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THE PENNSYLVANIA STATE UNIVERSITY

SCHREYER HONORS COLLEGE

SCHOOL OF ENGINEERING DESIGN, TECHNOLOGY, AND PROFESSIONAL

PROGRAMS

DIFFUSION-OF-INNOVATION THEORY APPLIED TO A STUDENT STARTUP

MRIDUL BHANDARI

SPRING 2015

A thesis

submitted in partial fulfillment

of the requirements

for baccalaureate degrees

in Chemical Engineering and Economics

with honors in Engineering Entrepreneurship

Reviewed and approved* by the following:

Robert Macy

Clinical Associate Professor of Entrepreneurship

Thesis Supervisor

Sven G. Bilén

Associate Professor of Engineering Design, Electrical Engineering, and Aerospace Engineering

Honors Adviser

* Signatures are on file in the Schreyer Honors College.

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ABSTRACT

Diffusion-of-Innovation methodology has been applied to a student entrepreneurship class

project to analyze how the information spread so that the knowledge can be applied by future

startups using social media as their main form of marketing and promotions. This research stems

from a business project that was conducted a year ago and that primarily used Facebook to channel

information about their food delivery service in State College, PA. Although the project only

covered weekends during a four-week time period—after which it was decided not to continue as

a business—the service continued to receive inquiries and orders from all over the United States.

This thesis uses Diffusion-of-Innovation theory to find the point at which the service should

have changed their marketing technique. Other questions that were analyzed were to find if social

media affected accuracy of information and geographical distance as a function of each other and

a function of time.

The results indicate that the service should have changed their marketing technique from

early adopter marketing to mainstream marketing within two weeks of the initiation of their

venture. It is also seen that geographical distance, accuracy of information, and time do not have

any statistically significant correlations. This shows that social media accomplishes its purpose in

eradicating issues with the reach of information.

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TABLE OF CONTENTS

LIST OF FIGURES ..................................................................................................... iv

ACKNOWLEDGMENTS ........................................................................................... v

Introduction and Objectives ........................................................................ 1

Background ................................................................................................. 3

Methodology ............................................................................................... 11

Drunk Deliveries .............................................................................................................. 11 Research Questions .......................................................................................................... 17 Metrics ............................................................................................................................. 18 Data Analysis ................................................................................................................... 20

Results and Discussion ................................................................................ 22

Finding the Optimal Time for Marketing Strategy Change ............................................. 24 Correlational Analysis of Raw Variables ......................................................................... 28

Conclusions ................................................................................................. 32

Implications ...................................................................................................................... 32 Limitations ....................................................................................................................... 33 Future Research ................................................................................................................ 33

Appendix A Drunk Deliveries’ Text Message Examples ........................................... 35

Appendix B Cities and States that Inquired about Drunk Deliveries ......................... 36

BIBLIOGRAPHY ........................................................................................................ 37

ACADEMIC VITA ...................................................................................................... 40

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LIST OF FIGURES

Figure 1 Diffusion of Innovation Curve (8)............................................................................... 6

Figure 2 DoI Curve with Chasm, Tipping Point, and 16% Rule (22) ........................................ 8

Figure 3 S-Curve/Cumulative Adoption Curve Compared to DoI Curve (9) ............................ 9

Figure 4 S-Curves of Various Products throughout American History (19) .............................. 10

Figure 5 Drunk Deliveries’ Flyer ............................................................................................. 13

Figure 6 Process Flow Diagram for Drunk Deliveries’ Operations ......................................... 15

Figure 7 U.S. Map of Text Messages Received by Drunk Deliveries ..................................... 22

Figure 8 Number of States Reached as a Function of Time .................................................... 23

Figure 9 Predicted Frequency of Text Messages per Week using Bass’s DoI Equation ......... 24

Figure 10 Predicted Adoption Curves per Week using Bass’s DoI Equation .......................... 25

Figure 11 Coefficient of Innovation and Imitation as Potential Market Size Varies ............... 26

Figure 12 Optimal Week to Change Marketing Strategy Based on Market Size .................... 26

Figure 13 Drunk Deliveries' Adoption Curve Compared to Predicted Curves ........................ 27

Figure 14 Frequency of Text Messages Received Per Day ..................................................... 28

Figure 15 Distance between State College and Delivery City as a Function of Time ............. 29

Figure 16 Accuracy Score of Raw Data as a Function of Time ............................................... 30

Figure 17 Accuracy Score of Raw Data versus Geographical Distance .................................. 31

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ACKNOWLEDGMENTS

I would like to express my deepest appreciation for my honors adviser, Dr. Sven Bilén,

Associate Professor of Engineering Design, Electrical Engineering, and Aerospace Engineering,

Faculty of The Pennsylvania State University, for agreeing to help me in my time of need. His

immediate responses, critiques and references helped me immensely in the process to complete

this thesis.

I would also like to thank my thesis supervisor, Dr. Robert Macy, Clinical Associate

Professor of Entrepreneurship, and Director of the Farrell Center for Corporate Innovation &

Entrepreneurship, Faculty of The Pennsylvania State University¸ for being an amazing professor

to me and for teaching me about life in his own quirky way.

Robert Beaury, Instructor for Engineering Entrepreneurship and Leadership, Faculty of

The Pennsylvania State University, for always being encouraging while pushing me out of my

comfort zone. For allowing the ideas of Drunk Deliveries as a project in his class to helping me

with my own startup, Bob has been nothing but generous with his time and advice. He must also

be recognized for his wisdom and his help in editing this thesis on extremely short notice.

Dr. Scarlett Miller, Assistant Professor of Engineering Design and Industrial

Engineering, Faculty of The Pennsylvania State University, has my respect and thanks for assisting

with the analysis aspect of this thesis during the final hours.

Dr. Christian Brady, Dean of Schreyer Honors College, Faculty of The Pennsylvania

State University, deserves immense appreciation for being extremely understanding, helpful, and

reachable within my time of need. Dr. Nichola Gutgold, Associate Dean for Academic Affairs,

Faculty of The Pennsylvania State University, has my gratitude for being super approachable. No

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words are enough to thank Debra Rodgers, Coordinator of Student Records, Faculty of The

Pennsylvania State University, for her help in formatting, solving major system issues, and her

awesome emails.

My teammates for Drunk Deliveries deserve to be thanked for those countless nights of

staying awake and delivering Taco Bell – Blair Hutto, Dolly Grullon, and Zack Meyer. It really

was a roller coaster ride, thank you for sticking through it.

Finally, I’d like to thank my family and friends for their immense support throughout my

college career, and especially during this study.

All trademarks used in this thesis are property of their respective owners.

Mridul Bhandari

Spring 2015

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Introduction and Objectives

For a service-oriented company that is ready to start selling its services to the market, time

and resources are of utmost importance. Social media is an inexpensive and practical method for

companies that are starting up to promote their services. As a startup, companies do not have large

budgets for interns or employees to constantly be managing a social marketing campaign. Finding

out when marketing strategies need to be changed, and what they need to be changed to, is

important for saving time and money. It is also important to know the impact of your social media

campaigns in terms of reach and accuracy of information.

The aim of this research study is to analyze the growth of Drunk Deliveries, an

entrepreneurship class business project—akin to a “startup” and hence the term is used

throughout—conceived by a team of students at The Pennsylvania State University to deliver food

from Taco Bell to students at later hours of the night. Using quantitative methods, this thesis

examines two distinct topics. The first part attempts to use the adoption profile of Drunk Deliveries

to find out the best time to convert from scarcity marketing to social-proof marketing. The second

part is to find correlations between geographical distance (between State College, PA and the city

of requested delivery), how accurate the information that was distributed was, and time.

A year’s worth of Drunk Deliveries’ data, extracted from orders and inquiries messaged to

Drunk Deliveries, are analyzed. All Drunk Deliveries’ text messages are time stamped, have a

geographic location attached, and consist of a message that may or may not be accurate to Drunk

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Deliveries’ requirements. Data are analyzed using statistics, graphical methods, and Diffusion-of-

Innovation charts and methods.

I seek to validate my hypothesis that social media and viral trends do not follow the S-

curve described by Diffusion-of-Innovation (DoI) theory and that it will take approximately one

month (four weeks) before a marketing change needed to be made for Drunk Deliveries. My

second hypothesis researched in this study is that the accuracy of information distribution and

geographical distance will be inversely related, accuracy of information will diminish over time,

and that geographical distance will grow over time.

This research study aims to create a pattern that other startups could follow for their

planning or analysis. Using concepts from DoI theory, this thesis attempts to answer questions

such as when promotions should be conducted and how accurate the information is when it reaches

a certain distance, and how quickly the information travels. These are all questions that a startup

company deems important when moving from the idea stage to the selling stage of their business.

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Background

Diffusion of Innovation (DoI) is a theory that tries to understand how, why and at what rate

ideas spread through cultures. This concept is used to analyze and understand how Drunk

Deliveries spread from State College, PA to widespread corners of the U.S. Drunk Deliveries is at

the heart of this thesis, and will be clearly explained in the methodologies part of this paper. The

data from the inquiries and orders that Drunk Deliveries received were analyzed using various

methods to quantify the growth of Drunk Deliveries’ market reach, and to answer questions that

Drunk Deliveries would find imperative had they chosen to stay in business.

DoI attempts to explain the adoption process of an idea by modeling the product life cycle

as it applies to society and consumers who had not previously heard of the idea and human

information interactions. DoI is of broad interest because any innovation or new idea, inherently,

is difficult to get into the due to the challenges with getting the target audience to accept the idea.

The theory of DoI has been researched and applied across many fields of study, ranging from

marketing, economics, sociology, and technology management to anthropology and agriculture.(27)

A diffusion model’s goal is to graphically measure the spread of innovation among new markets,

and then to mathematically represent that spread of innovation using a simple quantitative function

with respect to time.(6) This theory has been around for over a century and was popularized by

sociologist, writer, teacher and communications scholar Everett M. Rogers in 1962 with the release

of his book ‘Diffusion of Innovations’.(13)

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French sociologist Gabriel Tarde was the first person to introduce the sigmoid, or the S-

shaped curve for the diffusion processes.(1) His research in 1903 stated that there were three stages

of innovation in relation to his sigmoid graph: a slower advance in the beginning, followed by

accelerated slope, and ending with slacking progress until the diffusion ceases. Sociologists Bryce

Ryan and Neal Gross popularized the idea amongst sociologists when they personally interviewed

345 farmers and analyzed data based on 259 respondents to calculate “diffusion of hybrid seed

among Iowa farmers.”(27) This study in 1943 popularized DoI studies amongst academics,

marketing firms, businesses and advertisers.

There are many aspects to DoI studies including the process of innovation, the types of

adopters, the sequential stages, factors in the process of adoption, types of innovative decisions,

factors that contribute to those decisions and much more.(10) For this thesis, only explanations of

the parts of the DoI theory that apply will be given. There are two different streams of research in

DoI. One is developed by Rogers and consists of all the aforementioned parts. Another stream of

research is referred to as the Bass Diffusion model and was developed by Frank Bass in 1969.(4)

This model is currently the most cited empirical generalization to date with over 5740 citations in

Google Scholar.(3) It has been widely used to forecast technology and new product sales. The Bass

model states that the number of adopters is approximately the same as the number of sales

throughout most of the diffusion process, allowing us to approximate sales on the basis of adoption

of the idea for a given period of time.

Both Rogers’ and Bass’s models are extremely useful in this field. Rogers’ model allows

for the understanding of the diffusion process and the factors that influence it. Bass’s model

provides a quantitative analysis of the adoption curve distribution that can be used for prediction

and forecasting. Bass converted the Riccati equation, which is any first-order ordinary differential

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equation in which the unknown function is quadratic, into the Bass diffusion equation.(26) The Bass

equation simplified is of the form(16):

𝑁𝑡 = 𝑝(𝑚 − 𝑁𝑡−1) + 𝑞𝑁𝑡−1

𝑚(𝑚 − 𝑁𝑡−1), (1)

where the variables are as follows:

Nt = number of adoptions as of period t,

m = potential market,

p = coefficient of innovation, and

q = coefficient of imitation.

This equation basically states that the possibility of those adopting an idea will be a linear

function of those who had previously adopted.(28) Throughout this thesis, the ideas of Rogers have

been used extensively, with the mathematical mindset and the notion that sales is equal to adoption,

which Bass used in his model. The Bass equation of the adoption model is used throughout the

analysis of this thesis, as well.

Rogers’ stated that there were four elements that were of utmost importance in the DoI

process that can be explained and put into context as following sentence: Innovation is that which

is marketed and spread through a communication channel over time to a social system.(5) In the

context of Drunk Deliveries, the service was the innovation, the communication channel was the

internet, namely Facebook, the time span was over a year, and the social system that it reached out

to be primarily intoxicated people from various locations in the United States. As Rogers deems

these four components to be critical to a DoI process, and Drunk Deliveries utilizes all these

components, it would be understandable to use DoI to analyze Drunk Deliveries.

The DoI Curve, also known as the Innovation Adoption Curve, created by Rogers is one

of the most important curves in this field. It is also referred to as the Multi-Step Flow Theory.(17)

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This curve classifies the various categories of adopters based on when they adopt new ideas. This

is based on the simple principle that some people are more open to adaptation than others.(15) There

are five different types of adopters as defined by Rogers:

Innovators, the first 2.5% to adopt,

Early Adopters, consist of the next 13.5% who adopt the innovation,

Early Majority, making up the next 34%,

Late Majority, 34% near the end of the curve, and

Laggards, the final 16% to adopt.(7)

Figure 1 Diffusion of Innovation Curve (8)

The innovators are those who are eager to try new ideas and are considered daring by their

peers. Early adopters are the type of people who are referred to as ‘trendsetters’ and ‘visionaries’.

Combined, these two types of adopters are known as the early market. Early majority adopters are

known as the pragmatists and tend to be opinion leaders who like to safely try out new ideas. Late

majority adopters are skeptical people who will only adopt new ideas after the majority of the

population has adopted it. Laggards are traditional people who do not care for new ideas. They are

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the last ones to adopt, who will only do so after the idea has been accepted by everyone else around

them, or if the idea has become the new tradition.(11) These final three types of adopters make up

the mainstream market. After Rogers’ version of the DoI curve was released, an adaptation was

made by Geoffrey Moore and Malcolm Gladwell.

Geoffrey Moore is an organizational theorist, management consultant and author of

Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers.(12)

This book was released in 1991 and explains that the mindset of those in the early market and

those in the mainstream market are completely different and, therefore, different marketing

techniques have to be applied to get those people to adopt a new idea. Moore defined ‘the chasm’

to be between the early market and the mainstream market. Malcolm Gladwell is the author of the

book The Tipping Point: How Little Things Can Make a Big Difference.(25) In his book, he defined

that the ‘tipping point’ in relation to marketing was the point at which the mainstream market

begins to adopt the idea, hence growing the market share immensely. These two books combined

slightly altered the adoption curve to what is seen in Figure 2. Both books arrive at the same

conclusion that the marketing strategy must be changed from a scarcity marketing strategy to a

social-proof strategy when approximately 16% of the population has adopted the idea. In Figure 2,

the image mentions this as Maloney’s 16% Rule. Chris Maloney is an acclaimed Australian

marketer who coined the 16% Rule that Moore and Gladwell had touched upon in their books.

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This 16% Rule will be used extensively in the analysis aspect of this thesis. Though

Maloney’s 16% Rule is not an acclaimed theory, it can be seen as a summary of concepts that were

derived in Crossing the Chasm and The Tipping Point. These books state that the early market has

a different psychology when it comes to adopting new ideas than the mainstream market. This is

the reason for the gap between the two types of markets in the innovation curve that is called

chasm. Marketing towards the early market is strategy based on scarcity. Key words such as

‘limited time offer’ or ‘be the first one to...’ are attractive to the adopters in the early market,

whereas they are repulsive to mass market. Marketing towards the mass market must be done by

providing proof of consistency of quality and product/service. Key words such as ‘join the 100,000

customers’ or ‘join the new tradition’ are phrases that appeal to the mainstream market.

Figure 2 DoI Curve with Chasm, Tipping Point, and 16% Rule (22)

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Another curve of importance that goes hand-in-hand with the DoI curve is referred to as

the S-curve or the cumulative adoption curve. This curve shows cumulative adoption till the market

share reaches 100%. In Figure 3, the S-curve is yellow and the DoI curve is blue.

Figure 3 S-Curve/Cumulative Adoption Curve Compared to DoI Curve (9)

There are many products whose S-curves/Cumulative Adoption curves were plotted to find

their pattern of adoption, see Figure 4. Many of these curves follow a similar trend to the curve

obtained for Drunk Deliveries, which indicated that DoI theory could be used for the analysis of

Drunk Deliveries.

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Figure 4 S-Curves of Various Products throughout American History (19)

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Methodology

Diffusion-of-Innovation methodology has been applied to a student startup to analyze how

the information spread so that the knowledge can be applied by future startups using social media

as their main form of marketing and promotions. This research stems from a business project that

was conducted a year ago. Drunk Deliveries primarily used Facebook to channel information about

their food delivery service in State College, PA. After some time, it was noticed that orders were

coming in from all over the country. The company, Drunk Deliveries, its operations, and its data,

are primarily used and analyzed in this research study to understand how information diffuses

using social media, to determine what correlation exists between the accuracy of the information

diffused and geographical distance, and to find the optimal time delay for reinserting promotions

into the system.

Drunk Deliveries

Drunk Deliveries stemmed from an idea that started six months before it came to fruition.

The idea was born in Panera Bread® while standing in a long line for lunch. The first idea started

off with delivering Panera Bread, and then expanded as a service that would deliver all fast food.

The logistics were planned out with no real intention to start the project.

Six months later, in an Engineering Entrepreneurship class, my group was tasked with

making money in an innovative way. ENGR 407 (Technology-Based Entrepreneurship) is one of

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the final classes that needs to be taken to complete the Engineering Entrepreneurship Minor. This

class is designed to be experiential, rather than theoretical, and is focused around groups and

teamwork. Groups were assigned for each project. One of the hardest and most rewarding projects

throughout this class was a project where groups were asked to go out and ‘earn money’. The

groups were given specific guidelines to make sure that students did not do anything that would

harm the University’s reputation. The instructor vetted the project ideas before groups were

allowed to proceed. The project was very open-ended, and was very much dependent on the group,

and less on the instructor. Groups were to invest their own money and their own time as the point

of this project was to give students a taste of how it would be to run their own ventures.

My group brainstormed for a couple of hours on how we could earn the most money in the

four weeks during which this project was to occur. The idea for delivering fast food during hours

that people are incapable or less willing to get it themselves was pitched to the team, and it was

unanimously agreed to pursue. Taco Bell was decided to be the only fast food would be delivered,

because it was conveniently located in the center of the downtown area, and it was always busy at

nights. After deciding that inebriated college students would be our primary market, we chose to

make our hours from 11 PM to 4 AM, Thursday through Saturday. These hours were chosen

because they are historically the prime hours that college students choose to consume alcoholic

beverages. The logistics of ordering and delivering had been planned out approximately six months

earlier. Because there was not enough manpower to handle calls or enough capital to create a

website that would handle the orders, it was decided that everything would be done via cell phones

using text messages as the only method of taking orders. A 20% markup of the order price was

chosen to be the charge for the delivery fee.

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Publicity of Drunk Deliveries was primarily done using a popular social media site,

Facebook. An event with all the information regarding hours and method of ordering was created

on Facebook for each weekend that Drunk Deliveries would run. Drunk Deliveries’ method of

delivery was through the use of cars. A few flyers were put up on campus notice boards (shown in

Figure 5). The same flyer was digitized and also put up on the Facebook page with additional

instructions in the information box of the event. The event was updated constantly with any

changes and the team’s photo in an attempt to make Drunk Deliveries as safe as possible for

everyone involved.

Figure 5 Drunk Deliveries’ Flyer

Safety measures were taken for our privacy and physical safety. To ensure that no team

member’s phone number would be out on a public space, a Google Voice number was used. There

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were a few benefits to using a Google Voice number instead of a regular cell phone number.

Google Voice numbers can be forwarded to more than one cell phone number, so it was able to set

it up such that all four team members received every text message. This made it so that

communication amongst the team was clear at all times. Another benefit of Google Voice was that

all messages exchanged using it could be viewed on the computer. Google Voice added a time

stamp and showed the city and state of each text message, which was used to analyze the data in

this research study. Google Voice was a safe and efficient method for our team to be able to receive

many text messages without giving away our phone number, and then being able to analyze those

messages later on.

For the team’s physical safety, the rule was made that the two females on the team would

not go out to make deliveries. Because Drunk Deliveries primary market was expected to be

intoxicated students during its business hours, it was safer for the males on the team to make the

deliveries, while the females on the team handled customer relations and operations. For the safety

of Drunk Deliveries’ customers, a picture of the team was posted on the Facebook event page, so

the customers knew not to accept food from anyone else. To ensure that the team was not escorted

out of Taco Bell, the team talked to the General Manager of the establishment to ensure that the

group could sit there and run Drunk Deliveries. After receiving permission to do so, our team sat

in a cornered area that kept us slightly removed from Taco Bell’s customers to avoid any

disruptions and unnecessary issues.

The operations of Drunk Deliveries were split into various parts: customer relations,

ordering of food, logistics, and food delivery. Customer relations consisted of everything that

involved communicating with the customer. This includes responding to the customer’s initial text

message, calculating and informing the customer of the final price of their order, and letting the

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customer know when to expect their delivery. Ordering of the food consists of standing in the line

at Taco Bell to place orders (usually multiple orders) with correct receipts for each order, receiving

the food from Taco Bell, and marking the receipts with final prices including delivery fees.

Logistics involved making sure that thermal bags were ready for our deliverers with the orders that

made the most efficient driving route without the food getting cold. Lastly, food delivery

comprised of delivering the food, impressing customers, and receiving payment and tip. Figure 6

shows the process flow diagram for Drunk Deliveries’ operations.

Figure 6 Process Flow Diagram for Drunk Deliveries’ Operations

In Figure 6, the colored boxes represent the four distinct operations of the process. The

smaller boxes represent steps within these operations. Each black arrow represents an immediate

action, with no waiting time in the middle. The white arrow represents a step that requires waiting

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time. The information regarding the cost and the time delivery is provided only after the fee is

calculated and written on the receipt. Drunk Deliveries’ payments were only taken in cash or

through a mobile payment application called Venmo. Venmo is an application that accesses each

user’s bank account and allows them to pay other Venmo users from money in their accounts

through the application.

Drunk Deliveries’ publicity was a success. To publicize this service, a Facebook event and

some fliers were put up on notice boards in university campus buildings. After the event was

launched, Onward State, a well-read collegiate blog run by Penn State students contacted us for an

interview to learn more about Drunk Deliveries. That Onward State article regarding Drunk

Deliveries received over 2,500 ‘likes’ on Facebook and approximately 120 ‘tweets’ on Twitter

within the first couple hours of its publication. A day after Onward State published an article, a

national website geared towards college fraternities, Total Frat Move, published a story about

Drunk Deliveries as well. A couple days after, a local radio station in Pittsburgh was heard

speaking about Drunk Deliveries’ services. The publicity and marketing of Drunk Deliveries all

started from one Facebook event. That event was updated every weekend that Drunk Deliveries

ran.

Drunk Deliveries ran from February 27th, 2014 to March 23rd, 2014, with a break from

March 8th to March 20th due to Spring Break. The distinct days that Drunk Deliveries was open

were from Thursday, February 27th to Sunday morning, March 2nd, Thursday, March 6th to Friday

morning, March 7th, and from Thursday, March 20th to Sunday morning, March 23rd. Drunk

Deliveries only ran for seven nights on three distinct weekends. With the limited time and

resources that our team had, it can be deemed that Drunk Deliveries was a success. However, this

was not a venture that any one of founders wanted to pursue full time. Drunk Deliveries took a toll

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on sleeping habits, time and lifestyles during the weekends. While it was an enjoyable venture for

a short time, and the response that the service received was amazing, it was a venture that started

and ended as a class project. The data that were received from Drunk Deliveries was analyzed to

answer the following research questions.

Research Questions

1. When should have Drunk Deliveries changed their marketing techniques according to DoI

theory?

2. Is there a correlation between time and geographical distance when using social media as

means of marketing?

3. Is there a correlation between time and accuracy of information when using social media

as means of marketing?

4. Does social media convey messages accurately as a function of geographical distance?

The answer to the first question would have been extremely important to Drunk Deliveries

had they continued their business. It would have allowed them to know when to change their

marketing techniques and therefore allowed them to penetrate the market in a more efficient

manner.

The final three questions allow for startups using social media as their main means of

marketing to know the efficiency of their usage. The second question, when answered, will show

if social media actually overcomes or not the problem of marketing to various distances over time.

This is important for startups who are looking for customers from all over, instead of just one local

area. The third question will answer if accuracy depletes over time despite information being

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readily available on the internet. This would mean that companies will have to change the

frequency of their marketing promotions to keep information accurate. Lastly, the final question

would help startups figure out if their marketing messages are being warped as distance from

starting point increases. If so, this would mean that the either the messages need to be changed,

starting point needs to be changed, or that there need to be more starting points simultaneously.

Metrics

Drunk Deliveries received text messages through Google Voice. Because of this, it is

possible to view the text message with a time and location stamp. For this research study, there

were four main raw variables:

Frequency of text messages (F),

Geographical distance from State College, PA to the requested delivery city (x),

Accuracy index of the information (A), and

Time (t).

Frequency of text messages (F) is the number of unique customer’s text messages received in a

given time. Geographical distance (x) is the distance from Drunk Deliveries (located in State

College, PA) to the location to which the text message states that the delivery should go to. The

distance resolution is at the level of the city because many text messages mentioned the city and

did not mention an entire street address. It was not possible to make the distance more granular

because of the requests received and the fact that phone number area codes denote cities. Accuracy

(A) of the information in the text message is calculated based on an average of five binary

questions. The basic set of questions were asked to get the most holistic accuracy score: Who,

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What, Where, When and How, and Why. However, it was not possible to analyze the ‘Why’ part

of the basic questions because it was impossible to get that information without reaching out to the

customer to ask their reasoning for contacting Drunk Deliveries. Each text message was analyzed

against these five questions in a binary method. A score of 1 was given in the ‘Who’ section if the

text message included the phrases ‘Taco Bell’ or ‘Drunk Deliveries’ because that indicates that the

customer knew who they were contacting. The only accepted answer for ‘Where’ was within the

city of ‘State College’ because that is the only place Drunk Deliveries committed to delivering to.

For the question of ‘What’, the text message was required to include the words exactly or

synonymous to ‘food delivery’, which shows that the customer knew what they were looking to

achieve from their action. ‘When’ was not answered by analyzing the text message, instead the

date and time of the receipt of the text message were used. Because Drunk Deliveries states that

their services are only open from Thursday to Saturday, between the hours of 11 PM to 4 AM, a

score is only awarded if the text message is received within those hours. Drunk Deliveries had

explained a set of instructions for placing orders via text messaging, if the text messages followed

those instructions, it received a 1 for the ‘How’ section; if it did not follow the directions, the text

message received a 0. These five binary scores were averaged to give each text message an

accuracy score that ranged from 0 to 1. The score of 1 denoted that the text message received was

100% accurate.

The last variable in this research study is time (t). While many other variables could have

been selected, these are the raw variables that most pertain to the research study.

To utilize DoI and Bass models, the frequency of text messages (F) received every week

was used. To use the Bass’s model, four variables were required:

Coefficient of innovation (p),

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Coefficient of imitation (q), and

Potential market (m).

Coefficient of innovation (p), also known as the coefficient of external influence represents the

effect of external factors, such as media communication. Coefficient of imitation (q), also known

as coefficient of internal influence, represents effects of internal influences, prior adoptions and

word or mouth on the rate of adoption. Potential market (m) is the total market to be penetrated,

or the ultimate number of adopters.

Data Analysis

For this research study to be possible, the following assumptions were required to compress

the scope of the study:

Each customer is assumed to send their first text message according to all the information

they have in regards to Drunk Deliveries;

Area code of the phone denotes the delivery city if it is not explicitly mentioned in the

received text message; distances are calculated accordingly; and

If the ‘How’ question is answered accurately, then the ‘Who’ and ‘What’ questions are

automatically answered correctly.

The first message is the initial point of contact between the customer and Drunk Deliveries; hence,

it is assumed to be the most accurate representation of the information that the customer has before

any further clarification, giving rise to the first assumption. For those text messages that did not

follow the instructions for ‘How’ and did not explicitly mention the city of delivery, the city

denoted by the area code of the customer’s phone number is used as the city of delivery. The

21

assumption is made that those people did not move to a different city geographically, which may

be a fairly weak assumption for a college town, but was a required assumption to make so that

each text message could have a delivery city attached to it. Every message from State College, PA

had mentioned the delivery address, so this assumption was not required. This reduces the errors

due to this assumption as a college town is where majority of the people immigrate to from their

hometowns. The majority of the text messages had a delivery city mentioned in the message, so

this assumption was not made often. The last assumption correlates the ‘How’ question to the

‘Who’ and ‘What’ question. Even though the instructions on how to place an order does not

explicitly say to mention ‘Taco Bell/Drunk Deliveries’ or ‘food delivery’, it is assumed that if the

customer follows the instructions precisely, they are aware of the ‘Who’ and ‘What’. Based on

these assumptions, the raw data were created and then analyzed using graphical methods.

Using the frequency of text messages sent per week, the cumulative of the texts received

became the adoption curve, as each text message was by a unique sender. Using the adoption curve

and the varying values of m, the solver add-in was used to find values for p and q. From those

values, predicted text messages and predicted adoption curves were created for each m value using

the Bass’s DoI equation. These curves were used to find the closest adoption curve that fit the

actual adoption curve of Drunk Deliveries. For each of the adoption curves graphed from varying

the potential market size, the calculation was done to find at what predicted week 16% of the

market would have adopted the idea for Drunk Deliveries.

For the next set of analysis, raw data involving frequency of text messages (F),

geographical distance (x), accuracy index of the information (A), and time (t) were graphed to find

correlations between these variables.

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Results and Discussion

Drunk Deliveries was a service that delivered Taco Bell on Thursday to Saturday nights.

The service was catered to Penn State’s students and ran from February 27th, 2014 to March 23rd,

2014. After it was shut down, the information regarding the services continued to spread. This

research study analyzes Drunk Deliveries’ orders via text message.

To understand how impactful the very limited marketing of Drunk Deliveries was, it is

necessary to show the reach of the company. Drunk Deliveries received many orders via text

messaging since its conception in 2014, which was over a year ago. Within 55 weeks, 246 inquiries

were received from a total of 66 cities in 22 states. Social media, namely a Facebook page, was

used to market and advertise Drunk Deliveries. Orders were received from all corners of the U.S.

Though Drunk Deliveries started and catered only to State College, PA, orders came in from the

extreme West in California, extreme North in Alaska, and extreme South in Puerto Rico (seen in

Figure 7).

Figure 7 U.S. Map of Text Messages Received by Drunk Deliveries

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Drunk Deliveries received messages from 22 different states over the course of one year.

Figure 8 shows how many states had been reached as a function of time stemming from one

marketing promotion on a Facebook page.

Figure 8 Number of States Reached as a Function of Time

Now that it has been shown that there was not only a diffusion of innovation in terms of

adopters, but also diffusion related to geographical distance and states, these aspects will be

analyzed using Bass’s quantitative diffusion-of-innovation model and graphical methods. The first

question, in regards to finding the optimal time for Drunk Deliveries to have changed their

marketing strategy, is investigated using Bass’s Equation (1) in the method that was previously

explained in the Data Analysis section of the Methodology in this thesis.

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Finding the Optimal Time for Marketing Strategy Change

As mentioned in the Data Analysis section, the Drunk Deliveries’ frequency of text

messages per week was used to find the cumulative adoption rate of the innovation. It is acceptable

for text messages to be equivalent to the adoption curve because only unique text messages were

analyzed; therefore, each text message represents a new adopter. By varying the potential market

size (m) and using the adoption values, the coefficients (p) and (q) were calculated using a solver

to best fit Bass’ DoI equation. The values for the coefficients and the market size were reinserted

into the Bass’ equation (1) to find the predicted frequency of text messages per week and predicted

adoption curve per week if Drunk Deliveries’ had followed the Bass equation perfectly. The graphs

for the predicted frequency of text messages per week (Figure 9) and the cumulative adoption

curve per week (Figure 10) with varying market sizes are seen below. For both Figure 9 and Figure

10, the legend shows the various potential market sizes in terms of new users.

Figure 9 Predicted Frequency of Text Messages per Week using Bass’s DoI Equation

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Figure 10 Predicted Adoption Curves per Week using Bass’s DoI Equation

A chart was developed to analyze how p and q varied as a function of m in terms of Drunk

Deliveries’ data. Typically, p ranges between 0.01 and 0.03 and q ranges between 0.3 and 0.5. The

values that were seen using Drunk Deliveries for p ranged between 0.0069 and 0.167, whereas the

range for q was seen to be between 0 and 0.87. These values are very different from typical. The

histogram is seen below in Figure 11. This analysis attempts to predict how much information of

Drunk Deliveries was spread using mass communication and the Facebook group as compared to

word-of-mouth advertising dependent on the size of the potential market. It is interesting to see

how q diminishes to 0 as the potential market reaches the actual size of the market that Drunk

Deliveries reached (246). It is also curious to note that, while p has a reducing trend as market size

increases, there is an increase seen when the market size goes from 150 to 200. There is no

explanation that can be given at this time for this change in trend.

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Figure 11 Coefficient of Innovation and Imitation as Potential Market Size Varies

By applying Maloney’s 16% rule that summarizes ideas from Crossing the Chasm and

The Tipping Point, the week at which 16% of the market size was reached was calculated. This

weeks is to be used as an estimation of when the marketing strategy needs to change from aiming

towards the innovative and risk-taking population to the mainstream adopters.

Figure 12 Optimal Week to Change Marketing Strategy Based on Market Size

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The last aspect of this analysis was to find what the optimal time for Drunk Deliveries

was to change their marketing strategy. Using the adoption curves from Figure 10, the predicted

adoption curves that most closely resembled the actual adoption curve of Drunk Deliveries were

graphed. Based on Figure13, it is seen that the predicted adoption curve of 225 people being the

potential market fit the adoption curve of Drunk Deliveries the best. Based on the Figure 12, the

optimal week to change the marketing strategy for a market size of 225 was predicted to be

within 2 weeks.

Figure 13 Drunk Deliveries' Adoption Curve Compared to Predicted Curves

The estimation of changing the market strategy for Drunk Deliveries within two weeks

seems reasonable as it is a service that is advertised on social media. This answer intuitively makes

sense.

28

Correlational Analysis of Raw Variables

Using the raw data that the text messages showed, three graphs were created to show how

the variables F, x, and A changed over time. Then a graph was created to see how accuracy reacted

as a function of distance. The graphs are provided below, along with a discussion of the data they

represent.

The graph of the frequency (messages per day) of text messages (Figure 14) is as expected;

it shows a high number of responses to Drunk Deliveries during the first couple months, which is

then reduced over time because Drunk Deliveries ceased to exist.

Figure 14 Frequency of Text Messages Received Per Day

The graph of geographical distance as a function of time (Figure 15) shows no real

correlation. A linear trend line was drawn to see if there were any relationship or trends could be

found. The linear trend line showed a steady increase in the distance as time goes on. The trend

line does not have much qualitative significance for two reason: one would be due to the fact that

29

their intercepts are so vastly negative and therefore do have a qualitative meaning, and secondly

the R2 values are extremely low so that neither of the trend lines are good fit for the data. There

are many points seen at the distance of approximately 2500 miles. This is due to the number of

text messages received from different cities in California, Oregon and Washington. There is no

particular trend with time and the data is too scattered to be analyzed efficiently.

Figure 15 Distance between State College and Delivery City as a Function of Time

The accuracy score as a function of time also shows no real correlation (Figure 16). It

decreases as a function of time according to the linear trend line. This is an expected result

qualitatively, because it would be assumed that as time goes on, people tend to forget and,

therefore, the accuracy of the information diminishes. However, the trend line is statistically

insignificant because the R2 values is too low to state that the trend line is a good fit for the data.

30

Figure 16 Accuracy Score of Raw Data as a Function of Time

The final graph created from the raw data was the graph of accuracy score (A) versus

geographical distance (x), seen in Figure 17. This graph is important in finding the relation between

distance and accuracy of the information. Looking at the graph, no clear correlation is seen. The

linear trend, while it shows almost perfect inverse relationship, is not statistically significant due

to its low R2 value.

31

Figure 17 Accuracy Score of Raw Data versus Geographical Distance

After looking at these graphs and seeing the raw data, the data in all the graphs were too

scattered to analyze efficiently. This could be due to social media marketing and due to the fact

that this was an open population sample research study. Looking at the graphs, it is possible to

state that there is no correlation found between geographical distance as a function of time,

accuracy index as a function of time, and accuracy index as a function of geographical distance.

This shows that social media does what is intuitively expected of it, and information does not

depend on distance or time when it is marketed through the use of social media. Accuracy is also

seen to not be affected by changes in distance or time.

32

Conclusions

Drunk Deliveries stopped providing their service within four weeks of their launch, and

therefore do not run anymore. However, if Drunk Deliveries had chosen to expand from a project

to a company, this study would have been able to give them a few key points that could have been

utilized to further their business by saving money or time. Learning from Drunk Deliveries’

experience, other startups could follow similar patterns in their marketing. In conclusion, it was

seen using Diffusion-of-Innovation theory and Bass’s equation that Drunk Deliveries should have

changed its marketing technique from scarcity marketing to social proof marketing after the second

week of their venture. It was also found that there are no statistically significant correlations

between geographical distance and time, accuracy index and time, and accuracy index and

geographical distance. This matches with the intuitive nature of social media diffusion, and does

not show that geographical distance or time to be much of a hindrance when social media

marketing is concerned.

Implications

This research is an attempt to quantify social experiments. Being aware of when a

marketing technique change needs to be made and what technique to adopt is extremely important

for startup companies. This study defines this point for Drunk Deliveries using an analysis method

that can be used by other startups as well. Finding that social media is not affected by geographical

33

distance and time shows social media is a valid tool for marketing to the mass public without

letting geographical distance be an issue. Because there was no correlation found for accuracy

indices, it can be assumed that accuracy of the information does not differ much based on

geographical distance from starting point or starting time through the use of social media. These

correlations, while showing the same results as intuition would, are important to understand that

intuition does stand true at this point.

Limitations

There were many limitations in this research study. Because it was not possible to contact

the customers and ask questions in regards to where exactly they were from, why they were

contacting Drunk Deliveries, and how they received the information, the data can be skewed

because it is open to a certain amount of interpretation. There was also no way to find the analytics

of Facebook from the starting to the end of Drunk Deliveries, which lowered the accuracy of the

analysis. Drunk Deliveries is only one startup. This research study could have been bettered if

many startups were analyzed.

Future Research

There are many ways to further this research study. Analysis performed on different

startups and then analyzed together in the method outlined in this thesis would allow better

understanding of the relationship accuracy index, geographical distance, and time. It would have

also been possible to get an estimate for the average optimal time for the startups to pivot from

34

early adopter to mainstream adopter marketing. Using network theory and social network analysis

is also a possibility for growth in this area.

35

Appendix A

Drunk Deliveries’ Text Message Examples

Text Messages Accuracy

Index

Are you tacobell man? 0.6

Hi Taco Bell angels what time are you delivering until tonight? 0.6

Ay u finna get me sum t-bell doe? 0.4

Hi do you guys deliver chipotle? 0.2

Hi can I have Three xxl chalupas? 0.8

Cool ranch locos tacos...loaded potato griller x2 chili cheese fries loaded

griller!! And the beefy nacho griller and the fiesta taco chicken salad! 0.8

Breakfast at open or nah? 0.2

Hey I know it's out of hours but my roommates and I are dying to try out this

delivery service thing and will definitely tip! ;) 0.4

Crunchwrap supreme and grilled stuffed burrito (beef) ..... hopefully it works

for STL 0.6

Taco bell? Need that shit asap jahh feell🙌👍? 0.4

Can you deliver I have kids I can't drive I've been drinkn 0.4

It was all a lie! A well-orchestrated lie? 0.2

Hey just wondering if you can deliver on marine corps base in San Diego 0.5

Do you deliver Taco Bell in a Arlington, VA? 0.4

Are you guys still delivering t bell? Failed an exam and its a 911 0.4

Can I get Taco Bell delivered like soberly...? 0.4

Will you deliver to Westfield State university? 0.2

2 chicken ranch loaded grillers, 1 Doritos loco taco (nacho cheese), 2 mini

chicken quesadilla, 2 beefy nacho loaded grillers, 1 beef queserito(no sour

cream). Name. Address.

1

36

Appendix B

Cities and States that Inquired about Drunk Deliveries

Delivery City Delivery State

State College PA

Whittier CA

Rancho Cordova CA

Santurce PR

Norristown PA

Santa Barbara CA

Arlington VA

New York City NY

Beaumont TX

Orlando FL

Waldorf MD

Chester PA

Leesburg VA

Caldwell NJ

Fayetteville AR

Buffalo NY

Mckinney TX

Sacramento CA

Anchorage AK

Saint Louis MO

Los Angeles CA

Fort Collins CO

Garden City NY

Selden NY

Anaheim CA

Deerfield Beach FL

Florissant MO

Collingswood NJ

Santa Monica CA

Beverly Hills CA

Pittsburgh PA

Bethlehem PA

Washington VA

Morristown NJ

Pullman WA

Tacoma WA

Lansdale PA

Burbank CA

Portland OR

Richland WA

West Bloomfield MI

Cincinnati OH

Westfield MA

Grand Prairie TX

Clifton OH

Glen Burnie MD

Oakland CA

Fort Lupton CO

Frederick MD

Baltimore MD

Wallace CA

Berkeley CA

Elk Grove CA

Miami FL

Omaha NE

Austin TX

Birmingham AL

Atlantic City NJ

Chicago IL

San Diego CA

Pleasanton CA

Alexandria VA

Princeton MN

Eugene OR

San Francisco CA

Blackwood NJ

37

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Mridul Bhandari

bhandarimridul@gmail.com

ACADEMIC VITA Education: The Pennsylvania State University, University Park, Pennsylvania, USA Bachelor of Science in Chemical Engineering Schreyer Honors College Bachelor of Science in Economics May 2015 Minor in Engineering Entrepreneurship with Honors Dean’s List: 6 semesters Minor in Six Sigma Minor in Mathematics National University of Singapore, Singapore, Singapore 2012 University Scholars Program International Engineering and Economics Study Abroad Program

Professional Experience: Chemical Engineering Internship, Procter & Gamble in Cape Girardeau, Missouri 2014 - Employed as Manufacturing Engineering Intern to work on five separate projects that integrated multiple skills - Made design decisions, received quotes, worked with contractors for installation, learned on the job to code VBA for quality analysis,

conducted feasibility studies, used SAP to create standardized maintenance plans, and worked with program developers to fix and test a manufacturing mobile app.

University Innovations Fellow, NCIIA and Stanford University’s Epicenter 2013 - Completed intensive training from Stanford about entrepreneurial ecosystems, strategic enhancement, and immediate implementation

plans within 6 weeks - Influenced 5 entrepreneurship minors and over 7 entrepreneurial activities at Penn State under NCIIA and Stanford University’s

National Center for Engineering Pathways to Innovation Chemical Engineering Internship, Dow Corning in Midland, Michigan 2012 - Employed as Manufacturing Engineering Intern to analyze and minimize impurities while increasing throughput of rubber intermediates. - Minimized distillation throughput time by correlating the time data with quality data - Worked with project team to increase projected production revenue by $3.6 million per year through Six Sigma DMAIC process. - Achieved results through Aspen, Excel, and PI Process Book

Research: Research, Dr. Darrell Velegol 2015 - Researching application of chemical engineering theories and methods in the community, business and finance. Area of study is called

Physics of Community which is spearheaded by Dr. Velegol. Research Lab, Dr. Manish Kumar 2012 - Conducted biochemical research to cultivate Aquaporin-Z Escherichicoli and purified 8 proteins in 14 weeks - Protein measurements were done using Bradford method - Conducted SDS-Page Electrophoresis

Learning by Teaching: Teaching Intern for Computational Tools for Chemical Engineering 2013 Grader for Heat Transfer and Phase and Chemical Equilibria 2013 Teaching Assistant for Entrepreneurial Leadership 2013 Facilitated Calculus with Analytical Geometry 1 2011

Achievements & Experiences: Lion Launch Pad Grant Recipient 2015 Leadershape Institute Participant 2014 Penn State Ghaamudyaz Captain (Indian Dance Team) 2012