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Keywords:

  • social networking;
  • social networking sites;
  • social capital;
  • online behavior;
  • user characteristics

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX

This study uses the amount of time users spend on social networking sites (SNSs) to differentiate user groups and investigates the following three issues: (1) the most common behavior of different groups when using SNS; (2) whether users have different perceptions of their social capital on SNSs versus in real-life environments; and (3) whether there are differences in the perceived social capital of different groups. This study discovered that users have different user behavior depending on their amounts of usage. In particular, heavy users tend to be willing to share information and often use application programs associated with SNSs. With regard to perceptions of social capital, the study found that different groups have somewhat different ideas as to what constitutes social capital. We summarize a novel individual social capital systematic behavior and discuss the practical implications of this work and suggestions for future research. Copyright © 2013 John Wiley & Sons, Ltd.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX

Social networking sites (SNSs) are currently the most popular Web application services and enable users to engage in online communication and information sharing with friends or social groups (Governatori and Iannella, 2011). Facebook and Twitter are among the most well-known SNSs, with a vast numbers of users, although there is considerable competition in this market. With the rapid growth of SNSs, both the Internet behavior and preferences of users have evolved. Among Internet users in the USA, 65% online adults use SNS (Madden and Zickuhr, 2011), which is more than those who use other online services, whereas approximately one-half of European Internet users use SNS (European Commission, 2011). In addition, SNSs are also attracting many young users in emerging nations, and the number of SNS users in such countries is increasing rapidly (boyd, 2008). According to a 2010 market research report published by ComScore, in the USA, people now spend more time using Facebook than Google (AFP.Com, 2010). We can conclude that social networking has already become the popular online activity of most people.

The tremendous popularity of SNSs derives from the fact that they allow people to maintain relationships with friends, as well as make new ones. This gives users a high degree of self-esteem and satisfaction, and provides service channels that enable the development of individual social capital (Ellison et al., 2007), which can be seen as a network of relationships that rely on reciprocity and trust (Lin, 1999). These networks can have many members, each with their own relationships and social systems, and these can be used to explain an individual's social capital (Nahapiet and Ghoshal, 1998, Adler and Kwon, 2002; Mathwick et al., 2008). Wellman et al. (2001) noted that when an individual engages in more frequent communication with other members in the social network, then this will transform their social capital. The question thus arises as to whether the social capital of users who spend large amounts of time using SNS gives them better relationships or benefits than the social capital of ordinary users. Burke et al. (2010, 2011) and Ellison et al. (2011) noted that users who spent more time on an SNS have more friends and concluded that such sites can help people to build and maintain relationships. Moreover, they proposed that people who use and do not use SNS have the same individual social capital, which implies that there are no differences with regard to the social capital that people have in virtual and real-life environments. However, White (2002) pointed out that social capital is a multidimensional construct, which varies depending on different types of behavior. Gilbert and Karahalios (2009) noted that SNSs are virtual environments and that not every one of a person's real-world friends may have an SNS account. Moreover, some people may have many different SNS accounts, and users may employ SNS to construct different types of social capital in their relationships (Donath and boyd, 2004). Huvila et al. (2010) compared social capital in real life and a virtual world, Second Life, and their respondents stated that it is easier to meet friends in real life and to have more opportunities for face-to-face interactions. In addition, Guo and Gong (2011) explored individual wealth differences in a virtual and the real one and suggested that future research should examine how the relationships formed in both environments differ. They also found that there are differences in users' perceptions of social capital due to differences in the SNS and real-world environments. Burke et al. (2010, 2011), Ellison et al. (2011) and Huvila et al. (2010) all raised the question of whether there are any differences in the individual social capital that arises on an SNS and in real life. However, to the best of the authors' knowledge, no studies have yet been published that consider this question and attempt to explain the relationship between the social capital acquired in real life and on an SNS.

In addition, according to Riva et al. (2003), people who use the Internet for different amounts of time will have different behaviors and characteristics. Although some past research sought to understand what users do on SNSs—for instance, Hargittai and Hsieh (2010) investigated different SNS's social behaviors among students and Miller et al. (2010) compared the Facebook and MySpace user behavior of undergraduate students—there is still a lack of research on differences in SNS user behavior and their relation to the amount of time spent using such sites. If we can gain a better understanding of user behaviors or motivations with regard to SNSs, then this information can provide a basis for the planning and development of better e-commerce systems (Lu and Lin, 2002; Amiel and Sargent 2004; Hong et al., 2006). The issue of how SNS use affects individual perceptions of social capital is currently a major academic research topic (e.g. Steinfield et al., 2008; Ji et al., 2010; Choi et al., 2011). Choi and Scott (2011) suggested that research should investigate the relationship between the use of SNS and social capital by adopting various perspectives. On the basis of the foregoing discussion, we use the amount of SNS usage to differentiate user groups, as this can then shed light on differences in user behavior and social capital. The following are research questions that are investigated in this study:

  1. What differences in user behavior exist among groups with different amounts of time spent using SNS?
  2. Are there differences in SNS and real-life perceived social capital among members of groups that spend the same amount of time using SNS?
  3. Are there differences in SNS and real-life perceived social capital among members of groups that spend different amounts of time using SNS?

We attempted to determine the actual SNS use behavior of users in Taiwan. Taiwan is part of the Greater China market, which includes Mainland China, Hong Kong and Macau. Thus, the results obtained in this study can be extrapolated to this larger market and thus have considerable reference value in practice.

LITERATURE REVIEW

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX

Behavioral Characteristics of Social Networking Sites Users

Investigating the behavior of Internet users is an interdisciplinary research topic. For instance, Casale and Fioravanti (2011) studied students' use of Internet in the context of psychosocial health, whereas Lykourentzou et al. (2012) investigated the user behavior on Wiki sites operated by businesses and proposed some specific implications for managers. People can employ SNS to engage in real-time online communication and information sharing with friends or social groups, and two main research frameworks are currently used by academic researchers to investigate SNS user behavior, using clickstream data and questionnaires.

The first involves the use of computer programs to collect clickstream data, followed by statistical treatment to analyse user behaviors (Benevenuto et al., 2009; Schneider et al., 2009), with these including searching, viewing video or photos, making scrapbooks, posting testimonials, sending messages, taking part in communities and updating profiles. Clickstream data consists of the browsing and user traffic data collected from websites, and researchers can thus use large amounts of data to analyse user behaviors via a number of statistical operations. In addition, van den Poel and Buckinx (2005) suggested that the analysis of clickstream data can allow users' website activities to be classified in detail, which can then be used to infer different behaviors. As a consequence, this method provides a highly scientific basis for data classification in this context.

The other method is the use of questionnaires, which can produce two types of results. One type is exemplified by Valenzuela et al. (2009), which includes five common Facebook activities as part of their survey. Most studies of this type investigate only samples with specific demographic characteristics, and are therefore unable to perform in-depth analysis of the issues being examined, as they do not focus on investigating the behavior of SNS users. The other type of questionnaire research primarily focuses on understanding user behavior. For instance, Kim et al. (2011) gathered SNS user behavior from past literature and then used factor analysis to derive five motivations for it. Barker (2009) similarly used factor analysis to obtain six types of user motivation among older adolescents. We found that the results of both Kim et al. (2011) and Barker (2009) include social networking, entertainment, searching for information and learning activities. Furthermore, a comparison with the results of studies that used clickstream data analysis revealed a number of similarities and differences; thus, these two research methods can be used in a complementary fashion to help researchers more fully understand the behaviors of SNS users.

Social Capital of Social Networking Sites

Social capital can be used to explain the overall or individual structural relationships that exist in society from a sociological perspective (Portes, 1998). This structural relationship is a social system (Adler and Kwon, 2002). A person's social capital refers to their relationships with others, which can serve as a source of benefits for that person (Coleman, 1988). In general, social capital can be divided into bonding and bridging capital (Katz and Aspden, 1997; Putnam, 2000). Bonding social capital refers to the cognitive similarity and higher homogeneity of internal social groups, whereas bridging social capital is the social relations among more heterogeneous people, in which there are greater cognitive differences; thus, it is possible for individuals to form relationships with greater levels of reciprocity and recognition. We believe that social capital consists of social networks possessing value, and it is based on the individual social relationships that make up a person's social system. For example, in a social network, individuals can use specific communication media to communicate with friends, and the relationships that exist between friends are unique. Some friends are valuable because they are amusing, others are more helpful and other friends may have many roles. An individual's social system will include relationships with different levels of trust and familiarity (Burt, 1992). In addition, significant emotional interchanges between individuals will alter their existing interpersonal relationship, causing changes to their social capital.

An SNS is a complex network, and each individual within it has his or her own set of social relationships, and if the structure of these relationships can be interpreted, then this can help us to understand each user (Piao et al., 2010). boyd and Ellison (2007) stated that SNSs permit individuals to openly or semi-openly disclose their basic individual information to groups of people with similar interests, allowing individuals to meet and engage new friends online. Because of this, users' SNS friends are not necessarily their friends met in real life, and their relationships only exist within the virtual community environment.

Wang et al. (2012) stated that a virtual community is a computer-mediated communication environment and that if enough functional benefits that improve system interactivity are offered, then it will be able to attract more members. Pasek et al. (2009) suggested that SNSs constitute virtual communities and people's social behavior and characteristics are limited by the constraints of the website system itself. As a consequence, users are not necessarily able to express conventional social behaviors in a virtual environment in exactly the same way as they would in a normal situation. In effect, the virtual environment has its own characteristics, such as the fact that the veracity of certain information cannot be known with full certainty, which implies that there is a clear discrepancy between how social capital is perceived in the real world versus on SNSs. Huvila et al. (2010) studied the differences between social capital in real life and social capital in a virtual world (Second Life) and found that people with different levels of use had different perceptions of social capital. However, it remains to be determined whether the same result will occur in the virtual environment of an SNS. Donath and boyd (2004) suggested that although SNSs serve as bridges enabling users to obtain and display their social capital, this does not mean that SNS users will always have good social capital. This study therefore uses a survey and employs statistical testing methods to determine whether there are any significant differences between social capital in real life and on SNSs.

RESEARCH DESIGN AND METHOD

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX

Questionnaire Design

The questionnaire used in this study consists of three parts. The first part, which concerns the users' basic details, asked about their gender, age, tools used, number of friends in real life and on SNSs and amount of time spent on SNSs. In particular, this study used the amount of SNS usage as a way of differentiating user groups. Wilson et al. (2010) employed weeks as a unit for assessing amount of time spent using SNS, and the current study also does this, with each usage option increasing in increments of 3 h (i.e. 0–3 h is the lowest option). Because the respondents consisted of members of the general public in Taiwan, we consulted the research of Chou and Hsiao (2000), who found in a study of college students in Taiwan that ‘heavy users’ normally spend three times as much time online as ‘light users’. Because of this, when investigating users' SNS usage time in the questionnaire, we felt it necessary to provide a time option that was three times greater than the minimum option. Consequently, we included four options: 0–3, 3–6, 6–9 h and over 9 h. From this, four user groups were formed according to the amount of time spent on SNSs: members of group A spent 0–3 h per week using SNS, those in group B spent 3–6 h, those in group C spent 6–9 h and the heavy users, in group D, spent more than 9 h per week on SNSs.

We chiefly referenced the works of Benevenuto et al. (2009), Kim et al. (2010) and Schneider et al. (2009) when designing the user behavior questions. The operational definitions of the most frequent types of user behavior are summarized in Table 1.

Table 1. Behavior of SNS users
Type of user behaviorDescription
  1. SNS, social networking site.

SearchSearching for interesting information or friends.
Scrapbook or share personal informationSharing information or gossip, or responding to messages.
MessagesEngaging in online real-time communication and messaging with others.
TestimonialGiving testimonials or recommendations.
Watch videosWatching videos online.
Watch photosWatching photo online.
Browse info posted by friend or community informationBrowsing information posted by friends or community members, for study, fun, and so on.
Browse info about the friends of friends or community membersBrowsing account profiles of friends of friends or community members.
Use other extended or embedded applicationsUse of other application programs or software incorporated on SNSs, such as Web games and multimedia editing programs.
Other activities, such as personal profile editing and idlingOther activities, including personal account editing or idling (i.e. away from keyboard) behavior.

The social capital measurement questions in the third part of the questionnaire are based on the measurement instruments in Huvila et al. (2010) and Onyx and Bullen (2000), and we do not consider the type of social capital (i.e. bridging and bonding). All 13 questions were written with a double list design by selecting suitable variables to examine the status of social capital on SNSs and in real life (see APPENDIX). Each question used a five-point Likert scale, ranging from ‘strongly disagree’ at 1 point to ‘strongly agree’ at 5 points.

Data Collection and Research Method

The questionnaires were gathered using an Internet survey method. We used online forums, bulletin board systems and SNSs as channels to publicize the questionnaire, and we encouraged respondents to complete them at an online questionnaire service provider (http://survey.youthwant.com.tw) or this study's survey website. Questionnaires were collected from 13 February to 31 July 2011, and the survey was primarily aimed at SNS users in Taiwan. A total of 768 people filled out the questionnaire, and 732 valid responses were obtained.

This study adopted descriptive statistics, multivariate analysis of variance and the t-test as the statistical methods. We used the statistical software package spss Statistics 17.0 (IBM, Armonk, NY, USA) to analyse the responses. The results of the reliability analysis for real-life and SNS social capital showed that the reliability values of both were roughly the same (Cronbach's alpha = 0.92). On the basis of the recommendation of Fornell and Larcker (1981) that the Cronbach's alpha value should be at least 0.7, the internal consistency of the questionnaire was thus excellent. In addition, Hair et al. (1998) suggested that factor loadings should exceed 0.5. We calculated the factor loading of the recovered sample, and this was greater than 0.5 in all cases, indicating excellent convergent validity.

STATISTICAL RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX

This study classified the sample on the basis of the amount of SNS usage, with four groups, from light to heavy, as noted earlier. The basic demographic distribution of the sample is as shown in Table 2. The total number of people in the sample was 732, of which 328 belonged to group A, 156 to groups B and D, and 92 belonged to group C. Close to equal numbers of men and women completed the questionnaire, with only 3% more of the latter. With regard to age, 96.2% were younger than 40 years, and the largest number was in the 18- to 23-year age group (41%). For the equipment used to access SNS, the majority still used desktop computers (73.8%) and notebooks (22.4%), with only 2.2% using smartphones to do so. Furthermore, with regard to the number of friends, 69.4% of respondents had more than 30 SNS friends, and 39.9% had more than 100. In contrast, no one had so many friends in real life, and only 19.7% of the respondents claimed to have more than 30 friends in this context.

Table 2. Profile of the respondents
 Group AGroup BGroup CGroup Dn = 732
  0- to 3-h users3- to 6-h users6- to 9-h users>9-h usersTotal (%)
  1. SNS, social networking site.

Gender     
 Male184722868352 (48.1)
 Female144846488380 (51.9)
Age (years)     
 <1816082044 (6.0)
 18–23140684052300 (41.0)
 23–2876282444172 (23.5)
 28–3360401628144 (19.7)
 33–4016204444 (6.0)
 >402000828 (3.8)
Equipment connected to SNS     
 Desktop2721165696540 (73.8)
 Laptop44283656164 (22.4)
 Smartphone480416 (2.2)
 Other mobile devices40004 (0.5)
 Other digital devices44008 (1.1)
SNS friends     
 03200032 (4.4)
 1–10561202088 (12.0)
 11–3060161216104 (14.2)
 30–10092602440216 (29.5)
 >10088685680292 (39.9)
Real-life friends     
 024041240 (5.4)
 1–396201216144 (19.7)
 4–10112682024224 (30.6)
 10–3072322452180 (24.6)
 >3024363252144 (19.7)

Table 3 shows the results of the statistical analysis, and it can be seen that the different groups exhibited somewhat different user behaviors. In terms of the overall results, the most common form of SNS behavior was the use of other application programs. Approximately 20.5% of the respondents commonly used SNS to play games or used other embedded applications, and this percentage was the highest in group D.

Table 3. Comparisons of social networking sites' user behavior between different user groups
 Group AGroup BGroup CGroup D 
Type of user behavior0- to 3-h users (%)3- to 6-h users (%)6- to 9-h users (%)>9-h users (%)Total (%)
Search60 (18.3)24 (15.4)12 (13.0)12 (7.7)108 (14.8)
Scrapbook or share personal information16 (4.9)8 (5.1)8 (8.7)12 (7.7)44 (6.0)
Messages44 (13.4)24 (15.4)16 (17.4)22 (14.1)106 (14.5)
Testimonial60 (18.3)8 (5.1)12 (13.0)25 (16.0)105 (14.3)
Watch videos8 (2.4)12 (7.7)0 (0.0)8 (5.1)28 (3.8)
Watch photos28 (8.5)20 (12.8)8 (8.7)16 (10.3)72 (9.8)
Browse info posted by friends or community information20 (6.1)9 (5.8)4 (4.4)14 (9.0)46 (6.3)
Browse info about the friends of friends or community members12 (3.7)14 (9.0)16 (17.4)5 (3.2)41 (5.6)
Use other extended or embedded applications (e.g. play Web game)60 (18.3)29 (18.6)16 (17.4)38 (24.4)150 (20.5)
Other activities, such as personal profile editing and idling20 (6.1)8 (5.1)0 (0.0)4 (2.6)32 (4.4)
Total (%)328 (100)156 (100)92 (100)156 (100)732 (100.0)

The paired-samples t-test was used to examine whether any differences existed between SNS and real-life social capital for each group (see Table 4). The results indicate that significant differences existed for groups A, B and C, as users in these had different perceptions of social capital in the two environments. The results of testing for group D did not reach the level of significance (p > 0.05) and indicated that users in this group did not perceive a significant difference between social capital on SNSs and in real life.

Table 4. t-test of differences in perceived social capital between social networking sites and real life
 Nt-value
  • *

    p < 0.001.

Group A (0–3 h)328−8.830*
Group B (3–6 h)156−7.384*
Group C (6–9 h)92−4.749*
Group D (>9 h)156−1.479

Multivariate of analysis was used to investigate social capital in the different groups, with the result having a significant Wilks's lambda (Wilk's lambda = 0.827, p < 0.001). The statistical results show that social capital on both SNS and in real life reached levels of statistical significance (SNS: F = 46.578, p < 0.001; real life: F = 27.021, p < 0.001), indicating that at least one pair of mean differences reached the level of significance. In addition, the results of Levene's test for equality of variances indicated that neither SNS nor real life social capital was significant, showing that the variances were not the same (SNS: F = 4.888, p < 0.05; real life: F = 2.987, p < 0.05). Therefore, we also used Dunnett's C method to perform post hoc testing and derived the differences between groups by comparing the results, which are shown in Table 5.

Table 5. MANOVA analyses to test differences in perceived social capitals of different user groups with regard to SNS and real life (Wilk's lambda = 0.827, p < 0.001)
  Sum of squaresDfMean squareFPost hoc test (Dunnett's C method)
  • SNS, social networking site.

  • A (0- to 3-h users); B (3- to 6-h users); C (6- to 9-h users); D (>9-h users).

  • *

    p < 0.001.

User groupsSocial capital on SNS9390.15833130.05346.578*B > A; C > A;
     D > A; D > B;
     D > C
Social capital in real life4730.24031576.74727.021*B > A; C > A;
     D > A
ErrorSocial capital on SNS48921.75472867.200  
Social capital in real life42481.34572858.353  

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX

Previous studies of user behavior on SNSs were preliminary explorations that did not distinguish the behaviors of different groups (e.g. Benevenuto et al., 2009; Valenzuela et al., 2009; Kim et al., 2011). In this study, we divided users into groups on the basis of the time spent on SNSs to better understand their behavior, based on Tokunage and Rains (2010), which noted that time spent using a particular media can reveal details of the user's psychological status, as well as how addicted they may be to the media being examined. In this section, we first discuss SNS user behavior and then investigate users' social capital, as explained in more detail as follows.

Social Networking Sites User Behavior

We derived the respondents' basic information and user behavior by means of a descriptive statistical analysis of the sample. The results of this analysis indicate that users with different intensities of SNS usage exhibit different behaviors. However, users in each group spent similar percentages of their usage time engaged in instant messaging and other forms of communication on SNSs. In addition, the same kinds of activities accounted for low amounts of the users' time in all groups, such as watching videos, posting or looking at photos and other activities, such as editing one's personal information or idling, and do not represent the users' main reasons for using SNS. In addition, searching behavior accounted for a relatively large percentage of time among users with low SNS usage, consistent with the finding of Emmanouilides and Hammond (2000) that people who do not often use websites engage in a relatively high amount of searching.

The users in group D, who spent most time on SNSs, spent a relatively large amount of their time using other applications or services (24.4%) provided by the SNSs, a much more time (16.0%) than they spent on giving testimonials, their second most common behavior. Furthermore, these users spend a relatively low amount of time on communication-related behaviors, which supports the view that heavy SNS users do not necessarily spending much of their SNS time engaging in social networking activities. Moreover, this group's most common behavior, the use of applications on the SNS, accounted for 20.5% of the usage time among the entire sample; thus, many SNS users engage in this activity. We thus argue that the various application programs incorporated in SNS are major factors that are attracting users to such sites. In addition, the more a user uses an SNS, the more time he or she spends using such applications. We thus recommend that SNSs that wish to attract more long-term users should work to provide application programs that users will feel are interesting or useful. If SNS operators do not have sufficient resources to invest in the development of new applications, they can instead form alliances with software developers to jointly develop the market, as such cooperative measures can reduce the risks of uncertainty (Chakraborty, 2012) and may lead to mutually beneficial outcomes.

It can be seen from Table 3 that users in group D spend a higher percentage of their time engaging in the two user behaviors of ‘Browse info posted by friend or community information’ and ‘Scrapbook or share personal information’ than the other groups. Because we do not consider whether the users are browsing or sharing information content, this result implies that the more people use SNS, the more they regard SNS as a major platform for sharing personal information and acquiring knowledge. On the other hand, these activities do not account for large percentages of total use time within the sample as a whole, and very few users share information, accounting for only 6% of the entire sample. Looking from an academic perspective, Chiu et al. (2006) combined social capital theory and social cognition theory in an investigation of the background motivations for knowledge sharing in virtual communities. Their research suggested that individuals who are willing to share knowledge hope to affirm their professionalism and earn the approval and attention of their colleagues. In other words, perhaps not all SNS users wish to seek attention from their friends. However, we infer from actual observations that the information sharing mechanisms of existing SNS in the Taiwanese market lack the ability for users to easily find historical information, and most information on SNSs consists of gossip. This is consistent with the argument of Yang and Lai (2011) that system and information quality will affect users' willingness to share knowledge. It is recommended that future SNS should provide better information sharing mechanisms, which enable people to post and search for more valuable information, as they may increase their willingness to do so.

Users' Social Networking Sites and Real-Life Social Capital

The results of our study provide empirical support for our proposition that there is a clear difference in the social capital that exists in the real world and on SNSs. Table 4 shows that, apart from group D, the heavy users, the other groups all have significant differences in their perceptions of social capital on SNSs and in real life, and all feel that the latter is more important than the former. This finding is consistent with the argument in Donath and boyd (2004), which stated that SNS will not necessarily increase strong ties, such as relationships with existing close friends, and thus that people will still have better relationships with their original close friends than those met through SNS.

The results of the statistical analysis in Table 5 reveal that there are significant differences in the perceptions of social capital, both on SNSs and in real life, between users in group A, the lightest users, and those in other groups. However, this does not necessarily mean that users in group A lag behind those in other groups in terms of their individual social capital structure or resources. We thus simply infer that users in group A do not have a higher perception of or regard for participation in social networking activities, and the interpersonal communication that can occur there, than users in other groups.

Although the results of this study show that perceived social capital on SNSs differs depending on the amount of time spent using such sites, they do not reveal how an individual's social capital can be changed by using SNS or explain the possible reasons why users in group D have similar perceptions of their real-life and SNS-related social capital. Tong et al. (2008) and Wang and Wellman (2010) indicated that the numbers of friends a person has can express the extent of his or her basic social relationships. This study thus examines the number of network friends that exist in the different groups and whether this is connected to the transformation of personal perceived social capital. The results are summarized in Figure 1. It can be seen that the proportion of SNS friends and real-world friends rises from group A to group C. However, as users spend more time on SNSs, there is a clear transformation in their perceived social capital. We conclude that this may be because individual social capital undergoes a systematic shift (see Figure 2), as follows. When people first start to use an SNS, they have few SNS friends and thus are likely to have more friends in the real world. However, as a person's number of SNS friends increases, he or she will spend more time using such sites. The results show a positive relationship between the number of SNS friends and time spent on SNSs. As a person's social relations gradually transfer to an SNS context, the levels of social capital perceived in the virtual and real environments will become increasingly similar, although the number of SNS friends a person has will not necessarily increase substantially. Moreover, Hsu et al. (2012) noted that higher levels of flow experience (i.e. perceived enjoyment) will increase a user's continuance intention with regard to system use. Because communicating with friends and playing games are enjoyable experiences, we conclude that users in group D have higher levels of flow experience on SNSs; thus, this is why they feel that there are no major differences between relationships with friend on SNSs and offline.

image

Figure 1. Transformation of perceived social capital with the time spent on a social networking site (SNS)

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image

Figure 2. Transformation of perceived social capital process: use time concept. SNS, social networking site

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CONCLUSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX

The rise of online social networks (SNSs) provides a convenient way for people to make and maintain friendships and thus engage in new forms of social behavior. It is anticipated that the results of this study should enable SNS operators to better understand user behaviors and their perceptions of social capital. Because the results of this work show that roughly one-fifth of users use other services provided by SNSs, and not just those for communicating with friends, operators should work to develop functions that do not focus only on traditional social networking activities, so as to increase the attractiveness of their sites and retain long-term users. Furthermore, this study also examined differences in perceived social capital between users with different levels of SNS usage and found that those who spend more time on SNSs do not see any significant differences between their real-life and SNS social capital. In contrast, users who spend less time on SNSs do not place as much importance on the latter. We thus verified that users with different amounts of SNS usage have different user behaviors and perceptions of social capital and proposed a process to explain the transformation of perceived social capital that occurs in this context. The results of this study may provide a foundation for subsequent research on social capital and SNSs.

In the face of stiff competition in the SNS market, e-commerce firms must work to understand and meet users' preferences. Hampered by current technologies, today's SNSs mostly provide only text communication and few emoticons, but future advances in communication methods will allow SNSs to offer other means of communication, such as richer emoticons or video, and this will increase the level of perceived communication and thus enable users to create even better social relationships.

LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX

This study has the following limitations, which suggest directions for future research. First, the research sample only included subjects from Taiwan, and different results may be obtained with subjects from a different country or culture. However, because Taiwan has many similarities with China, the findings of this study should still have considerable value. Second, this study had a 96.2% questionnaire recovery rate, with most of the respondents being younger than 40 years and only 3.8% older than this. As a result, this study does not reflect the views and behaviors of older users, and more research is thus needed to determine the SNS behaviors and perceived social capital of this group. Third, this study used the amount of SNS use as a basis for differentiating user groups, and different classification criteria may lead to different results. Finally, we recommend that other researchers perform a more in-depth investigation of SNS addiction and related behaviors. The current study examines SNS use from the perspective of the positive aspects, and an examination of the negative ones may lead to a different understanding of SNS user behavior.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX
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APPENDIX

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. LITERATURE REVIEW
  5. RESEARCH DESIGN AND METHOD
  6. STATISTICAL RESULTS
  7. DISCUSSION
  8. CONCLUSION
  9. LIMITATIONS AND FUTURE RESEARCH RECOMMENDATIONS
  10. REFERENCES
  11. APPENDIX

ITEMS OF SOCIAL CAPITAL QUESTIONNAIRE

Social capital on social networking sites.
1. I feel that social activities on a social networking site have value.
2. I believe that helping other people on a social networking site is the same as helping myself.
3. I have served as a volunteer helping other people on a social networking site.
4. Most people on social networking sites are trustworthy.
5. When you need help, you can get help from your friends on social networking sites.
6. I have participated in a group activity held by a social networking site during the past 6 months.
7. I am an active member of a social networking site.
8. Yesterday I chatted with many people on a social networking site.
9. I frequently recommend to my social networking site friends that they join me when I take part in an online activity.
10. I have given a testimonial on a social networking site for a friend within the past 6 months.
11. The multicultural interchanges they allow make me feel that social networking sites are great.
12. If I do not see eye to eye with someone on a social networking site, I will still freely express my opinion.
13. I would be very willing to obtain mediation if I had a conflict with a friend on a social networking site.
 
Social capital in real life.
1. I feel that social activities in real life have value.
2. I believe that helping other people in real life is the same as helping myself.
3. I have served as a volunteer helping other people in real life.
4. Most people in real life are trustworthy.
5. I can obtain help from a friend in real life if I need assistance.
6. I have participated in a real-life group activity during the past 6 months.
7. I am a cheerful member of social groups.
8. Yesterday I chatted face to face with many people.
9. When I take part in an activity, I frequently ask my friends to join me.
10. I commended my friend in real life within the past 6 months.
11. In real life, the multicultural interchange makes me feel great.
12. If I do not see eye to eye with someone in real life, I will still freely express my opinion.
13. I would be very willing to obtain mediation if I had a face-to-face conflict with a friend.