Influences of strong tie with opinion leaders in an interconnected network of korea

Choi, [email protected]

Department of Interaction Sciencein Sungkyunkwan University

is.skku.edu

Influences of Strong Tie with Opinion Leaders in an Inter-

connected Network of Korea

RQ1: For the Korean, what is the relationship between a tie

strength with opinion leaders and the degree of information ex-

change?

RQ2: For the Korean, what is the relationship between a strong

tie with opinion leaders and an influence on people who are

on the periphery of the network?

Research Questions

Go to Hypothesis ☞

Do you think that information inequality exists in the in-

formation society?

Introduction

Do you think that opinion leaders are the most important

people in disseminating information as we know?

Introduction

Literature Review

Information Inequality and Information Exchange

There have been two separate but similar research fields, which are information

divide and digital divide, with one interdisciplinary area, information inequality

(Yu, 2011).

Early studies of information divide had defined information divide as the dis-

parity between the less advantaged in society (e.g., the disabled, the poor and the

aged) and mainstreams (Yu, 2006).


Van dijk (2000) said that information divide is the inequality that results from

the disparity of possession and usage for information and communication chan-

nels.

Britz and Blignaut (2001) used the term, information poverty, instead of in-

formation divide, defining it as the situation that social entities (e.g., individuals

and communities) do not have adequate skills, abilities, and materials in obtain-

ing information.

Literature Review (Cont.)


While the discourse on Information Divide has concentrated on the situation to

get information, the research interest of Digital Divide have been concerned for

an access to ICT.

Since in the society that the adoption rate of internet and computer is too high,

however, it is not sufficient to examine information inequality with variables of

digital divide, a new approach in building theoretical frame is necessary

(Verdegem & Verhoest, 2009)



This study is to examine the phenomenon of information inequality based on

the interdisciplinary approach rather than to define two concepts, information di-

vide and digital divide.

Information inequality is defined as multifaceted disparity of information us-

age and access to digital technologies between individuals and communities in

organizing information resources (Yu, 2011).


Social Network Perspective and Information Exchange

Social Network Analysis is the study to represent the social structure with actors

(e.g., individuals and communities) and relationships between actors. It helps to

find the patterns of relationships which represent the exchange of resources

between social entities (Haythornthwaite, 2002; Wasserman & Faust, 1994).

In order to examine how the social network affects to the social behavior such

as an exchange of resources, it is necessary to approach from 1) “relational” or

“Ego-centered” and 2) “Positioned” or “Entire” levels (Burt, 1987; Haythornth-

waite, 1996).


Social Network Perspective and Information Exchange

The pattern of information exchange provide the explanation that individuals

have their own access to and control of information (Haythonthwaite, 1996).

To understand of the social structure with a relationship of information ex-

change may help to explain the disparity of information between individuals

(Johnson, 2007).


Opinion Leaders and Influentials Hypothesis

This study uses the important concept in the diffusion of innovation theory

which describes in neutral position that information inequality is the naturally oc-

curring phenomenon (Yu, 2011), in order to examine the phenomenon of informa-

tion inequality itself.

The most important people in disseminating information of innovation refer to

opinion leaders in the diffusion of innovation theory.



The importance of opinion leaders was re-emphasized as the two-step of flow

theory, which underlines the role of opinion leaders who allow messages from

media easily to disseminate, played an important role in the media sociology

(Katz & Lazarsfeld, 1955/2006).

Rogers (2003) defined opinion leaders as people who have uneven influences

on behaviors or attitudes of others.

Chatman (1987) said that opinion leaders are those who play an important role

in transferring information to others. She underlined the role of opinion leaders

in the information environment.



However, previous studies of opinion leaders have not given definite explana-

tions about dissemination of influences of opinion leaders in process of the diffu-

sion of innovation or information exchange (Watts & Dodds, 2007).

Watts and Dodds (2007) said that although they modestly agreed on the impor-

tance of opinion leaders, most of social changes are triggered by those who are

easily influenced by opinion leaders, not opinion leaders.



Being influential to something in a network depends on a structure of an entire

network, not the characteristics of specific individuals. Thus, it is necessary to ex-

amine local environments as well as environments around opinion leaders and

those who are directly influenced by them (Watts, 2007).


Influence, Tie Strength and Multiplexity

Opinion leaders which involve relationships with objects influenced by them

are possible to be explained by the social network perspective that consider rela-

tionships such as ties and connections (Scott, 2000).

An influence between two people in a network indicates the degree of cohesion

which represents the strength of relationship between them. That is, the more in-

timate relationship they have, the easier they are influenced each other.



The strength of ties is a combination of the amount of time, the emotional in-

timacy, and friendliness (Granovetter, 1973).

Since the strong ties represent the intimate relationships among people, the

strong ties allow the opponents to have strong motivation and reduce uncertain-

ties in receiving information (Krackhardt, 1992).

The strong ties in a network of information flow make have more influences on

people who receive information rather than the weak ties (Brown & Reingen,

1987).



Multiplexity is the term that since the relation between two people can consist

of more than one relationship (Monge & Contractor, 2001), there can be multi-

plex relationships such as friend, fellow, and neighbor etc. (Burt, 1982; Hansen,

Mors, & Lovas, 2005; Hite et al., 2006) between them.

One study showed that the behaviors in multiplex and strong relationships can

occur the same way (Brass, Butterfield & Skaggs, 1998). That is, the multiplex re-

lations indicate the strong relations (Granovetter, 1973).


Hypothesis 1: In an entire network, the stronger ties people have with opinion leaders, the more they exchange information with others.

Hypothesis 2: In an entire network, there are significant differences between the groups which include opinion leaders and those which dose not include them.

Hypothesis 3: In an eco-centered network, those who have strong ties with opinion leader have more influences on the remains in disseminating information.

Hypothesis

Method

Measuring Opinion leaders and Tie Strength

In order to examine the influence of an individual in a network, this study

uses two type of roles for opinion leaders; closure and brokerage (Burt, Kil-

duff & Tasselli, 2012).

UCINET 6.0, a software package for social network data, is easy to calcu-

late in-degree and betweenness centrality (Borgatti et al., 2002).


For measuring the degree of closure, the in-degree centrality was calcu-

lated (Valente, 2010). In-degree centrality indicates a number of ties directed

to the actor.

For measuring the degree of brokerage, the value of betweenness centrality

was calculated. Betweenness centrality represents the number of times a sub-

ject acts as a bridge along the shortest path between two other objects

(Wasserman & Faust, 1994).

Method (Cont.)


Haythornthwaite (2005) explained the relationship between the strength of

ties and media uses, which referred to “Media Multiplexity.” He said that the

more channel between two people maintain, the more influences they have

each other.

This study define tie strength as multiplexity of media (e.g., face-to-face,

cellphone, Mobile Instant Messenger, and SNS).

Method (Cont.)

Influences of people who have strong ties with opinion leaders

This study divides all of members into three groups ;1) Opinion leaders, 2)

People who have strong ties with opinion leaders, and 3) the remainders,

based on media multiplexity.

Strong ties indicate the relations which use a number of offline and online

channels in communicating with others (Haythornthwaite, 2005). This study

determined the criteria of classification of strong ties as usages of all of off-

line and online channels.

Method (Cont.)

Influences of people who have strong ties with opinion leaders

Density, and then, was used for examining influences among three groups.

Influences among social entities indicate the degrees of cohesion. The den-

sity, overall measure of cohesion, indicates the degree to which members are

connected to all members of a population (Haythornthwaite, 1996).

For a valued graph, the density can average the values attached to the lines

across all lines (Wasserman & Faust, 1994).

This study examines densities among groups of opinion leaders (A), people

who have strong ties with opinion leaders (B), and the remainders(C), by

comparing densities of each group

Method (Cont.)

Information Exchange

Method (Cont.)

This study modified the Cerise’s six information exchange relationships:

Giving work (GW), Receiving work (RW), Collaborative writing (CW),

Computer programming (CP), Sociability (Soc), and Major emotional sup-

port (MES) (Haythornthwaite & Wellman, 1998).

Computer programming was excluded from out list of Information Ex-

change relationships, since there are a few tasks related to computer pro-

gramming in the department of Interaction Science rather than the Cerise.

Questionnaire

Method (Cont.)

Respondents reported with whom they have contact in a various channels:

face-to-face, cellphone, Mobile Instant Messenger (e.g., kakaotalk, a multi-

platform texting application), and SNS (e.g., Facebook, Twitter, Path, and

etc.). They identified 48 IS students from a list of the IS students.

Figure 1. Format of Questionnaire for Media Multiplexity

Questionnaire

Method (Cont.)

Respondents were asked to with whom they communicate with, modified

by Cerise members’ six information exchanges (Haythornthwaite & Well-

man, 1998). Surveymonkey, an online questionnaire tool, was used for col-

lecting data. It is useful to reach people who are hard to see in the depart-

ment.

Figure 2. Format of Questionnaire for Information Exchange

Sample

Total IS graduates Population included 61 members (33 females, 28

males); 4 international students and 57 domestic students, and 9 absences

and 52 attendances. However, this study excluded the international students

and those who are absence from school in a survey. Questionnaire completed

by 48 of students of the department of Interaction Science in Sungkyunkwan

University.

The response rate was 0.458 (22 out of 48). They were asked to report the

behavior of information exchange by a specific medium.

Method (Cont.)

Sample

If all of students report had listed 48 correspondents, there would have

been 48 x 47 = 2256 pairs. And if respondents had fully connected with all

of students, the pairs would be (22 – 1) x 48 = 1008 pairs. The number of re-

spondents gave a total of 410 pairs. The density is 0.1817 (410 / 2256).

Method (Cont.)

Data Analysis Plan

The rate of information exchange was based on the frequency of informa-

tion exchange between two people. Full matrix of 48 x 48 was created with

limited information exchange relationships from 22 members with 48 stu-

dents (22 respondents x 48 students).

The most important task was to find opinion leaders in the network of IS

department. After calculating values of in-degree and betweenness centrali-

ties in the information exchange network of IS department, this study found

the opinion leaders which stayed on the top 10% of the two indices (Valente

& Pumpuang, 2007).

Method (Cont.)

Data Analysis Plan

Then, for testing Hypotheses 1, the regression analysis was conducted with

the degree of Information Exchange and the degree of tie strengths which

represent media multiplexity with opinion leaders. T-test was conducted for

testing Hypothesis 2.

Method (Cont.)

Data Analysis Plan

Lastly, this study used ANOVA density model for testing Hypothesis 3.

ANOVA density model tests the probability that the density of within-group

differs from all relations of between-groups (Hanneman and Riddle 2005).

That is, it tests whether the relationship of a network is patterned by a cate-

gorical variable. We examine whether the relationships of influence defined

as media multiplexity are patterned by groups of opinion leaders (A), people

who have strong ties with opinion leaders (B), and the remainders(C).

Method (Cont.)

Opinion leaders and tie strength

There are four opinion leaders among 48 members in the department of In-

teraction Science. IS29, whose in-degree centrality is 135 and betweenness

centrality is 177.269, topped the list, followed by IS15 (In-degree = 111, Be-

tweenness = 181.707), IS48 (In-degree = 139, Betweenness = 26.399), IS06

(In-degree = 149, Betweenness = 5.551).

Findings

In-Degree Centrality

Betweenness Centrality

N 48 48Mean 77.521 14.375Std Dev 36.247 36.174Minimum 15.000 0.000Maximum 149.000 181.707

Table 1. Descriptive Statistics for In-degree and Betweenness centralities


While IS29 and IS15 have low in-degree centralities and high betweenness

centralites, IS48 and IS06 have high in-degree centrality and low between-

ness centrality.

IS01

IS03

IS05

IS07

IS09

IS11

IS13

IS15

IS17

IS19

IS21

IS23

IS25

IS27

IS29

IS31

IS33

IS35

IS37

IS39

IS41

IS43

IS45

IS47

020406080

100120140160180200

Indegree Betweenness

Figure 3. Indices of In-degree and Betweenness centralities for the department of Interaction Sci-ences’ students

Findings (Cont.)


The low in-degree and high betweenness centrality show the characteristics

of brokerage which have relatively equal chances of information exchange

with others.

The high in-degree and low betweenness centrality represent the character-

istics of closure which exchange information with some specific individuals

in a network.

They have different features of opinion leaders.

Findings (Cont.)


This study made 48 x 48 symmetrical matrix of tie strength based on the

average of media multiplexity between two people. The students in the de-

partment of Interaction Science build relationship throughout more than one

or two channels in an average.

Findings (Cont.)

Opinion Leader,

IS29

Opinion Leader,

IS15

Opinion Leader,

IS48

Opinion Leader,

IS06

Mean 2.448 2.469 1.833 1.448

SD 1.182 1.187 1.449 1.346

Table 2. Media multiplexity with opinion leaders

Influences of opinion leaders in a global level

Several scholars have emphasized the role of a brokerage which connects

relations between people for opinion leaders (Burt, 1999; Goldenberg et al.,

2009).

Findings (Cont.)

Table 3. Results of regression analysis for the relationship between tie strength with opinion leader and the degree of information exchange

　 β SE T R2

Opinion LeaderIS29

13.453** 15.08 9.38 6.449

Opinion LeaderIS15

4.222** 3.988 3.499 4.099

Opinion LeaderIS48

3.19* 3.78 2.68 1.57

Opinion LeaderIS06

0.195 0.254 0.146 0.056

Note: N = 44, * p < .05, ** p < .01


Betweenness centrality of IS06 (5.551) is lower than the average of be-

tweenness centrality in the network (Table 1). It means that the low ability of

a brokerage reduce the influence on an overall network.

Findings (Cont.)

Table 3. Results of regression analysis for the relationship between tie strength with opinion leader and the degree of information exchange

Note: N = 44, * p < .05, ** p < .01

　 β SE T R2

Opinion LeaderIS29

13.453** 15.08 9.38 6.449

Opinion LeaderIS15

4.222** 3.988 3.499 4.099

Opinion LeaderIS48

3.19* 3.78 2.68 1.57

Opinion LeaderIS06

0.195 0.254 0.146 0.056


This study compared the degree of information exchange between groups

that include opinion leaders and do not include them for examining influ-

ences of opinion leaders on groups.

There are 4 laboratories which include opinion leaders out of 10 laborato-

ries in the department of Interaction Science. A number of students in the

group which include opinion leaders are 28, and 20 for the another group.

Findings (Cont.)


The result showed that the degree of information exchange for the group

which have opinion leaders (M = 91.679, SD = 36.382) is higher than have-

not (M = 57.700, SD = 26.424).

The difference, t(45.9) = -3.73, between two groups proved to be signifi-

cant at the p < .001 level.

Hypothesis 2, “In a whole network, there are significant differences be-

tween the groups which include opinion leaders and those which do not in-

clude them,” was supported.

Findings (Cont.)

Influences of opinion leaders in a local level

For testing hypothesis 3, the densities between B and C in the network of

media multiplexity which indicates influences have to be higher than those

between A and C at the significant level.

Findings (Cont.)

Table 5. Densities between and within groupsa of in the ego-network for media multi-plexity

a The groups indicate opinion leader (A), people having strong ties with opinion leader (B), and the remains of members (C).

Opinion Leader29

Opinion Leader15

Opinion Leader48

Opinion Leader06

A – A 0.000 0.000 0.000 0.000

A – B 4.000 4.000 4.000 4.000

A – C 3.152 3.364 1.974 1.750

B – A 4.000 4.000 4.000 4.000

B – B 2.149 1.851 2.250 2.938

B – C 1.758 1.639 2.035 1.794

C – A 0.576 0.455 0.763 0.350

C – B 0.333 0.355 0.592 0.575

C – C 0.273 0.338 0.440 0.556

Influences of opinion leaders in a local level

It is not sufficient to fully support the third hypothesis, because the A - C

densities are higher than B – C for opinion leader 29 and 15.

Findings (Cont.)

Table 5. Densities between and within groupsa of in the ego-network for media multi-plexity

a The groups indicate opinion leader (A), people having strong ties with opinion leader (B), and the remains of members (C).

Opinion Leader29

Opinion Leader15

Opinion Leader48

Opinion Leader06

A – A 0.000 0.000 0.000 0.000

A – B 4.000 4.000 4.000 4.000

A – C 3.152 3.364 1.974 1.750

B – A 4.000 4.000 4.000 4.000

B – B 2.149 1.851 2.250 2.938

B – C 1.758 1.639 2.035 1.794

C – A 0.576 0.455 0.763 0.350

C – B 0.333 0.355 0.592 0.575

C – C 0.273 0.338 0.440 0.556

This study examined how opinion leaders influence on individuals at the

global and local level.

Global Level: An importance of Opinion Leaders in having access and ex-

changing information.

- The stronger ties people maintain with opinion leaders, the more chances

to get information they have.

- And the degree of information exchange in the groups involving opinion

leaders is much higher than the groups that have not opinion leaders.

Discussion

Local Level: Influences of opinion leaders depending on their role in a net-

work

- The opinion leaders as a brokerage have great influences on all of indi-

viduals in exchange information with multiple communication channels.

- The opinion leaders as a closure influence just on people who have

strong ties with them.

Discussion (Cont.)

Local Level: Influences of opinion leaders depending on their role in a net-

work

- The opinion leaders as a brokerage have great influences on all of indi-

viduals in exchange information with multiple communication channels.

- The opinion leaders as a closure influence just on people who have

strong ties with them.

While we admit the importance of opinion leaders, the finding shows that

people who have strong ties with opinion leaders are more likely to influence

on individuals, depending on types of opinion leaders.

Discussion (Cont.)

Theoretical implication on the studies for opinion leaders:

− This study supports “Influentials Hypothesis” with the empirical case

study of information flow in small organization.

Practical implication:

− The government has to discover opinion leaders in every field who are

available for multiple communication channels in order to allow people

to access novel information.

− Aral and Van Alstyne (2011) suggest that in the high-dimensional infor-

mation society, a brokerage of high communication bandwidth has an

advantage on access to information.

Discussion (Cont.)

Practical implication:

− And it is important for opinion leaders and easily influenced people to

help people to learn how to use information throughout a government

support policy.

− The government must do more to support the regions which have been

insufficient in opinion leaders as a brokerage.

Discussion (Cont.)

While this study has insightful implications, the results of this study should

be interpreted with caution for several reasons.

1. Conceptualization of personal influence is limited and applied partially.

Weinmann (1991) argued that influences consist of three personal ele-

ments:

1) Personification which represents a specific value relating to

personal characteristics;

2) Competitiveness relating to an intellectual level; and

3) Social position relating to social capital, and social elements.

Limitation

While this study has insightful implications, the results of this study should

be interpreted with caution for several reasons.

2. The sample of this study is limited as it focused on one specific organi-

zation. This limitation is related to external validity in generalizing the

results for understanding the phenomenon of information inequality in

Korea.

Limitation (Cont.)

Reference Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012, April). The role of social networks in information diffusion. In Proceed-

ings of the 21st international conference on World Wide Web (pp. 519-528). ACM. Barzilai-Nahon, K. (2006). Gaps and bits: Conceptualizing measurements for digital divide/s. The Information Society, 22(5),

269-278. Borgatti, S.P., Everett, M.G. & Freeman, L.C. (2002). Ucinet for Windows: Software for Social Network Analysis [computer

sofeware]. Harvard, MA: Analytic Technologies. Brass, D. J., Butterfield, K. D., & Skaggs, B. C. (1998). Relationships and unethical behavior: A social network perspective.

Academy of Management Review, 14-31. Britz, J. J., & Blignaut, J. N. (2001). Information poverty and social justice. South African journal of library and information sci-

ence, 67(2), 63-69. Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 350-362. Burt, R. S. (1982). Distinguishing relational contents. Survey Research Center, University of California. Burt, R. S. (1987). Social contagion and innovation: Cohesion versus structural equivalence. American journal of Sociology,

1287-1335. Burt, R. S. (1999). The social capital of opinion leaders. The Annals of the American Academy of Political and Social Science,

566(1), 37-54. Burt, R. S., Kilduff, M., & Tasselli, S. (2012). SOCIAL NETWORK ANALYSIS: FOUNDATIONS AND FRONTIERS ON ADVANTAGE. Chan, K. K., & Misra, S. (1990). Characteristics of the opinion leader: A new dimension. Journal of Advertising, 53-60. Chatman, E. A. (1987). Opinion Leadership, Poverty, and Information Sharing. Rq, 26(3), 341-53. De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek. Cambridge University Press. Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 35-41. Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social networks, 1(3), 215-239. Friedkin, N. (1980). A test of structural features of Granovetter's strength of weak ties theory. Social Networks, 2(4), 411-

422. Fritsch, M., & Kauffeld-Monz, M. (2010). The impact of network structure on knowledge transfer: an application of social net-

work analysis in the context of regional innovation networks. The Annals of Regional Science, 44(1), 21-38. Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of the 27th international confer-

ence on Human factors in computing systems (pp. 211-220). ACM. Goldenberg, J., Han, S., Lehmann, D., & Hong, J. (2009). The role of hubs in the adoption processes. Journal of Marketing,

73(2). Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 1360-1380. Granovetter, M. (1983). The strength of weak ties: A network theory revisited.Sociological theory, 1(1), 201-233. Hansen, M. T., Mors, M. L., & Løvås, B. (2005). Knowledge sharing in organizations: Multiple networks, multiple phases.

Academy of Management Journal, 48(5), 776-793. Hargittai, E. (2008). The digital reproduction of inequality. Social stratification, 936-944. Haythornthwaite, C. (1996). Social network analysis: An approach and technique for the study of information exchange. Li-

brary & Information Science Research, 18(4), 323-342. Haythornthwaite, C. (2002). Strong, weak, and latent ties and the impact of new media. The Information Society, 18(5), 385-

401.

Reference (Cont.) Haythornthwaite, C. (2005). Social networks and Internet connectivity effects. Information, Community & Society, 8(2), 125-

147. Hite, J. M., Williams, E. J., Hilton, S. C., & Baugh, S. C. (2006). The role of administrator characteristics on perceptions of in-

novativeness among public school administrators. Education and urban society, 38(2), 160-187. Johnson, C. A. (2007). Social capital and the search for information: Examining the role of social capital in information seek-

ing behavior in Mongolia. Journal of the American Society for Information Science and Technology, 58(6), 883-894. Katz, E., & Lazersfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications. Glen-

coe, IL: Free Press. Katz, E., & Lazarsfeld, P. F. (2006). Personal influence: The part played by people in the flow of mass communications. New

Brunswick, N.J: Transaction Publishers. Kennedy, T., Wellman, B., & Klement, K. (2003). Gendering the digital divide. It & Society, 1(5), 72-96. Krackhardt, D. (1992). The strength of strong ties: The importance of philos in organizations. Networks and organizations:

Structure, form, and action, 216, 239. Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of trust in effective knowledge

transfer. Management science, 50(11), 1477-1490. Lu, Y. (2007). The human in human information acquisition: Understanding gatekeeping and proposing new directions in

scholarship. Library & information science research, 29(1), 103-123. Monge, P. R., & Contractor, N. S. (2001). Emergence of communication networks. The new handbook of organizational com-

munication: Advances in theory, research, and methods, 440-502. Monge, P., & Contractor, N. (2003). Theories of communication networks. Oxford: New York: Oxford University Press. Nisbet, M. C., & Kotcher, J. E. (2009). A two-step flow of influence? Opinion-leader campaigns on climate change. Science

Communication, 30(3), 328-354. Payton, F. C. (2003). Rethinking the digital divide. Communications of the ACM, 46(6), 89-91. Roe, K., & Broos, A. (2005). Marginality in the information age: the socio-demographics of computer disquietude. A short re-

search note. Communications, 30(1), 91-96. Rogers, E. (2003). Diffusion of innovations (5th ed.). Free Press: New York. Sassi, S. (2005). Cultural differentiation or social segregation? Four approaches to the digital divide. New Media & Society,

7(5), 684-700. Scott, J. (2000). Social network analysis: A handbook. Sage Publications Limited. Valente, T. W. (1996). Network models of the diffusion of innovations. Computational & Mathematical Organization Theory,

2(2), 163-164. Valente, T. W. (2010). Social networks and health: models, methods, and applications. New York: Oxford University. Valente, T. W., & Pumpuang, P. (2007). Identifying opinion leaders to promote behavior change. Health Education & Behav-

ior, 34(6), 881-896. Van den Bulte, C., & Joshi, Y. V. (2007). New product diffusion with influentials and imitators. Marketing Science, 26(3), 400-

421.

Reference (Cont.) Van Dijk, J. A. G. M. (2000). Widening information gaps and policies of prevention. Digital democracy: Issues of theory and

practice, 166-183. Van Dijk, J. A. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4), 221-235. Van Dijk, J., & Hacker, K. (2003). The digital divide as a complex and dynamic phenomenon. The information society, 19(4),

315-326. Van Eck, P. S., Jager, W., & Leeflang, P. S. (2011). Opinion leaders' role in innovation diffusion: A simulation study. Journal of

Product Innovation Management, 28(2), 187-203. Verdegem, P., & Verhoest, P. (2009). Profiling the non-user: Rethinking policy initiatives stimulating ICT acceptance.

Telecommunications Policy, 33(10), 642-652. Warren, M. (2007). The digital vicious cycle: Links between social disadvantage and digital exclusion in rural areas.

Telecommunications Policy, 31(6), 374-388. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press. Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of consumer research, 34(4),

441-458. Watts, D. (2007). Challenging the influentials hypothesis. WOMMA Measuring Word of Mouth, 3(4), 201-211. Wellman, B. (1992). Which types of ties and networks give what kinds of social support. Advances in group processes,

9(207), 35. Wellman, B., & Wortley, S. (1990). Different strokes from different folks: Community ties and social support. American jour-

nal of Sociology, 558-588. Weimann, G. (1991). The influentials: back to the concept of opinion leaders?. Public Opinion Quarterly, 55(2), 267-279. Weimann, G. (1994). The influentials: People who influence people. Albany, NY: State University of New York Press. Wu, S., Hofman, J., Mason, W., & Watts, D. (2011). Who says what to whom on Twitter. Proceedings of WWW’11. Yu, L. (2006). Understanding information inequality: making sense of the literature of the information and digital divides.

Journal of Librarianship and Information Science, 38(4), 229-252. Yu, L. (2011). The divided views of the information and digital divides: A call for integrative theories of information inequal-

ity. Journal of Information Science, 37(6), 660-679.

Thank you

Choi, [email protected]

Department of Interaction Sciencein Sungkyunkwan University

is.skku.edu

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Influences of strong tie with opinion leaders in an interconnected network of korea