Post on 25-Mar-2022
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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.
22
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
23
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
25
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
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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