UNDERSTANDING THE SOCIAL BENEFITS OF SOCIAL NETWORKING SERVICES: APPLYING THE INFORMATION SYSTEM SUCCESS MODEL

Transcription

1 UNDERSTANDING THE SOCIAL BENEFITS OF SOCIAL NETWORKING SERVICES: APPLYING THE INFORMATION SYSTEM SUCCESS MODEL Tse-Ping Dong, Graduate Institute of Global Business and Strategy, College of Management, National Taiwan Normal University, 162, He-ping East Road, Section 1, Taipei 10610, Taiwan, R.O.C Yen-Chun Jim Wu, Dept. of Business Management, National Sun Yat-Sen University, 70. Lianhai Rd., Kaohsiung, 804 Taiwan, Nai-Chang Cheng, Graduate Institute of Global Business and Strategy, College of Management, National Taiwan Normal University, 162, He-ping East Road, Section 1, Taipei 10610, Taiwan, R.O.C ABSTRACT Using information system success model, this study develops a research model to examine the external effect of social networking site quality (system quality, information quality and information privacy concerns) on user satisfaction, intention to reuse and social benefits of social networking services. Based on a survey of 322 participants, this study uses a structural equation modeling approach to investigate the research model. The findings indicate that perceived of user satisfaction and intentions to reuse are determinants of social benefits of social networking services. In addition, system quality, information quality and information privacy concerns influence both user satisfaction and intention to reuse. This study contributes to a theoretical understanding of whether information system factors can explain and predict the social benefits of social networking services. It obtains relatively satisfactory results with an extended DeLone and McLean s model, with the results supporting all the proposed hypotheses and explaining a relatively high proportion of the variation in the intention Keywords: Social networking service, DeLone and McLean s model, user satisfaction, social benefits

2 INTRODUCTION Social networking services (SNS) are Internet-based applications that are built on the ideological and technological foundations of Web 2.0, and allow the creation and exchange of user-generated content (Kaplan & Haenlein, 2010), as seen on sites such as Facebook, Twitter, Google+ and MySpace. SNS allow users to share ideas, activities, events, and interests within their individual networks, as well as to make contact with people who have similar interests, and have become one of the most powerful sources for users to send and receive news updates, using platforms like Twitter and Facebook (Eldon, 2011). However, since SNS are based on user-generated content, there are obvious privacy concerns with regard to how this data is stored and used (Dwyer, Hiltz, & Passerini, 2007; Shin, 2010), related to issues such as cyber stalking, location disclosure, social profiling and third party disclosure of personal information. This study thus examines how the issue of privacy impacts social benefits, user satisfaction, and intention to reuse by applying information system success model (Delone & Mclean, 1992, 2002). This model has received considerable attention among researchers, and provides a foundation for many studies in the SNS domain (Joiner, 2004; Lin, 2007; Petter, Delone, & Mclean, 2008). THEORETICAL BACKGROUND DeLone and McLean s model (D&M model) Delone and Mclean (2003) proposed an Undated Information Systems Success Model in order to provide a general and comprehensive definition of IS success that covers various dimensions. Specifically, the modified model consists of six interrelated dimensions: information, system and service quality, intention to reuse, user satisfaction, and net benefits. Motivated by DeLone and McLean s call for further development and validation of their model, many researchers have attempted to extend or refocus it (Chen & Cheng, 2009; Lin, 2007; Wu & Wang, 2006). The D&M model is the most widely used theory for explaining IS success, and studies have applied it to measure the benefits of utilizing an IS or the factors related to e-commerce success at the individual and organizational levels (see Table 2). However, the research findings from such studies that use the model to examine SNS have often been inconsistent and incomplete. The main purpose of this study is to identify the antecedents that support SNS success from the perspective of D&M model. This study explores the external factors (information quality, system quality, and information privacy concern) and internal factors (intention to reuse and user satisfaction) to examine their impacts on social benefits. Accordingly, user satisfaction is a key measure of information system success (Delone & Mclean, 1992; Ong & Lai, 2007). User satisfaction deals with user attitudes to computer systems in the context of their

3 environments. In a broader sense, the definition can be extended to user satisfaction with any computer-based device or application, and in this study we define user satisfaction as a key measure of IS success. Privacy concerns with social networking services SNS allow users to upload information to a public profile, create a list of online friends, and browse the profiles of other users. However, the data that is generated in this way raises obvious concerns about privacy, despite the fact that privacy within SNS is often not expected or remains a vague and ill-defined concept (Dwyer, et al., 2007). Moreover, while studies have indicated that SNS users are worried about how their data is used, they are not very vigilant about safeguarding it (Awad & Krishnan, 2006; Buchanan, Paine, Joinson, & Reips, 2006). Social benefits of social networking services Social networking services play a vital role in social interactions and relationships for many millions of people. For, example, Facebook is related to attitudes and behaviors that can enhance an individual s social capital (Valenzuela, Park, & Kee, 2009) in ways that are fast, convenient and essentially free. People use SNS to make new friends, find old friends, or locate people who have the same interests as they have. In short, people get friendship, community, and a sense of belonging out of SNS, with many relationships that are formed online then developing into offline friendships. RESEARCH MODEL AND HYPOTHESES System quality 0.15* 0.13* User satisfaction R 2 = ** R 2 =0.68 Information quality 0.24** 0.18** 0.36*** Social benefits R 2 =0.66 Information Privacy concern 0.32*** 0.28*** Intention to reuse 0.34*** FIG. 1. Results of structural modeling analysis This study investigated the applicability of the D&M model to SNS. The model can be interpreted as follows (see Figure 1). First, when measuring the success of a single system, the

4 key factors may be information or system quality (Delone & Mclean, 2003). A better system can increase how much work a person can do, making them more efficient /effective, and thus more satisfied. System quality is measured in terms of ease-of-use, functionality, reliability, flexibility, accessibility, integration, and importance (Nelson, Todd, & Wixom, 2005; Wixom & Todd, 2005). Therefore, this research model assumes that system quality is linked to user satisfaction and intention to reuse. These rationales lead to the following set of hypotheses: H1a: System quality positively affects user satisfaction with SNS. H1b: System quality positively affects intention to reuse. Second, information quality describes the quality of the content of IS. Generally, the more useful and interesting the content that a website or IS has, the more successful it will be. This is because more people will want to visit it repeatedly, and this is especially true if a system is adding content on a regular basis. Information quality is measured in terms of accuracy, context, relevancy, timeliness, completeness, and accessibility (Lee, Strong, Kahn, & Wang, 2002; Wang & Strong, 1996). Therefore, the research model presented in this work assumes that information quality is linked to user satisfaction and intention to reuse. These rationales lead to the following set of hypotheses: H2a: Information quality positively affects user satisfaction with SNS. H2b: Information quality positively affects intention to reuse. Third, recent studies have included the construct of information privacy concerns in their research models to explore consumer acceptance of SNS (Fogel & Nehmad, 2009). Concerns about information privacy could decrease both the willingness to use online services and user satisfaction (Chen, Hsu, & Lin, 2010; Shin, 2010). D&M model includes three quality characteristics of IS success: information, system and service quality. Accordingly, this study consider information privacy concern as a kind of service quality (Liu, Du, & Tsai, 2009; Yang, Cai, Zhou, & Zhou, 2005), and propose that they are not only positively related to user satisfaction, but also intention to reuse. Fourth, information privacy concerns are provided by most, if not all, SNS in order to protect users personal information (Shin, 2010). For example, a range of privacy settings are available on Facebook, and these allow users to block certain individuals from seeing their profiles and to otherwise limit who access to their posts, pictures and videos. Consequently, this study presents the following two hypotheses: H3a: Information privacy concerns positively affect user satisfaction with SNS. H3b: Information privacy concerns positively affect intention to reuse. Fifth, user satisfaction is defined as the opinions that a user has about a specific computer application or IS, especially with regard to whether or not it meets their information

5 requirements (Doll & Torkzadeh, 1988; Ives, Olson, & Baroudi, 1983). Other terms for user satisfaction that appear in the literature are user information satisfaction, system acceptance, management information systems (MIS) appreciation and feelings about information system. In addition, the term user information satisfaction is often used as a surrogate measure of IS success /effectiveness (Delone & Mclean, 2003; Doll & Torkzadeh, 1988; Ong & Lai, 2007; Wang & Liao, 2007). Finally, the model presented in this work can be interpreted as follows based on the original D&M model: An IS can be evaluated in terms of information quality and system quality, and these characteristics affect subsequent user satisfaction and intention to reuse, and thus influence social benefits. This study thus proposes the following hypotheses: H4: User satisfaction positively affects social benefits. H5: User satisfaction positively affects intention to reuse SNS. H6: Intention to reuse positively affects social benefits. Measurement RESEARCH METHODOLOGY In recent years, many studies have developed and validated instruments for measuring the D&M model (Chen & Cheng, 2009; Delone & Mclean, 2004; Liao, et al., 2011; Lin, 2007; Wu & Wang, 2006). Therefore, the items used in this questionnaire were derived from the existing literature and slightly modified to suit the context of SNS, as shown in Table 3. Items for measuring system and information quality were adapted from Delone and Mclean (2003) and Lin (2007). The items for privacy-information quality were developed by Malhotra, Kim, and Agarwal (2004). The items for social benefits were adapted from Chiu, Hsu, and Wang (2006) and Sweeney and Webb (2007), with modifications to suit the context of this study. Each item was measured on a seven-point Likert scale, ranging from strongly disagree (1) to strongly agree (7). Table 3. Formal definitions of constructs Construct Operational definition Items 1. System quality Measured in terms of ease-of-use, functionality, reliability, 3 flexibility, accessible and importance. 2. Information quality Measuring the value which the information provides to the user Information privacy The protection of personal information by the SNS. 3 concerns 4. User satisfaction The degree of one s feelings of pleasure or displeasure regarding 3 SNS. 5. Intention to reuse The degree to which an individual believes they will reuse SNS Social benefits The strength of the relationships, the amount of time spent, and communication frequency among members of social networks

6 Data collection The target population of this study was current SNS users in Taiwan. According to the Institute for Information Industry, in 2012 there were 10.7 million Internet users in Taiwan. Moreover, SNS dominate Taiwan's top 100 online sites, with Facebook at No. 1, according to an annual survey by a Business Next magazine. In total, 27 of the top 100 are social networking websites, including four of the top 10(Facebook named No. 1 in Taiwan's top 100 sites list, 2011). The study primarily used online surveys, because they have several advantages over traditional paper-based mail surveys (Tan & Teo, 2000). Specifically, they are cheaper to conduct, elicit faster responses, and are geographically unrestricted. Moreover, such surveys have been widely used in recent years, and international researchers are coming to accept the validity of online research (Wright, 2005). To solicit a pool of respondents who would be as close to the general population of Internet users as possible, the link to the survey was distributed through university listservs, online discussion boards and popular SNS-related websites, including Facebook ( Wretch (Wretch.cc), youthwant ( and campus BBS (bbs.ptt.cc). The online survey yielded 322 usable responses out of 495 online questionnaires, giving a response rate of 65%. The respondents were 54% male and 46% female. All the respondents had experience of using SNS, with 80% claiming more than five months of such experience. The respondents had a wide variety of occupations, as can be seen from the details shown in Table 4. DATA ANALYSIS AND RESULTS The research model was tested using the structural equation modeling (SEM) application in AMOS 18. Anderson and Gerbing (1988) proposed a two-step procedure which starts by developing an effective measurement model by using confirmatory factor analysis (CFA) to analyze the data. In the first step, the measurement model used in this work was thus estimated using CFA to test the reliability and validity of the constructs. The structural model was then analyzed to examine the hypothesized relationships. Analysis of the measurement model Table 5 lists the means, standard deviations, Cronbach s α and coefficients for each summed scale. The internal consistency reliability is a statement about the stability of individual measurement items across replications from the same source of information (Straub, 1989). Cronbach s α assesses the internal consistency reliability, and the values ranged from 0.821(for information quality) to (for intention to reuse), over the benchmark of 0.7 (Hair, Black, Babin, & Anderson, 2006). The results of the analysis show that the square correlations for each construct are less than the variance extracted by the indicators

7 measuring that construct, indicating that the measures have adequate discriminant validity. Convergent validity is the degree to which multiple attempts to measure the same concept are in agreement. This study conducted a CFA to test the convergent validity of each construct, and the results showed that all items had factor loadings higher than 0.7. The standardized path coefficients for the research model are also presented in Figure 1. All the paths were significant in the expected direction. Analysis of the structural equation model We examined the structural equation model by using AMOS 16 with maximum likelihood estimation to test the hypothesized relationships among the research variables. The standardized path coefficients for the research model are presented in Figure 1. All the paths were significant in the expected direction. The overall model fit was assessed in terms of five common measures: normed χ2 (the ratio of χ2 to the degree of freedom), goodness-of-fit index (GFI), comparative fit index (CFI), non-normed fit index (NNFI), and root mean square error of approximation (RMSEA). The results of the structural model analysis are shown in Figure 1. Normed χ2 was ( χ2 = , d.f. = 123), which is less than the recommended level of 3 (Bagozzi & Yi, 1988). Other fit indices also show good fit for the structural model. The GFI is 0.887, which exceeds the recommended cutoff level of 0.8 (Browne & Cudeck, 1992). The CFI is and the NNFI is 0.834, both of which also exceed the recommended cut-off level of 0.8 (Joreskog & Sorbom, 1996). In addition, the RMSEA is 0.079, which is below the cut-off level of 0.08 (Browne & Cudeck, 1992). Therefore, the structural model exhibited a fairly good fit with the data collected. DISCUSSION AND CONCLUSION This study makes several contributions to IS research. For example, prior studies have been confined to exploring satisfaction, whereas social benefits is a new construct in IS use research, and thus one contribution of this work is that it conceptualizes this construct and validates its antecedents and consequences with regard to SNS usage. To the best of our knowledge, this is the first study that specifically addresses social benefits in the SNS context, and while several previous IS researchers have identified information quality, system quality, or service quality as important antecedents of user satisfaction (Liao, et al., 2011), no studies have explored their effects on social benefits, and the current work addresses this gap in the literature. The results show that information quality, system quality, and information privacy concerns are stronger predictors of user satisfaction than intention to reuse. In addition, they also show that user satisfaction may be the key to explaining discontinued use of IS, a little-understood phenomenon in IS use research. To sum up, this study finds that user satisfaction play important roles in influencing the social benefits of SNS. This study shows

8 that intention to reuse not only has a direct influence on social benefits, but also acts as a mediator between user satisfaction and social benefits. Practitioners typically measure user satisfaction after using an SNS, and the results of this study suggest that they should also measure intention to reuse, since this affects social benefits. Practitioners should thus aim to make users feel that they have improved their social interactions and relationships when using an SNS, as this will make them more likely to continue to use the site. This study contributes to a theoretical understanding of whether information system factors can explain and predict the social benefits of social networking services. It obtains relatively satisfactory results with an extended D&M model, with the results supporting all the proposed hypotheses and explaining a relatively high proportion of the variation in the intention to use SNS and certain social benefits will be achieved. Based on the empirical findings outlined above, this study reaches several conclusions, as follows. First, SNS should actively seek methods of improving system and information quality, since these factors significantly affect user satisfaction and intention to reuse. SNS should thus make full use of the accuracy, relevance, timeliness, usefulness, and completeness of the information that they gather in order to increase user satisfaction and reuse. Second, the results also indicate that information privacy concerns have a strong and significant influence on user satisfaction and reuse. Therefore, instructors and system designers should put a high priority on protecting the users privacy. Third, consistent with intention based models, the results show a significant and positive linkage between intention to reuse and social benefits. This result suggests that the social benefits tend to be based on the user satisfaction and intention to reuse that occurs there. Moreover, social benefits of SNS are predicted jointly by user satisfaction and intention to reuse, and thus various social factors could be examined in subsequent SNS research. LIMITATIONS AND FURTHER RESEARCH As in most empirical research, this study has several limitations. First, in addition to the environmental and personal factors included in this study, there may be other factors influencing the intention to use an SNS. Second, besides the information service attributes and social benefits included in this study, there could still be other factors that influence behavioral intention. For example, Cheng, Tsai, Cheng, and Chen (2012) presented risk and critical mass as factors that influence the acceptance of online services, and further research considering these could enhance understanding of the determinants of success for SNS. Finally, social capital theory suggests that the social capital that is obtained on the Internet via SNS tends to be bridging capital, although such virtual social capital is a new area of research (Ellison, Steinfield, & Lampe, 2007). Three dimensions of social capital (i.e.,

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