Perception of Food Apps

MKTG 482

April 27, 2016

Research Report

Rashard Brooks, Kayla Brown, DeAndre Ishmael, Vaquacious Lundy, Jasmyn Oliver

Table of Contents

Executive Summary…………………………………………………………………………….2

Introduction to the Project, the Research Problem and the Research Objectives………….4

The Research Methodology…………………………………………………………………….6

The Sample and Sample Characteristics…………………………………………………….13

Data Analysis, Discussion, and Integration…………………………………………………16

Executive Summary

Introduction

A survey was conducted by our Food Apps team in Dr. Patwardhan’s Marketing

Research class. The participants, students of Winthrop University and other South Carolina

colleges, answered questions pertaining to perception of food apps, in categories based on

usefulness, security and convenience.

Objectives and Method

This survey had four objectives: to quantify how food apps are perceived, to understand

if the usefulness of a food app has an effect on whether the consumer will order from it, to

understand if the convenience of a food app has an effect on whether the consumer will order

from it, to understand how consumers perceive security on food apps, and lastly, how food apps

are perceived in comparison to ordering online or in-store.

The survey was taken through an online survey website, Qualtrics. The link for the web-

based survey was distributed via email and text messages. Questions were multiple choice and

likert scales. The survey was conducted from March 28, 2016 to April 13, 2016.

Key Results

Though the results showed a lack of students who actually have apps downloaded to their

mobile devices, the results showed that, many students perceived that food app’s were time a

convenient option to ordering food. However, in relation to usefulness, the data displayed that

many participants agreed in that usefulness was not a strong variable when looking to download

an app. Data also showed that many people agreed that food apps have a low rate of

trustworthiness, with many participants feeling uncomfortable with saving credit card

information and believing their credit card information is protected. Lastly, we found that many

participants would not rather order on a mobile app, than going in-store to order.

Recommendations/ Key Messages

The most important insight gained from this survey is that students do agree that food

apps are convenient and useful, but convenience and usefulness are not necessarily factors that

persuade them to be interested in downloading/using a food app or not. It would be best that

when advertising to college students, mobile app marketers should increase the awareness of

mobile app specific coupons and promotions, as well as promoting the safety features of

credit/debit card protection to these consumers.

Introduction

The definition of a “Food App”, is a mobile application that curates a visual collection of

specific food or beverages a company offers for promotion or sale, in which consumers can

purchase, provide recommendations, or review food or beverages categories. Restaurants no

longer have a choice between running as a brick and mortar retailer or having a strong online and

mobile presence. Today, even the food industry must have a strong presence in both. In 2015,

restaurant chains invested a large amount in recruiting top technology experts into executive

offices in order to thrive during this mobile and digital innovation in the food industry. As a

group, we were fascinated by this trend and wanted to analyze whether or not investing into food

apps was an efficient and effective marketing tactic when advertising to college students.

Our group used Qualtrics to create our “Food Apps” survey. The focus of the survey was

centered around how our specific target audience, college students in South Carolina, perceive

foods apps. Our main research question was: How do consumer perceive food apps in regards to

usefulness, security and convenience. Our sample consisted of students from Winthrop

University, University of South Carolina Columbia, South Carolina State University, Charleston

Southern, Claflin University, North Carolina Agricultural and Technical State University and

Winston-Salem State University.

Our project had four overall objectives. The first objective was to understand how food

apps are perceived to college students in general. The second objective was to understand how

having a user-friendly (i.e. useful) food app has an effect on whether consumers will order food

off of it. The third objective was to understand how consumers perceive the trustworthiness (i.e.

security) of food apps. The fourth objective was to understand if consumers choose convenience

over price.

Research Methodology

The research process was strategically planned to ensure that our group had sufficient

time to analyze the data collected and avoid any faults or mistakes that had arisen during the

distribution and collection period for out surveys. On January 13th of 2016, our group began

analyzing different trends amongst our generation to find a topic that was not only relevant but

also appealed to college students. With extensive research, we came across the growing market

for Food Applications which many restaurants have begun implementing in order to reduce

lines/and or waiting times, while increasing consumer customization and brand loyalty. On

February 1st, after the finalization of our research topic, we began identifying research objectives

based upon the usage and overall benefits of using Food applications. These objectives focused

around four main categories, which include general perceptions, usefulness, security, and lastly

convenience. These objectives were used to help our group design and create sample

questionnaire questions on February 3rd, which were later revised by Dr. Patwardhan on February

17th to ensure that the questions matched our research objectives and met all requirements. With

the finalized questions and research objectives, it was time to create the surveys by inputting our

questionnaires into Qualtrics to begin the distribution phase.

Qualtrics, a powerful online survey tool that allows one to build surveys, distribute

surveys and analyze responses from one convenient online location, enabled our team to input

our survey questions into a software that was distributed through a link that was emailed to our

target audience through non-probability convenience sampling. Convenience sampling, where

subjects are chosen simply because they are easy to recruit, was used because food applications

are so prevalent amongst young adults, specifically college students, and Winthrop University

has approximately 4,974 enrolled undergraduate students. With so many resources surrounding

us, we were then able to focus on sending out our survey link to collect data.

Before our group was able to send out the survey link and collected data, we needed a

strong introductory statement that showcased who we were as a group and why we were

requesting data from our fellow student body, as well as students from other universities. The

introductory statement used for our survey is as follows. “As students of Marketing Research we

are conducting a survey on the use of mobile food apps on Winthrop University's campus. Some

examples of food apps are Starbucks, Panera Bread, and Domino's. On these apps, users are able

to order and purchase food through their mobile phones. Please complete the survey below and

answer each question with your honest opinion. Your participation is not expected to exceed 10

minutes. Your responses will remain anonymous and confidential. Thank you for your

participation”. The statement clearly defines our goal as marketing research students and briefly

informs/reminds students of what food applications are. As a group, we wanted to quickly

remind students of what food applications were since we feared that many people might use

“slang” terms to define the apps so it was important to clarify this so it would not interfere with

our results.

In addition to a brief summary, we also added a time estimate to show respondents that

the survey would not exceed 10 minutes. As college students, we have very short attentions

spans since we tend to have so many activities and assignments happening all at once. The time

frame was also used a guide during the questionnaire designing process. The 10-minute deadline

was used to formulate questions in a manner that was short but precise and to the point. It was

important to us that our respondents would take the survey seriously by answering all questions

honestly and to the best of their ability. Lastly. We also included that responses and identities

from respondents would be remain confidential in order to ensure that respondents felt

comfortable to answer the questions truthfully without the fear of their identity being leaked or

shown for others to see. With this in mind, my group and I formulated an efficient and effective

survey that did not require too much time to take which resulted in respondents giving their

honest opinions.

During the creation and designing process of the survey, it was important that we

strategically ordered questions to ensure the survey flowed in a manner that followed effective

research guidelines discussed in class. As mentioned before, the survey was created in Qualtrics

with the assistance of Dr. Patwardhan and Mr. Brian Hipp. The software gave our group the

ability to order the questions using the drag and drop feature, skip questions using the force-

response validation option, and skip logic if certain questions did not apply to all respondents

such as the question in regards to being employed. If respondents selected “No” to being

currently employed then the surveyor skipped the question “What is your hourly pay?” The

software also gave us the ability to preview how it would look on mobile compatible devices.

This feature was most important since it was obvious that many students would be taking the

surveys using mobile devices.

With that being said, it was crucial that we designed the survey to flow in a manner that

not only appealed to the eye but also gave the respondent the ability to clearly see and read all

questions from their small screen. In addition to using the preview feature, we as a group were

able to sit down with each other and take the survey to test for any glitches, spelling and coding

errors. In order to ensure that the survey was ready for distribution, we scheduled a one on one

appointment with Dr. Patwardhan where he personally sat down and took the survey to test for

any problematic issues. His assistance helped our group to finalize our survey and prepare for

this distribution process.

The finalized survey was comprised of an introductory statement as well as 12 close-

ended questions that included Likert scales, categorical data, and etc. The questions used focus

on the respondent’s perception of usefulness, security, and convenience in regards to Food

Applications. Figure 1.1 below displays an example of two questions that fall under the Likert-

type scale.

Figure 1.1

These two questions specifically focus on the perception and usefulness of Food applications by

analyzing how comfortable consumers feel about saving credit card information, using the app

rather than going into the store, using the app when hungry, downloading the app when there are

incentives to do so, and etc. Other questions included in the survey focus on the convenience

provided by using a food application. This includes ease of access from ordering online through

the app, joining frequent purchase list, smoother delivery during ordering, comfortability with

receiving notifications from the Food Applications and etc. These questions were placed at the

beginning of the survey to help us better understand how Food Applications are viewed amongst

our target audience. The demographic questions were placed at the end to better understand what

category our respondents represented. These questions included race/ethnicity, gender, age,

residency, phone operating system and etc. as shown in figure 2.1.

Figure 2.1

With a finalized survey, it was time to launch the survey and distribute the link amongst

our classmates and other college students attending universities in South Carolina.

The survey links were attached to the emails of our group and were sent through the daily

student announcements, posted within Winthrop pages on Facebook, distributed within Winthrop

organizations such as Enactus, and the Association of Ebonites, as well as other universities in

South Carolina through friends and acquaintances to help us reach larger audiences. In addition,

to ensure that our group reached a larger audience, we also collected data by uploading the

survey onto an iPad while walking around the DiGiorgio Campus Center during common time.

With the iPad, we had the ability to walk around and ask students to participate in our research

study. This method seemed to be very effective and helped us obtain more respondents than

expected. During the identifying and finalizing process of our research topic, my group and I set

a target of reaching a sample size of approximately 80 people. However, with the help of friends

and coworkers throughout Winthrop’s campus, the survey reached and collected information

from a total of 106 respondents which exceeded our goal by 26 people.

With so many responses from surveyors, it was important that our information was

accurate and fully complete while minimizing any errors. To ensure this, our group adding

coding variable as mentioned before to ensure that respondents fully addressed all the variables

in a question before moving on to the next one. This is called the force validation response,

which is used to prevent respondents from skipping questions, especially our most important

ones. We also used skip logic to enable the response to skip questions that did not apply to them.

For example, our first question after the introductory statement read as follows “Do you

currently have any food apps downloaded on your phone”. If the respondent selected “yes” then

they were directed to the next question and if the respondent selected “no” then they were

directed to the end of the survey to a screen that said “thank you for your participation, please

pass this survey along to others”. This question helped us to deduct the amount of surveyors who

did not have surveys downloaded onto their phones.

However, with this coding function, we ran into some minor problems. Many people

began misinterpreting the question. Some failed to understand the term of “Food Apps” and did

not realize that they actually did have the examples of these applications downloaded onto their

phones. These respondents were selecting no which took them to the end of the survey. Because

of this, our survey results were showing no significant change in perception because the

surveyors did not understand the term “Food Apps” even though they had used one of

downloaded one before. Once my group and I became aware of this issue, we quickly went in

and changed the coding by deleting the skip logic. As a result, we had to delete the previous

survey results and start fresh with a new link for redistribution. During the process of doing so,

our coding that denied surveyors to ability to move on without fully answering questions

disappeared. Because of this, some questions on our survey were intentionally being skipped by

surveyors, which took a small toll on our results. This was a mistake that we all took full

responsibility for but simply fixed by doing some recalculations.

Sample and Sample Characteristics

The demographics of our Food App survey included: gender, race/ethnicity, age, and

employment information. Overall females ruled the majority of taking the survey. Our

percentage rating for males were twenty-five percent, and females were seventy-five percent. For

race/ethnicity, the majority taking the survey were Black/African-American including fifty-eight

percent. Thirty-four percent were White/Caucasian, four percent are Hispanic/Latino, and four

percent marked other or preferred not to answer. The majority of our survey participants were

ages twenty to twenty-two years old. Thirty-one percent were seventeen to nineteen years old,

eight percent are ages twenty-three to twenty-five, and five percent were twenty-five and older.

The majority of our participants are employed with a total of sixty-seven participants, and thirty-

three percent were not employed.

Gender

Race/Ethnicity

Age

Employment

Data Analysis and Discussion Integration

Consumer cognizance on the following were measured:

1. Perception of Food Apps

2. Usefulness of Food Apps

3. Security of Food Apps

4. Convenience of Food Apps

Additionally their age, gender, race/ethnicity, income and residency(commuter/on campus)

categories were also measured along with what phone/operating system they currently have.

RQ #1: How are food apps perceived?

I.Hypothesis: To see whether different ages result in their general perception of food apps.

ANOVA

Independent Variable: Age

Dependent Variable: General perception of food apps

Directions:

Analyze

Compare Means

One-Way ANOVA

Dependent List: Your Perception About Food Apps Questions (Total of 4)

Factor: Age

Options

Check “Descriptive”

Continue

Click “Ok”

Interpretation:

1. From the first table, the mean perception about food apps of the subjects in question 1 (I

think that using a food app is a convenient way to order food), question 2 (I think my

private info [credit card number] is secure while using a food app), question 3 (I would

feel more comfortable with using a food app, rather than going in store), and question 4 (I

rely heavily on the use of mobile apps in my everyday life) are 3.85, 3.25, 2.66 and 3.84

respectively.

2. Checking the ANOVA table;

As F increases, we are more likely to reject the null.

3. Conclusion through testing;

Since the p-values are all above 0.05, we accept the null hypothesis and conclude that

there is no significant difference in age and general perceptions of food apps. When looking at

the millennial generation, there is no significant difference between ages groups (17-19, 20-22,

23-25, and 25 and older). Food app companies do not need to target their messages and app

promotions by age.

II. Hypothesis: To see whether different genders have a result in their general perception of

food apps.

Independent Sample T-test

Independent Variable: Gender

Dependent Variable: General perception of food apps

Directions:

Analyze

Compare Means

Independent Samples T-Test

Place Convenience Question () Into “Test Variables”

Place Residency Into “Grouping Variables”

Click “Define Groups”

Name Group 1, 1

Name Group 2, 2

Click “Ok”

Interpretation:

1. From the first table, the mean perception about food apps of the subjects in question 1 (I

think that using a food app is a convenient way to order food) was 3.85, the mean for

question 2 (I think my private info [credit card number] is secure while using a food app)

was 3.25, the mean for question 3 (I would feel more comfortable with using a food app,

rather than going in store) was 2.66, and the mean for question 4 (I rely heavily on the

use of mobile apps in my everyday life) was 3.84 respectively.

2. Conclusion through testing;

Four of the p-values are all above 0.05, so we accepting the null hypothesis and conclude

that there is no significant difference in gender and general perceptions of food apps. However,

only 21 males agreed that they rely heavily on the use of mobile apps in their everyday life,

which means that food app companies may want to focus on solely targeting females or

attempting to build loyalty to food apps within males.

RQ #2: Does the usefulness of a food app have an effect on whether consumers are willing

to download or order from it?

I.Hypothesis: To see if usefulness (Questions 1,2,3,4 and 5) are associated with the residency

(Commuter and Live on Campus); Is there any difference in perceptions of usefulness and living

on or off campus.

Independent Sample T-test

Independent Variable: Residency

Dependent Variable: Usefulness

Directions:

Analyze

Compare Means

Independent Samples T-Test

Place Convenience Question () Into “Test Variables”

Place Residency Into “Grouping Variables”

Click “Define Groups”

Name Group 1, 1

Name Group 2, 2

Click “Ok”

Interpretation:

1. All of the p-values are all above 0.05, so we accept the null hypothesis and conclude that

there is no significant difference in whether you live on or off campus and the usefulness

of food apps. Our suggestion to food app marketers would be to not focus on usefulness

as a main factor of if students will download/use a food app, because they will use it

regardless of where they whether they live on or off campus.

RQ #3: Does the convenience of a food app have an effect on whether consumers will order

from it?

I.Hypothesis: To see if there is a significant difference in residency when looking perceptions of

convenience.

Independent Samples T-test

Independent Variable: Residency

Dependent Variable: Perceptions of convenience

Directions:

Analyze

Compare Means

Independent Samples T-Test

Place Convenience Question () Into “Test Variables”

Place Residency Into “Grouping Variables”

Click “Define Groups”

Name Group 1, 1

Name Group 2, 2

Click “Ok”

Interpretation

From the first table, the mean sales under the on-campus and commuter are 3.05 and 3.55,

respectively. The test statistic, t, for this observed difference is 2.49(t= 2.493). The p-value for

this t-statistic is 0.051(Sig.(2-tailed)=0.0255). Since p-value (0.051) is less than 0.05, we accept

the null hypothesis and conclude that there is not a significant difference in perception of

convenience and residency.

RQ #4: How do consumers perceive security on food apps?

I.Hypothesis: To see if security is positively related to the students’ use of app.

Simple Regression?

Independent Variable: Security

Dependent Variable: Usefulness

Directions:

Analyze

Regression

Linear

Add in - All security questions (Independent Box)

Hourly Pay (Dependent Box)

Click “Ok”

Interpretation:

1. From the last table, the estimated regression coefficients for question 1 are: βo

(Constant)= 3.793 and β1 (coefficient for X1)= -.174. The p-value for testing Ho: β1 = 0

is 0.000. Therefore, we accept the null hypothesis and we conclude that the likeliness for

the consumer to encourage friends/family to use a food app based on their personal

experience (Y) is not significantly affected by hourly pay(X1).

The estimated regression coefficients for question 2 are: βo (Constant)= 3.793 and β1

(coefficient for X1)= .182. The p-value for testing Ho: β1 = 0 is 0.000. Therefore, we

reject the null hypothesis and we conclude that the likeliness for the consumer to save a

credit card information on a food app for future purchases (Y) is significantly affected by

hourly pay(X1).

From the last table, the estimated regression coefficients for question 1 are: βo

(Constant)= 3.793 and β1 (coefficient for X1)= -.120. The p-value for testing Ho: β1 = 0

is 0.000. Therefore, we accept the null hypothesis and we conclude that the likeliness for

the consumer to download a food app if they have to receive emails (Y) is not

significantly affected by hourly pay(X1).

The estimated regression coefficients for question 2 are: βo (Constant)= 3.793 and β1

(coefficient for X1)= .108. The p-value for testing Ho: β1 = 0 is 0.000. Therefore, we

reject the null hypothesis and we conclude that the likeliness to order food through an app

rather than online or in-store (Y) is significantly affected by hourly pay(X1).

2. From the second table, R2 = .066 (or 6.6%). This indicates that the 6.6% of total variance

of security (Y) is explained by the estimated regression equation, or by the hourly pay.

As F increases, we are more likely to reject the null hypothesis(Ho).

3. Conclusion through testing;

P-value associated for this F-statistic is 0.000. Therefore, we conclude that the current

regression equation does not explain the relationship between the security (Y) and hourly pay

(X1).

RQ #5: How are food apps perceived compared to online or in-store?

Hypothesis: To see if consumer perceptions of food apps in comparison to online or in-

store differ significantly by residency?

Independent Samples T-test

Independent Variable: Residency

Dependent Variable: Perceptions food apps compared to online or in-store

Directions:

Analyze

Compare Means

Independent Samples T-Test

Place Convenience Question () Into “Test Variables”

Place Residency Into “Grouping Variables”

Click “Define Groups”

Name Group 1, 1

Name Group 2, 2

Click “Ok”

Interpretation

Under the second table, Levene’s Test for Equality of Variances the Sig. value is greater than

0.05, which means that the residency and using a food app rather than going in-store is not

significantly different. The differences between condition means are likely due to chance and not

likely due to the manipulation of the independent variable. We can conclude that consumer

perception of going in-store rather than using a food app is not related to residency. Our

suggestion to food app marketers would be that whether a student lives on or off campus does

not affect their perception of using a food app rather than going in-store. When marketing to

students, advertising the fact that they do not have to go in -store would not be a factor in if they

would download the food app.