Literature Review: Understanding the “Second-Level Digital Divide” Teresa Correa Doctoral student College of Communication University of Texas at Austin December 2008 *This is an unpublished manuscript prepared for the class Mass Communication Theory I, School of Journalism, UT-Austin One of the great promises of the media is that they reach people that otherwise could not have been reached by other channels (Severin & Tankard, 2000). Therefore, the media can potentially serve as glue that bring people together and, eventually, help to alleviate inequalities in information and opportunities. This positive outcome prevailed as a possible consequence when the mass media, especially television, irrupted as a new technology (Prior, 2007). The same happened with the emergence of digital technologies. In the beginning, they were seen as decentralizing, globalizing, harmonizing, and empowering tools (Negroponte, 1995). It has been found, however, that rather than alleviating existent inequalities, the media can exacerbate the information gaps between segments of the population. Tichenor and colleagues (Tichenor, Donohue, & Olien, 1970) surprisingly found that increasing the amount of information in a society leads to a greater acquisition of knowledge among people from higher socioeconomic status compared to those from lower classes. As a result, the media widens the knowledge gap between these groups (Tichenor, Donohue, & Olien, 1970). In the same period, the sociology of science field predicted a similar outcome in the “Matthew effect” (Merton, 1968) and the accumulation of advantage hypothesis (Zuckerman, 1977). These hypotheses basically assert that accumulation of advantage in science occurs when certain groups benefit from the allocation of resources at accelerating rates than other groups. As a consequence, this process contributes to elite formation and stratification (Zuckerman, 1977). The arrival of digital technologies, such as the internet, has shown the emergence of a digital divide. In other words, this new technology has benefited at greater rates those who already had access to other resources than people who had fewer resources (de Haan, 2004; van Dijk, 2006). These digital gaps may exacerbate existent inequalities between social groups because new technologies provide opportune access to information, a necessary tool for participating in a democratic society, as well as allows access to trade, education, employment, health, and wealth. In the beginning, the digital divide was conceptualized as a gap between those who do and those who do not have access to digital technologies (van Dijk, 2006). This conceptualization suggested that once the gaps are bridged, the internet use would be homogeneous (Peter & Valkenburg, 2006). Consequently, to narrow these inequalities, policy makers have focused their efforts on providing physical access to new digital technologies such as computers and the internet A well-known example is Nicholas Negroponte’s initiative “one laptop per child.” The purpose of this program is to provide affordable laptops to children in developing countries to promote self-empowered learning (one laptop per child, n.s.) . Although this binary approach of “have” and “have-nots” is useful to understand the inequalities regarding internet access or the basic usage of the Web, it has important shortcomings: First, it has been mainly descriptive, linking the physical access or basic usage of the technology (or lack of thereof) to socio-demographic characteristics of the population (de Haan, 2004; Hargittai, 2002; Newhagen & Bucy, 2004; van Dijk, 2004, 2006). For instance, studies have found differences by age, race, socioeconomic status, and geographic area (e.g., Hoffman & Novak, 1998; Howard, Rainie, & Jones, 2001; Katz & Aspden, 1997; LaRose, Gregg, Strover, Straubhaar, & Carpenter, 2007). In addition, this approach may also lead to the conclusion that as the technology spreads to the population the problem of the digital divide is resolved. Furthermore, it may suggest that digital inequalities are disappearing among developed countries and connected people (Hargittai & Hinnant, 2008; Peter & Valkenburg, 2006; van Dijk, 2006). Having access to the internet, however, is different from having access to the content that resides on it (Newhagen & Bucy, 2004; van Dijk, 2004). If people lack motivation, appropriate skills, cognitive ability, and self-confidence to assertively use the Web, a large portion of them may get left behind socially, economically and politically (Newhagen & Bucy, 2004). Therefore, as more people are using the Web to communicate, retrieve information and even contribute with content, academics and policy-makers have increasingly conveyed that it is necessary shift the focus from a simplistic and binary conceptualization of internet access to a more advanced and complex approach. For example, the office in charge of the United Kingdom’s online government services has clearly recognized this necessity, Encouraging remaining non-users onto the first rung of the internet ladder will remain an important challenge to guide policy in the next few years. However, for individuals to fully realise the benefits of the internet we must help them move up the ladder – to move from basic activities such as e-mail and browsing to more advanced uses (Office of the e-Envoy, 2004, as cited in Livingstone & Helsper, 2007). As a result, since 2002 research on the digital divide is moving beyond physical access to pay closer attention to a multifaceted concept of access that involves cognitive access, social access, and differentiated uses of the web (e.g., de Haan, 2004; Hargittai, 2002; Newhagen & Bucy, 2004; van Dijk, 2006). The emergence of this second wave of research on digital inequality has been called “usage gap” (van Dijk, 2004), “second-level digital divide” (Hargittai, 2002), and “emerging digital differentiation” (Peter & Valkenburg, 2006). Although this new area has also focused on socio-demographic predictors, it has incorporated other factors that affect the web usage, such as skills and social and psychological predictors. The purpose of this article is to present a literature review of the so-called “second-level digital divide.”By uncovering factors that prevent or facilitate the participation in the internet, web content producers, policymakers, and professors interested in increasing media access can have a better understanding of how to make the internet more relevant and approachable to include different groups in the digital world. LITERATURE REVIEW Socio-demographics and the “Second-level Digital Divide” Age: Perhaps one of the most powerful socio-demographic predictors of the digital divide is age. Teens and young adults are more likely to be online than older generations. The Pew Internet Project —one of the main authorities that keep track on American’s internet uses— has reported that over 80 percent of people between 12 and 34 years old go online. After that age, the percentages steadily decrease. Only half of the Americans in the sixties go online and merely 26 percent of people between 70 and 75 have accessed the web (Fox & Madden, 2005). Regarding specific uses of the internet, younger users are more likely to engage in communicative and interactive activities such as instant messaging, and blogging than older generations (Bonfadelli, 2002; Fox & Madden, 2005) .People between 29 and 69 years old tend to engage in activities that require more capital such as online banking and make travel reservations (Fox & Madden, 2005). Studies that have paid closer attention to younger cohorts —the most wired group— have revealed age differences in the interactive uses of the web. For example, Chen (2007) found that among college students, 18-21 years old were more likely to use social network sites and video-sharing applications than 22-plus-years old. These figures suggest that there might be a generational effect. That is, the “second-level digital divide” would disappear as younger people grow up. However, there is evidence that even among the most wired group there are differences in the participation the web by socioeconomic status and gender (e.g., Hargittai & Walejko, 2008; Livingstone & Helsper, 2007). Socioeconomic status/ education: In a broad sense, the knowledge gap (Tichenor, Donohue, & Olien, 1970) as well as the matthew effect (Merton, 1968) and the accumulation of advantage hypothesis (Zuckerman, 1977) predicted that segments of society that are better-off will get more benefits from an infusion of information than those that are less-well-off. In this sense, the differences in internet use by socioeconomic status —usually measured by education and/or income— follow this prediction. Generally, studies have demonstrated that people from higher socioeconomic status use more advanced applications of the web for informational, educational, communicational or service-oriented purposes while users from lower classes use simpler applications for communication and entertainment (Bonfadelli, 2002; Howard, Rainie, & Jones, 2001; Madden, 2003; Peter & Valkenburg, 2006; Van Dijk, 2005). Similarly, Cho and colleagues (2003) found that users high in socioeconomic status are more effective to attain gratifications they seek, such as connection, learning and acquisition, while people from lower status took a more indirect route to achieve a simpler goal: They were more likely to engage in consumptive activities to get connected with others. Among young adults, Hargittai and colleagues (2008) have found that the less education the users have, the less likely they are to visit capital-enhancing web sites (i.e., political, governmental, financial, career-related, and health web pages). They (Hargittai & Walejko, 2008) also demonstrated that college students that are better-off (measured by parental schooling) are more likely to engage in creative activities than those that are less-well off. Similarly to the predictions of the 1970s’ hypotheses (i.e., the knowledge gap, the matthew effect, and the accumulation of advantage), these findings suggest that internet use may increase social inequalities because people that are in more privileged positions are more likely to use the medium for activities from which they may get benefits. Race/ethnicity: Although whites have the greatest internet access among all ethnic groups (64 percent), English-speaking Hispanics are very close (62 percent). African Americans; access rates are the lowest (51 percent) These figures are misleading because they do not include Spanish-speaking Hispanics. (Madden & Rainie, 2003). A closer look at the online activities reveals that in very few activities all groups report similar levels of participation. While minority groups lag behind in their use of e-mail, they participate more in chat rooms and online discussions. Similarly to what happen in the socioeconomic divide, minorities engage less in informational uses of the internet (i.e., seek for information about government, health, and politics), as well as e-banking and online purchases compared to whites. However, regarding hobby and entertainment activities, such as downloading music, playing online games or looking for sports information, minorities outnumber whites (Madden & Rainie, 2003). Although the data is somewhat old, Howard and associates (Howard, Rainie, & Jones, 2001) found similar patterns the uses of the internet by race. But when they incorporated Asian Americans, they found that compared to white respondents, Asian Americans are more likely to research politics, do financial transactions, and make travel plans. Among the younger population, studies have not found significant ethnic differences in internet use: Adolescents (13-18 years) from different ethnicities were as likely as to use the internet as an informational, social, and entertainment medium (Peter & Valkenburg, 2006) It is important to note that Peter and Valkenburg’s (2006) measure of ethnicity is somewhat simplistic because they operationalized it as “Dutch” and “non-Dutch.” This operationalization overlaps with citizenship, which may hinder ethnic differences within each group. . Cotten and Jelenewicz (2006) —who focused on potential racial differences among college freshmen in the U.S. — demonstrated that there are no racial gaps in the students’ usage levels, and that few differences exist in general uses of the internet such as e-mail, instant messaging and surfing the web. The only differences appeared in a few activities: whites are more likely to play games, and less likely to use chat rooms than non-whites. Similarly, Jackson et al.(2001) found that racial differences among college students were small and only limited to e-mail use Regarding the racial differences on browsing when web sites target minorities, a study found that blacks spend more time and recall more when the web site targets African Americans while whites show no difference in browsing and information recall depending on the target of the site (Appiah, 2003). . Gender: Even though figures regarding internet access suggest that the gender gap disappeared, there are significant differences in how men and women use the web (Fallows, 2005; Meraz, 2008). Research has consistently found that men use computers and the internet more than women, spend more time online, and are more motivated to learn digital skills (Cooper, 2006; Fallows, 2005; Livingstone & Helsper, 2007; Losh, 2004). Paying closer attention to the differentiated uses of the web, women utilize the web to communicate (i.e., e-mail, social network sites, and blogging) while men are considered information seekers (i.e., search engines, search for news, for work or school, surf for hobbies, and e-banking). Except from blogging, males are also more likely to get involved in the technical —and more complex— aspects of online content creation, such as remixing files, maintaining their own website, and using iPods or Mp3 players (Bonfadelli, 2002; Fallows, 2005). Among the young generation, Livingstone and Helsper (2007) found that there is no difference between girls and boys among younger children (9-15 years) but boys make a broader use of the internet at an older age, suggesting that as the number of opportunities to use the web increases, the differences by gender open up. The use of the participatory web is also differentiated. For example, while teenage girls tend to be more avid bloggers, boys are more likely to upload videos (Lenhart, Madden, Rankin Mcgill, & Smith, 2007). Similarly, Chen (2007) found that while there are no gender differences among college students in social network site use, males were more likely to use video-sharing applications than women. In addition, Hargittai and Walejko (2008) found that women students were less likely to share online content (e.g., photos, videos, and text) than men. Psychological Predictors of the Internet Uses Although users’ sociodemographic characteristics are important in predicting the different levels of access and uses of the web, novice internet users may also face psychological barriers: their personality may prevent them to engage in certain activities, they may be less motivated using the web, less confident about their skills, or more likely to encounter stressful situations and develop computer anxiety. These psychological predictors may or may not be associated with their sociodemographics. Personality: Generally, the literature has demonstrated that people’s personality is associated with different internet uses. Hamburger and Ben-Artzi (2000) found that levels of extroversion and neuroticism showed different patterns between men and women in their approach to the web. For example, extroverted men —friendly and impulsive people who seek company and take risks— were more likely to use the internet for leisure activities. In the case of women, while extraversion was negatively related to the use of social sites, greater levels of neuroticism —i.e., an anxious, worrying, and emotional person— were positively associated with the use of social sites. These findings suggest that introverted and worrying females tend to use the internet for social network support (Hamburger & Ben-Artzi, 2000). Similarly, another study found that people who are open to new experiences and show greater levels of neuroticism are likely to be bloggers. These personality predictors are especially relevant for women (Guadagno, Okdie, & Eno, 2008). An exception to these findings is Hyles and Argyle’s study (2003), which found that personality was a weak predictor of internet use. Motivation: Motivation has been deemed critical in the digital inclusion process (de Haan, 2004; van Dijk, 2004, 2005, 2006). Van Dijk (2004) proposed that motivation is a critical step to get access to the internet. In his model, motivational access comes prior to physical access. However, once access is provided, motivation still plays a key role in the internet usage. It has been found that motivation is a stronger predictor than demographics (Sun, Rubin, & Haridakis, 2008). To date, motivations on internet use have been measured from a uses-and-gratifications approach only (e.g., Cho, Gil de Zuniga, Rojas, & Shah, 2003; Jackson, Ervin, Gardner, & Schmitt, 2001; Papacharissi & Rubin, 2000; Sun, Rubin, & Haridakis, 2008). That is, the goals and reasons why people use the web. Early studies associated motivations with certain uses such as informational and social uses, entertainment and escapism, as well as diversion and interactivity (Eighmey & McCord, 1998; Ko, 2002; Korgaonkar & Wolin, 1999). Other research has also tested the factors that influence certain motivations and its effects on internet uses. For example, Papacharissi and Rubin (2000) found that those who were economically secured, satisfied with life, and confident about their interpersonal communication favored instrumental uses such as information seeking while those who were less satisfied and less confident about face-to-face interactions preferred to use the internet to compensate interpersonal communication. Information seeking and entertainment motives predicted e-mail use; and economic security and information seeking were related to web browsing. In a similar vein, Cho and colleagues (2003) related people’s socioeconomic status with certain motivations — such as connection and learning— and internet use. Self-efficacy: Developed by Bandura (1986), self-efficacy is a form of self-evaluation that influences people’s decisions on what they can do with a given skill. In the case of internet, self-efficacy is the individuals’ beliefs in their capacity to perform certain internet actions in order to produce a given goal (Eastin & LaRose, 2000). Eastin and LaRose (2000) developed a scale to measure internet self-efficacy. They found that this psychological construct influences the amount of internet usage, as well as the perceived likelihood of developing relationship (social outcome) and finding information over the web (informational outcome). Interestingly, they found that up to two years of internet experience was necessary to develop enough self-efficacy. Using this scale, subsequent studies have found that internet self-efficacy is the stronger predictor of frequency of internet usage among adolescents (13-18 years), especially among male teens (Broos & Roe, 2006). Livingstone and Helsper (2007) also found that self-efficacy is a significant predictor of the number of activities performed by children and adolescents (9-19 years) in the web, although it was weaker compared to age, gender, and skills. Anxiety/stress: According to Bandura (1977), the amount of anxiety and stress negatively affects the levels of efficacy, and consequently, the successful performance of a task. Research has shown that one important factor explaining the digital divide is computer anxiety, especially in the case of the gender. Girls experience greater levels of anxiety compared to boys (for a review of the literature, see Cooper, 2006). Regarding internet use, having problems to get connected or having the computer freeze up can raise the level of stress or anxiety, which may affect the levels of self-confidence and, eventually, the performance in the web (Eastin & LaRose, 2000). Eastin and LaRose (2000) found that stress was negatively related to self-efficacy, but it was not related to internet use. Jackson and colleagues (2001) revealed that computer anxiety was higher among African Americans college students, and negatively correlated with web use. Locus of control: Developed by Rotter (1966), this psychological mechanism refers to how people perceive and expect certain outcomes depending on how much they control them. If they expect that outcomes in their life are under their control they have internal locus of control; if they think that the events are controlled by other factors (e.g., luck or fate), they have external locus of control. People with predominantly internal locus of control believe that outcomes are dependent on the effort they put in them, therefore, they tend to commit to difficult tasks while “externals” tend to avoid difficult situations. Studies that have applied this psychological construct to predict internet use have found that people who believe that outcomes are due to external factors are less likely to pursue instrumental or goal-directed behaviors (i.e., shopping, online purchases and gathering information) (Hoffman, Novak, & Schlosser, 2003). Although Broos and Roe (2006) did not find that locus of control by itself predicted internet use, their results revealed that when the construct was applied to computers (i.e., expectations of computer control), the mechanism was a powerful predictor of web usage. Attitudes: Although the relationship between attitudes and behavior has been demonstrated (Eagly & Chaiken, 1993), little research has tested the association between internet or computer attitudes and web use. One the few studies that explored this relationship found that the positive belief that computers help to increase control in your life mediated the racial divide between African Americans and whites in e-mail and web use (Jackson, Ervin, Gardner, & Schmitt, 2001). Another study revealed that negative attitudes toward technology prevented minorities from accessing the web (Rojas, Straubhaar, Roychowdhury, & Okur, 2004). Social Resources and Internet Uses Besides psychological mechanisms that help or prevent internet usage, social resources also emerge as relevant predictors. Social resources refer to social contacts. In the case of information technologies such as the internet, they consist on access to people in one’s social setting that possess these technologies, use them, and provide advice and support (de Haan, 2004). It has been found, for example, that ethnicity remains as factor that explains the digital divide even after controlling for income and education (Hoffman & Novak, 1998). Why increasing education and income do not solve the problem entirely? There is evidence of cultural or social factors that are more deeply rooted (Newhagen & Bucy, 2004). For example, using qualitative analyses, Rojas et al. (2004) found that among Hispanic poor families and teenagers a demotivating habitus was at play: relatives and neighbors reinforced a negative attitude toward technology, especially among boys (e.g., “keyboarding” was “woman’s work” in front of a “boring” computer). In addition, an ethnography (Kvasny, 2005) conducted in a municipal technology training program targeted to working class minorities found that the discourse was only constructed toward the instrumental, production-oriented use of information technologies as resources for economic advancement, ignoring the communicative, socially-oriented uses of the web. The problem of this rhetoric is that committing to the program but failing to achieve the desired outcome (i.e., employment), may affect individuals’ attitudes toward technologies, their levels of self-efficacy, and, eventually, exacerbate the inequalities. Expertise and the Internet Uses Related to socio-demographics and psychological factors, there is another set of predictors that have demonstrated to be powerful influences of the digital inclusion process. Because they are linked to the opportunities for experience, training, learning, and mastery regarding the web uses, they have been labeled as “expertise.” Within this category, there two subcategories: experience and skills. Experience Number of years and frequency of usage: Years and frequency of internet use have been consistently tested in almost every single study, and have demonstrated necessary predictors of greater and more complex web usage (e.g., Eastin & LaRose, 2000; Gil-Garcia, Helbig, & Ferro, 2006; Hargittai & Hinnant, 2008; Livingstone & Helsper, 2007; Peter & Valkenburg, 2006). For instance, Hargittai and Hinnant (2008) found that individuals who have used the web for fewer years and go online less than once a day are less knowledgeable about it; and Livingstone and Helsper (2007) revealed that children and adolescents who have used the web for longer and more frequently engage in more internet activities. Access locations and autonomy: It has been demonstrated that the more places the people have access to the web —also called ubiquitous internetting (Peter & Valkenburg, 2006)— the greater their internet skills and usage (Hargittai & Hinnant, 2008). Scholars argue that home access is the place that has the greatest impact on digital mastery because it offers the most autonomy for use (de Haan, 2004; Hargittai & Hinnant, 2008; Livingstone & Helsper, 2007). Its crucial impact has been demonstrated by Livingstone and Helsper (2007). The authors found that children and adolescents from lower class who have internet access use it as much as those from higher classes, which suggest that providing home access, may help to close socioeconomic gaps. It is important to note, however, that home access does not have the same influence in reducing age and gender divides. Similarly, de Haan (2006) demonstrated that the presence of computers and internet in the household, especially in children’s own rooms, positively influences their digital skills. Following the argument of the greater the autonomy, the greater the digital mastery, Hargittai and Hinnant (2008) not only found that home usage positively affects internet uses, but also freedom in using internet at work. Skills According to van Dijk (2006), there are three types of skills: operational skills, which are the ability to operate hardware and software; information skills, which refer to the capacity to search, select and process information in a computer; and strategic skills, which are the ability to use the computer and the internet to attain particular goals. One important finding is that people learn more (operational) digital skills through trial and error than through formal training (de Haan, 2004). To date, most of the research has focused on self-reported levels of operational and information skills (van Dijk, 2006). For example, using self-reported abilities, Livingstone and Helsper (2007) revealed that skills have the greatest impact on number of activities performed in the web. The influence was bigger than age. Despite these findings, self-reported skills suffer validity problems (van Dijk, 2006). To overcome these shortcomings, Hargittai (2002, 2005) developed performance tests to rate information skills in a controlled setting, and found that there are significant differences in accomplishing and time needed to complete a task: age is negatively associated with people’s level of online skill, experience with the technology is positively correlated; and gender has little impact on online skills (Hargittai, 2002). The author later found that although men and women do not differ greatly in their skills, females have lower perceived competences, which may affect their online behavior (Hargittai & Shafer, 2006). Based on these controlled tests, Hargittai developed questions to measure people’s knowledge of internet terms (e.g., jpg, frames, preference settings) in surveys. According to her (Hargittai, 2005), these measures are better proxies of actual users’ skills than self-perceived abilities. Using these measures, she found that people with greater skills are more likely to visit web sites that enhance their political, cultural and knowledge capital such as governmental, financial, and health web pages. Results from studies that have explored online skills reveal that although the access gaps are closing in many developed countries, skills gaps are opening (van Dijk, 2006), which is leading to the emergence of the so-called “second-level digital divide.” CONCLUSIONS The literature on the “second-level digital divide” suggests that although internet access gaps may be closing, especially in developed countries, internet uses gaps are emerging. The research that has tracked these inequalities has assertively moved beyond the demographic markers as only predictors of the differences in the digital inclusion process. As Bacy and Newhagen (2004) asserted, “sociodemographic markers, which serve as external indicators, do not specify the psychological processes involved in accessing, apprehending, and making gainful use of online content” (p.18). As a result, this second wave of research has found that digital inequality results from various factors, including demographics (e.g., gender and age), socioeconomic status (i.e., income and education), psychological aspects (e.g., personality, self-efficacy, motivation, attitudes), and social context (i.e., social environment and contacts). Despite these advancements, the literature on the digital divide lacks a systematic theoretical foundation, which leads to conceptual confusions (Peter & Valkenburg, 2006; van Dijk, 2006). Studies draw from different theoretical approaches to propose relationships between variables. For example, studies combine Bandura’s concept of self-efficacy, Rotter’s concept of locus of control, and uses and gratifications’ approach on motivation to predict web usage. Although this approach may be helpful to predict the impact of psychological factors on the differentiated internet uses, it does not provide guidance to organize the different layers of influence, contribute to model building and, eventually, indicate to policy-makers and schools a starting point to tackle the problem. Future research should offer a systematic theoretical approach to test the influence of psychological factors on differentiated internet uses. REFERENCES Appiah, O. (2003). Americans online: Differences in surfing and evaluating race-targeted web sites by black and white users. Journal of Broadcasting & Electronic Media, 47(4), 537-555. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Bonfadelli, H. (2002). The internet and knowledge gaps: A theoretical and empirical investigation. European Journal of Communication, 17(1), 65-84. Broos, A., & Roe, K. (2006). The digital divide in the playstation generation: Self-efficacy, locus of control and ICT adoption among adolescents. Poetics, 34(4-5), 306-317. Chen, H.-T. (2007). Sharing, connection, and creation in the web 2.0 era: Profiling the adopters of video-sharing and social-networking sites. Paper presented at the Association for Education in Journalism and Mass Communication, Washington D.C. Cho, J., Gil de Zuniga, H., Rojas, H., & Shah, D. V. (2003). Beyond access: The digital divide and internet uses and gratifications IT & Society, 1(4), 46-72. Cooper, J. (2006). The digital divide: The special case of gender. Journal of Computer Assisted Learning, 22, 320-334. Cotten, S. R., & Jelenewicz, S. M. (2006). A disappearing digital divide among college students? Peeling away the layers of the digital divide. Social Science Computer Review, 24(4), 497-506. de Haan, J. (2004). A multifaceted dynamic model of the digital divide. IT & Society, 1(7), 66-88. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Eagly, A. J., & Chaiken, S. (1993). The psychology of attitudes. Orlando, Fl: Hartcourt Brace Jovanovich. Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide [Electronic Version]. Journal of Computer-Mediated Communication, 6. Retrieved September 28, 2008 from http://jcmc.indiana.edu/vol6/issue1/eastin.html. Eighmey, J., & McCord, L. (1998). Adding value in the information age: Uses and gratifications of sites on the world wide web. Journal of Business Research, 51, 187-194. Fallows, D. (2005). How men and women use the internet: Pew Internet & American Life Project. Fox, S., & Madden, M. (2005). Generations online: Pew Internet & American Life Project. Gil-Garcia, J. R., Helbig, N. C., & Ferro, E. (2006). Is it only about internet access? An empirical test of a multi-dimensional digital divide. Electronic Government, Proceedings, 4084, 139-149. Guadagno, R. E., Okdie, B. M., & Eno, C. A. (2008). Who blogs? Personality predictors of blogging. Computers in Human Behavior, 24, 1993-2004. Hamburger, Y. A., & Ben-Artzi, E. (2000). The relationship between extraversion and neuroticism and the different uses of the internet. Computers in Human Behavior, 16, 441-449. Hargittai, E. (2002). Second-level digital divide: Differences in people's online skills [Electronic Version]. First Monday, 7. Retrieved October 10, 2008 from http://www.firstmonday.org/Issues/issue7_4/hargittai/. Hargittai, E. (2005). Survey measures of web-oriented digital literacy. Social Science Computer Review, 23, 371-379. Hargittai, E., & Hinnant, A. (2008). Digital inequality: Differences in young adults' use of the internet. Communication Research, 35(5), 602-621. Hargittai, E., & Shafer, S. (2006). Differences in actual and perceived online skills: The role of gender. Social Science Quarterly, 87(2), 432-448. Hargittai, E., & Walejko, G. (2008). The participation divide: Content creation and sharing in the digital age. Information, Communication & Society, 11(2), 239-256. Hills, P., & Argyle, M. (2003). Uses of the internet and their relationships with individual differences in personality. Computers in Human Behavior, 19(1), 59-70. Hoffman, D. L., & Novak, T. P. (1998). Bridging the racial divide on the internet. Science, 280, 390-391. Hoffman, D. L., Novak, T. P., & Schlosser, A. E. (2003). Locus of control, web use, and consumer attitudes toward internet regulation. Journal of Public Policy and Marketing, 22, 41-57. Howard, P. N., Rainie, L., & Jones, S. (2001). Days and nights on the internet: The impact of diffusing technology. American behavioral scientist, 45(3), 383-404. Jackson, L. A., Ervin, K. S., Gardner, P. D., & Schmitt, N. (2001). The racial digital divide: Motivational, affective, and cognitive correlates of internet use. Journal of Applied Social Psychology, 31(10), 2019-2046. Katz, J., & Aspden, P. (1997). Motivations for and barriers to internet usage: Results of a national public opinion survey. Internet research: Electronic networking applications and policy, 7(3). Ko, H. (2002). A structural equation model of the uses and gratifications theory: Ritualized and instrumental internet usage. Paper presented at the Association for Education in Journalism and Mass Communication. Korgaonkar, P., & Wolin, L. (1999). A multivariate analysis of web usage. Journal of Advertising Research, 39, 53-68. Kvasny, L. (2005). The role of habitus in shaping discourses about the digital divide. Journal of Computer-Mediated Communication, 10(2). LaRose, R., Gregg, J. L., Strover, S., Straubhaar, J., & Carpenter, S. (2007). Closing the rural broadband gap: Promoting adoption of the internet in rural America. Telecommunications Policy, 31(6/7), 359-373. Lenhart, A., Madden, M., Rankin Mcgill, A., & Smith, A. (2007). Teens and social media: The use of social media gains a greater foothold in teen life as they embrace the conversational nature of interactive online media. Retrieved November 22, 2008, from http://www.pewinternet.org/pdfs/PIP_Teens_Social_Media_Final.pdf Livingstone, S., & Helsper, E. (2007). Gradations in digital inclusion: Children, young people and the digital divide. New Media and Society 9(4), 671-696. Losh, S., Carol. (2004). Gender, educational, and occupational digital gaps. Social Science Computer Review, 22(2), 152-166. Madden, M. (2003). America's online pursuits. Washington, D.C. : Pew Internet & American Life Project. Madden, M., & Rainie, L. (2003). America's online pursuits: The changing picture of who's online and what they do: Pew Internet & American Life Project. Meraz, S. (2008). Women and technology: How socialization created a gender gap. In P. M. Poindexter, S. Meraz & A. Schmitz-Weiss (Eds.), Women, men and news: Divided and disconnected in the news media landscape (pp. 99-116). New York: Routledge. Merton, R., K. (1968). The matthew effect in science. Science, 159, 56-63. Miserandino, M. (1996). Children who do well in school: Individual differences in perceived competence and autonomy in above-average children. Journal of Educational Psychology, 88(2), 203-214. Negroponte, N. (1995). Being digital. London: Hodder & Stoughton. Newhagen, J. N., & Bucy, E. P. (2004). Routes to media access In J. N. Newhagen & E. P. Bucy (Eds.), Media access: Social and psychological dimensions of new technology use. Mahwah, NJ: Lawrence Erlbaum Associates. one laptop per child. (n.s.). One laptop per child. Retrieved November 10 2008, from http://laptop.org/en/ Papacharissi, Z., & Rubin, A. M. (2000). Predictors of internet use. Journal of Broadcasting & Electronic Media, 44(2), 175. Pelletier, L. G., Tuson, K. M., & Haddad, N. K. (1997). Client motivation for therapy scale: A measure of intrinsic motivation, extrinsic motivation, and amotivation for therapy. Journal of Personality Assessment, 68(2), 414-435. Peter, J., & Valkenburg, P. M. (2006). Adolescents' internet use: Testing the "Disappearing digital divide" versus the "Emerging digital differentiation" approach. Poetics, 34(4-5), 293-305. Prior, M. (2007). Post-broadcast democracy: How media choice increases inequality in political involvement and polarizes elections. Cambridge, NY: Cambridge University Press. Rojas, V., Straubhaar, J., Roychowdhury, D., & Okur, O. (2004). Communities, cultural capital and the digital divide. In E. Bucy & J. N. Newhagen (Eds.), Media access: Social and psychological dimensions of new technology use. Mahwah, NJ: Lawrence Erlbaum Associates. Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs, 80. Ryan, R. M., & Deci, E. L. (2000a). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54-67. Ryan, R. M., & Deci, E. L. (2000b). Self-determination theory and the facilitation of intrinsic motivation, social development and well-being. American Psychologist, 55(1), 58-68. Sun, S., Rubin, A. M., & Haridakis, P. M. (2008). The role of motivation and media involvement in explaining internet dependency. Journal of Broadcasting & Electronic Media, 52(3), 408-431. Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. Public Opinion Quarterly, 34(2), 159-170. Tremayne, M. (2008). Manipulating interactivity with thematically hyperlinked news texts: A media learning experiment. New Media & Society, 10(5), 703-727. Van Dijk, J. (1999). The network society: Social aspects of new media. London: Sage. van Dijk, J. (2004). Divides in succession: Possession, skills, and use of new media for societal participation. In J. N. Newhagen & E. Bucy (Eds.), Media access: Social and psychological dimensions of new technology use (pp. 233-254). Mahwah, NJ: Lawrence Erlbaum Associates. Van Dijk, J. (2005). The deepening divide inequality in the information society. Thousand Oaks: Sage. van Dijk, J. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4-5), 221-235. Zuckerman, H. (1977). Scientific elite: Nobel laureates in the United States. New York: The Free Press.  Figure 1: Self-determination continuum Source: Based on Deci and Ryan (2000a) Figure 2: A Hierarchical Model to Predict the Web Usage PAGE 2 Amotivation Extrinsic motivation ntrinsic motivation External Internal Lack of interest Peer pressure Self-endorsement Interest/enjoyment Personal characteristics -Age -Gender -Race/ethnicity -Education -Income -Personality -Perceived competence -Autonomy -Usefulness Motivation Skills Internet use