The purpose of this study is to explore the factors that affect the use of social networking Web sites. In doing so, this investigation focuses on two dimensions of social networking site use frequency (i.e., how often people use social networking sites) and amount (i.e., how much time people spend on social networks). Integrating the technology acceptance model with uses and gratification and other consumer characteristics, this study found that interpersonal utility, perceived ease of use, privacy concerns, and age predict the frequency of social networking site use. Interpersonal utility motive, escape motive, and Internet experience explain the time spent on social networking sites.
Social networking Web sites have experienced explosive growth over the past few years. According to the Pew Internet & American Life Project (2009), 46 percent of online American adults 18 and older used a social networking site in 2009. When the demographics are narrowed down to college students, 89 percent of of the 960 college students who participated in a survey conducted by the College Board in the fall of 2009 were using at least one social networking site (College Board and Arts & Science Group, 2009). Social networking Web sites have succeeded in attracting not only users but investors and advertisers too.
Media conglomerates have tried to acquire or invest in social networks. News Corp. acquired MySpace, the largest U.S. social network, for US$580 million in 2005. Venture capital firms put up US$25 million in funding for Facebook (Rosenbush and Mullaney, 2006; Vara, 2007). With respect to advertisers, MySpace generated about US$125 million in advertising revenue in the fourth quarter of 2006 (Havenstein, 2007) and US$440 million in revenue in the 2007 fiscal year. There is little doubt advertising is the primary source of revenue (Williamson, 2007). Facebook generated US$150 million in revenue and US$30 million in profit during the 2007 calendar year, through August; an advertising deal with Microsoft accounted for half of that revenue (Vara, 2007).
Along with this explosive growth of social networking sites over the past few years, research focusing on social networking sites has also been growing. However, peer–reviewed published research still lacks an assessment of the impact of this medium (Raacke and Bonds–Raacke, 2008). Given that social networking sites became one of the essential media among the majority of college students, and advertisers’ interest in social networking sites has been growing, the current study attempts to identify factors that promote and hinder college students’ use of social networking sites.
When examining the factors that affect the use of Internet–related media or content, existing research has employed different notions such as frequency, amount, or intensity (i.e., the duration of each session). Although several researchers have emphasized the need to distinguish those concepts (Tidwell and Walther, 2002; Walther, 1992; 1996), there was hardly any research on why one type of time measurement was used over another. The predictors of media use might depend on whether the focus is on frequency, amount, or intensity. Therefore, the present study recognizes the distinction between the frequency (i.e., how often people use social networking sites) and the amount of social networking site use (i.e., how many hours people spend on social networking sites per week) in examining the predictors of the use of social networking sites.
One of the primary theoretical foundations of this study is the Technology Acceptance Model (TAM), which is widely used to explain the adoption of a new technology and information system. The theory is parsimonious and applicable in a variety of contexts, but, on the flip side, it is not sufficient to provide a richer understanding of the adoption of a system (Park, et al., 2008). The present study aims not to test the generality of the TAM in the context of social networking sites but to identify a more comprehensive model that predicts the use of social networking sites. To that end, this study integrates the TAM with the Uses and Gratifications (U&G). While the TAM examines how perceptions of social networking Web sites affect their use, the U&G reveals consumers’ motivations for using these sites. To build a more comprehensive model that presents the use of social networking sites, this study also utilizes other consumer characteristics including online privacy concerns, Internet experience, innovativeness, gender, and age along with the constructs in TAM and U&G.
Perceived characteristics of social networking sites
TAM was derived from the theory of reasoned action (TRA) suggested by Ajzen and Fishbein (1980). While TRA posits that general attitudes toward technology along with subjective norms determine acceptance intention, the TAM employs more specific attitude constructs perceived usefulness and perceived ease of use as the determinants of the intention to use and actual use of a new technology (Davis, 1989; Peters, et al., 2007). Perceived usefulness is defined as “the degree to which an individual believes that using a particular system would enhance his/her job performance” . Perceived ease of use refers to “the degree to which an individual believes that using a particular system would be free of physical and mental efforts” .
Prior research indicates that TAM is successfully applied to Internet–related technologies and services such as Internet banking, online shopping, and software applications (Suh and Han, 2002; Gefen, et al., 2003; Chau, 1996). Beyond the context of the Internet, the impact of perceived usefulness on adoption or usage intention is, in general, seen as a stronger and more direct factor than that of perceived ease of use. Past research found direct or indirect effect of the perceived ease of use on the adoption of a technology (e.g., Davis, et al., 1989; Hu, et al., 1999; Jackson, et al., 1997).
Many empirical studies using the TAM as a theoretical foundation did not examine actual use of a system or technology, which is a construct in the original TAM’s path model (e.g., Chau and Hu, 2002; Hong, et al., 2002; Thong, et al., 2002). Given that, this study tests how perceived usefulness and ease of use in the TAM are related to consumers’ actual use of social networking sites. Based on the findings from previous studies, the present study proposes that perceived usefulness and perceived ease of use are related to the frequency and amount of social networking site use.
H1a: Perceived usefulness and perceived ease of use of social networking Web sites will be positively related to the frequency of social networking site use.
H1b. Perceived usefulness and perceived ease of use of social networking Web sites will be positively related to the amount of social networking site use.
Motives for using the Internet
Another main theoretical foundation for this study lies in U&G perspectives. The U&G is one of the predominant theories used to explain audiences’ choices and consumptions of media. The U&G suggests that audiences select a specific medium to gratify their goals or needs (Blumler, 1979; Katz, et al., 1974; Rayburn, 1996). Employing the U&G, several studies have attempted to explain the use of the Internet and Internet–related communications (Morris and Organ, 1996; Ferguson and Perse, 2000; Flaherty, et al., 1998; Leung, 2001; Papacharissi and Rubin, 2000). Papacharissi and Rubin (2000) identified interpersonal utility, passing time, information seeking, convenience, and entertainment as five motives for using the Internet. Similarly, Yang and Kang (2006) suggested that entertainment, habit, social interaction, information, and escapism were five motivations for Internet use.
While information seeking and entertainment are critical gratifications sought from the Internet in general (Papacharissi and Rubin, 2000), individual Internet services show differences in the relevance of gratifications sought. Peter, et al. (2006), focusing specifically on Internet chat rooms, found that social inclusion, maintaining relationships, meeting new people, social compensation, and entertainment are the gratifications for using these chat rooms. It is apparent in the research that most of the motives are associated with social interaction or interpersonal utility. In comparing general Internet use and Internet chat rooms, it has been found that people are more likely to use Internet chat rooms for socialization than information seeking. In addition, Ko, et al. (2005) discovered that the social–interaction motivation for using the Internet has a positive effect on the use of human–human interaction features on the Internet that have characteristics of connectedness and reciprocal communication.
Accordingly, it is plausible to hypothesize that interpersonal utility motivation for using the Internet is related to social networking site use. This study further identifies how other motivations for using the Internet are related to the frequency and amount of time spent using social networking Web sites.
H2a. Interpersonal utility motive will be positively related to the frequency of social networking site use.
H2b. Interpersonal utility motive will be positively related to the amount of social networking site use.
RQ1. How will other motivations for using the Internet be related to the frequency and amount of social networking site use?
Generally speaking, privacy refers to “the interest that individuals have in sustaining their personal space, free from interference by other people and organizations” (Clarke, 1988). With the emergence of the Internet, information privacy arose as a salient issue. Specifically, information privacy is defined as “the interest individuals have in controlling, or at least significantly influencing, the handling of data about themselves” (Clarke, 1988). Hoffman, et al. (1999) asserted that consumers’ expectations of privacy depend on the type of media. While consumers do not pay much attention to privacy in traditional media, they do want control and protection of privacy in electronic media (Hoffman, et al., 1999). In the context of the Internet, privacy is important because it builds a sense of trust in consumers, which can lead to increased use of Web sites.
Sheehan and Hoy (1999) found that as individuals’ concerns about privacy increase, the frequency with which they register for a Web site decreases. More recently, Wang, et al. (2006), in their empirical study on the relative weight of Internet commerce factors, identified privacy as one of the most critical factors in online marketing along with safety and product quality. The findings indicate that as marketers of Web sites improve privacy with safety and product quality, more people sign up for, and shop on, the Web sites.
When considering inherent characteristics of social networking Web sites, privacy is presumably a critical issue to those who use them. A vast amount of identifiable personal information such as full name, school, photos, and e–mail address is available on many social networking sites. In addition, disclosing some personal information is necessary to network on such sites. Indeed, Bart, et al. (2005) empirically discovered that privacy is an important driver of trust, particularly for community Web sites compared with other sites, including traveling, e–tailer, finance, computer, sports, and automobile Web sites because sharing information among members on community Web sites is prevalent; this results in users’ susceptibility of risking private information. Therefore, this study proposes there is a relationship between privacy concerns and social networking Web site use.
H3a. Privacy concerns on the Internet will be negatively related to the frequency of social networking site use.
H3b. Privacy concerns on the Internet will be negatively related to the amount of social networking site use.
Zajonc (1968) suggested a “mere exposure effect” postulating that continuous exposure tends to increase people’s liking for given stimuli. That is, as individuals’ exposure to a particular stimulus increases, he or she establishes a positive attitude toward the stimulus (Monroe, 1976; Wilson, 1979; Zajonc, 1968). Similarly, the more people use and become familiar with given technologies, the more comfortable they become using these technologies. Prior studies show that this theory holds true on the Internet. The Internet and Society Report by the Stanford Institute for the Quantitative Study of Society (2000) found that Internet experience has a positive correlation with the amount of Internet use. The longer people have used the Internet, the more hours they spend and the more activities they engage in on the Web. Several prior studies specifically focused on the relationship between Internet experience and purchase behaviors on the Internet. Aldridge, et al. (1997) argued that the likelihood of buying via the Internet should increase as use of the Internet increases. Hoffman, et al. (1999) found that Internet experience has a positive relationship with purchase behaviors on the Internet. When the findings of previous studies are applied to the context of social networking Web sites, the following hypotheses are proposed.
H4a. Internet experience will be positively related to the frequency of social networking site use.
H4b. Internet experience will be positively related to the amount of social networking site use.
Innovativeness is defined as an individual’s tendency to be more receptive to new ideas (Leung and Wei, 1998; Lin, 1998; Lin and Jeffres, 1998; Li, 2003; Rogers, 1995). Innovativeness depends on individuals and is seen as critical in consumers’ technology adoption. Individual innovativeness tends to differentiate adopters from non–adopters of new technologies (Lin, 1998; Lin and Jeffres, 1998; Busselle, et al., 1999). Rogers (1995) argued that a high degree of individual innovativeness triggers early adoption of a new technology and/or idea. Individual innovativeness is also introduced into the TAM research to expand the scope of TAM applicability. Lin (2004) pointed out that recent studies on innovative attributes and computer–mediated technology adoption generally support the influences of this personality trait on adoption of an innovation. Busselle, et al. (1999) found that an individual’s innovativeness is a positive predictor for the frequency of Internet use. Lin (1998) and Lin (2004) also demonstrated that innovativeness is a significant predictor for personal computer adoption and Webcasting adoption. Based on the findings of prior studies, this study suggests the following hypotheses.
H5a. Innovativeness will be positively related to the frequency of social networking site use.
H5b. Innovativeness will be positively related to the amount of social networking site use.
Madden and Savage (2000) found that age has an inverse relationship with Internet use. More recently, Pew Internet & American Life Project (2004) echoed the findings of Madden and Savage (2000). Similar findings can be found in the adoptions of specific Internet–related technologies such as online chat rooms and Webcasting (Peter, et al., 2006; Lin, 2004). Therefore, this study proposes a negative relationship between the use of social networking sites and age.
H6a. Age will be negatively related to the frequency of social networking site use.
H6b. Age will be negatively related to the amount of social networking site use.
Traditionally, innovation diffusion literature suggests that males are more likely than females to adopt a new technology earlier (Dutton, et al., 1987; LaRose and Atkin, 1988; Jeffres and Atkin, 1996). The adoption of the Internet is no exception. More males used the Internet in its nascent years than females (Ernst & Young, 1999; Pew Research Center for the People and the Press, 1998). Male dominance in Internet usage was, however, overturned. A recent report by Tech Crunchies (2008) indicates that more women used the Internet than their male counterparts in the U.S. as of 2008 and the trend will continue in the near future. Specifically, focusing on the frequency and amount of Internet–based technology usage, Leung (2001) found that female college students seek more socialization gratification through instant messages than relaxation and entertainment. Further, the study revealed that female college students chat through messengers more often and longer per session than male college students. Taken together, the current study proposes that there is a gender difference in social networking site usage.
H7a. Females use social networking sites more often than males do.
H7b. Females spend more time on social networking sites than males do.
Each of the aforementioned hypotheses addresses a one–on–one relationship between the use of social networking sites and each construct on which this study focuses. This study further explores how the models that contain both perceptions of social networking sites and consumer characteristics predict the use of social networking sites.
RQ2. How will perceptions of social networks, motives behind using the Internet, Internet privacy concerns, Internet experience, and demographic characteristics predict the frequency and amount of social networking site use?
To investigate the hypotheses and research questions, a survey method using a sample of college students was employed. Even though the use of college students can be viewed as convenient, Basil (1996) suggested that the use of college student samples is valid if the demographic group is of interest to the topic of study. The use of a college student sample is reasonable in this study because college students represent a significant portion of the demographic age group that social networking sites and related retailers target for marketing (Market Watch, 2008).
Two pretests comprising samples of 30 and 45 students, respectively, were carried out at a large southeastern university. Based on feedback from the pretests, the questionnaire items were carefully reworded and refined. For the main survey, a total of 251 college students across the campus was employed. The makeup of the sample was 41.4 percent male and 58.6 percent female; 8.0 percent of the students were first year, 30.3 percent sophomore, 37.8 percent junior, and 23.5 percent senior. The mean age was 20.51 years old (SD = 2.14). The descriptive statistics revealed that 98.41 percent of the participants (n = 247) said that they were using at least one social networking Web site. The average number of social networking Web sites they were using was 1.64 (SD = .73).
The Appendix presents the measures, reliabilities, and descriptive statistics of the survey question items. The reliabilities for all the constructs were acceptable in that the generally agreed lower limit for Cronbach’s α for reliability is .70, but it may decrease to .60 in exploratory research (Hair, et al., 1998). To measure motives for using the Internet, six constructs of motives for using the Internet were adapted from Papacharissi and Rubin (2000) and Yang and Kang (2006). The six constructs of motives included 1) interpersonal utility; 2) boredom relief; 3) learning; 4) convenience; 5) entertainment; and, 6) escape. Items were ordered randomly on the questionnaire such that no two items from the same motive dimension appeared sequentially. The measures for perceived ease of use and perceived usefulness constructs were adapted from Davis (1989) and Davis, et al. (1989). To measure innovativeness, five items from Yang (2005) were employed. The measurement items for privacy concerns on the Internet were borrowed from Dinev and Hart (2004). For the constructs listed above, the respondents were asked to indicate their agreement with each of the statements on a 7–point Likert scale (1 = strongly disagree, 7 = strongly agree). Internet experience was measured with 10 checklist items designed by Graphic Visualization Usability Center’s (GVU) WWW User Survey (1998). The items were used in previous studies and yielded a valid result (e.g., Barbeite and Weiss, 2004). Examples of the activities include 1) ordered a product/service from a business, government, or education entity on the Internet; 2) created a Web page; 3) changed your cookie preference; 4) listened to a radio broadcast online, etc. The scores of the 10 items were averaged to reflect Internet experience. Respondents were also asked to specify their age as an open–ended form. Finally, to measure the frequency of using social networking Web sites the respondents were asked how often they used them on a 7–point scale (1 = never, 7 = all the time). To establish the amount of social networking site use, the respondents were asked to write the number of hours they spent on social networking Web sites per week. The hours were then categorized into eight categories: 1) 0–2 hours; 2) 3–5 hours; 3) 6–8 hours; 4) 9–11 hours; 5) 12–14 hours; 6) 15–17 hours; 7) 18–20 hours; and, 8) 21 and more hours. The means for the frequency and amount were 5.96 (SD = 1.61) and 2.83 (SD = 1.97), respectively.
The first stage of the analysis was to investigate the first research question and the seven sets of hypotheses. Pearson correlations were used to test the bivariate relationship between the perceptions of social networking sites or consumer characteristics and social networking site usage. The second stage was conducted to explore the second research question, which addresses the statistical significance of each construct in the model simultaneously. Two separate hierarchical regressions were used to examine the relative contributions of motive, perceptions, and consumer characteristics to predict the frequency and amount of time spent using social networking sites. In hierarchical regressions, consumers’ general characteristics, such as age, gender, and innovativeness, were first entered followed by consumers’ Internet–related characteristics, including Internet experience and Internet privacy concerns. In the third and final blocks, the six motives behind using the Internet and perceptions of social networking sites (i.e., perceived usefulness and perceived ease of use) were entered, respectively.
Results from correlation analyses are shown in Table 1. With respect to research question 1 asking the correlation with the motive for Internet and social networking site use, entertainment (r = .220, ρ <.001), boredom relief (r = .235, ρ <.001), interpersonal utility (r = .435, ρ <.001), escape (r = .229, ρ <.001), and convenience (r = .268, ρ <.001) motives were positively related to the frequency of social networking site use. The results for the amount of social networking site use parallels the results for the frequency of using social networking sites. Entertainment (r = .197, ρ <.01), boredom relief (r = .215, ρ <.01), interpersonal utility (r = .392, ρ <.001), escape (r = .288, ρ <.001), and convenience (r = .312, ρ <.01) were positively related to the time spent on social networking sites per week. The learning motive behind using the Internet was related to neither the frequency nor the amount of social networking site use.
The correlation analyses showed that both hypotheses 1a and 1b were supported. Perceived usefulness (r = .195, ρ <.01) and ease of use (r = .306, ρ <.001) were positively related to the frequency of social networking site use. Perceived usefulness (r = .179, ρ <.01) and ease of use (r = .152, ρ <.05) were also positively related to the amount of social network use. Both perceived usefulness and ease of use had stronger relationships with frequency than with the amount of social networking site use.
The correlation analyses also discovered strong relationships between Internet-related constructs and the use of social networking sites. Hypotheses 2a and 2b posited that there are relationships between the interpersonal utility motive and use of social networking sites in terms of frequency and amount. The results indicate that people who use the Internet for social interaction tend to use social networking sites more often (r = .435, ρ <.001) and spend more time on social networking sites (r = .392, ρ <.001). Hypotheses 3a and 3b postulated that privacy concerns are negatively related to the frequency and amount of using social networking sites, respectively. As expected, the more concerned people are about Internet privacy, the less frequently they use social networking sites (r = -.156, ρ <.05). However, Internet privacy concerns are not related to how much time people spend on social networking sites. Hypotheses 4a and 4b proposed that Internet experience is positively related to the frequency and amount of using social networking sites. A correlation between Internet experience and the frequency of social networking site use (r = .149, ρ <.05) was detected. There is also a correlation between Internet experience and the amount of social networking site use (r = .209, ρ <.01).
As for general consumer characteristics, hypotheses 5a and 5b proposed that people who are innovative use social networking sites more often and spend more time on them. Neither of the hypotheses was supported. Hypotheses 6a and 6b postulated that age is negatively related to the frequency and amount of social networking site use. Hypothesis 6a was supported, showing that younger college students (r = -.221, ρ <.001) use social networking sites more frequently. There is, however, no relationship between age and the time spent on social networking sites. With respect to the relationship between gender and social networking site use, it was found that females spend more time on social networking sites than males (r = -.140, ρ <.05), which supports hypothesis 7b. However, gender was not related to the frequency of using social networking sites. Hypothesis 7a is not supported.
Research question 2 inquired how perceptions of social networking sites, motives behind using the Internet, privacy concerns on the Internet, Internet experience, and other demographic characteristics predict the use of social networking sites. The results of two separate hierarchical regressions are reported in Table 2. Variance inflation factor (VIF) was consulted to see if there are multicollinearity problems in both regression models. The general rule of thumb for the existence of multicollinearity is that it exists among variables when a VIF is larger than 10 (Guo, et al., 1996). VIF values for the current study ranged from 1.11 to 2.20 in both models, which indicates no muticollinearity problem in each of the models. The hierarchical regression showed that interpersonal utility motive (β = .362, ρ <.001), Internet privacy concerns (β = -.153, ρ <.01), perceived ease of use (β = .179, ρ <.05), and age (β = -.226, ρ <.001) are statistically significant in predicting the frequency of social networking site use. In explaining the amount of time in social networking site use, interpersonal utility motive (β = .216, ρ <.05), escape motive (β = .150, ρ <.05), and Internet experience (β = .165, ρ <.01) are statistically significant.
Table 2: Multiple hierarchical regressions. Predictors Frequency Amount Final β ΔR2 Final β ΔR2 Block 1. General characters .056 .039 Age -.226*** -.066 Gendera .015 -.122 Innovativeness -.013 .021 Block 2. Internet related characters .046 .051 Internet experience .056 .165** Privacy concern on the Internet -.153* .050 Block 3. Motives for using the Internet .187 .141 Entertainment -.136 -.084 Learning -.024 .006 Boredom relief .083 .070 Interpersonal utility .362*** .216* Escape .099 .150* Convenience .041 .096 Block 4. Perceptions of social networks .022 .046 Perceived usefulness -.033 .046 Perceived ease of use .179** .029 Final model F 8.018 5.436 Total R2 .311 .234 Total adjusted R2 .272 .191 Note: * ρ <.05; ** ρ <.01, *** ρ <.001.
a. Gender was coded as male = 1, female = 0.
The present study attempted to identify factors affecting the use of social networking sites. In that regard, this study focused on two dimensions of the medium’s use — frequency and amount to explore whether there are similarities or differences between frequency and amount of the medium’s use.
With respect to the bivariate relationship between social networking site use and perceptions of social networking sites, the two constructs in TAM perceived usefulness and perceived ease of use were related to both frequency and the amount of social networking site use. The correlation result also indicated that people with more Internet experience use social networking Web sites more often and spend more time on the venues. With regard to motives for using social networking sites, the present study found that entertainment, boredom relief, interpersonal utility, escape, and convenience motives are correlated with both frequency and amount of social networking Web site use. Learning motive was the only motivation that is related to neither frequency nor the amount of using social networking Web sites. As Papacharissi and Rubin (2000) noted, the current study supports that the learning motive (M = 5.372) is one of the most representative motives for Internet use along with entertainment motive (M = 5.819). It is, however, noteworthy that learning motive is not related to social networking site use. As expected, online privacy concerns reduced the frequency of using social networking Web sites, but these concerns did not hinder time spent on social networking sites. Another difference between frequency and amount is that females spend more time on social networking sites than males, but there is no gender difference in relation to the frequency of using social networking sites. This study confirms that female college students are more active users of online media that function primarily for socialization, as Leung (2001) found in the context of instant messaging. In addition, younger people tend to use social networking more often, but there is no relationship between age and college students’ time spent on social networking sites.
The results from regression analyses identify the predictors of the frequency and amount of social networking site use and the relative importance of each construct in the proposed models. It was found that college students tend to use social networking sites more often as they are younger, use the Internet more for interpersonal utility, have fewer privacy concerns, and perceive social networking Web sites as easy to use. On the other hand, college students spend more time on social networking sites, as they have more Internet experience and they use the Internet more for interpersonal utility and escape.
Examining the relative importance of each variable to predict the frequency and amount of social networking site use, this study found that the strongest determinant for both frequency and amount of social networking site use is the interpersonal utility motive. The finding from this study is consistent with Ko, et al. (2005), who discovered that the social interaction motivation for using the Internet has a positive impact on the frequency of using human–human interaction features on the Internet. While Ko, et al. (2005) concentrated on frequency, the present investigation further found that interpersonal utility motive also increases the amount of time spent on social networking sites. The result regarding the amount of time spent on social networking Web sites corroborates the study by Papacharissi and Rubin (2000), who found that interpersonal utility motivation predicts the amount of general Internet use. Note that entertainment, boredom relief, and convenience motives have bivariate relationships with social networking site use, but these motives do not predict use when the perceptions of social networking sites and consumer characteristics are taken into account concurrently.
Along with the interpersonal utility motive, the escape motive is another predictor of the time spent on social networking sites. The more college students use the Internet to escape worries and problems, the more time they spend on social networking sites. This result parallels the findings of Kraut, et al. (1998) and Papacharissi and Rubin (2000), who found that people with a higher desire to escape from worries and problems are more likely to use the Internet. The impact of the escape motive on the amount of time spent on social networking sites can be linked to psychology literature. Psychology research found that dependents of the Internet are more likely to use the Internet to escape from negative feelings (Young, 1998; Whang, et al., 2003). A widely used criterion to distinguish dependents and non-dependents of the Internet is the amount of time spent on the Internet (Whang, et al., 2003; Leung, 2004). The critical role of the escape motive for using the Internet might explain why none of the perceptions of social networking sites (i.e., perceived usefulness and ease of use) does not influence the time spent on social networks. Another interesting point is that the escape motive for Internet use does not affect how often college students use social networking sites.
This study’s findings also indicate that the more college students perceive social networking sites as easy to use, the more frequently they use them. In other words, establishing an easy interface and navigation for social networking Web sites is essential to turn visitors to regular users. However, the perceived ease of use of social networking sites does not necessarily spur college students to spend more time on them. The descriptive statistics show that perceived usefulness of social networking sites, which reflects the degree to which social networking sites increase efficiency and productivity in life, is relatively lower (M = 3.95) than perceived ease of use (M = 5.31). There is a bivariate relationship between perceived usefulness and social networking site use, but perceived usefulness has no influence on social networking site use when other factors are taken into account. Given that, another possible explanation regarding the lack of effect of the perceived usefulness of social networking sites would be that other factors such as interpersonal utility and escape motives presumably overshadow it.
In addition, this study found that online privacy concerns are a deterrent for the frequency of using social networking sites, but not for the amount of social networking site use. This study’s finding adds an empirical support to prior Internet–based system research that suggests privacy concerns are a barrier for using the Internet and adopting e–commerce (Hoffman, et al., 1999; Sullivan, 2005; Eastlick, et al., 2006). Sheehan and Hoy (1999) found that privacy concerns reduce the frequency of Web site registration. While prior studies found that online privacy concerns can negatively impact a person’s decision to adopt an Internet–based technology system, the present study highlights the influence of privacy concerns on the ongoing use of a medium even after the system is adopted. The current study suggests it is essential for social networking site operators to ensure online privacy to turn potential users into regular users of their sites because potential users will be reluctant to register for, and continue to log into, social networking sites on a regular basis if they fear privacy infringement. Even though the reputation of firms may reduce risk perceptions (Van den Poel and Leunis, 1999; Eastlick, et al., 2006) and social networking site operators have been diversifying privacy features on social networking Web sites from the beginning, this study shows that the benefits of using social networking sites do not mitigate the negative effects of privacy concerns.
As expected, Internet experience predicts the time spent on social networking sites. College students with more diverse Internet experience spend more time on social networking Web sites than those with less Internet experience. Note that the present study measured Internet experience with respect to depth (i.e., the diversity of the activities the individuals engaged in on the Internet), while some of the previous studies measured Internet experience with duration (i.e., the number of years individuals have been using the Internet) (Stanford Institute for the Quantitative Study of Society, 2000) or the frequency of ongoing Internet use (Hoffman, et al., 1999; Aldridge, et al., 1997). The finding provides a positive outlook for social network operators to migrate from advertising–based business models to the combination of advertising and user–based revenue structures because people with more diverse Internet experience are eager to try something new on the venues.
With respect to age, it is noteworthy that most studies and industry reports have focused on the impact of age on adoption likelihood or frequency of media use. This paper specifically found that younger college students use social networking sites more often, but do not necessarily spend more time using them. The age factor has the second strongest impact on frequency, following the interpersonal utility motive. The reverse relationship between age and frequency of using social networking sites can be seen as consistent with other reports pertaining to Internet and chat room use (Pew Internet & American Life Project, 2004; Peter, et al., 2006).
While some previous studies succeeded in integrating consumers’ innovativeness with other factors that explain the adoption of computer and Webcasting (Lin, 1998; Lin, 2004) and the frequency of Internet use (Busselle, et al., 1999), this study suggests that individuals’ innovativeness is not a salient factor that predicts the ongoing use of social networking sites. Considering that approximately 85 percent of college students had already adopted social networking sites as of 2005 (Arrington, 2005) and 98 percent of the participants in the current study were using at least one social networking site, the impact of individuals’ innovativeness on the ongoing use of the system appears to be diluted by the saturated adoption rate of the system among college students.
Even though an effort to recruit college students with diverse backgrounds was made, the data for this study were collected at a single university. The interpretation of the results needs to be treated with caution. Future studies can expand the samples to the general U.S. population to examine the impact of diverse demographic and consumer characteristics on social networking site consumption. The current study did not dichotomize the respondents into users and non-users of social networking sites because most college students use social networking sites, and the focus of this study was to identify factors that are relevant to, and predict, the incremental use of social networking sites. Expanding the population of the study from college students to Internet users, future research can further identify a greater diversity of factors that might hinder the adoption and ongoing use of social networking sites by grouping Internet users into users and non-users of these sites. This study also suggests a future research direction that addresses how social networking sites compete with other online media that are functionally similar in gratifying consumers’ social interaction needs.
About the author
Jiyoung Cha (Ph.D., University of Florida) is an assistant professor in the Department of Radio, Television, and Film at the University of North Texas. Her research interests include the relationship between the media and the audience and the interaction between emerging new media and traditional media from management and marketing perspectives. She received her Ph.D. in mass communication with a minor in marketing from the University of Florida and her master’s degree in Television, Radio, and Film at the S.I. Newhouse School of Communications at Syracuse University.
E–mail: jcha [at] unt [dot] edu
1. Davis, 1989, p. 320.
2. Davis, 1989, p. 323.
Icek Ajzen and Martin Fishbein, 1980. Understanding attitudes and predicting social behavior. Englewood Cliffs, N.J.: Prentice–Hall.
Alicia Aldridge, Karen Forcht, and Joan Pierson, 1997. “Get linked or get lost: marketing strategy for the Internet,” Internet Research, volume 7, number 3, pp. 161–169.http://dx.doi.org/10.1108/10662249710171805
M. Arrington, 2005. “85% of college students use FaceBook,” TechCrunch (7 September), at http://www.techcrunch.com/2005/09/07/85-of-college-students-use-facebook/, accessed 18 June 2008.
Francisco G. Barbeite and Elizabeth M. Weiss, 2004. “Computer self–efficacy and anxiety scales for an Internet sample: Testing measurement equivalence of existing measures and development of new scales,” Computers in Human Behavior, volume 20, number 1, pp. 1–15.http://dx.doi.org/10.1016/S0747-5632(03)00049-9
Yakov Bart, Venkatesh Shankar, Fareena Sultan, and Glen L. Urban, 2005. “Are the drivers and role of online trust the same for all Web sites and consumers? A large–scale exploratory empirical study,” Journal of Marketing, volume 69, number 4, pp. 133–152.http://dx.doi.org/10.1509/jmkg.2005.69.4.133
Michael D. Basil, 1996. “Standpoint: The use of student samples in communication research,” Journal of Broadcasting & Electronic Media, volume 40, number 3, pp. 431–440.http://dx.doi.org/10.1080/08838159609364364
Rick Busselle, Joey Reagan, Bruce Pinkleton, and Kim Jackson, 1999. “Factors affecting Internet use in a saturated–access population,” Telematics and Informatics, volume 16, number 1, pp. 45–58.http://dx.doi.org/10.1016/S0736-5853(99)00018-0
Jay G. Blumler, 1979. “The role of theory in uses and gratifications studies,” Communication Research, volume 6, number 1, pp. 9–36.http://dx.doi.org/10.1177/009365027900600102
Patrick Y.K. Chau, 1996. “An empirical assessment of a modified technology acceptance model,” Journal of Management Information Systems, volume 13, number 2, pp. 185–204.
Patrick Y.K. Chau and Paul J. Hu, 2002. “Examining a model for information technology acceptance by individual professionals: An exploratory study,” Journal of Management Information Systems, volume 18, number 4, pp. 191–229.
Roger A. Clarke, 1988. “Information technology and dataveillance,” Communications of the ACM, volume 31, number 5, pp. 498–512.http://dx.doi.org/10.1145/42411.42413
College Board and Arts & Science Group, 2009. “Social networking sites and college–bound students,” studentPOLL, volume 7, number 2, at http://professionals.collegeboard.com/data-reports-research/trends/studentpoll/social-networking, accessed 20 January 2010.
Fred D. Davis, 1989. “Perceived usefulness, perceived ease of use, and user acceptance of information technologies,” MIS Quarterly, volume 13, number 3, pp. 319–340.http://dx.doi.org/10.2307/249008
Fred D. Davis, Richard P. Bagozzi, and Paul R. Warshaw, 1989. “User acceptance of computer technology: A comparison of two theoretical models,” Management Science, volume 35, number 8, pp. 982–1,003.
Tamara Dinev and Paul Hart, 2004. “Internet privacy concerns and their antecedents — measurement validity and a regression model,” Behaviour & Information Technology, volume 23, number 6, pp. 413–422.http://dx.doi.org/10.1080/01449290410001715723
William H. Dutton, Everett M. Rogers, and Suk–Ho Jun, 1987. “Diffusion and social impacts of personal computers,” Communication Research, volume 14, number 2, pp. 219–250.http://dx.doi.org/10.1177/009365087014002005
Mary Ann Eastlick, Sherry L. Lotz, and Patricia Warrington, 2006. “Understanding online B–to–C relationships: An integrated model of privacy concerns, trust, and commitment,” Journal of Business Research, volume 59, number 8, pp. 877–886.http://dx.doi.org/10.1016/j.jbusres.2006.02.006
Ernst & Young, 1999. “The second annual Ernst & Young Internet shopping study,” at http://www.e-consultancy.com/knowledge/whitepapers/9/the/second-annual-Internet-shopping-study.html, accessed 10 August 2007.
Douglas A. Ferguson and Elizabeth M. Perse, 2000. “The World Wide Web as a functional alternative to television,” Journal of Broadcasting & Electronic Media, volume 44, number 2, pp. 155–174.http://dx.doi.org/10.1207/s15506878jobem4402_1
Lisa M. Flaherty, Kevin J. Pearce, and Rebecca B. Rubin, 1998. “Internet and face–to–face communication: Not functional alternatives,,” Communication Quarterly, volume 46, number 3, pp. 250–268.http://dx.doi.org/10.1080/01463379809370100
David Gefen, Elena Karahanna, and Detmar W. Straub, 2003. “Trust and TAM in online shopping: An integrated model,” MIS Quarterly, volume 27, number 1, pp. 51–90.
Graphic, Visualization, & Usability (GVU) Center, Georgia Institute of Technology, 1998. “GVU’s 10th WWW user survey,” at http://www-static.cc.gatech.edu/gvu/user_surveys/survey-1998-10/graphs/, accessed 12 February 2007.
Shumei S. Guo, William C. Chumlea, and David B. Cockram, 1996. “Use of statistical methods to estimate body composition,” American Journal of Clinical Nutrition, Supplement, volume 64, number 3, pp. 428S–435S.
Joseph F. Hair, Jr., Rolph E. Anderson, Ronald L. Tatham, and William Black, 1998. Multivariate data analysis. Fifth edition. Upper Saddle River, N.J.: Prentice–Hall.
Donna L. Hoffman, Thomas P. Novak, and Marcos A. Peralta, 1999. “Building consumer trust online,” Communications of the ACM, volume 42, number 4, pp. 80–85.http://dx.doi.org/10.1145/299157.299175
Paul J. Hu, Patrick Y.K. Chau, Olivia R. Liu Sheng, and Kar Yan Tam, 1999. “Examining the technology acceptance model using physician acceptance of telemedicine technology,” Journal of Management Information Systems, volume 16, number 2, pp. 91–112.
Cynthia M. Jackson, Simeon Chow, and Robert A. Leitch, 1997. “Toward an understanding of the behavioural intentions to use an information system,” Decision Sciences, volume 28, number 2, pp. 357–389.http://dx.doi.org/10.1111/j.1540-5915.1997.tb01315.x
Leo Jeffres and David Atkin, 1996. “Predicting use of technologies for communication and consumer needs,” Journal of Broadcasting & Electronic Media, volume 40, number 3, pp. 318–330.http://dx.doi.org/10.1080/08838159609364356
Hanjun Ko, Chang–Hoan Cho, and Marilyn S. Roberts, 2005. “Internet uses and gratifications: A structural equation model of interactive advertising,” Journal of Advertising, volume 34, number 2, pp. 57–70.http://dx.doi.org/10.1080/00913367.2005.10639191
Robert Kraut, Michael Patterson, Vicki Lundmark, Sara Kiesler, Tridas Mukophadhyay, and William Scherlis, 1998. “Internet paradox: A social technology that reduces social involvement and psychological well–being,” American Psychologist, volume 53, number 9, pp. 1,017–1,031.
Robert LaRose and David Atkin, 1988. “Satisfaction, demographic, and media environment predictors of cable subscribers,” Journal of Broadcasting & Electronic Media, volume 32, number 4, pp. 403–413.http://dx.doi.org/10.1080/08838158809386712
Louis Leung, 2004. “Net–generation attributes and seductive properties of the Internet as predictors of online activities and Internet addiction,” CyberPsychology & Behavior, volume 7, number 3, pp. 333–348.http://dx.doi.org/10.1089/1094931041291303
Louis Leung, 2001. “College student motives for chatting on ICQ,” New Media & Society, volume 3, number 4, pp. 483–500.http://dx.doi.org/10.1177/14614440122226209
Louis Leung and Ran Wei, 1998. “Factors influencing the adoption of interactive TV in Hong Kong: Implications for advertising,” Asian Journal of Communication, volume 8, number 2, pp. 124–147.http://dx.doi.org/10.1080/01292989809364766
Shu–Chu Sarrina Li, 2003. “Electronic newspaper and its adopters: Examining the factors influencing the adoption of electronic newspapers in Taiwan,” Telematics and Informatics, volume 20, number 1, pp. 35–49.http://dx.doi.org/10.1016/S0736-5853(02)00002-3
Carolyn A. Lin, 2004. “Webcasting adoption: Technology fluidity, user innovativeness, and media substitution,” Journal of Broadcasting & Electronic Media, volume 48, number 3, pp. 446–465.http://dx.doi.org/10.1207/s15506878jobem4803_6
Carolyn A. Lin, 1998. “Exploring personal computer adoption dynamics,” Journal of Broadcasting & Electronic Media, volume 42, number 1, pp. 95–112.http://dx.doi.org/10.1080/08838159809364436
Carolyn A. Lin and Leo Jeffres, 1998. “Factors influencing the adoption of multimedia cable technology,” Journalism & Mass Communication Quarterly, volume 75, number 2, pp. 341–352.http://dx.doi.org/10.1177/107769909807500209
Gary G. Madden and Scott J. Savage, 2000. “Some economic and social aspects of residential Internet use in Australia,” Journal of Media Economics, volume 13, number 3, pp. 171—185.http://dx.doi.org/10.1207/S15327736ME1303_2
Kent B. Monroe, 1976. “The influence of price differences and brand familiarity on brand preference,” Journal of Consumer Research, volume 3, number 1, pp. 42–49.http://dx.doi.org/10.1086/208649
Merrill Morris and Christine Ogan, 1996. “The Internet as mass medium,” Journal of Communication, volume 46, number 1, pp. 39–50.http://dx.doi.org/10.1111/j.1460-2466.1996.tb01460.x
Zizi Papacharissi and Alan M. Rubin, 2000. “Predictors of Internet use,” Journal of Broadcasting & Electronic Media, volume 44, number 2, pp. 175–196.http://dx.doi.org/10.1207/s15506878jobem4402_2
Namkee Park, Kwan Min Lee, and Pauline Hope Cheong, 2008. “University instructors’ acceptance of electronic courseware: An application of the technology acceptance model,” Journal of Computer–Mediated Communication, volume 13, number 1, pp. 163–186.http://dx.doi.org/10.1111/j.1083-6101.2007.00391.x
Jochen Peter, Patti M. Valkenburg, and Alexander P. Schouten, 2006. “Characteristics and motives of adolescents talking with strangers on the Internet and its consequences,” CyberPsychology & Behavior, volume 9, number 5, pp. 526–530.http://dx.doi.org/10.1089/cpb.2006.9.526
Cara Peters, Christie H. Amato, and Candice R. Hollenbeck, 2007. “An exploratory investigation of consumers’ perceptions of wireless advertising,” Journal of Advertising, volume 36, number 4, pp. 129–145.http://dx.doi.org/10.2753/JOA0091-3367360410
Pew Internet & American Life Project, 2009. “Adults on social network sites, 2005–2009,” at http://pewinternet.org/Infographics/Growth-in-Adult-SNS-Use-20052009.aspx, accessed 27 January 2009.
Pew Internet & American Life Project, 2004. “Older American and the Internet,” at http://www.pewinternet.org/Reports/2004/Older-Americans-and-the-Internet.aspx, accessed 23 November 2010.
Pew Research Center for the People and the Press, 1999. “Basic patterns of Internet use,” at http://people-press.org/report/?pageid=338, accessed 15 August 2007.
John Raacke and Jennifer Bonds–Raacke, 2008. “MySpace and Facebook: Applying the uses and gratifications theory to exploring friend–networking sites,” CyberPsychology, volume 11, number 2, pp. 169–174.http://dx.doi.org/10.1089/cpb.2007.0056
J.D. Rayburn, 1996. “Uses and gratifications,” In: Michael B. Salwen and Don W. Stacks (editors). An integrated approach to communication theory and research. Mahwah, N.J.: Erlbaum, pp. 145–163.
Steve Rosenbush and Timothy J. Mullaney, 2006. “Social networking’s gold rush,” at http://www.businessweek.com/technology/content/apr2006/tc20060419_514268.htm?campaign_id=rss_daily, accessed 10 March 2007.
Kim.B. Sheehan and Mariea G. Hoy, 1999. “Flaming, complaining, abstaining: How online users respond to privacy concerns,” Journal of Advertising, volume 28, number 3, pp. 37–51.
Stanford Institute for the Quantitative Study of Society, Stanford University, 2000. “Internet and society: A preliminary report,” at http://www.stanford.edu/group/siqss/Press_Release/Preliminary_Report-4-21.pdf, accessed 6 January 2006.
Bomil Suh and Ingoo Han, 2006. “Effect of trust on consumer acceptance of Internet banking,” Electronic Commerce Research and Applications, volume 1, numbers 3–4, pp. 247–263.
Bob Sullivan, 2005. “Data leaks stunt e–commerce, survey suggests,” MSNBC.com (15 June), at http://www.msnbc.msn.com/id/8219161/, accessed 10 December 10 2006.
Tech Crunchies, 2008. “Internet statistics and numbers,” at http://techcrunchies.com/males-vs-females-internet-users-in-usa/, accessed 10 January 2009.
Lisa Collins Tidwell and Joseph B. Walther, 2002. “Computer–mediated communication effects on disclosure, impressions, and interpersonal evaluations,” Human Communication Research, volume 28, number 3, pp. 317–348.http://dx.doi.org/10.1111/j.1468-2958.2002.tb00811.x
Dirk Van den Poel and Joseph Leunis, 1999. “Consumer acceptance of the Internet as a channel of distribution,” Journal of Business Research, volume 45, number 3, pp. 249–256.http://dx.doi.org/10.1016/S0148-2963(97)00236-1
Vauhini Vara, 2007. “Facebook gets personal with ad targeting plan,” Wall Street Journal (23 August), p. B1, and at http://online.wsj.com/article/SB118783296519606151.html?mod=technology_main_whats_news, accessed 10 September 2007.
Joseph B. Walther, 1996. “Computer–mediated communication: Impersonal, interpersonal, and hyperpersonal interaction,” Communication Research, volume 23, number 1, pp. 3–43.http://dx.doi.org/10.1177/009365096023001001
Joseph B. Walther, 1992. “Interpersonal effects in computer–mediated communication: A relational perspective,” Communication Research, volume 19, number 1, pp. 52–90.http://dx.doi.org/10.1177/009365092019001003
Eric T.G. Wang, His–Yin Yeh, and James J. Jiang, 2006. “The relative weights of Internet shopping fundamental objectives: Effect of lifestyle differences,” Psychology & Marketing, volume 23, number 5, pp. 353–367.http://dx.doi.org/10.1002/mar.20116
Leo Sang–Min Whang, Sujin Lee, and Geunyoung Lee Chang, 2003. “Internet over–users’ psychological profiles: A behavior sampling analysis on Internet addiction,” CyberPsychology & Behavior, volume 6, number 2, pp. 143–150.http://dx.doi.org/10.1089/109493103321640338
William R. Wilson, 1979. “Feeling more than we can know: Exposure effects without learning,” Journal of Personality and Social Psychology, volume 37, number 6, pp. 811–821.http://dx.doi.org/10.1037/0022-35220.127.116.111
Debra A. Williamson, 2007. “Social networks ad spending is rising,” eMarketer (13 August), at http://www.emarketer.com/Article.aspx?id=1005257, accessed 13 February 2008.
Kenneth C.C. Yang, 2005. “Exploring the factors affecting the adoption of mobile commerce in Singapore,” Telematics and Informatics, volume 22, number 3, pp. 257–277.http://dx.doi.org/10.1016/j.tele.2004.11.003
Kenneth C.C. Yang and Yowei Kang, 2006. “Exploring factors influencing Internet users’ adoption of Internet television in Taiwan,” First Monday, volume 11, number 3, at http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/1319/1239, accessed 20 November 2006.
Kimberly S. Young, 1998. “Internet addiction: The emergence of a new clinical disorder,” CyberPsychology & Behavior, volume 1, number 3, pp. 237–244.http://dx.doi.org/10.1089/cpb.1998.1.237
Robert B. Zajonc, 1968. “The attitudinal effects of mere exposure,” Journal of Personality and Social Psychology, volume 9, number 2, pp. 1–27.http://dx.doi.org/10.1037/h0025848
Appendix: Constructs and reliabilities
Constructs M SD Items Entertainment motive
5.820 .973 Because it’s entertaining Because it amuses me Because it’s enjoyable Learning motive
5.364 1.618 Because it lets me explore new things Because it opens me up to new ideas Because it advances my knowledge Because it extends my mind Boredom relief motive
5.829 1.138 To pass time when bored When I have nothing better to do To occupy my time Interpersonal utility motive
4.408 1.084 To belong to a group To express myself freely To meet people Because I wonder what other people said To feel involved with what’s going on with other people To strengthen my relationships with my friends To keep contact with my friends To keep my friends up–to–date Escape motive
2.927 1.524 To forget my problems To escape my worries Convenience motive
4.488 1.637 Because it is easier to communicate on the Internet than tell people Because people don’t have to be there to communicate on the Internet Privacy concerns on the Internet
5.212 1.052 I am concerned that the information I submit on the Internet could be misused. When I shop online, I am concerned that the credit card information can be stolen while being transferred on the Internet. I am concerned about submitting information on the Internet because of what others might do with it. I am concerned about submitting information on the Internet because it could be used in a way I did not foresee. Perceived ease of use
5.313 .798 It is easy to become skillful at using social networking Web sites. Using social networking Web sites will be a frustrating experience. Learning to use social networking Web sites is easy for me. Perceived usefulness
3.946 1.251 Using social networking Web sites makes me more efficient. Using social networking Web sites helps me accomplish things more quickly. Using social networking Web sites makes my life easier. Using social networking Web sites would be useful in my life. Innovativeness
5.407 .919 I am very curious about how things work. I like to experiment with new ways of doing things. I like to take a chance. I like to be around unconventional people who dare to try new things.
Received 10 March 2010; accepted 15 October 2010.
Copyright © 2010, First Monday.
Copyright © 2010, Jiyoung Cha.
Factors affecting the frequency and amount of social networking site use: Motivations, perceptions, and privacy concerns
by Jiyoung Cha.
First Monday, Volume 15, Number 12 - 6 December 2010
A Great Cities Initiative of the University of Illinois at Chicago University Library.
© First Monday, 1995-2016.