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Food Ordering Survey
Survey Research
Anchor 1


October 2022 - December 2022


Understand the impact of food ordering methods on user satisfaction and preference


Qualtrics, MS Excel, Google Suite (Sheets, Slides, Doc)


Literature Review, Survey, T-Test, Chi-Square Test

For an academic project in the course, HF 795: Research Methods for Human Factors, I conducted a study with two other students to explore the impact of various food ordering methods (including delivery applications) on user satisfaction and preference. This study was conducted by distributing a 19-question survey created through Qualtrics to understand food ordering method preferences through device and software application types.

Project Journey

With my team, I created a research plan, designed a survey, recruited participants, analyzed data, wrote a research report, and presented our findings to the class. I took the lead on conducting quantitative analysis (t-test and chi-square) to evaluate our hypotheses for the study.

Research Question:

Are users satisfied with the available order methods (e.g., device type and application) for the delivery of food or goods?


Foundational Research

To formulate our research hypotheses, my team and I conducted a literature review of past studies surrounding our research question. As a result, we discovered:

  • Studies have examined the effect of device type, such as the device impact on learnability, or device preference when ordering plane tickets.

  • Limited research has been conducted on:

    • Device type preference or performance when it comes to ordering takeout

    • Differences between order method preferences versus satisfaction

    • Impact of personality type (introvert versus extrovert) on the preferred order method


  1. The percentage of users who prefer ordering through mobile applications versus other methods will be greater for younger participants compared to older participants.

  2. Self-service kiosk within a restaurant will be the least preferred method for all age groups.

  3. People that self-report as having an introverted personality type will prefer self-service kiosk over phone call-in verbal interaction.

  4. Older participants prefer ordering methods involving less advanced technology (e.g., home phone and desktop).


We presented a between-subjects study design to 31 participants. Each participant answered the same set of survey questions on Qualtrics without any counter-balancing of questions.

1. Independent Variables:

5 survey questions (multiple choice)

  • Device Type: Mobile Phone, Tablet, Desktop, Self-Service Kiosk, Home Phone

  • Application: Grubhub, Seamless, DoorDash, Uber Eats, Postmates

  • Age: Gen Z (1997 - 2022), Millennials (1981 - 1996), Gen X (1965 - 1980), Baby Boomers (1946 - 1964)

  • Gender: Male, Female, Non-Binary, Prefer Not to Answer

  • Residence Location: Urban, Suburban, Rural

2. Dependent Variables:

11 survey questions (9 5-point Likert scales, 1 ranking, and 1 free response)

  • Order method preference

  • Ordering app satisfaction

3. Recruitment Details:

  • Slack: Survey link was posted to Bentley MSHFID Slack channel & Falcon Grad Slack workspaces

  • Direct Message: The team sent direct messages with the survey link to other participants

4. Participant Requirement:

  • Used at least one device type and one app in the previous year for ordering food or goods


Survey design

Copy of Team Avocado - Final Presentation (4).jpg

Participant demographics from the survey


Using our survey results, I performed the following calculations using Google Sheets and Excel:

  • Mean preference ranking by order method

  • Mean satisfaction rating by ordering app

  • T-tests of survey results (e.g., app vs website)

  • Chi-squared analysis of hypotheses

Mean preference ranking by order method

Chi-squared analysis of one hypothesis

Copy of Team Avocado - Final Presentation (1).jpg

Mean satisfaction rating by ordering app

Screenshot 2023-07-10 012810.png
Screenshot 2023-07-10 012851.png

Some calculations, including t-tests, on Google Sheets


Some notable insights that I found from my data analysis were:

  • Most to least preferred ordering methods

  • Highest to lowest satisfaction for apps

  • Which null hypotheses to reject

Three of the null hypotheses could not be rejected. For the remaining hypothesis, however, we could reject the null hypothesis, meaning that a statistically significant difference exists.

Copy of Team Avocado - Final Presentation (7).jpg

Null hypothesis to reject

At the end of this project, my team submitted our final deliverables:

  • Survey

  • Research report

  • Final presentation (Google Slides)

Key Takeaways

Facing study limitations. While conducting our research, my team and I faced a few limitations:

  • None of the respondents lived in rural areas.​​

    • ​This could be a study artifact, because respondents described their residence location in one of the survey questions. However, potential rural respondents may have been disqualified in the first few questions–where they were asked how often they use apps to order food.

  • There was no option on the survey's ordering methods question for ordering "in-person."

    • It will be difficult to determine if self-reported introverts prefer ordering through self-service kiosk over face-to-face interaction.​

  • Order methods of older participants was difficult to determine due to limited sample size.

    • Only 5 of 28 participants (18%) of participants were classified in the older age group (Born in 1946 - 1964 or Born in 1965 - 1980). 23 of 28 participants (82%) were classified in the younger age group (Born in 1997 - 2022 or Born in 1981 - 1996).​

  • Participants may have been impacted by a survey order bias.

    • Introverts reported a higher preference in ordering through self-service kiosk compared to ordering through the home phone call-in method, however an order bias was presented – self-service kiosk was defaulted to precede the home phone option.​

Conducting further studies. Looking back on this project, there are a few areas that would have been interesting to dive into:

  • Explore preferred applications based on geographic location.

    • Residence Type (Urban, Suburban, or Rural) paired with qualitative feedback suggests application preferences may be specific to the city of residence.​

  • Explore the “why” behind app non-satisfactory ratings.

    • Usability tests should be conducted on the apps to determine why no apps received a mean satisfaction rating indicating “Satisfied” or “Highly Satisfied.”​

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