Social capital: the benefit of Facebook 'friends'

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Social capital: the benefit of Facebook ‘friends’ a

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Kevin Johnston , Maureen Tanner , Nishant Lalla & Dori Kawalski

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Information Systems Department, University of Cape Town, South Africa Version of record first published: 07 Mar 2011.

To cite this article: Kevin Johnston , Maureen Tanner , Nishant Lalla & Dori Kawalski (2013): Social capital: the benefit of Facebook ‘friends’, Behaviour & Information Technology, 32:1, 24-36 To link to this article: http://dx.doi.org/10.1080/0144929X.2010.550063

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Behaviour & Information Technology, 2013 Vol. 32, No. 1, 24–36, http://dx.doi.org/10.1080/0144929X.2010.550063

Social capital: the benefit of Facebook ‘friends’ Kevin Johnston*, Maureen Tanner, Nishant Lalla and Dori Kawalski Information Systems Department, University of Cape Town, South Africa

Downloaded by [Mount Allison University 0Libraries] at 06:40 12 April 2013

(Received 28 July 2009; final version received 9 December 2010) This research investigated the role Facebook use plays in the creation or maintenance of social capital among university students in South Africa. Data were collected using questionnaires completed by over 800 students from 7 universities. The questionnaire was obtained from a study conducted in Michigan State University (Ellison N.B., Steinfield, C., and Lampe, C., 2007. The benefits of Facebook ‘‘Friends’’: social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168.). Empirical research has linked social capital to many positives in society, such as improved mental and physical health, economic wellbeing, etc. Thus, social capital is important for the success of civil society. This research examined the relationships between Facebook use and the formation and maintenance of social capital amongst university students. The study also examined factors specific to the South African context and drew comparisons to the results of the original study. Analysis of the results suggests a strong association between the intensity of Facebook use and perceived bridging, bonding and maintained social capital. This paper broadens the understanding of Facebook usage by introducing the dimensions of race and age. Facebook usage was found to interact with measures of psychological well-being, suggesting that it might be beneficial to students experiencing low self-esteem and low life satisfaction. Keywords: Facebook, social capital, South Africa, intensity

1. Introduction The internet enabled a communication revolution, allowing users to send and retrieve information irrespective of geographical location, thus changing the way human beings live, work and communicate (O’Murchu et al. 2004). As the internet became an integral part of life, researchers became interested in its effects on social exchange and relationships (Williams 2006). Social networking is based on the idea that society exists as a structured set of relationships between people (Toomey et al. 1998). With the arrival of web 2.0 technologies, a new wave of online social networking tools and services emerged. These include weblogs, social network sites, forums and instant messaging. Social network sites attracted millions of users including the attention of academic and industry researchers (Boyd and Ellison 2007). There are currently many social network sites, offering a variety of services targeted at diverse audiences across the globe (Boyd and Ellison 2007). Online social network sites bring together a vast number of people who share common interests, views and goals (Boyd and Ellison 2007). These new forms of social networking impact the development and maintenance of social capital (DiMaggio et al. 2001).

*Corresponding author. Email: [email protected] Ó 2013 Taylor & Francis

Facebook is a social networking site of particular interest to researchers due to its heavy usage patterns and its technological capabilities, which allow for the bridging of online and offline relationships (Ellison et al. 2007). This empirical research replicates a study from the Michigan State University (MSU) (Ellison et al. 2007), which assessed the intensity of Facebook use and its association with social capital. This research broadens the understanding of Facebook usage by introducing the dimensions of race and age across a range of universities in South Africa. Network theorists argue that understanding social capital requires a finegrained analysis of the specific quality and configuration of network ties (Adler and Kwon 2002). The paper begins by introducing concepts of social networking and social capital. The research methodology is then explained, followed by the analysis of the data. The implications of the results are discussed, and the paper is concluded by highlighting key aspects of the study. 2. 2.1.

Review of literature Online social networking

Online social networks are defined as virtual communities which interact and pool resources through


Behaviour & Information Technology computer-mediated relationships (Toomey et al. 1998). Often, these networks consist of people who share common affinities or interests and may or may not be separated geographically (Boyd and Ellison 2007). Social networking has changed as a result of the improvement and transformation of communication technologies, primarily the internet. Bargh and McKenna (2004) state that the internet is special as it combines into one tool, features of many previous breakthroughs: person-to-person communication (such as telephones), mass medium (such as televisions) and information resources (such as libraries). While the internet was initially assumed to be merely a standardised mean of accessing information, current online social networking tools such as social network sites have allowed individuals to connect with each other more effectively than was anticipated (Weaver and Morrison 2008). An online social network site can be defined as a set of web-based services that allows 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, (3) view and transverse their list of connections and those made by others within their system’ (Boyd and Ellison 2007, p. 221). Many of these social network sites are primarily used to articulate existing social networks rather than expand networks (Boyd and Ellison 2007), thus reflecting real-life social relationships fairly accurately (Ellison et al 2007).

social capital (Ellison et al. 2007) and are all part of the cognitive social capital class. 2.2.1. Bridging social capital Bridging social capital focuses on external relations (Adler and Kwon 2002) and refers to the ‘weak ties’ between individuals (Putnam 2000). These ‘weak ties’ usually form between individuals of different ethnic and occupational backgrounds (Islam et al. 2006) and can cut across geographic and socio economic distance (Carter and Maluccio 2003). ‘Weak tie’ relationships are considered provisional and lack depth (Williams 2006), thus making bridging social capital inherently heterogeneous (Putnam 2000). Bridging social capital between individuals provides useful information and new perspectives. It expands social horizons but does not provide much emotional support (Granovetter 1983, Williams 2006, Ellison et al. 2007). Individuals with ‘weak ties’ or bridging social capital tend to have a broader set of information and access to opportunities. This phenomenon is known as the ‘strength of weak ties’ (Granovetter 1983). Donath and Boyd (2004) suggested that social network sites could increase the formation and maintenance of ‘weak ties’ or bridging social capital because the costs of use are low. Solidarity can also emerge from weak ties and can thus otherwise bridge diverse racial groups as in the case of South Africa (Adler and Kwon 2002). 2.2.2.

2.2.

Social capital

Social capital refers to resources accumulated from various relationships (Coleman 1988) and lies in the structure of relationships within which the actors are located (Portes 1998). It can be considered as a byproduct of social relationships resulting from social exchanges in structured social networks (Islam et al. 2006), and promotes co-operation between individuals (Fukuyama 2001). Social capital can be broken into two classes, namely cognitive and structural. Cognitive social capital is linked to personal aspects such as beliefs, values, norms and attitudes (Islam et al. 2006). It is also a by-product of cultural norms like religion, tradition and shared historical experiences (Fukuyama 2001). Structural social capital is the outwardly visible features of social organisations such as patterns of social engagement or density of social networks (Islam et al. 2006). The structural class reflects the strength of associational links, density of social associations and indicators of social interactions (Islam et al. 2006). Figure 1 outlines the forms of social capital relevant to this study. These relate to bridging, bonding (Putnam 2000) and maintained

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Bonding social capital

In contrast, bonding social capital exists between family members, close friends and other close relations and focus on internal ties between actors (Morrow 2001, Adler and Kwon 2002). These relations are generally homogenous and inclusive, existing between strongly knit, emotionally close relationships (Williams 2006, Ellison et al. 2007). Bonding relationships provide emotional support and a way of transmitting behavioural norms between individuals in the relationship (Islam et al. 2006). The type of relationships and structures within a social network determines the types of social capital formed in these networks (Williams 2006). ‘Strong tie’ relationships or bonding social capital does not provide links to individuals of differing backgrounds (Williams 2006). Bonding social capital is closely related to the Chinese concepts of ‘guanxi’ and ‘renqing’. Guanxi is embedded in daily social practices of people and relates to personal connections and relationships (Smart 1993, Chung 2006). Guanxi is essential to the completion of tasks in social life, and has both positive (such as reciprocity and gifts) and negative (such as bribes and nepotism) connotations (Smart


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Figure 1.

K. Johnston et al.

Social capital.

1993, Gold, Guthrie and Wank 2002, Chung and Hamilton 2003). This study only focuses on the positive aspects of guanxi. 2.2.3. Maintained social capital Ellison et al. (2007) established a third type of social capital called ‘Maintained social capital’. Maintained social capital is created when individuals maintain connections to their social networks having progressed through life changes (Bargh and McKenna 2004, Ellison et al. 2007). The use of technology can assist people to maintain relationships threatened by changes in geographical location (Bargh and McKenna 2004). The terms ‘friendsickness’ refers to the anguish caused by loss of contact with friends (Paul and Brier 2001). ‘Friendsickness’ is usually caused when school students move away to tertiary learning institutions (Paul and Brier 2001). Research shows that university students use email and instant messaging services to stay in touch with old high school friends (Ellison et al. 2007). Proximity does not affect these relationships; however, the level of communication does affect the relationship (Oswald and Clark 2003).

Generally, when social capital is low within a community, there tends to be greater social disorder and more distrust amongst participants (Helliwell and Putnam 2004). However, with greater social capital comes a general commitment to collective action which generally results in a positive effect with regard to interaction (Ellison et al. 2007). 2.3.

Negative social capital

In spite of the advantages of social capital, negative aspects still prevail (Portes 1998), as people tend to divide the world between friends and enemies. For instance, social capital might lead to the creation of hate groups as can sometimes be seen on Facebook (Fukuyama 2001). Strong ties between members of a group can exclude outsiders’ access to a group (Portes 1998). Weaker group members can make excess claims on stronger members (the free-riding problem) (Portes 1998). Participation in a group generally dictates certain levels of conformity (Portes 1998), and this can restrict individual freedom. Situations where group solidarity is formed by common experiences are termed downward levelling norms, individual success stories undermine group cohesion (Portes 1998).

2.2.4. Benefits of social capital Social capital has been shown to be a forecaster of school attrition, academic performance, physical and mental health, children’s intellectual development, sources of employment, juvenile delinquency and its prevention, and economic development (Coleman 1988, Portes 1998, Putnam 2000, Mcckenzie et al. 2002, Ellison et al. 2007).

2.4.

Measurement of social capital

The concept of social capital is elusive, both in terms of its meaning and measurement (Islam et al. 2006, Williams 2006). There is no consensus on the measurement of social capital (Fukuyama 2001, Williams 2006). Researchers have to identify observable variables and develop methods to use those

Behaviour & Information Technology


variables as proxies for social capital (Islam et al. 2006). Williams (2006) developed tools to measure internet social capital known as Internet Social Capital Scales or ISCS, which have been validated in previous studies (Valenzuela et al. 2008, Ellison et al. 2007) and were used in this study. These scales provide clarification of social capital and its formation online and offline, as well as the tradeoffs between the two (Williams 2006). The relationship between Facebook use and social capital may be explored by examining individuals’ self-esteem (Rosenberg 1965) and satisfaction with life (Pavot and Diener 1993) two validated measures of subjective well-being. 2.5.

Technology and social capital

Some researchers say that the internet diminished social interactions regarding face-to-face exchanges (Bargh and McKenna 2004). However, it can be argued that changing technology changes forms of associations (Fukuyama 2001). The ‘uses and gratifications’ theory suggests that ‘the particular purpose of individuals within communication settings will determine the outcome of the interaction’, regardless of the type of communication channel used (Bargh and McKenna 2004, p. 578). Another viewpoint proposes that elements of internet communication impact interaction outcomes, and the impacts depend on the social context (Bargh and McKenna 2004). Wellman et al. (2001) argued that online interaction provides new means of communication between parties, as well as supplementing or replacing physical interactions, thereby strengthening relationships. One reason for the initial confusion of the effects of the internet and social capital is the lack of controlled longitudinal research (Williams 2006). Online social network tools might be useful to individuals who generally struggle to form and maintain both strong and weak ties. Some studies demonstrated that the internet might help individuals having low psychological well-being and few ties to friends and neighbours (Bargh and McKenna 2004). Barriers to interactions can be lowered and self-disclosure encouraged through some form of computer-mediated communication (Tidwell and Walther 2002, Bargh and McKenna 2004). 2.6.

The South African context

No study of South Africa can be complete without some understanding of the social context of the country. Apartheid in South Africa actively oppressed and humiliated non-white people, limiting their access to education, and ability to accumulate and use assets (Carter and May 2001). All citizens were racially

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classified and issued identification numbers that contained racial classification. South African governments of the 1920–1990s used laws and power to separate and exclude non-white people. Non-white South Africans were repeatedly degraded, ill treated, and informed of their inferior status (Ramphele 2008). People of different racial classifications were legally forbidden to marry, socialise, live, play, learn, or be buried together. South Africa held its first democratic election in 1994. Fifteen years later, ‘the persistent matter of race and identity’ continue to permeate all aspects of life and plague South Africans (Boesak 2009, p. 9). South Africa continues to be a divided nation, a first world part mainly populated by whites, and a third world part populated by blacks (Adato et al. 2006) and this distorts most statistics (Waddock 2007). South Africans have a life expectancy of 51.5 (the average for Africa was 53.9), an adult literacy rate of 88 (Africa 63.3), and GDP per capita $9.757 (Africa $2.729) (Klugman 2009). In 2001, South Africa had a GENI coefficient of 0.73 and is ‘one of the most unequal societies in the world, more than half of all South Africans live in poverty, more than 10% of South Africans live in absolute poverty, and the situation is getting worse’ (Hall 2007, p. 21). South Africa has a population of 49.32 million, of which 52% were females, 79% of the population were black, and 9% white (Lehohla 2009). The Digital Divide phenomenon which is prevalent in South Africa is defined as ‘the gap between those who can effectively use or have access to new information and communication tools, such as the internet, and those who cannot’ (Mutula 2005, p. 123). The digital divide can also exist between gender, physical disability, racial and age differences (Singh 2004). These differences are especially prominent in South Africa due to its apartheid legacy (Singh 2004). 3.

Hypotheses and research questions

Table 1 summarises the hypotheses from the paper by Ellison et al. (2007), which were replicated in this study. Seven exploratory questions were constructed to examine specific aspects relating to the South African context. Three questions examined the role race played in the relationship between Facebook use intensity and the three forms of social capital. Similarly, three questions examined the role age played in the relationship between Facebook use intensity and the three forms of social capital. The seventh question examined the demographics of Facebook members versus nonFacebook members amongst South African university students. All questions were submitted to and approved by the University Ethics committee.

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K. Johnston et al.

Table 1.

Hypotheses from Ellison et al. (2007).


H1: Intensity of Facebook use will be positively associated with individuals’ perceived bridging social capital. H2: Intensity of Facebook use will be positively associated with individuals’ perceived bonding social capital. H3a: The relationship between quantity of Facebook use and bridging social capital will vary depending on the degree of a person’s self-esteem. H3b: The relationship between quantity of Facebook use and bridging social capital will vary depending on the degree of a person’s satisfaction with life. H4a: The relationship between quantity of Facebook use and bonding social capital will vary depending on the degree of a person’s self-esteem. H4b: The relationship between quantity of Facebook use and bonding social capital will vary depending on the degree of a person’s satisfaction with life. H5: Intensity of Facebook use will be positively associated with individuals’ perceived maintained social capital.

4.

Methodology

The underlying philosophy for this research is positivist. This study made use of primary data sources retrieved first hand. Quantitative data were collected through questionnaires obtained from a previous study (Ellison et al. 2007). The original questionnaire was trimmed down due to its length, in consultation with the original authors. The questionnaire is available for future use. Various statistical techniques were used to analyse the data. Most of the analytical methods were obtained from the study by Ellison et al. (2007). Additional demographic data (e.g. ethnicity, gender, etc.) were collected through the questionnaires. All questionnaires were returned in a two-week window, implying that a snapshot of the current situation was taken. The data acquired were used in an attempt to support or reject the hypotheses. A random sample of students was selected from seven tertiary institutions within South Africa (University of Cape Town, University of Witwatersrand, University of Fort Hare, Cape Peninsula University of Technology, University of Johannesburg, Stellenbosch University, and University of Western Cape). Permission to distribute the questionnaire was obtained prior to distribution. Eight hundred and twenty-three responses were captured. The raw data were cleaned and formatted, using Microsoft Excel 2007, so that it could be used in Statistica. All partially completed responses were removed before analysis, reducing the number of valid responses to 572, of which 67% (383) were members of Facebook. 5.

Data analysis

Five-point Likert scales were extensively used in the questionnaire. Various measures and constructs were obtained from the original study. These were used in assessing demographics, general life satisfaction at university, self-esteem, forms of social capital and intensity of Facebook use. These measures were then

used in building regression models to assess the impact of Facebook use on social capital. Two sets of regressions were used to build models to predict statistics for each form of social capital. The first regression was used as a comparison with the original study. The second regression included ethnicity and age variables in order to assess age and ethnicity factors in the social capital models. Interaction terms used in the regression analysis were constructed by multiplying the variables, for example: ‘self-esteem by Facebook Intensity’ was constructed by: (self-esteem) 6 (Facebook intensity). 5.1. Chi-squared goodness-of-fit test The chi-squared goodness-of-fit test was used to assess that the sample was an accurate representation of the population. The most recent figures regarding student enrolment in South African universities was used (HEMIS 2007). It is assumed that university demographics have not changed significantly since then, and that these figures are still representative of general university demographics. The chi-squared test confirmed that the sample used was a good representation of South African students both in terms of race and gender.

5.2.

Measures of Facebook usage

5.2.1. Facebook intensity The Facebook intensity scale attempted to obtain a measure of Facebook intensity other than measures of frequency or duration of Facebook use (Ellison et al. 2007). The construct consisted of two questions (13 and 14) regarding Facebook use, to assess how actively involved respondents are on Facebook, as well as six Likert-scale questions (sub questions of question 17), which assessed respondents’ attitudes towards Facebook. The attitudinal questions related to the extent to which a respondent feels emotionally connected to

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Behaviour & Information Technology Facebook, as well as assessing how significant a role Facebook plays in a participant’s regular activities. Table 2 summarises statistics associated with the Facebook Intensity construct. The construct yields a Cronbach’s alpha of 0.84, indicating a high reliability of the sub-items in supporting the construct. The analysis indicates that time spent using Facebook per day (Q. 14) fell within the 10– 30 minute category, and that the average South African student indicated he/she has between 100 and 150 friends. The MSU students’ time averages fell within the 10–30 minutes, while the average number of friends ranged between 150 and 200. Researchers at Sheffield Hallam University and the University of Liverpool have found that most people have five-core online friends (the same number they have offline) despite claiming 150 or 200 online friends (Lim 2007). The Likert-scale questions provided rather neutral results, with mean values around three. However, the

Table 2.

standard deviation of all of these items was relatively high (i.e. above 1). 5.2.2. Facebook usage: elements in profile and perceptions of who has viewed profiles Respondents were asked to indicate which profile elements they included when developing their Facebook profile (Figure 2). Respondents were also asked to indicate who they thought had viewed their Facebook profile (Figure 3). These items are valuable in assessing Facebook use regarding the development of new relationships or the maintaining of existing relationships. Contact details were the most common profile element included (77%), and ‘My classes’ the least common (10%). More than 70% of MSU students included university classes in their profiles (Ellison et al. 2007).

Summary statistics for Facebook intensity (N ¼ 383).

Question

Individual items and scale

Facebook intensity (Cronbach’s alpha ¼ 0.840) Q. 13 About how many total Facebook friends do you have? 0 ¼ 10 or less, 1 ¼ 11–50, 2 ¼ 51–100, 3 ¼ 101–150, 4 ¼ 151–200, 5 ¼ 201–250, 6 ¼ 251–300, 7 ¼ 301–400, 8 ¼ more than 400 Q. 14 In the past week, on average, approximately how many minutes per day have you spent on Facebook? 0 ¼ less than 10, 1 ¼ 10–30, 2 ¼ 31–60, 3 ¼ 1–2 hours, 4 ¼ 2–3 hours, 5 ¼ more than 3 hours Q. 17 1. Facebook is part of my everyday activity. 2. I am proud to tell people I am on Facebook. 3. Facebook has become part of my daily routine. 4. I feel out of touch when I haven’t logged onto Facebook for a while. 5. I feel I am part of the Facebook community. 6. I would be sorry if Facebook shut down.

Mean

SD

3.02 3.17

0.97 2.31

1.69

1.44

3.03 3.53 3.07 2.74 3.36 3.58

1.31 1.00 1.27 1.37 1.08 1.23

Notes: Individual items were first standardised before taking a mean to create a scale. Unless provided, response categories ranged from 1 ¼ strongly disagree to 5 ¼ strongly agree.

Figure 2.

Profile elements.

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K. Johnston et al.


Figure 3.

Perceived profile viewers.

Figure 3 shows who respondents perceived have viewed their profiles, ‘High school friends’ (88%) and ‘Friends other than high school friends’ (85%) were the most selected responses. 5.2.3. Use of Facebook to meet new people vs. connect with existing offline contacts The purpose of this construct is to ascertain whether respondents are inclined to use Facebook to find new people online or to find contacts with whom they already have an offline relationship. Table 3 shows Cronbach’s alpha for the construct ‘Offline to online: use of Facebook to connect with offline contacts’ is 0.60 and is not considered to be highly reliable. The MSU study yielded a Cronbach’s alpha of 0.70 and was considered to be reliable in supporting the construct. The single item measure ‘On to offline: use Facebook to meet new people’ has a mean of 2.55. The MSU study mean was 1.97 which is lower than the current study’s finding. The question ‘I use Facebook to keep in touch with my old friends’ has a particularly high mean of 4.47 and a low standard deviation (0.89), indicating that the majority of Facebook members agreed with this statement. 5.3.

Table 3. Summary statistics for Facebook use for prior contacts and meeting new people (N ¼ 383). Questions

Individual items and scales

Off to online: use Facebook to connect with offline contacts (Cronbach’s alpha 0.60) Q. 16 1. I have used Facebook to check out someone I met socially. 2. I use Facebook to learn more about other people in my classes. 3. I use Facebook to learn more about other people living near me. 4. I use Facebook to keep in touch with my old friends. On to offline: I use Facebook to meet new people (single-item measure)

Mean

SD

3.37

0.81

3.57

1.33

2.81

1.26

2.64

1.29

4.47

0.89

2.55

1.45

Notes: Individual items ranged from 1 ¼ strongly disagree to 5 ¼ strongly agree, scales constructed by taking mean of items.

specific social capital were also included (Ellison et al. 2007). The factor loadings analysis was calculated for all the social capital items. The results show that the social capital items actually represent three distinct factors.

Measures for psychological well-being

Self-esteem was measured using the Rosenberg selfesteem scale (Ellison et al. 2007) in question 20. The scale is made up of seven items, posed on a five-point Likert-scale. The scale exhibited high reliability with a Cronbach’s alpha ¼ 0.81 (Table 4). The satisfaction with university life was adopted from the Satisfaction with Life Scale (Ellison et al. 2007) in question 21. The scale exhibited high reliability with a Cronbach’s alpha ¼ 0.82. 5.4. Measures of social capital The three types of social capital were measured by adapting existing scales. Items which capture internet-

5.4.1.

Measure for assessing bridging social capital

This measure assessed the degree to which respondents experience bridging social capital (Ellison et al. 2007), using Williams’ (2006) bridging social capital subscale. The scale exhibited high reliability with a Cronbach’s alpha ¼ 0.86. 5.4.2.

Measure for assessing bonding social capital

This measure assessed the extent to which respondents experienced bonding social capital. Bonding social capital makes use of five items from the bonding subscale developed and validated by Williams (2006).

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Behaviour & Information Technology Table 4.

Summary statistics for self-esteem and satisfaction with university life items (N ¼ 383).

Questions Q.20


Q. 21

Individual items and scales

Mean

SD

Self-esteem scale (Cronbach’s alpha ¼ 0.81) I feel that I am a person of worth, at least on an equal plane with others. I feel that I have a number of good qualities. All in all, I am inclined to feel that I am a failure (reversed). I am able to do things as well as most other people. I feel I do not have much to be proud of (reversed). I take a positive attitude toward myself On the whole, I am satisfied with myself Satisfaction with university life scale (Cronbach’s alpha ¼ 0.82) In most ways my life at my university is close to my ideal. The conditions of my life at my university are excellent. I am satisfied with my life at my university. So far I have gotten the important things I want at my university. If I could live my time at my university over, I would change almost nothing.

4.16 4.24 4.40 4.02 4.09 4.06 4.15 4.13 3.35 3.30 3.38 3.57 3.52 2.96

0.62 0.82 0.71 1.03 0.88 1.10 0.90 0.86 0.78 1.06 1.02 0.93 0.96 1.15

Notes: Individual items ranged from 1 ¼ strongly disagree to 5 ¼ strongly agree, scales constructed by taking mean of items.

The scale did not exhibit high reliability Cronbach’s alpha ¼ 0.69. 5.4.3. Measure for assessing maintained social capital This measure assessed the extent to which respondents experienced maintaining social capital. This scale was adapted from the MSU study. The items were adapted from traditional measures of social capital which gauge a persons’ ability in finding support. The scale exhibited high reliability with a Cronbach’s alpha ¼ 0.80. 5.5.

Analysis of demographics

Having 33% of respondents not being Facebook members provided an interesting aspect to the study which was unavailable to the MSU study (6% were not Facebook users). South African student respondents were more balanced with regards to gender, 55% male verses 34% male in MSU. The average respondent was in 2nd year with a mean age of 20.58 years. The current study showed a split of 61% of students whose home residence is within the city in which they are studying, and 39% being from ‘out-oftown’. Thirty-seven percent of students resided on campus, in comparison with MSU results of 55%. The South African study indicated that more students (43%) are members of clubs or societies than their American counterparts in the MSU study (8%). 5.6.

Analysis of regression models

The aim of the research is to identify whether Facebook use plays a significant role in the creation of or maintaining of social capital. Regression models were constructed to predict whether certain variables

were significant in affecting the various forms of social capital. For each form of social capital, an initial regression model of control variables was constructed and then assessed. An additional variable of the intensity of Facebook use was then added to the model, and the results assessed. Finally, interaction variables of ‘self-esteem by Facebook intensity’ and ‘satisfaction with university life by Facebook intensity’ were introduced and the model was again assessed. 5.6.1. Regression model: bridging social capital The regression model constructed was used to assess which variables are predictors of bridging social capital. To assess the impact of Facebook use on bridging social capital, the regression was initially investigated with the control variables: demographics, general internet use, and measures of psychological well-being. This yielded an adjusted R2 of 0.37. The ‘intensity of Facebook’ variable was then included in the model to investigate if it has an impact on the predicted bridging social capital, which yielded an adjusted R2 of 0.38 (see Table 5). Two interaction variables were introduced into the regression for bridging social capital, resulting in two further models: . Model 1 aimed to predict bridging social capital by using ‘control factors’, ‘Facebook intensity’ and ‘self-esteem by Facebook intensity interaction’ as independent variables. . Model 2 aimed to predict bridging social capital by using ‘control factors’, ‘Facebook intensity’ and ‘satisfaction with university life by Facebook intensity interaction’ as independent variables.

32 Table 5.

K. Johnston et al. Regressions predicting the amount of bridging social capital from demographic, attitudinal and Facebook variables. Model 1: Control factors, Facebook intensity, and self-esteem 6 Facebook intensity interaction


Independent variables

Stand. beta

Intercept Gender: male Ethnicity: white Year in university Home residence: out-of-town Local residence: university residence Club/society member Hours of internet use per day Self-esteem Satisfaction with life at university Facebook (FB) intensity Self-esteem by FB intensity2 Satisfaction by FB intensity n ¼ 383

p

1.09 0.09 70.17 70.14 0.04 0.13 0.14 70.07 0.34 0.37 0.51 70.48

* * **** *** ** *** ** **** *

F ¼ 23.05, **** Adjusted R2 ¼ 0.39

Model 2: Control factors, Facebook intensity, and satisfaction with university life 6 Facebook intensity interaction Stand. beta 1.87 0.09 70.17 70.14 0.04 0.14 0.14 70.07 0.14 0.38 0.13

P **** * **** ** ** ** ** **

70.03 F ¼ 22.55, **** Adjusted R2 ¼ 0.38

Notes: *p 5 0.05, **p 5 0.01, ***p 5 0.001, ****p 5 0.0001. Only one interaction term was entered at a time in each regression.

These yielded adjusted R2 values of 0.39 (model 1) and 0.38 (model 2), compared with that of the MSU study of 0.44 (model 1) and 0.46 (model 2), respectively. For model 1, factors considered to be significant predictors in bridging social capital were satisfaction with university life (p ¼ 0.000) being most significant, ethnicity (white) (p ¼ 0.0007), year in university (p ¼ 0.0007), member of clubs or societies (p ¼ 0.0009), local residence: university (p ¼ 0.006), self-esteem (p ¼ 0.004), gender (p ¼ 0.037) and intensity of Facebook use (p ¼ 0.025). Self-esteem by Facebook intensity factor is not significant in predicting bridging social capital (p ¼ 0.07). Model 2 differed slightly from model 1, mainly regarding the levels of significance. Factors considered to be significant predictors of bridging social capital included: ethnicity (white) (p ¼ 0.0001) being most significant, year in university (p ¼ 0.001), member of clubs or societies (p ¼ 0.001), self-esteem (p ¼ 0.001), satisfaction with university life (p ¼ 0.002), residence: university (p ¼ 0.003), and gender: male (p ¼ 0.04). Satisfaction with university life by Facebook intensity is not significant in predicting bridging social capital.

measures of psychological well-being. This yielded an adjusted R2 of 0.17. The ‘intensity of Facebook’ variable was then included in the regression model to investigate whether it has an impact on the predicted bonding social capital, which yielded an adjusted R2 of 0.19. Similarly, two interaction variables were introduced into the regression for bonding social capital, resulting in two further models. These yielded adjusted R2 values of 0.18 for both models 1 and 2, compared with that of the MSU study of 0.23 (model 1) and 0.22 (model 2), respectively. For model 1, the factors considered to be significant predictors in bonding social capital were: satisfaction with university life (p ¼ 0.000) being most significant and gender (p ¼ 0.01). Intensity of Facebook use was found not to be significant in predicating bonding social capital (p ¼ 0.29). Self-esteem played some significance in model 2 in predicting bonding social capital. Factors considered to be significant predictors of bonding social capital for model 2 included: self-esteem (p ¼ 0.007), gender: male (p ¼ 0.01), and satisfaction with university life (p ¼ 0.002). Satisfaction with university life is less significant in model 2 than in model 1, but is still a significant predictor in the model.

5.6.2. Regression model: bonding social capital The regression model constructed was used to assess which factors are predictors of bonding social capital. This regression model was initially investigated with the same control variables as the previous model. These were: demographics, general internet use, and

5.6.3. Regression model: maintained social capital The regression model constructed was used to assess which factors are predictors of maintained social capital. To assess the impact of Facebook use on

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Behaviour & Information Technology maintained social capital, the regression was first investigated with the control variables: demographics, general internet use, and measures of psychological well-being. This yielded an adjusted R2 of 0.09. The ‘intensity of Facebook’ variable was then included in the regression model to investigate if it has an impact on the predicted bridging social capital, which yielded an adjusted R2 of 0.15. Similarly, two interaction variables were introduced for maintaining social capital, resulting in two further models. These yielded adjusted R2 values of 0.15 (model 1) and 0.16 (model 2), compared with that of the MSU study of 0.16 (model 1) and 0.17 (model 2), respectively. For model 1, the following factors were considered to be significant predictors in maintained social capital: intensity of Facebook use (p ¼ 0.003) being most significant, self-esteem (p ¼ 0.004), satisfaction with university life (p ¼ 0.02), and year in university (p ¼ 0.02). Model 2 offered some additional results. These included: intensity of Facebook use (p ¼ 0.0000) being highly significant, satisfaction with university life (p ¼ 0.001), self-esteem (p ¼ 0.004), satisfaction by Facebook intensity (p ¼ 0.009), and year in university (p ¼ 0.02). In model 2, intensity of Facebook use is more significant than in model 1. Table 6 highlights the major differences between the MSU study and the South African study.

Table 6.

6.

Discussion of implications

In the sample of students surveyed (N ¼ 572), 67% were Facebook users while in the MSU study, 94% were registered Facebook members. The MSU study yielded an insignificant sample size of non-Facebook members and provided inconclusive findings regarding demographics of non-members. The 33% of South African non-Facebook members are predominantly African people, due to the context as detailed in Section 2.3. The South African Facebook members spent 10–30 minutes per day using Facebook, similar to the MSU study. The average South African student has between 100 and 150 friends while MSU students on average have 150–200 friends. This may be due to higher technology adoption rates in developed countries, or because MSU students have been using Facebook for longer periods. The MSU students primarily use Facebook as a tool to keep in touch with students in the same university and classes. This can be seen by the high response rate of MSU students indicating that they have ‘My classes’ as a Facebook profile item. Only 2.57% of surveyed Facebook members indicated that they include ‘My classes’ as a Facebook profile element. South African students do not use Facebook in the same manner as the students at MSU. This

Major differences between the MSU study and the South African study. MSU

SA

Comments

Number of friends

150–200

100–150

Time spent of Facebook daily

10–30 minutes

10–30 minutes

Differences in number of friends might be due to higher technology adoption rates in developed countries or because MSU has been using Facebook for a longer period of time. Students (both in SA universities and MSU) might spend similar amounts of time in lectures, and tutorials, thus limiting the amount of time that they can spend on Facebook daily. As 50% of SA respondents were non-white and are thus more likely to come from disadvantaged backgrounds, they might be affected by the digital divide. This might in turn affect their access to computers and consequently their Facebook membership. The South African first year classes tend to be very large (over 600 students), thus limiting the ability of students to get to know each other. Hence, they might be less inclined to interact using the My Class Profile on Facebook. SA study has a more balanced Facebook usage rate across the genders as opposed to what can be seen in the MSU study. The fact that only 37% of SA respondents reside on campus might account for the lower Facebook membership rate in SA. In essence, on-campus residents are more likely to have Internet access, and thus access to Facebook. The population sample of MSU might be more uniform as opposed to SA respondents which are more diverse, thus explaining the non-white high adoption rate for MSU. The population sample of MSU might be more uniform as opposed to SA respondents which are be more diverse, thus explaining the high white non-adoption rate for MSU.

Members of Facebook

94%

67%

My class profile on Facebook

70%

10%

Male

34%

55%

Reside on campus

55%

37%

Race (white)

87%

28%

Non-members of Facebook (white)

87%

14%


34

K. Johnston et al.

means that the measure for ‘Off to online: use Facebook to connect with offline contacts’ will need to be adjusted to fit the South African context. The sub-item ‘I use Facebook to keep in touch with my old friends’ has a mean of 4.47 and a standard deviation of 0.89. This means that most of the respondents indicated that they strongly agree with this statement. This is an interesting aspect as it gives insight into the intended usage of Facebook by South African university students. In assessing Hypothesis 1, the regression model resulted in an adjusted R2 value of 0.37. After introducing the intensity of Facebook use variable, the adjusted R2 increased to 0.39. The Facebook intensity variable was considered to be significant, with standardised beta of 0.11 (p 5 0.05), indicating that Facebook intensity is positively associated with perceived bridging social capital. Thus Hypothesis 1 and the findings of the MSU study are supported. It is interesting to note that, similarly to the MSU study, hours of internet use per day is not significant in predicting the amount of bridging social capital. A similar procedure was followed in assessing Hypothesis 2, with various regression models being constructed, and introducing additional variables at each regression. The adjusted R2 value for the control variable regression yielded a value of 0.17, increasing to 0.19 after introducing the Facebook intensity variable. Standardised beta of 0.14 (p 5 0.01) for the Facebook intensity variable suggests that this variable is significant in the model. Hypothesis 2 is supported, namely that intensity of Facebook use is positively associated with perceived bonding social capital. However, considerably less (0.19 vs. 0.39) of the variation is explained by the regression model, Facebook use has less of an impact on bonding social capital. This could be due to the structure and features of Facebook which promote the creation of ‘weak ties’ (bridging). Facebook intensity is a less likely predictor for bonding social capital with South African university students. The gender variable was significant in the bonding social capital regression model where males appear to have higher bonding social capital then females. The relationship between intensity of Facebook use and bridging social capital (Hypotheses 3a and 3b) did not vary with different levels of psychological wellbeing, namely self-esteem and satisfaction with university life. The regression models show these factors as non-significant predictors of bridging social capital with standardised beta of 70.03 (p ¼ 0.89) for the selfesteem, and standardised beta of 70.48 (p ¼ 0.07) for the satisfaction with university life. Hypothesis 3a and 3b are therefore rejected. Rejecting 3a means that the relationship between Facebook intensity and bridging

social capital does not vary by differing levels of selfesteem. Rejecting 3b means that students with low levels of Facebook intensity, reporting low satisfaction with university life, do not necessarily report lower levels of bridging social capital than similar students with higher Facebook intensity levels. The MSU study found that students with low levels of Facebook intensity, reporting low self-esteem or low satisfaction with life at college, reported lower bridging social capital than those who frequent Facebook. The regression model with a standardised beta of 0.24 (p ¼ 0.43) for the self-esteem by Facebook Intensity interaction variable was not significant, and therefore Hypothesis 4a is rejected. This means that students with low levels of Facebook intensity, reporting low satisfaction with university life, do not necessarily report lower levels of bonding social capital than students with higher Facebook intensity levels. The relationship between Facebook intensity and bonding social capital does not vary by differing levels of satisfaction with university life. The MSU study produced similar results. The standardised beta of 70.06 (p ¼ 0.79), for the satisfaction with university life by Facebook intensity interaction variable was not significant, Hypothesis 4b is rejected. This means that students with low levels of Facebook intensity, reporting low satisfaction with university life, do not necessarily report lower levels of bonding social capital than similar students with higher Facebook intensity levels. The relationship between Facebook intensity and bonding social capital does not vary by differing levels of satisfaction with university life. The results of the MSU study were similar. Finally, in assessing Hypothesis 5, the regression models were again constructed, initially with the control variables and then with the intensity of Facebook variable, and intensity of Facebook interaction variables. The control variables yielded an adjusted R2 for the regression of 0.09, with the introduction of the Facebook intensity variable increasing adjusted R2 to 0.15 with standardised beta of 0.80 (p 5 0.0000). This is highly significant implying that Facebook intensity plays a significant role in predicting maintained social capital of South Africa university students. H5 is therefore supported. This is further supported by the figures in Table 3, which show higher mean values for offline-to-online than online-tooffline measures, suggesting that South African university students predominantly use Facebook as a means for maintaining and strengthening existing offline relationships. It seems that Facebook is used by South African university students as a communication medium to maintain already existing relationships. Overall, it seems that Facebook intensity is

Behaviour & Information Technology


strongly correlated with the formation of maintained social capital. Students surveyed seemed to use Facebook to maintain relationships and develop maintained social capital. The Facebook intensity is positively correlated with all three types of social capital, with the strongest correlation to maintained social capital. Having been undertaken in a more diverse setting with a range of universities, this study extends the original study. This was done by revealing how factors such as age, race, and multi-universities impacts the benefits of Facebook towards creating social capital in a developing country such as South Africa. This confirms the findings of the original study and extends its generalisability. 7.

Conclusion

This empirical research replicated a study performed at MSU and adapted it for a South African context. The study aimed to assess the intensity of Facebook use amongst South African university students and whether this would be useful in the maintenance and creation of social capital. The research provided a cursory analysis of demographic use of Facebook and attempted to answer potential research questions. Results indicate that intensity of Facebook use plays a role in the creation of social capital, but is particularly significant regarding the maintenance of social capital in the South African context. Comparisons were made with the MSU study which had a high rate of Facebook members, rendering the analysis of non-Facebook members inconclusive. Research questions were answered providing new avenues for future research regarding Facebook, or more generally, online social networking within South Africa. Future research could attempt to solidify the outcomes of some of the research questions regarding more detailed and rigorous studies of demographic use of social network sites within South Africa, demographic breakdown of non-use of social network sites within South Africa, as well as more formalised studies on the roles that race and age plays in the creation and maintenance of social capital within South Africa. References Adato, M., Carter, M.R., and May, J., 2006. Exploring poverty traps and social exclusion in South Africa using qualitative and quantitative data. Journal of Development Studies, 42 (2), 226–247. Adler, P.S. and Kwon, S., 2002. Social capital: prospects for a new concept. The Academy of Management Review, 27 (1), 17–40. Bargh, J. and McKenna, K., 2004. The Internet and social life. Annual Review of Psychology, 55 (1), 573–590. Boesak, A., 2009. Running with horses. Reflections of an accidental politician. Cape Town: Joho Publishers.

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Boyd, D.M. and Ellison, N.B., 2007. Social network sites: definition, history, and scholarship. Computer-Mediated Communication, 13 (1). Article 11. Carter, M.R. and Maluccio, J.A., 2003. Social capital and coping with economic shocks: an analysis of stunting of South African children. World Development, 31 (7), 1147–1163. Carter, M.R. and May, J., 2001. One kind of freedom: poverty dynamics in post-apartheid South Africa. World Development, 29 (12), 1987–2006. Chung, C., 2006. Beyond Guanxi: network contingencies in Taiwanese business groups. Organization Studies, 27 (4), 461–489. Chung, W. and Hamilton, G.G., 2003. Social logic as business logic: Guanxi, trustworthiness, and the embeddedness of Chinese business practices. Chapter 11. In: R.P. Applebaum, W.L. Felstiner, and V. Gessner, eds. Rules and networks. Portland: Hart Publishing. Coleman, J.S., 1988. Social capital in the creation of human capital. The American Journal of Sociology, 94, 95–120. DiMaggio, P., et al., 2001. Social implications of the Internet. Annual Review of Sociology, 307–336. Donath, J. and Boyd, D., 2004. Public displays of connection. BT Technology Journal, 22 (4), 71–82. Ellison, N.B., Steinfield, C., and Lampe, C., 2007. The benefits of Facebook ‘‘Friends:’’ social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12 (4), 1143–1168. Fukuyama, F., 2001. Social capital, civil society and development. Third World Quarterly, 22 (1), 7–20. Gold, T., Guthrie, D., and Wank, D. eds. 2002. Social connections in China. Institutions, culture and the changing nature of Guanxi. Cambridge: Cambridge University Press. Granovetter, M.S., 1983. The strength of weak ties: a network theory revisited. Sociological Theory, 1, 201–233. Hall, M., 2007. Poverty, inequality and the university. Michigan: Institute for the Humanities, University of Michigan. Helliwell, J.F. and Putnam, R.D., 2004. The social context of well-being. Philosophical Transactions of the Royal Society, 1435–1446. HEMIS, 2007. Higher education management information system report. Cape Town: National Department of Education. Islam, K.M., et al., 2006. Social capital and health: does egalitarianism matter? A literature review. International Journal for Equity in Health, 5 (3), 1–28. Klugman, J., 2009. Human development report 2009. New York: United Nations Development Program (UNDP). Lim, K., 2007. Facebook, we’ve got a problem . . . . Available from: theory.isthereason: http://theory.isthereason.com/ ?p ¼ 1856 [Accessed 2 April 2009]. Lehohla, P.J., 2009. Mid-year population estimates 2009. Pretoria: Statistics South Africa. Mcckenzie, K., Whitley, R., and Weich, S., 2002. Social capital and mental health. British Journal of Psychiatry, 280–283. Morrow, V., 2001. Young people’s explanations and experiences of social exclusion: retrieving bourdieu’s concept of social capital. International Journal of Sociology and Social Policy, 21, 37–63. Mutula, S.M., 2005. Peculiarities of the digital divide in SubSaharan Africa. Program: Electronic Library and Information Systems, 39 (2), 122–138.


36

K. Johnston et al.

O’Murchu, I., Breslin, J.B., and Decker, S., 2004. Online social and business networking communities. Galway, Ireland: Digital Enterprise Research Institute (DERI). Oswald, D.L. and Clark, E.M., 2003. Best friends forever?: High school best friendships and the transition to college. Personal Relationships, 10 (2), 187–196. Paul, E.L. and Brier, S., 2001. Friendsickness in the transition to college: precollege predictors and college adjustment correlates. Journal of Counseling & Development, 79 (1), 77–89. Pavot, W. and Diener, E., 1993. Review of the satisfaction with life scale. Psychological Assessment, 5 (2), 164–172. Portes, A., 1998. Social capital: its origins and applications in modern sociology. Annual Review of Sociology, 1–24. Putnam, R.D., 2000. Bowling alone. New York: Simon & Schuster. Ramphele, M., 2008. Laying ghosts to rest. Dilemmas of the transformation in South Africa. Cape Town: Tafelberg. Rosenberg, M., 1965. Society and the adolescent self-image. Princeton: Princeton University Press. Singh, A.M., 2004. Bridging the digital divide: the role of universities in getting South Africa closer to the global information society. South African Journal of Information Management, 6 (2). Smart, A., 1993. Gifts, bribes, and guanxi: a reconsideration of bourdieu’s social capital. Cultural Anthropology, 8 (3), 388–408.

Tidwell, L.C. and Walther, J.B., 2002. Computer-mediated communication effects on disclosure, impressions, and interpersonal evaluations: getting to know one another a bit at a time. Human Communication Research, 28, 317–348. Toomey, L., et al., 1998. Designing virtual communities for work. ACM SIGGROUP Bulletin, 19 (3), 6–7. Valenzuela, S., Park, N., and Kee, K.F., 2008. Lessons from Facebook: the effect of social network sites on college students’ social capital. International symposium on online journalism. Austin, TX: University of Texas at Austin, 1–39. Waddock, S., 2007. Leadership Integrity in a fractured knowledge world. Academy of Management Learning & Education, 6 (4), 543–557. Weaver, A.C. and Morrison, B.B., 2008. Social networking. Computer, 41 (2), 97–100. Wellman, B., et al., 2001. Does the Internet increase, decrease, or supplement social capital? Social networks, participation, and community commitment. American Behavioral Scientist, 45 (3), 436. Williams, D., 2006. On and off the net: scales for social capital in an online era. Journal of Computer-Mediated Communication, 11 (2), 593–628.