Rutgers University Dining Services Mobile Application
Like so many other millions of students in America, students at Rutgers University lead very busy lives and often lack the time to plan their meals. By researching Rutgers students’ lifestyles and how it directly affects their nutrition and eating habits, it came to the fore that Rutgers University students’ experiences with the campus eateries and dining halls are not the most satisfying. For students with dietary restrictions due to personal choices or allergies, the dining experience is not entirely seamless and most try to just work around it. And so students could benefit greatly from additional assistance with meal planning.
Design Process
Empathize - User Research
Define & Discovery - Identify the challenges and pain points
Ideate
Prototype
Test
Implement
ROLE:
User Research
Contextual Inquiry - Interviews, Surveys-
Journey Mapping
Sketching
Wireframing
Screen Flows
Visual Design
TOOLS:
Adobe XD, Adobe Illustrator, GSuite, Pen & Paper
Objective
The objective of the app is to provide Rutgers University students with a quick and easy way to find the right food at the right time by providing an application that allows users to specify their food preferences, requirements, meal plan information, and anticipated location, and receive a list of establishments and foods that meet their needs. In addition, users will be able to place orders for takeout or delivery through the application, without the need for additional applications.
A true “one-stop” experience!
Challenges
Diverse student population
Large university with different systems throughout campuses
Different eateries available across campuses
UX Methodology Implemented
Data & Insight Collection through User Interviews to best figure out
User Expectations
Pain Points
User Behavior Patterns
User Preferences
Personas & Journey Map Development reflecting Rutgers students with different nutritional lifestyles in order to clearly identify and fine-tune pain points with the current Rutgers food service system. Assets developed:
Capability Identification. Outlined user expectations and scenarios prioritizing a Minimum Viable Product. Developed:
Crazy 8-s
Wireframe
Designed a High-Fidelity Prototype. Used Adobe xd to develop an interactive prototype that allowed for internal heuristic testing, wireframe validation and Usability Testing.
Usability Testing allowed for recommendations and insights to be gathered, as well as feedback for future analysis.
IDENTITY MODELS DISCOVERED:
The speedster
The vegetarian
The foodie on a budget
The picky eater with an allergy
The healthy type
The chef
Quantitative Research
Several surveys were conducted to assess student satisfaction and uncover behavioral patterns.
SURVEY FINDINGS
When asked about the most important and least important factors that play into making the decision of where to eat, or grab a bite, our survey findings were reflective of several main points that came up on the conversational interviews. Out of all the students polled, 90% felt the most important factor was the value for their money as well as the ability to take their meals to go, followed by how fast they can go through the overall transaction from ordering all the way to paying at 70%. It’s interesting to note that for the most part, students considered most of the factors on the survey of importance, or on neutral ground and worth considering.
The contributing factors on a more neutral ground or split decisions were the ability to use their meal plan to pay, having healthier options and being able to pre-order and pick up their food or call for delivery and have the food brought to them.
Most important factors vs. least important factors when choosing where to eat
Student food behavior between 10AM- 2PM vs. Dinner (past 2PM)
Overall dining experience
LOW FIDELITY SCHEMATIC WIREFRAMES
Prototype Test Results
The feedback received from the students was positive for the most part. Students considered the application to be ready for development. Most of the users complimented how the interface was smooth and easy to navigate, allowed them to customize their order, and also mentioned how the application layout is easy for beginners which will help them find information easily. Students also liked that they were able to access their profiles and review their RU Express balance. Overall we received great feedback and no major issues from the student’s perspective. Minor issues reported involved device compatibility for users using an older iPhone version, making the bottom navigation hard to see and experiencing slow transitions when swiping through the Menu. Testers were made aware the interface is a prototype with limited capabilities and functionality.
Menu navigation discrepancies
Bottom navigation labeling issues to be explored in future iterations
The peak hour indicator needs more descriptive labeling
Make Express Pay feature also accessible from the home page
Conclusion and Next Steps
A college student’s life is incredibly and increasingly busy, which often comes at the expense of proper nutrition. The lack of which is detrimental to their mental and physical well-being. The purpose of this application is to help alleviate Rutgers University students’ stress and difficulty choosing nutritious meals that fit within their budgets, preferences, timeframes, and other parameters, by providing an intuitive interface with real-time data from participating local eateries.
The initial prototype was well received by students. Nevertheless, for future iterations, the most negative feedback from users tested should be analyzed in order to determine what changes can be made, what caused the negative feedback, and ultimately how to eliminate it or if there is a learning curve involved and how to alleviate it.
Recommendations for design improvements:
The peak hour indicator needs more descriptive labeling
Bottom navigation labels need to be more intuitive. A few of the navigation icons are currently inactive which caused some confusion and the question as to what they are intended for. Future iterations will allow full functionality removing the inherent issue.
In addition, beyond testing and initial implementation stages, the ultimate idea is to be able to integrate a small fleet of food delivery robots in order to expand delivery times and areas.