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Social Network Theory


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Social network theory focuses on the role of social relationships in transmitting information, channeling personal or media influence, and enabling attitudinal or behavioral change. Since the 1960s, social network theory has significantly expanded the horizon of media effects research, with increasing application of network analytic methods in various empirical contexts. The two-step flow of communication hypothesis, the theory of weak ties, and the theory of diffusion of innovations are three major theoretical approaches that integrate network concepts in understanding the flow of mediated information and its effects.
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Social Network Theory
University of Southern California, USA
Research history and key concepts
A diverse array of research traditions has shaped the current state of social network
theory. As Scott (1991) summarizes, there are three lines of research that contributed
to the theory’s early development: the sociometric analysis tradition, which relies on
graph theory methods from mathematics; the interpersonal relations tradition, which
focuses on the formation of cliques among a group of individuals; and an anthropol-
ogy tradition that explores the structure of community relations in less developed
ese research traditions did not evolve into a coherent theoretical framework until
the 1960s. A number of sociologists signicantly advanced the social network approach
by synthesizing previous theoretical traditions and extending them to understand both
formal and informal social relations. For example, the sociometric view of social net-
works was elaborated, emphasizing structural properties, such as the relative location
of individual nodes in the network. Researchers during this time also advanced social
networktechniquesbyproposingblock modeling and multidimensional scaling.Block
modeling considers the particular position of a node in a social network. is method
enables researchers to identify nodes that have similar network positions, or what is
called structurally equivalent nodes. e scaling technique, on the other hand, allows
researchers to convert social relationships into sociometric distance,therebymapping
these relationships in a social space (Wasserman & Faust, 1994).
ree key network concepts that have organized research on network eects are
centrality, cohesion, and structural equivalence. Freeman (1979) proposed three dis-
tinct measures to indicate structural centrality: degree, closeness, and betweenness.
is seminal paper aorded a nuanced understanding of centrality, and it established
a process through which new network measures were developed to have a raw form,
a normalized form, and a network-level form. Freemans (1979) paper also motivated
subsequent research to assess how dierent forms of network centrality interact with the
ow of information dierently. For example, Borgatti’s simulation study (2005) identi-
ed a typology of ow processes, and he showed that the values of dierent central posi-
tions depend on the characteristics of the process (e.g., gossip diusion versus goods
Network cohesion measures the degree of interconnections among a group of nodes.
is measure has long been useful to detect subgroups or cliques within the larger
e International Encyclopedia of Media Eects.
Patrick Rössler (Editor-in-Chief), Cynthia A. Honer, and Liesbet van Zoonen (Associate Editors).
© 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.
DOI: 10.1002/9781118783764.wbieme0092
social network (Burt, 1987). In the context of media eects research, network cohesion
serves as an important structural feature that moderates the inuence of interpersonal
networks. Friedkins (1993) longitudinal study, among others, found that personal inu-
ence grows stronger within more cohesive social networks than less cohesive ones.
Finally, structural equivalence indicates two or more network positions that share a
similar pattern of connections with the rest of the network. Actors that occupy struc-
turally equivalent positions oen have similar characteristics, such as social status or
other individual traits. Because equivalent nodes are connected to a similar set of actors,
they are more likely to receive similar information or social inuence. In understanding
the process of diusion, Burt’s (1987) study found that innovations were more likely to
ow via structural equivalence than direct ties, suggesting equivalence inuence may
be a stronger predictor of behavioral adoption than cohesive inuence. Burt (1999)
further elaborated on these mechanisms to explain the role of opinion leaders in the
media eects context. He argued that there were two dierent network mechanisms at
play: a two-step ow process that consisted of opinion leaders spreading information to
the group, and a contagion process via structural equivalence that generated adoption
behaviors within the group.
e years since the 1990s have witnessed extensive applications of key network
concepts in diverse research contexts, and the eld has also constantly been updated
with more rened network measures and analytic tools. In the arena of media
eects research, the fundamental question is: How do social networks, including the
quality and quantity of relational ties, the structural position of individual actors in
modularity) impact the ow of media messages and their eects on the audience?
ese eects include public opinion formation, marketing, uses and gratications of
media consumption, and behavior change due to prosocial campaigns.
Although communication research did not substantially shape the initial develop-
ment of social network theory, there is an emerging trend of cross-pollination between
social network theory and media eects research. In large part, this cross-pollination
stems from the emergence of computer-mediated communication, which aords
explicit social networks as well as the modes of communication that bind them. e
following sections review three theoretical approaches that best represent the inuence
of social network theory.
Two-step flow of communication hypothesis
e two-step ow of communication hypothesis was rst proposed by Lazarsfeld,
Berelson and Gaudet in the book e People’s Choice (1944). In their study of voting
decisions, they found personal inuence, which was largely derived from peoples
social contacts and friendship networks, signicantly aected voting decisions. And
this eect was even more pronounced among people who were less committed to their
existing beliefs or who changed their minds during the course of a campaign. e
hypothesis is called two-step because the mass media initially inuence opinion leaders,
individuals who are perceived as inuential, who in turn inuence their social contacts.
Building on its initial formulation, Katz (1957) reviewed a number of corroborating
studies on this hypothesis and further elaborated on three important aspects of it.
First, the magnitude of personal inuence could be greater than that of mass media,
as rst identied in the 1940s voting study. Similar ndings also emerged from
subsequent cases, such as the Decatur Study, which examined individuals’ fashion
decision making (Katz & Lazarsfeld, 1955). Second, in terms of the ow of personal
inuence, opinion leaders are not always concentrated at certain social strata, nor
does personal inuence always ow from a higher social stratum to a lower one. On
the contrary, studies have observed many cases of local leadership or issue-specic
leadership. at is, leaders dier for dierent groups of people and leaders lead in
some domains but not others. Finally, personal inuence does not necessarily work in
isolation from mass media. e voting study revealed that opinion leaders tended to
be those who were more exposed to the mass media. And, depending on the specic
context, personal inuence can either reinforce or attenuate the eect of traditional
mass media.
Central to the two-step ow of communication process is the concept of opinion
leaders, a group of individuals inuential in specic domains. Numerous studies have
attempted to identify the key characteristics associated with being inuential along
three lines (Katz, 1957): who one is, the individual characteristics of the opinion leaders,
such as personality traits, charisma, or demographic and socioeconomic backgrounds;
what one knows, the characteristics pertaining to individuals’ competence, such as
their knowledge, expertise, or ability to provide information or guidance on particular
issues; and whom one knows, the characteristics related to an individual’s structural
position in a network. In other words, individuals may become opinion leaders
not only because they possess certain attributes but also because they occupy the
right network positions that enable them to eectively spread information and exert
personal inuence. Centrality measures have been particularly useful for identifying
leaders based on their network position.
As discussed, social network theory has proposed three types of network centrality
measures to identify the advantageous position that opinion leaders usually occupy:
degree, betweenness, and closeness (Freeman, 1979). Degree centrality measures the
number of links to and from an individual in a network. Individuals with high degree
centrality are more likely to become opinion leaders because more social ties can mean
greater opportunities to receive as well as disseminate information (see Figure 1, black
node). Betweenness centrality measures the frequency at which an individual node
lies on the shortest path connecting other nodes in the network. Individuals high in
betweenness centrality are more likely to serve as a bridge in the network—dened as a
node that connects otherwise unconnected network clusters. Just like gatekeepers in a
network, if individuals high in betweenness centrality oppose the dissemination of an
idea, this piece of information may not be able to ow to other areas of the network. In
Figure 1, the light gray node occupies this critical position. Finally, closeness central-
ity measures the average distance between an individual node and all other nodes in
the network. Individuals with higher closeness centrality need relatively fewer steps to
reach all other individuals in the network and thus can potentially move information
faster. e ability to eectively reach other contacts in one’s network makes individuals
Opinion leaders with high
degree centrality
Opinion leaders with high
closeness centrality
Opinion leaders with high
betweenness centrality
Figure 1 Network illustration of opinion leaders with high degree centrality, closeness central-
ity, and betweenness centrality. Source: Adapted from Everett’s kite, in Brandes and Hildenbrand,
with high closeness centrality inuential. In Figure 1, the dark gray nodes have high
closeness centrality.
As one of the most applied theories in media eects research, the two-step ow of
communication hypothesis has been rigorously tested in various empirical settings.
e research on public opinion formation and agenda-setting, for instance, has
studied how inuential individuals, such as early recognizers of social issues, may
mediate the public and the media agendas by identifying emerging issues in the
media, diusing them among public audiences, and ultimately aecting the media
agenda (Brosius & Weimann, 1996). Research on health interventions has examined
(1998) study of a reproductive health communication campaign, they found both
the mass media campaign and personal networks were associated with individuals’
contraceptive adoption, but the impact of mass media was stronger for low threshold
individuals, those whose personal networks were composed of few contraceptive
users, than high threshold individuals, those whose networks contained a majority
of users.
Although the two-step ow hypothesis has been validated in numerous studies,
scholarship since the 1980s has pointed out that media inuence may take multiple,
recursive steps, and the overall process is more complex thana singular, one-directional
ow. With the rapid change in today’s media and communication environment, some
scholars also argue that the role of opinion leaders is becoming less pivotal. Bennett
and Manheim (2006), among others, have proposed that the traditional two-step ow
of media messages has gradually transformed into a one-step ow process, where
mass media are becoming more fragmented and niche media increasingly engage
in narrowcasting. Under this changed landscape, media messages may directly reach
their audience and opinion leaders thus would play a less signicant role than was
previously theorized.
e theory of weak ties, articulated in Granovetter’s (1973) seminal piece “e Strength
of Weak Ties,” concerns the role of weak social ties in diusing ideas and informa-
tion. In his labor-market study, Granovetter observed that people more oen found jobs
through their weak social ties, as opposed to relying on their family or close friends. He
measured tie strength through the frequency of contact, asking respondents how oen
In addition to contact frequency, studies have proposed a combination of factors to
indicate the strength of social ties, such as the duration of interaction, the amount of
eort individuals invest in a relationship, the extent to which the social ties provide
reciprocal utility (e.g., social support), and the level of intimacy exchanged in a relation-
ship. Based on these criteria, weak ties are generally dened as social relations requiring
little investment, and they are composed mostly of acquaintances or other loosely con-
nected actors, as opposed to kin or close friends.
Why are weak ties more likely to channel novel information than strong ties? To
explain the underlying mechanism of Granovetters ndings, it is necessary to return
to the network concept of bridging, mentioned previously in the denition of between-
ness centrality. Bridging ties are social connections that link two otherwise unconnected
network clusters. In other words, bridging ties provide the only path between two dis-
connected clusters, such as Cluster A and Cluster B in Figure 2. Granovetter found weak
ties were more likely to be bridging ties, because weak ties’ peripheral position made
them better able to reach outside information than strong ties. In Figure 2, imagine each
network cluster represents a circle of close friends, as all the nodes in each cluster are
connected to each other. In such highly interconnected circles, each person is likely to
Cluster A
Bridging tie
Cluster B
Figure 2 Bridging ties.
receive a similar set of information. e bridging tie (sitting between the two clusters),
on the other hand, becomes the only opportunity for any nodes in Cluster A to access
novel information from Cluster B.
Although strong ties oen emerge from the center of a network, which gives
them greater capacity to diuse information and exert social inuence, Granovetter’s
thesis highlights the bridging function of weak ties and their ability to spread novel,
nonredundant information. e strength of weak ties, therefore, is not about the
potentially more heterogeneous, set of information sources. In the process of job
hunting, for example, the utility of strong ties diminishes, because they provide similar
and potentially redundant information to individuals.
text of general information seeking, organizational knowledge sharing, the diusion
of innovations, community building, and many more. In the area of media eects,
studies have explored the role of weak discussion ties in promoting civic engagement.
For example, de Zúñiga and Valenzuela (2011) examined the association between
strong versus weak ties and individuals’ online and oine civic engagement. Among
factors such as discussion frequency and discussion network size, they found the
frequency of weak-tie discussion was the strongest predictor of individuals civic
e emergence of new media and social networking sites has created an increase in
online weak ties. Indeed, new media provide novel platforms through which individuals
can connect with geographically distant others, and functions such as add friends,”
“follow the post,” mention,” and “retweet” have been theorized as forms of weak social
ties. Some research has found evidence of online, mediated weak ties in maintaining
individuals’ bridging social capital, such as Ellison, Steineld, and Lampe’s (2007) study
on college students’ Facebook friends networks, whereas other scholars have argued
that online connections may breed slacktivism, as they fail to nurture meaningful civic
participation. e rise of new media platforms thus urges scholars to reconsider the
denition, conceptual boundary, and new typologies of weak ties. It also encourages
new research to examine the role of mediated weak ties in diusing information and
exerting social inuence.
Diffusion of innovations
e diusion of innovations occurs between individuals or organizations in a social
vations can be viewed as a social network, where network connections may take the
form of friendship, advice, communication, or social support. e diusion process is
of social connections, the structure and characteristics of this network can determine
how widely and how soon the innovations get adopted (Valente, 1995).
InseveraleditionsofthebookDiusion of Innovations, Rogers (2010) formally intro-
duced the model for diusion and dened it as the process in which an innovation
is communicated through certain channels over time among the members of a social
system” (p. 5). Rice (2011) dened the process of diusion in the context of media eects
“as the process through which an innovation (an idea, product, technology, process, or
service) spreads (more or less rapidly, in more or less the same form) through mass
and digital media, and interpersonal and network communication, over time, through
a social system, with a wide variety of consequences (positive and negative)” (p. 1).
e groundbreaking study in the eld of diusion was conducted by Ryan and
Gross in 1943 while they were investigating the diusion of hybrid corn seeds among
farmers in Iowa (see Valente, 1995). e early network approach of diusion studies
looked at how opinion leader status, indicated by the number of times an individual
was nominated as a network partner, was correlated with the time of adoption. Later
there emerged a more structural approach, and this approach shied the focus to
examine how the overall network pattern inuenced the adoption of innovations,
such as network density, the presence of weak ties (Granovetter, 1973) or structurally
equivalent positions (Burt, 1987), and so forth. Valente (1995) proposed a social net-
work threshold model, and this model is particularly characterized by its system-level
A large body of diusion studies has focused on identifying the factors and forces
that lead to the adoption of innovations among members of a certain population. ese
studies also aim to understand why some individuals or organizations adopt the inno-
vation sooner while others take more time to accept the same idea or practice. Current
scholarship has identied four main elements of the diusion model (Rogers, 2010;
Valente, 1995): the rate of adoption, which can be inuenced by the perceived char-
acteristics of the innovation and can be measured by mathematical models (Valente,
1995); the rate of adoption over time, which yields a cumulative S-shaped pattern; the
various stages during the adoption process, which can be further classied as knowl-
edge, persuasion, decision, implementation, and conrmation; and the modication of
the innovation.
In general, the adoption process entails learning about a new product, getting more
information about it, making a decision to adopt it or not, experimenting with it, and
eventually conrming the use of the product. e pace of diusion can be determined
by certain characteristics of the innovation, which include its: relative advantage, com-
patibility, complexity, trialability, observability, cost, and radicalness. Less radical, less
complex, and less expensive innovations, and innovations perceived as more advanta-
geous, compatible, trialable, and observable, spread more rapidly.
e ve adoption stages—knowledge, persuasion, decision, implementation, and
conrmation—have long been useful for understanding media eects and behavioral
change. Additional theories have further developed the adoption stages to evaluate
media campaign eects. For example, the hierarchy of eects model proposed 12
steps leading to behavioral change, and it estimated that individuals usually proceed
from one step to the next at a rate of 80% (Valente, 1995). e transtheoretical model
proposed specic cognitive stages of change—precontemplation, contemplation,
preparation, action, and maintenance—for quitting a behavior such as smoking
(Prochaska, DiClemente, & Norcross, 1992). It should be noted that a homogenous
model usually cannot capture the varying diusion processes in dierent contexts.
network weight matrices, such as relational, positional, and centrality measures, and
the weights based on social distance.
Diusion research peaked in the early 1960s, and it has been rekindled with the
rapid emergence of newer and more advanced network models and technology. e
application of diusion theory spans a wide array of disciplines, such as marketing,
economics, mathematics, sociology, anthropology, and epidemiology, among many
others. In the area of media eects research, the main premise of the theory is that
innovations enter into communities from external sources such as mass media or tech-
nological advancements, and then they spread via social networks and interpersonal
Mass media play a critical role in initiating diusion among opinion leaders and low
threshold adopters, as these individuals are more likely to rely solely on media infor-
mation to adopt an innovation (Figure 3). For opinion leaders or early adopters, their
initial decision of adoption may be independent of their social network ties. rough
processes such as the two-step ow, opinion leaders then spread the innovation to their
adjacent social network partners. At this stage, the early majority and late majority
may seek additional validating information to reduce uncertainty regarding the inno-
vation, from both traditional mass media and online media platforms such as Facebook
and Google. Toole, Cha, and González (2012) studied the spatiotemporal adoption of
Twitter, a microblogging web application, while also considering the interplay between
media and word of mouth. Media inuences at later stages of adoption increased the
Twitter user base twofold to fourfold. A study on news diusion across various social
media platforms analyzed over 386 million Web documents over a 1-month period in
2011. It found that, depending on issue domains, dierent types of media had varying
Cluster A
Bridging tie
Mass media
Cluster B
Figure 3 Two-step ow. 1: early adopter; 2: early majority; 3: late majority; 4: laggards.
in politics and culture, news media in the arts and economics, social media in controver-
sial topics such as protests, and single social platforms in entertainment (Kim, Newth, &
Christen, 2013). erefore, media can inuence one’s perception of innovations as well
as one’s adoption behaviors.
Key contributions and future directions
Social network theory and methods oer a distinct perspective on and set of tools
with which to understand media eects, enabling consideration of how micro- and
macrosocial structures mediate and moderate media eects. e theories of two-step
ow and diusion of innovations examine the paths by which mediated messages travel
through social networks, and the concepts of opinion leadership and tie strength oer
insights into critical variables that aect this ow. While each of the theories discussed
here was developed in the twentieth century during the golden age of mass media tech-
nologies, their theoretical contributions endure as scholars continue to test and rene
them in an era of social media and rapid evolution in media technologies. ree direc-
tions for current and future research are highlighted below.
First, new media technologies such as social networking sites, microblogging tools,
and online recommendation systems oer intriguing opportunities for further appli-
cation and extension of social network theory in the study of media eects. Current
research in this area falls into two broad categories. One category investigates whether
and how network-based media eects theories such as diusion and the strength of
weak ties operate dierently in dierent forms of social, as opposed to mass, media.
opinion leadership discussed above may not be the best indicators of social inuence
on Twitter (Gruzd & Wellman, 2014). A second category of research capitalizes on the
large amount of and novel types of data available through social media to rigorously
test network-based media eects theories in ways not previously possible. For example,
can be used to create randomized controlled experiments of the diusion of consumer
and political behavior on Facebook.
Second, media eects researchers have begun to extend social network theory and
methods beyond classic social contagion processes to engage in what Ognyanova and
Monge (2013) describe as a “relational reinterpretation of numerous mass communi-
cation phenomena. Hyperlink networks, for example, in which the nodes are websites
and the ties are the hyperlinks that connect them, may be analyzed to trace the dif-
fusion of content between mainstream media and blogs, or to determine the extent to
which prominent mainstream media versus bloggers wield inuence in media and pub-
lic agenda-setting. Semantic networks, in which the nodes are words and the ties are
cooccurrences of those words in various media, may be mapped to identify patterns
in how content is framed across dierent outlets over time. ese network approaches
oer promising new methods for research on core media eects theories.
ird, ongoing advances in the statistical approaches used in social network analy-
sis promise continued improvement in the sophistication with which researchers are
able to model how social structure shapes or is shaped by media eects. In particular,
the development of models that allow for multiplex (multiple types of ties), multimode
(multiple types of actors), and multilevel networks enables consideration of greater
complexity in the study of diusion and mediated social inuence. ese developments
are particularly relevant in a new media environment in which actors may be both pro-
ducers and consumers (potentially necessitating multiplex ties), and people may access
content from many dierent types of sources and using many dierent types of media
(requiring multiple modes and levels). In sum, communication research has never been
more promising or relevant, and the theories introduced here oer insights into how to
move communication research forward.
SEE ALSO: Agenda-Setting: Individual-Level Eects Versus Aggregate-Level Eects;
Diusion eories: Logic and Role of Media; Diusion eories: Media as Innovation;
Diusion eories: News Diusion; Multistep Flow of Communication: Evolution of
the Paradigm; Multistep Flow of Communication: Network Eects; Multistep Flow of
Communication: Online Media and Social Navigation; Multistep Flow of Communica-
tion: Opinion Leadership and Personality Strength; Network Society: Networks, Media,
and Eects; Social Networking
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tive health communication campaign in Bolivia. Communication Research,25(1), 96–124. doi:
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Further reading
Burt, R. S. (2000). e network structure of social capital. Research in Organizational Behavior,
22, 345–423. doi: 10.1016/s0191-3085(00)22009-1
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry,40(1),
35–41. doi: 10.2307/3033543
Granovetter, M. S. (1983). e strength of weak ties: A network theory revisited. Sociological
eory,1, 210–233. doi: 10.2307/202051
Valente, T. W. (2010). Social networks and health: Models, methods, and applications.NewYork,
NY: Oxford University Press.
Weimann, G. (1982). On the importance of marginality: One more step into the two-step ow
of communication. American Sociological Review,47, 764–773. doi: 10.2307/2095212
Wenlin Liu is a doctoral candidate at the Annenberg School for Communication,
University of Southern California, USA. Her research interests lie at the intersection
of interorganizational communication and social network theory and methodology.
Wenlin is a research member of the Center for Applied Network Analysis, led by
Dr. omas W. Valente.
Anupreet Sidhu is a doctoral student at the Department of Preventive Medicine, Uni-
versity of Southern California, USA. Her research interests lie in the area of health
campaign evaluation and social networks, specically in health promotion contexts.
Anupreet is a member of the Center for Applied Network Analysis, led by Dr. omas
W. V a l e n t e .
Amanda M. Beacom is a doctoral candidate at the Annenberg School for Communica-
tion, University of Southern California, USA. She conducts research on organizational
communication and social networks, particularly in health contexts.
omas W. Valente is professor in the Department of Preventive Medicine, Univer-
sity of Southern California, USA. He is the author of the books Social Networks and
Health: Models, Methods, and Applications (2010) and Network Models of the Diusion
of Innovation (1995).
... Small businesses are the bedrock of economic development as they contribute a stable source of revenue for the region which comes in form of taxies and levies. [31][32][33][34][35][36][37][38] Thus, small businesses innovative performance can be achieved from the benefit obtained from strong network relationships and weak network ties. Small enterprises compete to create goods and services for customers and achieve competitive advantage over and above their rivals. ...
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This study empirically evaluates social capital and the performance of small enterprises in Cross River State and Ebonyi, Nigeria. Small enterprises are not proficient to generate and increase utilization of the affluence of social capital to enhance performance. The underpinning theories for this study were based on Barnes (1954) network theory and resource based theory. Descriptive survey design was adopted for the study. The population for the study was 2332. Taro Yamane’s formula (1967) was used to determine a sample size of 341 from the population. Descriptive statistics were used to determine their frequencies, percentages, mean and standard deviations. Ordered logit regression model was adopted in data analysis and the hypotheses were tested at 0.05 degree of significance using Statistical Package for Social Sciences (SPSS) version 25. The study found out amongst others that social capital provides information through access to broader sources of information and provides information quality, relevance, and timeliness. Based on the findings, this study recommends amongst others that small enterprises should build social relationships that can provide greater access to tangible and intangible resources that can help promote their businesses. Based on the discussion of the findings and theoretical foundations, this study concludes that network relationship helps enterprise to harness tangible and intangible resources to foster innovation and enhance performance.
... Mapping and understanding the relevance and the nature of relationships among actors of these social organizations is crucial to explore the dynamic and the context of agricultural innovation. This study is therefore also grounded in social network theory (Liu et al. 2017), where modelbased approaches describe the proximity of actors within networks and the dynamics of innovations diffusion. This theory takes a non-linear perspective when examining actor interactions in the context of different relationship groups. ...
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Agriculture can benefit from crop diversification to facilitate its transition to more sustainable agrifood systems. However, these practices remain rare in Europe. One major barrier is the existence of sociotechnical lock-ins. To clarify the dynamics at work, we analyzed the relationships between actors involved in 23 crop diversification experiences across 11 European countries. The novelty of this paper lies in the systemic analysis of the network of actors involved in crop diversification experiences. Using data from qualitative interviews and cognitive mapping approaches, we identify and describe the role of actors and the key relationships in crop diversification and detect relationships that are currently missing. Our study shows that in the different European countries, similar relationships act as levers or barriers to crop diversification, with farmers and researchers playing a crucial role. The most important cognitive factors that influence the choice of farmers to diversify are environmental and health concerns and the desire to make profit and innovate. We relate the cognitive factors to organizational, technical, economic, and political factors and suggest levers for crop diversification based on successful crop diversification experiences.
... Since such relationships represent both affective and normative associations (Ding et al., 2019), we contend that such associations are actually expressive ties and that these ties become stronger when employees receive recognition via PDAs. This supposition is also in concordance with the existing evidence that shows that receiving recognition and appreciation from others establishes and strengthens connections, bonding, and social relationships (e.g., Bryant and Veroff, 2017;Liu et al., 2017), thereby improving expressive ties. Based on the preceding discussion, we propose the following: ...
... The nodes are individuals or an organization's actors, while the ties are the relationships between the actors. Social network analysis can be used to assess structural equivalence, centrality, and cliques [26]. Structural equivalence occurs when two nodes have the same connections to other nodes. ...
Social media platforms are powerful for businesses to gain insight into how their customers feel about their company, product, or service. This chapter discusses the different types of social media analytics methods available to businesses to track their social media performance. With the help of natural language processing, businesses can understand the emotions associated with their brand and develop strategies to better serve their customers. With its insights into customer behavior and its ability to detect shifting trends in social data, social media analytics can provide a clear picture of how customers move from your social media presence to making purchases.
... The variable of perceived ease of use (PE) was measured by easily operate (X 4 ); flexible to use (X 5 ) and simple and understandable (X 6 ) (Michael et al., 2015). Meanwhile, information quality variables were indicated by relevance (X 7 ); accurate (X 8 ) and complete information (X 9 ) (Liu et al., 2017). The perceived risks variables were explained by the risks itself (X 10 ) and security issues (X 11 ) (Perner, 2017). ...
Technological advancements have brought innovation in the financial industry with various platforms. Although these platforms are easily accessible, some Indonesians are still reluctant to use them, hence the need for proper introduction and information delivery. Peer-to-peer (P2P) lending platforms need to develop and implement applications that can monitor the activities and increase the productivity of direct sales agents (DSAs) to gain the trust of potential borrowers. This study analyses the influence of perceived usefulness, ease of use, risk, and information quality on the actual behaviour of DSAs when using sales productivity applications with behavioural intention as an intervening variable to be statistically processed using the Structural Equation Method. The questionnaire was distributed to DSAs of Modalku platform. The results showed that DSA's actual behaviour in using the application was significantly influenced by intention to use, perceived usefulness, ease of use, risk, and information quality. Perceived risk has the highest influence.
... Higher proximity centrality means fewer steps to reach all network members and quicker information transfer. Influential people with strong proximity centrality can reach other network contacts [34]. ...
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With a focus on bibliometric analysis, this study intends to analyze the patterns in the literature regarding knowledge sharing and student development. To find studies that were specifically focused on the topic under research, the authors searched Scopus using the Boolean operators AND, OR and NOT, together with the terms "knowledge sharing" and "student development," as well as their synonyms. In general, the study did not take the publication date into account. There were 1,154 documents in total in the final data set. The data were analyzed using Biblioshiny and VOSviewer software based on R. The use of bibliometric analysis to examine the connection between knowledge sharing and student development revealed several themes, influential authors, highly cited papers, prominent organizations and powerful nations. In addition, the study showed how the relationship between knowledge sharing and student development has changed over time and how it may interact with student performance to give educational institutions a long-term competitive advantage. To the author's knowledge, this study is the first to conduct a bibliometric analysis on knowledge sharing and student development. This study can be a starting point for scholars interested in understanding how knowledge sharing can relate to student development.
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Peer-to-peer (P2P) energy trading has gained significant importance in recent years due to the growing energy needs worldwide. To ensure the effective and efficient implementation of P2P energy trading, it is necessary to analyze the concept from multiple dimensions. This study aims to investigate the challenges that may hinder the smooth flow of P2P energy trading and identify strategies to overcome them. Technical, cybersecurity, renewable energy integration, economic, pricing mechanisms, and regulatory challenges are among the key obstacles that may curtail the full potential of P2P energy trading. In addition, the full achievement of the P2P energy trading potential requires a global response from stakeholders to ensure widespread acceptance and adoption. Game theory and agent-based modeling can effectively address these challenges and facilitate the successful implementation of P2P energy trading.
This research investigates the creation of social capital among members of the online community. In this case, social networking refers to interpersonal connections among members of a rural maritime community. The major goal of this study is to determine how much the maritime community uses social online networking and how social capital grows within the community via the internet. The study applied a triangulation method to analyze data from participants with several points of view and to engage people appropriately for a better understanding of the phenomenon. Main findings extracted from the interviews have been categorized into three themes: (1) patterns of online social networking and social media use, (2) social networking and trust, and (3) social capital development. Hence, it is apparent that online networking can be used to reduce the social capital divide between urban and rural communities in Malaysia.
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Purpose This study aims to explore the influence of science fiction on innovators and present a comprehensive model using the theory of planned behavior and social support theory to discuss the impact of science fiction on the intention of becoming an innovation worker. Design/methodology/approach Partial least squares structural equation modeling (PLS-SEM) was adopted in this study and responses were obtained from 244 Chinese innovators. Findings The results revealed the adequacy of the proposed model and the above-mentioned constructs in explaining innovation intention. Science fiction perception was found to influence the intention of becoming an innovation worker directly. Subjective norm, perceived behavioral control, and attitude directly influence the intention of becoming an innovation worker. Additionally, attitude is a mediator between science fiction perception and the intention of becoming an innovation worker. Moreover, social support network moderates the relationship between attitude and intention. Originality/value These results shed light on the mechanism by which science fiction influence innovators as well as provide critical managerial implications for policymakers and practitioners.
Opinion leaders are more precisely opinion brokers who carry information across the social boundaries between groups. They are not people at the top of things so mulch as people at the edge of things, not leaders within groups so much as brokers between groups. The familiar two-step flow of communication is a compound of two very different network mechanisms: contagion by cohesion tl through opinion leaders gets information into a group, and contagion by equivalence generates adoptions within the group. Opinion leaders as brokers bear a striking resemblance to network entrepreneurs in social capital research. The complementary content of diffusion and social capital research makes the analogy productive. Diffusion research describes how opinion leaders play their role of brokering information between groups, and social capital. research describes the benefits that accrue to brokers.
Social Networks and Health provides a comprehensive introduction to how social networks influence health behaviors. Section one provides an introduction to major research themes and perspectives used to understand how networks form, evolve, and channel the spread of ideas and behaviors. An intellectual history of the field is provided as well as conjectures on why network science took so long to develop. Methodologies for studying networks and assessing personal network data are discussed. Section two covers algorithms and applications of the most common network metrics divided into four chapters: centrality, groups, positions, and network level. For each chapter, descriptions of how the metrics are calculated and how they influence health behavior are presented. Section three reviews applications of social network analysis to health behaviors. The actor-oriented stochastic evolution model is presented first which provides a way to statistically test network evolution properties. Diffusion of innovations models are presented next which describe how networks influence the spread of ideas and practices within and between communities. Network interventions are also presented and a typology describing network interventions and evidence from empirical studies presented. This book enables researchers to understand how network data are collected and processed; and how to calculate appropriate metrics and models used to understand network influences on health behavior. Simple examples and data are presented throughout so researchers can adopt this methodology and perspective in their own investigations. Examples of health behaviors include smoking, substance use, contraception, HIV/AIDS, obesity, and many others.
An incredible number of centrality indices has been proposed to date (Todeschini & Consonni, 2009). Four of them, however, can be considered prototypical because they operationalize distinct concepts of centrality and together cover the bulk of analyses and empirical uses: degree, closeness, betweenness, and eigenvector centrality.
This is a review of argument and evidence on the connection between social networks and social capital. My summary points are three: (1) Research and theory will better cumulate across studies if we focus on the network mechanisms responsible for social capital effects rather than trying to integrate across metaphors of social capital loosely tied to distant empirical indicators. (2) There is an impressive diversity of empirical evidence showing that social capital is more a function of brokerage across structural holes than closure within a network, but there are contingency factors. (3) The two leading network mechanisms can be brought together in a productive way within a more general model of social capital. Structural holes are the source of value added, but network closure can be essential to realizing the value buried in the holes.
This special issue presents leading-edge work into how the characteristics of social media affect the nature of influence in networks. Our central thesis is that social influence has become networked influence. Influence is networked in two ways: by occurring in social networks and by propagating through online communication networks. We want to understand online social influence in its diversity: who is exercising influence, how it is done, how to measure influence, what its consequences are, and how online and offline influences intertwine in different contexts.
I examine the relationship between interpersonal power and influence during the resolution of an issue in an organization. Controlling for elementary bases of power (rewards, coercion, authority, identification, and expertise), I investigate three bases of power that arise from the structure of social networks (cohesion, similarity, and centrality). An analysis of longitudinal data on actors' bases of social power, frequency of interpersonal communications, and interpersonal influences indicates that cohesion, similarity, and centrality have significant effects on issue-related influence net of the elementary power bases. The effects of the structural bases are mediated by the frequency of issue-related communication. The primary structural determinant of the frequency of issue-related communication is network cohesion.