Facebook Is a Source of Social Capital Building ... - SAGE Journals

Article

Facebook Is a Source of Social Capital Building Among University Students: Evidence From a Developing Country

Journal of Educational Computing Research 0(0) 1–28 ! The Author(s) 2016 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0735633116667357 jec.sagepub.com

Syed Ali Raza1, Wasim Qazi2, and Amna Umer1

Abstract This study analyzes the influence of Facebook usage on building social capital among university students in Karachi by using a modified framework of technology acceptance model. Important information was gathered utilizing organized questionnaire containing items of Facebook intensity, social self-efficacy, perceived ease of use, perceived usefulness, perceived playfulness (independent variables), intention to continue use (mediating variables), bridging social capital, and bonding social capital (dependent variables), while the specimen size includes 560 university students. Furthermore, the procedures utilized as a part of the study are reliability analysis, confirmatory factor analysis, partial least square-structural equation modeling to check the impact of these factors on the building of social capital. Findings show that Facebook intensity, perceived ease of use, perceived usefulness, perceived playfulness, and social self-efficacy have a positive and significant impact on intention to continue use, while intention to continue use has a positive and significant impact on both dependent variables bridging social capital and bonding social capital concluding that social networking sites (Facebook) are helpful in building and maintaining social capital by creating intention to continue using them. This study provides useful insights about the youth experience of using Facebook and sharing information.

1

Department of Management Sciences, IQRA University, Karachi, Pakistan Department of Education and Learning Sciences, IQRA University, Karachi, Pakistan

2

Corresponding Author: Syed Ali Raza, Department of Education and Learning Sciences, IQRA University, Karachi 75300, Pakistan. Email: [email protected]

2

Journal of Educational Computing Research 0(0)

Also, marketers would get benefit as this study would allow them to adopt effective marketing strategies. Keywords Facebook intensity, social self-efficacy, bridging social capital, bonding social capital, technology acceptance model, Karachi, social networking sites, social media

Introduction The Internet was largely used for human interaction, allowing them to get engaged and being involved in the formation of societies (Ong, 1982). Humans are social and innovative by nature (Lu¨ders, 2007); therefore, socializing through Internet has developed to become the norm for people all around the globe. It has given a new meaning to communication by enabling them to share information regardless of their location. This has resulted in changes to their personal and professional lifestyles and the mode of sharing and exchanging information (O’Murchu, Breslin, & Decker, 2004). With the advent of web 2.0 technologies, a new set of advanced features and services appeared which included social networking sites (SNSs), blogs, message boards, and instant messaging services. Millions of people, involving educational and industrial investigators, are enticed by these SNSs (Boyd & Ellison, 2007). Among other SNSs, Facebook is one of the most attention-grabbing sites and is the prime interest to the proposed investigation owning to its extensive usability and its technological capabilities helping to maintain contacts (Ellison, Steinfield, & Lampe, 2007). According to Ellison, Gray, Vitak, Lampe, and Fiore (2013), people use Facebook in order to communicate with new people as well as with the people they know already. Furthermore, he describes Facebook as a platform where people can comment on their contact’s activities, visit their friends’ profiles, and post about their own activities and viewpoints. It has been stated in the studies that Facebook helps people to gather relevant information, develop relationships, and synchronize conducts to pinpoint mutual affairs (Gershuny, 2002; Norris & Jones, 1998). According to DiMaggio, Hargittai, Neuman, and Robinson (2001), these SNSs have greatly influenced the creation and continuance of social capital. Social capital, created by the combination of bridging social capital (BRS) and bonding social capital (BOS) (Putnam, 2001) has been regarded as the resources gathered through the contacts among people (Coleman, 1988; Ellison et al., 2013; Jin, 2013; Johnston, Tanner, Lalla, & Kawalski, 2013). It is considered to be the combined worth of publicly recognized social networks and the tendency of doing something for people, which emerges from these networks. According to Fukuyama (2001), social capital enhances association

Raza et al.

3

and relationship among people, while Lin (2002) sums it up as ‘‘investment in social relations with expected returns in the marketplace’’ (p. 3). The investment and return can be in terms of gaining relevant information, trend, opinions, and news. Furthermore, BRS places emphasis on extrinsic connections (Adler & Kwon, 2002) which are concerned with the weak ties among people (Putnam, 2001). These weak ties or loose connections help people in giving relevant information and different views about each other without providing any emotional assistance (Ellison et al., 2007; Granovetter, 1983, Williams, 2006). Contrary to bridging, BOS establishes closely tied relations between and among those who are attached emotionally like family, bosom friends, and special associates (Adler & Kwon, 2002; Morrow, 2001). Some researchers (Ellison et al., 2007; Williams, 2006) have regarded BOS as congruent with inner kinds of associations which are also capable of providing spiritual uplift to the people who are the members of those relationships (Islam, Merlo, Kawachi, Lindstro¨m, & Gerdtham, 2006). Various studies have been conducted to investigate the impact of the Internet on building social capital (Bharati, Zhang, & Chaudhury, 2015; Chang & Zhu, 2012, Richardson & Hessey, 2009) and have shown both an increased and decreased effect of the Internet on building social capital. A negative association between the Internet and social capital building has been reported by researchers such as Kraut et al. (1998), Nie, Hillygus, and Erbring (2002), and Nie and Hillygus (2002), while the positive association between the Internet and social capital has been demonstrated through the work of Williams (2006), Kavanaugh, Carroll, Rosson, Zin, and Reese (2005), Hampton and Wellman (2003), Gershuny (2002), Wellman, Haase, Witte, and Hampton (2001), and Norris and Jones (1998). Moreover, the effect of Facebook on the social capital building among teenagers and students of universities has also been studied (Ahn, 2012; Bohn, Buchta, Hornik, & Mair, 2014; Ellison et al., 2007; Jin, 2013; Johnston et al., 2013). Extensive research (Burke, Kraut, & Marlow, 2011; Ellison et al., 2007; Ellison, Steinfield, & Lampe, 2011; Johnston et al., 2013; Seinfeld, Ellison, & Lampe, 2008; Shah, Subramanian, Rouis, & Limayem, 2012; Valenzuela, Park, & Kee, 2009) shows that a positive relationship exists between the use of Facebook and social capital. The Internet service in Pakistan has been available since 1990. In 2001, around 1.3% of the population were using the Internet. By the end of 2006, the figure had risen to 6.5% and in 2012, it had reached 10% of the population. The latest figures of 2015 indicate that a further rise to 11% of the population is using the Internet in Pakistan, while information technology think tanks claims that Internet users have crossed 30 million at present. The usage of Facebook is increasing day by day. In June 2013, there were more than 9 million Facebook users in Pakistan. Statistics showed that 6.4 million were men, and 2.7 million were women, while 70% of the users were aged below 26 years (Farooqi et al., 2013; Memon et al., 2015). According to the Pakistan Social Media

4


Report—March 2014, the number of Facebook users had further risen up to 13 million out of which 72% were male and 28% were female, ranging between the age of 18 and 34 years (Pakistan Social Media Report, 2014). Although usage of Facebook is increasing tremendously, its impact on the creation of social capital is barely investigated in Pakistan. Therefore, there is a need to determine those factors which could impact the usage of Facebook resulting in the building of social capital. The increased popularity of SNSs especially Facebook in developing countries regard them as essential tools for students to create and sustain social capital. Students of higher education, particularly, are required to build social capital for their professional growth. However, there is a very little research on how the higher education students belonging to developing countries could gain and sustain social capital. This study makes imperative contributions by investigating the consequences for the maintenance of social capital through the use of Facebook by the students of higher education. It also sheds light on how students can widen their social networks and gain diverse information in the professional environment. Moreover, this study determines the influence of factors on the building and maintenance of both bridging and bonding social capital in a developing country. It is anticipated that this study will inspire future researches on the influence of other SNSs on the creation and usability of social capital. Lately, various researches have been conducted to see Facebook as a source of building social capital by either using technology acceptance model (TAM) or by considering demographics only, but this kind of modified model has not yet been studied, especially in developing countries. Also, social capital, the dependent variable of this study is not considered as one variable like previously studied in Jin (2013 and Sheng-Yi, Shih-Ting, Liu, Da-Chain, and Hwang (2012), but it is bifurcated as BRS and BOS. The aim of this study is to determine whether this extensive use of Facebook in Pakistan results in the building and maintenance of social capital, which is actually the accumulation of resources through the connections between individuals.

Literature Review Theoretical Background TAM is a chief conventional acceptance theory in the area of technology (Awa, Eze, Urieto, & Inyang, 2011). It lays a foundation to study exogenous factors which could influence the decision regarding acceptance of technology. The theory is proposed by Davis (1989). This TAM model, used for behavioral opinions and intentions, is widely utilized in studies (Davis, Bagozzi, & Warshaw, 1992; Klobas, 1995; Lee Cheung, & Chen, 2007; Venkatesh & Davis, 2000; Venkatesh, Speier, & Morris, 2002; Thong, Hong, & Tam, 2002; etc.).

Raza et al.

5

Moreover, social capital theory proposed by Bourdieu (1986) and Coleman (1988) is about the capability to exchange the resources among individuals or group of individuals enclosed in their social network. This social capital is then transformed into different types of capital like human capital or intellectual capital (Resnick, 2001). This research is based upon the modified model of technology acceptance (Davis, 1993; Venkatesh & Davis, 2000) and social capital theory (Bourdieu, 1986; Coleman, 1988; Putnam, 2001). Components of TAM and social capital are discussed in the following text.

Empirical Studies and Hypothesis Online social networking. Toomey, Mark, Tang, and Adams (1998) describe online SNSs to be the online groups which communicate and gather assets through these computer-regulated connections. Most of the time, these systems contain individuals of mutual bonds or interests who might belong to different territories (Boyd & Ellison, 2007). With the advancement in the technology, Internet has gone through a great deal of change over a period of time. As it is put up by Bargh and McKenna (2004) that the Internet is basically a combination of former innovations such as telephones, televisions, and libraries which were used as sources of communication and information. Initially, the Internet was understood to be the source of providing information only, but the emergence of these SNSs has provided people with an effective mean of connecting with each other and that too beyond expectation (Weaver & Morrison, 2008). Taking Facebook into account, when it is used in order to collect and share information rather than for socializing, more positive outcomes are anticipated (Junco, 2012). Also, the study carried out by Junco (2012) suggested that particular uses of Facebook which imitate academic behaviors such as collecting and sharing information are linked with positive academic results. Another research conducted by Junco, Heiberger, and Loken (2011) indicated that the communication of students about the content of course lead to enhanced achievements in academic performance. Social capital. Coleman (1988) defines social capital as the assets gathered via the connections between individuals. It is not confined to one meaning rather different meanings in various disciplines, as suggested by Adler and Kwon (2002), and is devised as a stimulus and a response (Resnick, 2001; Williams, 2006). The accumulation of assets which can be real or virtual emerges because of the possession of reliable collections of nearly standardized connections of an individual or a set of individuals, built through joint contacts and acknowledgments (Bourdieu & Wacquant, 1992, p. 14). It depends on the connections that how these assets work. According to Adler and Kwon (2002), social capital is associated with optimistic societal results which include good physical

6


condition of citizens, decreased unlawful acts, and effective capital market. Moreover, Putnam (2001) encounters elevated societal chaos, less involvement in communal affairs, and conceivably increased mistrust in the society due to the deteriorating social capital. While discussed earlier are the negative outcomes, exactly opposite results are produced by elevated levels of social capital such as enhanced devotion toward a society and a capability to encourage joint activities along with other advantages. Also social capital can be utilized negatively, it predominantly affects positively on the communication among the members of a social network (Helliwell & Putnam, 2004). Furthermore, social capital can be divided into two categories, cognitive and structural. Cognitive social capital encompasses personal features including opinions, ethics, norms, and attitudes (Islam et al., 2006), and it is formed by the behavior of a society such as religion, custom, and history (Fukuyama, 2001). On the other hand, structural social capital deals with the apparent characteristics of a society like systems of societal involvement or compactness of social network (Islam et al., 2006). Bridging and bonding social capital (Putnam, 2001), discussed later, also belong to the cognitive category. Bridging social capital. BRS emphasizes on extrinsic connections (Adler & Kwon, 2002) which are concerned with the ‘‘weak ties’’ among people (Putnam, 2001). These weak ties or loose connections help in giving relevant information and different views about each other without providing any emotional assistance (Ellison et al., 2007; Granovetter, 1983; Williams, 2006). Islam et al. (2006) proposes that these connections are formed among the people belonging to distinct cultural or professional settings. Also, BRS or loose connections are said to be increasingly devised and maintained by these SNSs due to the low-cost usage (Donath & Boyd, 2004). Bonding social capital. Contrary to bridging, BOS establishes among closely tied relations who are attached emotionally like family, bosom friends, and special associations (Adler & Kwon, 2002; Morrow, 2001). Many researchers (Ellison et al., 2007; Williams, 2006) have regarded BOS as congruent and inner kind of associations and which are capable of providing spiritual uplift to the people who are the members of those relationships (Islam et al., 2006). The revised TAM. Many previous researches have been conducted in order to study the effect of TAM on social media usage (Rauniar, Rawski, Yang, & Johnson, 2014) and in building social capital (Jin, 2013). In this study, we determine the impact of TAM along with other variables in building social capital. Davis (1989) came up with the TAM model in order to study the behavior related to the use of information technology. A second theory known as the theory of reasoned action, embraced TAM, describing an individual’s conduct through their intentions (Fishbein & Ajzen, 1975). TAM basically describes the

Raza et al.

7

technology acceptance which could in turn describe the usage behavior of the individuals using technology where as theory of reasoned actions generally describes human behavior (Davis, Bagozzi, & Warshaw, 1989). Furthermore, the attitude is divided into perceived ease of use (PEOU) and perceived usefulness (PU). Perceived playfulness (PP) has been regarded as a cognitive factor (Hsu & Lin, 2008). Several studies (Davis, 1993; Venkatesh & Davis, 2000) have revised or remodel TAM by adding factors which could be used in the specific setting of technology. Along with PEOU, PP and PU two other factors, social self-efficacy (SSE) and Facebook intensity (FI) have been incorporated to see their impact on intention to use and then ultimately on social capital building (bridging and bonding social capital). Perceived usefulness. According to an individual’s viewpoint, the extent to which his or her work output increases due to the usage of a specific system is called PU (Lu, Yu, Liu, & Yao, 2003; Rauniar et al., 2014). PU gives a clear vision about the factors influencing the actual use and intention to use (Awa et al., 2011). Both these factors of TAM impact a person’s attitudes toward the usage intention of technology (Rauniar et al., 2014); therefore, the hypothesis of PU would be as follows: H1: PU has a significant impact on intention to continue using Facebook.

Perceived ease of use. Davis (1989) and Venkatesh (2000) describe PEOU as how effortless would the use of a specific system be, in an individual’s perspective. It is a concept linked to a person’s evaluation of the struggle immersed in the procedure of using technology (Davis, 1989). Saade´ and Bahli (2005) explain PEOU as an individual’s evaluation that the use of technology would be without cognitive load and they would not require to devote much of their time and efforts while using it. PEOU defines an individual’s intention toward the use of technology (Taylor & Todd, 1995), and it can have both direct and indirect effects on ICU (Saade´ & Bahli, 2005). Therefore, the hypothesis in order to analyze the impact of PEOU would be as follows: H2: PEOU has a significant impact on intention to continue using Facebook.

Perceived playfulness. Social media is one platform which is joyful and enjoyable helping individuals to release stress and triggering creativity (Van der Heijden, 2004). Studies have shown that integrating work with joy results in enhancing productivity and output (Stephenson, 1964). PP can be elaborated in terms of Facebook as the limit to which Facebook associated activities can provide

8


enjoyment besides the predicted performance outcome (Raunir et al., 2013). Through various present researches, it has been observed that PP has an important impact on the behavior to use, such as computer technology in an organization (Hwang, 2005; Igbaria, Parasuraman, & Baroudi, 1996), instant messaging usage (Li, Chau, & Lou, 2005), and intention to continue the usage of mobile devices (Nysveen, Pedersen, & Thorbjørnsen, 2005a, 2005b). Therefore, the hypothesis for PP is as follows: H3: PP has a significant effect on intention to continue using Facebook.

Social self-efficacy. The degree of self-confidence owned by people which allow them to cope up with stress causing factors demonstrate self-efficacy (Bandura, 1977; Jerusalem & Schwarzer, 1992). Moreover, Gecas (1989) suggested that SSE is a person’s perspective about their capability to start up a social interaction and form new connections and so in an online system this would impact people’s stance and behavior in such group like Facebook (Wu et al., 2012). SSE is also employed in various fields concerning students (Fan, Meng, Gao, Lopez, & Liu, 2010; Hagedoorn & Molleman, 2006; Lin & Betz, 2009; Wei, Russell, & Zakalik, 2005). Self-efficacy has an important impact on the intention of using and adopting technology (Compeau & Higgins, 1991; Hill, Smith, & Mann, 1987; Davis et al., 1989). According to the argument placed by Bandura (1982, 1986), the perspective of self-efficacy acts as an important generator of intention to use technology. Moreover, Hill et al. (1987) discovered that self-efficacy has an important impact on technology acceptance and it has a significant impact on influencing the decision of using them. Therefore, the hypothesis would be as follows: H4: Social self-efficacy has a significant impact on the intention to use Facebook.

Facebook intensity. Social networking sites provide a forum to interact and to get a feeling of association with the personal and social contacts (Valenzuela et al., 2009). Through researches, it has been seen that individuals devote notable time on their desirable social network expressing comparatively optimistic attitude (Chen & Haley, 2010) which would in turn impact the intention to continue using it, since attitude influences intention as evident through the theory of reasoned action (Fishbein & Ajzen, 1975). Therefore, the hypothesis would be as follows: H5: Facebook intensity has a significant impact on the intention to continue using Facebook.

Raza et al.

9

Intention to continue using. TAM suggests that using the technology is decided by the intention to use it (Rauniar et al., 2014). It has been proposed by Jin (2013) that social media provides a platform in which participants make their virtual accounts, transfer resources, participate in various communal activities, and build connections with other participants. Association between Facebook usage and building of social capital has been determined by many researchers suggesting that the extensive use of social media would tremendously increase social capital. Also, individuals log in to these sites in order to nourish the connection with their old and new contacts (Ellison, Heino, & Gibbs, 2006). There are also many studies (Hu & Kettinger, 2008; Lin & Lu, 2011; Pinho & Soares, 2011) which have been carried out observing the influence of social capital on the intention to continue using Facebook. This research, however, investigates the opposite relationship, that is, impact of intention to continue using Facebook on social capital building. Hence, consistent with these observations, Jin (2013) proposed that intention to use Facebook influences the maintenance of social capital building (bridging and bonding social capital). Therefore, it can be hypothesized that: H6a: Intention to continue using Facebook has a significant impact on BRS. H6b: Intention to continue using Facebook has a significant impact on BOS.

Methodology The extended model of TAM is represented by Figure 1, which is the conceptual model of this research. This model comprises of three conventional factors of TAM which are PEOU, PU, and PP along with two additional factors, SSE and FI. In our research, we use these factors to study their influence on intention to continue using Facebook and ultimately on social capital building among university students of Karachi.

Measurement Instrument In accordance with this study, the model of TAM (Davis, 1989) was further transformed and extended. Instrument, intended to use in this research, is a questionnaire whose items are designed according to the demand of the factors. Moreover, the content of the questionnaire is validated by educational and industrial professionals. To reassure the relevancy and convenience of the questionnaire, a preliminary test has been carried out by distributing the questionnaire among students who belong to various universities. The questionnaire is constructed on a 5-point Likert scale which ranges from strongly disagree (1) to strongly agree (5) which is utilized to estimate the role of Facebook on building social capital. With this respect, items of the factors,

10


Facebook Intensity

Social Self-Efficacy

Perceived Playfulness

Perceived ease of use

Social Capital

Intention to continue

Bridging social capital Bonding social capital

Perceived Usefulness

Figure 1. Conceptual model.

PEOU (Gefen & Straub, 2000; Jin, 2013), PU (Jin, 2013), PP (Wakefield & Whitten, 2006), intention to continue using Facebook (Hamari & Koivisto, 2013; Jin, 2013), SSE (Shen-Yi, Shih-Ting, Liu, Da-Chain, & Hwang, 2012), FI (Ellison et al., 2007), BRS (Chang & Zhu, 2012; Ellison et al., 2007), and BOS (Ellison et al., 2007) are adapted and transformed according to the situation of this research. The data along with the demographic statistics, which include gender, age, and qualification, are gathered through survey method. The technique used is nonprobability sampling technique which is also called convenience sampling. The focus of this research is to target students who are the users of Facebook. Considering the factor analysis, a sample of 50, 300, 500, and 1,000 is regarded as poor, good, very good, and excellent, respectively (Ali & Raza, 2015; Comrey & Lee, 1992). Hence, in order to continue this research, business students were selected from seven Business Schools of Karachi (Iqra University, Institute of Business Management, Institute of Business Administration, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, National University of Computer and Emerging Sciences, Bahria University, Karachi University Business School) as a sample. Consent was obtained regarding the distribution of questionnaire, from the relevant authority. Six hundred and fifteen responses were collected initially, and after removing the missing responses, the number of valid responses were reduced to 560. Items in a questionnaire should be at least 25 (Hair, Black, Babin, Anderson, & Tatham, 2006), and this requirement is met by our questionnaire since it has 45 items in total. Voluntary responses are taken

Raza et al.

11

from the participants, while all the information shared by them is kept confidential. Furthermore, the instrument comprises independent variable (FI, SSE, PEOU, PU, and PP and intention to continue using Facebook), while dependent variable comprises social capital: bridging and bonding social capital. In accordance with the above finding, there would be two mathematical equations representing regression models. They would be as follows: BRS ¼ þ 1 FI þ 2 SSE þ 3 PEOU þ 4 PU þ 5 PP þ 6 ICU þ E

ð1Þ

BOS ¼ þ 1 FI þ 2 SSE þ 3 PEOU þ 4 PU þ 5 PP þ 6 ICU þ E

ð2Þ

where BRS ¼ Bridging social capital, BOS ¼ Bonding social capital, FI ¼ Facebook Intensity, SSE ¼ Social self-efficacy, PEOU ¼ Perceived ease of use, PU ¼ Perceived usefulness, PP ¼ Perceived playfulness, ICU ¼ Intention to continue using Facebook, E ¼ error.

Demographics The sample represented the responses of the students from different business universities, and in total, 615 questionnaires were filled and returned. After deletion of outliers and erroneous responses, 560 responses were found useable. Table 1 represents the demographics of the participants which are 560 in number (excluding 10 missing responses). Out of this 560 responses, 279 are male (50%) and 281 are female (50%) students from different universities. Majority of the students’ age is between 18 and 25 years making up to 73%, while 21% of the students are aged between 26 and 30, 5% of the students have their ages ranging between 31 and 35, and only 1% of the students are of age 36 or more. Furthermore, the data of educational level show that the greater section of students are either intermediate or A levels (42%), 37% are graduate, 16% are postgraduate, while 6% are PhD. In addition to that, the intensity of Facebook usage is determined by the total number of friend’s respondents have on Facebook and the number of hours they spend on Facebook. Through the statistics, it is observed that 14% of the students have 51 to 100 friends on Facebook, 12% have more than 400 friends, while only 8% have 11 to 50 friends. Moreover, 28% of the students use Facebook for 10 to 30 minutes, while only 12% uses Facebook for 1 to 2 hours.

Data Analysis and Results To analyze the data, structural equation modeling in SmartPLS 3.2.3 (Ringle, Wende, & Becker, 2014) along with the bootstrap resampling technique of 5,000 subsamples has been used in this study (Hair, Ringle, & Sarstedt, 2011). This technique is utilized to evaluate the measurement model and the structural

12

Journal of Educational Computing Research 0(0) Table 1. Profile of Respondents. Demographic items Gender Male Female Age 18–25 26–30 31–35 36 or more Education level Inter/A levels Graduate Postgraduate PhD Facebook intensity 10 or less 11–50 51–100 101–150 151–200 201–250 251–300 301–400 more than 400 Facebook usage less than 10 10–30 31–60 1–2 hours 2–3 hours more than 3 hours

Frequency

Percentile (%)

279 281

50 50

409 118 28 5

73 21 5 1

235 207 90 28

42 37 16 6

50 45 79 73 56 67 73 50 67

9 8 14 13 10 12 13 9 12

78 158 95 95 67 67

14 28 17 17 12 14

Source: Author estimations.

model. Moreover, partial least square (PLS)-structural equation modeling technique is appropriate for the analyses of the complex framework like the one used in this study (Hair et al., 2011; Henseler et al., 2014), and it is capable of evaluating this model properly (Hair et al., 2011; Hair, Sarstedt, Ringle, & Mena, 2012).

Raza et al.

13

Wold (1975, 1980) and Joreskog and Wold (1982) introduced PLS. This technique is capable of representing relationships among multiple latent variables. A latent variable is an unobserved variable which correlates other measured variables (Aibinu & Al-Lawati, 2010). PLS has the ability to function with hidden latent constructs and can interpret the measurement error in the improvement of latent variables (Chin, 1998). This study incorporates perception-based items, represented on a Likert scale, which have unknown distribution and their normality cannot be presented. Furthermore, the methods used to examine the effectiveness of the model are individual item reliability analysis, convergent validity of the measures associated with individual latent variables (Cook & Campbell, 1979), and discriminant validity (Campbell & Fiske, 1959) of the research instruments. For the assessment of individual item reliability, the standardized loadings (or simple correlation) are examined. As proposed by Tabachnick and Fidell (2007) and Raza and Hanif (2013), 0.55 is a cutoff point. All the items in this study have loadings above 0.55 as shown in Table 2. Furthermore, two techniques are utilized by PLS in order to determine the convergent validity of the measured constructs (Fornell & Larcker, 1981): (a) Cronbach’s alpha and composite reliability scores (rc) and (b) average variance extracted (AVE). As shown in Table 2, all the variables have Cronbach’s alpha greater than 0.7 (Cronbach, 1951), which means that all the variables are reliable. However, all the variables meet the requirement of composite reliability as its value should be 0.7 or more (Nunnally, 1978; Raza, Jawaid, & Hassan, 2015). Moreover, Fornell and Larcker (1981) suggested that in order to determine convergent validity for the variables, the AVE is supposed to exceed 0.5. Table 2 shows that all variables have AVE values exceeding 0.5 except for BRS. To check the discriminant validity, two tests are carried out: analysis of crossloadings and analysis of AVE. Table 3 representing correlation matrix, follows the rule proposed by Fornell and Larcker (1981) according to which the square root of AVE should be greater than the correlation of two latent variables. Table 4. shows that the loadings of all items are higher on their respective constructs than on the other constructs. Also the differences of cross-loadings are greater than the suggested threshold (Gefen and Straub, 2005). Also, the heterotraitmonotrait ratio of correlations results in Table 5 which shows that none of the heterotrait-monotrait criteria are greater than 0.85 (Henseler, Ringle, & Sarstedt, 2015). The explanatory power of the structural model can be investigated by assessing the amount of variance in the dependent variable which can be explained by the model. Breiman and Friedman (1985) suggest that R2 is essential for the assessment of a structural model. Figure 2 shows that the R2 for ‘‘intention to continue using Facebook (ICU)’’ is 0.5060 implying that 50.06% of the changes in the intention of students to continue using Facebook are due to the five latent variables in the model. Furthermore, R2 for BRS is 0.4640 showing that 46.40%

14


Table 2. Measurement Model Results.

Constructs Bonding social capital (BOS)

Bridging social capital (BRS)

Facebook intensity (FI)

Intention to continue using (ICU)

Perceived ease of use (PEOU)

Perceived playfulness (PP)

Items BOS1 BOS2 BOS3 BOS4 BOS5 BRS1 BRS2 BRS3 BRS4 BRS5 BRS6 BRS7 BRS8 BRS9 BRS10 FI1 FI2 FI3 FI4 FI5 FI6 ICU1 ICU2 ICU3 ICU4 ICU5 PEOU1 PEOU2 PEOU3 PEOU4 PP1 PP2 PP3 PP4

Loadings 0.7160 0.6790 0.7850 0.8080 0.7960 0.6620 0.7830 0.7110 0.6320 0.7590 0.7240 0.7110 0.6910 0.6530 0.5140 0.7590 0.7310 0.7970 0.7670 0.7600 0.6100 0.7710 0.8510 0.8650 0.6810 0.7180 0.8470 0.8530 0.8260 0.8090 0.7960 0.8670 0.8600 0.7810

Cronbach’s a

Composite reliability

Average variance extracted

0.8190

0.8680

0.5720

0.8720

0.8980

0.5820

0.8320

0.8760

0.5450

0.8340

0.8840

0.6080

0.8540

0.8970

0.6890

0.8430

0.8950

0.6820

(continued)

Raza et al.

15

Table 2. Continued

Constructs

Items

Perceived usefulness (PU)

Social self-efficacy (SSE)

PU1 PU2 PU3 PU4 SSE1 SSE2 SSE3

Loadings 0.765 0.739 0.807 0.671 0.744 0.754 0.731

Cronbach’s a

Composite reliability

Average variance extracted

0.7440

0.8310

0.5560

0.7390

0.7840

0.5490

Table 3. Summary Statistics. Correlation matrix

BOS BRS FI ICU PEOU PP PU SSE

BOS

BRS

FI

ICU

PEOU

PP

PU

SSE

0.756 0.519 0.395 0.461 0.034 0.345 0.289 0.348

0.687 0.653 0.674 0.298 0.595 0.524 0.472

0.738 0.523 0.2 0.444 0.399 0.328

0.779 0.263 0.627 0.469 0.432

0.830 0.36 0.549 0.581

0.825 0.479 0.438

0.745 0.561

0.740

Note. BRS ¼ bridging social capital; BOS ¼ bonding social capital; FI ¼ Facebook intensity; SSE ¼ social self-efficacy; PEOU ¼ perceived ease of use; PU ¼ perceived usefulness; PP ¼ perceived playfulness; ICU ¼ intention to continue using Facebook. The diagonal elements (bold) represent the square root of AVE (average variance extracted).

of BRS is explained by ICU, while the R2 for BOS is 0.2180 which means that 21.80% of BOS is explained by ICU.

Path Analysis Table 6 and Figure 2 show the results of path analysis in which each path correlates with the hypothesis. The hypotheses between latent variables and dependent variables are checked by evaluating their respective coefficients on the basis of sign, size, and significance (Wixom & Watson, 2001). The greater the

16


Table 4. Loadings and Cross-Loadings. BOS

BRS

FI

BOS1 0.7160 0.3700 0.3220 BOS2 0.6790 0.2080 0.2560 BOS3 0.7850 0.3450 0.2880 BOS4 0.8080 0.4590 0.3030 BOS5 0.7960 0.5010 0.3270 BRS1 0.3450 0.6620 0.5140 BRS2 0.4960 0.5140 0.3780 BRS3 0.3590 0.7830 0.4980 BRS4 0.4320 0.7110 0.4010 BRS5 0.2740 0.6320 0.3750 BRS6 0.3420 0.7590 0.5370 BRS7 0.3300 0.7240 0.4560 BRS8 0.2490 0.7110 0.3980 BRS9 0.3400 0.6910 0.5230 BRS10 0.4550 0.6530 0.4050 FI1 0.2410 0.4370 0.7590 FI2 0.4170 0.5170 0.7310 FI3 0.2510 0.4580 0.7970 FI4 0.1900 0.4240 0.7670 FI5 0.3390 0.6080 0.7600 FI6 0.2810 0.4200 0.6100 ICU1 0.3580 0.5540 0.3970 ICU2 0.4180 0.5780 0.4030 ICU3 0.4310 0.5840 0.4370 ICU4 0.2780 0.4260 0.3720 ICU5 0.2950 0.4750 0.4310 PEOU1 0.0020 0.2390 0.1160 PEOU2 0.0030 0.2550 0.1740 PEOU3 0.0850 0.2510 0.1870 PEOU4 0.0430 0.2490 0.1940 PP1 0.2680 0.4980 0.3870 PP2 0.2850 0.4790 0.3990 PP3 0.3630 0.5190 0.3600 PP4 0.2190 0.4670 0.3260 PU1 0.225 0.388 0.262

ICU

PEOU

PP

PU

SSE

0.2510 0.2340 0.3400 0.3590 0.4730 0.3600 0.3550 0.5360 0.4890 0.4600 0.5410 0.4750 0.4440 0.4870 0.4370 0.3830 0.4500 0.3950 0.3380 0.4430 0.2630 0.7710 0.8510 0.8650 0.6810 0.7180 0.2220 0.2520 0.1730 0.2180 0.4750 0.4840 0.5620 0.5400 0.344

0.0800 0.1830 0.0140 0.0610 0.1680 0.1940 0.0050 0.2570 0.2680 0.1890 0.2980 0.2920 0.3140 0.1020 0.0630 0.3100 0.0700 0.2180 0.0940 0.1150 0.0680 0.0990 0.1840 0.1670 0.3500 0.2500 0.8470 0.8530 0.8260 0.8090 0.4280 0.2910 0.2550 0.2300 0.491

0.1820 0.1210 0.2580 0.2500 0.3960 0.3830 0.2840 0.5050 0.4190 0.3520 0.4550 0.4050 0.4760 0.4250 0.3480 0.4360 0.3380 0.3510 0.2640 0.3570 0.1700 0.3990 0.4680 0.5040 0.5530 0.5350 0.3100 0.3660 0.2360 0.2650 0.7960 0.8670 0.8600 0.7810 0.32

0.1140 0.0480 0.2390 0.2390 0.3390 0.4030 0.2000 0.4930 0.4340 0.2790 0.3570 0.3350 0.4000 0.3510 0.3180 0.3410 0.2680 0.3410 0.2340 0.3370 0.2290 0.2750 0.3400 0.4100 0.4090 0.4000 0.4410 0.4300 0.4290 0.5320 0.5110 0.4040 0.3920 0.2910 0.765

0.1290 0.0680 0.2250 0.3380 0.4160 0.3000 0.1670 0.4520 0.4010 0.2320 0.3650 0.3160 0.3590 0.3060 0.2910 0.3070 0.2320 0.2630 0.2100 0.2930 0.1050 0.3130 0.3500 0.3520 0.3420 0.3320 0.4620 0.5080 0.4820 0.4860 0.3640 0.3670 0.3870 0.3300 0.477 (continued)

Raza et al.

17

Table 4. Continued

PU2 PU3 PU4 SSE1 SSE2 SSE3

BOS

BRS

FI

ICU

PEOU

0.147 0.351 0.035 0.456 0.25 0.054

0.293 0.543 0.237 0.304 0.355 0.396

0.154 0.435 0.249 0.218 0.257 0.259

0.24 0.472 0.261 0.355 0.258 0.333

0.467 0.278 0.517 0.266 0.388 0.641

PP

PU

SSE

0.277 0.483 0.279 0.262 0.247 0.453

0.739 0.807 0.671 0.294 0.404 0.555

0.344 0.428 0.424 0.744 0.754 0.731

BRS ¼ bridging social capital; BOS ¼ bonding social capital; FI ¼ Facebook intensity; SSE ¼ social self-efficacy; PEOU ¼ perceived ease of use; PU ¼ perceived usefulness; PP ¼ perceived playfulness; ICU ¼ intention to continue using Facebook.

Table 5. Heterotrait-Monotrait Ratio (HTMT) Results.

BOS BRS FI ICU PEOU PP PU SSE

BOS

BRS

FI

ICU

PEOU

PP

PU

0.599 0.464 0.520 0.163 0.378 0.324 0.493

0.759 0.782 0.340 0.688 0.606 0.643

0.615 0.242 0.517 0.460 0.451

0.314 0.749 0.558 0.600

0.421 0.729 0.808

0.575 0.605

0.831

SSE

BRS ¼ bridging social capital; BOS ¼ bonding social capital; FI ¼ Facebook intensity; SSE ¼ social self-efficacy; PEOU ¼ perceived ease of use; PU ¼ perceived usefulness; PP ¼ perceived playfulness; ICU ¼ intention to continue using Facebook.

coefficient, the greater is the impact of the independent variable on the dependent variable. Hypotheses are supported by considering the level of significance to be 0.1, and according to this, all paths are significant as shown in Figure 2. Moreover, as shown in Figure 2, the coefficients of paths connecting PU, PEOU, PP, SSE, FI with intention to continue using Facebook are all positive and significant; hence supporting H1, H2, H3, H4, H5, respectively. Moreover, the coefficient of path connecting intention to continue use (ICU) and BRS is positive and significant (H6), while the coefficient of path connecting intention to continue using and BOS is also positive and significant (H7).

18


Facebook Intensity 0.2520

Social Self-Efficacy

Social Capital R ²=0.5060 0.1510 0.6810

Perceived Playfulness 0.4130

Intention to continue 0.4670

Perceived ease of use

Bridging social capital Bonding social capital

R ²=0.4640

R ²=0.2180

0.090

0.1420

Perceived Usefulness

Figure 2. Path analysis.

Table 6. Standardized Regression Weights for the Research Model. Hypothesis

Regression path

H1 H2 H3 H4 H5 H6a H6b

PU > ICU PEOU > ICU PP > ICU SSE > ICU FI > ICU ICU > BOS ICU > BRS

Effect type Direct Direct Direct Direct Direct Direct Direct

effect effect effect effect effect effect effect

SRW

Remarks

0.1420* 0.0900* 0.4130*** 0.1510* 0.2520*** 0.4670*** 0.6810***

Supported Supported Supported Supported Supported Supported Supported

Note. SRW ¼ standardized regression weight; BRS ¼ bridging social capital; BOS ¼ bonding social capital; FI ¼ Facebook intensity; SSE ¼ social self-efficacy; PEOU ¼ perceived ease of use; PU ¼ perceived usefulness; PP ¼ perceived playfulness; ICU ¼ intention to continue using Facebook. *p < .10. ***p < .01.

Discussion The aforementioned results show that all the paths between independent and dependent variables are positive and significant which mean that all the hypotheses are supported. The path connecting PU with intention to continue using Facebook is positive and significant (b ¼ 0.1420, p < .1); hence supporting the hypothesis (H1). This result is congruent with the outcomes of other researches

Raza et al.

19

(Jin, 2013; Rauniar et al., 2014). Therefore, it can be concluded that PU positively impacts intention to continue using Facebook. Similarly, the path connecting ‘‘perceived ease of use’’ and ICU is positive and significant (b ¼ 0.0900, p < .1), supporting H2 and thus it is consistent with various other studies (Jin, 2013; Rauniar et al., 2014). This again shows that PEOU impacts ICU but not greatly. Again, the path connecting PP is also positive and significant (b ¼ 0.4130, p < .1), which supports the hypothesis (H3). Different other literatures have supported this result (Jin, 2013); hence PP can have a great impact on ICU. According to Jin (2013), participants regard social media as the supplier of information which is conveniently approachable, easily usable, and also enjoyable. Thus, the above findings sum up that the social media like Facebook is favorable in meeting the demands of the customers due to its ease of use, usefulness, fun providing attribute (Jin, 2013). Furthermore, SSE also impacts positively and significantly on ICU (b ¼ 0.1510, p < .1); hence supporting the hypothesis (H4). This result is also consistent with the various researches (Compeau & Higgins, 1991; Davis et al., 1989; Hill et al., 1987). According to Hill et al. (1987), self-efficacy has a positive impact on the intention to continue using Facebook. Furthermore, Wu et al. (2012) suggest that SSE creates positive relation in the online Internet world specially in terms of their emotional connectivity and the sharing of knowledge. Hence, individuals’ SSE results in the building of social capital through their communication with participants. As indicated by the results, FI has a positive and significant impact on ICU (b ¼ 0.2520, p < .1), thus supporting H5. Similar results could be found in mentioned researches (Chen & Haley, 2010; Park & Kim, 2013). Hence, the findings suggest that the use of Facebook helps students to establish social capital, since it is easier and cheaper to maintain bonds (Donath & Boyd, 2004) and it also removes the hurdle, through its affordability, for those who are hesitant in communicating directly (Ellison et al., 2007). ICU has a positive and significant impact on both BRS (b ¼ 0.681, p < .1) and BOS (b ¼ 0.467, p < .1); hence supporting both the hypotheses H6 and H7, respectively. According to Jin (2013), intention to continue using Facebook is facilitated by PEOU, PU, and PP of the users, while social capital building is mediated by intention to continue using Facebook. This study explored that the elements of TAM along with three variables play positive and significant role in the building of social capital. Facebook, hence, plays a vital part in the course of building both bridging and bonding social capital. The outcomes of the research indicate that Facebook usage assists students in gathering and sustaining BRS since it allows participants to sustain such bonds conveniently and at a much lower cost. However, outcomes show that as compared with bridging, the use of Facebook seems to form and maintain BOS at a lower level.

20


Conclusion and Recommendations This study analyzes the influence of Facebook usage on building social capital among Business University students in Karachi, Pakistan. The elements of TAM model along with three variables play positive and significant role in the building of social capital. Facebook, hence, plays a vital part in the course of building both bridging and bonding social capital. The outcomes of the research indicate that Facebook usage assists students in gathering and sustaining BRS since it allows participants to sustain such bonds conveniently and at a much lower cost. However, outcomes show that as compared with bridging, the use of Facebook seems to form and maintain BOS at a lower level. This research provides fresh perceptions about youth experience of using social media along with their experience of getting connected to the world. Also, it gives an idea of how the information can be transferred and utilized through Facebook. Furthermore, the outcomes of this study should be replicated in the development of marketing agendas and subject congruent with consumer preferences. The outcomes can be of value to the marketers who are looking for comprehensive approaches for attracting and acquiring customers who embrace and use SNSs. Largely, social interaction is responsible for the creation of social capital. SNSs have earned popularity in this modern society since they assist chain interactions. Hence, the findings of this study should inspire SNSs marketers to incorporate those techniques which could expose the characteristics of consumers and then to segment users with related attributes into separate divisions. The findings are also fruitful for the studies to be conducted on social networking marketing as it would help marketers to embrace effective social marketing strategies by providing them vital strategic guidelines. By developing marketing strategies using Facebook, the connection between the customer and the marketer will be intensified, since the use of Facebook will enhance the interaction of users. This research should moreover help marketers in the formation of marketing programs which are analogous to the preferences of the consumers. Furthermore, this study should also lead to the formation of strategies that could enhance marketing communication through SNSs and also to entice consumers who embrace these sites. Comprehending the driving factors of consumers to use these SNSs is the basis to fully utilize these social networking media. Moreover, by this research, participants also come to know that if they are provided with the required information or not through Facebook. This revised model of TAM can act as a reference for practitioners, researchers, and educators of social media. The outcomes of this research can also assist social network marketing studies by proposing vital strategic instructions for social marketers who are looking for the operative social marketing strategies.

Raza et al.

21

This research is also fruitful for educators and students. Higher education professionals, by familiarizing themselves with social media, can help students to gather relevant information through SNSs such as Facebook and can also assist them to use this information in a positive manner, thus enhancing the building and usability of social capital. These SNSs also encourage educators to interact with their students through social media such as Facebook which could lead to a thought provoking discussions even outside a classroom setting as according to Junco et al. (2011), the use of SNSs in an academically appropriate way enhanced involvement of students and upgraded their results. Teachers or educators can make themselves easily relatable and approachable by interacting with their students through Facebook. SNSs are the cost-efficient and effective tools which can be used by teachers as they complement and enhance the growth of essential intellectual skills. Like any other study, this study also has some limitations. Since this study has gathered data from university students with the similar age group as that of other studies and having similar kind of live styles; therefore, the result of this research cannot be generalized. Nevertheless, people who belong to different demographic groups demonstrate different usage of Facebook and dissimilar time out activities. Most likely, the research could be extended by other factors such as subjective norm, positive and negative technology readiness, satisfaction, perceived behavioral control, attitude, and trustworthiness which could help in the formation and maintenance of social capital. Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The authors received no financial support for the research, authorship, and/or publication of this article.

References Adler, P. S., & Kwon, S. W. (2002). Social capital: Prospects for a new concept. Academy of Management Review, 27, 17–40. Ahn, J. (2012). Teenagers’ experiences with social network sites: Relationships to bridging and bonding social capital. The Information Society, 28, 99–109. Aibinu, A. A., & Al-Lawati, A. M. (2010). Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding. Automation in Construction, 19, 714–724. Ali, M., & Raza, S. A. (2015). Service quality perception and customer satisfaction in Islamic banks of Pakistan: The modified SERVQUAL model. Total Quality Management & Business Excellence, 0, 1–19.

22


Awa, H. O., Eze, S. C., Urieto, J. E., & Inyang, B. J. (2011). Upper echelon theory (UET) a major determinant of information technology (IT) adoption by SMEs in Nigeria. Journal of Systems and Information Technology, 13, 144–162. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Upper Saddle River, NJ: Prentice-Hall, Inc. Bargh, J. A., & McKenna, K. Y. (2004). The Internet and social life. Annual Review of Psychology, 55, 573–590. Bharati, P., Zhang, W., & Chaudhury, A. (2015). Better knowledge with social media? Exploring the roles of social capital and organizational knowledge management. Journal of Knowledge Management, 19, 456–475. Bohn, A., Buchta, C., Hornik, K., & Mair, P. (2014). Making friends and communicating on Facebook: Implications for the access to social capital. Social Networks, 37, 29–41. Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). New York, NY: Greenwood. Bourdieu, P., & Wacquant, L. J. (1992). An invitation to reflexive sociology. Chicago, IL: University of Chicago Press. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Computer-Mediated Communication, 13, 210–230. Breiman, L., & Friedman, J. H. (1985). Estimating optimal transformations for multiple regression and correlation. Journal of the American Statistical Association, 80, 580–598. Burke, M., Kraut, R., & Marlow, C. (2011). Social capital on Facebook: Differentiating uses and users. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 571–580). New York, NY: ACM. Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81. Chang, Y. P., & Zhu, D. H. (2012). The role of perceived social capital and flow experience in building users’ continuance intention to social networking sites in China. Computers in Human Behavior, 28, 995–1001. Chen, H., & Haley, E. (2010). The lived meanings of Chinese social network sites (SNSS) among urban white-collar professionals: A story of happy network. Journal of Interactive Advertising, 11, 11–26. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295, 295–336. Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120. Compeau, D., & Higgins, C. A. (1991). A social cognitive theory perspective on individual reactions to computing technology. Proceedings of ICIS, USA, 187–198. Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Hove, England: Psychology Press.

Raza et al.

23

Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis for field settings. Skokie, IL: Rand McNally. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340. Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38, 475–487. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of Applied Social Psychology, 22, 1111–1132. DiMaggio, P., Hargittai, E., Neuman, W. R., & Robinson, J. P. (2001). Social implications of the Internet. Annual Review of Sociology, 27, 307–336. Donath, J., & Boyd, D. (2004). Public displays of connection. BT Technology Journal, 22, 71–82. Ellison, N., Heino, R., & Gibbs, J. (2006). Managing impressions online: Self-presentation processes in the online dating environment. Journal of Computer-Mediated Communication, 11, 415–441. Ellison, N. B., Gray, R., Vitak, J., Lampe, C., & Fiore, A. T. (2013, July). Calling All Facebook Friends: Exploring Requests for Help on Facebook. Proceedings of the Seventh Annual International Conference on Weblogs and Social Media, Boston, MA, 155–164. Ellison, N. B., Steinfield, C., & 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, 1143–1168. Ellison, N. B., Steinfield, C., & Lampe, C. (2011). Connection strategies: Social capital implications of Facebook-enabled communication practices. New Media & Society, 13, 873–892. Fan, J., Meng, H., Gao, X., Lopez, F. J., & Liu, C. (2010). Validation of a US adult social self-efficacy inventory in Chinese populations 1c7. The Counseling Psychologist, 38, 473–496. Farooqi, H., Patel, H., Aslam, H. M., Ansari, I. Q., Khan, M., Iqbal, N., . . . Nadeem, A. (2013). Effect of Facebook on the life of medical university students. International Archives of Medicine, 6, 1. Fishbein, M., & Ajzen, I. (1975). Belief, attitudes, intention, and behavior. An introduction to theory and research. Boston, MA: Addison-Wesley. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. Fukuyama, F. (2001). Social capital, civil society and development. Third World Quarterly, 22, 7–20. Gecas, V. (1989). The social psychology of self-efficacy. Annual Review of Sociology, 15, 291–316.

24


Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example. Communications of the Association for Information systems, 16, 91–109. Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1, 8. Gershuny, J. (2002). Social leisure and home IT: A panel time-diary approach. IT & Society, 1, 54–72. Granovetter, M. S. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1, 201–233. Hagedoorn, M., & Molleman, E. (2006). Facial disfigurement in patients with head and neck cancer: The role of social self-efficacy. Health Psychology, 25, 643. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19, 139–152. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433. Hamari, J., & Koivisto, J. (2013). Social motivations to use gamification: An empirical study of gamifying exercise. Proceedings of the European Conference on Information Systems, Urecht, The Netherlands, 105. Hampton, K., & Wellman, B. (2003). Neighboring in Netville: How the Internet supports community and social capital in a wired suburb. City & Community, 2, 277–311. Helliwell, J. F., & Putnam, R. D. (2004). The social context of well-being. Philosophical Transactions-Royal Society of London Series B Biological Sciences, 359, 1435–1446. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W.,. . . & Calantone, R. J. (2014). Common beliefs and reality about PLS comments on Ro¨nkko¨ and Evermann (2013). Organizational Research Methods. doi: 10.1177/ 1094428114526928 Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. Hill, T., Smith, N. D., & Mann, M. F. (1987). Role of efficacy expectations in predicting the decision to use advanced technologies: The case of computers. Journal of Applied Psychology, 72, 307. Hsu, C. L., & Lin, J. C. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45, 65–74. Hu, T., & Kettinger, W. J. (2008). Why people continue to use social networking services: Developing a comprehensive model. Proceedings of 29th International Conference on Information Systems, Paris, France. Hwang, Y. (2005). Investigating enterprise systems adoption: Uncertainty avoidance, intrinsic motivation, and the technology acceptance model. European Journal of Information Systems, 14, 150–161.

Raza et al.

25

Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13, 127–143. Islam, M. K., Merlo, J., Kawachi, I., Lindstro¨m, M., & Gerdtham, U. G. (2006). Social capital and health: Does egalitarianism matter? A literature review. International Journal for Equity in Health, 5, 1. Jerusalem, M., & Schwarzer, R. (1992). Self-efficacy as a resource factor in stress appraisal processes. In R. Schwarzer (Ed.), Self-efficacy: Thought control of action (pp. 195–213). Washington, DC: Hemisphere Publishing Corp. Jin, C. (2013). The perspective of a revised TRAM on social capital building: The case of Facebook usage. Information & Management, 50, 162–168. Johnston, K., Tanner, M., Lalla, N., & Kawalski, D. (2013). Social capital: The benefit of Facebook ‘friends’. Behaviour & Information Technology, 32, 24–36. Jo¨reskog, K. G., & Wold, H. O. (1982). Systems under indirect observation: Causality, structure, prediction (Vol. 139). Amsterdam, The Netherlands: North Holland. Junco, R. (2012). Too much face and not enough books: The relationship between multiple indices of Facebook use and academic performance. Computers in Human Behavior, 28, 187–198. Junco, R., Heiberger, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27, 119–132. Kavanaugh, A., Carroll, J. M., Rosson, M. B., Zin, T. T., & Reese, D. D. (2005). Community networks: Where offline communities meet online. Journal of Computer–Mediated Communication, 10(4), 00. Klobas, J. E. (1995). Beyond information quality: Fitness for purpose and electronic information resource use. Journal of Information Science, 21, 95–114. Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukophadhyay, T., & Scherlis, W. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist, 53, 1017. Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2007). Understanding user acceptance of multimedia messaging services: An empirical study. Journal of the American Society for Information Science and Technology, 58, 2066–2077. Li, D., Chau, P. Y., & Lou, H. (2005). Understanding individual adoption of instant messaging: An empirical investigation. Journal of the Association for Information Systems, 6, 102–129. Lin, K. Y., & Lu, H. P. (2011). Intention to continue using Facebook fan pages from the perspective of social capital theory. Cyberpsychology, Behavior, and Social Networking, 14, 565–570. Lin, N. (2002). Social capital—A theory of social structure and action. Cambridge, MA: Cambridge University Press. Lin, S. P., & Betz, N. E. (2009). Factors related to the social self-efficacy of Chinese international students. The Counseling Psychologist, 37, 451–471. Lu, J., Yu, C. S., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless Internet. Internet Research, 13, 206–222. Lu¨ders, M. (2007). Being in mediated spaces. An enquiry into personal media practices (Unpublished dissertation). University of Oslo, Norway. Memon, A. R., Azim, M. E., Kiyani, M. M., Ameer, M. R., Ali, S. M., & Malik, A. N. (2015). Perceptions, practices, and use of Facebook: A cross-sectional survey on

26


physiotherapy students in Pakistan. Journal of Community Medicine & Health Education, 4, 5. 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. Nie, N. H., & Hillygus, D. S. (2002). The impact of Internet use on sociability: Time-diary findings. IT & Society, 1(1), 1–20. Nie, N. H., Hillygus, D. S., & Erbring, L. (2002). Internet use, interpersonal relations, and sociability. In The Internet in everyday life (pp. 215–243). Wiley-Blackwell. Norris, P., & Jones, D. (1998). Virtual democracy. Harvard International Journal of Press Politics, 3(1), 1–4. Nunnally, J. C. (1978). Psychometric methods. New York, NY: McGraw Hill. Nysveen, H., Pedersen, P. E., & Thorbjørnsen, H. (2005a). Explaining intention to use mobile chat services: Moderating effects of gender. Journal of Consumer Marketing, 22, 247–256. Nysveen, H., Pedersen, P. E., & Thorbjørnsen, H. (2005b). Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33, 330–346. O’Murchu, I., Breslin, J. B., & Decker, S. (2004). Online social and business networking communities. Galway, Ireland: Digital Enterprise Research Institute (DERI). Ong, W. (1982). Orality and literacy: The technologization of the word. London, England: Methuen. Pakistan Social Media Report. (2014). Retrieved from http://www.pas.org.pk/pakistansocial-media-report-march-2014/ Park, C. H., & Kim, Y. J. (2013). Intensity of social network use by involvement: A study of young Chinese users. International Journal of Business and Management, 8, 22–33. Pinho, C. M. R., & Soares, A. M. (2011). Examining the technology acceptance model in the adoption of social networks. Journal of Research in Interactive Marketing, 5, 116–129. Putnam, R. D. (2001). Bowling alone: The collapse and revival of American community. New York, NY: Simon and Schuster. Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: An empirical study on Facebook. Journal of Enterprise Information Management, 27, 6–30. Raza, S. A., & Hanif, N. (2013). Factors affecting internet banking adoption among internal and external customers: A case of Pakistan. International Journal of Electronic Finance, 7, 82–96. Raza, S. A., Jawaid, S. T., & Hassan, A. (2015). Internet banking and customer satisfaction in Pakistan. Qualitative Research in Financial Markets, 7, 24–36. Resnick, P. (2001). Beyond bowling together: Sociotechnical capital. In J. M. Carroll (Ed.), HCI in the new millennium (pp. 247–272). Boston, MA: Addison-Wesley. Richardson, K., & Hessey, S. (2009). Archiving the self? Facebook as biography of social and relational memory. Journal of Information, Communication and Ethics in Society, 7, 25–38. Ringle, C. M., Wende, S., & Becker, J. M. (2014). SmartPLS 3. Hamburg, Germany: SmartPLS.

Raza et al.

27

Saade´, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42, 317–327. Seinfeld, C., Ellison, N., & Lampe, C. (2008). Online social network use, self-esteem, and social capital: A longitudinal analysis. Journal of Applied Developmental Psychology, 29, 434–445. Shah, V., Subramanian, S., Rouis, S., & Limayem, M. (2012). A study on the impact of Facebook usage on student’s social capital and academic performance. Proceedings of AMCIS 2012. Retrieved from http://aisel.aisnet.org/amcis2012/proceedings/ ISEducation/27 Sheng-Yi, W. U., Shih-Ting, W. A. N. G., Liu, E. Z. F., Da-Chain, H. U., & Hwang, W. Y. (2012). The influences of social self-efficacy on social trust and social capital–A case study of Facebook. TOJET: Turkish Online Journal of Educational Technology, 11, 246–254. Stephenson, W. (1964). The play theory of mass communication. Piscataway, NJ: Transaction Publishers. Tabachnick, B. G., & Fidell, L. S. (2007). Multivariate analysis of variance and covariance. Using Multivariate Statistics, 3, 402–407. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6, 144–176. Thong, J. Y., Hong, W., & Tam, K. Y. (2002). Understanding user acceptance of digital libraries: What are the roles of interface characteristics, organizational context, and individual differences? International Journal of Human-Computer Studies, 57, 215–242. Toomey, L., Mark, G., Tang, J. C., & Adams, L. (1998). Designing virtual communities for work. ACM SIGGROUP Bulletin, 19, 6–7. Valenzuela, S., Park, N., & Kee, K. F. (2009). Is there social capital in a social network site?: Facebook use and college students’ life satisfaction, trust, and participation1. Journal of Computer-Mediated Communication, 14, 875–901. Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28, 695–704. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 342–365. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186–204. Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33, 297–316. Weaver, A. C., & Morrison, B. B. (2008). Social networking. Computer, 41, 97–100. Wei, M., Russell, D. W., & Zakalik, R. A. (2005). Adult attachment, social self-efficacy, self-disclosure, loneliness, and subsequent depression for freshman college students: A longitudinal study. Journal of Counseling Psychology, 52, 602. Wellman, B., Haase, A. Q., Witte, J., & Hampton, K. (2001). Does the Internet increase, decrease, or supplement social capital? Social networks, participation, and community commitment. American Behavioral Scientist, 45, 436–455. Williams, D. (2006). On and off the’ net: Scales for social capital in an online era. Journal of Computer-Mediated Communication, 11, 593–628.

28


Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25, 17–41. Wold, H. (1975). Modelling in complex situations with soft information. Paper presented at third world congress of econometric society (pp. 21–26), Toronto, Canada. Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce. In J. Kmenta & J. B. Ramsey (Eds.), Evaluation of econometric models (pp. 47–74). Cambridge, MA: Academic Press.

Author Biographies Syed Ali Raza is associated with IQRA University as an assistant professor and deputy director research & publications. His areas of interest include financial economics, energy economics, tourism economics, and corporate finance. He has published numerous paper in International Referred Journals including Tourism Management, Energy Policy, Economic Modelling, Social Indicators Research, Current Issues in Tourism, Physica A, Quality and Quantity, International Migration, Total Quality management and Business Excellence, Journal of Business Economics and Management, Studies in Higher Education, Journal of Transnational Management, Transition Studies Review, Global Business Review, Journal of Chinese Economic and Foreign Trade Studies and others. Wasim Qazi is associated with IQRA University as a professor and vice president. He completed his PhD in Educational Management from Hamdard University, Pakistan and Postdoctorate from Education Eastern Kentucky University, USA. He has published numerous papers in international refereed ISI indexed journals. Dr. Qazi is the Editor-in-Chief of Journal of Management Sciences (JMS), a biannual online Double Blind Peer Reviewed Research Journal in the area of management sciences. Dr. Qazi is also heading the Editorial Team of Journal of Education and Social Sciences (JESS) a biannual online Double Blind Peer Reviewed Research Journal in the areas of education and social sciences. Amna Umer is associated with IQRA University as a research associate. She completed her MBA degree in Human Resource Management from IQRA University. Currently, she is pursuing her PhD from IQRA University. Her areas of interest are human resource management, training and development, strategic performance management, social sciences, and behavioral sciences.