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The 11th Public Management Research Conference

June 2–4, 2011

Maxwell School of Syracuse University

TOWARD THE NEW PHASE OF E-GOVERNMENT: AN EMPIRICAL STUDY ON

CITIZENS’ ATTITUDE ABOUT OPEN GOVERNMENT AND GOVERNMENT 2.0

Taewoo Nam

Rockefeller College of Public Affairs and Policy

University at Albany, State University of New York

tnam@ctg.albany.edu

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Abstract

This paper sees Open Government and Government 2.0 as a new goal and tool of e-

government in the United States. By conducting the structural equation model analysis on the

data from the Pew Internet and American Life Project’s national survey (2009 Government

Online), the study found what influences American citizens’ attitude about Open Government

and Government 2.0. The structural equation model estimation decomposes the causal

relationships among multiple variables into direct and indirect effects. The statistical analysis

suggests several noteworthy findings. Frequent users of e-government service are more likely to

have positive attitudes toward Open Government and Government 2.0. Citizens’ perceived

values of e-government affect their attitudes about the new goal and tool of e-government.

Citizens’ trust in government leads to a positive attitude concerning the new version of e-

government. Conventional determinants of the digital divide, socio-demographic conditions,

have indirect effects on citizens’ attitudes through e-government use.

Key Words

E-government; Open government; Government 2.0

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TOWARD THE NEW PHASE OF E-GOVERNMENT: AN EMPIRICAL STUDY ON

CITIZENS’ ATTITUDE ABOUT OPEN GOVERNMENT AND GOVERNMENT 2.0

Something New for E-Government

Since taking office in January 2009, the Obama Administration has envisioned a new

direction for the U.S. government: Open Government. Labeling this term as ―new‖ may be

misleading because citizens have long felt the need for Open Government. In addition, for as

long as governments have existed, there have been efforts to create Open Government in a range

of contexts. This paper, nevertheless, addresses something different from ―government as usual‖

(Golembiewski & Gabris, 1995; Holzer & Halachmi, 1996). Today’s government is exposed to

new opportunities, enabled and facilitated by Information and Communication Technologies

(ICTs), to accommodate citizens’ values: e.g., accessibility to information and services,

efficiency and effectiveness of service delivery, and transparency and accountability in operation

and administration (Lathrop & Ruma, 2010).

Two buzzwords currently infiltrating the public sector are Open Government and Government

2.0. On the trajectory of e-government development, Open Government and Government 2.0

seem to be, respectively, the new ends and new means of e-government. This is true not only in

the U.S., but also in other advanced industrial democracies, as well as certain developing

countries. The platform and standard for technology-enabled government are moving from e-

government (Government 1.0) to Open Government and Government 2.0 (Parycek & Sachs,

2009).

This study spotlights the transition of e-government into new modes in terms of goals and

tools. The new aspects of e-government are not just for government but for the public as

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customers and users. The focus of this paper is on citizens’ attitudes toward Open Government

and Government 2.0. Given this research focus, the following research question arises: ―What

influences citizens’ attitudes about Open Government and Government 2.0?‖. Previous research

found that citizens’ evaluation on the performance of new initiatives may vary and change with

their adoption of ICTs, usage of e-government services, trust in government, perceived value of

the role of e-government, and personal socio-demographic backgrounds. This study considers

those factors as potential determinants for attitudes toward the new aspects of e-government.

The paper will be structured into five sections, including the foregoing introduction. Drawing

on the extant literature, the paper explores both new (i.e., Open Government, Government 2.0)

and old (i.e., citizens’ attitudes and perceptions of e-government) themes culminating in current

discussions of e-government research, and then establishes a research model and several

hypotheses. After describing details of the data (2009 Government Online survey conducted by

the Pew Internet and American Life Project) and measurements, the paper presents results of the

structural equation model analysis and tests hypotheses. Finally, the last section addresses policy

implications and concluding remarks.

New Issues and Old Issues of E-government Research

New Goal: Open Government

As Open Government has been historically used in various contexts including freedom of

information, anti-corruption, and transparency (Birkinshaw, 1997; Dawes, 2010; Dawes &

Helbig, 2010; Parks, 1957; Rose-Ackerman, 2008), the concept, per se, cannot be considered

novel or recent. The U.S. government’s strong initiative, notwithstanding, is making Open

Government its new priority objective. On January 21, 2009, his very first day in the White

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House, President Barack Obama signed the Memorandum on Transparency and Open

Government, ushering in a new era of open and accountable government meant to bridge the gap

between the American people and their government (Executive Office of the President, 2009a).

The Obama Administration seeks to create and institutionalize a culture of Open Government,

requiring that federal agencies’ Open Government plans address transparency, participation, and

collaboration, describe a flagship initiative, and offer various opportunities for public and agency

engagement (Executive Office of the President, 2009b). This study follows that practical

conceptualization of Open Government as an explicitly addressed governmental goal.

Through greater openness and new technologies, the Obama Administration hopes to

empower the public to influence the decisions that affect their lives (McDermott, 2010). The role

of ICTs is central and fundamental to opening government, though technology cannot account

for all recent changes in government (Dawes, 2008). The Open Government Initiative upholds

transparency, participation, and governance (through collaboration), which are the core values of

e-government. Changing technologies (social networking, visualization, and virtualization) may

offer the new means for electronically-mediated governance (Dawes, 2009). Now, with the

continual advancement of e-government, public values stated in the Open Government Directive

may become relatively more specific and tangible as goals, compared to earlier years of e-

government when researchers (Moon, 2002; Reddick, 2004b; West, 2004) reported no more than

rhetorical performance of e-government—the gap between the actual status of e-government

(limited utilization of e-government chiefly for cataloguing information on governmental

webpages) and the envisioned elevated and functioning stage (harnessing participatory,

democratic and communicative potential of e-government).

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New Tool: Government 2.0

A variety of new technological instruments are available for the development of e-

government. The Obama Administration prioritizes the extensive technological support for Open

Government initiatives (Executive Office of the President, 2009b). Especially Web 2.0 is

considered a new tool for those government initiatives.

Contemporaries are seeing the ubiquitous, prevailing fashion of Web 2.0, which is the second

generation of Web access and use, characterized as participatory, pervasive and integrated

(Mintz, 2008). The second generation Web technologies have the potential to change the way

government delivers services and its relationship with the public. A suite of popular Web 2.0

technologies, such as social networking (Facebook, MySpace), wikis, blogs, micro blogs

(Twitter), mash-up, and multimedia sharing (YouTube, Flickr) can promote open and user-driven

governance (Bertot et al., 2010a, b, c; Millard, 2009).

Government 2.0—the government’s merger with Web 2.0 (Johannessen, 2010)—is a new

notion for describing the current use of Web 2.0 technologies to socialize government services,

processes, and data (DiMaio, 2009). The government’s use of collaborative technologies is at the

heart of Web 2.0. It permits a two-way interaction between government and citizens via online

comments, live chats, and message threads.

There are a variety of expectations on the performance and functions of Government 2.0.

Government 2.0 may facilitate achievement of e-government goals for efficiency, effectiveness,

and democracy (Eggers, 2005; DiMaio, 2009). It may heighten the public’s awareness of policy

and their ability to provide feedback on policymaking (Anttiroiko, 2010; Cho & Hwang, 2010;

DiMaio, 2009; Osimo, 2009). It can be a promising tool for transformation and innovation in

government (Eggers, 2005; Ferro & Molinary, 2009; Yong & Koon, 2005). It can also lead to an

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enhanced level of transparency and anti-corruption in the public sector (Bertot et al., 2010a, b, c).

Those positive expectations are partially or substantially realized in some areas, but remain

illusive in others. Despite some hopeful expectations for Government 2.0, its status is somewhat

controversial. While DiMaio (2009) does not view Government 2.0 as a new kind of government,

but rather, as a means to an end, Tapscott et al. (2008) lauded Government 2.0 as the next

generation of e-government.

Several academics have raised concerns about the extent to which Government 2.0 is utilized.

The performance of Government 2.0 is still not so much fact or reality as fiction or hype (Mintz,

2008; Osimo, 2009). Millard (2009) sees current governmental adoption of Web 2.0 as

something between Government 1.0 and Government 2.0. Using the metaphor of Government

1.5, he compares his evaluation of governmental utilization of Web-based technologies to half-

full (positive expectation of current 1.5 status) and half-empty (negative expectation of current

1.5 status) glasses. The earlier recognition that the reality of e-government lags behind its

rhetoric (Moon, 2002) seems to recur with the rapid mushrooming of Government 2.0.

There are several reasons for the rhetoric-reality gap. The public sector is slow in utilizing

Web 2.0 due to privacy, security, and information policy, compared to businesses that are able

and enthusiastic to employ Web 2.0. Placing policies of Open Government into practice requires

a cultural shift as well as the operationalization of new policies yet undefined. Bertot et al.

(2009), recognizing the policy-technology gap, claim that the Obama Administration is now

seeking a Government 2.0 administration, while still residing in a Government 1.0 environment.

The fact that excessive enthusiasm for ICTs normally results in failure to develop and use

technology systems in government (Goldfinch, 2007) cannot be ignored, and e-government and

Government 2.0 may be no exception.

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Citizens’ Attitudes about E-government

The attitudes of citizens may be influenced by services which are enabled and advanced by

the employment of new technologies implemented for government workings. Previous empirical

studies have surveyed citizens’ attitudes and/or perceptions of e-government in terms of trust,

satisfaction and values. There has been a gap between public expectation and perceived

governmental performance (Nye et al., 1997; Peters, 2009), which underlines the importance of

actual government performance since the objective—that is, the idea or notion—of performance

only raises citizens’ expectations, and if this objective is not achieved, the previously mentioned

gap widens. The public expectation-perception gap can lead to a decline in the public’s trust of

government, also applying to e-government (Welch et al., 2005).

Individuals’ longstanding perceptions on government may be unyielding, so that while

elaborate technology may positively affect some citizens’ attitudes, these advances may also fail

to influence even a slight change in others. Therefore, Open Government driven by Government

2.0 needs to be evaluated from the viewpoint of citizens. Combined with the continuous

expansion and progress of conventional e-government functions, various new initiatives of e-

government are believed to boost citizens’ positive expectations on government performance by

championing the core values of transparency, public participation, and collaboration.

Despite theoretical importance of the relationship between technology, governmental

workings and citizens’ perception, there is a weakness inherent in empirical research, since the

degree to which citizens recognize and are satisfied with e-government strategies is often not

clearly articulated as a measure of empirical investigation (Welch et al., 2005). The way by

which citizens view their government seems quite abstract in terms of conceptual validity and

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measurability. Considering such a limitation, an array of prior studies have developed and

employed several effective strategies for measuring citizens’ perceptions and attitudes.

Empirical research can be conducted to determine how citizens view and use e-government,

for example, by evaluating perceptions, satisfaction, efficacy, trust, and confidence. Focusing on

these areas, existing research presents or assumes various causal models. Antecedents and

determinants of online political participation matter to the extent that use of e-government affects

citizens’ attitudes (Tolbert & Mossberger, 2003). In particular, attention to socio-demographic

conditions reveals that the impact of e-government varies across segments within the population

(Mossberger et al., 2003, 2008; Niehaves & Becker, 2008; Tolbert & Mossberger, 2003; Welch

et al., 2005; West, 2004). Drawing on the ―perceived usefulness‖ factor of the Technology

Acceptance Model (Davis, 1989), some empirical works found that individuals’ perception on

the usefulness of e-government (perceived use value of e-government) influences their

satisfaction with e-government (Kolsaker & Lee-Kelley, 2008) and consequently, continuous

usage of e-government and attitude about e-government adoption (Carter & Bélanger, 2005;

Wangpipatwong et al., 2008). Perceived risk of e-government use, the flip side of its perceived

usefulness, also affects intention to engage in e-government (Alsaghier et al., 2009). Frequent

use of e-government has a great impact on trust in government, attitudes toward e-government,

and expectation or perception of e-government use (Kolsaker & Lee-Kelley, 2008; Sweeney,

2007; Tolbert & Mossberger, 2003; Morgeson III et al., 2011).

Technological factors also deserve consideration. The disparity in degrees of Web use among

socio-demographic groups captures a digital divide, which fundamentally impedes the

nationwide spread of e-government use. Therefore, including Web use as an explanatory variable

aids in demonstrating the influence of the digital divide on citizens’ attitudes concerning e-

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government. Another technological factor is the adoption of broadband Internet connectivity.

Since e-government services, especially Government 2.0-enabled functions, require a

(moderately) high level of Web connectivity, dial-up Internet users may lag behind high-speed

Internet users who can benefit by using any new features of e-government.

Research Model and Hypotheses

Drawing from the literature review, this study identifies the following factors which influence

citizens’ attitudes toward e-government: e-government usage intensity, perceived value of e-

government, general trust in government, and general use of the Internet. In light of those

primary factors, this study generates a path model illustrated in Figure 1.

[Insert Figure 1 about here]

A rich, accumulative body of empirical research on the digital divide has found the

determining effects of socio-demographic characteristics. Socio-demographic backgrounds in

terms of age, gender, race, the level of education and income (proxy for socioeconomic status or

SES), and residential place may influence the degree of Internet use and e-government use. As

well, the degrees may vary with whether individuals use high-speed Internet (broadband).

Frequent Internet users may become frequent e-government users.

More importantly, various causal relationships among key factors of interest are

interconnected. In this sense, those factors are endogenous, which means that they are

determined within a system. Frequent e-government use can lead to positive perception of e-

government value, general trust in government, and positive attitude about the new goal (Open

Government) and tool (Government 2.0) of e-government. Positive perception of e-government

value may contribute to trust in government and attitude about Government 2.0 and Open

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Government. Those with trust in government may be more positive on what a government does

newly than their counterparts. The hypothetical relationship is thus the causality from trust in

government to attitude about Government 2.0 and Open Government. Trust in government and

attitude about Open Government may be substantially influenced by political affiliation (or

partisanship) as an exogenous factor, because Democrats may be more supportive of the

incumbent administration and its Open Government initiatives. Another causal possibility is

from attitude about Government 2.0 to attitude about Open Government. Positive attitude for

technological tools may lead to positive attitude for national policy enabled by the tools.

This path model has uniqueness, compared to usual linear regression. The nature of causal

relationships can be direct or indirect. The combined effects of both direct and indirect

causalities are of special interest in this study. For example, e-government use may directly

affect attitude about Government 2.0, and e-government value perception may mediates the

causal flow from e-government use to attitude about Government 2.0.

This study does not account for all relationships identified. Instead, corresponding to the

research question addressed in the introduction, the study hypothesizes the causal relationships

between key determinants derived from existing literature and citizens’ attitudes about

Government 2.0 and Open Government.

Hypothesis 1. Citizens’ frequent use of e-government positively influences their attitudes

concerning the new goal (Open Government) and tool (Government 2.0) of e-

government.

Hypothesis 2. Citizens’ perceived value of e-government as beneficial positively influences

their attitudes concerning the new goal (Open Government) and tool

(Government 2.0) of e-government.

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Hypothesis 3. Citizens’ trust in government positively influences their attitudes concerning

the new goal (Open Government) and tool (Government 2.0) of e-government.

Hypothesis 4. Citizens’ attitudes about the new technological tool of e-government

(Government 2.0) positively influence their attitudes about the new goal of e-

government (Open Government).

Hypothesis 5. A set of conventional determinants (socio-demographic conditions) of the

digital divide significantly influence citizens’ attitudes concerning the new

goal (Open Government) and tool (Government 2.0) of e-government.

Measurements and Empirical Strategy

This study analyzes the publicly-available data (2009 Government Online) from the national

survey conducted by the Pew Internet and American Life Project via telephone interviews during

December 2009 (Data and interview questions are sourced from www.PewInternet.org/Shared-

Content/Data-Sets/2009/December-2009--Government-Online.aspx). By keeping only responses

pertinent to this analysis, the dataset (N=927), used in the study, was extracted from the original

random-sampled dataset (N=2,258). All respondents are Internet users, but the frequency of

Internet use varies among them.

[Insert Table 1 about here]

Table 1 exhibits the demographic distribution of the sample. When age is categorized into

four cohorts in terms of birth year—following generational divisions by Zukin et al. (2006)—

Baby Boomers take the largest proportion (40%). Education and household income fall into five

strata (and also valued in five-point ordinal scale). The dataset also includes residential contexts

to further classify respondents: urban, suburban, and rural area. When the category of high-speed

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Internet users includes all other ways of networking faster than dial-up connection, eighty-nine

percent of the respondents adopt high-speed connection to the Internet (e.g., broadband adoption:

DSL, FiOS, or Wi-Fi). Self-identified partisanship is quite evenly distributed in the sample, but

the proportion of Republicans is somewhat smaller than that of Democrats and independents.

Measurements

Table 2 presents the descriptive statistics of all variables employed. While those personal

background characteristics are exogenous variables, a set of endogenous variables include

Internet use intensity (frequency of Internet use), use of e-government (transactions, information),

perceived value of e-government use, trust in government, attitude about Government 2.0, and

attitude about Open Government. Portrayed in Figure 1, an ultimate outcome variable is citizens’

attitudes about Open Government. Details of these measures are as follows.

[Insert Table 2 about here]

Internet use intensity Internet use intensity is measured as the frequency of Internet use

according to seven ordinal points: 1) Never (5%), 2) Less often (4%), 3) Every few weeks (4%),

4) 1–2 days a week (13%), 5) 3–5 days a week (15%), 6) About once a day (22%), and 7)

Several times a day (37%).

Attitude about Open Government This variable is measured in terms of the perception of

openness and accessibility of government. The original question is: ―Would you say government

is now more open and accessible, less open and accessible, or about the same as it was two years

ago?‖ The proportional results of three response options are: 1) Less open and accessible (16%),

2) About the same (46%), and 3) More open and accessible (38%).

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Attitude about Government 2.0 Two variables are germane to citizens’ attitudes about

Government 2.0 tools, such as blogs and social networking sites (e.g., Facebook, MySpace, and

Twitter). Those variables measured on the Likert scale are responses to two statements:

―[Government 2.0] makes government accessible‖ and ―[Government 2.0] helps keep people

informed.‖ The proportional distribution of responses is: 1) Strongly disagree (10% for making

government accessible, 9% for keeping people informed), 2) Somewhat disagree (10% and 7%,

respectively), 3) Neutral (1%, 1%), 4) Somewhat agree (45%, 48%), and 5) Strongly agree (34%,

35%). The two variables are combined as a composite by principal component factor scoring

(eigen value=1.880, factor loading=0.889, uniqueness=0.199).

E-government use This variable is made by extracting a common factor (principal component

factor analysis) from the two composite measures: use of information service and use of

transactional service (eigen value=1.869, factor loading=0.886, uniqueness=0.215). The measure

for citizens’ use of informational service is the summation of binary (1 for Yes or 0 for No)

responses (Cronbach’s α=0.75). The data provides ten items related to information acquisition

through e-government: ―Information about a public policy or issue‖ (49% for Yes), ―Advice or

information about a health or safety issue‖ (26%), ―Recreational or tourist information‖ (33%),

―Official government documents or statistics‖ (37%), ―Information about benefits‖ (21%),

―Information about how to apply for a government job‖ (16%), ―Government data on data.gov,

recovery.gov or usaspending.gov‖ (17%), ―Information on who contributes to the campaigns of

elected officials‖ (14%), ―Text of any legislation‖ (24%), and ―How money from the recent

federal government stimulus package is being spent‖ (24%). The measure for citizens’ use of

transactional service is also an additive index of aggregating five binary variables (Cronbach’s

α=0.71). The collapsed items are: ―Renewing a driver’s license or auto registration‖ (32%),

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―Applying for a fishing, hunting or other recreational license‖ (11%), ―Paying a fine such as a

parking ticket‖ (12%), ―Downloading government forms‖ (44%), and ―Looking up what services

a government agency provides‖ (47%).

Perception of e-government value Respondents’ perceptions of two statements—―A

government agency provides general information to the public on its website‖ and ―A

government agency allows people to complete tasks on the website, such as submitting

applications or renewing licenses‖—are measured on a four-point scale: 1) Not important at all

(74% for value on information, 70% for value on transaction), 2) Not too important (20% and

22%, respectively), 3) Somewhat important (4%, 4%), and 4) Very important (2%, 4%).

Principal component factor analysis creates a composite of the two ordinal variables (eigen

value=1.588, factor loading=0.812, uniqueness=0.324).

Trust in government Citizens’ trust in federal, state and local government is measured on a

four-point scale. The level of trust in government (an answer to the question ―How much of the

time can you trust?‖) is: 1) Never (17% for federal, 13% for state, and 11% for local), 2) Some of

the time (55%, 51%, and 45%, respectively), 3) Most of the time (25%, 32%, and 38%), and 4)

Just about always (3%, 4%, and 6%). While other composites are produced by principal

component factor scoring, the composite of trust in government is created by simply summing

raw scores. The more parsimonious measure that estimates less parameters (based on Tau-

equivalent assumption) is preferred over a more complicated measure or factored score (based on

congeneric assumption), given statistically little difference between both measures in terms of

the likelihood ratio test (χ2

Tau-equivalent – χ2

Congeneric = 4.23, where the degree of freedom as the

difference in the number of parameters estimated = 2). The simple summation of the four-battery

responses represents the general level of citizens’ trust in government (Cronbach’s α=0.77).

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Method

This study suggests a structural equation model to examine the complicated relationships

among multiple composite indices and tests a model fit of the path analysis by LISREL 8.8

software package. Constructing the structural model is based on maximum likelihood estimation.

An advantage from the path analysis is the decomposability of the causal effect into direct and

indirect one (Kelloway, 1998). Total effects are produced through path decomposition, where

direct path coefficients and indirect effects mediating through other variable(s) are multiplied

and summed. All causal effects are standardized and thus presented with standardized

coefficients. Standardized total effects offer insight into which variables are relatively more

important in determining an ultimate outcome variable—attitude about Open Government.

Intervening variables can substantially change the interpretation of a direct effect if an indirect

effect is significantly large (Kline, 2005).

Results

This paper views the new direction of e-government through the lens of citizens’ attitudes,

which are shaped by perception of Open Government performance and efficacy of Government

2.0. To test hypotheses, this structural equation model analysis examines causal effects among

multiple factors identified in the literature review section. The results of the analysis are

presented in three tables (correlation matrix, structural equation model estimation, and

decomposed causal effects) and one diagram (illustration of total causal effects).

[Insert Table 3, 4, 5 and Figure 2 about here]

Before discussing the results of hypothesis test, model fit measures merit consideration. Three

types of goodness-of-fit measures are usually employed. Exhibited in Table 4, absolute,

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incremental, and parsimonious model fit statistics all pass rule-of-thumb cutoff criteria. Each

structural equation shows varying levels of squared multiple correlation or R2. Thirty percent of

the variance in scores of attitude about Open Government is significantly explained by other

endogenous variables such as e-government use intensity, e-government perceived value, trust in

government, and attitude about Government 2.0. The decomposition of causal effects (Table 5)

reveals that indirect effects reinforce direct effects in causal relationships that have both effects.

While indirect effects are smaller in magnitude in most relationships, some between-variable

relationships show larger indirect effects over direct ones. Such cases will be explained with

discussion of hypothesis test.

Hypothesis 1. Citizens’ frequent use of e-government positively influences their attitudes

concerning the new goal (Open Government) and tool (Government 2.0) of e-

government.

The direct effect shows different results between attitudes about Government 2.0 and Open

Government. While the frequent use of existing e-government services leads to positive attitude

about the new tool of e-government, citizens’ e-government use itself does not contribute to

attitude about Open Government. However, it is notable that the indirect effect of e-government

use on Government 2.0 and Open Government is larger than the direct effect. The extent to

which e-government use influences attitude about the new modes of e-government is estimated

more indirectly—mediated through e-government value perception and trust in government—

than directly. Decomposed causal effects indicate that current e-government users, especially

heavy users, can ultimately have more positive attitude about the new goal and tool of e-

government, but the degree to which e-government use intensity turns to positive attitude about

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Government 2.0 and Open Government depends on perceived value on e-government and

general trust in government to a substantial extent.

Hypothesis 2. Citizens’ perceived value of e-government as beneficial positively influences

their attitudes concerning the new goal (Open Government) and tool

(Government 2.0) of e-government.

This study employs a composite measure of citizens’ perceived value on e-government—

availability of information on government webpages, and seamless and satisfactory transaction

via e-government. Higher expectations on e-government performance would likely lead to

positive attitude about Open Government and Government 2.0. It is intriguing that decomposed

effects distinguish between attitude about Open Government and attitude about Government 2.0.

E-government value perception has a larger direct effect on attitude about Government 2.0 than

an indirect effect. By contrast, the indirect effect on attitude about Open Government

overwhelms the direct effect. The effect of e-government value perception on attitudes toward

Open Government and Government 2.0 is mediated through trust in government. Trust in

government boosts the direct effect of e-government value perception. The direct effect of e-

government value perception on attitude about Open Government is also mediated through

attitude about Government 2.0. Thus the effect of attitude about government 2.0 on attitude

about Open Government magnifies the total effect of perceived value on attitude about Open

Government.

Hypothesis 3. Citizens’ trust in government positively influences their attitudes concerning

the new goal (Open Government) and tool (Government 2.0) of e-government.

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The level of citizens’ general trust in government positively affects their attitude toward Open

Government and Government 2.0. Those with a high level of general trust in government tend to

maintain that trust, despite changes in public policies and environments (Putnam, 2000). Such

people are likely to have positive attitude about Open Government and Government 2.0. E-

government use does not have a direct effect on trust in government, but perceived usefulness of

e-government use contributes to increasing trust in government. Sketched in Figure 2, the role of

trust in government is not only directly causal to attitudes toward Open Government and

Government 2.0 but also mediating from e-government use and value perception to the attitudes.

Hypothesis 4. Citizens’ attitudes about the new technological tool of e-government

(Government 2.0) positively influence their attitudes about the new goal of e-

government (Open Government).

The path model sets attitude about Government 2.0 not only as an outcome variable but as a

mediating and explanatory variable. The analysis result shows that positive attitude toward new

technologies in e-government is translated to positive attitude toward the new direction of e-

government. The causal effect specified in this hypothesis reflects complexity in the

relationships among variables, capturing the multifaceted effects of e-government use, e-

government value perception and trust in government on attitude toward technological

innovation in the public sector.

Hypothesis 5. A set of conventional determinants (socio-demographic conditions) of the

digital divide significantly influence citizens’ attitudes concerning the new

goal (Open Government) and tool (Government 2.0) of e-government.

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The new modes of e-government are not free from conventional concerns over the digital

divide. The indirect effect of typical determinants of the digital divide (socioeconomic and

demographic characteristics) on attitude about the new version of e-government is not trifle. That

effect is mediated through internet use and e-government use, which shows the digital divide

driven by personal backgrounds. Shown in Table 5, younger people, men, the better-educated,

and the more affluent are likely to have more positive attitude about Government 2.0 and Open

Government. This result inevitably supports extant empirical findings (the heavy leverage of

socio-demographic conditions on e-government use) in much research on e-government use

(Akman et al., 2005; Bélanger & Carter 2006a, b; Becker et al., 2008; Goldfinch et al., 2009;

Mossberger et al., 2003, 2008; Niehaves & Becker, 2008; Reddick, 2004a; Sipior & Ward, 2005;

Tolbert & Mossberger, 2003). The further extension of e-government faces the challenge of a

participation or usage divide in regard to adopting the new modes of e-government.

By the structural equation model analysis, all hypotheses turn out to become valid arguments.

However, the path model includes relationships that have not been hypothesized. Attention to

those relationships is worthwhile as well. Two findings merit consideration. First, access to high-

speed Internet has an indirect effect on citizens’ attitudes toward Open Government and

Government 2.0 through e-government use and perceived usefulness of e-government. Second,

the analysis result reports an obvious attitudinal gap between Republicans and Democrats in self-

identified partisanship. Whereas Open Government retains strong support from individuals who

identify as Democrats, Republicans have significantly negative attitude on the level of openness

in the current administration. Almost half of respondents said that their feelings were ―about the

same‖ concerning governmental openness between the Obama Administration and its

predecessor. Most of those people may come from individuals self-identified as Republicans.

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Positive attitude about Government 2.0 is also only significant for Democrats. Government use

of ICTs for openness is not yet appealing enough to the large population to significantly affect

their attitudes about government.

Findings and Implications

This concluding section presents implications for government practitioners and researchers.

The statistical analysis carried some hope while presenting a challenge in extending to Open

Government beyond the existing e-government. Along with testing hypothetical causal effects,

the analysis also highlighted likely advocates of Open Government and Government 2.0. While

certain users of conventional e-government services perceive potential benefits of e-government

and would translate their positive attitude toward e-government directly to support of Open

Government and Government 2.0, others, who neither use e-government services nor value e-

government use, do not have much interest in the new options of e-government. Practitioners and

academics of e-government need to be aware of the overarching findings and their further

implications.

First, citizens’ perception of potential value on e-government use is as important as actual

experience of e-government in shaping attitude about the new direction of e-government. Those

who value potential benefits of currently available services through e-government are supportive

of Open Government and Government 2.0. A distinction for policy exists between those who

have used e-government services and those who perceive potential value of the services. If a

government should care about its citizens’ attitudes toward their government, then it is crucial to

identify what shapes the citizens’ perceived values of government. Since e-government value

perception is key to creating positive attitude toward Open Government, a government needs to

22

educate citizens (especially, the service-needy, the technology-illiterate, and the

socioeconomically disadvantaged) about the value of e-government (potential benefits) so that

citizens keep aware of usefulness of e-government (Jaeger & Thompson, 2003).

Second, any government should know that trust in standard government (government without

―e-‖) heavily influences citizens’ attitudes about e-government. If governmental efforts to change

citizens’ attitudes hinge only on technological innovation, government would overlook the more

fundamental factor that affects attitudes about government—namely, citizens’ trust in

government itself. The fact that citizens’ trust in government anchors their support for the new

initiative of e-government requires government to consider factors for inspiring general trust, as

well as to improve technological convenience for using e-government. However, trust is not

simply based on a feeling or emotion easily and quickly altered by external stimuli. Trust-

building requires a long-term investment by government because trust is established through

longstanding relationships (van de Walle et al., 2008; Warkentin et al., 2002). While both Open

Government and Government 2.0 are new to most individuals, e-government can also be a still

unfamiliar channel to some people. It is rare that the adoption of new technologies in government

will exert a radical impact on the level of trust in government. To those who have little trust in

government, the new ends and means of e-government may seem illusive. General trust in

government is central to attitude toward the new direction of (e-)government.

Last but not least, a crucial policy concern for Open Government and Government 2.0 is to

identify who advocates are and are not—policy targets. Personal political affiliation heavily

affects attitude toward Open Government. Socioeconomically disadvantaged and thus

technologically marginalized segments of the population are less likely to be positive toward

23

Open Government and Government 2.0. In this sense, the existing gap in adoption and use of e-

government induces the attitudinal difference.

Conclusively, this study offers practical insight into contributors to positive attitude for the

new direction of e-government. For government practitioners, the result of the analysis makes

reasonable sense. However, its underlying message is that their job of making citizens’ attitude

positive or turning their current negative attitude to enthusiasm is not easy, even though Open

Government as a new ends of government in the Obama Administration is a positive objective

for both government and society as a new norm for public values such as accessibility,

transparency, and citizen engagement. To that end, improving the level of e-government

adoption is an initial job for government. Then letting people aware of potential usefulness of e-

government is important for creating their positive attitudes. Facing the continuous decline of

trust in government (Putnam, 2000), enhancing that trust is the most difficult work but still

pivotal to obtaining compliance to new policy direction. Government priorities necessitate

considerable popular support from the generic public. To reach the larger populace of citizens

and secure support from them, government needs to implement strategies to ensure effectiveness

of new technological tools and turn new e-government initiatives from hype and rhetoric into

hope and actual achievement.

24

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Figure 1. The Path Model

Demographics

Broadband

Adoption

Internet Use

E-Government

Use

E-government

Value Perception

Trust in

Government

Attitude about

Government 2.0

Attitude about

Open Government

Political

Affiliation

32

Figure 2. The Focused View of Total Causal Effects between Endogenous Variables

Internet Use

E-Government

Use E-government

Value Perception

Trust in

Government

Attitude about

Government 2.0

Attitude about

Open Government

Republican

Age

Male

Caucasian

Education

Income

Suburban

Urban

Broadband

Democrat

0.177*

0.107*

0.175*

0.055

0.131*

0.130*

0.216*

0.150*

0.177*

0.023

0.175*

-0.163*

0.282*

Note. * < 0.05, This diagram omits the description of the effects by socio-demographic exogenous variables.

33

Table 1. The Sample Distribution

N = 927 Categories Proportion

Generation DotNets (born after 1976) 24%

GenXers (born from 1965 to 1976) 18%

Baby Boomers (born from 1946 to 1964) 39%

Dutifuls (born before 1946) 19%

Gender Male 45%

Female 55%

Race Caucasian 80%

Non-Caucasian 20%

Education High school incomplete 5%

High school graduate 23%

Some college level 29%

Four-year college graduate 25%

Post-graduate education 18%

Annual Household Income $30,000 or less 24%

$30,001 up to $50,000 22%

$50,001 up to $75,000 18%

$75,001 up to $100,000 15%

$100,001 or more 21%

Residential Place Rural residence 21%

Suburban residence 52%

Urban residence 27%

Internet Connection Dial-up connection 11%

High-speed connection 89%

Self-reported Partisanship Republican 26%

Democrat 37%

Independent or others 37%

34

Table 2. The Descriptive Statistics

Variables Mean Std. Dev. Min Max

E-government Use 0.000 1.252 -1.628 4.278

E-government Perceived Value 0.000 1.178 -4.113 0.864

Trust in Government 6.804 1.895 3 12

Attitude about Government 2.0 0.000 1.000 -2.529 1.027

Attitude about Open Government 2.191 0.698 1 3

Age 51.363 18.238 18 95

Female or not 0.560 0.496 0 1

Caucasian or not 0.800 0.400 0 1

Education 4.550 1.669 1 7

Annual Household Income 4.910 2.334 1 9

Suburban Residence 0.513 0.500 0 1

Urban Residence 0.254 0.435 0 1

High-Speed Internet 0.893 0.309 0 1

Republican 0.243 0.429 0 1

Democrat 0.397 0.489 0 1

35

Table 3. The Correlation Matrix

[A] [B] [C] [D] [E] [F] [G] [H] [I] [J] [K] [L] [M] [N] [O]

[A] E-government Use 1.000

[B] E-government Value 0.356 1.000

[C] Trust in Government 0.062 0.146 1.000

[D] Attitude about Gov 2.0 0.176 0.289 0.190 1.000

[E] Attitude about Open Gov 0.120 0.183 0.288 0.266 1.000

[F] Age -0.071 -0.193 -0.091 -0.161 -0.072 1.000

[G] Female or not -0.100 0.044 -0.026 0.043 0.064 -0.033 1.000

[H] Caucasian or not -0.034 -0.037 0.024 -0.025 -0.090 0.165 -0.032 1.000

[I] Education 0.305 0.171 0.121 0.032 0.039 0.132 0.045 0.008 1.000

[J] Income 0.233 0.064 0.019 -0.038 -0.030 0.085 -0.146 0.108 0.376 1.000

[K] Suburban residence 0.033 -0.040 -0.048 -0.015 0.038 -0.002 -0.014 0.040 0.013 0.085 1.000

[L] Urban residence 0.045 0.063 0.010 -0.018 0.006 -0.044 0.025 -0.128 0.026 -0.012 -0.630 1.000

[M] High-speed Internet 0.184 0.081 0.048 0.046 0.000 -0.065 -0.026 0.036 0.120 0.170 0.065 0.050 1.000

[N] Republican -0.007 -0.046 -0.064 -0.035 -0.302 0.027 -0.007 0.191 -0.002 0.113 -0.004 -0.059 0.049 1.000

[O] Democrat 0.041 0.080 0.176 0.089 0.378 0.018 0.091 -0.206 -0.022 -0.075 -0.088 0.121 -0.075 -0.462 1.000

36

Table 4. Structural Equation Model Estimation

N = 927

Independent variables

Internet Use

Intensity

E-government Use

E-government Value

Trust in Government

Attitude about Government 2.0

Attitude about Open

Government

Exogenous variables

Demographics

Age

-0.131*** (0.031)

-0.080* (0.031)

Female

-0.070* (0.031)

-0.086* (0.031)

Caucasian

0.006 (0.031)

-0.034 (0.031)

Education

0.189*** (0.033)

0.233*** (0.033)

Income

0.047 (0.034)

0.099** (0.034)

Suburban residence (vs. Rural residence)

0.080* (0.040)

0.036 (0.039)

Urban residence (vs. Rural residence)

0.049 (0.040)

0.050 (0.039)

Technology adoption

High-speed Internet

0.246*** (0.031)

0.076* (0.032)

Political affiliation

Republican (vs. Independent or others)

0.023

(0.036)

-0.168*** (0.032)

Democrat (vs. Independent or others)

0.175*** (0.036)

0.244*** (0.032)

Endogenous variables

Internet use intensity

0.177*** (0.032)

E-government use

0.356*** (0.031)

0.009 (0.034)

0.081* (0.033)

0.044 (0.031)

E-government value

0.130*** (0.034)

0.239*** (0.034)

0.061* (0.031)

Trust in government

0.150*** (0.031)

0.189*** (0.029)

Attitude about government 2.0

0.177***

(0.030)

R2 (Squared multiple correlation) 0.209 0.258 0.177 0.095 0.150 0.300

Absolute model fit statistics Normed χ2 test = Minimum fit function χ2 (131.90) / degree of freedom (44) = 2.99 (Cutoff is less than 5) Root mean Square Error of Approximation (RMSEA) = 0.05 (Cutoff is equal to or lower than 0.05) P-value for test of close fit (RMSEA < 0.05) = 0.77

Incremental model fit statistics

(Baseline model comparisons)

Bentler-Bonett Normed Fit Index (NFI) = 0.94 (Cutoff is larger than 0.9) Incremental Fit Index (IFI) = 0.96 (Cutoff is larger than 0.9) Comparative Fit Index (CFI) = 0.96 (Cutoff is larger than 0.9)

Parsimonious model fit statistics (Akaike Information Criterion)

Model AIC (311.77), Saturated AIC (272.00) < Independence CAIC (2397.94) Model CAIC (848.31), Saturated AIC (1065.15) < Independence CAIC (2491.25) (Criterion is that model and saturated parameters should be smaller than independence parameters)

Note. *** p < 0.001, ** p < 0.01, * p < 0.05, Standardized direct effects, Standard errors in parentheses

37

Table 5. Standardized Direct, Indirect, and Total Effects

N = 927

Independent variables

Internet Use

Intensity

E-government Use

E-government Value

Trust in Government

Attitude about Government

2.0

Attitude about Open

Government

Exogenous variables

Age a. -0.131*** b. c. -0.131***

a. -0.080* b. -0.023** c. -0.103**

a. b. -0.037** c. -0.037**

a. b. -0.006 c. -0.006

a. b. -0.018** c. -0.018**

a. b. -0.011* c. -0.011*

Female a. -0.070* b. c. -0.070*

a. -0.086* b. -0.012* c. -0.098**

a. b. -0.035** c. -0.035**

a. b. -0.005 c. -0.005

a. b. -0.017** c. -0.017**

a. b. -0.011* c. -0.011*

Caucasian a. 0.006 b. c. 0.006

a. -0.034 b. 0.001 c. -0.033

a. b. -0.012 c. -0.012

a. b. -0.002 c. -0.002

a. b. -0.006 c. -0.006

a. b. -0.004 c. -0.004

Education a. 0.189*** b. c. 0.189***

a. 0.233*** b. 0.033*** c. 0.266***

a. b. 0.095*** c. 0.095***

a. b. 0.015 c. 0.015

a. b. 0.047*** c. 0.047***

a. b. 0.029** c. 0.029**

Income a. 0.047 b. c. 0.047

a. 0.099** b. 0.008 c. 0.107**

a. b. 0.038** c. 0.038**

a. b. 0.006 c. 0.006

a. b. 0.019** c. 0.019**

a. b. 0.012* c. 0.012*

Suburban residence (vs. Rural residence)

a. 0.080* b. c. 0.080*

a. 0.036 b. 0.014 c. 0.050

a. b. 0.018 c. 0.018

a. b. 0.003 c. 0.003

a. b. 0.009 c. 0.009

a. b. 0.005 c. 0.005

Urban residence (vs. Rural residence)

a. 0.049 b. c. 0.049

a. 0.050 b. 0.009 c. 0.059

a. b. 0.021 c. 0.021

a. b. 0.003 c. 0.003

a. b. 0.010 c. 0.010

a. b. 0.006 c. 0.006

High-speed Internet a. 0.246*** b. c. 0.246***

a. 0.076* b. 0.044*** c. 0.120***

a. b. 0.043*** c. 0.043***

a. b. 0.007 c. 0.007

a. b. 0.021** c. 0.021**

a. b. 0.013* c. 0.013*

Republican (vs. Independent or others)

a. 0.023 b. c. 0.023

a. b. 0.003 c. 0.003

a. -0.168*** b. 0.005 c. -0.163***

Democrat (vs. Independent or others)

a. 0.175*** b. c. 0.175***

a. b. 0.026*** c. 0.026***

a. 0.244*** b. 0.038*** c. 0.282***

Endogenous variables

Internet use intensity a. 0.177*** b. c. 0.177***

a. b. 0.063*** c. 0.063***

a. b. 0.010 c. 0.010

a. b. 0.031*** c. 0.031***

a. b. 0.019** c. 0.019**

E-government use

a. 0.356*** b. c. 0.356***

a. 0.009 b. 0.046*** c. 0.055

a. 0.081* b. 0.093*** c. 0.174***

a. 0.044 b. 0.063*** c. 0.107***

E-government value

a. 0.130*** b. c. 0.130***

a. 0.239*** b. 0.019** c. 0.258***

a. 0.061* b. 0.070*** c. 0.131***

Trust in government

a. 0.150*** b. c. 0.150***

a. 0.189*** b. 0.027*** c. 0.216***

Attitude about government 2.0

a. 0.177***

b. c. 0.177***

Note. *** p < 0.001, ** p < 0.01, * p < 0.05, a: Standardized direct effects, b: Standardized indirect effects, c: Standardized total effects