A comparison of Facebook, Twitter, and LinkedIn: Examining motivations and network externalities for the use of social networking sites
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A comparison of Facebook, Twitter, and LinkedIn: Examining motivations and network externalities for the use of social networking sites by Mijung Kim and Jiyoung Cha



Abstract
Although the winner-takes-all approach is often theorized in the use of an information communication technology, more than one popular social networking site exists in the market. Integrating uses and gratification (U&G) theory with network externalities, this study examines why social networking sites can coexist in the market and whether predictors of using social networking sites differ across popular social networking sites. Three separate surveys were conducted for Facebook, Twitter, and LinkedIn. The results show that motivations for using each SNS differ; these motives exert a greater influence on SNS use than network externalities for all three SNSs.

Contents

Introduction
Literature review
Research questions
Method
Results
Discussion and conclusion

 


 

Introduction

Over the past decade, social networking sites (SNSs) increasingly have become an essential part of life for many U.S. Internet users. As of 2017, nearly 70 percent of American adults use at least one SNS (Pew Research Center, 2017). It has been estimated that the average person, in a lifetime, will spend five years and four months on SNSs (Campbell, 2017). The winner-takes-all approach is often theorized for information communication technologies (Abrahamson and Rosenkopf, 1997; Brynjolfsson and Kemerer, 1996; Lee and O’Connor, 2003). According to that theory, an SNS with the largest network size should dominate the SNS marketplace. Interestingly, however, more and more SNSs are being introduced in the market, and some of them have become popular, attracting a huge number of users (Make A Websitehub, 2017).

Given the coexistence of popular SNSs in the U.S. marketplace, a lingering question is whether popular SNSs serve different purposes. Scholarly research regarding SNSs has increased in tandem with the growth of the SNS as a medium, but scant research has compared different SNSs and examined whether they serve different needs. In light of the coexistence of different SNSs in the social media market, this study examines whether motives for using SNSs and predictors of SNS use differ. Specifically, this study focuses on Facebook, Twitter, and LinkedIn, which are the three SNSs that receive the most user visits in the U.S. and throughout the world (eBizMBA, 2014; American Society of Media Photographers (ASMP), n.d.).

This study conceptualizes SNS use as a function of motives for using the medium and network externalities. Hence, it integrates uses and gratification (U&G) theory with network externalities. Network externalities are important for the adoption and use of information communication technologies (Strader, et al., 2007). Network externalities refers to the change in product value or utility resulting from the number in the user base (Allen, 1988). Considering that networking is the most essential and fundamental element of SNSs as media, network externalities may be core determinants of SNS use. Thus, this study dissects the sources of network externalities and integrates them with uses and gratifications (U&G) theory to predict SNS use. Furthermore, this research offers a an investigation into how motives and network externalities exert relative influence on SNS use.

 

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Literature review

Uses and gratifications (U&G)

U&G theory is widely used to explain the adoption and use of a medium. Numerous studies have applied U&G theory to identify motives for using various media and the link between motives and use. U&G is a psychological communication perspective positing that individuals actively select and use a certain medium or content to fulfill their specific gratifications (Blumler, 1979; Katz, et al., 1974; Rayburn, 1996; Rubin, 2002). In tandem with the growth of SNS use, studies employing U&G to learn why people use SNSs have also emerged. Two approaches have been widely used. One is to identify the motives for using SNSs in general. This approach does not focus on a specific SNS. Although some studies asked participants to choose an SNS they often use, the SNSs that participants mentioned varied. Thus, the motives found in these studies are not bound to a specific SNS. Rather, the motives represent the reasons why participants used various SNSs. Another approach is to single out one or two SNSs and focus on motives for using them.

The first approach, which focuses on SNSs in general, aims to formulate typologies of motives for SNS use in general. Studies employing this approach found that individuals use SNSs for entertainment, learning, boredom relief, interpersonal utility, and escape (Brandtzæg and Heim, 2009; Cha, 2010; Dumrongsiri and Pornsakulvanich, 2010). Brandtzæg and Heim (2009) emphasized the role of SNS as an interpersonal utility, revealing that people use SNSs to get in touch with new people, keep in touch with their friends, and for socializing. This finding supports the idea that SNSs can serve as functional alternatives to other existing mediated and interpersonal communication systems. Meanwhile, SNSs also serve motives that traditional media fulfill. Cha (2009) found that entertainment and boredom relief are the most salient motives for using SNSs.

The second approach, which focuses on a specific SNS or specific SNSs, has tended to focus on Facebook or Myspace (e.g., Ancu and Cozma, 2009; Chen, 2011; Cheng, et al., 2011; Choi, et al., 2013; Smock, et al., 2011). These studies were aimed at finding motives for using a particular SNS or SNSs. Although some studies investigated two specific SNSs, the majority of them aggregated data regarding motives and use of SNSs and did not compare the results between the two. Some findings from studies that examined motives for the use of a specific SNS are consistent with studies that examined SNS use in general. They found that social utility or social interaction is a motive for using a specific SNS; other motives included entertainment, escapism, and boredom relief (Papacharissi and Mendelson, 2010; Sheldon, 2008; Wasike and Cook, 2010).

With respect to motives for using Facebook, a common ground from different studies was that the social interaction motive diverged into more specific and independent forms of motivation, such as expressive information sharing, relationship maintenance, developing new relationships, and virtual community (Alhabash and McAlister, 2014; Papacharissi and Mendelson, 2010; Sheldon, 2008; Wasike and Cook, 2010). People use Facebook more to maintain relationships with off-line connections than to develop new relationships (Ellison, et al., 2007; Lampe, et al., 2006). Some researchers have emphasized the role of Twitter as an informational medium rather than a communication medium (Kwak, et al., 2010), but others have identified two common motivations for using Twitter: information sharing and social interaction (Johnson and Yang, 2009; Johnson, 2014; Lee and Kim, 2014; Zhao and Lu, 2012).

Network externalities

Aggregate network

One’s motive for using an SNS may influence the choice and use of the SNS. The literature on diffusion of innovation has emphasized social influence in the decision-making processes of adoption (e.g., Abrahamson and Rosenkopf, 1997; Delre, et al., 2007; Dickinger, et al., 2008; Strang and Macy, 2001; Tucker, 2008). Social norms are correlated to adoption behavior (Dickinger, et al., 2008). Furthermore, social influence can lead to bandwagon processes in the diffusion of innovation (Abrahason and Rosenkopf, 1997; Strang and Macy, 2001). In particular, the present study examines the role of network externalities in SNS use.

The network externalities proposed by Rohlfs (1974) and Katz and Shapiro (1986) refer to the change in product value or utility associated with the number in the user base (Allen, 1988). Network externalities occur in situations where the perceived benefit of using a product increases as more and more people use that product (Katz and Shapiro, 1986). Rogers (2003) suggested that firms and consumers benefit not from the creation of a technology per se but from the diffusion of that technology. Thus, the network externalities of a new technology influence its adoption and diffusion.

Network externalities are critical in predicting the adoption of innovations, particularly communication and interactive technologies, because the value of a communication technology is directly linked to the number of prior adopters (Ilie, et al., 2005; Nysveen, et al., 2005; Song, et al., 2009; Wasike and Cook, 2010).

Individual network

In examining the influence of network effects on the adoption of a technology, the majority of prior studies has tended to emphasize the total number of people who adopted the technology at the aggregate level. This approach disregards the impact of an individual’s own network. Abrahamson and Rosenkopf (1997) emphasized that the potential adopters of an innovation learn information about that innovation through the adopters’ own social networks. A computer simulation of the diffusion model with social network effects indicated that the greater the number of links in social networks, the greater the amount of information the adopters have, which results in a greater number of bandwagon adopters (Abrahamson and Rosenkopf, 1997; Hatt, 2006). The information obtained through one’s own social networks also affects the perceived usefulness of innovations (Dickinger, et al., 2008). Thus, the number and ties among network members (those whom you know) are important factors in the adoption and acceptance processes (Katona, et al., 2011; Lonkila and Gladarev, 2008; Smoreda and Thomas, 2001; Song, et al., 2009).

In the same vein, Tucker (2008) suggested that the benefit of adopting a technology comes not only from the total number of users of the technology at the aggregate level, but also from how many people in one’s own network use the technology at an individual level. Some prior studies have found that an individual’s own network size with regard to a technology predicts the perceived benefit and social support in the context of instant messaging (Lin and Bhattacherjee, 2008; Lin and Bhattacherjee, 2009). Focusing on network externalities based on one’s individual network, Tucker (2008) found that the people in one’s own network with whom one communicates influence the individual’s adoption decision regarding a technology.

 

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Research questions

Motivations for using Facebook, Twitter, and LinkedIn

Previous studies exploring motivations for SNS use have mostly focused on Facebook or SNS use in general (e.g., Ancu and Cozma, 2009; Brandtzæg and Heim, 2009; Cha, 2010; Papacharissi and Mendelson, 2010; Quan-Haase and Sheldon, 2010; Urista, et al., 2009; Wasike and Cook, 2010). Recent data show that Internet users around the world have an average of five SNS accounts (Bennett, 2014). Although the number of SNSs in the marketplace and multiple SNS use among Internet users has increased, little research has examined whether different SNSs serve various user needs. Some studies have examined gratifications sought from more than one SNS, specifically Facebook and Myspace (e.g., Kujath, 2011; Raacke and Bonds-Raacke, 2008; Bonds-Raacke and Raacke, 2010), but they have not compared the gratifications between the two, aggregating the motives for using either of the sites.

Many Internet users in the U.S. use Twitter and LinkedIn, but studies examining Twitter and LinkedIn are relatively limited compared to those examining Facebook. Given the rapid growth in the number of Twitter and LinkedIn users in the U.S., this study is intended to identify motives for using Facebook, Twitter, and LinkedIn and whether the three SNSs differ with respect to motives for use. A few studies have reported the highest rated motives for Facebook and Twitter use (e.g., Alhabash and McAlister, 2014; Papacharissi and Mendelson, 2010), but their results are limited to their samples because the reports are based on descriptive statistics. Thus, this study further intends to identify the salient motives for use of Facebook, Twitter, and LinkedIn.

RQ1: What are the motives for using Facebook, Twitter, and LinkedIn? What are the salient motives for using each of these SNSs?

Predictors of SNS use

Motivations

Since motivation is driven by interest or enjoyment in the task itself (i.e., SNS use), the motives are controlled by individual users. Thus, the motives for using an SNS involve the individual’s psychological factors that affect his or her usage behavior. Previous studies have reported a relationship between motive and SNS use. Ross, et al. (2009) found that a group of users who had high motivation to use Facebook spent more time (31 to 60 minutes per day) on Facebook than those who had low motivation (l0 minutes per day or less). Also, the level of motivation was related to the frequency of checking behavior in SNS use.

Additional studies further examined the link between specific motives for SNS use and the use of SNSs. Cha (2009) distinguished between frequency of and amount of time in SNS use and found that the motive of interpersonal utility affects the frequency of SNS use. The same study indicated that the motives of interpersonal utility and escape influence the amount of time in SNS use. Focusing on Twitter, Chen (2011) found that gratification of the need to connect with others influences the number of months of active use of Twitter as well as total tweets and replies. Kim and Lee (2010) found that gratification of the need for entertainment predicted the amount of time spent on Twitter as well as the frequency of checking behavior on Twitter. Information seeking, boredom relief, and controlling/promoting work predicted the frequency of posting links on Twitter (Holton, et al., 2014). Lin and Lu (2011) simplified the motives for using Facebook into two constructs, perceived usefulness and perceived enjoyment, and found that perceived enjoyment of using Facebook is associated with continued intention to use it.

Integration of network externalities

In addition to motivations, network externalities may influence SNS use. Perceived network externalities are critical in predicting communication and interactive technologies because the value of a communication technology is directly linked to the number of prior adopters (Ilie, et al., 2005; Nysveen, et al., 2005; Song, et al., 2009; Wasike and Cook, 2010). Thus, the present study integrates motivations with network externalities to predict SNS use. Tucker (2008) suggested that network externalities at the individual network level influence the adoption and use of a technology. In gauging the effect of network externalities on SNS use, Lin and Lu (2011) distinguished between the total number of Facebook members and the number of one’s peers who use Facebook. They found that the perceived number of Facebook members at the aggregate level has a positive effect on the perceived usefulness of Facebook, whereas the perceived number of one’s peers who use Facebook at the individual level is a positive predictor of perceived enjoyment and perceived usefulness of Facebook.

Building on prior studies regarding network externalities, this study dissects the sources of network externalities into two levels. Aggregate network refers to network externalities based on the perceived total number of people who use the technology. Individual network refers to the perceived number of the technology’s users in one’s own network. Some studies have distinguished between aggregate network and individual network in terms of network externalities, but the majority did not test the effects of both on the adoption of an information communication technology. Instead, they gauged the role of one of the network externalities’ sources (i.e., aggregate network or individual network) on use of a technology (e.g., Lin and Bhattacherjee, 2008; Lin and Bhattacherjee, 2009). Although Lin and Lu (2011) examined the role of network externalities at different levels, their study confined one’s individual network to friends. In the real world, one’s individual social circle is not limited to friends. SNS users have other groups of people with whom they connect and communicate on SNSs. Thus, the individual network in this study includes friends, colleagues, acquaintances, and others.

RQ2a: How do the motivations for using an SNS, aggregate network, and individual network predict SNS use? Between motives and network externalities (i.e., aggregate network and individual network), which ones are more important predictors of SNS use?

RQ2b: Do differences exist across SNSs with respect to the predictors of SNS use?

 

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Method

Data collection and participants

Data for this study were collected through online surveys. A directory that encompasses the populations of Facebook, Twitter, and LinkedIn users is not publicly available. Thus, this study used a combination of convenience and snowball sampling methods to recruit diverse users of the SNSs in terms of demographics. Online surveys were administered to undergraduate and graduate students with various majors at four large public universities in the United States. Specifically, the online surveys were administered at three universities in the South and one university in the Midwest area of the United States. Those college students who were contacted were asked to forward the online survey links to friends, colleagues, family members, and acquaintances who use Facebook, Twitter, or LinkedIn. The online survey links were also posted on the Web pages of various online groups and communities available on Facebook, Twitter, and LinkedIn.

People who were willing to participate in the survey were first directed to the screening page to identify which SNS they were using. Of the 546 Internet users who visited the screening page, 507 reported using one of the SNSs selected for data analysis. The data analysis used 226 respondents (44.5 percent) identifying themselves as Facebook users, 133 respondents (26.2 percent) reporting themselves as Twitter users, and 148 respondents reporting themselves as LinkedIn users (29.1 percent). The participants’ demographic information for the samples across the SNSs is summarized in Table 1. The participants were asked to choose from Facebook, Twitter, and LinkedIn and to stick to the selected SNS to answer all questions throughout the survey.

 

Table 1: Sample profile.
 Facebook
(n=226)
Twitter
(n=133)
LinkedIn
(n=148)
Gender (%)   
Male50.950.443.2
Female49.144.452.7
Mean age (SD)21.85 (3.24)22.44 (4.06)27.58 (10.34)
Education level (%)   
High school13.312.05.4
College84.172.268.2
Graduate school2.710.523.0
Income (%)   
Less than US$20,00055.350.438.5
US$20,000–$39,99913.710.510.8
US$40,000–$59,9998.48.38.1
US$60,000–$79,9996.67.59.5
US$80,000–$99,9995.34.56.1
US$100,000 or more8.89.018.9
Ethnicity (%)   
Black8.818.08.1
White66.857.967.6
Hispanic11.56.84.7
Asian5.36.012.2
Native Hawaiian.4.0.0
Multiracial5.33.02.7
Other.93.0.7

 

Measures

Motivations for SNS use

Motivations measure psychological reasons why people use an SNS. The items for motives behind SNS use were adapted from several previous studies (Cha, 2010; Park, et al., 2009; Papacharissi and Mandeson, 2010; Sheldon, 2008). The present study included 10 a priori motive categories of possible SNS motives: entertainment, information seeking, information providing, information sharing, socialization, boredom relief, companionship, escape, habit, and professional advancement. Twenty-four items were used to measure motives for SNS use. The respondents were asked to indicate how much they agreed with each reason on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree).

Aggregate network

Aggregate network measures network externalities based on the perceived total number of people who use an SNS. To measure the aggregate network for each SNS, four items were adapted from Lee’s (2006) work. For their selected SNS, the respondents were asked to indicate their level of agreement with the following statements: (a) Most people use Facebook; (b) There will be more people using Facebook; (c) As more and more people use Facebook, I think related services will be developed soon; and, (d) As more and more people use Facebook, I think that related applications will be developed soon. The respondents who chose Twitter or LinkedIn answered questions corresponding to the SNS they chose. The items for aggregate network were measured using a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). The measures were reliable across different SNSs (Facebook: M = 6.08, SD = 1.00, Cronbach’s α = .88; Twitter: M = 5.10, SD = 1.10, Cronbach’s α = .80; LinkedIn: M = 4.97, SD = 1.09, Cronbach’s α = .79).

Individual network

Individual network measures the perceived number of people who use the SNS in one’s own network. To measure the individual network of an SNS, four measurement items were adapted from Song and Walden’s (2007) study. The respondents were asked to indicate how much they agreed with the following statements: (a) Most people I know use Facebook; (b) Of the people I communicate with regularly, many use Facebook; (c) Most people I communicate with use Facebook; and, (d) A large percentage of people I know use Facebook. The respondents who chose Twitter or LinkedIn answered questions corresponding to the SNS they chose. All items for the individual network were measured using a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). Reliabilities of the measurement items are ensured across Facebook, Twitter, and LinkedIn (Facebook: M = 6.39, SD = 1.05, Cronbach’s α = .96; Twitter: M = 3.76, SD = 1.76, Cronbach’s α = .94; LinkedIn: M = 3.55, SD = 1.75, Cronbach’s α = .95).

SNS use

SNS use measured the frequency of using an SNS. Respondents were asked to indicate how often they use the SNS on a 7-point Likert-type scale (1 = never, 7 = all the time). The descriptive statistics indicated that Facebook users use Facebook (M = 5.54, SD = 1.24) more often than Twitter users (M = 4.57, SD = 1.72) use Twitter or LinkedIn users use LinkedIn (M = 3.70, SD = 1.36).

Statistical analysis

RQ1 asked about the motives for using each SNS and identified the prevalent motives for each SNS. To identify motives for using Facebook, Twitter, and LinkedIn, a factor analysis with varimax rotation was performed utilizing 24 motivation items for each SNS. The factor analysis required an eigenvalue greater than 1.0 with a 60/40 loading criterion. As a result, four, five, and two motive items were eliminated for Facebook, Twitter, and LinkedIn, respectively, due to high cross loadings. To identify the salient motives for using each SNS, repeated measures of analysis of variance (ANOVA) were carried out for each SNS. Prior studies merely compared the means of motives for using an SNS to identify the salient motives for using the SNS. The use of descriptive statistics limits their findings to the samples they used. Thus, this study used inferential statistics instead. A one-way repeated measures ANOVA is used to compare means within each sample, and thus detects statistically significant mean differences of motives for using an SNS. Bonferroni simultaneous 95 percent confidence intervals were consulted to compare the mean scores of the motives within each SNS. Three separate ANOVAs with repeated measures revealed the primary motives for using Facebook, Twitter, and LinkedIn, respectively.

RQ2a asked about the predictors of use for each SNS and the relative effects of motives, aggregate network, and individual network as determinants of such use. To find the predictors for each SNS’s use, a hierarchical multiple regression was performed for each. In the first step, aggregate network and individual network were entered in the prediction model. In the second step, motivations were entered. RQ2b asked about the differences among Facebook, Twitter, and LinkedIn in terms of the predictors of use of each. The significant predictors from the three separate hierarchical regression analyses were compared across Facebook, Twitter, and LinkedIn.

 

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Results

SNSs usage motivations

RQ1 gauged the motives for using Facebook, Twitter, and LinkedIn and the salient motives for using each SNS. The factor analysis for Facebook yielded five motives: (1) expressive information networking; (2) entertainment; (3) professional advancement; (4) escape through companionship; and, (5) boredom relief. These five factors explained 65.46 percent of the total variance (see Table 2). The factor analysis for Twitter identified four factors, accounting for 67.28 percent of the total variance: (1) expressive information networking; (2) boredom relief through entertainment; (3) escape through companionship; and, (4) professional advancement (see Table 3). The factor analysis yielded five motives for using LinkedIn: (1) expressive information networking; (2) boredom relief through entertainment; (3) escape through companionship; (4) professional advancement; and, (5) self-presentation. These factors explained 64.83 percent of the total variance (see Table 4).

 

Table 2: Motivations for using Facebook.
 Facebook motivations
“I use Facebook ...”12345
Factor 1: Expressive information networking     
To tell others a little bit about myself.800    
To provide personal information about myself.699    
To present information about a special interest of mine.695    
To talk about something with others.638    
To feel like I belong to a community.616    
To share information that may be of use or interest to others.612    
Because it opens me up to new ideas.585    
To get peer support from others.555    
To meet interesting people.412    
Factor 2: Entertainment     
Because it’s entertaining .830   
Because it’s enjoyable .830   
Because it amuses me .788   
Factor 3: Professional advancement     
To help me network with professional contacts  .810  
Because it is helpful for my professional future  .795  
To post my resume and/or other work online  .758  
Factor 4: Escape through companionship     
To escape my worries   .879 
To forget my problems   .788 
So I won’t have to be alone   .782 
Factor 5: Boredom relief     
When I have nothing better to do    .853
To pass time when bored    .745
Mean4.405.453.372.835.80
SD1.101.081.451.501.24
Cronbach’s α.86.85.79.83.73
Eigenvalue6.272.851.651.331.00
Variance explained (%)19.2712.0212.7012.338.15

 

 

Table 3: Motivations for using Twitter.
 Twitter motivations
“I use Twitter ...”1234
Factor 1: Expressive information networking    
Because it opens me up to new ideas.825   
To provide personal information about myself.665   
To get peer support from others.655   
Because it makes me feel less lonely.644   
To meet interesting people.584   
To feel like I belong to a community.529   
To tell others a little bit about myself.516   
Factor 2: Boredom relief through entertainment    
To occupy my time .878  
Because it amuses me .838  
To pass time when bored .829  
Because it’s enjoyable .746  
When I have nothing better to do .739  
Because it’s entertaining .720  
Factor 3: Professional advancement    
To post my resume and/or other work online  .811 
Because it is helpful for my professional future  .789 
To help me network with professional contacts  .766 
Factor 4: Escape through companionship    
To forget my problems   .844
To escape my worries   .833
So I won’t have to be alone   .827
Mean3.895.353.472.56
SD1.231.281.631.48
Cronbach’s α.83.91.79.87
Eigenvalue3.346.171.311.96
Variance explained (%)19.2322.3411.9313.79

 

 

Table 4: Motivations for using LinkedIn.
 LinkedIn motivations
“I use LinkedIn ...”12345
Factor 1: Expressive information networking     
To present information about a special interest of mine.767    
To share information that may be of use or interest to others.741    
Because it extends my mind.739    
To talk about something with others.723    
To meet interesting people.662    
To feel like I belong to a community.623    
Because it opens me up to new ideas.603    
Factor 2: Boredom relief through entertainment     
When I have nothing better to do .797   
To occupy my time .778   
To pass time when bored .762   
Because it amuses me .732   
Because it’s entertaining .633   
Factor 3: Professional advancement     
Because it is helpful for my professional future  .787  
To post my resume and/or other work online  .730  
To help me network with professional contacts  .620  
To get peer support from others  .582  
Factor 4: Escape through companionship     
To escape my worries   .884 
So I won’t have to be alone   .828 
To forget my problems   .815 
Because it makes me feel less lonely   .641 
Factor 5: Self-presentation     
To provide personal information about myself    .821
To tell others a little bit about myself    .695
Mean3.862.535.811.524.47
SD1.361.211.03.801.62
Cronbach’s α.87.83.71.81.64
Eigenvalue6.363.601.691.441.17
Variance explained (%)18.0515.5713.6310.257.34

 

To identify salient motives for using each SNS, a one-way repeated measures ANOVA was performed for each site. The results indicated that statistically significant mean differences exist between various motives for each SNS (see Table 5). The repeated measures ANOVA result of Facebook (F (4, 860) = 289.47, η2 = .57, p <.001) revealed that boredom relief (M = 5.80, SD = 1.24) is the most salient motive and entertainment (M = 5.45, SD = 1.08) is the second most salient motive for its use. For Twitter (F (3, 372) = 103.53, η2 = .46, p <.001), boredom relief through entertainment (M = 5.35, SD = 1.28) is the primary reason for Twitter use. Expressive information networking (M = 3.88, SD = 1.23) and professional advancement (M = 3.47, SD = 1.62) are the second and third most salient motives for using Twitter. The repeated measures ANOVA results also revealed statistically significant differences among the motives for using LinkedIn (F (4, 524) = 329.27, η2 =.72, p <.001). Professional advancement (M = 5.81, SD = 1.03) is the most salient motive for using LinkedIn, followed by self-presentation (M = 4.47, SD = 1.62).

 

Repeated measures ANOVA for paired comparisons of motivations
 
Note: Larger version of table available here.

 

Dynamics of motivations and network externalities in SNS use

RQ2a asked about the predictors for use of each SNS and the relative effects of motivations, aggregate network, and individual network on use of each. The hierarchical regression analyses indicated that both aggregate network and individual network were statistically significant in predicting the frequency of SNS use before motives were entered in the prediction model (see Table 6). This result was consistent across the three SNSs. However, the relative effects of aggregate network and individual network on SNS use differed across the SNSs. With respect to Facebook and Twitter, the aggregate network had a slightly stronger relationship with the frequency of use than the individual network. In contrast, the hierarchical regression analysis for LinkedIn showed that the individual network (β = .36, p < .001) had a stronger relationship than the aggregate network (β = .24, p < .01) with the frequency of LinkedIn use.

 

Table 6: Hierarchical regressions predicting frequency of SNS use.
Note: * p < .05; ** p < .01; *** p < .001
Facebook  
 Step 1Step 2
 βt-valueβt-value
Aggregate network.1972.346*.063.886
Individual network.1952.321*.066.908
Expressive information networking  .066.927
Entertainment  .4416.117***
Boredom relief  .1432.085*
Professional advancement  -.015-.233
Escape through companionship  .0671.034
 F (2, 198) = 14.029***F (7, 193) = 18.273***
 R2 = .124, adj. R2 = .115R2 =.413, adj. R2 = .391
Twitter  
 Step 1Step 2
 βt-valueβt-value
Aggregate network.2612.427*-.036-.407
Individual network.2472.298*.1091.266
Expressive information networking  .1982.235*
Boredom relief through entertainment  .6257.782***
Professional advancement  .2563.470**
Escape through companionship  -.088-1.177
 F (2, 106) = 13.765***F (6, 102) = 22.047***
 R2 = .206, adj. R2 = .191R2 =.565, adj. R2 = .539
LinkedIn  
 Step 1Step 2
 βt-valueβt-value
Aggregate network.2372.684**.014.151
Individual network.3574.036***.3634.667***
Expressive information networking  .4995.691**
Boredom relief through entertainment  -.111-1.370
Professional advancement  .1041.215
Escape through companionship  .030.377
Self-presentation  -.032-.416
 F (2, 120) = 21.355***F (7, 115) = 14.636***
 R2 = .206, adj. R2 = .250R2 =.471, adj. R2 = .439

 

When the constructs for network externalities (i.e., aggregate network and individual network) were integrated with motives for SNS use, the motives were stronger predictors of SNS use than network externalities. As seen in Table 6, the final model for Facebook revealed that entertainment (β = .44, p < .001) and boredom relief (β = .14, p < .05) were significant predictors of the frequency of Facebook use. For Twitter, boredom relief through entertainment (β = .63, p < .001), expressive information networking (β = .20, p < .05), and professional advancement (β = .26, p < .01) were significant predictors of the frequency of use. For LinkedIn, expressive information networking (β = .50, p < .01) and individual network (β = .36, p < .05) were significant predictors of the frequency of use.

RQ2b asked about the differences among the three SNSs in terms of predictors. The hierarchical regression analyses above showed that motives were more important predictors of SNS use for Facebook, Twitter, and LinkedIn. The motives of entertainment, boredom relief through entertainment, and expressive information networking had the strongest relationship with use of Facebook, Twitter, and LinkedIn, respectively. Neither aggregate network nor individual network was a significant predictor of Facebook or Twitter use when those network externalities were combined with motives. In contrast, individual network was still a significant predictor of LikedIn use.

 

++++++++++

Discussion and conclusion

The contribution of this study is threefold. First, the study revealed the motivations for using the three most visited U.S. SNSs and compared them. Second, it integrated U&G theory with network externalities and examined the relative influence of motives and network externalities on SNS use. Third, this study delved into the role of network externalities in SNS use by dissecting the construct network externalities into two different sources (i.e., aggregate network and individual network).

Recognizing the role of network externalities in predicting the adoption and use of an information communication technology, this study proposed a theoretical model that combines U&G and network externalities to predict use of an SNS. The empirical tests documented that motives have a greater impact than network externalities on the frequency of using an SNS. In other words, the specific gratifications users seek from an SNS exert more influence on how often they use the SNS than the perceived benefit and outcome of the SNS derived from how many people use the SNS or how many people they know and communicate with use it. This result was shared across Facebook, Twitter, and LinkedIn. The finding implies that one’s internal motivations are more important than the reinforcement value of outcomes in predicting the repeated use of an SNS in the post-adoption phase.

Network externalities are strong predictors of SNS use before they are integrated with motives. Interestingly, network effects on SNS use disappeared or critically weakened when integrated with motives. This means that network externalities are strong determinants of use of an SNS until before users develop specific motives for the SNS. The results imply that network externalities are more likely to influence one’s decision to adopt or reject an SNS during its initial diffusion stage when users are not familiar with the gratifications that the SNS may provide. However, the effects of network externalities on use of an SNS weaken in the post-adoption phase as more and more people establish specific motives for using the SNS. If early and late majority adopters in a society use SNSs, certain SNSs will attract a large number of users and be established with large networks. Meanwhile, their users become more familiar with the features and services of those SNSs, and thus they develop specific motivations for using them through repeated and continuing use. Then, specific gratifications sought from an SNS rather than network externalities more heavily influence consumption patterns of the SNS.

After SNSs are established with stable and large network sizes, service providers should tackle specific gratifications they will satisfy and delve into why individuals use their sites. Based on the understanding of users’ motives for using a service provider’s SNS, service providers should develop features and services to maximize the gratification of user needs to increase traffic. Merely focusing on increasing the network size will not help an established SNS build constant traffic. Theoretically, this study suggests that timing in relation to diffusion stages of an information communication technology might be considered in examining the dynamics of motives and network externalities in relation to technology use. Future studies could examine how the relative effects of motives and network externalities on the use of SNSs shift throughout the diffusion phases and life cycle of SNSs and other information communication technologies.

This study identified the motives for using the three most visited U.S. SNSs, which enabled us to compare the sites. The salient motivations for using Facebook and LinkedIn differ substantially. Boredom relief and entertainment are the primary motivations behind Facebook use. This study used inferential statistics to identify the salient motives for use of each site, and the salient motives for Facebook use are consistent with prior studies regarding Facebook (Cheng, et al., 2011; Papacharissi and Mendelson, 2010) which used descriptive statistics. Unlike the motives for Facebook use, professional advancement and self-presentation are the primary motives for using LinkedIn. Facebook use has ritualistic orientations, whereas LinkedIn use has more instrumental orientations. In addition, the findings show that the motives for using Facebook and LinkedIn differ, indicating that the unique features and content of an SNS diversify individuals’ motives for using social media. Thus, the results explain why individuals actively choose a particular SNS over other SNSs.

These different motives behind Facebook and LinkedIn use help in understanding why people might use more than one SNS. The substitution theory posits that consumers tend to switch from one product to another that fulfills the same purpose. On the other hand, if the two products fulfill different needs, they complement each other and are used in tandem (Nicholson, 1995). When the substitution theory is applied to the findings of this study, Facebook and LinkedIn are likely to complement one another because the salient motives for LinkedIn use are completely different from those for Facebook use. Researchers have theorized the winner-takes-all approach in the market by emphasizing network externalities in the adoption of new technologies (Abrahamson and Rosenkopf, 1997; Brynjolfsson and Kemerer, 1996; Lee and O’Connor, 2003). Based on their arguments, the SNS with the largest network size should dominate the SNS marketplace. However, the findings of this study suggest that the different gratifications sought from SNSs explain why more than one SNS exists, attracting a large number of users in the marketplace.

On the one hand, the motives for using Twitter are similar to those for using Facebook. Like Facebook, boredom relief through entertainment is the primary motive for using Twitter. In addition, boredom relief through entertainment is the strongest predictor of the frequency of using Twitter. On the other hand, Twitter differs from Facebook. Expressive information networking is the second most salient motive for using Twitter. While entertainment and boredom relief predict the frequency of Facebook use, the motives of professional advancement and expressive information networking are the second and third most important determinants of frequent Twitter use. The results showed that Twitter satisfies instrumental needs that Facebook is weak at satisfying. The gratifications an SNS fulfills better or unique motives behind use of one SNS versus others may explain why people use a particular SNS although it has a smaller network size.

The three most visited SNSs have different motives and factors that influence their use. The finding that the motives and determinants of SNS use are not the same among the three most-visited SNSs provides further implications. If two established SNSs have features and attributes that fulfill the same needs, the one with the largest network size might expel the one with the smaller network size in the long run. Therefore, it would be critical for the SNS with a smaller network size to differentiate its services from its dominant competitors and fulfill different needs that the dominant competitors do not satisfy. The present study revealed functional similarities and differences among the three most visited SNSs. Future studies can further examine how the functional similarities between two SNSs in terms of the gratifications people seek influence their coexistence and competition in the long run at the macro level.

Marwick and boyd (2010) contended that every participant in a communication act has an imagined audience. The imagined audience is a “mental conceptualization of the people with whom users are communicating” [1]. Social media users consciously or unconsciously recognize their imagined audiences; thus, they control their performance and tailor their presentation of self to achieve the ideal conditions that they want (Hogan, 2010; Papacharissi, 2002). Likewise, each SNS provider is also likely to have an imagined audience, and they develop attributes and benefits of their SNS to satisfy that imagined audience. However, there is no guarantee that the SNS serves its imagined audience, or that its features and benefits fulfill the needs of its imagined audience.

This study reveals the motives for using each of the most visited SNSs and how those motives predict the use of each SNS. The results help Facebook, Linkedin, and Twitter bridge the knowledge gap between their imagined users and actual users. Twitter’s unique features and interface, which introduced the hashtag concept and forced 140 character limits, might allow Twitter to occupy a distinct position in SNS users’ and its competitors’ minds. As a result, Twitter and its users might have a distinct imagined audience that has different needs from those of users of other SNSs. However, the results interestingly reveal that the most important predictors of Facebook and Twitter use are quite similar, although the secondary motives differ. Boredom relief and entertainment are the strongest predictors of using Facebook and Twitter. The results help Facebook, LinkedIn, and Twitter to better understand the motives of their actual users and further provide insight into how an SNS can differentiate itself from others.

This study investigated the role of network externalities in SNS use at both the aggregate level and the individual level, whereas most prior studies have not distinguished between aggregate network size and individual network size of a technology (e.g., Chiu, et al., 2013; Strader, et al., 2007). The relative effects of the aggregate network and individual network differed across the three most visited SNSs. In the cases of Facebook and Twitter, the aggregate network was slightly more important than the individual network in predicting Facebook and Twitter use. In contrast, the individual network was more pivotal than the aggregate network in predicting how often individuals use LinkedIn. The results imply that whether an SNS is primarily used to meet ritualistic needs or instrumental needs might determine the relative effects of the aggregate network and individual network. Given the aforementioned salient motives for using the SNSs, ritualistic needs are more prevalent than instrumental needs for Facebook and Twitter although Twitter is used to satisfy both. In contrast, LinkedIn is used to fulfill instrumental needs rather than ritualistic needs.

These results are relevant to Lin and Lu (2012), who found that the number of peers on an SNS has greater influence than the aggregate network of the SNS on the perceived usefulness of the SNS. While they focused exclusively on peers, specifically friends, in examining the effect of individual network size, the present study’s individual network is more comprehensive, including people one knows and with whom one communicates. The results of the present study are also corroborated by Lian (2015), who revealed the effect of the individual network on adoption of a cloud-based e-invoice service, from which individuals are more likely to fulfill instrumental needs than ritualistic needs. SNSs designed primarily to satisfy instrumental needs should provide an interface and features that help users easily locate and connect with their friends, colleagues, and acquaintances. In addition, encouraging their users to communicate with those that they know will be key to increasing frequent use.

Despite the contributions of this study, it is worth noting that the results should be interpreted with caution. No such sampling frame enables researchers to generate a probability sample for the three most visited U.S. SNSs. To overcome this drawback, the data were collected from various regions and age groups across the U.S. Despite these efforts, the mean age of each SNS’s users is younger than the mean age reported in industry reports (e.g., Google Ad Planner). Future studies should include more groups of older users and replicate this study to examine whether the results are valid for those populations. This study is one of the first to compare the three most visited U.S. SNSs. As an exploratory study, it used a relatively small sample for each SNS. Future studies should employ larger samples for each site to revalidate results.

Culture may also play an important role in why and how often people use SNSs. This study, conducted in the U.S., found that an individual’s motives for using SNSs have greater influence on how often people use SNSs than do network effects. However, network effects may play a more significant role than individual motives for using the medium in other countries with cultures based on collectivism. Given that some U.S. SNSs have more users outside the U.S. than inside, it would be interesting to examine how motives for using SNSs and network effects come into play in predicting SNS use in different countries. End of article

 

About the authors

Mijung Kim, Ph.D., is a senior research associate at Future IT Innovation Laboratory, Pohang University of Science & Technology (POSTECH) in South Korea. Her research focuses on new media effects, social media analysis, media habit formation based on her academic background of Social Sciences. Recently, she joined the research team of Institute for Sports Engineering at POSTECH and has collaborated with great engineering scholars to address interactive technology in sports. Specifically, her team’s research aims to explore the technical potential of sensors (e.g., wearable computing) and the use of ICT to motivate people to move or exercise by providing social support or through gamification, and create innovative interaction techniques.
Direct comments to: mijungkim [at] postech [dot] ac [dot] kr

Jiyoung Cha, Ph.D., is an assistant professor in the Department of the Broadcast and Electronic Communication Arts at San Francisco State University. Her research aims to understand the competitive dynamics of the media marketplace, how new media change audiences’ media consumption patterns and the business principles of media firms, and why audiences adopt or reject new communication technologies. Her research has appeared in peer-reviewed journals, including the Journal of Media Economics, International Journal on Media Management, Journalism and Mass Communication Quarterly, Telematics and Informatics, Journal of Electronic Commerce Research, First Monday, and Journal of Advertising Research among others. She earned her Ph.D. in mass communication with a minor in marketing from the University of Florida.
E-mail: jycha [at] sfsu [dot] edu

 

Note

1. Litt, 2012, p. 331.

 

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Editorial history

Received 21 August 2017; revised 12 October 2017; accepted 26 October 2017.


Copyright © 2017, Mijung Kim and Jiyoung Cha.

A comparison of Facebook, Twitter, and LinkedIn: Examining motivations and network externalities for the use of social networking sites
by Mijung Kim and Jiyoung Cha.
First Monday, Volume 22, Number 11 - 6 November 2017
http://firstmonday.org/ojs/index.php/fm/article/view/8066/6563
doi: http://dx.doi.org/10.5210/fm.v22i111.8066





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