles, along with an analysis of existing systems, helped us further identify our users’ daily challenges and what technologies are currently of access to them (i.e. mobile applications, allergen sensing devices).
research objectives
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What food allergies do our users have, and do these allergens include items not known as a "common food allergy?"
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How do users typically search and decide on a place to eat? What factors do they consider?
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What are some anxieties and pain points for our users - Millennials with food allergies?
Primary Research
- Surveys -
Goal: Understand the scope of food allergies our users have, how frequent they dine out, and if they have a budget set aside for this activity.
Method Justification: Surveys allowed us to quickly receive responses to collect additional data that could confirm or deny our initial hypothesis.
--- We hypothesized that those with food allergies and strict dietary restrictions would eat out less frequently than those without food allergies due to anxieties and challenges that may come with eating at restaurants. ---
Method Details:
Survey Design. Our online survey had a total of 10 questions. It was designed to be adaptive in order to group respondents into certain categories:
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People with food allergies
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People with strict dietary restrictions
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People who do not have food allergies or dietary restrictions but dine with someone who does
Distribution. We narrowed down the demographic to those above the age of 18 and shared the survey across various platforms:
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Subreddit thread r/SampleSize (104,000 members)
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Internal Slack channel within the MS-HCI workspace (129 members)
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Our social media accounts.
--- At this time, we explored a user group which included both people with food allergies and people with strict dietary restrictions. But this was too broad, so we ended up pivoting toward helping only those with food allergies. ---
Findings: Using this method, we received 28 viable responses and discovered...
The spectrum of how people describe their food allergies included words such as “mildly,” “deadly,” “sensitivity,” and “intolerance.”
Range of budget per meal when dining out:
$8
$30
Respondents with food allergies eat out less than 3 times a week, whereas others eat out between 2 – 5 times a week
Foods that people are allergic to that go beyond common food allergies.
- Interviews -
Goal: Uncover specific challenges people with food allergies face when trying to find food options.
Method Justification: Interviews allowed us to collect qualitative data about our users including feelings, motivations, behaviors, and attitudes.
Method Details:
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Semi-structured interviews with 6 participants
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Questions mirrored our survey but also included questions to better understand users’ end-to-end experience with food allergies
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Conducted over the phone, via Skype, or in person
Sample Questions:
How could the process of finding food be improved for you?
Tell me about the last time you ate out at a restaurant.
What kind of tools, if any, do you use to assist you when finding food options?
How frequently do you dine out a week?
Findings: Using this method, we discovered...
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Most participants are hesitant to try new restaurants and stick to familiar places
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Information they need to make an informed decision are not readily available
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Participants parse through nutrition information across multiple platforms – Yelp, restaurant websites, and various mobile applications
- Contextual Inquiries -
Goal: Uncover users’ rationale behind every decision they make, from choosing a place to eat and ordering a meal to taking their first bite.
Method Justification: This method allowed us to understand users’ end-to-end journey when dining out. Since we observed our users in context while asking questions, any unconscious, nonverbal behaviors not explicitly explained.
Method Details:
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4 participants with food allergies (peanuts, kiwi, pineapple, and lactose)
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Participants went through the entire process of eating out (i.e. choosing a restaurant, placing their order, enjoying their meal)
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Asked questions about their decisions
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Documented actions and behaviors
Findings: Using this method, we discovered...
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At the restaurant, they need to alert wait staff about their food allergies and sometimes have to remind wait staff about their food allergies.
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If the meal they ordered is incorrect, some of them feel embarrassed to send the meal back.
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Online recommendations that are catered toward certain food allergies are difficult to find.
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Participants include location and cuisine as factors when searching for places to eat.
- Analysis and Synthesis -
Affinity Mapping
We interpreted notes from our interviews and contextual inquiries, while generating follow-up questions and design ideas along the way. We spent hours grouping and regrouping sticky notes, working our way up from detailed yellow stickies to high-level green stickies that illustrated the core aspects of the data collected.
We love sticky notes!
Major Pain Points:
1. People with food allergies need to know that their food allergies can be accommodated.
2. They have a complex information gathering process when searching for a place to eat.
3. Most restaurants and food places (not including food chains) do not provide adequate information about their menu items.
4. They are hesitant to try new restaurants due to worries about lack of accommodation and risk of cross contamination.
5. They have anxieties about eating out in a social setting.
6. They are unable to view visual information about menu items.
7. They do not have access to recommendations catered to their dietary needs and taste preferences.
design
Iteration 1
Goal: create divergent designs, varying in medium and intended use in order to address users’ pain points
Approach: brainstorm ideas individually based on research findings, share with team, and then do Round Robin session to critique ideas and rapidly iterate off of each other’s ideas
Concept 1 -
Would Recommend is a mobile application that allows users to search for food options and recommends food options based on users’ profiles, which include both dietary and taste preferences. Users can engage with the community by leaving reviews, recommending food options to other users, and reacting to others’ posts.
Concept 2 -
See-Food Spectacle is a wearable in the form of glasses that incorporates elements of augmented reality. When wearing these glasses, users can see “pins” of food options nearby that accommodate their food allergies. At the restaurant, users can view AR renderings of menu items and then inspect their order after it was brought out to them.
Concept 3 -
SmartScreen is a four-sided digital display placed in areas near multiple restaurants. Users can search for food options accommodating to their needs by indicating their allergens and types of cuisine. They are provided with a pin or “sticker” to use as their login for their next user of the display.
Feedback and Internal Team Analysis:
After presenting these concepts to users and colleagues, we did an internal team analysis of these 3 design alternatives based on the following:
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Address of Problem Space
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Overall Usefulness
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Novelty and Innovation
We found Would Recommend, the mobile application, the most favorable concept since it
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Had potential to address all of our users’ pain points rather than just focusing on a few
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Could seamlessly integrate into users’ daily lives
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Was easy to understand because it borrows aspects from existing systems
Iteration 2
Goal: create one convergent design, distill user needs into features
Approach: focus on initial concept while incorporating aspects from other design concepts to address all of users’ pain points
Major System Functionalities:
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Personalization. Users input their location, allergens, favorite cuisines, and restaurants to create a profile. Along with custom searches, they received recommendations tailored to their profile.
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Gamification. Users earn badges based on how they contribute to the online community, from posting reviews to recommending restaurants and menu items for others.
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Wayfinding. After selecting a food option, users receive directions to that restaurant location based on their current location.
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Seamless Selection. Menu items and ingredients are aggregated into the application so users can select specific dishes and review additional information. Optical Character Recognition (OCR) is added so users can scan menus and receive alerts about items to avoid based on their allergens.
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Community Engagement. Users can post information beyond the overall rating of the restaurant – how well the restaurant accommodates their dietary restrictions, rating of their order, comments about customer service, and more.
Click the arrows to view more!
evaluative research
Moderated User Testing and Expert Evaluations
Goal: Determine whether users can successful complete critical tasks when using the prototype and see if the design of our interface supports the purpose of the application.
Method Justification: Giving users tasks to complete allowed us to evaluate our prototype and specific user flows. This method also reveals any fundamental usability issues with the overall system and enabled to ask additional questions for clarification.
Method Details:
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5 users (either had food allergy or dines with someone who does) and 2 experts
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Used foam core model of iPhone X with printouts of wireframe
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After each task, users and experts were asked to respond to statements from the After Scenario Questionnaire (ASQ)
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Users and experts filled out a System Usability Scale (SUS) at the end of the evaluation session
Team Roles: Moderator, Facilitator, Notetaker
--- Based on time constraints, we conducted evaluation sessions individually. For our experts, they completed the same tasks to familiarize themselves with the system while providing usability feedback ---
Tasks:
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Create a profile
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Search for a list of dining options and decide on a restaurant to visit
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Find directions to the restaurant
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Decide on a dish to order
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Leave a review
ASQ Results:
Users
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Struggled the most on Task 4, deciding on what dish to order.
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Able to complete Task 2, searching for a list of dining options and deciding on a place to eat, without additional help.
Experts
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Found Task 1, creating a profile, the most challenging.
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Able to complete Task 3, finding directions to the restaurant, easily.
SUS Results:
Users
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Average SUS Score: 60
Experts
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Average SUS Score: 52.5
--- The goal was a score within the 80th percentile but since this was one of our first high-fidelity iterations of the application, we knew that could be challenging to achieve. ---
Findings:
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Most found the system easy to use and one they could use frequently
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Users could learn to use this system fairly quickly
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Additional feedback, support information, and more customizability would improve the user’s overall experience
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Functions within the system could be better integrated (i.e. OCR feature should be optional, not a given part of the process)
ITERATION 3
Version 1
Version 1
Version 1
Version 1
Version 1
Version 1
Version 2
What Changed?
Decreased number of questions on Signup page
Allergens and Cuisine preferences can be selected from drop down menus
Added checkbox for user to allow location permission and edited text to improve clarity
Version 2
What Changed?
Users’ information is displayed at the top
Level number is more prominent to highlight gamification
Preferred cuisines include both text and visual aids
Recommendations can be sorted by distance, cuisine, and price
Recommendations now have a simpler text rating, “Would Recommend” or “Would Not Recommend”
Global navigation added at the bottom
Version 2
What Changed?
User can now select sections of their profile – Reviews, Badges, Photos, Rewards.
Version 2
What Changed?
Search results can now be filtered by rating, distance, cuisine, and price
Ratings are simplified with color-coded and word descriptions – Green is highly recommended, yellow is in between, and red is not recommended
Version 2
What Changed?
Embedded navigation app
Expanded restaurant view includes reviews, menu items, and photos of the restaurant
Version 2
What Changed?
Added field for restaurant name and dedicated section for review
Added clear rating system for “Would Recommend” and “Would Not Recommend”
Clear submission button
pains and gains - lessons learned from this project
Pains
As I was conducting interviews, I soon realized that our problem space was much more complex than anticipated. There were lots of nuances associated with having food allergies, from varying levels of severity and uncommon allergies to time-consuming ordering processes. It was challenging to navigate at first but by working with my team to find patterns in our research, I was able to carefully put pieces together. We also had to pivot from original user group which was scary but necessary to keep the project moving. I learned that when the whole team’s on board with the change, you end up getting richer data and better designs!
Gains
From this project, I learned the importance of applying different research methods and creating thorough research plans. At each step, we asked ourselves: Why are we using this method? What makes this method better than others? How will we carry this method out and what do we hope to find?
In regards to the design phase, I found it was beneficial to use paper prototypes to quickly explore a design idea before utilizing lots of resources. This allowed us to then move onto a higher fidelity digital prototype soon after our evaluation sessions. With working on Would Recommend, I also discovered a passion for uncovering user needs and research insights to inform designs.