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E-Political Empowerment: Age Effects or Attitudinal Barriers? Lisa E. Thrane Mack C. Shelley, II Stuart W. Shulman Sally R. Beisser Teresa B. Larson Lisa Thrane is a postdoctoral research associate at the Research Institute for Studies in Education at Iowa State University. Her research interests include digital government and social inequality, as well as mental health and criminology. Iowa State University, E005A Lagomarcino Hall, Ames, IA 50011-3190 (E-mail:
[email protected]). Mack Shelley is professor of Statistics and professor of Educational Leadership and Policy Studies, and Director of the Research Institute for Studies in Education, at Iowa State University. He also holds a courtesy appointment in Political Science. His research has addressed American government, public policy, and research methods and statistics. Iowa State University, E005A Lagomarcino Hall, Ames, IA 50011-3190 (E-mail:
[email protected]). Stuart Shulman is assistant professor in Information Sciences and Public Administration at the University of Pittsburgh. He is a Senior Research Associate in the University of Pittsburgh’s Center for Social and Urban Research and in the Universit de Genve, European Union Institute, and Oxford Internet Institute-based E-Democracy Centre and a member of the National Science Foundation-funded Digital Government Organization. His other research looks at the role of electronic rulemaking in citizengovernment interaction. His postal address is: 121 University Place, Suite 600, Pittsburgh, PA 15260 (E-mail:
[email protected]). Sally Beisser is associate professor of Education at Drake University. She publishes and presents in the areas of service learning and gender and technology. She has developed a Service Learning Scholarship for education majors at Iowa State University. Drake University, 124A Education Building, Des Moines, IA 50311 (E-mail:
[email protected]). Teresa Larson is Digital Citizenship service learning lab instructor at Drake University. She holds the M.S. in Curriculum and Instructional Technology from Iowa State University. Her thesis was entitled: “Private Pocketbooks and Public Interest: Forces Affecting Public Library Access to the Proposed National Information Infrastructure, 1993-1994.” Drake University, 30C Harvey Ingham, Des Moines, IA 50311 (E-mail:
[email protected]). This research was made possible with a grant (EIA-0113718) from the National Science Foundation. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation. The authors thank anonymous reviewers for their helpful comments. This manuscript is based on a poster presented at the dg.o 2004 Conference (May 24-26, 2004). Journal of E-Government, Vol. 1(4) 2004 http://www.haworthpress.com/web/JEG 2004 by The Haworth Press, Inc. All rights reserved. Digital Object Identifier: 10.1300/J399v01n04_03
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ABSTRACT. Results of data from a 2003 national computer-assisted telephone interview random sample survey (n = 478 completed surveys were returned) are reported. Adult respondents living in Colorado, Iowa, and Pennsylvania were eligible for participation. Of respondents 55 and older, 49% had a home computer, 46% used e-mail, and 43% used the Internet. For seniors 75 and older, 19% had a home computer, 15% used e-mail, and 19% used the Internet. A fully saturated structural equation model with observed variables was estimated. Our survey results leave little doubt that demographics (age, education), attitudes toward the role of technology (IT advantages, computer apathy), and behavior (use of technology in daily life) play a role in determining patterns of electronic citizenship. Most (74%) of the negative total effect of age on e-politics was indirect, as was nearly half (47%) of the effect of education on e-politics. Since attitudes toward technology are formative barriers to digital citizenship, service-learning may be a key ingredient in challenging technological attitudes and increasing electoral participation of marginalized groups. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: Website: © 2004 by The Haworth Press, Inc. All rights reserved.]
KEYWORDS. Technology, attitude, age, e-political, SEM
Digital inequality is compounded further as the technology changes by leaps and bounds, existing skills become antiquated, and no ready path is available to acquire new skills. The 1999 National Research Council (NRC) Report, Being Fluent with Information Technology, challenges researchers to look beyond a reductive skills-based notion of computer literacy. Due to these rapid shifts in information technology (IT), lifelong learning and adaptation are critical to counteract these trends. The aim of this study is to elucidate the attitudinal measures that might be targeted for interventions to lessen the digital divide and encourage e-political involvement. Becoming a digital citizen is a process influenced by attitudes toward technology that may have the effect of widening the digital gap. Life course differences in orientations to politics and age-related differences in levels of political interest and involvement, up to and including voting in elections at all levels, are well documented (Sigel & Hoskin, 1977; Steckenrider & Cutler, 1989). These trends convey the intriguing
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possibility that the differential spread of (information technology literacy) in the short run may reduce somewhat the generational gap in political participation, and that in the longer run, through generational replacement, the differential spread of ITL may help to produce an electorate that is more engaged and more participatory than those they are supplanting. At a minimum, the uneven spread of ITL implies that those who are technologically literate will engage with politics differently than their less technologically literate contemporaries, and negative or ambivalent attitudes toward technology may prove to be a significant barrier to epolitical empowerment. Through this process of attitudinal change, enhancement of IT skill may empower marginalized citizens to engage more fully in e-democracy. LITERATURE REVIEW Compared to their older counterparts, citizens 30 years of age and younger are more likely to use the Internet as a news source on a weekly basis (Pew Research Center for the People and the Press, 2000). Findings from the Pew Research Center for the People and the Press (2000) showed that age, sex, education, size of locality, and e-information collection were significant predictors of political involvement. Shah and colleagues (Shah, McLeod, & Yoon, 2001) report that seeking Internetbased information increased “Generation X”ers’ levels of civic participation (e.g., index of volunteerism, club attendance, and participation in neighborhood projects) and interpersonal trust in others’ integrity. Lenhart and colleagues (Lenhart et al., 2003) reported that Internet users reported more reliance than did non-users on print and broadcast media, cell phones, and other forms of technology. Computer training for elders has many positive effects, according to Cody and colleagues (Cody, Dunn, Hoppin, & Wendt, 1999), including access to current events and news as well as medical information and improved psychological well-being. In a study of 1,812 respondents (with over-sampling of ethnic minorities) drawn from residential areas near the Los Angeles Civic Center, Loges and Jung (2001) reported that older persons were just as likely as their younger counterparts to view the Internet as an integral part of their lives, but that older people had a limited repertoire of Internet activities. The so-called “gray gap” shows that the likelihood of Internet holdout status is greatest among those over the age of 65. Among the
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foremost reasons for ambivalence about technology were concerns about privacy, irrelevance, cost, and perceptions of the steep learning curve required to use computers and the Internet (Lenhart et al., 2000). Lenhart and colleagues (Lenhart et al., 2000) suggest that fear of privacy invasion may be an important factor in limiting ITL usage by seniors. To this end, Loges and Jung (2001) recommend that Internet training should put seniors at ease by addressing these privacy concerns. Income and education are among the variables that correlate positively with levels of access to and familiarity with computers and the Internet (NTIA, 2002, 2000; UCLA, 2000; Wilhelm, 2000). The purchase of a home computer or general access to computers is largely dependent on income. In addition, education provides knowledge and skills that set the stage for a desire to acquire or sharpen technological skills. Seventy-five percent of individuals with a college degree have a home computer, compared to only 13% of those with a secondary education. Attitudes toward IT and desire to enhance computer skills may intervene and compound the impact of sociodemographic constructs. For instance, in a survey of Midwesterners (Shelley et al., 2004), interest in developing computer skills (e.g., basic computing, e-mail, Internet, and computer training) was positively associated with digital government. Digital government was measured as a factor score of five items, including support for holding e-elections and influencing the government via the Internet, credibility of the Internet as a source of political information, citizens’ effective use of computers, and the relevance of computers to solving the problem of unequal governmental participation (Shelley et al., 2004). It appears that willingness to acquire computer skills and technology attitudes shape Midwesterners’ views of digital government. Some research has concluded that the Internet provides a civic and political forum for citizens and strengthens community-level participation (Alexander, 1999; Brants, Huizenga, & Van Meerten, 1996). In the 1998 National Election Surveys sample of citizens with Internet access, surfing for campaign e-information did not predict voting behavior. It has been suggested that the pathways between IT and political involvement may be explicated by cognitive variables rather than access to a plethora of e-political information (Bimber, 2001). In a 1998 Internetbased survey of adult U.S. citizens, after controlling for demographic correlates (age, sex, education, and race) and civic group participation, engaging in Internet activities increased involvement in traditional poli-
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tics (Weber, Loumakis, & Bergman, 2003). Internet users are more aware than their counterparts of a wide range of political arguments (Horrigan, Garrett, & Resnick, 2004). A 2000 Web survey of 442 politically motivated Internet users showed that, after controlling for demographic characteristics, e-based shopping and finances were significant predictors of obtaining political guidance and advice via the Web. Using the Web as a source of political entertainment was predicted by age, education, e-technical orientation, and use of e-entertainment (e.g., online videos and music). Those who were highly-educated and had fewer years of experience on the Web were more likely to seek e-political information because of its convenience. The linkage between e-political interest and e-business could be a function of the sample characteristics because nearly 90% (88.1%) reported being “Internet literate.” Conducting one’s routine business via the Internet and e-political empowerment could go hand-in-hand because of the underlying level of Internet sophistication (Johnson & Kaye, 2003). In a national random telephone survey conducted between 1995 and 2000, Katz and colleagues (Katz et al., 2001) concluded that Internet participation enhanced everyday political and civic involvement and bolstered offline and online social exchange. Two components of online political participation were investigated. Nearly half (46%) of Internet users had participated in at least one of the following activities: reading discussion boards, viewing political Web sites, or following 1996 election coverage via the Internet. Over one-quarter (28%) had engaged in more participatory on-line political exchanges. Internet users were more likely to use e-mail as a forum to share political views about the campaign or the election, and to contact governmental officials. In addition, Internet users were more likely than nonusers to engage in traditional forms (e.g., read newspapers, attend rallies, and watch TV coverage of the 1996 campaign) of political participation. The authors suggested that reliance on other forms of technology and Internet use were mutually reinforcing. It appears that technology is fluid and melds the dimensions of lived experience (Katz et al., 2001). In a sample of Internet users surveyed during the 2000 presidential campaign, Krueger (2002) blends both active and passive dimensions of e-political engagement (e.g., surfing a candidate’s Website, online communication with a candidate, etc.). Statistically significant predictors of on-line participation were gender, income, interest, internal efficacy, free time, Internet skills, and broadband access. After controlling for other variables, Internet skill was the strongest predictor of e-poli-
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tics. Interestingly, age, education, and traditional civic engagement had no impact on the outcome measure. THEORY & HYPOTHESES Our theoretical framework combines the perspective of changes in political participation that are associated with the unfolding of the life course, based on longstanding research in political science, with diffusion of innovations theory, which traces its intellectual origins to adult education and sociology. With its focus on how individuals’ behavior and attitudes adapt to their changing chronological age and circumstances, life-cycle theory provides a micro-level structure for this framework to address the differential impact and acceptance of social and cultural innovations. A macro-systemic theoretic superstructure is afforded by research that addresses the diffusion of innovations throughout a society; Internet technology certainly fits comfortably within the broader context of how technological innovations spread, how they are resisted or adopted, and the societal implications of the ensuing crosscurrents. Life cycle and role transitions have been associated with varying levels of political engagement. As individuals become vested in simultaneous roles, they become more engaged in the political process (Sigel & Hoskin, 1977). In this sense, the life course is a series of age-graded pathways that channel individuals into and out of specific roles and activities over time and may lead to the adoption of political attitudes that reflect these roles. Thus, roles can shift political interest and involvement as people become more vested in the process (Sigel & Hoskin, 1977). Diffusion of innovations theory aptly applies to the digital divide by exploring demand-side explanations. Rogers’ (2003) model of adoption explains the process by which people implement an innovation. Adoption occurs over time and through a series of stages. First, an individual must learn about a new idea before forming an attitude about it. This model posits that an attitude must be formed before behavior can follow that either supports or rejects the new technology. In Rogers’ persuasion stage, an individual forms attitudes about a particular innovation. One can be persuaded to adopt an innovation if it has obvious advantages and is relevant to one’s life. One’s behavioral intention is manifested in the decision to accept or reject the new idea. Individuals put the innovation to use, and over time it becomes routinized in everyday life
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(Rogers, 2003). This entire process of the adoption and diffusion of innovation is influenced by social relationships and individuals’ knowledge base. However, personal characteristics shape one’s access to innovation and receptivity to exploring technological advances. The blending of these two theories suggests that historical context and life course transitions play critical roles in shaping political thought and contribute to both stability and change in participation across the life course. Hypotheses Having access to new technology and a willingness to adopt it are influenced by personal characteristics (Rogers, 2003). Younger people have grown up with technology and consider it part of their everyday lives. For them, the culmination of technological access to government and adopting adult roles may offer political participation in their own time and at their own pace. Among this group, e-political involvement may serve as an alternative to or may supplement traditional participation. Due to the technological lag across generations (Pew Research Center for the People and the Press, 2000) and educational disparities (NTIA, 2002, 2000; Shelley et al., 2004; UCLA, 2000; Wilhelm, 2000), we predict that older people (Hypothesis 1) and those with less education (Hypothesis 2) will have more negative technological attitudes. With a few exceptions (Shelley et al., 2004; Thrane et al., forthcoming), attitudinal dimensions of IT adoption have not been explored fully in the digital government literature. Consistent with diffusion of innovations theory (Rogers, 2003), we extend prior research by suggesting that attitudes may influence computer skill development and interest in everyday communication and entertainment technologies. Therefore, we hypothesize that a supportive view of technology will decrease computer apathy (Hypothesis 3) and increase the use of other technological devices (Hypothesis 4). Today’s older Americans began using technology in their work lives. As they transition out of the workforce, they may lack the interest and expertise to adapt to technological trends. This may have the net effect of leaving them on the outskirts of e-politics even though they remain engaged politically through voting and community involvement. This suggests that older Americans must redefine their roles as citizens to adapt to the changing face of government. However, given the lack of access to and experience with IT among older cohorts, we hypothesize that they will be more apathetic toward computer training (Hypothesis
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5) and the adoption of everyday technology (Hypothesis 6). Education puts in place knowledge and skills that promote technology adoption (NTIA, 2002); therefore, we predict that the more educated will have a desire to improve their computer competence (Hypothesis 7) and will be early adopters of other forms of technology (Hypothesis 8). We predict that technology use will decrease computer apathy (Hypothesis 9), and suggest that willingness to acquire new computer skills will have a positive direct effect on e-political participation (Hypothesis 10) (Krueger, 2002, Shelley et al., 2004). In addition, it is hypothesized that the adoption of other forms of technology will increase engagement in e-political participation (Hypothesis 11). We postulate that technological attitudes will continue to influence e-political involvement (Hypothesis 12). We suggest that attitudinal and behavioral mechanisms will decrease the path coefficients to non-significance from the demographic constructs to e-political participation (Hypothesis 13), consistent with Shelley and colleagues (2004). METHODS Sample Results of data from a 2003 national computer-assisted telephone interview (CATI) random sample survey (n = 478) are reported. The sample consisted of phone numbers appearing in telephone directory listings, which represented three regions of the country. Adult respondents living in Colorado, Iowa, and Pennsylvania were eligible for participation. These data were weighted by gender and age to correspond on those two demographic traits with the census 2000 national adult population. Women and men were nearly equally represented (52% and 48%, respectively). On average, respondents were 46 years old. Racial and ethnic origins were self-reported as 89% White, 5% Black, 4% Hispanic, 1% American Indian/Alaskan Native, and 1% Asian or Pacific Islander. The overall response rate was 31.4%, ranging from 37.4% in Iowa to 26.7% in Pennsylvania. Measures The following variables were employed in statistical analysis. The response categories for age were 1 = “18-37 years,” 2 = “38-50 years,” 3 = “51-64 years,” and 4 = “65+ years.” Education is treated as a continuous
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variable. Response categories are 1 = “non-completion of high school,” 2 = “high school diploma,” 3 = “trade school,” 4 = “some college,” 5 = “undergraduate degree,” and 6 = “graduate or professional degree.” IT Advantages is a linear composite derived from factor analysis of 3 items–viewing the Internet as a good source of information, e-mail as a good way to contact political officials, and enjoying the use of new technology–which produced fairly robust factor loadings (.76, .72, and .65, respectively). Individual item response categories range from 1 = “strongly disagree” to 4 = “strongly agree.” Higher values of the scale indicate more support for information technology. Computer Apathy is a construct designed to measure level of computer sophistication and interest in improving one’s computer skills. Individual item response categories range from 1 = “very high skill and improvement desired” to 9 = “very low skill and no improvement desired.” For the most part, respondents sought to improve moderate levels of computer training (Mean = 3.20). Higher values indicate greater apathy toward enhancing computer expertise. Use Technology is a count of affirmative responses to five items (VCR, cell phone, camcorder, digital camera, and PDA). On average, respondents had used more than three technological devices. E-Political Participation1 is a linear composite derived from factor analysis of 5 separate items. Using the Internet to get political information (.85), news (.75), and specifics about a political candidate (.67) have the largest factor loadings. Responding to an Internet petition (.58) and using e-communication to contact a public official (.56), which are more proactive and less passive forms of e-political participation, have more modest loadings on this factor. Individual response categories are 0 = no and 1 = yes. Higher scale values indicate greater involvement in e-politics. For this composite, missing values were not replaced. For the e-political participation and computer apathy measures, a square root transformation was used to induce normality. Unless otherwise indicated, all factor scores were obtained by principal components extraction and varimax rotation, with the Anderson-Rubin (Anderson & Rubin, 1956) procedure used to save the resulting factors as uncorrelated standard normal composite variables with mean zero and standard deviation one. Unless otherwise indicated, missing values were replaced with mean substitution. RESULTS Intercorrelations among the variables examined in this study, together with descriptive statistics, are presented in Table 1. The pattern
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JOURNAL OF E-GOVERNMENT TABLE 1. Correlation Matrix (n = 468) 1
1 Age 2 Education
⫺.27**
3 IT advantages (factor score)
⫺.34**
4 Computer apathy
2
3
4
5
-.28**
--
.47** ⫺.39** ⫺.44**
--
⫺.57**
.36**
.37**
⫺.58**
--
6 E-political participation (factor score) ⫺.42**
.41**
.40**
⫺.56**
.51**
5 Use technology
6
--
--
Mean
2.13
3.81
.00
1.93
3.42
1.37
Standard Deviation
1.11
1.53
1.00
.69
1.31
.36
p < .05; ** p < .01
of correlations indicates that the variables of interest are interrelated. A fully saturated structural equation model (estimated with LISREL 8.50 statistical software, using the maximum likelihood procedure) with observed variables (that is, a path model) was estimated (Jöreskog & Sörbom, 1996). By definition, in a saturated model, χ2 = 0.00; df = 0; p = 1.00 (Figure 1). There were 22 cases per parameter; consequently, the estimates meet the usual criteria for being stable and reliable (Bollen, 1989). Direct effects are displayed in Figure 1 by arrows that go directly from a predictor variable on the left to a dependent variable to its right, without passing through any other variable in between. In contrast, indirect effects are relationships between a left-side predictor variable and a right-side dependent variable that are mediated by passing through one or more variables in between. Crossmultiplying the regression coefficients for any combination of paths that connects a predictor variable on the left with a dependent variable on the right, and then summing these results, determines the overall magnitude of indirect effects. The total effect of a predictor variable on a dependent variable is the sum of its direct and indirect effects. There are statistically significant direct effects from age and education to technological attitudes, IT use, and e-political interest. As age declined, respondents reported more support for IT (ß = ⫺.28) and more interest in developing their computer skills (ß = .15). Younger respondents used other forms of technology (ß =⫺.47) and were more involved in e-political participation (ß =⫺.09) than were their older counterparts. Those with lower levels of education were more apathetic
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FIGURE 1. Saturated Model Computer .15** Apathy (3.57) –.09* –0.17** Age –.21** .28** (–2.04) (–4.50) (–5.56) –.35** (–6.36) (–7.77) Advantages –0.27 of IT –.16** .21** –.47** .17** (4.66) –.26 (–11.91) (3.95) (4.19) (–5.82) Education Technology Use .19**
–.29 (–6.11) 0.13** (3.12)
e-Political Participation
.19** (3.93)
(4.87)
* p < .05. ** p < .01.
toward computer training (ß =⫺.17) and saw fewer IT advantages (ß = .21). In addition, the more educated were more likely to use technology (ß = .19) and were more apt to be active civically via the Internet (ß = .17). Those who had a positive attitude toward information technology were interested in improving their computer skills (ß =⫺.21) and made use of everyday technology (ß = .16). Respondents with positive IT attitudes were more engaged in e-politics (ß = .13). Technology use decreased computer apathy (ß = ⫺.35). Direct effects also were found between IT use and both computer apathy and e-citizenship activities; respondents with little interest in additional computer training were less likely to support e-political participation (ß = ⫺.29), while e-citizens employed technology in their lives (ß = .19). The path model displayed in Figure 1 demonstrates how some predictors may have an intervening effect on the outcome measure; some of these indirect effects have an important influence. Total effects are decomposed into direct and indirect effects in Table 2. Nearly twothirds (62%) of the influence of age on computer apathy was indirect. The most prominent mechanism was through technology; because older people relied less on IT they had less interest in updating their computer skills. The indirect negative effect of age on e-politics (-.34) accounted for 74% of the total effect. The greatest impact again was through technology use. Younger people were more technologically savvy, which increased e-involvement. Furthermore, e-citizenship was promoted as
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JOURNAL OF E-GOVERNMENT TABLE 2. Decomposition of Total Effects for Reduced Model
Predictor Variable
Dependent Variable
Total Direct Indirect Standard t-statistic Direct Effect Effect Effect Error Effect as % of Total Effect ⫺.28
⫺.28
⫺6.36** 100%
Age
⇒ IT advantages
Education
⇒ IT advantages
.21
.21
.00
.03
4.66** 100%
Age
⇒ Computer apathy
.39
.15
.24
.03
3.57**
.00
.04
38%
Education
⇒ Computer apathy ⫺.29
⫺.17
⫺.12
.02
⫺4.50**
59%
IT advantages
⇒ Computer apathy ⫺.27
⫺.21
⫺.06
.03
⫺5.56**
78%
Technology use ⇒ Computer apathy ⫺.35
⫺.35
.00
.02
⇒ Technology use
⫺.51
⫺.47
⫺.04
.05
⫺11.91**
92%
Education
⇒ Technology use
.22
.19
.03
.03
4.87**
86%
IT advantages
⇒ Technology use
.16
.16
.00
.05
3.95** 100%
Age
Age
⇒ E-politics
⫺.34
⫺.09
⫺.25
.01
Education
⇒ E-politics
.32
.17
.15
.01
IT advantages
⫺7.77** 100%
⫺2.04*
26%
4.19**
53%
3.12**
57%
⇒ E-politics
.23
.13
.10
.01
Computer apathy ⇒ E-politics
⫺.29
⫺.29
.00
.02
⫺6.11** 100%
Technology use ⇒ E-politics
.29
.19
.10
.01
3.93**
Notes:
66%
Total Effect = Direct Effect + Indirect Effect. Significant at p ⱕ .05 (i.e., t-value ⱖ 1.96); ** significant at p ⱕ .01 (i.e., t-value ⱖ 2.56).
technological sophistication decreased computer apathy. In addition, older persons were more apathetic toward ITL and therefore less likely to be engaged in e-politics. Indirect effects accounted for 41% of the total effect of education on apathy. Positive IT attitudes and IT use were about equally as influential. Education led to more IT use, which in turn decreased apathy. In addition, positive IT attitudes were bolstered by education, linked in particular with increased desire for computer training. Only a bit more than half (53%) of the total effect of education on e-politics is direct. Through multiple pathways, education also had an indirect influence on e-politics. The most dominant indirect path was through education, as it diminished computer apathy, which in turn promoted e-citizenry. Being able to see the benefits of technology had an indirect effect on digital government. IT benefits operated primarily through de-
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creasing computer apathy, whereby it strengthened engagement in epolitics. CONCLUSION Our survey results leave little doubt that demographics (age, education), attitudes toward the role of technology (IT advantages, computer apathy), and behavior (use of technology in daily life) play a role in determining patterns of electronic citizenship. Consistent with our hypotheses, younger individuals (Hypothesis 1) and the more educated (Hypothesis 2) have more positive attitudes toward technology, which in turn decrease computer apathy (Hypothesis 3) and increase the use of other forms of technology (Hypothesis 4). Older individuals were more apathetic about computers (Hypothesis 5) and other technological devices (Hypothesis 6), contrary to the impact of higher levels of education on these outcomes (Hypotheses 7-8). Technological advantages (Hypothesis 12), interest in computer skills (Hypothesis 10), and use of technology (Hypothesis 11) led to higher levels of e-political engagement. Interestingly, technology use decreased computer apathy (Hypothesis 9). Contrary to Hypothesis 13, the demographic variables had a direct and significant impact on e-political participation. However, attitudinal constructs and technological behavior account for nearly 75% of the effects on e-politics, while these variables account for nearly 50% of education’s total effect on e-politics. The complexity of life-cycle effects on e-citizenship is demonstrated dramatically by the finding that most of the effect of age is indirect, mediated heavily by computer apathy, technology use, and IT advantages. Our technologically-driven society opens doors to engage more fully in e-democracy. As the Internet offers engagement in a medium that fits comfortably with mode of life, it may facilitate more meaningful involvement in the political system for all citizens. Technology cross-cuts many domains, with computers and the Internet harnessed for academic achievement, advancement at work, communication with family and friends, and leisure activities such as listening to music or watching DVDs. In turn, people may form more favorable opinions about technology because it improves their quality of life. These attitudinal shifts could lead to adoption of other forms of technology. People who enjoy learning about IT are more inclined to use a digital personal assistant or digital music player. As comfort level increases, the IT literate extend
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their e-repertoire to banking, shopping, and news. Political use of the Internet may be a natural step for those who choose this more contemporary and innovative mode of engagement. While IT should make it easier for all citizens to conduct their routine business with the government, in fact, it appears to be widening the gap between the IT-literate and those without basic navigational skills. Persons standing on the periphery of technology-driven change may not have embraced it as a matter of course because technology may not have punctuated their career trajectory. Fewer IT demands are placed on those who are not in the workforce or are in low-paying service-sector positions, so IT may have less relevance in their day-to-day lives. These citizens may be more likely to hold pessimistic attitudes about IT, which short-circuits their participation and interest in e-politics. As trends continue and technology becomes the glue that connects citizens to the government, the IT-challenged will have one less alternative route through which they can engage with the political system. Since attitudes toward technology are formative barriers to digital citizenship, service-learning interventions may link the mission of higher education explicitly to the “theory and practice of democratic citizenship” (Barber & Battistoni, 1993, p. 235). It could lay the groundwork as citizens, community leaders, students, and researchers work together to empower all citizens to have a voice in the changing landscape of democracy. For instance, a service-learning intervention conducted in Iowa with 158 elderly volunteers found that negative IT attitudes were associated with less interest in e-citizen activities. After completing the training, qualitative analysis suggests that the computer workshop created more positive attitudes and decreased IT fear, and increased seniors’ comfort level with computers. However, due to a limited number of computer sessions and other pragmatic issues, major gains in bridging the digital divide were not achieved (Thrane et al., forthcoming). We argue that empowering citizens to engage democracy is a longterm process that begins with service-learning interventions. Technology must be introduced appropriately, and not simply thrown to people as yet another challenge to be overcome. It is crucial that programs demonstrate the relevance of technology and its advantages in ways that participants will find most useful and meaningful. To reduce digital barriers, computer and Internet access must be made available and should fit as comfortably as possible into citizens’ lifestyles and needs. By and large, receptiveness to technology affects citizens’ willingness to engage government electronically. This study makes clear that technolog-
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ical attitudes are a significant impediment to digital government and may deepen existing social divisions. Service-learning may be a key ingredient in challenging technological attitudes and increasing electoral participation of marginalized groups. Two limitations of this study are its use of cross-sectional data and its sampling technique. Care must be taken in drawing conclusions about the processes at work because the data do not allow one to evaluate these mechanisms over time. The study’s results are based on data collected in three states and may not be generalizable to all regions in the United States. Although the model may fit well and be theoretically defensible, it does not imply causality. NOTE 1. An exploratory factor analysis was conducted to ascertain if passive and active dimensions were two distinct constructs. The resulting analysis did not elucidate a simple factor structure. Therefore, they were collapsed into the e-political participation measure.
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Received: 05/25/2004 Revised: 11/02/2004 Accepted: 11/05/2004
