Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
Agents of Diffusion – Insights from a Survey of Facebook Users
Rebecca Ermecke
CDTM, LMU Munich
[email protected]
Philip Mayrhofer
CDTM, LMU Munich
[email protected]
Abstract
In times of web 2.0 and its strong focus on user
interaction in business models, entrepreneurs and
investors of internet businesses often back up their
ambitious growth expectations with the argument of
viral distribution. However, there exists little
evidence on the determinants of consumers’ willingness to voluntarily and actively exchange information
about internet applications.
Based on survey data of 475 Facebook users, this
paper makes a contribution to fill this gap. Key
findings are that passive (observation of other users)
and active (purposeful recommendations from peers)
viral channels are equally important to make users
aware of a novel product. Active viral channels,
however, dominate in convincing users to actually
start using a product or service. We also find that
users are altruistically motivated to recommend
applications and predominantly use built-in
invitation mechanisms in the application as a
communication channel for recommendations.
This paper contributes to the field of electronic
marketing in two ways. First and foremost it brings
forward empirical evidence on the interpersonal
aspects of the mechanisms behind viral distribution
and word-of-mouth marketing. Second, it presents the
illustrative case of Facebook which has become a
fore-runner in online marketing when it comes to
taking into account user interaction and behavior.
1. Introduction
The term “viral” refers to the observation that
information about new products seem to spread like a
virus from one person to another without any central
coordination mechanisms. Depending on individuals’
inclination to pass on the information they received,
this distribution mechanism can result in an
exponential increase in “infected”, i.e. informed,
subjects. Viral distribution of product information
relies on social influence in the sense that actions of
an individual A affect another individual B in her
beliefs or actions.
Stefan Wagner
INNO-tec, LMU Munich
[email protected]
The purpose of this paper is twofold: First, it
examines how different forms of social influence
make other people aware of a new product or service
and impact someone’s subsequent decision whether
or not to adopt it. We will distinguish between two
different types of social influence depending on the
role of the influencing person. The influencing
person is said to be passive if she exerts influence on
others indirectly, e.g. by displaying the results of her
own adoption decision in a transparent manner. In
contrast, the influencing person is said to be active if
she undertakes a purposeful attempt to influence
others to also add an application, e.g. by explicitly
inviting her friends to install the application.
In principle, the information on novel products in
online social networks could be spread through
passive viral channels as other people’s actions are
transparent to their peers as well as through active
viral channels as most web-based applications
include tools for an easy generation of
recommendations through a myriad of online
communication features. It is the aim of this paper to
examine the relative importance of these inherently
different channels for both the spread of information
on new products as well as for the adoption decision
of individuals.
The second objective of the paper is the analysis
of those people who actively recommend certain
products or features and pass on information to their
peers. From a firm’s perspective, knowing what
segments of consumers are more likely to actively
disseminate information on their products is highly
relevant. Based on this knowledge specific marketing
activities can be tailored to this segment of users in
order to foster viral spread of information related to a
firms’ products.
The paper proceeds as follows: Section 2 provides
a review of the literature in the realm of viral
marketing and social influence. The background and
research environment of the study are introduced in
Section 3. Section 4 discusses our methodological
approach and describes the sample that was used for
the empirical analysis. Section 5 finally presents
results from our empirical analysis. It also derives
implications relevant for interested scholars as well
978-0-7695-3450-3/09 $25.00 © 2009 IEEE
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
as practitioners. Section 6 summarizes findings and
implications and concludes with a brief discussion of
possible limitations of the study that suggest further
research.
2. Literature review
Rogers [1] identifies consumers’ social system
and communication channels as important
determinants of the diffusion of innovations.
Furthermore, he stresses the importance of an
individual’s perception of the characteristics of an
innovation. One of the most important characteristics
from a consumer’s perspective is the relative
advantage of an innovation compared to previously
available products; perceived personal benefit
derived from the adoption of an innovation must be
superior compared to an existing system of use.
Moreover, perceived ease of use facilitates adoption,
whereas more complex innovations, and those that
are difficult to understand and to use, typically need
more time to spread. As an innovation by its very
definition is something new and previously unknown,
it involves uncertainty for the individual and possibly
brings about risk in its adoption. The lower the
perceived risk of adoption the faster is its diffusion.
We will use this framework to relate users’
perceptions to their adoption decisions as well as to
their propensity to recommend innovations to their
peers.
Social influence between individuals, in general,
works independently of third-parties, such as firms.
In the marketing literature, however, various
concepts that aim at managing and commercially
exploiting social influence can be found and are often
subsumed under the term “viral marketing”. This
literature argues that one of the most important
success factors of viral marketing campaigns is to not
to frame it in an overly intrusive way. Subramani and
Rajagopalan [2] note that the unwise use of viral
marketing can be counterproductive and shed a
negative light on products. It is essential, that
influencers are perceived as knowledgeable helpers
among their peers instead of just marketing agents of
some company. Schemes that make too obvious and
clumsy attempts to encourage users to promote
products and services are likely to destroy the
balance and reduce the effectiveness of the approach
which hurts both the company and the users who may
have derived value from the knowledge-sharing acts
of influencers [2].
The concept of word-of-mouth marketing
(WOM) is closely related to viral marketing and is
often used synonymously. WOM is defined as
“informal, person-to-person communication between
a perceived non-commercial communicator and a
receiver regarding a brand, a product, an
organization, or a service” [3]. It aims at spreading
marketing messages virally among potential
customers. Whereas earlier studies focused on faceto-face communication, the Internet makes it now
easier to connect to more peers and on a more
frequent basis. Web 2.0 with its focus on social
interaction brings about novel communication
channels over which product information are
transmitted. Mechanisms to forward digital products,
recommend them to friends or comment on them are
becoming industry standard. Social influence should,
thus, be even more compelling and pervasive than in
conventional, interpersonal interactions [2]. These
activities play an important role as both informational
and normative social influence [4] have an impact on
peers’ buying decisions [5] and thus often lead to
conformity within a network [6].
Exposure to favorable word of mouth was found
to increase the probability of purchase of a food
product, whereas exposure to unfavorable comments
decreased the probability [7]. In the same study
word-of-mouth communication was not motivated by
a desire to exert active influence on others but rather
to exchange opinions about a product. Bone [8]
examined the influence of word of mouth on shortterm and long-term product judgments in an
experimental setting. She finds that this influence is
more significant the source of the WOM
communication is an expert. Furthermore, her results
suggest that personal characteristics such as general
susceptibility to interpersonal influence and product
knowledge do not appear to significant factors of the
effectiveness of WOM. In a study of the adoption of
a new telecommunication service [9], Hill et al. show
that consumers linked to a prior customer adopt the
service at a 3-5 times higher rate than customers from
a reference group. Leskovec et al. [5] analyze a
person-to-person recommendation network, and find
that usually recommendations are not very effective
at triggering purchases and do not spread very far
(p.1). Most recommendation chains do not grow very
large; rather they end with the first purchase of a
product. Sometimes, however, a product will be
broadcasted
throughout
a
very
active
recommendation network (p.3). The authors further
find that an individual’s likelihood of purchasing a
product initially increases with the number of people
recommending it to them, but a saturation point is
quickly reached and additional recommendations do
not result in a greater propensity to buy a product
(p.3).
From a firm’s perspective it is particularly
interesting to know the determinants of customers’
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
willingness to voluntarily spread information on its
products. Possible explanations include individuals’
eagerness to share enthusiasm and frustration with
their peers and their increased joy of consuming the
good based on the possibility to “discuss, rave,
slaughter and define” themselves by the things they
like [10]. It has also been argued by [11] and [12]
that network externalities provide a rationale for
recommend products and services. Hill et al. [9] note
that some consumers “value the appearance of being
on the cutting edge or “in the know”, and therefore
derive satisfaction from promoting new, exciting
products”. Furthermore, there might be some kind of
extrinsic motivation to stimulate adoption among
peers created through a reward scheme of the
producer.
Socio-demographic attributes, i.e. gender were
also found to be an explanatory variable of active
recommendation behavior. Phelps et al. [13] found
females to be much more active in forwarding passalong e-mails. Likewise, Feick and Price [14] found
market mavens more likely to be female in a sample
of U.S. consumers and Wiedmann et al. [15] in a
sample of German consumers, although the latter
propose that this is about to change with increasingly
blurred gender-models (p.195).
of the platform and, therefore, to enforce viral
tendencies.
Recently, large social networks opened their
platform for third parties: Software developers have
been allowed to write and to distribute applications
that are based on the platforms’ APIs (application
programming interface) and their technical
infrastructure. As a consequence, third-party
applications can easily be installed individually by
users and integrate seamlessly into their user
accounts on the platform. Once third party
applications are installed they can access a user’s
address book. Based on this information many third
party applications provide their users with easy ways
to pass on information to a user’s friends
implemented as ‘recommend this’-buttons. Most
often, applications diffuse within social networks
through user recommendations based on these
mechanisms. In the following we describe how we
aim to derive information on the mechanics of the
nature of ‘viral’ recommendations within this setting
by analyzing the diffusion of novel third-party
applications on Facebook’s social network.
3. Third-party applications on Facebook
Our empirical analysis is based on a survey of
users of Facebook, a large internet-based social
network which is open for third-party developers.
Originally, when Facebook was launched in February
2004 (www.facebook.com) it was exclusively for
Harvard students. It soon opened for other colleges
and high schools (September 2005) and finally for
anyone with an email address (September 2006). In
May 2007, Facebook opened the technical
infrastructure of its website so that external
developers could write additional applications
providing additional functionality to Facebook users.
Very soon after Facebook’s initiative, competing
major social network platforms announced to open
their infrastructure (Google followed in November
2007 and MySpace in March 2008).
By January 2008, Facebook accounted for more
than 50 million active users which can create profiles
that include pictures, contact information and
interests. Access to user profiles can be restricted to
members of the same college, high school or work
network and to friends that have been actively
acknowledged. Facebook aims at mapping out “real
and pre-existing connections among people” [16].
For many younger users Facebook serves as the
central portal to stay up-to-date on activities in their
personal network (via the “News Feed”, a list of
friends’ recent activities).
3.1. Background: social networks turn to
programming platforms
Online social networks1 provide users with a
range of functions to manage their personal contacts
and to organize their private and business life based
on these contacts. Beyond mere messaging
functionalities, users can create customized profile
pages containing various types of content ranging
from professional and personal information, pictures
or even schedules of planned future activities.
There are two features common to most internetbased social networks which are important for the
following study. First, many social networks contain
automated news feeds informing users on updates on
their friends’ profiles pages including their adoption
of certain services offered on the platform. Second,
they contain mechanisms to pass on information (e.g.
recommendations) easily to their friends on the
platform based on predefined ‘recommend-this’functionalities. These two features can be expected to
accelerate the exchange of information among users
1
Prominent examples include MySpace, Facebook, Bebo, Orkut,
LinkedIn or Plaxo.
3.2. Object of study: Facebook application
platform
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
Technically, developers of third party applications
write web-based applications that are integrated in
Facebook but are hosted on servers provided by the
third party. In the simplest case, an application is a
piece of web content (e.g. a slide show of pictures)
but it can also be a sophisticated web application with
a complex functionality (e.g. an interactive game one
can play with other users). After an application is
registered with Facebook, users can easily install and
use it.
Three aspects make Facebook’s platform
attractive to developers. First, the degree of
integration and access to Facebook’s functionality is
unprecedented. Facebook also offers convenient tools
facilitating the development of applications. Second,
applications can integrate functionality that enables
them to virally activate Facebook’s existing user base
as they access users’ address books. In fact, users can
send invitations to their friends easily within
applications
providing
different
forms
of
‘recommend-this’ buttons. Facebook also shows a
notification when friends install new applications in
the news feed contained in users’ profiles. Third,
Facebook allows developers to monetize their
applications without charging a commission. The
description of different business models related to the
provision of applications for Facebook is beyond the
scope of this paper. However, we want to highlight
that often applications are used as a mean to promote
services offered by third-parties outside of Facebook.
For example, applications can generate revenues as
Facebook allows the inclusion of advertisements (e.g.
Google Ads) in the applications.
By February 2008, more than 15,000 applications
were registered with Facebook. Facebook reports on
its web page that more than 90% of Facebook’s users
have installed and used an application at least once
and the most successful applications are being used
by more than one million users daily.
Diffusion of some of the early applications has
been very rapid with achieving more than 850,000
users within three days [17]. Even though achieving
comparable growth rates has become harder with
more applications competing for users’ attention, the
number of new applications provided by third parties
is steadily increasing.
Based on the characteristics of Facebook’s
platform described above, we believe that this setting
provides an excellent environment for the study of
the role individuals play for the diffusion of novel
products. The setting allows an easy separation of
passive (observation of peers’ behavior either on their
profile pages or via updates in the news-feed) and
active forms (receipt of recommendations or
messages) of social influence as described in Section
1. Facebook includes a variety of channels which can
be used to influence peers. Whereas some are still
specific to Facebook, others (e.g. the News Feed)
have been adopted by most other social networks.
This makes Facebook an innovative object to study,
yet generalizable for other existing and future
networks and web applications.
4. Methodology and sample
4.1. Methodology: case-study setup and
triangulation
Our research follows a case study approach [18,
19] that uses different sources of information that
contribute to a better understanding of viral
marketing and social influence. The use of multiple
data sources is a common approach in studies that
focus on (innovative) social communities (compare,
e.g. [20]). The major advantage of this approach is
that it allows for the examination of a novel
phenomenon from various angles, i.e. through
triangulation [21, 22, 23].
In a first explorative step, we became accustomed
to the environment of Facebook as a social network
as well as with the community of application
developers. Explorative interviews were conducted
with both application users and developers. Apart
from casual conversations in various occasions, three
explicit, yet unstructured interviews were conducted
with developers of popular applications. They
provided insights on the design of applications,
particularly with the focus on features that facilitate
viral growth. Moreover, we conducted five
unstructured interviews with users of Facebook
which provided insights regarding their general
Facebook usage behavior. The more important
objective of these user interviews was to get detailed
information on the decision-making process
preceding the installation of applications as well as
the decision to actively recommend applications to
friends.
In a second step, we conducted an online survey
of Facebook users with regard to the adoption of third
party applications and the recommendation of those
applications. The questionnaire was based on the
insights from the explorative interviews as well as on
a thorough literature review. It was developed in
December 2007 and pretested in various stages. The
pretesting involved both paper-based questionnaires
as well as an electronic version of the survey.
Our final questionnaire contained a total of 47
questions. Respondents were confronted with a
choice of nine applications and were requested to
pick one application they had heard of. In the course
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
of the survey all the questions would then relate to
this chosen application. If the respondent had not
heard of any of the given applications, she would be
redirected to a base set of questions concerning her
personality (opinion leadership), her general usage of
Facebook and the applications and sociodemographic attributes such as her age, education etc.
If one answer was chosen, the respondent was
confronted with a variety of questions concerning e.g.
her perception of the application and how she first
made contact with it. In subsequent steps,
respondents were also asked whether or not and why
they had installed (i.e. adopted) the application. Then
they were asked whether they had exerted active
influence on others to also install the application.
We chose the strategy of confronting users with a
predefined set of successful applications in order to
focus their attention to a specific problem they faced
in the past. Compared to asking general questions
with regard to the adoption process without linking
them to a specific question, this approach should
yield more reliable answers.
Nine applications were randomly selected out of
the 50 most successful third party-applications (in
terms of installation on Facebook as of January 28th
2008) for this purpose.
We tried to address a broad variety of Facebook
users with our online-survey. However, due to the
strict non-spam policies of Facebook and the lack of
an official cooperation with the company, it was not
possible to pick a random sample of Facebook users
and to ask them to fill in the survey. Therefore, we
distributed invitations to the survey through various
channels. Invitations were sent to the authors’
personal contacts on Facebook who were again asked
to further forward it. Additionally, a link to our
survey was posted on more than 40 Facebook user
groups. Furthermore, the URL was posted in a
variety of online discussion boards, blogs, college
websites, forums and mailing lists in various
countries and institutions.
4.2. Sample description
The online-survey was accessible for 14 days. In
total, we received 475 completed responses from our
survey. The questionnaire was filled in by
respondents of 47 different nations. The US and
Germany are by far the most represented nations with
139 (29.3%) and 174 (36.6%) respondents
respectively (see Table 1). Not surprisingly, college
or university students account for about 75% of the
respondents. The age of the respondents varies from
14 to 47; about half of the participants are in their
early twenties with the average age being 23 years.
The percentages of male and female participant are
almost balanced; however, it is remarkable, that the
female fraction of the sample is on average two years
younger and much more likely to be from the US.
With regard to their use of Facebook, 75% of the
respondents reported to spend 5 hours or less on
Facebook per week. On average, users have added
slightly less than 7 applications to their profile (see
Table 1). A minority 12% of the respondents
indicated not having installed any third-party
application at all, while nearly three quarters of the
participants had installed between 1 and 10
applications. Less than 5% of the sample indicated
numbers higher than 22 with 77 as the maximum.
Table 1: Characteristics by gender.
Share of
nation in %
American
German
Other
Weekly time on Facebook in
h (mean)
No of installed apps (mean)
Female
72.66
34.71
51.88
Male
27.34
65.29
48.13
Total
36.63
29.26
34.11
6.71
3.95
5.38
7.39
6.02
6.74
As data on the size of the basic population was
not available, we are unable to report a precise
response rate. However, we conducted a nonresponse analysis comparing early to late respondents
[24] which yielded no indication of a non-response
bias.
5. Findings
As described above, the adoption of third-party
applications on Facebook can occur in three different
settings: A user might become aware of a specific
application independently of other users on the
platform and install it. Awareness and adoption might
also be triggered by observing other users’ adoption
decisions (in particular via the news-feed or by
browsing other people’s profile pages). Finally, users
could also be made aware of applications by
recommendations they receive from other users.
In the following we present results from our
survey which shed some light on the relative
importance of the three alternatives. Moreover, we
further investigate whether there are differences in
the probability that an application finally is adopted
by a user depending on the way he was made aware
of it. Finally, we also present evidence on
characteristics and motivations of users who actively
pass information on applications to their peers.
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
5.1. How do users get aware of available
applications?
Participants in our survey were confronted with
nine applications and were asked to choose one
application they had heard of. They were then asked
how they first took notice of the chosen application.
It turns out that in the realm of Facebook, viral
channels (observation of users’ behavior in the newsfeed or receiving recommendations) play an
overwhelming role compared to self-discovery by
e.g. browsing the directory of applications or noticing
the application in advertisements. At an aggregated
level, viral channels account for more than 90% of
first contact with an application (see Table 2).
Table 2: “How did you first take notice of the
application?”
Passive viral
channels
Active viral
channels
Non-viral
channels
“I do not remember”
“I observed that someone
else was using it”
“Someone invited me or
told me about it”
“I found out about the application by myself”
Total
n
%
22
197
5.1
45.8
197
45.8
14
3.2
430
100
In a subsequent question, participants that had not
indicated that they did not remember their first
contact, were asked to provide further details about
the way they became aware of the application. With
regard to passive channels (observation of others),
the data indicates that about 30% of our respondents
discovered the application on other people’s profile
pages while only 10% mentioned the News Feed.
Within the category of active viral channels, the
most frequent response was the receipt of an
invitation to join the application. Only a third of that
number was prompted by the application because
someone had tried to interact with them via the
application. Other active communication channels
seem to have played a less important role, adding up
to 3% of the answers (see Table 3).
Likewise, non-viral channels played almost no
role, merely 4% of respondents indicated to have
discovered the application via advertisement, the
Facebook directory or through reports on another
web page in the Internet (e.g. blogs).
Taken together, it seems that viral channels are
the most important source of information about new
applications. Passive and active social influence are
almost equally important in drawing users’ awareness
to third-party applications. Observation of other
people’s profile pages turned out to be the most
important passive viral channel while invitations sent
by other users constitute the most important active
channel.
This finding has interesting implications for firms
that intend to boost the distribution of their
applications by “traditional” online advertising
channels such as linking in directories, search engine
optimization or placement of text and banner ads. In
social systems that provide users with inherent
functionality to recommend applications to others,
this word-of-mouth appears to crowd out traditional
channels by capturing the majority of users’
attention. If this was found to hold in a variety of
contexts, advertising spendings and investments need
to be re-considered.
Table 3: “Please specify how you observed that
someone else was using/ how you were told/ how
you found out about the application”
Passive viral
channels
Active viral
channels
Non-viral
channels
Don’t remember
Profile page
News Feed
Request
Interaction attempt
Other Facebook channel
Other online channel
Offline channels
Directory
Internet
Advertisement
Total
n
7
127
44
149
49
6
4
4
7
3
2
404
%
1.7
31.4
10.9
36.9
12.1
1.5
1.0
1.5
1.7
1.7
0.5
100
5.2. What drives adoption of applications?
While respondents were first asked whether they had
heard of a specific application, they were
subsequently asked whether they had later added this
application to their profile. In total, 199 respondents
(about 42%) indicated to have done so for the
application they chose in the survey.
In Table 4 we report the frequency of different
reasons to install an application. Interestingly, active
channels of social influence account for two thirds of
installations, while the observation of others in
contrast plays a minor role (see Table 4). Only 26%
of the respondents indicated that passive influence
triggered their decision to add the application.
Information that the participants found via other
channels accounted for 7.6% of the installation
decisions.
We conclude that active recommendations by
other users are the most important determinant of the
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
diffusion of new applications. This is particularly
interesting when compared to the results in Section
5.1. While active and passive channels are equally
important in drawing users’ attention to applications
active channels induce subsequent installations more
frequently. Analogously to the above implication,
this means that firms need to primarily focus on peers
than on traditional advertising and even passive
observation when attention shall be converted to
usage.
Table 4: “What was the main trigger to add the
application?”
Invited / interaction
Observed
Self-discovered
Don’t remember
Total
Freq.
115
52
15
15
197
%
58.38
26.40
7.61
7.61
100
Cum.
58.38
65.99
92.39
100.00
2 out 199 adopters did not answer the question
5.3. Which users actively recommend?
Section 5.2 clearly demonstrated that user
recommendations are particularly effective in
fostering the diffusion of applications. In this section
we further investigate the characteristics and motives
of users who actively recommended applications.
Only users who had installed an application
themselves were asked about their invitation
behavior.
5.3.1. Active recommendations. About 56 % of the
users who installed an application actively
recommended it to at least one of their friends (see
Table 5). On average, recommendations were sent to
about 12.6 contacts. However, the distribution is
skew with a median of 4.5. Participants on average
estimated the success rates of their invitations to be
slightly over 60%.
Table 5: “Ever since you installed the application,
have you tried to influence any of your friends or
acquaintances to also use the application?”
Active influencer
No act.influencer
Total
n
109
86
195
%
55.90
44.10
100
4 out 199 adopters did not answer the question
Thus, viral growth through active channels does
not seem to work via few extremely active users but
rather through a broad basis of influencers – the
median indicates that 50% of the respondents sent not
more than 4.5 recommendations. Those users issue
targeted recommendations to a small number of their
friends. The impact of this behavior is shown by very
high reported conversion rates.
5.3.2. Motivation. Different items related to
motivational aspects proposed by existing literature
[9, 10, 11, 12] were included in the questionnaire and
presented to those respondents who had indicated to
have sent recommendations to other people. Altruistic aspects received the highest agreement rates.
For example, the statement “I thought other people
would find the application useful or entertaining.
Therefore, I told my friends about it” received
strongest agreement, followed by the motive to share
enthusiasm (see Table 6). On the other hand, more
selfish motivations that referred to increasing one’s
own benefit via invitations earned lesser agreement.
The particular low agreement to the item related to
extrinsic rewards has implications for practitioners
who seek to incentivize product recommendations.
While adding features that bring about local network
effects and further increase the benefit from getting
friends to use the could possibly increase recommendations, designing explicit reward schemes
might not warrant the effort as they are not a strong
motivation to pronounce recommendations.
Table 6: “If you told anyone about the application
please indicate what your motivation to do so
was”
“I thought other people
would find the
application useful or
entertaining”.
“I just wanted to share
enthusiasm or
frustration that I
experienced with the
application”
“The application is
more useful to me the
more of my friends use
the application”
“The application is
more useful to me the
more other people use
the application”
“I could earn rewards
within the application
for recruiting friends”
N
Mean*
p50*
105
4.17
4
100
3.21
4
99
3.10
4
100
2.39
2
100
2.01
1
*Answers on 5-point rating scale, min=1, max=5 for each item
Apparently, socially desirable answering needs to be
considered at this point. However, one can derive the
7
Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
implication that interface design becomes more
important than promotion. Thus, firms need to
employ various mechanisms (such as iterative
prototyping, experimentation) to integrate users early
in the product development process.
5.3.3. Communication channels. The enquiry which
communication channels are used to pass on
recommendations
shows
that
the
built-in
recommendation functions are much more frequently
used than alternative communication channels (such
as other Facebook, other online or offline
communication , see Table 7). Thus, designing
mechanisms for easy invitations and reduced friction
of recommendations seems to pay off by encouraging
diffusion.
Table 7: “What communication channels did you
use to let your friends know about the
application?”
“I used the built-in feature to invite friends”
“I told someone offline about the application”
“I used other Facebook communication to tell
friends”
“I told people using some sort of other online
com.”
Total
n
90
19
14
8
131
Multiple answers were allowed to this question
5.3.4. Factors impeding recommendations. When
asked about their reasons not to recommend a
specific application, respondents most frequently
indicated that they did not feel comfortable advertising for a company or that they could not see any
additional benefit for them if others joined (see Table
8). Much fewer people indicated that their friends
would use the application anyway or that they were
not convinced of the quality of the application.
Hardly any respondents agreed that doubts about the
effectiveness of a recommendation would keep them
from exerting active social influence.
An implication for firms which could be drawn
from these results is that firms should try to build
good reputation and trust with consumers. Moreover,
highlighting the network benefits from having one’s
friends join the application might foster recommenddations. Additional features that require interaction
could artificially increase network externalities and
encourage recommendation behavior.
Table 8: "Please indicate your reasons to NOT
invite people to the application or tell them to
join"
“I do not care who uses the application…”
“I do not want to advertise for the
application”
“Most of my friends use the application
anyway”
“I don’t know enough about the application
or am not convinced of the quality of it”
“I don’t think people would join if I told them
so”
Total
n
51
51
21
17
9
149
Multiple answers were allowed to this question
5.3.5. Characteristics of active recommenders. Our
data suggests that users that actively send recommendations differ from users not engaging in recommendations in various ways (compare Table 9).
First, differences in the perception of an
innovation’s characteristics were observed. The
perceived ease of use and triability found
significantly higher agreement among the influencers
than the non-influencers. Influencing behavior seems
to go along with tech-savvyness, which confirms the
findings from literature on opinion leadership. Likewise, risk perception varied significantly regarding
the fear of clutter on one’s profile page and of
negative privacy implications. There was no variance
in the perception that the application would be a
waste of time. Interestingly, the level of perceived
personal benefit did vary significantly for adopters
and non-adopters but not for influencers and noninfluencers in the sample.
Table 9: Perception of the innovation by
influencing behavior
Active
influencer
“I expected to benefit
from using the
application”
"Using the
application seemed
to be pretty easy and
self-explanatory"
"It seemed easy to
register and to try out
the application"
"I was afraid that my
profile would appear
messy if I added the
application"
Noninfluencer
n
mean
n
mea
n
pvalue
106
3.06
85
2.99
0.34
109
4.31
86
4.08
0.03
107
4.12
85
3.88
0.04
107
2.56
85
3.06
0.01
8
Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
cont.
"I was afraid, that
adding the
application would
endanger my
privacy"
"I was afraid that
using the application
might make me
waste too much
time"
n
mean
n
mean
pvalue
107
1.88
83
2.31
0.01
107
2.24
86
2.37
0.23
One-sided p-values for significantly smaller or larger means
respectively
Second, influencers and non-influencers differed
systematically with regard to certain socio-demographic attributes. As table 10 shows, females in the
sample were much more likely to be active
influencers than men.
Table 10: Active influencers by gender
Female
Male
Total
Active
influencer
80
27
107
No active
influencer
40
46
86
Total
120
73
Pearson chi2(1) = 16.1852 ; p-value = 0.000
7. Conclusion
This paper is the first to present findings on
person-to-person communication and influence on
Facebook. It brings forward evidence that informs
research on electronic marketing in general and viral
marketing in particular.
User behavior does have a strong influence on
awareness and adoption decisions of others.
According to our data, adoption of new products
(Facebook applications) is particularly driven by
active peer recommendations. Firms should evaluate
which recommendations channels impose minimize
friction on users and ideally integrate recommendation functionality into applications and services.
Extrinsic determinants do not seem to be important
motivational drivers for users to recommend a
service. Rather, firms should point out to users how
their peers benefit from the application.
Our study examines a rather novel phenomenon
and exhibits some limitations that provide room for
further research. One limitation concerns the static
character of the analysis. As the applications at hand
were already well established at the point of time
when the survey was conducted, the implications for
marketers might be different when they need to
disseminate information about a new application.
Further research is needed to enquire how the role of
viral versus non-viral channels and within the first
category, the role of active versus passive social
influence changes over the life cycle of a product.
With respect to survey design and distribution, we
point out that the selection of targeted respondents
was not totally random. This leads to a sample which
reflects the distribution channels of the survey and
the personal network of the authors (e.g.
overrepresentation of German respondents). Social
desirability presents a further challenge to some
answers. As Pronin et al. [25] note, social influence
might not be perceived correctly because of selfenhancement motives and a lacking cognitive
awareness of conforming behavior.
8. References
[1] E. Rogers (1983), Diffusion of innovations, Free Press,
New York, 1983
[2] M. Subramani, and B. Rajagopalan, “Knowledgesharing and influence in online social networks via
viral marketing”, Communications of the ACM, 2003,
Vol. 46(12), pp. 300–307.
[3] L. Harrison-Walker, “The Measurement of Word-ofMouth Communication and an Investigation of Service
Quality and Customer Commitment as Potential
Antecedents”, Journal of Service Research, 2001, Vol.
4(1), pp. 60–75.
[4] H. Kelman ,“Processes of Opinion Change, The Public
Opinion Quarterly”, 1961, Vol. 25(1), pp. 57–78.
[5] J. Leskovec, L. Adamic, and B. Huberman, “The
Dynamics of Viral Marketing”, ACM Transactions on
the Web (TWEB), 2007, Vol. 1(1), pp. 1-39.
[6] L. Rook, “An Economic Psychological Approach to
Herd Behavior”, Journal of Economic Issues, 2006,
Vol. 40, No. 1, pp. 75-95.
[7] J. Arndt, “Role of Product-Related Conversations in
the Diffusion of a New Product”, Journal of Marketing
Research, 1967, Vol. 4(3), pp. 291–295.
[8] P. Bone, “Word-of-Mouth Effects on Short-Term and
Long-Term Product Judgments”, Journal of Business
Research 1995, Vol. 32(3), pp. 213–223.
[9] S. Hill, F. Provost, and C. Volinsky, “Network-Based
Marketing: Identifying Likely Adopters Via Consumer
Networks”, Statistical Science, 2006, Vol. 22(2), pp.
256–276.
[10] M. Kretschmer, G. Klimis, and C. Choi, „Increasing
Returns and Social Contagion in Cultural Industries”,
9
Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
British Journal of Management, 1999, Vol 10(1), pp.
61-72.
[11] J. H. Block, and J. Henkel, “Peer Influence in Network
Markets: A Theoretical Analysis”, Working Paper,
Technische Universität München, TUM Business
School, 01/2007
[12] J.H. Block, and P. Köllinger (2006), “Peer Influence in
Network Markets: An Empirical Investigation”,
Working Paper, Technische Universität München,
TUM Business School, 08/2006
[13] J. Phelps, R. Lewis, L. Mobilio, D. Perry, and N.
Raman, “Viral Marketing or Electronic Word-of-Mouth
Advertising: Examining Consumer Responses and
Motivations to Pass Along Email”, Journal of
Advertising Research, 2005, Vol. 44(4), pp. 333–348.
[14] L. Feick and L. Price, “The Market Maven: A
Diffuser of Marketplace Information”, Journal of
Marketing, 1987, Vol 51(1), pp. 83–97.
[15] K. Wiedmann, G. Walsh, and V. Mitchell, “The
Mannmaven: an Agent for Diffusing Market
Information”, Journal of Marketing Communications,
2001, Vol. 7(4), pp. 195–212.
[16] “Face value | Book value on Mark Zuckerberg”, The
Economist, July 21st, 2007, p. 64.
[17] C. McCarthy, “Facebook's app feeding frenzy”, CNET
News.com,
June
15,
2007,
available
at
http://news.cnet.com/2100-1038_3-6191152.html,
retrieved June 15, 2008
[18] K. M. Eisenhardt, “Building Theories from Case Study
Research”, The Academy of Management Review,
1989, Vol. 14(4), pp. 532-550.
[19] R.K. Yin, Case Study Research: Design and Methods,
SAGE Publications, London, 1993
[20] L.B. Jeppesen and L. Frederiksen, “Why Do Users
Contribute to Firm-Hosted User Communities? The
Case of Computer-Controlled Music Instruments”,
Organization Science, 2006, Vol. 17(1), pp. 45-63.
[21] N.K. Denzin, The Research Act: a Theoretical
Introduction to Sociological Methods, McGraw-Hill,
New York, 1978
[22] R.D. Wimmer and J.R. Dominick, An Introduction to
Mass Media Research, Wadsworth Publishing
Company, California, 1994
[23] S.R. Barley and G. Kunda, “Bringing Work Back In”,
Organization Science, 2001, Vol. 12(1), pp. 76-95.
[24] J. S. Armstrong and T. S. Overton, “Estimating
nonresponse bias in mail surveys”, Journal of
Marketing Research, 1977, Vol. 14, pp. 396–402.
[25] E. Pronin, J. Berger, and S. Molouki, “Alone in a
Crowd of Sheep: Asymmetric Perceptions of
Conformity and Their Roots in an Introspection
Illusion”, Journal of Personality and Social Psychology,
2007, Vol. 92(4), pp. 585–95
7. Appendix
These are short-descriptions of the nine
applications the users could choose from in the
survey.
Super Wall (23 m installs / 1.6 m active users /
rank 2) amplifies the ordinary Facebook Wall. Super
Wall allows to also post pictures and videos and to
quickly forward messages on the wall to one’s
friends.
SuperPoke! (15.3 m installs / 460 k active users /
rank 7) widens the interaction possiblities on
Facebook. Users can kiss, hug or high five another
and keep in touch by occasionally throwing a sheep
at each other.
Growing gifts (6.7 m installs / 134 k active users
/ rank 26) allows users to send flower pots to their
friends. They can then watch a flower grow out of it
in the course of some days.
BumperSticker (7.2 m installs / 796 k active
users / rank 19) allows the user to choose from a wide
range of bumper stickers which can then be displayed
on one’s profile.
Are YOU interested? (7.6 m installs / 533 k
active users / rank 16) and HOT or NOT (4.2 m
installs / 208 k active users / Rank 44) are very
similar applications. Once a user has installed the
application he or she can rate other users of the
application according to their hotness or has to make
a statement about whether or not he or she is
interested in the person.
Causes (9.2 m installs / 92 k active users / rank
12) lets users start and join causes they care about.
The application displays the causes one has joined in
the profile box, ranging from supporting presidential
candidates to saving Darfur or raising awareness of
breast cancer.
Entourage (5.1 m installs / 154 k active users /
rank 34) puts pictures of one’s chosen friends onto
the user’s profile and also provides a handy overview
of the status updates of these friends.
Honesty Box (4.5 m installs / 181 k active users /
rank 40) displays a box on the user’s profile where
other people can enter adjectives that describe the
user anonymously.
10