WOULD RECOMMEND: UX DESIGN PROJECT
Would Recommend is a restaurant recommendation app concept developed over the course of 3 months. Intended to help travelers discover dining venues that accommodate their dietary restrictions, it would also aid in making informed decisions and allow user feedback as well as gamification.
At the beginning of the fall 2019 semester, our HCI class was charged with making an app themed to ‘On the Go’, where we decided to focus on travelers looking for places to eat while abroad. This led to our research and development of a high-fidelity prototype.
EVALUATION + TASKS
We approached this design project from a user-centered perspective, researching and observing both users and experts as they performed benchmark tasks while eating out.
1. CREATING A PROFILE
The intent of Would Recommend was to encourage people with food allergies to participate in an online crowdsourcing community. In order to ensure the quality and integrity of dining recommendations and reviews, users must create a profile. For this task, we expected participants to be able to indicate his or her name, location, food allergy, and cuisine preferences without any trouble. We needed to evaluate whether this form is intuitive for new users and whether users can quickly and easily complete the signup process.
2. SEARCH DINING OPTIONS + SELECT
There were several ways for users to find dining options on the mobile app. They can either search by dish, cuisine, or location. Because the homepage of the app showcases nearby dining recommendations based on users’ past cuisine preferences, the goal of this task was to let users search for dining options by dish. We needed to evaluate whether users can easily find and utilize this information-gathering option, and whether users comprehended how information is displayed on the app, such as star ratings, to support their decision-making.
3. NAVIGATING TO THE RESTAURANT
Once users make an informed decision on a restaurant at which to dine, the app also supports wayfinding so that users do not waste time shuffling between different apps and tools. We expected participants to be able to infer that they can tap on the map preview within the information box for a restaurant to find directions.
4. DECIDING ON A DISH
Would Recommend included a unique feature that leverages an OCR camera interface to help users find information in the restaurant as they’re perusing the menu. By pointing the camera to a menu item, the user can see more information about the menu item on his or her screen. Because this is a relatively unique and novel feature, we needed to evaluate whether participants could recognize that the camera icon on the homepage is for OCR, hover the prototype over the menu, and make sense of the onscreen renderings.
5. LEAVING A REVIEW
The user should be able to contribute to the crowdsourcing community on Would Recommend by leaving a review for other users who share the same food allergies.
We first observed casual users with dietary restrictions as they walked us through the five benchmark tasks. Following each benchmark task, the facilitator asked the participant the following three questions from the After Scenario Questionnaire (ASQ), using a 7-point Likert scale with 1 being Strongly Agree and 7 Strongly Degree:
Overall, I am satisfied with the ease of completing this task.
Overall, I am satisfied with the amount of time it took to complete this task.
Overall, I am satisfied with the support information (online help, messages, documentation) when completing this task.
We also conducted the same benchmark tests to several experts in the field, such as the department head of Human-Computer Interaction at Georgia Tech. They were also presented with the After Scenario Questionnaire.