A location-based, social, mobile application HoosOut'is'a'mobile'application'at'the'University'of'Virginia'that'will'allow'users'to'connect'with'others'in'a'similar'geographical'area'

Prepared by: Adan Aguerri Molly Berberian Marcos Da Silva Katherine Gruneisen Ryan Maher Ayah Manalastas

HoosOut'

HoosOut?'

Prepared for: Ms. Ann Wells April 27, 2015

Sabre Consulting

Sabre Consulting 125 Ruppel Dr.

Charlottesville, VA 22903 ammkra@sabreconsulting.com

April 27, 2015 Ms. Ann Wells, 3210 Blandemar Dr. Charlottesville, VA 22903 Dear Ms. Wells: As due diligence for your prospective business, we have investigated the marketability of a mobile application at the University of Virginia that will allow users to connect with others in a similar geographical area. As explained in our briefing, we understand that you hope to promote the application as a friend locator, social network, and public forum. We conducted marketing research to help you expand upon and build out the business concept for your mobile application. In the following enclosed document, we have compiled and presented our findings as related to your decision problems regarding the demand, preferred features of the application, market segmentation, and price, as well as an auxiliary question of the preferred name of the application. After analyzing the results of our primary research, we conclude that a majority of respondents are likely to download Ms. Wells’ application. The insights provided by undergraduate students through interviews and our questionnaire indicate that the application’s scope of services has the heaviest impact on respondents’ willingness to pay and their likelihood to download. According to student responses, considerable interest exists for an app that locates friends, connects with other users, and publishes details about live events around Grounds. In addition to our firsthand research, we found that secondary sources note the existence of similar mobile applications involving location sharing, such as Find My Friends. Despite the ubiquity of Find My Friends among smartphone owners, our research indicates that students at the University of Virginia are interested in Wells’ concept for a tailored, university-specific application. We hope that you can leverage our research and findings to make more informed business decisions regarding your mobile application. Thank you for engaging us; we greatly appreciate the opportunity. If you have any questions regarding our research or report, please contact us at ammkra@sabreconsulting.com. We hope you will work with us again in the future. Sincerely, Adan Aguerri Molly Berberian Marcos Da Silva Katherine Gruneisen Ryan Maher Ayah Manalastas

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Executive Summary Sabre Consulting conducted extensive market research for Ann Wells, who returned to Charlottesville seeking to launch a mobile application capable of connecting UVA students. While Wells knows she would like to create a GPS-based social application, she needs to determine if there is sufficient demand, the scope of services she should include in the application, how she should target her marketing efforts, and the price she can charge. At Sabre Consulting, our team set out to determine customer preferences and sentiment related to the proposed app. We also ventured to segment the market and determine feature preferences and interest based on year in school, gender, and socialization area. Secondarily, we sought to determine a name for the app. Secondary and primary exploratory research provides framework for further analysis Our team began our research on the marketability of Ms. Wells’ business concept by searching the Internet to learn more about location-based mobile applications that are currently on the market. We discovered that a variety of applications currently offer services similar to those proposed by Ms. Wells. Each comparable application showed none of the apps charged users a fee. In addition, we discerned that users enjoy real-time location tracking and built-in messaging features. Also of note, all of the applications researched limited users to interactions with friends and family. From the findings of our secondary research, we anticipated that our primary research would yield similar results regarding consumer sentiment among UVA undergraduates. We then conducted one-on-one interviews with students. From this primary exploratory research, our team learned that students already use a variety of apps that perform functions similar to the proposed application. In addition, we discovered that students have privacy concerns and varied in their level of interest in the application. We hypothesized that students would want the app to provide an experience customized to UVA while also including strict privacy measures. Questionnaire designed to reveal answers to initial research questions

We developed a questionnaire to determine the potential success of launching the proposed application at UVA and inform the research questions. The questionnaire emerged from the findings of our exploratory research. After extensive pretesting we distributed the questionnaire through Google Forms. The questionnaire was created and distributed with the intention of obtaining information and insights regarding market size, app use, features, location services, demand, pricing, naming, and demographic information. Data analysis shows trends affecting likelihood to download and willingness to pay After analyzing the responses of 164 respondents who completed the questionnaire, we were able to identify the most attractive target audience for Wells’ app and identify which features of her proposed app would be most appealing. We found that consumers who use Facebook Messenger, Snapchat, or GroupMe as their main app for messaging have a higher likelihood to download. In addition, consumers who prefer highly interactive social media apps, including Facebook,

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Instagram, and Snapchat, were more likely to download. The specific app features: seeing friends’ location, connecting with other users, and reading about live events nearby, increased willingness to download. Men were willing to pay a higher average price for her application than females. Limitations of survey method on research design should be considered in results In choosing an online survey our marketing research team made tradeoffs between our ability to monitor respondents as they took our survey and our ability to reach as many respondents as possible. The risks associated with this decision were a lack of guidance for respondents through the survey, an inability to control the environment in which the survey was taken, and an inability to measure refusal rate. Despite the limitations associated with our decision to conduct an online survey as opposed to an in-person analysis, we felt confident in our ability to counteract these issues. We addressed the lack of guidance in our online survey by providing general descriptions, sample answers, and helpful explanations throughout the survey and beneath specific questions. We scrutinized the answers given by our respondents to screen for valid responses and excluded responses that were obviously invalid. In our survey, we did not specify a price when we asked respondents how likely they were to download the application. The question asked respondents to indicate their likelihood based on a brief product overview. However, if a price had been established, this could have had an effect on reported likelihood to download. We believe that this could have caused the reported likelihood to download to be higher. Ms. Wells’ can leverage insights to maximize interest and likelihood of downloading In conclusion, we determined that significant interest exists at UVA for a GPS-based app that offers friend finding, safety, and event promoting services. We also noted that consumers’ likelihood to download and willingness to pay are impacted by several components of the app’s marketing strategy. These variables include the different features of the app that are emphasized, the location of the app’s promotion, and the consumers’ prior experience with similar apps. Guided by the insights revealed through our data analyses, Ms. Wells can now move forward to design the final product and create a marketing strategy for the app to most effectively target UVA students in an impactful manner. Given the market opportunity present at UVA, supported by both primary and secondary data, we recommend that Ms. Wells move forward with the product’s implementation.

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Table&of&Contents&

I.INTRODUCTION*..................................................................................................................................................*6!II.*RESEARCH*METHODOLOGY*.........................................................................................................................*7!SECONDARY!EXPLORATORY!RESEARCH:!ONLINE!RESOURCES!............................................................................................!8!Secondary*research*offers*insights*on*similar*applications*................................................................................*8!

PRIMARY!EXPLORATORY!RESEARCH:!INTERVIEWS!WITH!STUDENTS!...............................................................................!9!Interviews*reveal*students’*past*experiences*with*mobile*applications*vary*..............................................*9!Interviews*reveal*students’*concerns*about*the*application*............................................................................*10!Interviews*reveal*diverse*student*routines*and*hang*out*spots*.....................................................................*10!Interviews*lead*to*hypotheses*that*students*would*want*customized,*secure*app*................................*11!

DESCRIPTIVE!RESEARCH:!SURVEY!OF!UVA!UNDERGRADUATES!.....................................................................................!11!Questionnaire*aims*to*answer*initial*research*questions*.................................................................................*11!Expert*review*improves*quality*of*questionnaire*.................................................................................................*12!PreG*testing*improves*the*clarity*of*final*questionnaire*....................................................................................*13!Field*test*confirms*questionnaire*was*ready*to*be*released*............................................................................*14!

SAMPLING,!DATA!COLLECTION,!AND!HANDLING!................................................................................................................!14!Undergraduate*UVA*students*make*up*our*target*population*.......................................................................*14!NonGprobability*sampling*plan*reaches*164*UVA*undergraduates*..............................................................*14!Online*distribution*method*selected*for*questionnaire*to*maximize*reach*..............................................*15!Steps*were*taken*to*minimize*bias*and*collect*quality*data*............................................................................*15!

III.*RESULTS*.........................................................................................................................................................*16!SAMPLE!ACHIEVED!..................................................................................................................................................................!16!Sample*is*fairly*representative*of*target*population*..........................................................................................*16!

EXTENT!AND!NATURE!OF!APPLICATION!DETERMINED!BY!PRIMARY!RESEARCH,!DATA!ANALYSIS!............................!17!Live*events*and*location*of*friends*drive*likelihood*to*download*proposed*app*....................................*17!Selecting*more*functions*leads*to*increased*likelihood*to*download*the*app*..........................................*18!Showing*location*of*friends*on*map*increases*likelihood*to*download*the*app*......................................*19!

SEGMENTATION!FOR!TARGETED!MARKETING!....................................................................................................................!19!Students*who*frequent*14th*Street*are*more*likely*to*download*the*app*.................................................*19!Students*with*experience*using*GPS*app*are*more*likely*to*download*app*..............................................*20!Gender*is*not*significantly*associated*with*socialization*location*................................................................*20!Year*in*school*is*not*significantly*associated*with*socialization*location*.................................................*20!More*interactive*app*usage*impacts*likelihood*to*download*application*.................................................*21!Messaging*app*usage*impacts*likelihood*to*download*application*.............................................................*21!

DRIVERS!OF!PRICE!...................................................................................................................................................................!22!Males*have*a*higher*mean*willingness*to*pay*than*females*............................................................................*22!More*interactive*app*usage*impacts*price*...............................................................................................................*22!Messaging*app*usage*impacts*price*............................................................................................................................*23!

DRIVERS!OF!DEMAND!.............................................................................................................................................................!23!Majority*of*students*are*interested*in*downloading*the*proposed*app*......................................................*23!Price*drives*likelihood*to*download*proposed*application*...............................................................................*24!

ADDITIONAL!ANALYSIS!...........................................................................................................................................................!24!

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Respondents*prefer*“HoosOut?”*as*an*application*name*..................................................................................*24!IV.*LIMITATIONS*................................................................................................................................................*25!LIMITATIONS!OF!THE!DESIGN!................................................................................................................................................!25!Online*survey*traded*inability*to*monitor*respondents*for*convenience*and*reach*..............................*25!

PROBLEMS!ENCOUNTERED!....................................................................................................................................................!26!Reported*likelihood*of*download*may*have*differed*with*price*indication*...............................................*26!

V.*CONCLUSIONS*................................................................................................................................................*27!APPENDICES*........................................................................................................................................................*29!APPENDIX!A:!SUMMARY!OF!SECONDARY!EXPLORATORY!RESEARCH!.............................................................................!29!APPENDIX!B:!EXCERPT!FROM!SECONDARY!SOURCE!ON!OTHER!LOCATION!BASED!APPS!............................................!30!APPENDIX!C:!MODERATOR’S!GUIDE!FOR!ONEGONGONE!INTERVIEWS!............................................................................!31!APPENDIX!D:!QUESTIONGBYGQUESTION!SUMMARY!OF!INTERVIEWEES!RESPONSES!...................................................!33!APPENDIX!E:!MARKEDGUP!VERSION!OF!ORIGINAL!SURVEY!QUESTIONNAIRE!..............................................................!35!APPENDIX!F:!FINAL!EDITED!SURVEY!QUESTIONNAIRE!SENT!VIA!GOOGLE!FORMS!.....................................................!39!APPENDIX!G:!EXAMPLES!OF!REQUESTS!TO!STUDENTS!TO!COMPLETE!QUESTIONNAIRE!............................................!43!APPENDIX!H:!SAMPLE!DEMOGRAPHICS!REFLECT!FAIRLY!REPRESENTATIVE!SAMPLE!................................................!44!APPENDIX!I:!NO!SIGNIFICANT!DIFFERENCE!BETWEEN!SAMPLE!AND!POPULATION!DEMOGRAPHICS!.......................!45!APPENDIX!J:!FUNCTIONS!THAT!DETERMINE!LIKELIHOOD!TO!DOWNLOAD!THE!PROPOSED!APP!...............................!46!APPENDIX!K:!SELECTING!MORE!FUNCTIONS!IMPACT!LIKELIHOOD!TO!DOWNLOAD!THE!APP!....................................!47!APPENDIX!L:!MAP!FEATURES!THAT!DETERMINE!LIKELIHOOD!TO!DOWNLOAD!...........................................................!48!APPENDIX!M:!SOCIALIZING!ON!14TH!STREET!DRIVES!LIKELIHOOD!TO!DOWNLOAD!.................................................!49!APPENDIX!N:!REGRESSION!SHOWS!EXPERIENCE!WITH!GPS!APPS!INCREASE!LIKELIHOOD!TO!DOWNLOAD!...........!50!APPENDIX!O:!CONTINGENCY!TABLE!SHOWING!ASSOCIATIONS!BETWEEN!GENDER!AND!SOCIALIZATION!LOCATION!....................................................................................................................................................................................................!51!APPENDIX!P:!CONTINGENCY!TABLE!TESTING!FOR!ASSOCIATIONS!BETWEEN!YEAR!AND!SOCIALIZATION!LOCATION!....................................................................................................................................................................................................!52!APPENDIX!Q:!MORE!INTERACTIVE!APP!USERS!INCREASE!WILLINGNESS!TO!PAY!........................................................!53!APPENDIX!R:!MESSAGING!APP!USAGE!IMPACTS!LIKELIHOOD!TO!DOWNLOAD!APPLICATION!....................................!54!APPENDIX!S:!MORE!INTERACTIVE!APP!USAGE!IMPACTS!WILLINGNESS!TO!PAY!...........................................................!55!APPENDIX!T:!MESSAGING!APP!USAGE!IMPACTS!WILLINGNESS!TO!PAY!.........................................................................!56!APPENDIX!U:!WILLINGNESS!TO!PAY!DRIVES!LIKELIHOOD!TO!DOWNLOAD!..................................................................!57!

WORKS*CITED*.....................................................................................................................................................*58!

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I.INTRODUCTION Ann Wells, a McIntire School graduate, has returned to Charlottesville to launch and operate a

mobile application capable of connecting users living in a similar geographical area. Wells

hatched the idea for her business, a GPS-enabled social app, after growing tired of contacting

friends to inquire about their location and plans. Ms. Wells believes that she has identified a

significant need for this service among undergraduate students at the University of Virginia, and

she hopes to customize her service to meet student interests. While Ms. Wells possesses the

necessary financial resources to start her venture, she understands that she will be confronted

with a multitude of marketing issues surrounding the application’s go-to-market strategy. Thus,

prior to rolling out her new business, Ms. Wells expressed interest in conducting marketing

research to enlighten and inform her decision-making processes.

Ann Wells contacted our research team at Sabre Consulting in the hopes of learning more about

her target market of UVA undergraduates. Ms. Wells wanted to answer:

● Should she create and launch the application?

● What features should the final app include?

● Who is most interested in using the proposed app and how should she target her

marketing efforts towards them?

● What price should she charge for the app?

Secondarily, Wells wanted to finalize the name of her app by polling student opinion. She

initially conceived the name “HoosOutThere?” but ultimately decided to survey her target

market to form a short list of options.

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This led us to form the following research questions:

● Demand: How interested are UVA undergraduates in the proposed application? What are

the download intentions for the proposed application?

● Scope of services: What features influence students’ likelihood to download the app?

What apps do students currently prefer? What types of apps are favored?

● Market segmentation: Which years in school, gender, and socialization location are

most likely to download the app? Where do students most often hang out?

● Price: How much are students willing to pay for the app? Does willingness to pay

depend on the features included or demographics?

In our approach to research, our team implemented “backward market research.” We met with

Anne to discuss the decision problems she had and showed her a variety of mock tables and

graphs to increase the actionability of our findings. Sabre Consulting developed a research plan

that included the following:

● Analyze secondary data to determine what features and apps were popular. Discover

what apps already exist that serve similar functions.

● Conduct primary exploratory research through interviews to gain insight on initial

reactions.

● Create, pre-test, and implement an online questionnaire to address our research questions.

● Perform data analysis and provide recommendations.

II. RESEARCH METHODOLOGY Our marketing research team was tasked with learning more about the GPS-based social media

mobile application landscape. We used both secondary and primary exploratory research to learn

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about the similar services currently available to UVA students and initial reactions to the app.

Exploratory research helped the group form a questionnaire that was then pre-tested and

eventually sent out to UVA undergraduate students through a Google Form.

Secondary exploratory research: online resources We began our research journey by engaging in secondary exploratory research. Our team

scoured the Internet to learn more about location-based mobile applications that are currently on

the market in either the Apple App Store or Android’s Google Play.

Secondary research offers insights on similar applications

Through our research, we discovered that a variety of applications currently offer services

similar to those proposed by Ms. Wells. We researched four of the most population GPS based

apps: Find My Friends, Foursquare/Swarm, Glympse, and Tag. We analyzed what features they

had in common and user reviews. We found all of these apps were free to download.

Additionally, we discerned that users enjoy real-time location tracking and built-in messaging

features. Also of note, all of the applications limit users to interactions with friends and family. A

summary of our research can be found in Appendix A. A copy of the excerpts used in our

secondary research can be found in Appendix B.

From our secondary research, we hypothesized that university students would desire real time

location tracking features and communication capabilities. Our team also anticipated that our

primary research would indicate that UVA students are interested in privacy settings and

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features. Finally, based on the price points of comparable apps, we hypothesized that students

would expect and desire a free application from Ms. Wells.

Primary exploratory research: interviews with students To understand students’ past experiences with mobile apps and initial reactions to the proposed

application we conducted one-on-one interviews. We interviewed UVA students from each year

in school to get a broad understanding of our target market. A copy of our moderator guide can

be found in Appendix C. Students used a wide variety of mobile apps and expressed privacy

concerns with the location feature. A question-by-question summary of interviewees’ responses

can be found in Appendix D.

Interviews reveal students’ past experiences with mobile applications vary Interviewees currently used a variety of mobile apps to connect with friends and other students in

their area. Most of the interviewees used Twitter, Facebook, Instagram, Tumblr, Tinder, YikYak

and Snapchat. Students reported liking different features of each app. For example, on Snapchat

interviewees enjoyed seeing what others were up to with the “campus story” feature that allows

students to view pictures and videos that other students at the school submit to the app. On the

other hand, interviewees enjoyed being able to look people up and connect with friends on

Facebook. We asked students about their use of Find My Friends, a location-based mobile

application used to locate friends in your area. Only one interviewee had used the app. The

interviewee expressed that the app was slow to update and indicated that he/she would be more

likely to use that application if the time lag was not a problem.

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Some interviewees expressed concern that the present app did not differ significantly from free

mobile applications they already used. For this reason, interviewees did not express a willingness

to pay for the application. Interviewees expressed interest in simple applications that were easy

to use. They used mobile apps to kill time, connect with friends, and for entertainment.

Respondents did not like when apps spammed them.

Interviews reveal students’ concerns about the application Interviewees were concerned with the privacy features of the app and the possible stigma

stemming from an app used to meet people. We discovered in the interviews that many thought

the name “HoosOutThere?” was “creepy” and reminded them of dating apps. Interviewees

reported that they are interested in meeting new people, but prefer to first physically meet a

person prior to contact on social media. Other concerns stemmed from the apps’ location-based

services. One interviewee expressed concern that businesses could abuse the app and track

customers for sales purposes. Others expressed concern about having to turn their location on

and off; interviewees feared they would forget to turn location services off in a private location

they did not want broadcasted. Overall, interviewees only wanted friends to see their location.

Interviews reveal diverse student routines and hang out spots

Interviewees reported spending their time on weekends on the corner, at friends’ apartments, and

downtown. They typically studied alone in a variety of locations including libraries, apartments,

and coffee shops. Interviewees reported that they would like to know when friends were around,

but sometimes liked their alone time. Interviewees often separated from friends and walked

home alone at night. They reported feeling safe; however, lately their sense of security was low.

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Interviews lead to hypotheses that students would want customized, secure app After conducting interviews we hypothesized that students would want more than just

messaging, photo uploads, and friend-finding services. Mobile applications in this space cover

many of these features, and in order to be successful with students downloading Wells’

application, we would need to provide a customized experience to UVA and the surrounding

area. Recent increased safety concerns lead us to hypothesize students would want the app to

provide some safety measures they could use if they felt unsafe. Students concerns over privacy

also lead us to hypothesize that students would only want their friends to see their location.

Descriptive research: survey of UVA undergraduates

Questionnaire aims to answer initial research questions After conducting both secondary and primary exploratory research we developed a questionnaire

to determine the potential success of launching the application at UVA and inform our research

questions. We developed the questionnaire to gather data about:

● Market size: We asked students if they had a smartphone to determine the total

addressable market at UVA.

● App use: Respondents indicated what social media and messaging app they used most

often. They were also asked if they had ever used certain GPS-based applications. These

questions not only addressed our competition but also gave us insights into what

application features Wells should include. These questions were disguised, indirect ways

for us to get at what product features respondents would like without directly asking

them. This would also give us applications we could recommend Wells model the

proposed app after based on its features and user interface.

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● Features: We directly asked respondents to indicate which features from a list they

would be interested in using on the application. There was no limit to the number of

features respondents could choose. This directly addressed Ms. Wells’ research question

to determine the scope of services.

● Location services: Respondents were asked what they would like to see on a map

(friends, other people, events, etc.) if we were to include one. These questions addressed

the scope of services we would provide and the privacy features we would include.

● Demand: We asked respondents how likely they were to download Ms. Wells’ proposed

app. This question helps determine download intentions. This question would provide us

data to determine segmentation when coupled with the demographic data we collected.

● Pricing: Respondents indicated how much they would be willing to pay for Ms. Wells’

app. The answer to this question would determine willingness to pay.

● Naming: We asked respondents to indicate from three options or provide a suggestion of

their own for the name of the app. We included this question after initial interviews called

into question Ms. Wells’ preferred name.

● Demographic information. The demographic information included gender, year in

school, and locations where students socialize most with friends. This information would

help Ms. Wells to segment her target market. We could use this to see if responses,

particularly likelihood to download, varied by gender, year, or social location.

Expert review improves quality of questionnaire Before we sent out our questionnaire the team submitted a draft to Professor David Mick from

the McIntire School of Commerce’s marketing department to provide an expert opinion and help

improve our questionnaire. A copy of the marked up questionnaire can be found in Appendix E.

Professor Mick suggested that we make the question asking respondents price they would be

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willing to purchase the application at an open-ended question. He also suggested we change the

scaling of our question that asked how likely respondents were to download the proposed app to

include more points and begin at 0 instead of 1. Additionally, he recommended the demographic

questions be placed at the end of our questionnaire.

Professor Mick asked what would happen if respondents did not have a smartphone, answering

“no” to our first question. Our group decided that we would end the survey there for those

respondents as our target market included undergraduates with a smartphone who could

download the application. Respondents without a smartphone would not be able to answer the

majority of our questions, for example those that asked about past social media, messaging, and

GPS-based application use. We made several changes based on Professor Mick’s suggestions

before pretesting the questionnaire.

Pre- testing improves the clarity of final questionnaire Members of our team asked peers and acquaintances to fill out the questionnaire in front of them

and provide question-by-question feedback to determine if there were any issues before the final

survey was sent out. Individuals had difficulty answering the demographic question that asked

where they spent most of their time over the course of a typical week. The potential responses

included “home” and “dorms” which we realized were not mutually exclusive. We found

students were unclear if “home” meant their hometown before coming to UVA or where they

reside at school if they did not live in a dorm. We decided to eliminate both options and change

the question to ask to where respondents typically socialize with friends. Since the proposed app

is interested in public, social spots we determined that these choices were unnecessary. By

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changing the wording of the question and eliminating “home” and “dorms” as an option we

improved the clarity of the questionnaire.

Field test confirms questionnaire was ready to be released We conducted a field-test in the final step of the pre-testing process. Our group confirmed that

the electronic survey displayed each question properly and recorded data as it was collected. The

group created a Google Form where they could monitor the data collection process in real-time.

The group was then confident that the survey was ready to launch. A copy of the final

questionnaire, as viewed in the Google Form, can be found in Appendix F.

Sampling, data collection, and handling

Undergraduate UVA students make up our target population We defined the target population for our questionnaire as all UVA undergraduates. This

definition excludes all UVA personnel, graduate students, and other greater community members

from our sample. We decided on our target population and its bounds based on the target market

for Ms. Wells’ business: undergraduate students at the UVA. We can define the population as

male and female undergraduates, typically between the ages of 18 and 22. We collected data on

gender and year in school to access the representativeness of our sample.

Non-probability sampling plan reaches 164 UVA undergraduates We used a non-probability sampling plan, as a random sample was not feasible. We distributed

our survey through various social media groups and email listserves. This scope included our

ICE blocks, students from other classes, and student organizations that we were involved in. We

used both convenience and purposive sampling techniques. We first emailed the questionnaire

out to people that we knew, using convenience sampling to target groups of people we personally

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knew to increase the likelihood of response. Administering the survey online allowed us to

monitor the breakdown of respondents’ year in school and gender. We used purposive or

judgmental sampling to target underrepresented groups. For example, we noticed our sample

lacked males and sent the link to fraternity organizations to increase their representation.

Online distribution method selected for questionnaire to maximize reach Our team distributed the questionnaire via email and social media outlets in an attached Google

Form link. We chose our distribution method due to its advantages over traditional, in-person

techniques. As students, we recognized that undergraduates frequently check emails and are on

social media sites and thus would be likely to see and read our messages with accompanying

questionnaire links. In addition, by sending our questionnaire out electronically, we were able to

reach a wide range of undergraduates more efficiently. The online questionnaire format allowed

students to respond on their own time in a self-paced manner that reduced barriers to

questionnaire participation and competition that plague traditional in-person techniques, such as

schedule conflicts and brief availability between classes.

Steps were taken to minimize bias and collect quality data Our survey suffered from bias as a product of online distribution. We took steps to limit the bias

from administering the survey online.

● We emailed the survey out to a large, diverse group of students that were involved in

different organizations and represented a broad range of demographics.

● We avoided the possibility of the same person taking our survey twice and limited the

responses to UVA students by using Google Forms which requires an email log-in

through Netbadge.

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● We excluded data from respondents we deemed did not fill out the survey accurately or

truthfully.

● We kept our questionnaire short to limit the possibility that respondents would lose focus

while filling it out.

We closed the survey after approximately 2 weeks, the number of incoming responses had

dropped off and our networks had been exhausted. We determined that 164 responses was an

adequate size to conduct proper statistical analyses. The cover letter, which describes the purpose

of our questionnaire, can be seen at the top of Appendix F. The prompts our team sent to

listserves and social media sites to distribute the questionnaire and obtain responses are included

in Appendix G.

III. RESULTS

Sample achieved

Sample is fairly representative of target population We estimate that we distributed the survey to approximately 2,000 undergraduates. A sample of

164 gave us a response rate of about 9%, including those we excluded from our analysis for not

having a smartphone. The data collected was primarily

segmented according to year in school and gender.

The UVA undergraduate population of 16,087 is

approximately evenly distributed among year in school,

and is composed of 55% female and 45% male students.

As shown in Figure 1, our sample yielded 54% female

Figure 1: Sample percentage breakdown by gender is fairly representative

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and 46% male, which closely mirrors the target

population’s distribution. Additionally as seen in

Figure 2 our sample was composed of 27% first years,

19% second years, 32% third years, and 22% fourth

years. Both the sample and target population

distributions can be seen side-by-side in Appendix H.

We ran a chi-square goodness of fit test and determined

that our sample distribution was not statistically

significantly different from the target population for both gender and year in school as seen in

Appendix I. In summary, although our results do not exactly match the target population, we

have a large enough sample size of 164 students and a fairly representative sample based on

gender and year to allow us to comfortably extrapolate results from our data analysis.

Extent and nature of application determined by primary research, data analysis

Live events and location of friends drive likelihood to download proposed app To address Ms. Wells’ decision problem regarding the nature and extent of services her proposed

application will offer, we ran a regression analysis with the dependent variable (DV) of

likelihood to download app and the independent variables (IVs) consisting of the coded dummy

variables for the different functions that the respondents would be interested in using through the

app.

The regression model was significant, p= 0.0023. We found that respondents who want to see the

location of their friends and those who want to read about live events are more likely to

Figure 2: Sample percentage breakdown by year in school is fairly representative

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download the app. To appeal to students, the application should emphasize these two as the most

important features. No other IVs proved to be individually significant in increasing or decreasing

likelihood to download. Refer to Appendix J to see the results of our regression analysis.

Selecting more functions leads to increased likelihood to download the app

We subsequently ran a regression between users’ likelihood to download (DV) and the total

number of functions (IV) they wanted to see included in the application. The overall model was

significant with at p= 0.0001. Additionally, the IVs came out significant. We concluded that the

more features respondents wanted in the application, the more likely they were to download. See

Appendix K for the regression output.

However, implementing all these functions in the app could negatively impact ease of use. We

suggest to focus on the functions people care most about (live events and location of friends) and

add functions if there is sufficient demand. Location of security services and chat functions were

also popular choices. We suggest that Wells concentrate on features with enough demand and

then consider expanding into more functions like uploading pictures only after the proposed app

proves to be successful. Figure 3 displays the distribution of percentages of function desirability.

Figure 3: Distribution of functions desired shows live events and location of friends are popular features

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Showing location of friends on map increases likelihood to download the app We also ran a regression analysis between the dependent variable (DV) of likelihood to

download the application and the independent variables (IVs) consisting of the coded dummy

variables for the different functions that the respondents would want to view on a map. See

Appendix L for the regression output.

The results showed that seeing the location of friends on the map was the only feature that

significantly increased likelihood

to download, p= 0.0012. We

suggest that Wells highlight this as

a main feature of the map;

however, we recommend she still

consider including live events on

the applications map as 76% of

respondents indicated this as a

desirable feature. Figure 4 displays

the percentage distribution of

responses.

Segmentation for targeted marketing

Students who frequent 14th Street are more likely to download the app We ran a multiple regression between likelihood to download the app (DV) and gender, year,

and socialize location (IVs). The model was not significant p=0.0796, although one IV (14th

street) came up significant p=0.00116. We concluded that respondents who spend most of their

time socializing on 14th street were more likely to download the proposed application. Both

Figure 4: Distribution of map features desired shows live events and friends’ locations are popular features but viewing other people’s locations is unwanted

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gender and year in school have no linear relationship with the likelihood to download the app.

See Appendix M for the regression analysis output.

Students with experience using GPS app are more likely to download app

We continued our single variable analysis by running a one-way ANOVA test between

respondents’ prior usage of GPS applications and their corresponding likelihood to download to

determine if past use varied with likelihood to download. We ran likelihood to download (DV)

against each possible GPS application separately and found a statistically significant relationship

exists for the full response set. Therefore, we can conclude that the prior experience of

consumers with GPS applications impacts their willingness to download Wells’ application. See

Appendix N for the output of the ANOVA.

Gender is not significantly associated with socialization location

We ran a chi square analysis to determine if there was an association between gender and

socialization area. We found the overall model to be not significant p= 0.0698. The p-value of

the output is only slightly greater than the .05 alpha confidence level, and if it had been smaller,

we could’ve concluded that more females and fewer men socialize at the JPA location than

expected. See Appendix O for the contingency table.

Year in school is not significantly associated with socialization location

We ran a chi-square analysis to determine if a statistically significant association existed between

respondent’s year in school and their most frequented location to socialize. The contingency

table output can be found in Appendix P. The test revealed that the two independent variables are

unrelated. This is helpful to know that there is no association between respondents year in school

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and where they hang out often; if Wells wished to target her marketing to a specific year in

school, location based promotions would not make sense.

More interactive app usage impacts likelihood to download application

We ran a second one-way ANOVA isolating the responses from Question #2 again and ran them

against the dependent variable Likeliness to Download from Question #7 to determine if the

social media app that customers most often use has an impact on their willingness to download

the product. We found the overall model to be significant, and the individual responses to also be

significant. Therefore, we should be aware that a user’s likelihood of downloading proposed

application will depend on the social media apps they currently use the most. See Appendix Q for

the ANOVA output.

Messaging app usage impacts likelihood to download application We ran the individual responses from Question #3 in separate ANOVA tests against Likeliness

to Download. We found that consumers who prefer Facebook Messenger, Snapchat, or Group

Me as their primary messenger app have a statistically significant variance with the their

likelihood to download the proposed application. The only app to not have a significant

relationship was Whatsapp. These results tell us that the type of messenger app that consumers

already use impacts the likelihood of their downloading another messenger app, the proposed

app, with the exception of Whatsapp users. Typically, users in the US download Whatsapp when

going abroad given that the brand differentiates with its free international messaging ability. The

proposed app is much more focused on the domestic market at local levels, and thus the

statistically significant output relates much more closely with Wells’ product. When promoting

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the proposed app during the implementation phase of the product launch, we should be aware

that Facebook, Snapchat, or GroupMe apps will have a significant impact on consumers’

likelihood to download the app, and she should select the promotional channels accordingly. See

Appendix R for the ANOVA output.

Drivers of price

Males have a higher mean willingness to pay than females

We ran a multiple regression to determine if there was an association between the price

respondents were willing to pay for the app (DV) and their gender, year, and socialize location

(IVs). The model was not significant, p= 0.3438. Year level and socialization location had no

relationship with the willingness to pay.

We then ran a two-tailed t-test to determine if mean willingness to pay differed according to

gender. Males (M = 0.964, SD = 1.19) reported a statistically significant higher willingness to

pay than females (M= 0.617, SD = 0.885), t (162) = -2.14, p= 0.0339.

More interactive app usage impacts price We ran a one-way ANOVA using isolated responses from Question #2, "Which social media app

do you use most often?” and the dependent variable Price from Question #8 to measure whether

the social media app that customers use most often will have an impact on the average price they

would be willing to pay. We found that users who prefer Facebook, Instagram, and SnapChat

had a statistically significant relationship with the price they were willing to pay. The

respondents who preferred YikYak and Twitter did not have a significant difference in the price

range they were willing to pay. These results were useful for our market research given the

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multi-faceted interfaces of Facebook, Instagram, and SnapChat relative to Twitter and YikYak,

which are strictly used for posting blog commentary. We can conclude that for users of highly

interactive media apps, a statistically significant relationship exists between the specific social

media app that they use most often and the price they’d be willing to pay for the product. Since

the proposed app will most likely be highly interactive, this information is important to us. The

features Ms. Wells includes, such as GPS and photo sharing, will impact the price customers are

willing to pay. See Appendix S for the ANOVA output.

Messaging app usage impacts price Similar to the approach previously discussed, we isolated the individual responses for Question

#3, “Which of these messaging apps do you like best?”, to use as an independent variable and

run in a one-way ANOVA test against the dependent variable of Price. We wanted to determine

if there was a statistically significant relationship between the messaging apps that customers

prefer and the average price they are willing to pay for the proposed app. After running the

analyses, we found a significant variance for Snapchat, GroupMe, and Whatsapp consumers. The

only consumer group that did not have a statistically significant relationship with Price was those

who liked Facebook Messenger the best. Given that Facebook messenger is operated by the

largest social network in the world, the results that we found for the smaller messaging apps,

which were all significant, relates more closely to the proposed app. See Appendix T for the

ANOVA output.

Drivers of demand

Majority of students are interested in downloading the proposed app To determine the interest level and download intention of the undergraduate population at UVA,

we asked respondents how likely they were to download the proposed application. Students were

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asked from 0 to 6 to indicate likelihood of download. We found an average likelihood of 3.65

with a standard deviation of 1.36. See Figure 5 for the distribution of reported likelihood to

download. With 7 points on our scale we categorized responses of 0-2 as unlikely, 3 as neutral,

and 4-6 as likely. As shown in Figure 6, 57% of respondents were likely to download the

application, with only 28% of respondents reporting they were unlikely.

Price drives likelihood to download proposed application We ran a regression analysis between the price respondents reported willing to pay and their

reported likelihood of downloading. We found that the higher the price respondents were willing

to pay, the more likely they were willing to download the app. The regression model was

significant at p=0.000000297. See Appendix U for the regression output.

Additional analysis

Respondents prefer “HoosOut?” as an application name We surveyed the opinion of respondents to

determine a final name for the application.

The results showed that the most popular

Figure 5: The majority of respondents indicated they were likely to download the app Figure 6: 57% of respondents indicated they

were likely to download the app

Figure 7: The majority of respondents favor the name HoosOut?

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name was HoosOut? (51%), followed by HoosOutThere? (40%). See Figure 7 for a breakdown

of responses.

IV. LIMITATIONS

Limitations of the design In making decisions related to data collection for the design of our research study our consulting

group had to consider certain limitations. Although there are limitations to using an online

survey tool, we worked hard to balance these and mitigate the limitations of an online survey. On

top of the bargains with reality involved in how our data was collected, there were also

limitations to the way data could be analyzed. In order to address Wells’ concerns effectively all

these factors were considered and countered as much as possible within our analysis.

Online survey traded inability to monitor respondents for convenience and reach We carefully considered possible tradeoffs between using an in-person or online survey method.

Online surveying would expand our reach beyond what we would be capable of in-person.

However, there would be no way of guiding respondents through the survey if there was any

confusion or misinterpretation and we would have no way of pre-screening respondents or

personally scrutinizing their responses as valid until after they took the survey. We were able to

combat respondent confusion or misinterpretation by providing general descriptions and helpful

explanations in the survey and beneath specific questions to guide respondents. However, the

anonymity of the online survey medium still left open the possibility that some respondents

would dismiss it and not take it seriously. If we had used in-person surveying we wouldn’t have

had to find and exclude invalid responses after reviewing our survey results. In-person surveying

would have allowed us to pre-emptively exclude these respondents from the survey itself.

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Despite the existence of these tradeoffs, our ability to minimize their effect on our findings made

us satisfied with our data collection methods and the results they yielded.

Our survey limited our ability to determine refusal rate and further engage respondents

The lack of personal communication in our choice of survey method also prevented our market

research team from accurately determining a refusal rate. To counteract this limitation we could

have strictly opted for in-person fieldwork or a combination of the online survey and in-person

survey methods to get a sense of the refusal rate. Knowing the refusal rate would have allowed

us to understand the current level of engagement and the level of incentive we could have then

used to minimize the non-response rate and improve the sample. Although never quantified, one

insight our market research team came across that made a significant difference in response rate

was the effect of individual messaging and email on respondent participation.

Problems encountered

Reported likelihood of download may have differed with price indication In our survey, we did not specify a price when we asked respondents how likely they were to

download the application. The question asked respondents to indicate their likelihood based on a

brief product overview. However, if a price had been established, this could have had an effect

on reported likelihood to download. For example, if the price of the application had been

determined to be free this could have increased likelihood. The lack of specification on price

prevented us from calculating the price elasticity of demand for the app which would have been

given by percent change in likelihood to download over the percentage change in price.

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V. CONCLUSIONS After reviewing the data analyses from our research sample, we identified the demographic that

would be most interested in downloading the application, and created marketing

recommendations to most effectively target these consumers. By looking at the respondents who

reported higher levels of likelihood to download and/or pay higher prices for the product, we

defined the target audience by specific geographic areas, gender, and prior application usage

habits. We then designed a recommended promotional approach accordingly.

In order to promote the product, we recommend running advertisements around the 14th Street

area. We found that users who frequently socialize at this location were more interested in

downloading the app, so promotions, such as handing out flyers, will have the greatest impact

here. Our analyses further determined that a user’s likelihood of downloading the product varies

with the social media app that they currently use the most, though we were unable to determine

the direction of the relationship. Depending on the specific social media channel Wells wishes to

use, she should be aware of the application’s specific impact on download rates by conducting

further, more concentrated research. In addition, consumers who prefer Facebook Messenger,

Snapchat, or GroupMe as their primary communication app are more likely to download the

product, therefore advertising through these mediums would be efficient.

If Wells chose to prioritize making a profit over achieving the highest possible number of

downloads, then we would recommend making slight adjustments to the strategy. When

targeting consumers through the online channels, we’d narrow the channel scope to exclusively

highly interactive applications. Our data analysis confirmed that users who chose Facebook,

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Instagram, or Snapchat as their preferred communication interface were more likely to pay a

higher average price for the product. In addition, males were willing to pay a higher average

price for Wells’ product than females, so we would cater advertisements to appeal predominantly

to the male demographic.

The results from our market research also helped us to determine the actual content that should

be portrayed in our promotions. The regression analysis showed us the specific features of the

proposed app that impacted consumers’ likelihood to download. The only attribute that had a

significantly improved consumers’ download rates was the ability to find friends’ geographic

location on the map. When we first looked at the frequency distribution of the features that

respondents most preferred, the reading about live events nearby was slightly more popular than

showing the location of friends. However, the respondents’ having selected the Live events or

Finding other people nearby options did not make a statistically significant impact on likelihood

to download. In conclusion, Wells should spotlight the Find your Friends attribute of the

proposed product in advertisements to target this demographic of users who have the highest

likelihood to engage with and download the application.

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Appendices

Appendix A: Summary of secondary exploratory research

Application Name Price Features Selected Reviews

Find My Friends Free Locate friends and family, location-based notifications, privacy controls, parental restrictions

Location given within certain parameter/range, users must request permission to track location

Foursquare/Swarm Free Search for restaurants and shops, submit and read user reviews, keep track of friends through location-based check ins

Offers great recommendations on places to go, easy to check in and meet friends all over the world, enjoy conversing with friends through private messages

Glympse Free Real time GPS tracking of friends and family, share location with anyone via email or SMS

Enjoy that location updates can be sent to more than one person at a time, no “on all the time” setting, no ability to personalize messages

Tag Free See what nearby friends are up to, notify friends of location, attach videos, pictures, and messages, 100% private

Cannot track location real time, simply location at moment of sharing, enjoy messaging and communication features

Sources: Find My Friends, App Store, Apple Foursquare, App Store, Apple Swarm, App Store, Apple Glympse, Google Play, Google Tag, App Store, Apple

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Appendix B: Excerpt from secondary source on other location based apps

Source: How Universities Can Win Big with Location-Based Apps, Mashable, Sep 2010

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Appendix C: Moderator’s guide for one-on-one interviews Introduction ● Welcome interviewee and make introductions ● Inform interviewee of the purpose of this interview ● Inform interviewee that their personal information will remain confidential and their

responses will be used only for analysis related to our class project ○ Explain that names will be changed in the interview write-up will be changed for

confidentiality ● Ask for them to be completely honest with their responses ● Explain to interviewee that they may refrain from answering questions that they make

them uncomfortable or uneasy ● Ask for permission to record responses- we request this so that we can stay engaged in

the interview without having to focus on taking notes ● Ask if they have any questions before we begin the recording

General description of project: ● “ We are conducting marketing research on an app named “HoosOutThere”. The app

connects people on a geographical basis within a public forum-- specific to UVA-- but has the privacy options to interact with specific people only if desired. In order to best prepare for the launch of this product, we want to create an app that will best fit your needs, so please respond as candidly as possible.”

● We will use interview/survey data to gage general interest in product, scope of services, and extent of privacy controls.

● GPS - tag yourself/check in and share it (if you want) - lets you find your friends ● Sign up with UVA email and then link to contacts/social media? Ask if they want it

restricted to UVA student only ● What do you want to see on the app? People, events, etc?

Warm-Up (Begin recording) ● Ask for descriptive information about interviewee

○ Name ○ Year ○ Do you live on or off grounds? ○ Major

Specific Questions ● Describe your typical night out on the weekend; typical study spot; spot to grab lunch… ● Who are you with? Alone? ● How do you get home? Do you feel safe going home? ● How do you connect with your friends? Please tell us about all social platforms that you

use.

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● What are your favorite features about these platforms? What do you think is missing? ● How do you meet new people? Are you interested in meeting new people? ● Have you used a friend finding service in Charlottesville? ● If yes, describe your experience. ● How often do you take wish you knew if your friends are around you? ● When you do use a friend finding service, when do you use it? ● Would you pay for a service like this? If so, how much? ● Would you like this service exclusive to UVA students? How would you like to sign up

(email, sync with contacts, Facebook, etc.)? ● Do you see this app more as a safety measure or a social connection? ● What do you imagine the user interface looking like? ● What privacy measures would you like to see? What concerns do you have about this

app? ● Who do you think would be interested in this app? ● What do you think about the name HoosOutThere?

Conclusion ● Thank interviewee for their time and help ● Ask them if there is any additional comments or thoughts they have ● Let them know they are free to contact you with additional questions ● End Recording

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Appendix D: Question-by-question summary of interviewees responses • Describe your typical night out on the weekend; typical study spot; spot to grab lunch…

o Night out: Weekends were spent around JPA apartments, the corner, and restaurants. Often students socialized with friends either in an apartment or in a public place (bar, etc.). Students typically left around 9-10pm, hanging out in a more intimate setting first and then moving to a more open public setting, returning home at about 2-3am on weekends. Some nights students spent the evening at home alone, watching TV; however, this was not the norm.

o Study spot: Students often studied at home, a building on grounds when they needed to use resources, libraries, or Starbucks. Study spots varied widely but many students commented that they like to study in communal areas with their friends.

o Spot to grab lunch: Students often grabbed lunch on the corner, prepared food at home, or ate a meal on grounds. There was no one restaurant that stood out or was mentioned by more than one respondent. Students seemed to have varied preferences in food type and location.

• Who are you with? Alone? o Students spent time in all activities alone and together. Some students mentioned studying

alone to focus or grabbing lunch alone to speed up the process. Students spent time out with friends who were often roommates or from class.

• How do you get home? Do you feel safe going home? o Students often walked home and felt safe doing so. Cabs were infrequently used. Students

did mention they often do get separated from their friends. Students reported that they are lately feeling less safe than before. Male reported felling safe going home more often then female students.

• How do you connect with your friends? Please tell us about all social platforms that you use.

o Responses included Twitter, Facebook, Instagram, Timblr, Tinder, YikYak. Students reported they felt like Facebook had the most users and often used it to kill time and connect with friends. Some students reported using location based apps but being frustrated with functionality.

• What are your favorite features about these platforms? What do you think is missing? o Students seemed to appreciate different aspects of apps. Students liked how they could look

people up on Facebook. On Twitter they enjoyed reading entertaining posts and seeing what people were doing. Students thought Snapchat was fun and liked the “best friends” feature; however, the app took this away. Additionally students reported liking the ”story” and most recently “campus story” where students post videos and pictures as this let them see what everyone was up to. Often students reported not liking spam (such as random invites on Facebook). Overall students liked when apps were simple and easy to use with a good design.

• How do you meet new people? Are you interested in meeting new people? o Students reported that they meet new people and then make social media contact. Students

said there is a “stigma” of meeting new people online and apps such as Tinder are often used as a joke among friends or to “mess with people”. Many students will not go out of their way to meet new people and let it happen naturally.

• Have you used a friend finding service in Charlottesville? o Most students did not report using a friends finding app- although one had used Find My

Friends in the past.

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• If yes, describe your experience. o One student has an experience with Find My Friends. She only used it once as it drained her

battery and she often had to update her location. If it was easier to use and did not kill her battery she reported that she would use it more often.

• How often do you take wish you knew if your friends are around you? o Students reported that they would like to know where their friends were, but often were with

them and did not need to know this information. One student responded that sometimes she likes alone time and friends to not know her location in case she is lying.

• When you do use a friend finding service, when do you use it? o Students often used friends finding services out of boredom to pass the time.

• Would you pay for a service like this? If so, how much? o Students reported they were not willing to pay for the app. They thought there were free

alternatives such as direct messaging and other social media apps they could use in its place. They did note it would be useful to see events happening around you, however other features can be found on other social media outlets they already use.

• Would you like this service exclusive to UVA students? How would you like to sign up (email, sync with contacts, Facebook, etc.)?

o Students preferred that this be an exclusive app for UVA students. They would like to sign up via social media such as Facebook or through their phone contacts.

• Do you see this app more as a safety measure or a social connection? o Students see this app as a social connection.

• What do you imagine the user interface looking like? o Students imagine that the app would include a map where they could see their friend’s

location on it. Some imagined a chat feature below the app to share information about your location to friends and a live feed on the side to see events/ where clusters of people are.

• What privacy measures would you like to see? What concerns do you have about this app?

o Students were concerned about having to turn their location on and off (forgetting when in a private location they do not want broadcasting that they have location on). Other students voiced concern over businesses tracking users whereabouts for sales purposes. Some students wanted strict measures to ensure only friends could see their location and to ensure the app was not abused.

• Who do you think would be interested in this app? o Students either thought the app would not be used as there are already too many alternatives

out there or that young college students would use the app either to meet up with people or kill time.

• What do you think about the name HoosOutThere? o Some students liked the name but others found it “creepy” and the name made them think of

dating apps.

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Appendix E: Marked-up version of original survey questionnaire

36

37

38

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Appendix F: Final edited survey questionnaire sent via Google Forms

40

41

42

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Appendix G: Examples of requests to students to complete questionnaire Example Email:

Example Facebook Post:

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Appendix H: Sample demographics reflect fairly representative sample Sample demographics:

UVA undergraduate demographics:

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Appendix I: No significant difference between sample and population demographics

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Appendix J: Functions that determine likelihood to download the proposed app The table below shows the regression model using the dependent variable (DV) of likelihood to download app and the independent variables (IVs) of the coded functions that people are interested in using through the app. Only “read about live events” and “see location of friends” showed up significant (p-value<.05) and the overall model was also significant (p-value<.05).

47

Appendix K: Selecting more functions impact likelihood to download the app The table below shows the regression model using the dependent variable (DV) of likelihood to download app and the independent variables (IV) of the total number of functions that people are interested in using through the app. The IV showed up significant (p-value<.05) and the overall model was also significant (p-value<.05).

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Appendix L: Map features that determine likelihood to download The table below shows the regression model using the dependent variable (DV) of likelihood to download app and the independent variables (IVs) of the coded map features that people want to see on the app. Only “friends” street showed up as significant (p-value<.05) and the overall model was also significant (p-value<.05).

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Appendix M: Socializing on 14th Street drives likelihood to download The table below shows the regression model using the dependent variable (DV) of likelihood to download app and the independent variables (IVs) of gender, year levels, and socialization location. Only 14th street showed up as significant (p-value<.05) but the overall model was not significant (p-value>.05).

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Appendix N: Regression shows experience with GPS apps increase likelihood to download

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Appendix O: Contingency table showing associations between gender and socialization location

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Appendix P: Contingency table testing for associations between year and socialization location

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Appendix Q: More interactive app users increase willingness to pay

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Appendix R: Messaging app usage impacts likelihood to download application

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Appendix S: More interactive app usage impacts willingness to pay

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Appendix T: Messaging app usage impacts willingness to pay

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Appendix U: Willingness to pay drives likelihood to download

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<://itunes.apple.com/us/app/find-my-friends/id466122094?mt=8>. "Foursquare - Find Places to Eat, Drink, and Visit." App Store. N.p., n.d. Web. 27 Apr. 2015.

<https://itunes.apple.com/us/app/foursquare-find-places-to/id306934924?mt=8>. "Glympse - Share GPS Location." Google Play. N.p., n.d. Web. 27 Apr. 2015.

<https://play.google.com/store/apps/details?id=com.glympse.android.glympse&hl=en>. "How Universities Can Win Big with Location-Based Apps." Mashable. Accessed April 10,

2015. <http://mashable.com/2010/09/22/universities-geo-location/.> "Swarm by Foursquare." App Store. Accessed April 13, 2015.

<https://itunes.apple.com/us/app/swarm-by-foursquare/id870161082?mt=8.> "Tag - You're It." App Store. Accessed April 27, 2015. <https://itunes.apple.com/us/app/tag-

youre-it/id829344553?mt=8.>