Embed Size (px)
Citation preview
This article was downloaded by: [Mount Allison University 0Libraries]On: 12 April 2013, At: 06:40Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK
Behaviour & Information TechnologyPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tbit20
Social capital: the benefit of Facebook ‘friends’Kevin Johnston a , Maureen Tanner a , Nishant Lalla a & Dori Kawalski aa Information Systems Department, University of Cape Town, South AfricaVersion of record first published: 07 Mar 2011.
To cite this article: Kevin Johnston , Maureen Tanner , Nishant Lalla & Dori Kawalski (2013): Social capital: the benefit ofFacebook ‘friends’, Behaviour & Information Technology, 32:1, 24-36
To link to this article: http://dx.doi.org/10.1080/0144929X.2010.550063
PLEASE SCROLL DOWN FOR ARTICLE
Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions
This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form toanyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses shouldbe independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims,proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly inconnection with or arising out of the use of this material.
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
(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 amonguniversity students in South Africa. Data were collected using questionnaires completed by over 800 students from 7universities. 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 ofonline social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168.). Empirical researchhas linked social capital to many positives in society, such as improved mental and physical health, economic well-being, etc. Thus, social capital is important for the success of civil society. This research examined the relationshipsbetween Facebook use and the formation and maintenance of social capital amongst university students. The studyalso 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 introducingthe 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 informationirrespective of geographical location, thus changingthe way human beings live, work and communicate(O’Murchu et al. 2004). As the internet became anintegral part of life, researchers became interested in itseffects on social exchange and relationships (Williams2006).
Social networking is based on the idea that societyexists as a structured set of relationships betweenpeople (Toomey et al. 1998). With the arrival of web2.0 technologies, a new wave of online social network-ing tools and services emerged. These include weblogs,social network sites, forums and instant messaging.Social network sites attracted millions of users includ-ing 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 diverseaudiences across the globe (Boyd and Ellison 2007).Online social network sites bring together a vastnumber of people who share common interests, viewsand goals (Boyd and Ellison 2007). These new formsof social networking impact the development andmaintenance of social capital (DiMaggio et al. 2001).
Facebook is a social networking site of particularinterest to researchers due to its heavy usage patternsand its technological capabilities, which allow for thebridging of online and offline relationships (Ellisonet al. 2007).
This empirical research replicates a study from theMichigan State University (MSU) (Ellison et al. 2007),which assessed the intensity of Facebook use and itsassociation with social capital. This research broadensthe understanding of Facebook usage by introducingthe dimensions of race and age across a range ofuniversities in South Africa. Network theorists arguethat understanding social capital requires a fine-grained analysis of the specific quality and configura-tion of network ties (Adler and Kwon 2002).
The paper begins by introducing concepts of socialnetworking and social capital. The research methodol-ogy is then explained, followed by the analysis of thedata. The implications of the results are discussed, andthe paper is concluded by highlighting key aspects ofthe study.
2. Review of literature
2.1. Online social networking
Online social networks are defined as virtual commu-nities which interact and pool resources through
*Corresponding author. Email: [email protected]
� 2013 Taylor & Francis
Behaviour & Information Technology, 2013Vol. 32, No. 1, 24–36, http://dx.doi.org/10.1080/0144929X.2010.550063
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
computer-mediated relationships (Toomey et al. 1998).Often, these networks consist of people who sharecommon affinities or interests and may or may not beseparated geographically (Boyd and Ellison 2007).Social networking has changed as a result of theimprovement and transformation of communicationtechnologies, primarily the internet. Bargh andMcKenna (2004) state that the internet is special as itcombines into one tool, features of many previousbreakthroughs: person-to-person communication(such as telephones), mass medium (such as televisions)and information resources (such as libraries). Whilethe internet was initially assumed to be merely astandardised mean of accessing information, currentonline social networking tools such as social networksites have allowed individuals to connect with eachother more effectively than was anticipated (Weaverand Morrison 2008).
An online social network site can be defined as a setof web-based services that allows individuals to ‘(1)construct a public or semi-public profile within abounded system, (2) articulate a list of other users withwhom they share a connection, (3) view and transversetheir list of connections and those made by otherswithin their system’ (Boyd and Ellison 2007, p. 221).Many of these social network sites are primarily usedto articulate existing social networks rather thanexpand networks (Boyd and Ellison 2007), thusreflecting real-life social relationships fairly accurately(Ellison et al 2007).
2.2. Social capital
Social capital refers to resources accumulated fromvarious relationships (Coleman 1988) and lies in thestructure of relationships within which the actors arelocated (Portes 1998). It can be considered as a by-product of social relationships resulting from socialexchanges in structured social networks (Islam et al.2006), and promotes co-operation between individuals(Fukuyama 2001). Social capital can be broken intotwo classes, namely cognitive and structural. Cognitivesocial capital is linked to personal aspects such asbeliefs, values, norms and attitudes (Islam et al. 2006).It is also a by-product of cultural norms like religion,tradition and shared historical experiences (Fukuyama2001). Structural social capital is the outwardly visiblefeatures of social organisations such as patterns ofsocial engagement or density of social networks(Islam et al. 2006). The structural class reflects thestrength of associational links, density of socialassociations and indicators of social interactions(Islam et al. 2006). Figure 1 outlines the forms ofsocial capital relevant to this study. These relate tobridging, bonding (Putnam 2000) and maintained
social capital (Ellison et al. 2007) and are all part ofthe 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 ethnicand occupational backgrounds (Islam et al. 2006) andcan cut across geographic and socio economic distance(Carter and Maluccio 2003). ‘Weak tie’ relationshipsare considered provisional and lack depth (Williams2006), thus making bridging social capital inherentlyheterogeneous (Putnam 2000). Bridging social capitalbetween individuals provides useful information andnew perspectives. It expands social horizons but doesnot provide much emotional support (Granovetter1983, Williams 2006, Ellison et al. 2007). Individualswith ‘weak ties’ or bridging social capital tend to havea broader set of information and access to opportu-nities. This phenomenon is known as the ‘strength ofweak ties’ (Granovetter 1983). Donath and Boyd(2004) suggested that social network sites couldincrease the formation and maintenance of ‘weakties’ or bridging social capital because the costs of useare low. Solidarity can also emerge from weak ties andcan thus otherwise bridge diverse racial groups as inthe case of South Africa (Adler and Kwon 2002).
2.2.2. Bonding social capital
In contrast, bonding social capital exists betweenfamily members, close friends and other close relationsand focus on internal ties between actors (Morrow2001, Adler and Kwon 2002). These relations aregenerally homogenous and inclusive, existing betweenstrongly knit, emotionally close relationships (Williams2006, Ellison et al. 2007). Bonding relationshipsprovide emotional support and a way of transmittingbehavioural norms between individuals in the relation-ship (Islam et al. 2006). The type of relationships andstructures within a social network determines the typesof social capital formed in these networks (Williams2006). ‘Strong tie’ relationships or bonding socialcapital does not provide links to individuals ofdiffering backgrounds (Williams 2006).
Bonding social capital is closely related to theChinese concepts of ‘guanxi’ and ‘renqing’. Guanxi isembedded in daily social practices of people andrelates to personal connections and relationships(Smart 1993, Chung 2006). Guanxi is essential tothe completion of tasks in social life, and has bothpositive (such as reciprocity and gifts) and negative(such as bribes and nepotism) connotations (Smart
Behaviour & Information Technology 25
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
1993, Gold, Guthrie and Wank 2002, Chung andHamilton 2003). This study only focuses on thepositive aspects of guanxi.
2.2.3. Maintained social capital
Ellison et al. (2007) established a third type of socialcapital called ‘Maintained social capital’. Maintainedsocial capital is created when individuals maintainconnections to their social networks having progressedthrough life changes (Bargh and McKenna 2004,Ellison et al. 2007). The use of technology can assistpeople to maintain relationships threatened by changesin geographical location (Bargh and McKenna 2004).The terms ‘friendsickness’ refers to the anguish causedby loss of contact with friends (Paul and Brier 2001).‘Friendsickness’ is usually caused when school studentsmove away to tertiary learning institutions (Paul andBrier 2001). Research shows that university studentsuse email and instant messaging services to stay intouch 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).
2.2.4. Benefits of social capital
Social capital has been shown to be a forecaster ofschool attrition, academic performance, physical andmental health, children’s intellectual development,sources of employment, juvenile delinquency and itsprevention, and economic development (Coleman1988, Portes 1998, Putnam 2000, Mcckenzie et al.2002, Ellison et al. 2007).
Generally, when social capital is low within acommunity, there tends to be greater social disorderand more distrust amongst participants (Helliwell andPutnam 2004). However, with greater social capitalcomes a general commitment to collective action whichgenerally results in a positive effect with regard tointeraction (Ellison et al. 2007).
2.3. Negative social capital
In spite of the advantages of social capital, negativeaspects still prevail (Portes 1998), as people tend todivide the world between friends and enemies. Forinstance, social capital might lead to the creation ofhate groups as can sometimes be seen on Facebook(Fukuyama 2001). Strong ties between members of agroup can exclude outsiders’ access to a group (Portes1998). Weaker group members can make excess claimson stronger members (the free-riding problem) (Portes1998). Participation in a group generally dictatescertain levels of conformity (Portes 1998), and thiscan restrict individual freedom. Situations where groupsolidarity is formed by common experiences are termeddownward levelling norms, individual success storiesundermine group cohesion (Portes 1998).
2.4. Measurement of social capital
The concept of social capital is elusive, both in terms ofits meaning and measurement (Islam et al. 2006,Williams 2006). There is no consensus on themeasurement of social capital (Fukuyama 2001,Williams 2006). Researchers have to identify observa-ble variables and develop methods to use those
Figure 1. Social capital.
K. Johnston et al.26
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
variables as proxies for social capital (Islam et al.2006). Williams (2006) developed tools to measureinternet social capital known as Internet Social CapitalScales or ISCS, which have been validated in previousstudies (Valenzuela et al. 2008, Ellison et al. 2007) andwere used in this study. These scales provide clarifica-tion of social capital and its formation online andoffline, as well as the tradeoffs between the two(Williams 2006). The relationship between Facebookuse and social capital may be explored by examiningindividuals’ self-esteem (Rosenberg 1965) and satisfac-tion with life (Pavot and Diener 1993) two validatedmeasures of subjective well-being.
2.5. Technology and social capital
Some researchers say that the internet diminishedsocial interactions regarding face-to-face exchanges(Bargh and McKenna 2004). However, it can beargued that changing technology changes forms ofassociations (Fukuyama 2001). The ‘uses and gratifica-tions’ theory suggests that ‘the particular purpose ofindividuals within communication settings will deter-mine the outcome of the interaction’, regardless of thetype of communication channel used (Bargh andMcKenna 2004, p. 578). Another viewpoint proposesthat elements of internet communication impactinteraction outcomes, and the impacts depend on thesocial context (Bargh and McKenna 2004).
Wellman et al. (2001) argued that online interac-tion provides new means of communication betweenparties, as well as supplementing or replacing physicalinteractions, thereby strengthening relationships. Onereason for the initial confusion of the effects of theinternet and social capital is the lack of controlledlongitudinal research (Williams 2006). Online socialnetwork tools might be useful to individuals whogenerally struggle to form and maintain both strongand weak ties. Some studies demonstrated that theinternet might help individuals having low psycholo-gical well-being and few ties to friends and neighbours(Bargh and McKenna 2004). Barriers to interactionscan be lowered and self-disclosure encouraged throughsome form of computer-mediated communication(Tidwell and Walther 2002, Bargh and McKenna2004).
2.6. The South African context
No study of South Africa can be complete withoutsome understanding of the social context of thecountry. Apartheid in South Africa actively oppressedand humiliated non-white people, limiting their accessto education, and ability to accumulate and use assets(Carter and May 2001). All citizens were racially
classified and issued identification numbers that con-tained racial classification. South African governmentsof the 1920–1990s used laws and power to separate andexclude non-white people. Non-white South Africanswere repeatedly degraded, ill treated, and informed oftheir inferior status (Ramphele 2008). People ofdifferent racial classifications were legally forbiddento marry, socialise, live, play, learn, or be buriedtogether. South Africa held its first democratic electionin 1994. Fifteen years later, ‘the persistent matter ofrace and identity’ continue to permeate all aspects oflife and plague South Africans (Boesak 2009, p. 9).South Africa continues to be a divided nation, a firstworld part mainly populated by whites, and a thirdworld part populated by blacks (Adato et al. 2006) andthis distorts most statistics (Waddock 2007). SouthAfricans have a life expectancy of 51.5 (the average forAfrica was 53.9), an adult literacy rate of 88 (Africa63.3), and GDP per capita $9.757 (Africa $2.729)(Klugman 2009). In 2001, South Africa had a GENIcoefficient of 0.73 and is ‘one of the most unequalsocieties in the world, more than half of all SouthAfricans live in poverty, more than 10% of SouthAfricans live in absolute poverty, and the situation isgetting worse’ (Hall 2007, p. 21). South Africa has apopulation of 49.32 million, of which 52% werefemales, 79% of the population were black, and 9%white (Lehohla 2009).
The Digital Divide phenomenon which is prevalentin South Africa is defined as ‘the gap between thosewho can effectively use or have access to newinformation and communication tools, such as theinternet, and those who cannot’ (Mutula 2005, p. 123).The digital divide can also exist between gender,physical disability, racial and age differences (Singh2004). These differences are especially prominentin South Africa due to its apartheid legacy (Singh2004).
3. Hypotheses and research questions
Table 1 summarises the hypotheses from the paper byEllison et al. (2007), which were replicated in thisstudy. Seven exploratory questions were constructed toexamine specific aspects relating to the South Africancontext. Three questions examined the role race playedin the relationship between Facebook use intensity andthe three forms of social capital. Similarly, threequestions examined the role age played in the relation-ship between Facebook use intensity and the threeforms of social capital. The seventh question examinedthe demographics of Facebook members versus non-Facebook members amongst South African universitystudents. All questions were submitted to andapproved by the University Ethics committee.
Behaviour & Information Technology 27
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
4. Methodology
The underlying philosophy for this research ispositivist. This study made use of primary datasources retrieved first hand. Quantitative data werecollected through questionnaires obtained from aprevious study (Ellison et al. 2007). The originalquestionnaire was trimmed down due to its length, inconsultation with the original authors. The question-naire is available for future use. Various statisticaltechniques were used to analyse the data. Most of theanalytical methods were obtained from the study byEllison et al. (2007). Additional demographic data(e.g. ethnicity, gender, etc.) were collected through thequestionnaires. All questionnaires were returned in atwo-week window, implying that a snapshot of thecurrent situation was taken. The data acquired wereused in an attempt to support or reject thehypotheses.
A random sample of students was selected fromseven tertiary institutions within South Africa (Uni-versity of Cape Town, University of Witwatersrand,University of Fort Hare, Cape Peninsula University ofTechnology, University of Johannesburg, StellenboschUniversity, and University of Western Cape). Permis-sion to distribute the questionnaire was obtained priorto distribution. Eight hundred and twenty-threeresponses were captured. The raw data were cleanedand formatted, using Microsoft Excel 2007, so that itcould be used in Statistica. All partially completedresponses were removed before analysis, reducing thenumber of valid responses to 572, of which 67% (383)were members of Facebook.
5. Data analysis
Five-point Likert scales were extensively used in thequestionnaire. Various measures and constructs wereobtained from the original study. These were used inassessing demographics, general life satisfaction atuniversity, self-esteem, forms of social capital andintensity of Facebook use. These measures were then
used in building regression models to assess the impactof Facebook use on social capital.
Two sets of regressions were used to build modelsto predict statistics for each form of social capital. Thefirst regression was used as a comparison with theoriginal study. The second regression included ethni-city and age variables in order to assess age andethnicity factors in the social capital models. Interac-tion terms used in the regression analysis wereconstructed by multiplying the variables, for example:‘self-esteem by Facebook Intensity’ was constructedby: (self-esteem) 6 (Facebook intensity).
5.1. Chi-squared goodness-of-fit test
The chi-squared goodness-of-fit test was used to assessthat the sample was an accurate representation of thepopulation. The most recent figures regarding studentenrolment in South African universities was used(HEMIS 2007). It is assumed that university demo-graphics have not changed significantly since then, andthat these figures are still representative of generaluniversity demographics. The chi-squared test con-firmed that the sample used was a good representationof South African students both in terms of race andgender.
5.2. Measures of Facebook usage
5.2.1. Facebook intensity
The Facebook intensity scale attempted to obtain ameasure of Facebook intensity other than measures offrequency or duration of Facebook use (Ellison et al.2007). The construct consisted of two questions (13and 14) regarding Facebook use, to assess how activelyinvolved respondents are on Facebook, as well as sixLikert-scale questions (sub questions of question 17),which assessed respondents’ attitudes towards Face-book. The attitudinal questions related to the extent towhich a respondent feels emotionally connected to
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.
K. Johnston et al.28
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
Facebook, as well as assessing how significant a roleFacebook plays in a participant’s regular activities.Table 2 summarises statistics associated with theFacebook Intensity construct. The construct yields aCronbach’s alpha of 0.84, indicating a high reliabilityof the sub-items in supporting the construct.
The analysis indicates that time spent usingFacebook per day (Q. 14) fell within the 10–30 minute category, and that the average SouthAfrican student indicated he/she has between 100and 150 friends. The MSU students’ time averages fellwithin the 10–30 minutes, while the average numberof friends ranged between 150 and 200. Researchersat Sheffield Hallam University and the University ofLiverpool have found that most people have five-coreonline friends (the same number they have offline)despite claiming 150 or 200 online friends (Lim 2007).The Likert-scale questions provided rather neutralresults, with mean values around three. However, the
standard deviation of all of these items was relativelyhigh (i.e. above 1).
5.2.2. Facebook usage: elements in profile and percep-tions of who has viewed profiles
Respondents were asked to indicate which profileelements they included when developing their Face-book profile (Figure 2). Respondents were also askedto indicate who they thought had viewed their Face-book profile (Figure 3). These items are valuable inassessing Facebook use regarding the development ofnew relationships or the maintaining of existingrelationships.
Contact details were the most common profileelement included (77%), and ‘My classes’ the leastcommon (10%). More than 70% of MSU studentsincluded university classes in their profiles (Ellisonet al. 2007).
Table 2. Summary statistics for Facebook intensity (N ¼ 383).
Question Individual items and scale Mean SD
Facebook intensity (Cronbach’s alpha ¼ 0.840) 3.02 0.97Q. 13 About how many total Facebook friends do you have? 3.17 2.31
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 minutesper day have you spent on Facebook?
1.69 1.44
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. 3.03 1.312. I am proud to tell people I am on Facebook. 3.53 1.003. Facebook has become part of my daily routine. 3.07 1.274. I feel out of touch when I haven’t logged onto Facebook for a while. 2.74 1.375. I feel I am part of the Facebook community. 3.36 1.086. I would be sorry if Facebook shut down. 3.58 1.23
Notes: Individual items were first standardised before taking a mean to create a scale. Unless provided, response categories ranged from1 ¼ strongly disagree to 5 ¼ strongly agree.
Figure 2. Profile elements.
Behaviour & Information Technology 29
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
Figure 3 shows who respondents perceived haveviewed their profiles, ‘High school friends’ (88%) and‘Friends other than high school friends’ (85%) werethe most selected responses.
5.2.3. Use of Facebook to meet new people vs. connectwith existing offline contacts
The purpose of this construct is to ascertain whetherrespondents are inclined to use Facebook to find newpeople online or to find contacts with whom theyalready have an offline relationship.
Table 3 shows Cronbach’s alpha for the construct‘Offline to online: use of Facebook to connect withoffline contacts’ is 0.60 and is not considered to behighly reliable. The MSU study yielded a Cronbach’salpha of 0.70 and was considered to be reliable insupporting the construct.
The single item measure ‘On to offline: use Face-book to meet new people’ has a mean of 2.55. TheMSU study mean was 1.97 which is lower than thecurrent study’s finding. The question ‘I use Facebookto keep in touch with my old friends’ has a particularlyhigh mean of 4.47 and a low standard deviation (0.89),indicating that the majority of Facebook membersagreed with this statement.
5.3. Measures for psychological well-being
Self-esteem was measured using the Rosenberg self-esteem scale (Ellison et al. 2007) in question 20. Thescale is made up of seven items, posed on a five-pointLikert-scale. The scale exhibited high reliability with aCronbach’s alpha ¼ 0.81 (Table 4).
The satisfaction with university life was adoptedfrom the Satisfaction with Life Scale (Ellison et al.2007) in question 21. The scale exhibited highreliability with a Cronbach’s alpha ¼ 0.82.
5.4. Measures of social capital
The three types of social capital were measured byadapting existing scales. Items which capture internet-
specific social capital were also included (Ellison et al.2007). The factor loadings analysis was calculated forall the social capital items. The results show that thesocial capital items actually represent three distinctfactors.
5.4.1. Measure for assessing bridging social capital
This measure assessed the degree to which respondentsexperience bridging social capital (Ellison et al. 2007),using Williams’ (2006) bridging social capital subscale.The scale exhibited high reliability with a Cronbach’salpha ¼ 0.86.
5.4.2. Measure for assessing bonding social capital
This measure assessed the extent to which respondentsexperienced bonding social capital. Bonding socialcapital makes use of five items from the bondingsubscale developed and validated by Williams (2006).
Table 3. Summary statistics for Facebook use for priorcontacts and meeting new people (N ¼ 383).
Questions Individual items and scales Mean SD
Off to online: use Facebook to connectwith offline contacts (Cronbach’s alpha0.60)
3.37 0.81
Q. 16 1. I have used Facebook tocheck out someone I metsocially.
3.57 1.33
2. I use Facebook to learnmore about other peoplein my classes.
2.81 1.26
3. I use Facebook to learnmore about other peopleliving near me.
2.64 1.29
4. I use Facebook to keep intouch with my oldfriends.
4.47 0.89
On to offline: I use Facebook to meet newpeople (single-item measure)
2.55 1.45
Notes: Individual items ranged from 1 ¼ strongly disagree to5 ¼ strongly agree, scales constructed by taking mean of items.
Figure 3. Perceived profile viewers.
K. Johnston et al.30
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
The scale did not exhibit high reliability Cronbach’salpha ¼ 0.69.
5.4.3. Measure for assessing maintained social capital
This measure assessed the extent to which respondentsexperienced maintaining social capital. This scale wasadapted from the MSU study. The items were adaptedfrom traditional measures of social capital which gaugea persons’ ability in finding support. The scaleexhibited high reliability with a Cronbach’salpha ¼ 0.80.
5.5. Analysis of demographics
Having 33% of respondents not being Facebookmembers provided an interesting aspect to the studywhich was unavailable to the MSU study (6% were notFacebook users). South African student respondentswere more balanced with regards to gender, 55% maleverses 34% male in MSU. The average respondent wasin 2nd year with a mean age of 20.58 years.
The current study showed a split of 61% ofstudents whose home residence is within the city inwhich they are studying, and 39% being from ‘out-of-town’. Thirty-seven percent of students resided oncampus, in comparison with MSU results of 55%. TheSouth African study indicated that more students(43%) are members of clubs or societies than theirAmerican counterparts in the MSU study (8%).
5.6. Analysis of regression models
The aim of the research is to identify whetherFacebook use plays a significant role in the creationof or maintaining of social capital. Regression modelswere constructed to predict whether certain variables
were significant in affecting the various forms of socialcapital. For each form of social capital, an initialregression model of control variables was constructedand then assessed. An additional variable of theintensity of Facebook use was then added to themodel, and the results assessed. Finally, interactionvariables 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 assesswhich variables are predictors of bridging socialcapital.
To assess the impact of Facebook use on bridgingsocial capital, the regression was initially investigatedwith the control variables: demographics, generalinternet use, and measures of psychological well-being.This yielded an adjusted R2 of 0.37. The ‘intensity ofFacebook’ variable was then included in the model toinvestigate if it has an impact on the predicted bridgingsocial capital, which yielded an adjusted R2 of 0.38 (seeTable 5).
Two interaction variables were introduced into theregression for bridging social capital, resulting in twofurther models:
. Model 1 aimed to predict bridging social capitalby using ‘control factors’, ‘Facebook intensity’and ‘self-esteem by Facebook intensity interac-tion’ as independent variables.
. Model 2 aimed to predict bridging social capitalby using ‘control factors’, ‘Facebook intensity’and ‘satisfaction with university life by Face-book intensity interaction’ as independentvariables.
Table 4. Summary statistics for self-esteem and satisfaction with university life items (N ¼ 383).
Questions Individual items and scales Mean SD
Q.20 Self-esteem scale (Cronbach’s alpha ¼ 0.81) 4.16 0.62I feel that I am a person of worth, at least on an equal plane with others. 4.24 0.82I feel that I have a number of good qualities. 4.40 0.71All in all, I am inclined to feel that I am a failure (reversed). 4.02 1.03I am able to do things as well as most other people. 4.09 0.88I feel I do not have much to be proud of (reversed). 4.06 1.10I take a positive attitude toward myself 4.15 0.90On the whole, I am satisfied with myself 4.13 0.86
Q. 21 Satisfaction with university life scale (Cronbach’s alpha ¼ 0.82) 3.35 0.78In most ways my life at my university is close to my ideal. 3.30 1.06The conditions of my life at my university are excellent. 3.38 1.02I am satisfied with my life at my university. 3.57 0.93So far I have gotten the important things I want at my university. 3.52 0.96If I could live my time at my university over, I would change almost nothing. 2.96 1.15
Notes: Individual items ranged from 1 ¼ strongly disagree to 5 ¼ strongly agree, scales constructed by taking mean of items.
Behaviour & Information Technology 31
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
These yielded adjusted R2 values of 0.39 (model 1)and 0.38 (model 2), compared with that of the MSUstudy of 0.44 (model 1) and 0.46 (model 2),respectively.
For model 1, factors considered to be significantpredictors in bridging social capital were satisfactionwith 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 inten-sity of Facebook use (p ¼ 0.025). Self-esteem byFacebook intensity factor is not significant in predict-ing bridging social capital (p ¼ 0.07).
Model 2 differed slightly from model 1, mainlyregarding the levels of significance. Factors consideredto be significant predictors of bridging social capitalincluded: ethnicity (white) (p ¼ 0.0001) being mostsignificant, year in university (p ¼ 0.001), member ofclubs 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 intensityis not significant in predicting bridging social capital.
5.6.2. Regression model: bonding social capital
The regression model constructed was used to assesswhich factors are predictors of bonding social capital.This regression model was initially investigated withthe same control variables as the previous model.These were: demographics, general internet use, and
measures of psychological well-being. This yielded anadjusted R2 of 0.17. The ‘intensity of Facebook’variable was then included in the regression model toinvestigate whether it has an impact on the predictedbonding social capital, which yielded an adjusted R2 of0.19.
Similarly, two interaction variables were intro-duced into the regression for bonding social capital,resulting in two further models. These yielded adjustedR2 values of 0.18 for both models 1 and 2, comparedwith that of the MSU study of 0.23 (model 1) and 0.22(model 2), respectively.
For model 1, the factors considered to be sig-nificant predictors in bonding social capital were:satisfaction with university life (p ¼ 0.000) being mostsignificant and gender (p ¼ 0.01). Intensity of Face-book use was found not to be significant in predicatingbonding social capital (p ¼ 0.29).
Self-esteem played some significance in model 2 inpredicting bonding social capital. Factors consideredto be significant predictors of bonding social capital formodel 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 lesssignificant in model 2 than in model 1, but is still asignificant predictor in the model.
5.6.3. Regression model: maintained social capital
The regression model constructed was used to assesswhich factors are predictors of maintained socialcapital. To assess the impact of Facebook use on
Table 5. Regressions predicting the amount of bridging social capital from demographic, attitudinal and Facebook variables.
Independent variables
Model 1: Control factors, Facebookintensity, and self-esteem 6 Face-
book intensity interaction
Model 2: Control factors, Facebookintensity, and satisfaction with uni-versity life 6 Facebook intensity
interaction
Stand. beta p Stand. beta P
Intercept 1.09 * 1.87 ****Gender: male 0.09 * 0.09 *Ethnicity: white 70.17 **** 70.17 ****Year in university 70.14 *** 70.14 **Home residence: out-of-town 0.04 0.04Local residence: university residence 0.13 ** 0.14 **Club/society member 0.14 *** 0.14 **Hours of internet use per day 70.07 70.07Self-esteem 0.34 ** 0.14 **Satisfaction with life at university 0.37 **** 0.38 **Facebook (FB) intensity 0.51 * 0.13Self-esteem by FB intensity2 70.48Satisfaction by FB intensity 70.03
F ¼ 23.05, **** F ¼ 22.55, ****n ¼ 383 Adjusted R2 ¼ 0.39 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.
K. Johnston et al.32
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
maintained social capital, the regression was firstinvestigated with the control variables: demographics,general internet use, and measures of psychologicalwell-being. This yielded an adjusted R2 of 0.09. The‘intensity of Facebook’ variable was then included inthe regression model to investigate if it has an impacton the predicted bridging social capital, which yieldedan adjusted R2 of 0.15.
Similarly, two interaction variables were intro-duced for maintaining social capital, resulting in twofurther models. These yielded adjusted R2 values of0.15 (model 1) and 0.16 (model 2), compared with thatof the MSU study of 0.16 (model 1) and 0.17 (model2), respectively. For model 1, the following factorswere considered to be significant predictors in main-tained social capital: intensity of Facebook use (p ¼0.003) being most significant, self-esteem (p ¼ 0.004),satisfaction with university life (p ¼ 0.02), and year inuniversity (p ¼ 0.02).
Model 2 offered some additional results. Theseincluded: intensity of Facebook use (p ¼ 0.0000) beinghighly significant, satisfaction with university life(p ¼ 0.001), self-esteem (p ¼ 0.004), satisfaction byFacebook intensity (p ¼ 0.009), and year in university(p ¼ 0.02). In model 2, intensity of Facebook use ismore significant than in model 1. Table 6 highlights themajor differences between the MSU study and theSouth African study.
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 studyyielded an insignificant sample size of non-Facebookmembers and provided inconclusive findings regardingdemographics of non-members. The 33% of SouthAfrican non-Facebook members are predominantlyAfrican people, due to the context as detailed inSection 2.3.
The South African Facebook members spent 10–30minutes per day using Facebook, similar to the MSUstudy. The average South African student has between100 and 150 friends while MSU students on averagehave 150–200 friends. This may be due to highertechnology adoption rates in developed countries, orbecause MSU students have been using Facebook forlonger periods.
The MSU students primarily use Facebook as atool to keep in touch with students in the sameuniversity and classes. This can be seen by the highresponse rate of MSU students indicating that theyhave ‘My classes’ as a Facebook profile item. Only2.57% of surveyed Facebook members indicated thatthey include ‘My classes’ as a Facebook profileelement. South African students do not use Facebookin the same manner as the students at MSU. This
Table 6. Major differences between the MSU study and the South African study.
MSU SA Comments
Number of friends 150–200 100–150 Differences in number of friends might be due to highertechnology adoption rates in developed countries or becauseMSU has been using Facebook for a longer period of time.
Time spent ofFacebook daily
10–30 minutes 10–30 minutes Students (both in SA universities and MSU) might spend similaramounts of time in lectures, and tutorials, thus limiting theamount of time that they can spend on Facebook daily.
Members of Facebook 94% 67% As 50% of SA respondents were non-white and are thus morelikely to come from disadvantaged backgrounds, they might beaffected by the digital divide. This might in turn affect theiraccess to computers and consequently their Facebookmembership.
My class profileon Facebook
70% 10% The South African first year classes tend to be very large (over 600students), thus limiting the ability of students to get to knoweach other. Hence, they might be less inclined to interact usingthe My Class Profile on Facebook.
Male 34% 55% SA study has a more balanced Facebook usage rate across thegenders as opposed to what can be seen in the MSU study.
Reside on campus 55% 37% The fact that only 37% of SA respondents reside on campusmight account for the lower Facebook membership rate in SA.In essence, on-campus residents are more likely to haveInternet access, and thus access to Facebook.
Race (white) 87% 28% The population sample of MSU might be more uniform asopposed to SA respondents which are more diverse, thusexplaining the non-white high adoption rate for MSU.
Non-members ofFacebook (white)
87% 14% The population sample of MSU might be more uniform asopposed to SA respondents which are be more diverse, thusexplaining the high white non-adoption rate for MSU.
Behaviour & Information Technology 33
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
means that the measure for ‘Off to online: useFacebook to connect with offline contacts’ will needto be adjusted to fit the South African context.
The sub-item ‘I use Facebook to keep in touch withmy old friends’ has a mean of 4.47 and a standarddeviation of 0.89. This means that most of therespondents indicated that they strongly agree withthis statement. This is an interesting aspect as it givesinsight into the intended usage of Facebook by SouthAfrican university students.
In assessing Hypothesis 1, the regression modelresulted in an adjusted R2 value of 0.37. Afterintroducing the intensity of Facebook use variable,the adjusted R2 increased to 0.39. The Facebookintensity variable was considered to be significant, withstandardised beta of 0.11 (p 5 0.05), indicating thatFacebook intensity is positively associated with per-ceived bridging social capital. Thus Hypothesis 1 andthe findings of the MSU study are supported. It isinteresting to note that, similarly to the MSU study,hours of internet use per day is not significant inpredicting the amount of bridging social capital.
A similar procedure was followed in assessingHypothesis 2, with various regression models beingconstructed, and introducing additional variables ateach regression. The adjusted R2 value for the controlvariable regression yielded a value of 0.17, increasingto 0.19 after introducing the Facebook intensityvariable. Standardised beta of 0.14 (p 5 0.01) for theFacebook intensity variable suggests that this variableis significant in the model. Hypothesis 2 is supported,namely that intensity of Facebook use is positivelyassociated with perceived bonding social capital.However, considerably less (0.19 vs. 0.39) of thevariation is explained by the regression model, Face-book use has less of an impact on bonding socialcapital. This could be due to the structure and featuresof Facebook which promote the creation of ‘weak ties’(bridging). Facebook intensity is a less likely predictorfor bonding social capital with South African uni-versity students. The gender variable was significant inthe bonding social capital regression model wheremales appear to have higher bonding social capitalthen females.
The relationship between intensity of Facebook useand bridging social capital (Hypotheses 3a and 3b) didnot vary with different levels of psychological well-being, namely self-esteem and satisfaction with uni-versity life. The regression models show these factorsas non-significant predictors of bridging social capitalwith standardised beta of70.03 (p ¼ 0.89) for the self-esteem, and standardised beta of 70.48 (p ¼ 0.07) forthe satisfaction with university life. Hypothesis 3a and3b are therefore rejected. Rejecting 3a means that therelationship between Facebook intensity and bridging
social capital does not vary by differing levels of self-esteem. Rejecting 3b means that students with lowlevels of Facebook intensity, reporting low satisfactionwith university life, do not necessarily report lowerlevels of bridging social capital than similar studentswith higher Facebook intensity levels. The MSU studyfound that students with low levels of Facebookintensity, reporting low self-esteem or low satisfactionwith life at college, reported lower bridging socialcapital than those who frequent Facebook.
The regression model with a standardised beta of0.24 (p ¼ 0.43) for the self-esteem by FacebookIntensity interaction variable was not significant, andtherefore Hypothesis 4a is rejected. This means thatstudents with low levels of Facebook intensity,reporting low satisfaction with university life, do notnecessarily report lower levels of bonding social capitalthan students with higher Facebook intensity levels.The relationship between Facebook intensity andbonding social capital does not vary by differing levelsof satisfaction with university life. The MSU studyproduced similar results.
The standardised beta of 70.06 (p ¼ 0.79), for thesatisfaction with university life by Facebook intensityinteraction variable was not significant, Hypothesis 4bis rejected. This means that students with low levels ofFacebook intensity, reporting low satisfaction withuniversity life, do not necessarily report lower levels ofbonding social capital than similar students withhigher Facebook intensity levels. The relationshipbetween Facebook intensity and bonding social capitaldoes not vary by differing levels of satisfaction withuniversity life. The results of the MSU study weresimilar.
Finally, in assessing Hypothesis 5, the regressionmodels were again constructed, initially with thecontrol variables and then with the intensity ofFacebook variable, and intensity of Facebook inter-action variables. The control variables yielded anadjusted R2 for the regression of 0.09, with theintroduction of the Facebook intensity variable in-creasing adjusted R2 to 0.15 with standardised beta of0.80 (p 5 0.0000). This is highly significant implyingthat Facebook intensity plays a significant role inpredicting maintained social capital of South Africauniversity students. H5 is therefore supported. This isfurther supported by the figures in Table 3, which showhigher mean values for offline-to-online than online-to-offline measures, suggesting that South African uni-versity students predominantly use Facebook as ameans for maintaining and strengthening existingoffline relationships. It seems that Facebook is usedby South African university students as a communica-tion medium to maintain already existing relation-ships. Overall, it seems that Facebook intensity is
K. Johnston et al.34
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
strongly correlated with the formation of maintainedsocial capital. Students surveyed seemed to use Face-book to maintain relationships and develop main-tained social capital. The Facebook intensity ispositively correlated with all three types of socialcapital, with the strongest correlation to maintainedsocial capital. Having been undertaken in a morediverse setting with a range of universities, this studyextends the original study. This was done by revealinghow factors such as age, race, and multi-universitiesimpacts the benefits of Facebook towards creatingsocial capital in a developing country such as SouthAfrica. This confirms the findings of the original studyand extends its generalisability.
7. Conclusion
This empirical research replicated a study performed atMSU and adapted it for a South African context. Thestudy aimed to assess the intensity of Facebook useamongst South African university students andwhether this would be useful in the maintenance andcreation of social capital. The research provided acursory analysis of demographic use of Facebook andattempted to answer potential research questions.
Results indicate that intensity of Facebook useplays a role in the creation of social capital, but isparticularly significant regarding the maintenance ofsocial capital in the South African context. Compar-isons were made with the MSU study which had a highrate of Facebook members, rendering the analysis ofnon-Facebook members inconclusive.
Research questions were answered providing newavenues for future research regarding Facebook, ormore generally, online social networking within SouthAfrica. Future research could attempt to solidify theoutcomes of some of the research questions regardingmore detailed and rigorous studies of demographic useof social network sites within South Africa, demo-graphic breakdown of non-use of social network siteswithin South Africa, as well as more formalised studieson the roles that race and age plays in the creation andmaintenance of social capital within South Africa.
References
Adato, M., Carter, M.R., and May, J., 2006. Exploringpoverty traps and social exclusion in South Africa usingqualitative and quantitative data. Journal of DevelopmentStudies, 42 (2), 226–247.
Adler, P.S. and Kwon, S., 2002. Social capital: prospects fora new concept. The Academy of Management Review, 27(1), 17–40.
Bargh, J. and McKenna, K., 2004. The Internet and sociallife. Annual Review of Psychology, 55 (1), 573–590.
Boesak, A., 2009. Running with horses. Reflections of anaccidental politician. Cape Town: Joho Publishers.
Boyd, D.M. and Ellison, N.B., 2007. Social network sites:definition, history, and scholarship. Computer-MediatedCommunication, 13 (1). Article 11.
Carter, M.R. and Maluccio, J.A., 2003. Social capital andcoping with economic shocks: an analysis of stunting ofSouth 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. WorldDevelopment, 29 (12), 1987–2006.
Chung, C., 2006. Beyond Guanxi: network contingencies inTaiwanese business groups. Organization Studies, 27 (4),461–489.
Chung, W. and Hamilton, G.G., 2003. Social logic asbusiness logic: Guanxi, trustworthiness, and the embedd-edness of Chinese business practices. Chapter 11. In: R.P.Applebaum, W.L. Felstiner, and V. Gessner, eds. Rulesand networks. Portland: Hart Publishing.
Coleman, J.S., 1988. Social capital in the creation of humancapital. 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 connec-tion. BT Technology Journal, 22 (4), 71–82.
Ellison, N.B., Steinfield, C., and Lampe, C., 2007. Thebenefits of Facebook ‘‘Friends:’’ social capital andcollege students’ use of online social network sites.Journal of Computer-Mediated Communication, 12 (4),1143–1168.
Fukuyama, F., 2001. Social capital, civil society anddevelopment. Third World Quarterly, 22 (1), 7–20.
Gold, T., Guthrie, D., and Wank, D. eds. 2002. Socialconnections in China. Institutions, culture and the changingnature of Guanxi. Cambridge: Cambridge UniversityPress.
Granovetter, M.S., 1983. The strength of weak ties: a networktheory revisited. Sociological Theory, 1, 201–233.
Hall, M., 2007. Poverty, inequality and the university.Michigan: Institute for the Humanities, University ofMichigan.
Helliwell, J.F. and Putnam, R.D., 2004. The social context ofwell-being. Philosophical Transactions of the RoyalSociety, 1435–1446.
HEMIS, 2007. Higher education management informationsystem report. Cape Town: National Department ofEducation.
Islam, K.M., et al., 2006. Social capital and health: doesegalitarianism matter? A literature review. InternationalJournal for Equity in Health, 5 (3), 1–28.
Klugman, J., 2009. Human development report 2009. NewYork: United Nations Development Program (UNDP).
Lim, K., 2007. Facebook, we’ve got a problem . . . . Availablefrom: 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. Socialcapital and mental health. British Journal of Psychiatry,280–283.
Morrow, V., 2001. Young people’s explanations andexperiences of social exclusion: retrieving bourdieu’sconcept of social capital. International Journal ofSociology and Social Policy, 21, 37–63.
Mutula, S.M., 2005. Peculiarities of the digital divide in Sub-Saharan Africa. Program: Electronic Library and Infor-mation Systems, 39 (2), 122–138.
Behaviour & Information Technology 35
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
O’Murchu, I., Breslin, J.B., and Decker, S., 2004. Onlinesocial 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 thetransition to college: precollege predictors and collegeadjustment correlates. Journal of Counseling & Develop-ment, 79 (1), 77–89.
Pavot, W. and Diener, E., 1993. Review of the satisfactionwith life scale. Psychological Assessment, 5 (2), 164–172.
Portes, A., 1998. Social capital: its origins and applications inmodern 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 thetransformation 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 ofuniversities in getting South Africa closer to the globalinformation society. South African Journal of InformationManagement, 6 (2).
Smart, A., 1993. Gifts, bribes, and guanxi: a reconsiderationof bourdieu’s social capital. Cultural Anthropology, 8 (3),388–408.
Tidwell, L.C. and Walther, J.B., 2002. Computer-mediatedcommunication effects on disclosure, impressions, andinterpersonal evaluations: getting to know one anothera bit at a time. Human Communication Research, 28,317–348.
Toomey, L., et al., 1998. Designing virtual communities forwork. ACM SIGGROUP Bulletin, 19 (3), 6–7.
Valenzuela, S., Park, N., and Kee, K.F., 2008. Lessons fromFacebook: the effect of social network sites on collegestudents’ social capital. International symposium on onlinejournalism. Austin, TX: University of Texas at Austin,1–39.
Waddock, S., 2007. Leadership Integrity in a fracturedknowledge 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. AmericanBehavioral Scientist, 45 (3), 436.
Williams, D., 2006. On and off the net: scales for socialcapital in an online era. Journal of Computer-MediatedCommunication, 11 (2), 593–628.
K. Johnston et al.36
Dow
nloa
ded
by [
Mou
nt A
lliso
n U
nive
rsity
0L
ibra
ries
] at
06:
40 1
2 A
pril
2013
