Determinants of Use Intensity in Social Networking Sites

A Cross-cultural Study of Korea and USA

Cheol Park

, Jongkun Jun

and Thaemin Lee

Division of Business Administration, Korea University, 2511, Sejong-Ro, Sejong City 339-700, South Korea

Department of International Business, Hankuk University of Foreign Studies,

81, Oedae-ro, Cheoin-gu, Yongin-si, Gyeonggi-do 449-791, South Korea

Department of Business Administration, Chungbuk National University Gaeshing-Dong,

Heungduk-Gu, Cheongju, Chungbuk 361-763, South Korea

Keywords: Social Networking Sites, Cross-culture, Innovativeness, Propensity to Share Information, Privacy Concern,

Korea, USA.

Abstract: This study examined the antecedents and consequences of intensity of SNS use in a cross-cultural context.

A survey of 977 SNS users was performed in Korea and USA, and the causal relationship was tested using

structural equation modelling. Consumer innovativeness, propensity to share information, and privacy

concern affected intensity of SNS use and the usage of SNS enabled social capital. In addition, the effects of

innovativeness and privacy concern on intensity of SNS use were greater in the U.S. sample than in the

Korean sample. This moderation effects come from the differences of collectivism and individualism and

the implications and further researches were suggested.

1 INTRODUCTION

Recently, social networking sites are gaining

popularity throughout the world. The number of

Facebook user is estimated over one billion in the

world, 8 million in Korea, and 160 million in US

People can bridge and bond their relationships in the

social networking sites, and they are perceived to be

a good and easy tool of accumulating social capital

(Ellison et al., 2007). The differences in online

social networking may cause another type of digital

divide (Pfeil et al., 2009). Age difference is said to a

main source of digital divide (Prensky, 2001), but

there might be other personal or personality related

factors influencing social networking sites use and

social capital from the use.

There are studies on the use of social networking

sites and social capital (Ellison et al., 2007);

(Valenzuela et al., 2009). We attempt to extend

previous research in two ways. First, we examine the

impacts of three personality related variables -

privacy concern, consumer innovativeness, and

propensity to share information– on the use of social

networking sites. Second, we examine if there are

www.socialbakers.com/facebook-statistics

cross-cultural differences in the relationships

between personality-related variables and social

networking sites (SNS) use.

The findings from this research will provide

empirical evidence to the practitioners and

academics in the SNS related field, especially who

are interested in cross-cultural variations. We

established hypothesis after literature review and

presented results of an empirical study in the

remainder of this paper.

2 THEORETICAL

BACKGROUNDS

Ever since Ellison et al. (2007) examined the

relationship between Facebook intensity and social

capital, the dynamics of social capital in social

network sites have been widely tested (Valenzuela et

al., 2009); (Steinfield et al., 2008). Besides the topic

of social capital, a stream of study on the factors of

consumer behavior in online social networks is

gaining popularity recently. There were studies on

adoption of SNS (Cheung and Lee, 2010); (Joinson,

2008), electronic word-of-mouth (eWOM) on SNSs

(Jansen et al., 2009); (Chu and Kim, 2011),

119

Park C., Jun J. and Lee T..

Determinants of Use Intensity in Social Networking Sites - A Cross-cultural Study of Korea and USA.

DOI: 10.5220/0004497501190126

In Proceedings of the 4th International Conference on Data Communication Networking, 10th International Conference on e-Business and 4th

International Conference on Optical Communication Systems (ICE-B-2013), pages 119-126

ISBN: 978-989-8565-72-3

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

personality and SNS use (Correa et al., 2010);

(Pagani et al., 2011). Another group of research is

focused on examining how cultural contexts

influence the uses of SNS (Kim et al., 2011); (Chu

and Choi, 2011). Yet very few researches are

addressing the relationship between consumer

characteristics and SNS use in cultural contexts.

Most previous studies considered positive

determinants of SNS adoption or use (Pagani et al.,

2011); (Thorbjørnsen et al., 2007), but there are few

studies that considered both positive and negative

determinants of SNS use.

This research examines the impacts of consumer

innovativeness, propensity to share information, and

privacy concern on SNS use and SNS-enabled social

capital in a cross-cultural context.

2.1 Consumer Innovativeness

The concept of innovativeness is a personality trait

defined as the degree to which an individual makes

innovative decisions independently of the

communicated experience of others (Midgley and

Dowling, 1978). Especially, domain specific

innovativeness (DSI) reflecting the tendency to

adopt innovations within a specific product domain

has been a good predictor for consumer behavior on

the Internet (Goldsmith, 2001); (Park and Jun, 2003).

Consumer DSI was positively associated with

both creating new content (active use) and

consuming the contents of others (passive use) on

the social networking sites (Pagani et al., 2011).

Since social networks are still in the growth stage,

we propose that a person with high innovativeness in

the technology domain will spend more time using

SNS. Furthermore, as innovators are the first group

of consumers to adopt new features or functions of

products, they will likely use them more than others.

H1: High innovativeness in technology domain

will be positively associated with intensity of SNS

use.

2.2 Propensity to Share Information

Identity expressiveness has been regarded as a good

determinant of intention and behavior for symbolic

goods or in public settings of consumption

(Hirschman and Holbrook, 1981); (Richins, 1994).

Expressiveness was found to be a strong driver of

using mobile communication services (Thorbjørnsen

et al., 2007). Similarly, Pagani et al. (2011) showed

both self-identity expressiveness and social-identity

expressiveness are positively related to active use of

social networking sites. Although identity

expressiveness is a good motive for using SNS,

sharing content or information is another motive.

Many social network sites such as Facebook and

Myspace support users in sharing content, especially

LinkedIn is most commonly used for information

providing and gathering, not on socializing

(DiMicco et al., 2008). Social networks are based on

information sharing and revealing enough

information is one of the strongest motivator of SNS

use (Acquisti and Gross, 2006). Travel blogs have

been perceived as useful sources of information for

those who are planning trips, and the bloggers may

have high propensity to share information.

According to Constant et al. (1994), sharing tangible

information depends on pro-social attitudes and

norms whereas sharing expertise depends on

people's own self-expressive needs. Propensity to

share information is regarded as part of pro-social

transformation behaviors as well as a personal norm

reflecting the costs and benefits of sharing and is

significantly related with the use of collaborative

electronic media (Jarvenpaa and Staples, 2000).

H2: Propensity to share information will be

positively associated with intensity of SNS use.

2.3 Privacy Concern

Social networking sites may raise privacy concerns

since they allow users to search for profiles of other

members. Users are concerned about their privacy

when their personal information is used without their

permissions or knowledge (Phelps et al., 2000).

While most SNS users are aware of the visibility of

their profiles, they seem to believe their ability to

control the information revelation (Acquisti and

Gross, 2006). Some possible reasons for revealing

one’s information at the risk of privacy invasion

include cost benefit approach (Donath and Boyd,

2004), peer pressure and herding behavior, relaxed

attitudes towards (or lack of interest in) personal

privacy, incomplete information, faith in the

networking service or trust in its members(Gross and

Acquisti, 2005).

Another reaction to the privacy concern on SNS

may be staying away from it. Privacy concern was

associated with negative attitudes toward SNS

(Boyd, 2008), but not with the behavioral intention

(Tan et al., 2012). A survey on college students

showed that privacy concern was higher for non-

members of Facebook than for members, and non-

members showed stronger sensitivity towards

privacy than members (Acquisti and Gross, 2006).

Although the results of previous researches are

mixed, individuals with high privacy concern will

ICE-B2013-InternationalConferenceone-Business

120

have lower intensity of SNS use due to either not

using them or using them passively.

H3: Privacy concern will be negatively

associated with intensity of SNS use.

2.4 Intensity of SNS use and Social

Capital

The relationship between Facebook use and social

capital has been positively significant in most

previous researches (Papacharissi and Mendelson,

2008); (Burke et al., 2010). While Facebook use was

associated with social capital in general, the

relationship was moderated by the ability and

inclination of the users (Burke et al., 2010).

Although the impact of Facebook use on social

capital has been investigated in previous studies,

fewer studies have tested the relationship between

SNS use overall and social capital.

H4: Intensity of SNS use will be positively

associated with SNS-enabled Social Capital.

2.5 Cross-cultural Variations

Facebook has been allowing users to track the

actions, beliefs and interests of the larger groups to

which they belong, which may serve a social

searching or a surveillance function (Joinson, 2008).

The desire to meet new people on Facebook was a

primary motivation for opening their profile. Many

Facebook users may expect reciprocity in social

surveillance when they leave their privacy settings

relatively open (Gross and Acquisti, 2005). In

general, collectivism culture has stronger peer

pressure to adhere to societal norms than

individualism culture (Hofstede, 2001), which is

also related with herding behavior. Innovators tend

to perceive less risk in their adoption process

(Alda´s-Manzano et al, 2009), and once they started

to adopt SNS by leaving their privacy open, the

majority in collectivism culture will more likely

follow the same practice as a herding behavior than

those in individualism culture. In addition, people in

the culture of high peer pressure and herding

behavior will expect more reciprocity in social

surveillance, especially among in-group members

since they are interested in tracking others in the

group. This tendency might alleviate the negative

impact of privacy concern on intensity of SNS use.

A recent study on mobile phone adoption

showed that the innovation factor of Bass diffusion

model had higher impact on adoption in

individualism culture than in collectivism culture

(Lee et al., 2013). In contrast, in collectivism culture

imitation factor was more effective on adoption than

it was in individualism culture. These findings imply

that innovativeness may be less effective in

explaining adoption behavior in collectivism culture

than in individualism culture. Another recent study

reports that the relationship between consumer

innovativeness and adoption of innovation varies

across cultural norms and values (Truong, 2013).

Based on the discussion above, we propose the

following hypotheses.

H5a: The relationship between privacy concern

and intensity of SNS use will be moderated by cross-

cultural variations.

H5b: The relationship between innovativeness

and intensity of SNS use will be moderated by cross-

cultural variations.

3 METHOD

3.1 Samples

A total of 977 responses were collected through an

online survey in South Korea and the U.S. The

subjects for this study were confined to smartphone

users. Responses consist of 50.4% men and 49.6%

women. In the sample, 39.9% were in their twenties,

39.8% were in their thirties and20.3% were in their

forties. The sample consisted of white-collars

(30.2%), professionals (15.3%), (under)graduate

students (14.3%), housewives (12.2%), sales/service

(8.7%), government employees (3.6%), production

(2.7%), and etc. (13.1%).

3.2 Measures

All measurement items used seven-point scales

(1=very strongly disagree, 7=very strongly agree).

Consumer innovativeness was measured using five

items based from Keller and Holland (1978),

Goldsmith(2001): (1) “I can understand the latest

products or service without any help of others”, (2)

“I know about the new technology trend in my

interest area”, (3) “Compared to others, I am the first

person to accept the new technology”, (4) “In

general, others ask me advice of the new

technology”, (5) “I purchase new product before

most other people do”. Propensity to share

information was measured by the agreement with the

following five statements based from Davenport and

Prusak (2000), Hsu et al. (2007) : (1) “I frequently

share new information and my knowledge with

others”, (2) “I frequently talk about the information,

DeterminantsofUseIntensityinSocialNetworkingSites-ACross-culturalStudyofKoreaandUSA

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knowledge and know-how with others”, (3) “I

exchange information and data with others

regularly”, (4) “I share purchase information or

knowledge with others”, (5) “I share my knowledge

and experiences with others voluntarily”. Privacy

concern was measured using three items based from

Xin et al. (2012): (1) “I am concerned about the

negative consequences of unknown parties accessing

my private information on this mobile social

network”, (2) “I am concerned that my private

information on the mobile social network may be

misused”, (3) “I am concerned that unknown parties

have access to my private information on this mobile

social network”. Intensity of SNS use was

measured by the agreement with the following six

statements based from Ellison et al. (2007): (1)

“SNS is part of my everyday activity”, (2) “I am

proud to tell people I’m on SNS”, (3) SNS has

become part of my daily routine”, (4) “I feel out of

touch when I haven’t logged onto SNS for a while”,

(5) “I feel I am part of the SNS community”, (6) “I

would be sorry if SNS shut down”. The

measurement of intensity of SNS use was not

confined to a specific SNS; rather it was SNS use in

the aggregate. We did not measure the use of a

separate SNS since the types and popularity of SNS

between Korea and USA are different. Social capital

was measured using three items based from Ellison

et al. (2007): (1) “If I needed an emergency loan of

$100, I know someone at SNS I can turn to”, (2)

“There is someone at SNS I can turn to for advice

about making very important decisions”, (3) “The

people I interact with at SNS would be good job

references for me”.

4 RESULTS

4.1 Validity of Measurements

Following Anderson and Gerbing (1998), we

conducted the confirmatory factor analysis in order

to establish the reliability and discriminant validity

of the multi-item scales.

Although the chi-square value for this model was

significant (727.325, with 192 degrees of freedom

[df], p = .00), these statistics are sensitive to the

sample size and model complexity; as such, the

goodness-of-fit index (GFI), Tucker–Lewis index

(TLI), and comparative fit index (CFI) are more

appropriate for assessing the model fit here (Bagozzi

and Yi, 1988); (Bearden et al., 1982).

GFI (0.932), AGFI (0.911), TLI (0.965), CFI

(0.971), SRMR (0.039), and RMSEA (0.053)

Table 1: Confirmatory factor analysis results.

Construct

/items

Unstand

ardized

t-value

Construct

Reliability

AV E

Innovativeness 0.920

0.69

INNO1 0.749* 26.801

INNO2 0.832* 31.433

INNO3 0.911* 36.390

INNO4 0.889* 34.951

INNO5 0.788* 28.928

Propensity to share

information

0.901

0.64

PROP1 0.743* 26.297

PROP2 0.735* 25.695

PROP3 0.835* 31.706

PROP4 0.829* 30.895

PROP5 0.870* 33.275

Privacy concern 0.906

0.76

PRIV1 0.759* 27.386

PRIV2 0.960* 38.742

PRIV3 0.892* 34.433

Intensity of SNS use 0.943

0.73

INT1 0.847* 32.620

INT2 0.846* 32.438

INT3 0.830* 31.460

INT4 0.815* 30.557

INT5 0.938* 38.574

INT6 0.855* 32.965

Social Capital 0.888

0.72

SC1 0.787* 28.589

SC2 0.900* 35.054

SC3 0.864* 33.312

* Parameter estimates are significant at the .001 level.

** AVE = average variance extracted

indicate a satisfactory model fit. Furthermore, all the

individual scales exceed the recommended standards

proposed by Baggozi and Yi (1988) in terms of

construct reliability (greater than 0.60) and average

variance extracted (AVE) by the latent construct

(greater than 0.50). Further, all item loadings

indicate significant t-values, suggesting that

convergent validity was achieved.

The squared correlation between the two

constructs is less than all the AVE for each construct

(See table 1 and 2), suggesting discriminant validity

was achieved (Fornell and Larcker, 1981). In

addition, as shown in table 2, the confidence interval

for each pair-wise correlation estimate does not

include the value of 1. This result also suggested that

discriminant validity was achieved (Anderson and

Gerbing, 1988).

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Table 2: Correlation Matrix.

INNO PROP PRIV INT SC

INNO 0.498 0.000 0.160 0.162

PROP

0.706

(0.019)

0.007 0.187 0.218

PRIV

0.019

(0.034)

0.081

(0.034)

0.008 0.006

INT

0.400

(0.029)

0.433

(0.029)

-0.090

(0.034)

0.567

0.403

(0.030)

0.467

(0.029)

-0.079

(0.034)

0.753

(0.017)

INNO: Innovativeness, PROP: Propensity to share information,

PRIV: Privacy concern, INT: Intensity of SNS use, SC: Social

Capital.

Construct correlations (and standard errors) appear below the

diagonal. Squared correlations appear above the diagonal.

4.2 Hypotheses Test

AMOS 20.0 was used to test the model and

hypotheses. The covariance structure testing of the

research model resulted in a chi-square statistic of

766.420 (df = 195, p = 0.00). Although this chi-

square value was significant, this statistic is sensitive

to the sample size and model complexity; as such,

the goodness-of-fit index (GFI), non-normed fit

index (NNFI), and comparative fit index (CFI) are

more appropriate for assessing the model fit here

(Bagozzi and Yi, 1988; Bearden, Sharma and Teel,

1982). GFI (0.928), AGFI (0.907), NNFI (0.963),

CFI (0.969), SRMR (0.048), RMSEA (0.055)

indicate a satisfactory model fit.

The results of the hypotheses test are

summarized in Table3, which show that all proposed

relationships received strong support.

Table 3: Hypotheses Test Results.

H Path

Path

Coefficient

t-value Results

H1 INNO→INT 0.201 3.759

Support

H2 PROP→INT 0.378 6.773

Support

H3 PRIV→INT -0.135 -3.948

Support

H4 INT→SC 0.768 24.144

Support

INNO: Innovativeness, PROP: Propensity to share information,

PRIV: Privacy concern, INT: Intensity of SNS use, SC: Social

Capital.

* p< 0.01

4.3 Cultural Effect

In order to investigate the moderating effect of

national culture in explaining Intensity of SNS use

and social capital, we performed a multi-group

analysis for Korean sample and the U.S. sample. We

performed a multi-group analysis to test for

statistical differences in the structural relationships

across the two groups. The initial baseline model

(unconstrained model) was estimated by allowing all

the model parameters to be free estimates. Then we

constrained one path to be equal across the two

samples. A significant difference would imply that

the path coefficient is statistically different across

the two groups. An insignificant difference in-

between the constrained and unconstrained models

with respect to the degree of freedom would suggest

an equal path coefficient across the two groups. The

results of the multi-group comparison are

summarized in Table 4.

Table 4: Multi-group comparison results.

Path Korea U.S

Chi-square

difference

test

Results of

Multi-

group

comparison

INNO→INT

0.109

(1.425)

0.324

(4.556)





d(1) =

4.176

Korea <

U.S.

PROP→INT

0.345

(4.308)

0.283

(4.258)





d(1) =

0.356

Korea =

U.S.

PRIV→INT

-0.033

(-0.726)

-0.213

(-4.064)





d(1) =

6.719

Korea <

U.S.

INT→SC

0.773

(16.073)

0.696

(17.221)





d(1) =

1.538

Korea =

U.S.

**p< 0.01, * p<0.05

INNO: Innovativeness, PROP: Propensity to share information,

PRIV: Privacy concern, INT: Intensity of SNS use, SC: Social

Capital.

The coefficients are non-standardized values. t-values are in

parentheses.

As shown in Table 4, the effect of innovativeness

and privacy concern on intensity of SNS use was

greater for U.S sample than for Korean sample.

However, there is no significant difference in the

two paths (propensity to share information →

intensity of SNS use, intensity of SNS use → social

capital) between Korean sample and U.S. sample.

5 CONCLUSIONS

This study developed a causal model consisting the

antecedents and consequences of intensity of SNS

use and examined the model using survey data from

USA and South Korea. Among the antecedents of

intensity of SNS use, consumer innovativeness and

DeterminantsofUseIntensityinSocialNetworkingSites-ACross-culturalStudyofKoreaandUSA

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propensity to share information were positively

related to intensity of SNS use, whereas privacy

concern was the opposite. Since intensity of SNS use

determines social capital, the results imply that

social capital may be influenced by consumer

characteristics other than the age-related differences

although it is indirect. The study reports that

innovative users spend more time using SNS let

alone they adopt it earlier than others. The results of

this study also show that propensity to share

information affects intensity of SNS use. It implies

that social networking sites need to promote

information sharing beyond the mere expression of

user identity. Social networking sites may raise

privacy concerns since they allow users to search for

profiles of other members. Consumers who had high

privacy concern had lower intensity of SNS use due

to either not using them or using them passively.

Social networking sites need to understand that users

with high privacy concern can leave the sites and try

to reduce the concern on privacy by adding the opt-

in type of networking.

Culture was found to moderate the relationships

between the antecedents (privacy concern and

consumer innovativeness) and intensity of SNS use.

The negative impact of privacy concern on intensity

of SNS use was alleviated in the collectivism culture.

People in the culture of high peer pressure and

herding behavior tend to expect more reciprocity in

social surveillance, especially among in-group

members since they are interested in tracking others

in the group. This tendency might alleviate the

negative impact of privacy concern on intensity of

SNS use.

The positive impact of innovativeness on

intensity of SNS use was alleviated in the

collectivism culture. This is maybe because the

imitation factor predicts adoption behavior better

than the innovation factor in the collectivism culture.

This finding implies that social networking sites

should focus more on group behavior than individual

behavior in order to promote SNS use.

Despite several notable contributions, there are a

few limitations to this study, which may be

overcome by further research. First, this study only

considered consumer innovativeness and privacy

concern as antecedents of intensity of SNS use.

Various personal variables (i.e. demographics,

perception, attitudes, and etc.) will be considered in

the further research.

Second, most measurements were retrospective,

depending on the respondents’ memory of past

shopping behavior, so there were the errors of

measurements. More accurate measurement scale

will be developed in a further study.

Third, an experimental research will be needed to

obtain more accurate effects of antecedents on

intensity of SNS use in the next stage.

ACKNOWLEDGEMENTS

This research was supported by SSK 2011 of Korea

Research Foundation (NRF-2011-330-B00096).

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