｢정보시스템연구｣ 제19권 제2호

한국정보시스템학회

2010년 6월, pp. 1～21

An Empirical Study on e-Loyalty of Social Networking Sites

* Candidate PH.D., Dept. of MIS, Gyeongsang National University, jiaminghao@hotmail.com

** Corresponding Author, Professor, Dept. of MIS, Gyeongsang National University, dyjeong@gnu.ac.kr

Ming-Hao Jia*․Dae-Yul Jeong**

			<목 차>
Ⅰ. Introduction Ⅱ. Theoretical Background 2.1 Social Networking Sites 2.2 Satisfaction 2.3 E-loyalty Ⅲ. Research Method 3.1 Research Model 3.2 Hypotheses 3.3 Measurements of Variables 3.4 Sampling Design and Data Collection				Ⅳ. Data Analysis and Hypothesis Test 4.1 Measurement Model 4.2 Structural Model 4.3 Hypothesis test Ⅴ. Discussion and Conclusion 5.1 Implication 5.2 Limitations and Future Work References Abstract

Ⅰ. Introduction

Web 2.0 is the next generation application about the Internet. O’Reilly (2006) proposed a brief definition for web 2.0: “It is the business revolution in the computer industry caused by the move to the internet as platform, and an attempt to understand the rules for success on that new platform.” But, there is not a clear definition for web 2.0 yet. In short, web 2.0 refers to as a perceived second generation of web development and design that facilitates communication, secure information sharing, interoperability, and collaboration on the World Wide Web (http://en.wikipedia.org/wiki/Web_ 2.0, accessed 21 May, 2009).

The essence of web 2.0 is user generated content (UGC), also known as user created content (UCC) or consumer generated media (CGM). The internet contents what visitors can view were edited by a small number of web masters in web 1.0. Conversely, every visitor can be the constructors in web 2.0. Singel quotes Mayfield, a CEO of a software WIKI Solutions Company: “Web 1.0 was commerce. Web 2.0 is people.” Web 2.0 concepts have led to the development and evolution of web-based communities, hosted services, and applications; such as social networking sites, video-sharing sites, blogs, etc.

	An Empirical Study on e-Loyalty of Social Networking Sites

｢정보시스템연구｣ 제19권 제2호, 2010년 6월

Social networking sites such as Facebook, Myspace, Cyworld, Renren and Kaixin001 have an amazing development in recent years. There is a new way to show themselves, share information, connect with friends, and meet new people. Most of the existing articles (Barnes, 2006; Boyd, 2008; Cohen and Leslie, 2008; Dwyer et al. 2007; Fogel and Nehmad, 2009; Lewis et al. 2008; Strater and Richter, 2007) are only concerned about the security and privacy of SNSs. Although the security and privacy are very important factors from user perception, the user loyalty is more important for the corporation in comparison. But, there are no relevant research about user loyalty in SNSs.

The goal of this study is to propose a conceptual framework that customize for user loyalty of SNSs. We begin the study by reviewing the literatures about the relationship between site attractiveness, satisfaction and e-loyalty. Second, we identify some factors that affect the site attractiveness, satisfaction and e-loyalty, and then present a research model and hypotheses. Finally, we test the hypotheses and give out the implications.

In this study, through analyzing the factors that affect the user satisfaction and e-loyalty of SNSs, we propose what factors influence on site attractiveness and user satisfaction, and then examined the direct effect of site attractiveness and satisfaction on user e-loyalty. Furthermore, we tested whether there are mediators among these factors.

Ⅱ. Theoretical Background

2.1 Social Networking Sites

The basic theory of SNSs is called six degrees of separation theory which is popularized by Guare (1993). It refers to the idea that, the distant between any stranger in the world and you will not be more than 6 peoples. That is, you will be able to know any of a stranger through a maximum of six people if you found the right six people to make the right connection. As shown as Figure 1, there is a little relationship among most of your friends in the traditional interpersonal relationship mode. By contrast, each of your friends could be a transmitter in the SNSs, it allows people make link between new one and their friends. This kind of mode establish a link among these friends each other. Boyd and Ellison (2007) define SNSs as web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system.

<Figure 1> Traditional Interpersonal Relationship Mode & Social Mode of SNSs

SNSs is similar with a technology integrated sites. They have provided a variety of features, such as Bulletin(a brief announcement of news or opinion posted on the user’s profile page), Group (a place where people gather online, get updates and show support about specific subjects and interests), Mail(private messages much like e-mail and instead of posting comments on a user’s profile), IM (provides a instant message function for users to chat with each other), Application (usually third-party application created by other users for entertainment purpose), photos-sharing and etc. Table 1 showed some features of most popular SNSs in China Ineternet environment.  Several of SNSs also provide some characteristic functions. For instance, the Facebook and MySpace which are come from U.S. provide the video upload. The Kaixin001 and Chinaren which are come from China provide the function of webdisk. The 51.com and Qzone which are also provide unmodified domain name service, and the domain name can be modified only once in Myspace. The users are allowed to use domain name service by monthly payment. In contrast, the Cyworld comes from Korea provide this

<Table 1> Some Features of SNSs

	Facebook (CN)	Myspace (CN)	Cyworld (CN)	Renren	Kaixin001	51	Chinaren	Qzone
Bulleting	√	√		√	√	√		√
Group	√	√		√	√	√	√	√
Mail	√	√	√	√	√	√	√	√
Photo	√	√	√	√	√	√	√	√
Music	√	√	√	√	√	√		√
Video	√	√
IM	√	√	√	√	√	√		√
Webdisk					√		√
Apps	√	√	√	√	√	√		√
Domain Service		√	√	√		√		√
Mobile Support	√	√	√	√	√	√		√

service for free. It is worth mentioning that most of the SNSs have supported the mobile phone accessing. The users can browse the site anytime. The SNSs are keeping ongoing development and also the technology barriers are lower and lower. The site can provides the services what others can. The most critical and difficult thing is constant innovation.

Social networking sites have attracted millions of users and have the potential to fundamentally change our social lives. For examples, most of these users have integrated these sites into the daily practices (Boyd and Ellison, 2007).

2.2 Satisfaction

Oliver (1997) argued that satisfaction is the summary of psychological state resulting when the emotion surrounding disconfirmed expectations is coupled with a consumer’s prior feelings about the consumer experience. Stauss and Neuhaus (1997) defined satisfaction as a cumulative, attitude-like judgment that is based on customers past experiences. It is connected to varying emotional and cognitive states that influence customers’ future behavior towards the company. Following Hunt (1997), Rust and Oliver (1994) and Cronin et al. (2000), the customer satisfaction is described as “an evaluation of an emotion” that reflects the degree to which a consumer believes the service evokes positive feelings. In this research, satisfaction is defined as the contentment of the users during the experience of SNSs. It refers to fulfillment of the user's needs and expectation, and that the web site is perceived as satisfactory overall.

2.3 e-Loyalty

Oliver (1997) also define the loyalty as: “A deeply held commitment to re-buy or re-patronize a preferred product or service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational in influences and marketing efforts having the potential to cause switching behavior.” This general definition also appears to apply to online loyalty, or e-loyalty as well.  Srinivasan et al. (2002) present the briefer and more specific definition of e-loyalty as “a customer’s favorable attitude toward the e-retailer that results in repeat buying behavior.” Cyr et al. (2005) defined e-loyalty as intention to revisit a web site or to consider purchasing from it in the future. e-Loyalty has been conceived as a consumer's intention to buy from a web site and that consumers will not change to another web site (Flavian et al., 2006). For research purpose, we define the e-loyalty as the users’ favorable attitude toward the SNSs that result in visiting frequently and/or staying a long time.

Ⅲ. Research Method

3.1 Research Model

<Figure 2> A Research Model for e-Loyalty of SNSs

Liu and Arnett (2000) identify four factors that are critical to web site success in e-commerce: information quality, system use, playfulness and system design quality. Delone and Maclean (2003) consider the quality affect user satisfaction from three dimensions: system quality, information quality, and service quality. Based on the above two kinds of viewpoints, Cao et al. (2005) present the web site quality can be addressed via four components: functionality, contents, service and attractiveness. Web site quality should be used to evaluate a web site from a customer’s perspective (Barnes and Vidgen, 2001). In our opinions, we decide four factors which effect on user satisfaction of SNSs: system capability, information relevance, contents value, and site attractiveness. Figure 2 illustrates the research model.

3.2 Hypotheses

Attractiveness consists of the issues of whether the web site are fun to read and subjectively pleasing. No matter how well the content is or how reliable and easy to search the web site is, if users do not find the site appealing, they are not going to spend much time there (Smith and Merchant, 2001). Chen (2001) said playfulness includes the devices that attract the attention of web site users with enjoyable constructs such as online games, software downloads and Q&A. These features enhance communication by helping visitors find or interpret the information presented. Watson et al. (1998) also suggests that online users seek gratification in escape, entertainment and interaction. This suggests that there is a need for web site to motivate customers to participate, promoting customer excitement and concentration, and including charming features to attract customers and to help them enjoy the visit (Liu and Arnett, 2000). The SNSs combine the professional web sites such as specifically providing online music, webpage games, shared  video and blogging services together. To user's point of view, whether the site is functional; whether the information a site provide is high quality; whether the content is valuable; could make influence on the site attractiveness. In our study, we examine the site attractiveness from the aspects of system capability, information relevance, and contents value. Thus, the hypotheses are that:

H1a.  System capability has a significantly positive impact on site attractiveness.

H2a. Information relevance has a significantly positive impact on site attractiveness.

H3a.  Contents value has a significantly positive impact on site attractiveness.

System capability measures the functionality of a web site. For example, whether the system response time can be accepted by the user (Weinberg, 2000); whether the navigation is useful and the user can found what they want quickly (Smith and Merchant, 2001); and whether the users’ personal information can be protected appropriately or not (Zeithaml et al., 2002). Information relevance captures the content issues what provided by web site. It refers to the extent to which the information on the web site is related to the information needs of the customer (Cao et al. 2005). The information on the web site should be informative and updated (Ducoffe, 1996). The SNSs should provide the timely update information. For example, some new applications are developed and available for users, other applications are maintained and updated. These are signs of the SNSs’ competence, and therefore induce satisfaction. In general, the system capability and information relevance offers tangible services for users by site, on which they can base their assessment of the SNSs satisfaction and loyalty. The contents of SNSs are generated by the users. The users can record any of things they want on their own spaces. They can share information and discuss some popular topics with friends. Peer who has some information can also be aware of his situation. They also can capture the situation of their friends through messages, comments or other channels. The contents of User-Generated Content (UGC) have the uncertainty. Good information will be helpful for other users. On the contrary, there is no significance, and even have a negative impact. Thus, the hypothesis is that: Thus, the hypotheses are that:

H1b.  System capability has a significantly positive impact on satisfaction.

H2b. Information relevance has a significantly positive impact on satisfaction.

H3b. Contents value has a significantly positive impact on satisfaction.

Embedding playful features within the web site not only differentiate a site from others, but also enhance the user’s perceived level of satisfaction  (Eighmey, 1997). These features will also lead to increased customer activities (Schmidt, 1996). Furthermore, those will enhance the user’s perceived level of loyalty. Thus, the hypotheses are that:

H4a. Site attractiveness has a significantly positive impact on satisfaction.

H4b. Site attractiveness has a significantly positive impact on e-loyalty.

Customer satisfaction is generally considered a major driver of loyalty in e-commerce (Anderson et al., 1994; Ellinger et al., 1999; Oliver, 1980). There are many literatures indicate that satisfaction is likely to increase loyalty (Bolton, 1998; Jones and Suh, 2000; Oliver, 1997; Page and Eddy, 1999; Patterson et al., 1997). Thus, the hypothesis is that:

H5. Satisfaction has a significantly positive impact on e-loyalty.

3.3 Measurements of Variables

The questionnaire initially included 32 measurement items. Table 2 shows the measurements of all variables. For each item, five-point Likert scales which the ranging from 1 = strongly disagree to 5 = strongly agree with 3 = neutral (neither agree nor disagree) as the midpoint were used in this study.

3.4 Sampling Design and Data   Collection

The questionnaire was constructed as a web-based survey for one month during the survey period. Wang and Emurian (2005) indicate that the abilities of a web-based survey to allow respondents to feel anonymous and to overcome time and place constraints helped us to reach respondents more easily than using other data collection methods.

Internet usage in China is growing fast. As of the end of June 2008, the number of netizen in China reached 2.53 million (No.22 Internet Report from CNNIC, China Internet Network Information Center) and it has become the largest internet

<Table 2> Measurements

Factors	Measurements	References
System Capability	a. The site can be adapted to meet a variety of needs. b. The site loads its page fast. c. There is very little waiting time between my actions and this site response. d. I find this site is easy to navigate. e. I feel secure to provide private information to this site. f. I trust this site to keep my personal information safe.	Barnes and Vidgen (2002), Ribbink et al. (2004) Lin (2007), Parasuraman et al. (2005)
Information Relevance	a. The information on the web site is effective. b. The site provides timely information. c. The information provided by the web site is accurate. d. The site has comprehensive information. e. The site provides complete information. f. The information from this site always up to date.	Barnes and Vidgen (2002), Montoya-Weiss et al. (2003), Loiacono et al. (2007)
Contents Value	a. I can found the solution of problem on this site. b. The contents on this site are attractive. c. The contents on this site are useful. d. I can know the situation of friends via the web site. e. The contents on this site can enhance my knowledge. f. The contents on this site can enrich my life	Barnes and Vidgen (2002), Wolfinbarger and Gilly (2003)
Site Attractiveness	a. The site has features that are personalized for me. b. I can interact with this site in order to get information tailored to my specific needs. c. The site allows me to interact with it to receive tailored information. d. It’s really fun to enjoy at this site. e. I find the applications diverse and innovative. f. Overall, I believe this site is playful.	Wolfinbarger and Gilly (2003), Hsu and Lu (2007), Loiacono et al. (2007)
Satisfaction	a. I am very satisfied with the services of this site. b. I am very satisfied with the experience of this site. c. Compare with other sites, I like this site very much. d. Overall, I am very satisfied with this site.	Rust and Oliver (1994), Hunt (1997), Cronin et al. (2000)
e-Loyalty	a. I will visit this site more frequently than others. b. I will spend more time on this site than others. c. I will often talk about this site with others. d. I will recommend this site to others.	Oliver (1997), Hellier et al. (2003)

users in the world. So, the Chinese SNSs will be concerned in this study. The respondents who had been joined SNS at last half year were asked to answer the questionnaire having a familiar SNS in mind.

A total of 283 responses were received. After eliminating incomplete and inappropriate responses (e.g. giving the same rating for all items), 187 usable responses were included in the sample for construct validation and hypothesis test. The detailed descriptive statistics are shown in Table 3.

<Table 3> Descriptive Statistics

Measure	Value	Frequency	Percentage
Gender	Male	79	42.2%
	Female	108	57.8%
Age	Less than 20	6	3.2%
	20~25	113	60.4%
	26~30	66	35.3%
	Greater than 31	2	1.1%
Level of education	High school	3	1.6%
	College	109	58.3%
	Graduate	75	40.1%
Occupation	Student	107	57.2%
	Office Worker (White-collar)	74	39.6%
	Others	6	3.2%
Usage Internet per day	Below 3 hours	32	17.1%
	3~6 hours	59	31.6%
	6~9 hours	50	26.7%
	Above 9 hours	46	24.6%
Frequency of use SNSs	Several times per day	117	62.6%
	Several times per week	43	23%
	Several times per month	12	6.4%
	Long times per login	15	8%

Ⅳ. Data Analysis and Hypotheses test

This study conducted structural equations modeling (SEM) analysis. Both the measurement model and the structural model were assessed using AMOS 7.0 by the maximum likelihood method.

4.1 Measurement Model

The internal consistency reliability was assessed by the value of Cronbach’s alpha. The Cronbach’s alpha coefficients shown in Table 4 below indicated that each construct in our research model greater than 0.7, the lowest limit for Cronbach’s alpha suggested by Hair et al. (1998).

<Table 4> Internal Consistency Reliability

Construct	Item	Cronbach’s alpha
System Capability	3	.705
Information Relevance	3	.719
Contents Value	3	.759
Site Attractiveness	3	.746
Satisfaction	4	.847
E-loyalty	3	.859

A confirmatory factor analysis (CFA) was conducted to test the convergent validity, discriminant validity and reliability of model constructs. Fit measures used to assess the measurement model included the ratio of χ[eqn: {} ^{2}] to the degree of freedom (χ[eqn: {} ^{2}]/DF), Goodness of fit (GFI), Root Mean Square Residual (RMR), Root Mean Square Error of Approximation (RMSEA), Adjusted goodness of fit (AGFI), Normed Fit Index (NFI), Comparative Fit Index (CFI). As Table 5 shows, all of the indices were matched with the recommended value (Bollen, 1989; Byrne, 1998; Hair et al., 1998; Joreskog and Sorbom, 2003), and provide evidence of good model fit.

<Table 5> Confirmatory Factor Analysis

Fit Indices (Recommended Value)	Model Indices
χ[eqn: {} ^{2}]/DF (≤3)	2.219
GFI (≥0.9)	.912
RMR (≤0.05)	.063
RMSEA (≤0.08)	.067
AGFI > (≥0.8)	.865
NFI > (≥0.9)	.916
CFI > (≥0.9)	.953

The composite reliability coefficients for each construct was usually used for assess reliability. The coefficient was generated from standardized parameter estimates obtained from the confirmatory factor analysis (CFA) and calculated using Fornell and Larcker (1981) formula. In our study, the composite reliabilities range from .8684 (e-Loyalty) to .7515 (Site Attractiveness) which exceed the recommended level of .70. The Average Variance Extracted (AVE) measures range from .6896  (E-loyalty) to .5038 (Site Attractiveness) which also exceed the recommended level of .50 (Hair et al., 1998). All of the measured variables were statistically significant (see Table 6). Discriminant validity can be examined by the coefficients of squared correlation between each pair of constructs. As shown in Table 7, every coefficient of squared correlations between two constructs was less than the value of AVE. Therefore, the results of analysis demonstrate convergent validity and discriminant validity of measurement model.

<Table 6> Convergent Validity Test

Factors	Item	Standardized Loading	Error Variance	Composite Reliability	Average Variance Extracted
System Capability	SC2	.767	.4117	.7637	.5197
	SC3	.737	.4568
	SC5	.654	.5723
Information Relevance	IR1	.714	.4902	.7710	.5312
	IR2	.644	.5852
	IR3	.818	.3309
Contents Value	CV2	.712	.4931	.7681	.5265
	CV4	.803	.3552
	CV5	.654	.5723
Site Attractiveness	SA4	.625	.6094	.7515	.5038
	SA5	.735	.4598
	SA6	.762	.4194
Satisfaction	ES1	.664	.5591	.8439	.5785
	ES2	.665	.5578
	ES3	.808	.3471
	ES4	.882	.2221
E-loyalty	EL1	.871	.2414	.8684	.6896
	EL2	.897	.1954
	EL3	.711	.4945

4.2 Structural Model

The structural model was tested for examining the hypothesized relationships in this study. Comparison of all fit indices, with their corresponding recommended values, the value of ratio of χ[eqn: {} ^{2}] to the degree of freedom (χ[eqn: {} ^{2}]/df) was 1.785, which is with in the suggested value of 3.0. The Goodness of fit (GFI) was .883, which was good, closed with the suggested value of .90. The Root Mean Square Residual (RMR) was .066, closed with the suggested value of .05. The Root Mean Square Error of Approximation (RMSEA) was .065, which was good, below the suggested value of .08. The Adjusted goodness of fit (AGFI) was .840, which was good, exceed the suggested value of .80. The Normed Fit Index (NFI) was .907, which is good, above the suggested value of .90. The Comparative Fit Index (CFI) was .925, which was good, above the suggested value of .90. These results indicated a good model fit. The detail value of indices are shown in Table 8.

<Table 7> Squared Correlations and Discriminant Validity

Factors	SC	IR	CV	SA	ES	EL
System Capability	(.5197)
Information Relevance	.107**	(.5312)
Content Value	.038*	.058**	(.5265)
Site Attractiveness	.013	.186**	.231**	(.5038)
Satisfaction	.180**	.269**	.285**	.417**	(.5785)
E-loyalty	.041*	.188**	.095**	.393**	.379**	(.6896)
*p<0.05, **p<0.01 Average variance extracted measures are given along the diagonal.

<Table 8> Fit Indices for Structural Model

Fit Indices (Recommended Value)	Model Indices
χ[eqn: {} ^{2}]/DF (≤3)	1.785
GFI (≥0.9)	.883
RMR (≤0.05)	.066
RMSEA (≤0.08)	.065
AGFI > (≥0.8)	.840
NFI > (≥0.9)	.907
CFI > (≥0.9)	.925

4.2 Hypothesis Test

The path coefficients of the structural model provided direct evidence of hypotheses supported (see Figure 3). Table 9 shows the analysis results of hypotheses test. System Capability has significantly positive impact on satisfaction. The coefficient was .243 (p<0.05, t=2.604), providing support for H1b. Information relevance has significantly positive impact on site attractiveness. The coefficient was .408 (p<0.001, t=3.567), providing support for H2a. Contents value have significantly positive impact on site attractiveness and satisfaction. The coefficient were .395 (p<0.001, t=4.075) and .243 (p<0.01, t=2.748), providing support for H3a and H3b. Site attractiveness have significantly positive impact on satisfaction and e-loyalty. The coefficients were .425 (p<0.001, t=3.919) and .415 (p<0.001, t= 3.721), providing support for H4a and H4b. Satisfaction has significantly positive impact on e-loyalty. The coefficient was .331 (p<0.01, t=3.133), providing support for H5. On the other hand, system capability has not significantly positive impact on site attractiveness. Hence, H1a should be rejected. Information relevance has not significantly positive impact on satisfaction. So, H2b also should be rejected.

<Figure 3> Path Coefficients for Research Model

Ⅴ. Discussion and Conclusion

<Table 9> Result of Hypothesis Test

Hypotheses		Standard Estimate	S.E.	C.R.	P-value	Result
H1a	SC -> SA	.133	.097	1.238	.216	Rejected
H1b	SC -> ES	.243	.072	2.604	.009**	Accepted
H2a	IR -> SA	.408	.134	3.567	***	Accepted
H2b	IR -> ES	.174	.099	1.768	.077	Rejected
H3a	CV -> SA	.395	.087	4.075	***	Accepted
H3b	CV -> ES	.243	.068	2.748	.006**	Accepted
H4a	SA -> ES	.425	.093	3.919	***	Accepted
H4b	SA -> EL	.415	.136	3.721	***	Accepted
H5	SE -> EL	.331	.150	3.133	.002**	Accepted
*p<0.05, **p<0.01, ***p<0.001

5.1 Implications

As previously mentioned, SNSs provide most functions that general websites supported, thereby the users are satisfied with these services. According to the result of hypothesis H1a test, it is verified that system capability has significantly positive impact on satisfaction. SNSs are still in a beginning stage in China. Therefore, there is still a big space to improve and the function of these sites still need to be developed. Besides, the barriers of technology are getting lower and lower, the styles of SNSs are commonly the same. Copy from Facebook is quite a popular mode. So, self-characteristic of SNSs becomes an exigent need. Above all, system capability has no significant impact on site attractiveness.

With the development of SNSs, the information provided by sites is not limited to their own activities, message of new features, and notice of system improvements, also with more and more advertising. Inappropriate use of advertising will be lower customer satisfaction, but it will not affect the site attractiveness to users. Therefore, information relevance has significant impact on the site attractiveness. In contrast, information relevance has no effect on satisfaction.

The results of analysis indicate that the purpose of visit to SNSs are concern on getting the messages of friends (71.1%); play the applications (40.6%); shared knowledge or videos (36.9%); publish entries (34.8%); upload photos or videos (25.7%); and other things (5.3%). All of these purposes refer to contents except play the applications or other things. As previous mention, the contents are generated by the users. Therefore, contents value has significant impact on site attractiveness and also has significant impact on satisfaction.

As we described above, most SNSs offer additional features. The users can present themselves by designing their profile. The photos can be shared among the friends. The videos can include everything from users. The interesting applications are the good choose to spare time. These features meet the need of majority of the people. In summary, the SNSs should be an attractive place and therefore site attractiveness has significant impact on satisfaction. Also, site attractiveness and satisfaction has significant impact on e-loyalty.

Baron and Kenny (1996) suggested  the following steps for mediation: (i) the mediator must be associated with the independent variable. (ii) the independent variable must be associated with the dependent variable. (iii) the mediator must account for significant variance in the dependent variable, over and above that accounted for by the independent variable. In our case, as shown as Table 10, the coefficient of direct effect from site attractiveness to e-loyalty was .415 (p<0.001). The coefficient of indirect effect from site attractiveness to e-loyalty

<Table 10> Result of Path Analysis

To From		Site Attractiveness	Satisfaction	e-Loyalty
System Capability	Direct Effect	.133	.243**
	Indirect Effect	-	.057
	Total Effect	.133	.300**
Information Relevance	Direct Effect	.408***	.174
	Indirect Effect	-	.174**
	Total Effect	.408***	.348**
Contents Value	Direct Effect	.395***	.243**
	Indirect Effect	-	.168**
	Total Effect	.395***	.411***
Site Attractiveness	Direct Effect		.425***	.415***
	Indirect Effect		-	.141**
	Total Effect		.425***	.556***
Satisfaction	Direct Effect			.331**
	Indirect Effect			-
	Total Effect			.331**
*p<0.05, **p<0.01, ***p<0.001

was .141 which is significant(p<0.05). Therefore, satisfaction can be a mediator between site attractiveness and e-loyalty.

Perfect mediation is said to be evident when the addition of the mediator to the independent variable in the regression model results in previously significant relationship between the independent and dependent variable disappearing, as the mediator mops up the variance previously accounted for by the independent variable. In our case, the relation coefficient between information relevance and satisfaction was .348 (p<0.01). As shown in Table 10, the direct effect coefficient between information relevance and satisfaction was not significant. On the contrary, the indirect affect coefficients between them was significant. As a result, site attractiveness can be a mediator between information relevance and satisfaction although information relevance has no significantly impact on satisfaction.

5.2 Limitations and Future Work

Like other researches, our research also has its limitations. First, the survey was conducted using web-based survey. The online survey method was appropriate for collecting data from participants who were free of geographical constraints. For the future research, the sample should be enlarged. Seconds, the e-relationship should be an important factor in SNSs. Boyd and Ellison (2007) indicates that people can create real-life friendships through similar interests or groups in SNSs. As a result, they can establish important social relationships. Future research should attempt to further examine whether the e-relationship can be a moderator variable in research model.

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가명호 (Ming-Hao Jia)

저자 가명호는 중국 요녕과기대학교 학사, 경상대학교 컴퓨터과학과에서 석사학위를 취득하고, 현재 경상대학교 경영정보학과에서 박사수료 중에 있다. 주요 관심분야는 전자상거래, e-비즈니스, Social Networking Sites 등이다.

정대율 (Dae-Yul Jeong)

교신저자 정대율은 부산대학교 경영학과에서 경영학 학사, 석사, 박사학위를 받고, 현재 경상대학교 경영정보학과 교수로 재직중이다. 학회활동으로, 한국경영정보학회, 한국정보시스템학회, 한국지능정보학회, 한국데이터베이스학회 등의 종신회원이며, 한국정보시스템학회 상임이사를 역임하였다. 주요 관심분야로는 객체지향 시스템 분석, 지능형 정보시스템, 전자상거래, 인터넷 마케팅, e-러닝 등이다.

<한글초록>

소셜네트워킹 사이트의 e-고객충성도에 관한 실증연구

가명호․정대율

웹 2.0 기술의 급속한 발전은 인터넷을 통한 커뮤니티 형성을 효과적으로 가능하게 함으로서 소셜네트워킹 사이트(SNS)의 급속한 성장을 가져왔다. 본 연구는 소셜네트워킹 사이트의 고객충성도를 높이는 요인이 무엇인가를 실증적으로 밝히는데 있다. 연구모형은 세 개의 독립요인(시스템 능력, 정보 적절성, 콘텐츠 가치)과 두 개의 매개요인(사이트 매력도, e-고객만족)으로 구성되어 있다. 중국인들을 대상으로 한 실증연구 결과 정보 적절성과 콘텐츠 가치가 사이트 매력도에 직접 영향을 미치며, 이는 다시 고객만족을 통하여 고객의 충성도에 간접적으로 영향을 미치는 것으로 나타났다. 또한 사이트 매력도는 고객만족과 고객충성도에 강력한 영향을 미치는 것으로 나타났다. 구조모형에 대한 경로분석결과 사이트 매력도는 고객만족도를 높이는 중요한 매개변수임을 밝히고 있다. 본 연구에서는 소셜네트워크 사이트에서 이 매개변수들을 어떻게 하면 효과적으로 높일 것인가에 대한 다양한 시사점들을 제시하고 있다.

Keywords: Web 2.0, social networking sites, satisfaction, e-loyalty, site attractiveness, information relevance, contents value

* 이 논문은 2010년 2월 3일 접수하여 1차 수정을 거쳐 2010년 5월 5일 게재 확정되었습니다.