Electron Markets (2011) 21:185–195
DOI 10.1007/s12525-011-0066-y
SPECIAL THEME
The social embeddedness of decision making:
opportunities and challenges
Carsten Takac & Oliver Hinz & Martin Spann
Received: 28 June 2010 / Accepted: 12 July 2011 / Published online: 29 July 2011
# Institute of Information Management, University of St. Gallen 2011
Abstract Sociologists have long recognized that economic
decisions are socially embedded. Management sciences and
business practices have gradually begun to incorporate this
idea. With the rise of the Internet, large-scale data are
available on friendships, recommendations, transactions
and social interactions, which have led to a strong
momentum for research in this area. The aim of this article
is to inspire multidisciplinary research on the mechanisms
and consequences of social embeddedness on decision
making and to highlight opportunities and challenges by
synthesizing findings from various fields, such as IS
research, sociology, economics, marketing and other management disciplines. Key suggestions of this paper are to
identify the causality between social embeddedness and
decision making with small-scale experiments, and to learn
more about network formation by analyzing the evolution
of social networks.
Responsible editor: Thomas Hess
C. Takac
Goethe-Universität Frankfurt am Main,
Grüneburgplatz 1,
60323 Frankfurt, Germany
e-mail: [email protected]
O. Hinz
Technische Universität Darmstadt,
Hochschulstraße 1,
64289 Darmstadt, Germany
e-mail: [email protected]
M. Spann (*)
Ludwig-Maximilians-Universität,
Geschwister-Scholl-Platz 1,
80539 München, Germany
e-mail: [email protected]
Keywords Social network analysis . Decision making .
Consumers . Organizations . Experiments . Diffusion
JEL M15 . M20 . M30
Introduction
In times of omnipresent mass media and ubiquitous
Internet access, obtaining information presents less of a
problem than finding trustworthy information. Indeed,
the challenge is to filter relevant information from an
overwhelming mass of facts and figures. While firms,
such as Google, try to solve this problem with constantly
improved ranking algorithms, the challenger Facebook
tries to exploit the social structure among individuals and
provides a platform that allows one to utilize the social
environment, which is then used to obtain and validate
information to make decisions (Hinz & Spann 2008). In
the words of an economist, the allocation of scarce
resources, such as money or time, is determined by
individual choices, which, in turn, depend on social
interactions (Mayer 2009). Similarly, anthropologists have
emphasized the importance of networks and social
structure (Lévi-Strauss 1963). A social network is, in this
context, defined as a social structure made up of
individuals (or organizations) who are tied by one or more
specific types of interdependency, such as friendships,
kinships, common interests, financial exchanges, dislikes,
intimate relationships, or relationships of beliefs, knowledge or prestige (Wikipedia 2011). It is undisputed that
human decision making, in general, is strongly influenced
by the social environment and that individual action is
very closely embedded in networks of these interpersonal
relationships (Granovetter 1985).
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C. Takac et al.
Not only individual but also organizational decision
making is strongly influenced by peers, for example,
suppliers, customers or competitors (Teo et al. 2003). In
an economy and a society increasingly connected and
dominated by digital networks, the importance of such an
influence is expected to grow.
Therefore, social networks are receiving a growing
amount of attention from research and in practice. Recent
business developments, with respect to online social
networking platforms (OSNP), stimulate and may even be
ahead of empirical research (Van den Bulte 2010). We
anticipate that current changes in the way humans interact
is at the very core of ground shaking future developments
in business, information systems and society at large and
therefore deserve the focused attention of researchers from
various disciplines. Thus, the aim of this article is to inspire
multidisciplinary research on the mechanisms and consequences of social embeddedness on decision making and to
highlight rising opportunities and challenges by synthesizing findings from various fields, including network theory,
economics, information systems, sociology, communication, management and marketing.
We first discuss the challenge of disentangling the
causality chain of external influences, social network
formation and individual decision making. Next, we
describe what we call the “data opportunity” before we
introduce our research framework for structuring previous
findings and future research opportunities. Subsequently,
following the research framework, we summarize previous
findings with respect to the social embeddedness of
decision making at the individual, organizational and
aggregate level. We discuss the differences between online
and offline social networks and behavioral changes incurred
from the new means of online social interaction. Finally, we
conclude with describing what we perceive to be the most
promising avenues for future research and point out a few
specific research questions that need to be addressed by
researchers in this area.
Theoretical background
The causality challenge
Social networks are a simple, yet powerful abstraction
that are used to represent almost every type of human
interaction, connection or information flow (including
structure and dynamic), consisting of nodes representing
individual people or groups and ties for connections, such
as communication, dependence or vicinity (Borgatti et al.
2009; Oinas-Kukkonen et al. 2010). In the following,
the theoretical introduction refers to individual people,
although the mechanisms of social interaction similarly
apply to groups of people, such as organizations
(discussed later).
In general, the effect of social relations on behavior is that
people become increasingly similar because mechanisms of
social contagion operate among those that are proximate
in the social network, namely, cohesion and structural
equivalence (Burt 1987). Cohesion describes the phenomenon that evaluations of cost and benefit associated with
prospective behavior are aligned via strong communication relationships. People thus become more homogeneous
as a result of direct contact via social networking links
from node to node. This type of social contagion is
typically referred to as word-of-mouth.
However, even without direct interaction between
nodes, social networks can have an influence on
behavior. Two people in a social network, who are not
connected to each other, are likely to develop similar
behavior simply because they are both subject to frequent
and similar requests from their social networking neighbors or because a particular behavior is expected by their
peers (Fishbein and Ajzen 1975).
Moreover, people can behave similarly simply because
they are structurally equivalent in their social network;
thus, their behavior can become more homogeneous
(Borgatti et al. 2009). The pattern of structural equivalence
can lead to homogeneous behavior in people who are
similar in their relations with others and hence might be
able to replace each other. They compete for prestigious
social network positions and might adopt any innovation
that is perceived to make them more attractive as an object
or source of relations with others (Burt 1987). Figure 1a
depicts this view on social network positions influencing
decision making.
However, is it really the social network and mechanisms of cohesion and structural equivalence that
influence individual decision making and make individuals more similar? Or do people, for reasons of
homophily (McPherson et al. 2001), bond with similar
individuals and become part of the same social network,
Fig. 1 The causality challenge
causality
Social position
Decision making
a
Homophily
Social position
Exogenous
effects
Social position
Decision making
b
correlation
Decision making
c
Social embeddedness of decision making
as depicted in Fig. 1b? Indeed, research aiming at network
formation processes has found that common characteristics facilitate the formation of friendship networks
(e.g., Marmaros & Sacerdote 2006; Mayer & Puller
2008).
However, the similar behavior of individuals need not be
rooted in homophily if these individuals are actually subject
to a common external influence (as depicted in Fig. 1c). In
other words, “if at the beginning of a shower a number of
people on the street put up their umbrellas at the same time,
this would not ordinarily be a case of action mutually
oriented to that of each other, but rather of all reacting in the
same way to the same need of protection from the rain”
(Weber 1921, 1968). This is what Van den Bulte & Lilien
(2001) found when reanalyzing the frequently cited
Medical Innovation study by Coleman et al. (1966) where
social contagion was originally found to be a major driving
force in the decision regarding the prescription of a new
drug by U.S. physicians in the mid-1950s. As soon as
marketing effects, a contextual variable omitted in the
original study, had been controlled for, the social contagion
effect disappeared (cf. Aral et al. 2009 for another case of
over-estimated peer-influence).
The distinctions illustrated in Fig. 1 have not been
sufficiently addressed in previous research. Most previous
social network research uses measures derived from the
social network as explanatory variables. However, if one
aims at, for instance, explaining social status, the network
structure becomes the dependent variable. Thus, it is
essential for producing valid research results to account
for the larger framework of collective social dynamics,
which is currently beyond state of the art (Watts 2007).
To disentangle the causality chain and determine the
respective preconditions and the outcomes of social
embeddedness, we suggest conducting empirical and
experimental studies that (1) identify potential interpersonal
influences through social contagion (e.g., by observing
mutual influences on new product adoption or information
spreading behavior) and (2) observe the formation of the
social network over time. This would enable conclusions
about whether decision making is an outcome of social
position or if social position is an outcome of homophily or
exogenous factors.
We maintain that only if this basic “causality challenge” of social embeddedness is approached, we can get
closer to drawing sustainable conclusions about how
embeddedness in the social environment affects decision
making. This is essential, especially for IS research, if
we want to better understand what drives various
phenomena that seemingly stem from social contagion
or bandwagon effects, such as information cascades,
adoption processes, team effectiveness, the diffusion of
new technology and the success of OSNP.
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The data opportunity
Traditionally, social network related research was conducted via surveys asking participants for acquaintance
or friendship nominations. Surveys in the social networking context, however, may suffer from serious
limitations and problems (cf. Van den Bulte 2010), such
as poor memory and accuracy problems (Brewer 2000),
the results of the respondents’ varying interpretations of
the questions (Bearman & Parigi 2004), or measurement
bias (Feld & Carter 2002). Surveys are not only timeconsuming to conduct, but they also suffer from a rather
low response rate. Furthermore, their analyses are cumbersome and error-prone.
In spite of these potential problems when obtaining social
network data via surveys, network statistics generated from
this data are surprisingly robust even when only a part of the
network is surveyed. A sample can be considered representative for a social network if 50% of the members respond
(Valente & Pumpuang 2007; Costenbader & Valente 2003).
With techniques from graph theory, it has been demonstrated
that a satisfying level of accuracy can be reached even with
sample sizes as low as 15% of the overall network (Leskovec
& Faloutsos 2006).
We conjecture that the scarcity of research with a focus
on social networks has been due mainly to data availability
limitations. With new electronic data from OSNP, communities, and protocols of inter-personal communication (e.g.,
Hinz & Spann 2008; Hill et al. 2006) and social network
analysis tools with an unprecedented ability to trace,
visualize, analyze, explain and simulate structures and
behaviors of social networks (Oinas-Kukkonen et al.
2010), we see multiple opportunities for research to reexamine the assumptions and findings of earlier research
that lacked appropriate data.
It may be tempting to rely entirely on these new data
opportunities as marketing researchers did in the 1980s by
piggybacking on the emergence of scanner data in supermarkets. We agree with Van den Bulte (2010) that this
would be an approach similar to a drunk looking for lost
keys under lamp posts simply because this is where the
light is. Hence, we propose conducting complementary
field experiments (e.g., Hinz et al. 2011, 2012) based on the
information obtained from OSNP or based on the creation
of "artificial worlds" with varying network structures,
followed by an investigation on how these differences
affect user behavior.
Even though data availability in general has dramatically
improved, the wrong perspective on social networks and
bad quality data can produce misleading results (Huberman
et al. 2009). On OSNP, people tend to even add not-so-close
acquaintances to their friends’ lists; thus, distinguishing
weak links from strong links becomes a difficult problem
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for two reasons: first, the number of overt links is large, and
second, the number of observed interactions is rather small
in most cases (Trusov et al. 2010). Trusov et al. (2010)
developed a nonstandard form of Bayesian shrinkage
implemented in a Poisson regression and found that only
22% of so-called “friends” from an OSNP actually
influence a given user’s behavior. Not removing irrelevant
network ties and erroneously omitting relevant ties according to Páez et al. (2008) leads to biased results. It is often
ignored that there is not one social network connecting a
given set of individuals and influencing behavior, but
depending on the decision context, different types of
relationships have to be distinguished, including marriage,
friendship, work, advice, support, information transfer,
exchange, co-membership and many more (McPherson et
al. 2001; Wasserman & Faust 1994).
The enormous amount of data also poses substantial
challenges. Many global metrics from social networking
metrics are complex and require exponential time for
computation in the worst case scenario. Therefore, standard
social network analysis software is only suitable for
networks with 100–200,000 nodes; thus, Facebook’s social
graph with more than 500 million nodes cannot be
analyzed. This highlights the importance of smart network
boundary specification strategies (see Laumann et al. 1992
for a review on this topic). For many research questions or
business strategies, it is not necessary to consider the entire
dataset. It might be, for example, sufficient to examine the
behavior in one country, one city or the behavior among
users who were active in a given time period. Nodes and
ties outside of the boundary can then be discarded, and the
complexity is reduced dramatically. If the social network is
still too large for software packages, sampling might be a
second approach (Leskovec & Faloutsos 2006) as many
social network metrics are robust against sampling.
As tricky as the challenge to disentangle the causality
effects of social embeddedness seems to be, we are
Fig. 2 Research Framework
C. Takac et al.
confident that recent online developments provide
researchers with the rich data needed to proceed. Thus,
we present our framework for structuring previous and
future research.
Research framework
We suggest distinguishing between decision making on the
individual level and the organizational level. The individual
level refers to individual behavior and phenomena, such as
choice and product adoption processes. The objective of
research in this field is to analyze antecedents and outcomes
of an individual’s social network participation. Chapter 3
outlines previous literature and future research directions
for the individual level. It is important to distinguish the
individual level with individuals as actors from the
organizational level, where the organization, and thus a
group of individuals, itself becomes the actor and is socially
connected to other groups. This is especially important with
respect to management, economics, information systems
and marketing. Research topics in this field are presented in
more detail in chapter 4.
Decision processes can be separated into two classes of
decisions. The first class describes resource allocation
decisions, such as the adoption, purchase, promotion or
hiring decisions either made by an individual or by an
organization. The second class of decisions deals with the
question of how the individual or organization can change
its social position by entering or leaving relationships. We
discuss both classes on the microlevel before we examine
the impact of these microlevel decisions on the aggregate
level. In chapter 5, we discuss issues such as information
and product diffusion as well as the causalities of network
formation. As a cross-sectional dimension, we account for
similarities and differences between online and offline
social networks (see chapter 6). Therefore, we suggest the
research framework depicted in Fig. 2.
Social embeddedness of decision making
In the following, we want to outline the use of this
research framework by reviewing previous literature on
the impact of social embeddedness on decision making
and highlight challenges and new opportunities for
future research.
Individual level decision making
One of the most important issues for analyzing how social
embeddedness influences individual behavior is identifying
those individuals who exert the greatest influence on their
network neighbors. The first stream of research dedicated to
studying this phenomenon was the individual level adoption
of innovations. The process of individual decisions with
respect to purchase decisions of a new product was described
as a two-step flow (Lazarsfeld et al. 1944). First, awareness is
created by mass media, which, in most cases, is not strong
enough to trigger adoption decisions. In the second step,
interpersonal communication between potential buyers triggers purchase decisions. For this second stage, some
individuals are considered to be more important than others.
A relatively small number of people have a substantial
influence on the opinions and decisions of the majority (Katz
& Lazarsfeld 1955). These people are called opinion leaders
if they have a special expertise in a particular domain
(Barabási 2003; Valente 1995).
Identifying opinion leaders (sometimes called “influentials”) is a key problem in innovation and adoption
research, marketing, public opinion, epidemiology (in order
to stop spreading epidemics), communication, education
and many more fields. However, there are conflicting
findings on how to identify opinion leaders based on the
metrics of social network analysis. The social network
analysis concept of hubs (which refers to those people with
the highest number of social ties) is somewhat related to the
concept of opinion leadership, and frequently, individual
centrality is directly associated with power and influence (e.g.,
Borgatti et al. 2009; Iacobucci 1996).
Some claim that “there are no findings that connect
expertise or innovativeness to social connectivity and
having a large number of acquaintances” (Goldenberg et
al. 2009), which conflicts with other studies (Rogers &
Cartano 1962; Jacoby 1974) that report correlation coefficients ranging from 0.23 to 0.87 when comparing network
(degree) centrality and psychographic opinion leadership
metrics. Further research is required on how opinion leaders
can be identified based on their social network position as
current research still produces conflicting results (cf. Godes
& Mayzlin 2009; Coulter et al. 2002; Iyengar et al. 2011). It
appears, however, that opinion leaders are socially well
connected, whereas the well connected are not necessarily
opinion leaders.
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In addition to the question regarding how central
individuals influence others, it is interesting to ask how
the social network position (e.g., “degree centrality”
measured in terms of the number of links to others) affects
individual performance. Ahuja et al. (2003) find when
analyzing email communication that the degree of centrality
mediates the effects of role, status, and communication on
individual performance and that centrality is a stronger
direct predictor of performance than individual characteristics considered in this study. Central individuals may
receive more information than others and be more likely to
adopt new products, such as online auctions, mentioned
earlier. Similarly, social network growth processes are
believed to be strongly influenced by people having a large
number of ties to others (Goldenberg et al. 2009). Most
recent social network-related studies are based on the
concept of degree centrality.
A related idea was formalized by the concept of
“social capital” defined as the sum of the resources
accrued by an individual by virtue of possessing a
network of relationships of mutual acquaintances and
recognition (Bourdieu & Wacquant 1992). In other words,
social capital is a “metaphor about advantage”. This can
include the value of individuals with the ability to bridge
otherwise unconnected parts of the network, so-called
“structural holes” (Burt 2000). High social capital individuals stand at the crossroads and have the option of
bringing together otherwise disconnected individuals. This
idea is based on the famous argument of the strength of
weak ties (Granovetter 1973) and is captured by the social
network metric of betweenness centrality (Freeman 1978).
It is worth future empirical research to further investigate
the potentially different implications of being a hub (in
terms of a high degree of centrality) and standing at the
network crossroads (in terms of betweenness centrality) on
individual behavior.
We recommend introducing the notion of receptivity into
such studies pertaining to “who influences whom”. As
noted in computer simulations by Watts & Dodds (2007), it
might be easier to influence individuals who are not so well
connected rather than the influentials who are highly
connected because central individuals may be more difficult
to influence. They are subject to the influence of numerous
sources, and it might be difficult to start a diffusion process
with these individuals. Therefore, determining how important highly connected individuals are for the diffusion
process (e.g., social networking services) has created a
heated debate among practitioners (Thompson 2008). To
address this issue, we propose to study how individual
receptivity relates to social network position as a first step.
As introduced before, the tendency of humans to
interconnect becomes obvious in the plethora of successful Web 2.0 applications, such as Twitter, Facebook,
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MySpace and LinkedIn. It seems, however, that humans
may also suffer from too much (online) social embeddedness. A study from 2005, when online social activity was
still at a much lower level than it is today, reveals that an
average worker’s functioning IQ falls ten points when
distracted by ringing telephones and incoming emails
(Smith & Wilson 2005). It is, therefore, interesting to
determine optimal levels of (online) social interaction, and
it is necessary for IS research to find ways to manage the
information overload.
As a logical next step after addressing questions
pertaining to how social embeddedness influences individual behavior, there arise questions as to how individuals can
manipulate their social network position to improve their
social status, exert more influence on others, and acquire
more and/or more valuable information. Empirical evidence
on the extent to which individuals are able to strategically
manipulate their social network positions is scarce (cf.
Kossinets & Watts 2006).
There is some early experimental work in economics that
focuses on strategic models of network formation, explaining how decisions of individuals lead to social networking.
Literature in this area started with theoretical contributions
by Myerson (1977) and Jackson & Wolinsky (1996) and
attracted some interest in economics. Based on these ideas,
experimental economists tested theories of network formation in laboratory experiments with small groups of subjects
(Deck & Johnson 2004). While such experiments present a
useful technique for analyzing economic questions by
allowing for control of variables (such as costs, information
and valuations) that could possibly influence decision
making, the size of the networks must be rather small to
keep the experiment controllable.
First, results in IS research have been presented by Trier
(2008) with a method for event-based dynamic network
visualization and analysis. Based on longitudinal data of
corporate email communication, Trier (2008) demonstrates
how the exploration of animated graphs in combination
with measuring temporal network changes identifies measurement artifacts of static network analysis and measures
how network structures react to external events.
Mayer & Puller (2008) use data from Facebook and
develop a model of the formation of social networks that
decomposes the formation of social links into the effects
based on the exogenous school environment and the effects
of endogenous choice arising from preferences for certain
characteristics in one’s friends. Likewise, Putzke et al.
(2009) test different theories from sociology (e.g., transitivity, reciprocity, and homophily) on network formation
with data from a virtual world. Their results indicate that
structural effects and demographic variables active in the
real world also influence the formation of the players’
network in the virtual world.
C. Takac et al.
Organizational level decision making
Not only is individual decision making embedded in social
relationships but also in organizational decision making.
The ties that link different organizations constitute an
important value base for firms, especially in networked
economies. It has been shown that intra- and interfirm
social networks (e.g., Rindfleisch & Moorman 2001; Tsai &
Ghoshal 1998) and group collaborations (Grewal et al.
2006) drive values. Organizations are also embedded social
entities in inter-organizational networks. They are exposed
to the pressure from their environment, resulting from
interconnectedness and structural equivalence (Burt 1987).
Interconnectedness is characterized by inter-organizational
ties (e.g., transactions taking place between different
organizations, IT resources that are shared by different
organizations). If organizations occupy similar positions to
other organizations in an inter-organizational network, these
organizations are structurally equivalent. Accordingly,
institutional theory argues that the environment of an
organization has a significant impact on its structure and
actions (Burns & Wholey 1993), but the underlying
mechanisms of embeddedness are not quite clear. Similar
to the individual level, research should aim at disentangling
the causality chains of organizational embeddedness along
with identifying the type and level of inter-organizational
activity that drives organizational performance.
Over the last decade, a fundamental transformation has
been taking place as a networked economy has evolved in
which multiple organizations collaborate and create supply
chains and value networks. Such networks constitute webs
of relationships that generate both tangible and intangible
values through complex dynamic exchanges between two
or more organizations (Allee 2003). Any agent engaged in
these kinds of exchanges can be viewed as a value network
in itself, whether in private industry, government or the
public sector. This has given rise to important questions
regarding information diffusion, forecasting, the adoption
of new technology and business processes in such a closely
collaborating and networked economy. With the availability
of more and more information and the improvement of
research methods in this area, we expect that these effects
can be better identified and quantified (e.g., Berger and
Hinz 2008; Hochberg et al. 2007; Spann & Skiera 2003).
It is worth discussing the impact of social embeddedness
on organizational behavior separately from individual
embeddedness (as described in the previous chapter)
because there is evidence that some assumptions for the
individual level do not hold equally true on the organizational level. For instance, the individual benefit from
interaction through weak ties (Granovetter 1973) is based
on a low degree of redundant information between looselycoupled individuals. For organizations, on the contrary,
Social embeddedness of decision making
there is a high degree of redundant information between the
weakly-tied horizontal organizational alliances (i.e., among
competitors) and a low information redundancy between
closely-coupled vertical (i.e., firms operating adjacently in
the value chain) allies (Rindfleisch & Moorman 2001). On
the other hand, quite similar to the individual level,
centrality in a social environment is known to correspond
with performance in terms of an organization’s ability to
innovate or perform well financially (Powell et al. 1996;
Shipilov & Li 2008).
It has recently been demonstrated that economic value
lies in the social network between sellers in an online social
commerce market place (Stephen & Toubia 2010). The
authors demonstrate that economic value arises from links
between shops making them more accessible for the
customer; it is not the most central shops that benefit, but
those whose accessibility is most enhanced by the network.
However, it is left as an open question for future research to
determine whether and how strategic attempts to alter the
network structures (by linking to others or acquiring
reciprocal links from others) can be beneficial for a
particular shop.
Organizations steadily try to improve their social position
by engaging in new partnerships or co-operations, thus
highlighting the intersection of traditional organizational
science and social network analysis on the organizational
level.
Aggregate level: Diffusion through networks
and causality of network structure
The aggregated view on microlevel decisions in the social
networking context, be it on an individual or an organizational level, is commonly referred to as diffusion. Diffusion
through social networks can be defined as the transport
from node to node of some sort of quantity, such as
information, opinions, or disease (Goldenberg et al. 2009).
Since Bass’ (1969) seminal work, a variety of diffusion
models have been developed (an overview is given by
Mahajan et al. 1990). Previous research on information
diffusion is often focused on how information about cost,
return, risk, efficiency, and legitimacy influences the extent
of innovation diffusion, but it ignores the possibility that
this is channeled by social networks to only certain
potential adopters (Abrahamson & Rosenkopf 1997).
Market level diffusion models usually do not exploit the
underlying structures of heterogeneity, which would make
them more relevant for management decisions, according to
Song & Chintagunta (2003). Commonly, diffusion models
treat the market as homogeneous with the exception of
some general adopter categories, such as innovators or
laggards (Goldenberg et al. 2009). We conjecture that the
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evolving communication habits of emailing, instant messaging and communication via OSNP offer great opportunities to observe the spread of information through social
networks. Based on improved models for decisions at the
microlevel and information about the individuals’ or
organizations’ ties, researchers can model diffusion processes more accurately. Recent studies already highlight the
important influence of social structure on the diffusion
process (e.g., Bampo et al. 2008; Watts & Dodds 2007;
Hinz & Spann 2010). From an e-commerce perspective,
this leads one to question who should be targeted with
product information to maximize the dissemination of the
message. Hinz et al. (2012), for instance, conducted smallscale experiments on OSNP and experimentally tested
alternative targeting strategies proposed by previous research and found that targeting hubs (with a high number of
ties to other individuals) and individuals with a bridging
function (in terms of a high betweenness centrality) can
more than double the diffusion of information.
However, social network research in business disciplines
usually assumes that the network in question remains fixed
for the duration of the analysis and is unaffected by the
diffusion itself (e.g., Trusov et al. 2010; Hanaki et al. 2007),
while it has been demonstrated that networks change
dynamically over time (Trier 2008). As outlined in the
previous two chapters, individuals engage in new relationships, and organizations change their social positions by
deliberately choosing new partnering organizations. Therefore, research in economics often treats social networks as
dependent variables (e.g., Goyal & Vega-Redondo 2005;
Skyrms & Pemantle 2000). A combination of these two
perspectives seems fruitful for further research. To conduct
research in this area, access to data that captures the
formation of the network over time is necessary.
The social network formation is a complex process in
which many agents simultaneously attempt to satisfy goals
and thus, over time, create and deactivate social ties. There
are several motives for engaging in or disconnecting social
ties, including homophily, avoiding conflicts, choosing
friends of friends (known as “triadic closure”, cf. Rapoport
1957), global or local geographical proximity, or access to
information (cf. Kossinets & Watts 2006). We agree with
Kossinets & Watts (2006) that it is largely an empirical
question as to what extent each of these individually
plausible mechanisms manifest in individual or organizational network formation; this requires longitudinal network
data combined with individual attributes or group affiliations to proceed.
Based on such longitudinal data, it should be possible to,
for instance, investigate positive and negative contagion
effects across groups (cf. Van den Bulte 2010). Adoption of
prestigious goods (e.g., a rare and expensive digital item in
a virtual world like Habbo) by high status individuals might
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make it more appealing to mainstream or less affluent
individuals. Once the mainstream adopt a behavior (e.g., by
purchasing the product), the product becomes less attractive
to the high status individual.
Online vs. offline social networks and social activity
Supplemental to the analyses presented above, we need to
know more about how online and offline social networks
relate. Do people vary in their decision making process
when being influenced by online vs. offline acquaintances?
Why and how do people massively socialize online, and
does the recent tremendous increase in online social activity
substitute or complement traditional face-to-face socialization? Apart from studies that have begun to explore online
social network usage behavior (e.g., Subrahmanyam et al.
2008), we are not aware of any recent research that
satisfactorily addresses how the online changes to social
embeddedness affect decision-making behavior in general.
Many known features of real-world (offline) networks
can be similarly observed in online social networks. It is
well-understood that despite their often large size, in most
networks, there is a rather small distance between any two
nodes (defined as the number of ties along the shortest path
connecting them). This phenomenon is called the smallworld effect and produces small network diameters and
small average path lengths between network nodes. In his
famous study, Milgram (1967) found “six degrees of
separation”, that is, a typical shortest path length of no
more than six between most pairs of people in the U.S. The
small-world features of online social networks are, for
instance, shown by Adamic & Adar (2003). Moreover, both
online and offline social networks are known to exhibit a
higher level of clustering (i.e., hubs tend to be connected
with other hubs) than would be expected from random
connections, and the scale-free property of the degree
distribution (i.e., some nodes have a disproportionately high
number of connections) is common to online and offline
social networks (cf. Mayer 2009; Albert & Barabási 2002).
These commonalities may suggest that the underlying
mechanisms of network formation for online and offline
social networks are similar, and there is evidence that online
social networks are related to underlying offline social
networks (e.g., Mayer & Puller 2008; Putzke et al. 2009).
However, there are several important differences between online and offline socialization. The most important
difference is that there is almost no cost of communication
in online contexts; particularly, physical distance is no
longer a factor. Furthermore, online social networks make it
easier to spread false or misleading information because,
unlike trusted personal contacts, information acquired
online is hard to verify. Moreover, it is easier and virtually
C. Takac et al.
costless to find online communication partners with similar
interests or attributes (cf. Mayer 2009), and there is
evidence of increased information flow and price transparency via online social networks (e.g., Oestreicher-Singer &
Sundararajan 2011). Finally, involvement in virtual communities is a voluntary and conscious choice, whereas
membership in traditional offline communities may be
imposed by chance of birth or proximity of residence
(Bagozzi & Dholakia 2002). Thus, in economic terms, the
cost (i.e., the constraints) and benefits for utilitymaximizing agents associated with online socialization are
substantially different.
Furthermore, there seem to be negative outcomes
associated with increased online socialization, such as
congestion and information overload (cf. Mayer 2009;
Kamakura & Moon 2009). Due to the low cost of
accumulating online “friends”, online social networks can
quickly increase in size because there are usually low entry
and exit barriers (De Valck et al. 2009). Growing
communities, however, are known to reduce social pressure
and enforcement mechanisms which encourage behavior
that is beneficial to society. Furthermore, in small societies
(and when real-world behavior is visible), there are more
opportunities for sanctioning misbehavior. In summary, a
growing social network can inhibit pro-social behavior
(Putnam 1995; Allcott et al. 2007).
It is important to note that differences in offline
communication are not similar to all types of online
socialization but differ for group-based communication
where interaction usually occurs between a rather small
group (e.g., online role-playing games or chat-rooms) and
network-based interaction where interaction usually occurs
between different people (e.g., mailing lists and newsgroups). There are different purposes for participation
associated with these different types of networks. For
example, small group-based networks foster social benefits,
whereas network-based interactions are of informational
and instrumental value (De Valck et al. 2009).
When analyzing data from virtual communities, where the
data are not predominantly based on real-world relationships
and the users are identified by nicknames, trust as a basic
premise for social influence may not be given. It has even
been argued that face-to-face contact is irreplaceable for
building trust (Nohria & Eccles 1992), but studies in IS have
shown that very fragile and temporal forms of trust can
emerge in virtual relationships (Jarvenpaa & Leidner 1999).
It is, however, not clear how reliable the data from such
virtual environments are in contrast to data from real-world
relationships when one is analyzing social influence on
economic decision making. It is, however, likely that such
data include a higher fraction of irrelevant network ties.
We maintain that the differences between online and
offline social networks require further investigation. How-
Social embeddedness of decision making
ever, the newly-arising data opportunities from transactions
and conversations in online groups seem promising to
answer classic questions on human behavior and to
understand group and organizational processes (cf. Agarwal
et al. 2008). Complementary to retrospective accounts, we
suggest controlled social experiments as a suitable method
for further analyzing differences in the properties and the
formation of online vs. offline social networks and their
varying impact on decision making in a clear ceteris paribus
approach. For instance, online role-playing games or OSNP
(e.g., via Facebook widgets) may be a good environment
for conducting such experiments.
Conclusions and future research directions
We conclude that current research still lacks a coherent
understanding of how social connections impact individual and organizational decision making, thus providing
numerous opportunities for future research. Further, these
opportunities are even more promising when considering
the new data opportunities from digital social networks
and the possibilities of conducting online experiments in
these environments.
Moreover, we anticipate that the idea of social embeddedness is increasingly beginning to influence business
practices—particularly the business models of OSNP, such
as Facebook and LinkedIn. Marketers aim to use social
networks to foster word-of-mouth activities and thereby
promote social contagion.
ONSP are, however, still experimenting and services,
such as Beacon, have experienced a severe backlash for
such approaches. Beacon, as a part of Facebook’s advertisement system, sent data from external websites to
Facebook, ostensibly for the purpose of allowing users to
share their activities with their friends. Certain activities on
partner sites were published to a user’s News Feed. The
controversial service became the target of a class action
lawsuit and was shut down in September 2009.
In contrast, Procter & Gamble's subsidiary, the online
platform Vocalpoint, has successfully utilized social networking information for the introduction of new products
and has shown a doubling of sales in test locations.
Given these mixed results, empirically-tested targeting
strategies based on sociometric measures are of particular
interest. Who should marketers target with coupons, free
trials or price discounts to start a fast diffusion of new
products, and how should they do it without backlash and
negative publicity? While some empirical work has been
done to show the effect of cohesion, more studies
examining the effect of structural equivalence are necessary.
Many marketers believe that triggering competition effects
among consumers might be even more effective than
193
supporting word-of-mouth activities. This hypothesis has
yet to be tested empirically. Moreover, we need to
determine how social positions interrelate with characteristics that are used for traditional targeting strategies. Such
results would help to transfer insights from traditional
targeting to sociometric targeting.
While the increase in connectivity offers new opportunities for marketers, it also imposes new challenges for
marketers and communication specialists: upset customers
can easily reach a large audience with their complaints, thus
harming the firm’s image. One often cited example is the
“United breaks guitar” song by the Canadian musician
David Carroll. It chronicles the real-life experience of how
his guitar was broken during a trip on United Airlines and
the subsequent reaction from the airline. The song became
an immediate YouTube hit with more than 10 million views.
For IS research, it would be valuable to incorporate
detailed data from social networks into adoption models.
While adoption models primarily focus on the focal ego’s
characteristics, it is quite clear that social embeddedness
strongly influences the adoption decision. Data on information flows, and proxies for social influence may improve
adoption and diffusion models.
The study of social embeddedness in decision making,
however, will most likely become a prominent area of research
in information systems, marketing and related fields.
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