The Pennsylvania State University
The Graduate School
School of Public Affairs
FACTORS AFFECTING THE INTENTIONS OF VOTERS
TO PARTICIPATE IN INTERNET VOTING SYSTEMS
A Dissertation in
Public Administration
by
David P. Kitlan
2010 David P. Kitlan
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
August 2010
The dissertation of David P. Kitlan was reviewed and approved* by the following:
Jeremy F. Plant
Professor of Public Administration and Public Policy
Dissertation Adviser
Chair of Committee
Rhoda U. Joseph
Assistant Professor of Information Systems
James T. Ziegenfuss, Jr.
Professor of Management and Healthcare Systems
Steven A. Peterson
Director, School of Public Affairs
Professor of Politics and Public Affairs
*Signatures are on file in the Graduate School
iii
ABSTRACT
The purpose of this study is to identify and explore factors that affect the intentions of
potential voters to participate in Internet voting systems in the U. S. at the local, state, and federal
levels. These factors include voter characteristics as well as voter perceptions related to voting,
government, and the use of online technologies. This empirical analysis is based on theories from
technology adoption research and recent e-government models. The study uses constructs from
diffusion of innovation, technology acceptance, technology quality of service, and web trust
theories in a conceptual model to analyze the potential use of future Internet voting systems.
An online survey is used to collect data from several hundred university students and is
supplemented with focus group interviews. Multiple regression techniques are used for data
analysis. Results indicate that higher levels of online experience and user perceptions of
compatibility, relative advantage, trust in technology, and trust in government significantly affect
the intention of potential voters to vote online.
As an exploratory study, this research adds to the foundation of research involving online
voting systems. The study explores perceptions and characteristics from a population of potential
voters within a relatively narrow age range compared to the overall voting population. In spite of
this limitation, the study extends e-voting research by including a variety of college students, and
by considering Internet voting processes at the overall, local, state, and federal levels.
The study contributes to the understanding of factors that influence the intentions of
potential voters to use online voting. This can be useful for policy formulation, design, and
implementation decisions about Internet voting systems as well as to identify ways to increase
voter participation in a deliberative democracy. The study also relates to recent approaches in
public management for improving the ways that government interacts with the citizenry.
iv
TABLE OF CONTENTS
LIST OF FIGURES ................................................................................................................. vii
LIST OF TABLES ................................................................................................................... viii
ACKNOWLEDGEMENTS ..................................................................................................... ix
Chapter 1 Introduction and Background ................................................................................. 1
Public Administration and Governance ........................................................................... 3 Overview of Public Administration ......................................................................... 3 New Public Management and New Public Service .................................................. 5 New Governance and Engaging Citizens ................................................................. 8 Policy Development Stages ...................................................................................... 8
E-Government and Voting Processes ............................................................................... 10 Overview of E-Government ..................................................................................... 10 Democracy and the Importance of Voter Participation ............................................ 15 Initiatives to Increase Voter Participation ................................................................ 17 Voter Registration, Participation, and Behavior ...................................................... 21 Internet Usage Patterns Among Adults .................................................................... 23
E-Voting and Related Technologies ................................................................................ 25
E-Democracy and E-Voting ..................................................................................... 25 Impact of E-voting and Recent Initiatives ................................................................ 27 Benefits and Examples of Internet Voting Implementation ..................................... 29
Chapter 2 Literature Review ................................................................................................... 35
General Theories Related to Technology Adoption ......................................................... 35 Diffusion of Innovations (DoI) - Rogers (1995) ...................................................... 36
Technology Acceptance Models (TAM) - Davis (1989) ......................................... 42
Technology and Quality of Service (TQS) - Dabholkar (1996) ............................... 44
Trust Theory - Carter and Belanger (2005) and Others ........................................... 45
Integrated Theories for E-Government Initiatives ........................................................... 49 E-Government Adoption Model – Gilbert, Balestrini, and Littleboy (2004) ........... 49
E-Government Adoption Model – Carter and Belanger (2005) ............................... 51
Citizen Adoption of Online Voting System - Schaupp and Carter (2005) ............... 52
Chapter 3 Research Hypotheses and Conceptual Model ........................................................ 54
Research Hypotheses ....................................................................................................... 55 Internet Voting Conceptual Model ................................................................................... 58
Model Constructs ..................................................................................................... 59
Chapter 4 Research Design and Methodology ........................................................................ 64
v
Research Design ............................................................................................................... 64 Data Collection ......................................................................................................... 64
Survey Population .................................................................................................... 66
Survey Instrument .................................................................................................... 67
Focus Group Interviews ........................................................................................... 69
Intended Measures ........................................................................................................... 70 Validity and Reliability .................................................................................................... 71
Chapter 5 Data Analysis and Results ...................................................................................... 72
Descriptive Statistics ........................................................................................................ 72 Reliability Analysis .......................................................................................................... 74 Testing of Demographic and Other Characteristics ......................................................... 75 Testing of Model Significance ......................................................................................... 76
Hypothesis Testing ........................................................................................................... 79
H1: Age of Potential Voters vs. Intent to Vote Online ............................................ 80
H2: Internet Experience vs. Intent to Vote Online ................................................... 81
H3: Voting Experience vs. Intent to Vote Online .................................................... 82
H4: Trust of Internet Technology vs. Intent to Vote Online .................................... 82
H5: Trust of Government vs. Intent to Vote Online (Multiple Levels) .................... 83
Results from Focus Group Interviews .............................................................................. 85
Chapter 6 Summary and Conclusions ..................................................................................... 88
Summary of Research Results ......................................................................................... 89 Limitations of Study ......................................................................................................... 91 Suggestions for Future Research ...................................................................................... 92
Research Significance and Contributions ........................................................................ 95
REFERENCES ........................................................................................................................ 101
APPENDIX A Internet Voting Recruitment Script ................................................................ 117
APPENDIX B Annotated Survey Instrument ......................................................................... 118
APPENDIX C Focus Group Questions .................................................................................. 122
APPENDIX D Tables of Descriptive Statistics ...................................................................... 123
APPENDIX E Testing of Demographic and Other Characteristics ........................................ 130
APPENDIX F Listwise Regression Results – Overall Level of Government ........................ 132
APPENDIX G Stepwise Regression Results – Overall Level of Government ....................... 136
APPENDIX H Listwise Regression Results – Local Level of Government........................... 139
vi
APPENDIX I Stepwise Regression Results – Local Level of Government ........................... 143
APPENDIX J Listwise Regression Results – State Level of Government ............................. 146
APPENDIX K Stepwise Regression Results – State Level of Government ........................... 150
APPENDIX L Listwise Regression Results – Federal Level of Government ........................ 153
APPENDIX M Stepwise Regression Results – Federal Level of Government ...................... 157
vii
LIST OF FIGURES
Figure 1.1: Voter Turnout Among 18- to 29-Year-Olds, 1992-2008. .................................... 18
Figure 1.2: Voter Turnout Rates in U.S. Presidential Elections as Percentages of Overall
Voting Age Population (VAP) and Voting Eligible Population (VEP). .......................... 21
Figure 1.3: Relationships in e-Terminology ........................................................................... 26
Figure 2.1: Segmentation of Adoption Model (Rogers, 1995) ............................................... 38
Figure 2.2: Decision Innovation Process (Rogers, 1995). ...................................................... 38
Figure 2.3: Technology Acceptance Model (Davis, 1989). .................................................... 43
Figure 2.4: Technology and Quality of Service Models (Dabholkar, 1996)........................... 45
Figure 2.5: E-Government Adoption Model (Gilbert, Balestrini, & Littleboy, 2004) ............ 50
Figure 2.6: E-Government Adoption Model (Carter & Belanger, 2005) ................................ 51
Figure 2.7: E-Voting Adoption Model (Schaupp & Carter, 2005) ......................................... 53
Figure 3.1: Internet Voting Conceptual Model ....................................................................... 58
viii
LIST OF TABLES
Table 1.1: Interaction of E-Government and Public Administration ...................................... 11
Table 5.1: Reliability Analysis. .............................................................................................. 75
Table 5.2: Testing of Model Significance. .............................................................................. 77
Table 5.3: Hypothesis Testing of H1, H2, H3, and H4. .......................................................... 80
Table 5.4: Hypothesis Testing of H5. ..................................................................................... 84
ix
ACKNOWLEDGEMENTS
This research study involved the support of many contributors. I would like to thank my
dissertation committee Chair, Dr. Jeremy F. Plant, for his outstanding support and direction in
coordinating the efforts of the committee during the various stages of completion, as well as the
other committee members, Dr. Rhoda U. Joseph, Dr. Steven A. Peterson, and Dr. James T.
Ziegenfuss, Jr., who each provided excellent feedback and guidance throughout this project.
I would also like to thank the Pennsylvania State University for this rewarding learning
experience, and the faculty and staff of the School of Public Affairs at Penn State Harrisburg for
their exceptional academic and administrative support. I am thankful to Dr. Harold B. Shill for
teaching an informative colloquium on e-government that helped me to consider this field as one
for possible research. Also, I am thankful to Drs. Shill and Joseph for their exceptional guidance
and mentoring while sponsoring me in an independent study on e-voting. Those studies helped to
lay the foundation for this project.
I am thankful to Ms. Kathy Brode and Ms. Janice Smith in the Penn State Learning
Center for their helpful assistance regarding format and readability, to Dr. Elinor Madigan at
Penn State Schuylkill for arranging the use of the online survey application for this study, to the
faculty and staff of the School of Business Administration for their support in allowing me to
survey students, and to the students who voluntarily participated in the survey and interviews.
Becoming a Ph.D. would not have been achievable without the understanding and
support of my wife, Sue, and daughter, Jennifer, to whom this dissertation is dedicated. Finally, I
am thankful to my parents for encouraging me to pursue a lifetime of learning based on a
foundation of faith and perseverance.
Chapter 1
Introduction and Background
The use of Internet voting systems for government elections is a new application of
information technology that is being considered as a way to improve existing voting processes.
Internet voting is one specific type of technology within the field of electronic voting, or e-voting,
which is in turn related to the broader fields of electronic government and electronic democracy,
also known as e-government and e-democracy, respectively.
Although aspects of e-voting have been used in this country for decades and there are
numerous examples of its use at different levels within government, Internet voting has not yet
been adopted as a conventional method for citizen participation in elections. Alvarez and Hall
(2004) state that Internet voting is the future of voting in the U.S., and that the question is not
whether the Internet should be used for elections, but how and when it will be effectively
implemented. As with the implementation of any new technology, the development and use of
Internet voting systems has the potential to dramatically change the nature of related processes.
Because Internet voting is a new application of information technology, the impact of its
use on our democratic system is only beginning to be researched and understood. This empirical
study is designed to add to the body of knowledge in this field by identifying and exploring
factors that influence the intentions of potential voters to use online voting systems in government
elections in this country. These factors include voter perceptions about government, voting, and
the use of online technologies, as well as a variety of demographic and other user characteristics.
A better understanding of these factors can help to support future public policy and administrative
decisions related to voting processes, to improve the design of existing and future voting systems,
2
and to help in finding ways to increase the level of voter participation of the general public. The
study also adds to the existing foundation of research related to e-voting processes.
From a methodological standpoint, this study builds upon prior research that applies a
variety of technology adoption models to e-government initiatives and e-voting processes. The
study uses an online survey to obtain responses of students from a variety of majors at both the
undergraduate and graduate levels at multiple campuses of a large state university. By including
graduate students in the survey population, a wider range of demographic characteristics and user
perceptions is obtained. The study also extends previous research efforts by considering the use
of online voting systems at the overall, local, state, and federal levels of government.
This introductory chapter places the study in the overall context of the fields of public
administration and public management, as well as the area of e-government. The chapter also
provides background information related to e-voting processes in the U.S. and describes e-voting
as an important segment within these broader fields.
After considering the nature of public administration, related managerial and policy
approaches, and the concept of governance, an overview of e-government is presented. This
includes the impact that e-government is having on the ways that government provides services to
citizens. An overview of the nature of democracy is presented, as well as observations about the
importance of citizen participation in elections to democratic processes. Past and current
initiatives related to voter participation are reviewed, including trends about voter registration and
participation in recent U.S. elections. The chapter considers technology usage among adults,
including Internet usage patterns. The chapter concludes with a summary of specific issues
involving e-voting and related technologies, including the potential benefits and barriers of e-
voting and Internet voting processes.
3
Public Administration and Governance
Overview of Public Administration
In order to understand the importance of the application of new technologies to
democratic processes and voting systems and to place this research in an appropriate overall
context, it is important to briefly consider the nature and scope of the field of public
administration and related fields.
Public administration is a fragmented field and its domain includes a wide variety of
topics, disciplines, and foundational perspectives. These topics include aspects of organization
theory, matters of legal and regulatory importance, views of professionalism, aspects of political
reform and constitutional issues. Related disciplines include political science, economics,
finance, information sciences, philosophy, psychology along with other social and behavioral
sciences. The variety of related topics and disciplines indicates that public administration is a
field with a full and complex history. The field has evolved over the years as it continues to
provide both service and regulation to the public.
Public administration includes aspects of government that promote the public interest and
respond to the needs of citizens by providing solutions to societal problems and issues. Voting
processes are an important way for citizens to express their needs and interests to their
government and elected representatives.
A variety of definitions of public administration have been proposed in recent years,
indicating that it means different things to different people. Rosenbloom and Kravchuk (2005)
summarize commonly accepted definitions. The authors conclude that the diversity of definitions
indicates the usefulness and pervasiveness of public administration in our society. The following
definition is proposed, “Public administration is the use of managerial, political, and legal
4
theories and processes to fulfill legislative, executive, and judicial mandates for the provision of
governmental regulatory and service functions” (Rosenbloom & Kravchuk, 2005, p. 5).
As this definition indicates, public administration is more than a field of study. It is also
a profession. Those involved in the field need to understand the importance of a balance between
theory and practice, as well as the relationship between them. For example, theory provides input
to support practice, while practice has an impact on the type of research that takes place within
the field. Given the potential to support the development of policies and practices related to the
design, use, implementation, and management of online voting process, the results of this study
and related research efforts are expected to serve as a bridge between theory and practice within
the field.
Public administration in the U.S. includes the need to balance democratic, constitutional
government with the increasing need for knowledge and expertise in the governing process
(Rosenbloom & Kravchuk, 2005). Traditional public administration, as summarized by Lynn
(2001), is the concept of the design of a largely self-serving bureaucracy that was to be strictly
insulated from politics and that justified its actions based on a “science of administration” (p.
146) in which facts were to be separated from values, politics from administration, and policy
from implementation.
During the 1960s and 1970s, a series of accelerating world changes placed pressure on
government and created challenges and new opportunities for government transformation. A
distinct field of public management developed mainly in response to these challenges related to
industrial capitalism (Lynn, 2006).
Ott, Hyde, and Shafritz (1991) describe public management as a major segment of the
broader field of public administration. The focus is on public administration as a profession and
on the public manager as an actor and practitioner within the profession. Ott et al., (1991) state
5
that the field is concerned with internal operations and focuses on the tools, techniques,
knowledge, and skills used to turn ideas and policy into programs of action. This description
highlights the need to deal with two main types of management problems in government, namely
those involved with the politics of public administration and those involved with the problems of
management (Hood, 2005). By participating in elections at the local, state, and federal levels of
government, citizens can become more actively involved in helping to solve problems related to
both political and managerial issues within the public sector.
In recent years, several new approaches have emerged from public management which
relate either directly or indirectly to e-government and voting processes. These include the
concepts of “new public service” and “new governance”. These concepts are briefly described in
the following sections as a preface to the analysis of e-government and voting processes later in
this chapter.
New Public Management and New Public Service
An assortment of public management reforms, known as New Public Management
(NPM) or managerialism, developed in the 1980s and 1990s (Lynn, 2006). This movement has
become a contemporary, reform-oriented approach to public administration, the main goal of
which is to improve the performance of the public sector. Because the early 1990s were
characterized by a public view that government was generally ineffective and wasteful, reforms
became relatively easy to embrace. The movement applies methods from the private sector to
address the problems of the public sector and minimizes distinctions between public and private
administration. It is characterized by a more entrepreneurial approach to reinvent government
(Osborne & Gaebler, 1992).
6
The NPM approach has resulted in a shift from an emphasis on administration and policy
to a focus on management. With an emphasis on reform, networks, performance, and
competition, this approach has changed the nature of public management practices.
A group of related approaches that has extended the management philosophy of NPM
involves the concept of a “new public service”. Mosher is considered to be the originator of the
concept of public service professionalism (Plant, 2008). His influential book assesses the status
of democracy in this country in the context of a new public service and explores how to reconcile
the expertise of civil servants with respect for democratic governance (Mosher, 1982).
The meaning of the phrase “new public service” can vary depending on one’s viewpoint.
Light (1999) associates four characteristics with the new public service. These include: diversity;
rising interest in nongovernmental destinations; sector switching; and a commitment to making a
difference in the world (Perry, 2007).
A related but quite different viewpoint of “new public service” is provided by Denhardt
and Denhardt (2003). The authors argue for the emergence of an alternative, normative model,
known as New Public Service (NPS). NPS focuses on democratic and social criteria and involves
a set of alternative ideas that places citizens at its core. This citizen-centered movement involves
work in democratic citizenship, community and civil society, and organizational humanism and
discourse theory. The main focus of the movement is on the primary role of the public servant of
helping citizens to articulate and meet their shared interests rather than to attempt to control or
steer society. Thus, public administrators should focus on their responsibility to serve and
empower citizens as they manage public organizations and implement public policy, and public
institutions should be characterized by integrity and responsiveness.
Denhardt and Denhardt (2003) provide a useful comparison between their vision of NPS
and NPM, and the authors characterize NPS using the following seven principles: to serve rather
7
than to steer; the public interest is the aim, not the by-product; to think strategically, not
democratically; to serve citizens, not customers; accountability isn’t simple; to value people, not
just productivity; and to value citizenship and public service above entrepreneurship. These
principles can be considered as mutually reinforcing ideas that urge citizens and managers to
consider their respective roles within this vision of a new public service (Perry, 2007).
The argument of NPS is that, in a democratic society, the concern for democratic values
should take precedence when determining how to organize our government is a compelling one.
This collection of principles does not ignore the importance of values like efficiency and
productivity, but allows them to be viewed in a larger framework, in which other ideas and values
can be integrated for the overall good of the citizens.
Perry (2007) proposes four specific reforms as a path to reconcile democracy with the
new public service. These changes include: professional changes to enhance accountability and
citizen protection; institutional reforms to enhance popular participation and citizen agency;
reforming public service wage structures to further support accountability; and renewing civic
education to address threats to citizen protection and citizen agency.
Online voting system can provide support for the second of these reforms by offering
innovative ways to improve citizen participation through institutional changes. The
implementation of online voting systems and related uses of information technologies can provide
citizens with the means to identify and express their shared interests. This can be considered as
an important element of this type of reform-oriented, citizen-centered framework, which focuses
on democratic values and empowering citizens to improve public processes. Thus, the use of new
technologies to empower and engage citizens, such as online voting systems and forums, can be a
bridge between the current model of democracy and the vision of a new public service.
8
New Governance and Engaging Citizens
Ambiguity over the specific nature and scope of public management has given rise to the
term “governance”, which has been used to characterize traditional public administration and
NPM (Lynn, 2006). The term can be considered somewhat synonymous with both public
management and public administration (Frederickson & Smith, 2003).
Scholars have begun to use the term “governance” as a unifying concept. For example,
Kettl (2002) believes that governments are facing a growing set of problems that can only be
solved by a new paradigm of governance. Salamon (2002) identifies a paradigm of “new
governance” that includes an emphasis on tools of action, networks, public and private
collaboration, negotiation, persuasion, and the use of enablement skills. DeWitt, Kettl, Dyer and
Lovan (1994) refer to a set of ideas known as “new governance”, which combines previous
concepts, like engaging the public in deliberations about agencies and focusing on results rather
than input, with newer ideas. The result is a thorough approach centered on collaboration,
flexibility, results, and engaging citizens.
Similar to making a key contribution to the implementation of NPS, one can envision
online voting systems and related uses of information technologies as an important way to
support a new paradigm of governance focused on engaging citizens and collaboration, which
may allow government to better understand and meet the needs of the citizens. The next section
looks at the impact that online voting processes can have on the policy development process.
Policy Development Stages
In addition to the preceding concepts that help to position online voting systems in the
context of the field of public administration, it is helpful to briefly consider related aspects of
9
public policy and policy development. Although the public policy approach aimed to improve
public management, it rejected the traditional style of public administration with its efficiency,
economy and effectiveness (Bozeman, 1993). Kettl (1993) explains that the public policy school
emphasized how to identify the best decisions. According to the public policy school, public
managers had a tendency towards action and problem-solving, while emphasizing leadership
more than management. As the public policy approach matured, the focus involved the role of
high-level managers, or political appointees, and the political aspects of public management.
The assumptions that one makes about policy analysis affect the view of how policies
should be developed (Sabatier, 2007). Due to the complex nature of the policy process, a variety
of methods have been developed to analyze it. One useful approach, developed in the 1970s and
1980s, addresses this complexity by breaking down the policy process into a series of steps or
stages. Although different versions have been proposed in recent years, most include some form
of the following six steps: agenda setting/problem identification; analysis of policy issue;
formulation of policy options; adoption of specific policy; implementation of selected policy; and
evaluation of policy (Howard, 2005). In broader terms, the steps can be summarized as: agenda
setting; formulation; implementation; evaluation; and termination.
The stages heuristic is related to the rational model of decision-making and is a top-
down, legalistic framework (Sabatier, 2007). It is based on the assumptions that policies are
developed in a linear, simple manner, that there is one unified decision-maker, and that time is
not a major factor. In reality, public policies are not developed sequentially from one step to the
next. Also, the policy process often includes many participants and stakeholders, a lengthy time
frame, intergovernmental dependencies, numerous policy debates, and deeply held values and
interests (Sabatier, 2007). Critics believe that the assumptions on which the framework is based
limit its applicability in the public policy environment.
10
Although criticized by some as broad and overly simplistic, using this heuristic can be
helpful in understanding the how, why, and when of public policy (deLeon, 1988). Each stage in
the model is characterized by different purposes and actions, and it provides a useful framework
within which to analyze other approaches related to the policy process (deLeon, 1988).
Considering the five broad stages in the policy development process, online voting
methods can have an impact, either directly or indirectly, on all stages, although the greatest
impacts can occur within the policy formulation and implementation stages. Citizens may be
more apt to express their opinions regarding policy alternatives if given the option to do so using
voting methods or forums. Likewise, the policy implementation stage could benefit from input
provided by citizens using various applications of electronic technologies. For example, citizens
could provide real time feedback via online forums to their representatives regarding proposed or
pending legislation. These systems would apply the same technologies as developed for use in
online voting systems, and could thus benefit from the type of research performed in this study.
Given the preceding overview of public administration and related approaches that help
to define the context of online voting systems with respect to the public sector, the next section
discusses the topic of e-government as background for the analysis of online voting processes.
E-Government and Voting Processes
Overview of E-Government
In considering the importance of the use of new technologies to change the nature of
democratic processes and voting systems, it is helpful to first understand the nature and scope of
e-government. There are many issues involved in the use of information and communications
11
technologies, or ICTs, to support governmental processes. These technologies continue to
dramatically change the nature of how citizens interact with government on many levels.
E-government is one of the primary ways that a government can use ICTs to have a positive
impact on a wide variety of public administration functions and public policy issues.
E-government involves many functions within the field of public administration. E-
government consists of four categories of constituents, including citizens, employees, businesses,
and other governmental entities. Table 1.1 shows these e-government constituents along with
examples of areas of interaction that can occur across various functions of public administration
(Joseph & Kitlan, 2008). Many of these types of processes can be conducted in a web-based
environment. E-voting primarily involves the citizen constituency of e-government, but it can
also have an impact on other constituents as well as across all public administration functions.
Public Administration Functions
E-government
Constituents
Human
Services
Community
Services Transportation Justice
Land
Resources
Financial
Services
Citizens Consumer
safety Post offices Driver licenses
Law
enforcement
National
parks
College
scholarships
Businesses Safety
standards
Worker
training
Regulate
trucking
Control
cyber-crime
Water
conservation
Loans and
grants
Employees Evaluate
standards
Support
community
groups
Provide
transportation
Report
violations
Execute
transfers
Payroll
processing
Governments Military
bases
Flood
recovery Regulate trade Public safety
Land
transfers
Budget
creation
Table 1.1: Interaction of E-Government and Public Administration.
12
The role of government is to provide a variety of services to its citizens in the most
efficient and cost-effective manner (Fahnbulleh, 2005). Recent advancements in the use of
information technology have resulted in dramatic changes in the way that government entities are
providing services to citizens. In the early 1990s, Freeman (1993) identified the important role
that ICT would have in shaping public policy and cautioned governments about neglecting its
significance.
More recently, government departments and public policy administrators have shown an
increasing interest in the changes that occur as a result of the use of ICTs. Although information
about the quality and efficiency of e-government initiatives is limited, an increasing number of
governmental units are incorporating or expanding the use of ICTs into many of their activities
(Esteves & Joseph, 2008). E-government methods are expected to have an increasing impact on
public administration processes, public policy initiatives, and politics in the future as the use of
ICTs continues to change the nature of citizen interaction with government.
Because the use of e-government continues to evolve and expand, it is important to
examine its scope and the nature of its adoption, as well as its impact on various constituents. E-
voting is an important aspect of e-government and is primarily concerned with the citizen’s view
of e-government.
A variety of recent definitions of e-government have been developed and it is useful to
consider their common characteristics. In general, the concept of electronic governance involves
the use of new technologies in the governance of citizens (Moreno-Jimenez & Polasek, 2003). E-
government can also be considered as the process of providing public value by using ICTs
(Capati-Caruso, 2006). A report from the Council for Excellence in Government states that e-
government “has the greatest potential to revolutionize the performance of government and
revitalize our democracy” (Dearstyne, 2001, p. 17) by enhancing efficiency, decreasing
13
transaction time, bringing people closer to their government, and enhancing methods for citizens
to participate in governmental affairs.
A few other definitions of e-government are also useful to consider. Koh and Prybutok
(2003) describe e-government as the use of the Internet and other digital technologies in all facets
of the operations of a governmental organization to simplify or enhance the methods by which
citizens, employees, business partners, and other government organizations interact and transact
business. West (2005) states that e-government refers to the real-time availability of government
products, services, and information via digital technology, such as the Internet. Grant and Chau
(2005) consider e-government to be the leveraging of the capabilities and power of information
technology (IT) to deliver services provided by governments at local, municipal, state, and
national levels.
A report by the Organization for Economic Cooperation and Development (OECD)
(2003) describes e-government as the use of information and communication technologies,
particularly the Internet, as a tool to achieve better government. Bretschneider (2003) explains e-
government as the use of the Internet by governments to deliver services, to collect data, and to
enhance democratic processes. Dearstyne (2001) proposes the definition of e-government as the
emerging reliance of government on digital information to make information and services
available and to engage citizens in a way that meets their needs and reduces apathy and suspicion
of government.
In general, e-government is usually considered as consisting of four categories:
government-to-citizen (G2C), government-to-employee (G2E), government-to-business (G2B),
and government-to-government (G2G). G2C is the category most closely related to e-voting
processes. This category includes electronic communications and transactions that occur between
a government and one or more of its citizens. Governments tend to focus on this type of
14
interaction because a founding principle of governance is to serve its citizens. One international
study indicates that governments around the globe recognize that a customer-centric focus is
critical for e-government success (Hunter & Jupp, 2001).
G2E initiatives involve the human resource aspect between government and employees.
Three main benefits of G2E projects include improvement in strategic planning, cost reduction,
and service improvements between management and employees (Ruël, Bondarouk, & Looise,
2004). G2B initiatives refer to communications and transactions facilitated by electronic means
between a government and a non-profit or for-profit organization. For example, the collection of
corporate taxes would be a G2B process. G2G initiatives refer to units of governments
interacting with other governmental units. G2G occurs both vertically, where information is
exchanged between hierarchical levels of government, as well as horizontally, where one
department interacts with another similar branch of government (Layne & Lee, 2001).
Siau and Long (2005) propose a five-stage framework of e-government development
based on the synthesis of a variety of other models. The first stage is web presence, which
involves the use by a government entity of a non-interactive, descriptive website containing
general information. The second stage is interaction, which involves a two-way flow of
information between a government website and users. The third stage is the transaction stage,
which includes an advanced level of interaction by a government website, with features provided
for both citizens and business. The fourth stage is the transformation stage, which involves the
re-engineering of internal practices of a government entity into a web-based environment. The
final, and most advanced, stage of e-government development is e-democracy. This stage
involves full citizen participation in the democratic processes of government and includes the
domain of e-voting. The next section considers the importance of voter participation in a
democracy.
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Democracy and the Importance of Voter Participation
Democracy is often defined as government in which the power is vested in the people and
exercised by them, or by their elected representatives, under a free electoral system. Voting
processes are considered to be the foundation of the electoral system. Democracy is also
considered to be the political system in which citizens exercise their authority through
interventions in government based on the goal of improving their own conditions (Moreno-
Jimenez & Polasek, 2003). The U.S. Department of State (2008a) describes democracy as a set
of evolving ideas and principles that institutionalizes freedom. The Department identifies two
categories of democracies, direct and representative.
In a direct democracy, citizens can participate in making public decisions without relying
on elected or appointed officials as intermediaries. However, most governmental processes and
entities are too large and complex to allow direct democracy to effectively take place. Thus, the
most common form of democracy in existence today is representative in nature. Under this type
of democracy, citizens elect officials to make decisions, develop laws and regulations, and
implement and administer programs for the good of the public. Public officials, as a result, are
accountable to citizens for their actions and decisions. Another aspect of our democratic society
is that political decisions are made based on majority rule combined with an awareness of
individual human rights that protects the rights of minorities (U.S. Department of State, 2008a).
Curran and Nichols (2005) propose that the underlying core of democracy is an informed
and engaged citizenry. Although political scientists disagree on whether an informed citizenry is
necessary for democracy, it is generally accepted that an informed citizenry can improve the
effectiveness of a democracy.
As an example, Jefferson (1789) noted that when citizens are well informed, they can be
trusted with their own government. Thus, information creates trust and helps to ensure that
16
politicians serve the electorate. In 1822, Madison observed that democracy without information
is “but a prologue to a farce or a tragedy; or, perhaps both” (Hunt, 1900, p. 103). Democracy is
most effective when there is an uninterrupted flow of information between citizens and
government, as well as a high level of citizen participation in the political process (Watson &
Mundy, 2001). E-voting can support these key aspects of democracy by providing citizens with
access to online information and by providing the mechanisms for them to express their opinions.
Elections are considered to be a key part of democratic representative governments. This
is because the authority of the government comes from the consent of the citizens and the main
process for translating this consent into governmental authority is through free and fair elections.
The U.S. Department of State (2008b) identifies several requirements for free and fair elections,
including that they be competitive, periodic, inclusive, definitive, and involving policy issues.
Issues related to citizen participation, and how it can be improved, have long been the
focus of research, and research has confirmed the importance of citizen participation in
governmental processes on a variety of levels (Lowndes, Pratchett, & Stoker, 2006). Elgarah and
Courtney (2002) conclude that citizen input is crucial to government because it is the basis for
communicating the needs and wishes of the citizenry.
It is expected that the success of democracy in the future will be related to the number of
citizens who choose to participate and how accurately their votes represent the interests of society
as a whole (Morse & Hodges, 2002). Although voter turnout is not necessarily a measure of the
quality of a democracy, maintaining sufficient citizen participation in voting processes can be an
important element in the implementation of an effective democratic system.
Given the importance of voting processes and citizen participation, a number of
legislative efforts, programs, and other initiatives have been used to try to increase voter
participation in this country. Several of these initiatives are reviewed in the next section.
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Initiatives to Increase Voter Participation
A variety of initiatives have been used in this country to try to improve voting processes
and to increase the levels of voter participation. Examples include the passage of the Voting
Rights Act and the 26th Amendment, the implementation of the Motor Voter Law, Get Out the
Vote initiatives and the Help America Vote Act of 2002. The passage of the National Voting
Rights Act of 1965 was an important legislative change with respect to voting in this country.
This legislation prevented discriminatory voting practices and established extensive federal
oversight of elections administration (Alvarez & Hall, 2004).
Regarding the issue of voting by young adults, the passage of the 26th Amendment was a
critical legislative change. During the years of the Vietnam War in the 1960s, pressure began to
increase to lower the voting age from 21 to 18 years of age. It was considered by many to be
unfair for young Americans to be risking their lives at war while unable to participate in the
political processes that they were defending. The justification for this action involved five main
points: 18-year-olds deserved to vote; 18-year-olds are treated as adults in other respects; 18-
year-olds are well-qualified; granting the vote will combat youth alienation; and 18-year-old
voters will benefit democracy (Close Up Foundation, 2008).
Although ratified into law in 1971, the high expectations associated with its adoption
have not been fully realized. American youth have been less likely to exercise their voting right.
Voting among young adults declined significantly since the 1972 election, when almost 50% of
18- to 24-year-olds cast a ballot. Figure 1.1 indicates that the trend has begun to reverse in recent
elections and slightly over 50% of 18- to 29-year-olds turned out in the 2008 presidential election
(CIRCLE, 2009). In spite of this trend, there continues to be a need to find new ways to increase
voter participation (Close Up Foundation, 2008).
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In 1993, the U.S. House of Representatives introduced the National Voter Registration
Act of 1993, which became known as the "Motor Voter Bill." The bill combined the voter
registration and driver's license renewal and application processes. It also adopted national
registration or alternative state forms and provided the opportunity to register at any federal or
state agency. The result was that approximately 20 million Americans registered to vote during
the first 18 months of the bill's implementation. Within three years, the percentage of registered
voters rose to over 72% of the overall voting age population. This was the highest national voter
registration percentage since records were first kept in 1960. Since then, however, actual voter
participation has tended to decline, which again reinforces the need to consider other options,
such as those based on new technology, to increase registration and voter turnout (Morse &
Hodges, 2002).
Other nations have introduced related initiatives with the same purpose in mind. For
example, Switzerland approved the use of postal ballots in the 1970s to increase the level of
convenience for voters. This was seen as a major step toward “virtual voting” and the positive
results led that nation to commit to a long-term e-government strategy in the late 1990s (Geser,
Figure 1.1: Voter Turnout Among 18- to 29-Year-Olds, 1992-2008.
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2002). Brazil greatly reduced the level of voting fraud in the 1990s by replacing the use of paper
ballots with electronic boxes and printing systems. The use of these electronic ballots has
continued to grow at every voting location in that country (Grow, 2001).
There are a variety of non-partisan organizations involved in trying to encourage citizen
participation in U.S. elections. Some of these fall under a set of programs known as “Get Out the
Vote,” or GOTV, and have involved activities that are impartial as well as related to specific
campaigns. Political parties also engage in active GOTV efforts prior to most primary or general
elections. Related programs include, for example, the League of Women Voters, Women’s
Voice, Rock the Vote, and the Close-up Foundation, whose mission is to educate, inspire, and
empower individuals to become active citizens in our democracy (Close Up Foundation, 2008).
Another set of reform proposals was passed in 2002 as the Help America Vote Act, or
HAVA. This legislation was aimed at addressing some of the systemic problems that occurred in
the 2000 presidential election. Among other initiatives, HAVA proposed the adoption of
provisional voting, the development of computerized voter registration lists, the elimination of
problematic punch-card voting systems, and the creation of standards for states to follow in
election administration. It also provides funding for states to improve elections and to update
voting systems and requires that all voting systems are auditable (Alvarez & Hall, 2004).
Research related to voter participation initiatives is ongoing. For example, a Pew
Charitible Trusts Make Voting Work Grant was recently awarded to explore the efficacy of pre-
registration programs that permit persons as young as 15 years old to register so that they are in
the system and can immediately begin voting when they reach 18 years of age. Results from this
research project are expected to be released in 2010 (U.S. Elections Project, 2009).
Election reform initiatives in 2008 were designed to reduce or eliminate barriers to
voting. Such reforms included permitting voters to register on Election Day and early voting
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processes. The former reduces the burden of voter registration for first-time voters. Research has
shown a positive relationship between voter registration on Election Day and voter turnout
(Highton, 1997; McDonald, 2008a; Mitchell & Wlezien, 1995; Rhine, 1992).
Recent election trends show an increase in the use of nontraditional modes of election
participation by citizens. These include early voting, or allowing voters to cast their ballots prior
to Election Day, as well as voting by absentee ballot. About 30% of all votes were cast prior to
Election Day in the 2008 presidential election, which was up from 20% in 2004 (McDonald,
2008b). The two main forms of early voting initiatives include voting by mail and voting in
person. Early voting by mail has been increasing due to the use of absentee ballots and all-mail
elections. Early voting in person requires citizens to vote at special polling places located in a
variety of venues, such as shopping malls or centrally located administrative offices. More
research is needed to determine the impact of early voting initiatives on voter turnout.
Most states have provisions to allow voters to cast votes by absentee ballot if voters
expect to be absent or unable to go to a polling location to vote on Election Day. Absentee
ballots are usually permitted to be sent by mail or returned in person to locations specified by
election procedures.
Any reforms or modifications of election methods, including the use of Internet voting,
would need to take these alternative modes of participation into consideration. Given this
background on voter participation and related initiatives, the next section considers specific
information on voter registration and participation trends in this country.
21
Voter Registration, Participation, and Behavior
In this country, registering to vote is the responsibility of citizens, and voters are not
automatically registered to vote once they reach the age of 18. The United States has one of the
lowest rates of participation of any democracy in the world (Morse & Hodges, 2002). Voter
turnouts as a percentage of the overall population have been at or slightly above 50% in recent
general elections. Figure 1.2 shows voter turnout in U.S. presidential elections in the years from
1948-2008 (Infoplease, 2009).
Based on Figure 1.2, voter turnout in general elections averaged 54.5% between 2000 and
2008 (Infoplease, 2009). The 56.8% of the total U.S. population voting in the general election in
2008 equals approximately 62% of all eligible voters. Voter turnouts are usually lower in local
elections and primary elections, as well as in general elections in non-presidential years (U.S.
Figure 1.2: Voter Turnout Rates in U.S. Presidential Elections as Percentages of Overall Voting Age
Population (VAP) and Voting Eligible Population (VEP).
22
Census Bureau, 2008). Voter participation in non-presidential elections has averaged only 36.8%
since 1998, which equals 54.5% of all registered voters (Infoplease, 2009).
Another interesting aspect of voter turnout rates that can be seen in Figure 1.2 is that
turnout is no longer declining, but appears to have reverted to the higher levels experienced
during the 1950s and 1960s. The presidential turnout rate for those eligible to vote was 61.6% in
2008, which marks the third consecutive increase in presidential turnout rates since the modern
low point of 51.7% in 1996. This challenges some of the recent theories that have been proposed
to explain past voter turnout declines, including low citizen trust in government, voting apathy
among baby boomers, and an increase in the level of negativity of political campaigns
(McDonald, 2008b).
In spite of the recent trend toward increased voter participation, overall participation rates
in this country still remain relatively low compared to other democracies. One possible reason
for the relatively low voter turnouts in this country is that voting is optional. Other democracies
around the world have mandated voting participation by citizens, resulting in much higher
participation rates. For example, compulsory voting has been part of both state and national
elections in Australia since 1924. This is strictly enforced, and those who fail to vote can be
subject to fines. As a result, there is heavy voter turnout in Australia, typically over 90%, and the
majority of Australians support mandatory voting (Dean, 2003).
One potential factor for the increasing voter participation levels over the past three
presidential elections is an increase in the level of voter mobilization by campaigns, which is
often more intense in battleground states. The correlation between higher turnout and
mobilization campaign efforts suggests a causal relationship that has been confirmed by research
(McDonald, 2008b). For example, voter contact has been shown to be effective at increasing
turnout among those contacted (Green & Gerber, 2000).
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Voter behavior is a complex issue that has been studied for many years in this country.
Most observers of voting behavior agree that voter turnout is determined by a combination of
facilitative and motivational factors (Dalton, 2006; Rosenstone & Hansen, 1993; Verba,
Schlozman, & Brady, 1995). Facilitative factors include such characteristics as higher income,
more education, and more political experience. Motivational factors include levels of interest and
perceived political value. The evidence shows that the likelihood of people to vote increases as
these factors increase. Other factors, such as campaign voter contact and voter mobilization
programs, can have a positive impact on motivational factors.
In addition to facilitative and motivational factors, age can be a factor in determining
voter participation in this country. For example, citizens over the age of 45 are about twice as
likely to vote as those under the age of 25 (Alvarez & Hall, 2004). One possible indicator of
online voter participation is the level of experience that a citizen has with the use of technology in
general and with Internet usage in particular. This characteristic is often age dependent and is
considered in more detail later in this study. The next section looks at adult Internet usage
patterns and levels of technology experience as topics of possible relevance to the use of e-voting.
Internet Usage Patterns Among Adults
Recent studies have shown that people increasingly rely on the Internet as a source of
news and information. For example, the Pew Research Center has confirmed that, as time passes,
more people use the Internet to do research for work, entertaining, travel, shopping, and staying
in touch with friends and family (Pew Internet & American Life Project, 2008a). Likewise,
Internet-enabled technologies are facilitating new methods of both communication and
participation between citizens and government. For example, online blogs, discussion boards,
24
and communities are enabling new ways for issues to be discussed and deliberated (Lewis, 2001).
The use of public websites, wikis, blogs, and electronic forums can enhance citizen participation.
Electronic forums and other relatively new methods of electronic communication are helping to
connect citizens with government. However, one concern is that too much Internet-driven direct
democracy may undermine the processes involved in representative government (Eggers, 2005).
There is a significant difference in the use of the Internet and other information
technologies with respect to age. For example, one report shows that 89% of teens believe that
use of the Internet and other technological devices makes their lives easier, as compared to 71%
of their parents (Pew Internet & American Life Project, 2007). Regarding Internet usage, the
highest percentage of those who use online wireless technologies with a laptop away from home
or work is in the 18-29 year age group (Pew Internet & American Life Project, 2008a).
One recent way of assessing the level of information technology usage among adults is
known as the Pew Typology Test (Pew Internet & American Life Project, 2008b). This test
involves categorizing adults into 10 groups or typologies with respect to their use of ICTs.
Results show that the category with the highest reliance on Internet and other ICTs has the lowest
median age (28), and that technology usage tends to decrease with increasing age. Thus, there
appears to be a clear inverse relationship between age and the level of ICT usage. Further
research is required to determine if this relationship is best explained by age or generational
factors, but whatever the cause or explanation, this generational effect may have an impact on the
implementation and use of electronic and Internet voting systems in the future.
Having looked at the nature of democracy, past and current initiatives designed to
increase voter participation, voter participation and behavior, and Internet usage patterns among
adults, the following section focuses on the related concepts of e-voting and e-democracy. The
25
impact of e-voting and its relevance to democratic processes are also considered, as well as
potential benefits and problems associated with Internet voting systems.
E-Voting and Related Technologies
E-Democracy and E-Voting
Electronic democracy is described as a broad model which involves “the capacity of the
new communications environment to enhance the degree and quality of public participation in
government” (Kakabadse, Kakabadse, & Kouzman, 2003, p. 47). One goal of electronic
democracy is to use information technology to improve the effectiveness of democracy.
According to Watson and Mundy (2001), e-government and e-politics can both be
considered elements of e-democracy. According to these authors, e-government informs citizens
about their representatives and can improve government efficiency by enabling citizens to
complete transactions online. E-politics is defined as the use of Internet technology to improve
the effectiveness of political decision-making by making citizens aware of decision-making
processes and facilitating citizen participation (Watson & Mundy, 2001). Thus, the domain of e-
politics includes e-voting processes.
E-democracy is often considered to consist of two separate processes (Moreno-Jimenez
& Polasek, 2003). The first is a deliberative phase that includes on-line debates, the stating of
positions, and information exchange, using a range of online technologies as mentioned earlier.
The second phase involves the e-voting process. Although definitions of related e-terminology
vary, e-voting can be considered as a subset of e-politics, which is part of e-government, which in
turn is one aspect of e-democracy. Figure 1-3 shows these relationships.
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E-voting relies on the use of ICTs and also has the potential to change the nature of
citizen participation and involvement in governance. It involves the use of electronic means to
cast and/or to count votes. Given this broad definition, e-voting has been in use for decades,
since punch cards and optical scan cards have long been used to tabulate votes electronically.
These are mainly paper-based methods and are one of three main types of e-voting.
A second type of e-voting includes electronic systems that record the votes of citizens in
a secure and secret manner. Technologies such as direct electronic recording (DER) touch
screens or optical scanners are included in this category. In 2004, it is estimated that
approximately 30% of the U.S. voting population used a form of e-voting technology to record
their vote for president, most of which involved the first two types of e-voting (Bitpipe, 2008).
A third type of e-voting involves online or Internet voting and is the focus of this study.
Specific aspects of Internet voting are introduced later in this chapter. In most forms of electronic
voting, votes are stored on digital media, such as a tape cartridge, diskette, or smart card, before
being tabulated automatically at a central location. The next section considers the potential
impact of e-voting on our democratic system as well as recent examples of e-voting initiatives.
Figure 1.3: Relationships in e-Terminology.
27
Impact of E-Voting and Recent Initiatives
The potential impact of e-voting on our democratic system is not yet fully understood.
However, it is clear that before e-voting methods can be successfully implemented across all
levels of government, existing problems need to be addressed and overcome. These include
issues related to logistics, security, secrecy, privacy, legal obstacles, equal access, and equal
representation. One of the main concerns related to e-voting involves registration fraud, even
though there are numerous government services that are being provided using a variety of
identification methods (Larson, 2001).
Recent attempts at implementing e-voting initiatives have resulted in a variety of
problems. For example, Kohno, Stubblefield, Rubin, and Wallach (2004) analyzed one specific
e-voting machine in 2002 (known as a Diebold AccuVote-TS 4.3.1) and found that this voting
system exhibited serious security and other shortcomings. The problems included unauthorized
access, incorrect use of cryptography, vulnerability to network threats, and poor software
development processes. The authors demonstrated that unlimited votes could be cast without
detection by the voting system, and there was also a threat from insiders, such as poll workers,
who could modify votes. They concluded that this specific voting system was unsuitable for use
in a general election, and they further argued that these problems may be characteristic of other
electronic voting systems.
Other recent e-voting problems were identified in the 2006 mid-term elections in a joint
report by several groups (VotersUnite.org, VoteTrustUSA, & Voter Action, 2007). These groups
reviewed data collected from the Election Protection Coalition hotline and the Voter Action
hotline as well as reports submitted from poll workers and other sources. The results revealed
over 1,000 accounts of machine-related problems from more than 300 counties in 36 states. The
report summarizes these problems and categorizes them based on problem type.
28
Problems included machine malfunctions that prevented polls from opening, machine
failures during poll closings and vote tabulations, and votes that were lost or changed on voting
machine screens. There were incidents of voters leaving polling locations without casting their
ballots due to inoperable machines, and some machines apparently failed to properly record voter
choices on the ballot, causing them to question whether or not their votes were actually recorded.
There were also cases of votes being miscounted or lost. The report concludes that the problems
were too widespread to be considered just anomalies (VotersUnite.org et al., 2007).
Given these examples of potential and actual problems related to e-voting, there are
several points worth mentioning. Although there are clearly many difficulties that need to be
addressed, e-voting methods have been established as a viable alternative to the varied and
problematic voting methods of the past. Balloting methods have achieved significant
improvements since the 2000 presidential elections and these types of changes are expected to
continue in the coming years (Morse, 2002). The 2002 HAVA has provided states with funding
needed to pursue these changes. Since HAVA was implemented in 2002, over $3 billion in tax
dollars have been spent on new electronic voting equipment. However, the transition to e-voting
methods may have solved one set of problems while creating a new set of problems
(VotersUnite.org et al., 2007).
In addition to the problems previous mentioned, there are a variety of potential benefits to
e-voting, many of which help to offset some of the perceived and real weaknesses and problems.
These benefits include enhanced participation, reduced costs, and ease of registration. The use of
new technology has increased the accuracy, expedience, and convenience of election processes
and has opened the door to additional technology upgrades in the future (Morse, 2002).
Advocates of e-voting argue that e-voting can reduce election costs and increase civic
participation by making voting processes more convenient. Critics maintain that without a paper
29
trail, recounts are more difficult, and electronic ballot manipulation or poorly designed systems
affect election results. Obviously, government at all levels needs to do a better job to insure the
viability and proper implementation of e-voting systems in future elections.
Morse and Hodges (2002) provide a review of recent voting methods and consider the
impact on voter participation of making political participation easier and more convenient. Voter
participation is expected to improve if citizens are provided with more voting options and if
voting processes make better use of technology. For example, the decision to use remote voting
in Oregon in all elections by using mail-in balloting resulted in that state achieving one of the
highest election turnouts in the nation (Delk, 2001). The next section looks more specifically at
Internet voting and related issues, such as the potential benefits of its use.
Benefits and Examples of Internet Voting Implementation
Internet voting is already being used to supplement corporate annual meetings and the
collection of shareholder preferences. Some expect this type of technology to support the
transition from the current experimental stage of online voting with limited uses, to a widely
used, viable electoral tool in coming years.
Similar to the benefits of e-voting mentioned previously, there are some potentially
significant benefits to society that can be achieved by the proper implementation of Internet
voting. Internet voting would likely improve the convenience and accessibility of voting. It
could eliminate the need to travel to polling locations or to wait in voting lines in order to cast
ballots. Internet voting can potentially reduce the overall cost of elections, assuming that it would
eventually replace traditional voting methods. In this case, the costs associated with ballot
30
printing and hiring and training workers would be eliminated, and the costs of mailing absentee
ballots could also be reduced (Larson, 2001).
The use of Internet technology may provide more convenience for voter registration.
This would eliminate a significant barrier for a large percentage of potential voters, many of
whom do not make the effort to register to vote. Several websites currently provide citizens with
the opportunity to print out voter registration forms, which can then be completed and mailed to
the proper electoral registration location. Moving to a completely paperless method of voter
registration would simplify the process and eliminate a large barrier to voter participation.
It is expected that the use of Internet voting would benefit specific groups more than
others (Moglen & Karlan, 2001). There is some evidence to indicate that the use of Internet
voting would benefit only those groups who already intend to vote (Alvarez & Nagler, 2001).
However, the use of Internet voting may benefit those citizens who are more computer literate or
have a higher level of online experience.
On the other hand, Internet voting could be a barrier for citizens who do not have the
benefit of Internet access or who may not be as computer literate. This could include, for
example, citizens from minority groups or those with lower incomes. This is one aspect of the
societal issue known as the digital divide, which refers to the gap between individuals with
respect to access to online technology. By providing citizens with better access to information
and communication through technology, Internet voting offers the potential advantage of
increasing voter participation, as well as giving citizens a more direct voice in government.
It is interesting to consider how the use of the Internet could give citizens a more direct
voice in their government. A general decline in civic participation has been observed in recent
years due to a variety of potential causes and factors. Many experts agree that a large number of
citizens feel removed from the political processes that govern their day-to-day lives (Eggers,
31
2005). The use of technological advancements as an integral part of the future of democratic
voting is expected to provide greater opportunities for citizen participation. By reducing potential
barriers to civic involvement and providing new ways to express opinions, the Internet can open
up political discussions to citizens who feel as if their voices are not being heard (Eggers, 2005).
The availability and use of secure, reliable Internet voting systems can help to give citizens
confidence that their votes will be accurately received and counted.
Morse and Hodges (2002) conclude that providing citizens with more options, such as
online voting, would lead to a higher expected level of voter participation. In general, processes
that increase voter participation and maintain free and fair elections should benefit a democratic
society. Even if Internet voting provides only limited increases in voter participation, society
could move toward a more direct form of democracy (Morris, 2001).
Certain segments of the U.S. population could be given a more direct voice in
government by reducing or eliminating barriers by the use of Internet voting. Those with
disabilities can be limited with respect to access to e-government resources (Esteves & Joseph,
2008). For example, elections at traditional polling places often cause disabled citizens to be
confronted with a range of barriers to participation. A U.S. General Accounting Office report to
Congress in 2001 found that only 16% of polling places in 2000 had no impediments to access by
people with disabilities (USGAO, 2001). The other 84% of polling locations were identified as
having one or more physical impediments limiting access to people with disabilities in some way.
These barriers included issues such as inadequate capabilities to accommodate voters in
wheelchairs and an inability of ballots or voting equipment to be usable by voters who are
visually impaired. The report explains that access to polling places vary significantly from state
to state mainly because federal law allows each state to define the meaning of the term
accessibility (USGAO, 2001). Although improvements in polling place access have been made
32
over the last few years, barriers to those with disabilities still exist. In order for e-government to
truly represent democracy, citizens must be provided with equal access (Jaeger, 2004).
The USGAO report (USGAO, 2001) identifies Internet voting as one way to address
problems related to access, but states that its implementation presents a variety of challenges.
Although election officials and others have expressed concerns about the security and reliability
of Internet usage and limited access for some, the report refers to task force studies suggesting
that Internet voting could be successfully implemented in stages (USGAO, 2001).
As examples of this concept, Arizona and California have taken steps toward achieving a
phased implementation (Morse & Hodges, 2002). California developed a four-stage plan to
implement Internet voting while achieving a balance between public access and security (Delk,
2001). Although the plan has yet to be fully implemented, the state believes that using these four
stages can help to guarantee a successful transition to Internet voting in the coming years.
The first stage permits supervised Internet voting at traditional polling locations. Voters
would be given the choice of voting online or using other “offline” balloting methods. Votes cast
online would be electronically sent to the county election board to be counted. The second stage
would permit voters to cast their ballots online from any polling location within a local voting
district. These could include nontraditional polling locations, such as shopping centers, operated
by election officials.
The third stage would permit voters to vote over the Internet at a variety of remote and
unmanned stations set up and controlled by the county. Voters would use a special public key to
access electronic voter registration and to receive a personal identification number, or PIN, to
confirm identification at the time of voting. The final stage would enable citizens to cast votes
over the Internet at any time and any place. Voters could receive a confirmation that their vote
33
had been received by the county and tabulated. This four-stage process also has the flexibility to
allow the government and citizens to adjust to any needed technological changes (Delk, 2001).
Alvarez and Hall (2004) consider Internet voting to be casting votes over an Internet
connection using computers that are not under the physical control of election officials. The
authors distinguish this as remote Internet voting from three others types of potential Internet
voting, which include voting at kiosk locations, polling places, and local precincts on computers
that are under the physical control of election officials.
The world’s first use of true Internet voting for a binding nationwide referendum
occurred in Switzerland in September of 2004 (Kriesi & Trechsel, 2005). After several pilot
votes on the local level, Swiss citizens were given the opportunity to cast their votes by remote
Internet access. In this case, the main factor influencing voters to use Internet voting was the
convenience and saving of time relative to conventional voting. Although the method of voting
was different, the overall number of voters was similar to past referendum votes.
In this U.S., Internet voting has been attempted only sparingly in elections related mainly
to the selection of party delegates. In 2000, the Arizona State Democratic Party used a private
election process to choose delegates to its convention. Internet voting was one of four available
voting options that also included mail-in ballots, in-precinct paper ballots, or in-precinct
electronic ballots (Alvarez & Nagler, 2001). Similar to the plan in California, voters choosing to
cast ballots over the Internet received PINs, which were needed during the login process for
verification of identification prior to voting. It is interesting to know that the highest percentage
of voters chose to cast their ballots online. The Michigan Democratic Party implemented a
similar Internet voting election process in 2004 as a way to improve accessibility and voter
turnout for its presidential caucuses.
34
Although these experiences were considered successful overall, they were not without
problems (Alvarez & Nagler, 2001). For example, some voters could not access the websites due
to too many voters trying to cast their votes at the same time. In addition, a number of voters
tried to use hardware or software that was not compatible with the voting websites. Other
reported technical problems included ballots not appearing properly on computer screens,
confusing directions about voting processes, and problems with the verification of voter personal
information (Alvarez & Nagler, 2001).
Alvarez and Nagler (2001) propose that Internet voting could solve many problems with
current election processes in this country and may increase the levels of voter interest and
participation. Issues involving security concerns of online voting systems and the digital divide
need to be appropriately addressed. The authors propose a set of solutions to help make Internet
voting a reality in this country in the coming years. These solutions include: developing and
using experimental methods to properly evaluate Internet voting systems, providing federal
funding for states to experiment with Internet voting, establishing a program to gradually
implement Internet voting, using the Internet to promote civic participation and deliberative
democracy, increasing the level of research on Internet voting security, resolving legal barriers to
Internet voting, and eliminating the digital divide (Alvarez & Nagler, 2001).
As mentioned earlier, a key purpose of this study is to add to the body of knowledge
regarding the intent of citizens to participate in online elections. The next chapter considers
relevant research models that predict the willingness of users to adopt new technologies. Several
of these e-government models are integrated later in this study to define a conceptual Internet
voting model that assesses the potential use of online voting in this country.
35
Chapter 2
Literature Review
Having explained in the previous chapter how e-voting technologies and voting processes
relate to the broader concepts of e-governance and e-democracy, this chapter provides a literature
review of general theories related to technology adoption as well as theories that are specific to
the implementation and adoption of e-government services and initiatives. In considering these
various theories of technology adoption, emphasis is placed on how the approaches relate to
aspects of e-voting.
General Theories Related to Technology Adoption
This section provides a review of the literature of general theoretical approaches
involving the adoption of technological improvements and how they relate to the implementation
of e-voting. The general approaches considered include: diffusion of innovations by Rogers
(1995), technology acceptance models by Davis (1989), technology and quality of service by
Dabholkar (1996), and trust theory by Carter and Belanger (2005) and others.
The choice of a citizen or consumer to use an electronic service delivery method over
other traditional methods can be considered as an issue involving technology adoption. Research
in this area can be viewed as varying along a continuum from applying existing theories in a
technology context to the development of specific technology adoption approaches (Gilbert,
Balestrini, & Littleboy, 2004). The authors have identified three reliable approaches for assessing
the adoption of technological improvements. The first of these involves the diffusion of
36
innovations theory developed by Rogers (1995). The other two approaches are extensions of
existing theory related more specifically to technology. These include the technology acceptance
models by Davis (1989) and the application of diffusion to technology quality of service by
Dabholkar (1996).
A fourth approach involves trust models, or web trust theory, by Carter and Belanger
(2005) and others. This approach provides a better understanding of the application of the first
three approaches with respect to e-government initiatives. Each of these general approaches is
considered in more detail in the following sections along with an overview of how they have been
recently used in a variety of applications.
Several other approaches integrate two or more of these general approaches and have
been directly applied to studying the use and adoption of e-government and e-voting processes.
These integrated approaches are described in more detail later in this chapter.
Diffusion of Innovations (DoI) – Rogers (1995)
One theoretical approach related to the adoption of technology is known as innovation
diffusion theory or the diffusion of innovations theory (DoI). This theory attempts to explain
how, why, and at what rate new ideas and technologies spread through society. The theory also
seeks to understand the process by which innovations become distributed over time within and
across society.
In considering how DoI theory can address public policy issues related to e-democracy,
such as Internet voting, it is important to note that diffusion theory is not a single, unified theory.
It is instead composed of a number of theories from a variety of disciplines, each focusing on a
different aspect of the innovation process. In combination, they create a meta-theory of diffusion
37
(Surry, 1997). This literature review emphasizes the subset of meta-theory that relates more
directly to the adoption of information technology innovations.
Although the concept of diffusion was first studied in the late 19th century, its use was
popularized by the work of Rogers (1995), who provided extensive research while integrating
over 500 diffusion studies. His work established important foundational concepts and is
considered to be the best presentation of a unified theory of diffusion.
Rogers (1995) describes diffusion as the process by which an innovation is
communicated through certain channels over time among the members of a certain social
community as well as how it is adopted and gains acceptance by those members. Rogers states
that “the innovation is an idea, practice, or object that is perceived as new by an individual or
other unit of adoption” (Rogers, 1995, p. 11).
According to Rogers (1995), there are four key factors that influence the diffusion
process. These include the innovation itself, how information about the innovation is
communicated, the timing of the innovation, and the nature of the social system into which the
innovation is being introduced. Basic diffusion research looks at how these four factors interact
with one another, and with other factors, to increase or decrease the rate at which specific ideas or
practices are adopted by members of particular groups.
A five-part segmentation model of adoption by Rogers (1995) is shown in Figure 2.1. It
explains that an innovation will diffuse through a population over time, and the rate of adoption
will vary between those who adopt early, referred to as “innovators” and “early adopters,” and
those who adopt the innovation much later, referred to as “laggards” (Rogers, 1995). The
remaining two segments, “early majority” and “late majority”, account for the majority of users
who adopt an innovation over time. As a new technology is adopted by successive groups of
users, the market share eventually reaches a level of saturation, as also shown in Figure 2.1.
38
Rogers (1995) has also proposed an adoption process in which the diffusion of an
innovation into society occurs through five decision-making stages. This decision innovation
process is shown in Figure 2.2 and includes the stages of knowledge, persuasion, decision,
implementation, and confirmation.
Figure 2.1: Segmentation of Adoption Model (Rogers, 1995).
Figure 2.2: Decision Innovation Process (Rogers, 1995).
39
This theory proposes that diffusion is a process that occurs over time. As potential
adopters become aware of an innovation, they are persuaded about the value of the innovation,
make a decision regarding adoption and implementation of the innovation and finally, evaluate
the results. The DoI adoption process by Rogers (1995) is among the most useful and well-
known of diffusion theories. The varying rates of adoption indicate that some users are more
resistant to accepting an innovation. This is characteristic of many situations related to the
implementation of e-government initiatives. For example, the resistant-to-change phenomenon
can explain much of the hesitation that occurs on the part of constituents in moving from a paper-
based to a web-based system for interacting with government.
In addition to the rate of adoption and innovation decision process theories, the perceived
attribute diffusion theory (Rogers, 1995) has also become especially relevant to public sector
innovations. Since the adoption of innovations is represented as a process of gathering
information and reducing uncertainty as a means to evaluate the technology, an individual's
decision on whether to use the technology is based on perceptions of the technology. These
perceptions include image, relative advantage, compatibility, complexity, trialability and
observability.
Agarwal and Prasad (1998) propose that relative advantage, compatibility, and
complexity are the three factors supported the most by empirical studies. These studies have been
applied to the adoption of technology by employees using information systems to perform job
roles as well as by consumers to obtain products or services. Perceived attribute theory, with its
emphasis on user perceptions of image, relative advantage, benefits, and the ease of use of
technological innovations, is a key part of the methodology used later in this study to analyze the
use of online voting systems.
40
Another widely used diffusion model is known as the Bass model (Bass, 1969). This
model involves the timing of adoption of an innovation and first-purchase demand. Aspects of
the Bass model are consistent with the work of Rogers. The model is based on the conditional
probability that an adoption will occur at a given time, assuming that an adoption has not yet
occurred (Norton & Bass, 1987). This model, along with other similar theories, includes the
effects of diffusion and substitution and is considered to be conceptually sound.
Other related models, known as substitution models, focus on the demand for
technological innovations as a substitute for existing methods. One example is the Fisher-Pry
model (Fisher & Pry, 1971). This model is based on three assumptions that are each applicable to
e-voting adoption. The first assumption is that many technological advances can be considered to
be viable substitutions of one way of satisfying a need for another. The second assumption is that
often new technologies completely replace previous ones. The third assumption is that the rate of
substitution of new technologies for older ones is proportional to the amount of the old
technology that remains. Variations of the Fisher-Pry substitution model include a time
component that accounts for the delayed learning about the benefits of an innovation (Floyd,
1968; Sharif & Kabir, 1976).
Concepts of diffusion theory include the acceptance of new objects as well as ideas.
Some have considered diffusion to be the main driver of change in society (Bell, 1968). In this
regard, diffusion is related to the concept of technology-as-tools as well as the concept of
technology-as-organized-intelligence (Waller, 1982).
DoI theory has been used within a variety of fields to assess and improve the adoption of
innovative practices. The theory attempts to describe patterns of adoption, to explain this
process, and to predict the potential success of implementing a new idea, product, or process.
41
The concept has been applied to such fields as economics, marketing, sociology, anthropology,
medicine, and others (Brown, 1981; Hagerstrand, 1967).
Sabatier (2007) devotes a chapter in his edited book to the adoption of innovations at the
state level, and summarizes the work relating diffusion models to innovation adoption within
governmental entities by Berry and Berry (1990, 1992, 1994). Sabatier presents several diffusion
models, along with three reasons why states emulate each other. These reasons include: (a) states
learn from one another, (b) states compete with each other, and (c) public officials receive public
pressure from citizens to adopt policies from other states.
Other diffusion models described by Sabatier include the national interaction model, the
regional diffusion model, leader-laggard models, and vertical influence models. The national
interaction model is a learning model in which public officials learn about programs from peers in
other states using a communication network among state officials. The regional diffusion model
proposes that states are mainly influenced by nearby states. Leader-laggard models involve cases
in which some states are leaders in the adoption of certain policies, while others follow this
leadership (Walker, 1969). Vertical influence models consider states as followers of policies of
the federal government. These models can each relate to e-voting as states implement systems at
different rates and for different reasons.
In contrast to these diffusion models, which involve intergovernmental factors, the
concepts by Berry and Berry (1990, 1992, 1994) also include a second method by which states
adopt new programs, namely internal determinants (Sabatier, 2007). In this method of explaining
adoption, states are driven to innovate and adopt new programs or policies based on political,
economic, or social factors that are internal to the state.
DoI theory is often used to assess the effect of various attributes related to a particular
innovation and its adoption. The theory has potential significance when applied to the use of
42
information technologies. Researchers are interested in the factors that affect the adoption of IT
advances as well as other aspects within the field of information sciences. As new technologies
are developed and implemented, the application of diffusion theory can provide systematic
models that help to predict the adoption and diffusion of these technologies.
DoI has often been applied to problems and situations in the public sector. The theory
can be valuable to areas of public policy and public management. It can help to guide the
processes of policy development and implementation as well as to improve administrative
decisions and methods in the public sector. Due to the complex nature of the policy process,
many stakeholders do not understand how and why policies are developed and implemented. An
awareness of the many factors influencing the development and adoption of public initiatives can
help policy analysts and public managers to better understand the effects of these innovations as
well as to assess their potential impacts on society.
Technology Acceptance Models (TAM) – Davis (1989)
Having looked at a variety of DoI theories, a second approach to analyzing technology
adoption is known as technology acceptance models, or TAM. This approach is an extension of
the theory of reasoned action to technology, or TRA. TRA is from the social psychology
literature (Ajzen & Fishbein, 1980) and involves an individual’s evaluation of the potential to
perform a specific task.
TAM concepts have their basis in information systems theory and attempt to predict how
users accept and adopt the use of a new technology, such as the Internet. The primary model
within the TAM approach, shown in Figure 2.3, proposes that beliefs have an impact on attitudes
about new systems, such as those used for online voting.
43
These beliefs can lead to intentions and behaviors related to actual technology usage.
The beliefs that predict the use of technological systems include perceptions about the usefulness
of the technology related to improved performance, and perceptions about the ease of use of the
technology (Davis, 1989).
O'Cass and Fenech (2003) have shown that the risk of using the technology is a major
factor in determining the intention to use it. As a result, further extensions to the TAM have been
proposed, mainly in the area of subjective norms, which are a key part of the TRA but are not
included in the original TAM.
Other work has developed positive results for the adoption of technology with respect to
social norms (Karahanna, Straub, & Chervany, 1999; Lucas & Spitler, 1999). An example is the
case in which an individual’s behavior is affected by their beliefs about the evaluations that a
person held in high regard would have in using the technology. As a result, Venkatesh and Davis
(2000) developed an updated TAM which includes these types of subjective norms.
Attitudes about technology usage have also been proposed as having an influence on
Internet usage (Eastlick, 1993; Shim & Drake, 1990). Although most research related to
technology adoption and the use of the Internet considers the positive effects of other factors on
Figure 2.3: Technology Acceptance Model (Davis, 1989).
44
this behavior, O'Cass and Fenech (2003) have studied the factors that discourage individuals from
adopting the technology.
There is an increasing recognition of the need to research attitudes with respect to
Internet-related adoption. Several attitude-based theories, such as the TRA, theory of planned
behavior, and theory of trying, have been integrated with external factors, such as perceived risks,
to try to explain why individuals may prefer options based on the use of technology (Bobbitt &
Dabholkar, 2001). As a result, these theories each have some relevance to the adoption of e-
voting systems.
Technology and Quality of Service (TQS) – Dabholkar (1996)
In the DoI and TAM approaches, the perceptions of potential users and adopters of
technology determine the behavior about a product, service or technology, including the intent to
use or adopt that technology. A third approach is known as technology and quality of service
(TQS). This approach involves user intentions based on service quality to explain service
delivery by the use of technology. These models include perceptions that can relate to the
evaluation of service performance after the technology has been used. In other words, TQS
methods try to understand the precursors that can affect user behavior.
In a study of consumer evaluation of self-service delivery through technology, Dabholkar
(1996) proposes two models to determine the impact of service quality on the intention to use the
technology. These include: (a) the attribute based model, and (b) the overall affect model, each
of which is shown in Figure 2.4.
45
The attribute based model focuses on various attributes of quality while the overall affect
model is based on predetermined attitudes about the technology. The attribute model uses
dimensions that are similar to those used in other service quality literature. The work of
Dabholkar (1996) shows that speed of delivery, ease of use, reliability, enjoyment, and control are
all significant factors in assessing expected service quality. Other comparative models show that
consumers compare the innovative technology service delivery with traditional alternatives
(Meuter, Ostrom, Roundtree, & Bitner, 2000; Szymanski & Hyse, 2000).
Trust Theory – Carter and Belanger (2005) and Others
A fourth approach to technology adoption involves trust theory. Trust can be defined
along two dimensions: as an assessment of a current situation, or as an innate personality trait or
Figure 2.4: Technology and Quality of Service Models (Dabholkar, 1996).
46
predisposition (Driscoll, 1978). Trust is an important aspect of user decision making. For
example, one’s level of trust is an important factor affecting purchase or transaction decisions.
This concept has been used with respect to e-commerce (Jarvenpaa, Tractinsky, & Vitale, 2000;
Koufaris & Hampton-Sosa, 2004).
A citizen who has previously not established trust in the e-commerce domain can transfer
that lack of trust to other areas, such as e-government. The level of trust can vary depending on
the person and the situation. A lack of sufficient trust can limit the use of e-government
initiatives, such as e-voting systems, by citizens.
When evaluating voting processes, it is useful to consider the concept of “trust of
government” and its importance to a democratic society. Miller and Listhaug (1990) assert that
trust of government is an assessment of “whether or not political authorities and institutions are
performing in accordance with normative expectations held by the public” (p. 358). Levels of
declining trust in recent decades have been related to declines in political participation by several
scholars (Craig, 1996; Hetherington, 1999; Norris, 1999), and some researchers associate recent
declines in voter turnout rates in the U.S. with a decline in political trust (Putnam, 2000).
Beyond the issues of voting and citizen participation, trust is important in society for both
the legitimacy and stability of the political system (Mossberger & Tolbert, 2005). According to
Barber (1984), trust makes everyday life easier, less complex, and more orderly, which increases
the stability of a democratic society and reduces the level of fear and worry among citizens. Dahl
(1971) asserts that democratic society is unlikely to emerge without political trust. Putnam
(1993) claims that political trust makes democracy work. Trust of government also encourages
compliance with laws and regulations, which can be considered an important part of democratic
government (Ayres & Braithwaite, 1992; Levi, 1997; Scholz & Lubell, 1998; Tyler, 1990).
47
Distrust of government can diminish the legitimacy of government, so high levels of
distrust and cynicism are cause for concern in a democratic society (Craig, 1993; Donovan &
Bowler, 2003; Putnam, 2000). In addition, a lack of trust of governmental institutions can
undermine the rule of law and jeopardize a democratic society.
As significant as the level of citizen trust appears to be with respect to the proper
functioning of a democratic society, there has been no clear consensus on how to measure it. The
American National Election Studies (ANES, 1958) developed an index of five questions to
identify some basic aspects of trust in governmental processes, from which most measures of
political trust have evolved. These aspects included such issues as trusting the government to do
what is right, perceptions about the government being run by a few big interests, whether or not
tax revenues are wasted, and whether those running government are honest or intelligent.
Since research involving trust of government often implies that more trust is beneficial to
democracy, being able to effectively measure the level of political trust by citizens is important.
Recent research has assessed political trust using a single-item trust measure which asks citizens
how much of the time they think they can trust the government to do what is right (Alford, 2001;
Citrin & Luks, 2001; Hibbing & Smith, 2003).
Since trust is only one of many factors in making complex decisions about political
participation, and government involves many actors and institutions in society, the effects of
citizen trust and confidence remain difficult to measure (Levi & Stoker, 2000). Thomas (1998)
argues that little has been done in research to consider the exact means through which public
institutions can maintain or create trust in government. Hibbing and Theiss-Morse (2002)
conclude that the beliefs of citizens about the fairness and responsiveness of government
processes are important.
48
There are specific perceptions by citizens that government is no longer responsive to their
needs and preferences (Donovan & Bowler, 2003). Hibbing and Theiss-Morse (2001, 2002) have
shown that while the preferences of citizens may fall short of direct democracy, citizens want a
more participatory policymaking process than what they currently perceive as the norm in
American representative government. To this end, trust is one factor that is often used to try to
understand citizen confidence in government.
E-government has been proposed as a way to increase citizen communication with
government, which in turn can increase political trust (Chadwick & May, 2003; Ho, 2002; Seifert
& Peterson, 2002; Tapscott, 1997; Thomas & Streib, 2003; West, 2004). In spite of studies
suggesting differing effects of trust of government on voter turnout, it is clear that the successful
diffusion and acceptance of e-government initiatives, such as e-voting, requires two types of trust
by citizens. The first is trust in the technology that supports the service or initiative, while the
second is trust in the government that is involved in the service or initiative (Carter & Belanger,
2005; Lee & Turban, 2001). Thus, if a constituent has a lack of trust of either technology or of
government, his or her intention to use an e-voting system can be limited.
In addition to trust, security is a critical factor limiting the adoption of e-government
services (Gilbert et al., 2004). Therefore, it is important to maintain effective security within e-
government systems to promote and protect consumer trust and confidence. More information on
the issues of trust of government and of technology is provided in the next chapter when
describing the specific trust constructs used to assess the intentions of voters to vote online.
Given the review of literature in this section of a variety of general theories and models
that can be related to the adoption of innovations and technology, the next section considers
several theories that have been developed and applied specifically to the adoption of e-
government or e-voting initiatives.
49
Integrated Theories for E-Government Initiatives
The literature on citizen adoption of e-government initiatives is somewhat fragmented.
However, in recent years, researchers have begun to integrate approaches into models to identify
major factors, and the relationships among factors, that influence the adoption of online
government services by citizens. A major advantage of this technique is that the integration of
approaches can reduce the limitations of the individual approaches (Gilbert et al., 2004).
This section considers three integrated approaches that have been developed specifically
for assessing the implementation of e-government services and initiatives. These approaches
include the combination of DoI, TRA, and TAM theories (Gilbert et al., 2004), the integration of
DoI, TAM, and trust models to evaluate e-government initiatives (Carter & Belanger, 2005), and
a hybrid model to evaluate the adoption of online voting systems by citizens (Schaupp & Carter,
2005). Each of these three approaches is described in more detail in the following sections.
E-Government Adoption Model – Gilbert, Balestrini, and Littleboy (2004)
Gilbert et al. (2004) evaluated an e-government adoption model that combines attitude-
based constructs from DoI theory (Rogers, 1995) and TRA theory (Ajzen & Fishbein, 1980) with
aspects of service quality-based TAM theory (Davis, 1989). Attitude-based approaches are
supported by behavioral theory that links perceptions to user intentions, so it can be useful to link
attitudes to behaviors by combining attitude-based approaches with service-based approaches.
Figure 2.5 shows the e-government adoption model of Gilbert et al. (2004). In this
model, the dependent variable is the willingness of citizens to use e-government services, while
independent variables are perceived relative benefits and perceived barriers to the use of the e-
government services.
50
The perceived relative benefits include the avoidance of personal interaction, control,
convenience, cost, personalization, and time. The perceived barriers include confidentiality, ease
of use, enjoyment, reliability, safety, and visual appeal. The model includes age as a factor that
can influence the adoption of e-government initiatives.
The results of this study show that all factors, except the avoidance of interaction, are
correlated with a willingness to use e-government services. Time and cost factors are found to be
significant predictors of usage, including other modified factors such as financial security, trust,
and information quality. The study also concludes that a significant difference in the willingness
of a potential user to adopt e-government initiatives is due to the age of the user.
Figure 2.5: E-Government Adoption Model (Gilbert, Balestrini, & Littleboy, 2004).
51
E-Government Adoption Model – Carter and Belanger (2005)
Carter and Belanger (2005) developed a comprehensive e-government adoption model
that combines constructs from DoI theory (Rogers, 1995), TAM (Davis, 1989), and web trust
theory (Lee & Turban, 2001; McKnight, Choudhury, & Kacmar, 2002). The model identifies
factors that affect the adoption of online government services by citizens and can be applied to a
wide range of e-government initiatives at local, state, and federal levels. Figure 2.6 shows this
model.
The results of this study show that constructs from each of the DoI, TAM, and web trust
models make valuable contributions to the model. The model explains 85.9% of the variance in
citizen adoption of e-government. The perceived ease of use, compatibility, and trustworthiness
factors are found to be significant individual variables.
Figure 2.6: E-Government Adoption Model (Carter & Belanger, 2005).
52
The trustworthiness factor is composed of trust of the Internet and trust of government.
According to the results of the study, citizens perceiving the reliability and security of the Internet
to be low are less likely to adopt e-government services. Citizens who perceive government
agencies to be more trustworthy are more likely to adopt e-government services. Neither
perceived relative advantage nor perceived image is found to be significant, and age is not found
to be a significant demographic factor.
E-Voting Adoption Model – Schaupp and Carter (2005)
Schaupp and Carter (2005) extended the framework of Carter and Belanger (2005) to
explore the intention of young citizens to use an online voting system. College students were
surveyed to try to determine the factors that might influence their intention to use e-voting
systems. Voter intentions were assessed in spite of any existing security concerns. Figure 2.7
shows the model of e-voting adoption by Schaupp and Carter.
The results of this study indicate that user perceptions of compatibility, usefulness, and
trust significantly impact the intentions of young citizens to use an e-voting system. If a user
perceived that an e-voting system is compatible with their use of prior systems, such as e-
commerce or e-government services, the user is more likely to adopt an e-voting system. Also, a
voter’s intent to use an e-voting service increases if the service is perceived to be useful.
With respect to trust, a higher level of trust of the Internet is found to have a direct,
positive effect on the intention of a voter to use an e-voting system. Finally, for citizens to adopt
e-voting, they must believe that the government will take the steps necessary to ensure a fair and
reliable process. In this model, the only significant variables include the prior use of an e-
commerce service and the prior use of an e-government service.
53
The three preceding models demonstrate the use of innovative approaches to analyze and
predict the intention of users to adopt e-government initiatives or e-voting systems. However, the
authors of each model have expressed concerns about limitations of their work due to a variety of
design and methodological factors. The next chapter presents a research design that builds upon
the theoretical foundation of these three models while trying to address some of the limitations in
applying these concepts to the adoption and use of Internet voting systems.
Figure 2.7: E-Voting Adoption Model (Schaupp & Carter, 2005).
54
Chapter 3
Research Hypotheses and Conceptual Model
As shown in the previous chapter, recent research has relied on a variety of theories and
models to try to identify and predict factors that influence users to adopt new technologies related
to the governance of citizens. The results of this prior research have been mixed due in part to
limitations that researchers have identified regarding their respective studies.
As stated previously, a main objective of this exploratory study is to better understand the
factors and perceptions that would influence the intentions of voters to use an online voting
system. Among other uses, an understanding of these factors and the associated relationships can
help to determine how best to design and implement future Internet voting systems. A secondary
objective is to explore the relationships between demographic and other characteristics, such as
age, voting and Internet experience, and the intent of voters to participate in online elections.
Regarding user characteristics, factors like income, education, and a variety of other
personal characteristics can be contributing factors that affect one’s willingness to adopt new e-
government initiatives. For example, an individual’s resistance to change can be a contributing
factor in the decision to use any new system. Rogers (1995) observes that if a preference exists to
maintain the status quo, there is a greater chance that resistance to new processes will persist.
This chapter introduces a set of hypotheses and an Internet voting conceptual model that
builds on previous research in order to achieve the stated objectives of the study. Although a
wide range of possible factors can be analyzed, it was decided to use hypotheses that focus on
several key user characteristics, as well as the important, related issues of trust of technology and
government. After discussing the hypotheses, the model is presented and the individual model
55
constructs are introduced and described. The model is based in part on the technology adoption
approaches by Gilbert et al. (2004), Carter and Belanger (2005), and Schaupp and Carter (2005),
as described in the previous chapter.
Research Hypotheses
The following set of hypotheses is proposed, analyzed, and evaluated in this study.
H1: The age of potential voters is significantly related to the expressed likelihood to vote
online.
H2: Potential voters with greater Internet experience are more likely to vote online.
H3: People with previous voting experience are more likely to vote online than those who
have never voted.
H4: Potential voters with greater trust of Internet technology are more likely to vote online.
H5: Potential voters with greater trust of government at the overall, local, state, and federal
levels are more likely to vote online in elections at each respective level.
The factors being considered in these hypotheses have previously been shown to have
varying degrees of significance on the adoption of e-government and e-voting systems by
citizens. The evaluation of these hypotheses is expected to clarify the level of significance of the
factors, as well as to provide useful information for improving or refining the Internet voting
conceptual model prior to its implementation in future work involving the general voting
population in this country.
The first hypothesis deals with the relationship of the age of a potential voter to his or her
willingness to vote online. As mentioned earlier, age can be a factor, in addition to facilitative
56
and motivational factors, in determining voter participation, and older voters tend to have higher
voter participation rates compared to younger voters. For example, it was mentioned earlier that
citizens over the age of 45 are about twice as likely to vote as those under the age of 25 (Alvarez
& Hall, 2004).
Gilbert et al. (2004) conclude that a significant difference in the willingness of a potential
user to adopt e-government initiatives is due to the age of the user. However, Carter and
Belanger (2005) conclude that age is not a significant demographic factor in adopting online
government services. Thus, it is unclear whether the age of a potential user is a significant factor
to consider when exploring his or her willingness to adopt a new type of technology or system
such as those used for online voting.
The second hypothesis relates to whether or not a higher level of Internet or online
experience by a potential voter is associated with a higher likelihood to participate in Internet
voting. As mentioned earlier, the level of technology or Internet experience is often age
dependent. Younger adults are generally more likely to use new technologies, such as the
Internet, as well as new applications of older technologies. The use of computer technology has
become an increasingly essential part of the curriculum in K-12 grades in this country. Since
young people are being exposed to new information technologies at early ages, their abilities to
acquire technological skills, to adapt to changes in the use of technology, and to adopt new uses
of technology appear to be greater than those of older adults.
Schaupp and Carter (2005) conclude that a higher user perception of compatibility results
in a higher rate of participation in an e-voting system. If an e-voting system is perceived by an
individual to be similar to a system that has been previously used, such as one used for e-
commerce or e-government services, he or she would have a higher likelihood of using the e-
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voting system. Thus, the availability of Internet voting may result in an increased rate of voter
participation among younger voters due to their higher level of experience with technology usage.
The third hypothesis explores the relationship between voting experience and the
likelihood that a potential voter would decide to vote online. As mentioned earlier, political
experience can be a facilitative factor that increases the likelihood of a person to vote. Thus, a
higher level of voting experience by a citizen may be related to his or her decision to vote online.
The fourth hypothesis explores the relationship between trust of technology and the intent
to vote online. As described earlier, there appears to be a demonstrated link between trust of
technology and the adoption of technology. Schaupp and Carter (2005) conclude that a higher
level of trust in the Internet by citizens was found to have a direct, positive effect on the
intentions of those citizens to use e-voting systems. Similarly, Carter and Belanger (2005)
conclude that citizens perceiving the reliability and security of the Internet to be low were less
likely to adopt e-government services. Based on these results, it is expected that a higher level of
trust of technology by a voter would indicate that he or she is more likely to use Internet voting.
The final hypothesis explores the link between the level of trust of government and the
willingness of a citizen to participate in online elections at multiple levels. Recent work by
Schaupp and Carter (2005) and Carter and Belanger (2005) indicate that a higher perception of
trust in government by a citizen has a direct, positive effect on his or her likelihood to use e-
voting systems. A similar effect is expected to be observed in this study. However, since trust of
government has not been previously explored at multiple levels of government with respect to the
use of e-government services, the expected results at each individual level are uncertain.
Having reviewed the set of hypotheses being evaluated in this study, the next section
introduces the Internet voting conceptual model and discusses its constructs and variables.
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Internet Voting Conceptual Model
The Internet voting conceptual model is shown in Figure 3.1. The model includes eight
constructs, or summed indices, representing a total of 22 independent variables. Because the
complexity construct from DoI theory is considered to be equivalent to the perceived ease of use
construct from TAM, the model has a total of seven unique constructs. The model shown in
Figure 3-1 is a collection of four separate models, since each of the four dependent variables
represents the intention to use Internet voting at one of the four levels of government. The model
extends previously described technology adoption models by integrating constructs and by
considering the relationships between the independent variables and the user intention at the
overall, local, state, and federal levels. The model’s constructs are described in the next section.
Figure 3.1: Internet Voting Conceptual Model.
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Model Constructs
The Internet voting conceptual model includes a total of eight constructs as independent
variables and four dependent variables. Each of these constructs has been previously shown to be
valid in research involving the adoption of various systems and applications within e-government.
For many of these factors, positive relationships have previously been shown to exist with the
intention of citizens to use e-voting methods (Schaupp & Carter, 2005). This section introduces
the individual variables and explains them in more detail along with the expected relevance to the
adoption of Internet voting systems by potential users.
The following four constructs are obtained from the DoI research stream: (a)
compatibility, (b) relative advantage, (c) image, and (d) complexity. Compatibility is the degree
to which an innovation is seen to be compatible with values, beliefs, experiences, and the needs
of adopters (Rogers, 1995). This includes the perception of a match between an online voting
process and a user’s lifestyle (Schaupp & Carter, 2005). Citizens who regularly use the Internet
for communication and other activities are more likely to view e-voting as being consistent with
ways in which they prefer to interact (Carter & Belanger, 2005).
Relative advantage is the degree to which an innovation is seen as better than its
predecessor (Rogers, 1995). For Internet voting, this is the degree to which an online voting
system is perceived as being better than other traditional methods of voting. One aspect of
relative advantage includes the convenience with respect to time and location of being able to cast
votes online. Citizens who recognize and appreciate relative advantages are more likely to adopt
e-voting systems (Schaupp & Carter, 2005).
Perceptions of image include whether or not potential users view themselves as more
admired, modern, or intellectual due to adopting a new technology (Schaupp & Carter, 2005).
Those participating in online voting systems can perceive themselves as being more popular with
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their peers, or having a higher level of respect or status as a result of their participation in this
new application of technology.
Complexity is the degree to which a person believes that a system is effortless to use for
accomplishing a task (Davis, 1989). With respect to Internet voting, this includes characteristics
of the usability of online systems, such as the ability to comprehend the website, to interact with
it, and to navigate it in order to accomplish the related goals of obtaining voter information and
casting votes online. For example, if an online system is difficult to access or use, or if the design
makes it challenging for users to find desired information or to navigate the site, it will not be as
highly used. Thus, government agencies and other organizations who design online voting
systems need to insure that they are properly designed, easy to understand and navigate, and easy
for users to complete their voting transactions.
Storer and Duncan (2004) explain that some aspects related to this usability issue include
proper attention to the number of options on ballots, the maximum and minimum number of
options available for selection by voters, and the use of buttons to apply understandable orderings
to options being considered by voters. Online voting systems need to display ballots in a way that
permits voters to make and submit their voting choices as intended (Schaupp & Carter, 2005).
Online systems need to be usable by citizens who may have a very limited knowledge of e-voting
processes, and systems need to be convenient for voters to be able to accomplish their intended
objectives.
The following two constructs are obtained from the TAM research stream, each of which
has been previously shown to influence the intention of an individual to use a new system: (a)
perceived ease of use, and (b) perceived usefulness. Perceived ease of use is the degree to which
a person believes that using a system is free from effort (Davis, 1989). This construct is
considered to be equivalent to the DoI complexity construct described earlier.
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Perceived usefulness is the degree to which a person believes that using a system would
improve performance (Davis, 1989). With respect to Internet voting, this refers to the perceptions
of voters about online voting as a way to participate in elections more efficiently, including
saving time, being useful, or being convenient.
The following two constructs are obtained from the web trust research stream: (a) trust of
Internet technology, and (b) trust of government. Regarding trust of technology, trust theory
indicates that the successful adoption of Internet voting by citizens requires that they have
confidence with respect to three factors of online voting service providers: benevolence, integrity,
and competence (Schaupp & Carter, 2005). The belief of citizens in these three factors is
essential to the successful adoption of online voting.
Citizens must also believe that online service providers will ensure the privacy, security,
and reliability of an Internet voting process if they are to use online voting technologies (Toregas,
2001). Because online voting systems contain personal information and deal with confidential
voting transactions, the adoption of these systems may be opposed if system providers cannot
guarantee an accurate, consistent, and secure process (Jaeger & Thompson, 2003).
An overview of trust of government was provided in the chapter on literature review, but
a few aspects related to Internet voting require further explanation. Trust of government also
involves the confidence of citizens in the integrity and competence of individuals and entities
providing Internet voting services (Lee & Turban, 2001). These would include individuals who
are either inside or outside of government and who are involved in the development,
implementation, and maintenance of voting systems.
One important aspect of government trust is the level of government being considered.
Although the federal or national government is usually considered to be the most visible level of
government, state governments retain a certain level of sovereignty which allows them to make
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some decisions about policy development or implementation. Likewise, county and other local
forms of government retain control over certain aspects of local society. As a result, the
assessment of trust of government in this study is considered at each of these levels. Trust of
government at the overall level is included in the model as a way to compare overall results with
those at the individual levels as well as with the results of previous studies.
Although similar processes may occur at the federal, state, and local levels in
government, there may be different factors that have important influences across the levels of
government. For example, Hibbing and Smith (2001) have shown that there is a higher level of
approval by citizens in the operation of state government as compared to government at the
federal level. Similar examples can be found in the literature. Thus, citizens may evaluate trust
in government differently when considering the specific level of government.
In the past, economic conditions have been used to measure government performance and
have also been found to affect trust (Citrin & Green, 1986; Hetherington, 1998; Miller, 1983).
According to Craig (1996), government actions on policy issues important to citizens have an
influence on trust. Citizens are more likely to trust government when they believe it is pursuing
policies and producing outcomes that are consistent with their own preferences (Citrin & Green,
1986; Hetherington, 1998; Kimball & Patterson, 1997; Miller & Borrelli, 1991).
Actions of government can be considered as either process-based (whether government is
“making decisions fairly”) or outcome-based (whether government is “doing the right thing”).
This dichotomy can impact perceptions of citizens and can produce different responses with
respect to trust. Hibbing and Theiss-Morse (2001, 2002) have shown that citizens are more
trusting of government when asked to evaluate the process. The model in this study follows the
trend in recent research of considering measures of political trust that include both process-based
and outcome-based perceptions of potential voters.
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The four dependent variables represent the various levels in government at which
potential voters can express an intention to vote online. These include the intention of potential
voters to use Internet voting at the overall, local, state, and federal or national levels. This multi-
level set of dependent variables provides information needed to analyze the hypothesized
relationships regarding user intentions to adopt Internet voting as well as to further explore the
importance of level of government with respect to trust. Each of these dependent variables is
evaluated individually as described in the upcoming chapter on analysis and results.
The next chapter summarizes the research design for the analysis and testing of the set of
hypotheses and the Internet voting conceptual model. Included are summaries of the data
collection process, survey population, survey instrument, focus group methods, and the intended
measures.
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Chapter 4
Research Design and Methodology
This chapter describes the research design and methodology used in this exploratory
study. The overall approach used for data collection is presented, followed by details about the
survey population and the survey instrument. The process used to conduct the focus group
interviews is summarized. Finally, the intended measures are described and information about
the validity and reliability of the model and its constructs is presented.
Research Design
Data Collection
Data collection in this study is conducted using mixed methods research in two stages.
The first stage involves surveying college students regarding their opinions and perceptions about
issues related to online voting. The second stage involves focus group interviews of a subset of
survey participants, which are used to supplement the survey process.
Mixed methods research usually involves the combination of both quantitative and
qualitative methods for data collection and/or analysis within a single study or series of studies
(Creswell & Plano, 2007). Creswell and Plano (2007) describe a key principle that combining the
use of multiple approaches can lead to a better understanding of research problems than by using
either approach separately.
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Quantitative data collection methods, such as surveys, can help to isolate and identify
relationships between variables, while qualitative methods, such as interviews, focus groups, or
the observation of participants can help to further explore and/or explain the opinions of
participants in more detail (Creswell & Plano, 2007). As evidenced by the research described
earlier in this paper, collecting data by surveys has been used effectively in recent years to
research topics related to the implementation of e-government initiatives.
The first stage of data collection in this study involved a survey of several hundred
college students at both the undergraduate and graduate levels of a large state university to obtain
opinions and perceptions related to the potential use of Internet voting systems. The sample of
students surveyed is considered to be at least moderately representative of the overall college
student population of voting age in this country.
The survey was conducted using an online format in order to be convenient for
participants as well as to facilitate the collection and analysis of the data. The use of an online
survey for data collection helped to obtain timely responses from a relatively large group of
participants and resulted in a high rate of participation. This surveying took place during the
early fall of 2009.
The second stage of data collection included a series of two focus group interviews that
provided supplemental information to the survey process. One advantage of the use of focus
groups is that it can help to deal with the issue of self-reporting of user behavior that can occur
when using a survey. In general, the use of mixed methods of data collection is one way to
address and minimize this potential problem.
Each of the follow-up focus group interviews was conducted within 2 weeks of the
completion of the survey process. This helped to insure that the issues addressed in the survey
were more easily recalled.
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Survey Population
College students at both the undergraduate and graduate levels from two branch
campuses of the Pennsylvania State University participated in this study. Although the majority
of students who completed the survey were pursuing business or information science degrees at
the undergraduate and graduate levels, students from a variety of other programs and majors
within the university also participated. The online survey format helped to facilitate the
participation of students from different majors and across multiple campuses. The use of a
diverse student population can help to minimize bias of the collected data.
The survey was taken by students before, during, or after their regularly scheduled
classes with permission from their professors. The decision of each student whether or not to
participate was voluntary and had no impact on his or her class grade.
Students were provided with a recruitment script, either verbally or in written form, and
were also given some general background information about the topic and the nature of the
research study. The recruitment script is shown in Appendix A.
A link to the online survey was provided to students through Penn State’s ANGEL online
course management system although the data collection process was handled independently from
this system. The ANGEL acronym stands for “A New Global Environment for Learning”. This
course management environment is a Web-based tool that enables faculty to use the Web to
enhance courses offered through the university. The ANGEL system is supported by a
collaboration of several Penn State departments, including Information Technology Services,
Teaching and Learning with Technology, and Consulting and Support Services.
Since all Penn State students receive training on the features and use of the ANGEL
environment as part of their education, those participating in the survey were familiar with this
course management system. As a result, using this system to access the online survey helped to
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maximize the response rate of the participants as well as to minimize the potential bias due to
variations in the levels of technology and online experience in the student population.
Survey Instrument
The initial survey instrument was adapted from surveys used in recent research studies
involving e-government initiatives. Survey questions and constructs from related e-government
adoption models were customized for use in this study. The specific models used as the source of
constructs in this study are identified later in this chapter in the section on validity and reliability.
Since this was the first use of this customized survey, the initial survey instrument was
pilot tested using a small group of student volunteers from the larger survey population. This trial
run of the survey helped to identify potential problems with the survey process as well as
potential problems with the wording of specific survey questions. The students in the pilot group
completed their survey responses online, and their comments and observations about the survey
were then discussed informally as a group.
During the pilot survey, the online survey process worked smoothly, and the duration of
the survey was judged to be acceptable by the group of student volunteers. Problems with the
wording of several survey questions were identified. Based on the student feedback, the wording
of two questions was revised to help clarify the meaning. One question (38) was modified from:
“People who use the Internet to vote would have a high profile” to: “People who use the Internet
to vote would be more popular with their peers.”
The wording of a second question (39) was modified from: “People who use the Internet
to vote would have more prestige” to: “People who use the Internet to vote would be more highly
respected.” Also, the pilot group expressed unfamiliarity with the type of government services
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available online, so a technology usage question was modified to add examples of renewing a
driver’s license and vehicle registration online.
The revised annotated survey instrument is shown in Appendix B. The survey includes a
total of 62 questions, and participants took an average of about 15 minutes to complete the
survey. To help streamline the survey process and to make it more convenient for participants,
survey questions were grouped into the following categories: demographic information (questions
1-6), past voting behavior (questions 7-11 and 16-19), voter intentions to participate in Internet
voting by level of government (questions 12-15), technology and Internet usage information
(questions 20-30), perceptions of users related to Internet use and Internet voting (questions 31-
47), the level of trust of technology and of government (questions 48-58), and the intent to use
online technology (questions 59-62).
Survey questions involving demographic information (questions 1-6), and those
involving perception constructs and intent to use online technology (questions 31-52, 59-62) were
obtained from prior e-government adoption models, with the exception of questions 38 and 39 as
mentioned earlier. The remaining survey questions were newly developed for use in this study.
Questions about demographics and other voter characteristics were presented to
respondents using discrete choices. Most other questions utilized a 5-point Likert scale of: 1
(strongly disagree); 2 (disagree); 3 (neutral); 4 (agree); and 5 (strongly agree).
Initially, the survey questions assessing the level of trust of government were based on
indices developed by the American National Election Studies (ANES, 1958). Recent research
indicates that problems may exist with these standard indices for evaluating trust in government
(Gershtenson & Plane, 2006). These authors claim that a modified method of measuring trust in
government is easier to use, avoids problems associated with past standard questions, and
improves the ability to determine the frequency with which citizens trust government.
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As a result, the survey questions regarding trust with respect to decision-making
processes and policy outcomes at the local, state and federal government levels were modified to
use the following recommended 5-point, closed-ended trust scales of: 1 (almost never); 2 (once in
a while); 3 (about half of the time); 4 (frequently); and 5 (almost always).
Based on similar recommendations by Gershtenson and Plane (2006), questions for
assessing the level of trust of government in decision-making processes (53, 54 and 55) were
reworded as: “How much of the time do you think you can trust the (level = local, state, or
federal) government to make decisions in a fair way?” Likewise, the questions related to trust in
policy outcomes (56, 57 and 58) were reworded as: “How much of the time do you think you can
trust the (level = local, state, or federal) government to do what is right?”
The modified online survey was designed and implemented using version 2.0.2 of the
php Easy Survey Package, also known as phpESP. This application is an open source program
that is designed to allow users to create and administer online surveys, and allows basic statistics
and results to be viewed prior to detailed analysis. The software was installed on a networked PC
server, which helps to minimize operational problems.
Focus Group Interviews
As mentioned earlier, the use of mixed methods of data collection can help to address the
problem of self-reporting of user behavior that may occur when using a survey. In this study, a
series of two focus group interviews was used to supplement the survey data collection process.
Each of the focus group interviews included 10-12 students who had previously taken the survey
online and who had volunteered to participate in an interview session.
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The focus group interviews were conducted in a classroom environment during open
student activity periods. The audio from each of the focus group sessions was digitally recorded
for convenience of analysis at a later time. Students were advised in advance that the audio from
each interview would be recorded. The interviews included a set of 12 questions with several
follow-up questions, which are shown in Appendix C. Although the interview sessions were
originally expected to last approximately 30 minutes each, both interviews took longer to
complete due to the nature of the focus group discussions.
Since some of the focus group questions were open-ended in nature, the interviews
proved to be quite helpful in gathering additional opinions and reactions from the participants.
Each focus group was conducted by a two-person team, one of whom was responsible for asking
questions and facilitating the discussion, while the other person took notes about what was being
discussed during the interviews. The resulting notes were reviewed along with the audio
recordings as part of the data analysis stage, which is discussed in the next chapter.
Intended Measures
The dependent variable being evaluated in this study measures the intent of a potential
voter to use an Internet voting system. Because this measure was assessed at each of four levels
of government (overall, local, state, and federal), four separate models were evaluated in this
study. Thus, each model includes one dependent variable to measure the intent of a voter to vote
online at the overall, local, state, and federal levels of government, respectively.
Regarding the independent variables, a total of 28 factors were used, each of which is
associated with DoI, TAM, and trust model constructs as shown in Figure 3.1 and described in
the previous chapter. Since there are two constructs for trust of government used to evaluate each
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level of government, the four intended measures were evaluated separately using 22 independent
variables at each respective level of government.
Validity and Reliability
It is important in any research to consider the validity and reliability of data and the
methods used for its collection and analysis. Validity refers to the extent to which an empirical
measure reflects the meaning of the concept being studied. Reliability refers to the same results
being obtained when measures are applied repeatedly to the same concept. One way to minimize
problems or concerns related to validity and reliability in research is to use instruments and
constructs that have been previously demonstrated to be valid and reliable.
In this study, the survey instrument was based on a combination of valid instruments and
constructs used in recent models to assess the adoption of technology and e-government
initiatives. The constructs of relative advantage, compatibility, and image were adapted from
Van Slyke, Belanger, and Comunale (2004). The measures of perceived usefulness and
complexity or perceived ease of use were adapted primarily from Davis (1989). Items measuring
intention of use were adapted from Van Slyke et al. (2004) and Davis (1989). Measures of trust
of government and trust of technology were adapted from Van Slyke et al. (2004) and McKnight
et al. (2002).
Additional steps to check for validity and reliability were performed in the data analysis
stage, as described in the next chapter. For example, Cronbach’s alpha (Cronbach, 1970) is used
to assess the reliability of each of the constructs in the model. Having reviewed details about the
research design and methodology used in this study, the next chapter presents the analysis of the
collected data as well as a summary and discussion of the results.
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Chapter 5
Data Analysis and Results
This chapter describes the analysis of the collected data and summarizes the results. The
chapter includes sections on pertinent descriptive statistics, verification of data internal
consistency through reliability analysis, testing the significance of demographic and other
characteristics, testing the overall significance of the model at each of the levels of government
being considered, hypothesis testing, and a summary of results obtained from the two focus group
interview sessions. The data analysis for this study was performed during the fall of 2009.
Descriptive Statistics
After completion of the survey process, the responses were transferred from the online
survey application into SPSS for pre-analysis. A total of 220 survey responses were obtained,
which was considered acceptable for providing adequate variation of data. Eighteen of these
survey submittals had one or more missing responses. Consideration was given to replacing
missing values with mean scores of the responses, but since most of these 18 survey submittals
had multiple missing answers, it was decided that these responses should be eliminated from the
data analysis. This resulted in 202 acceptable survey submittals that were used for analysis.
Other pre-analysis data screening steps for outliers, normality, linearity, and
homoscedasticity yielded no problems with the assumptions associated with these data properties.
As part of the pre-analysis, a total of seven variables were transformed by reversing the scales.
This was done to maintain consistency of the scales across all variables.
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Summary tables of the descriptive statistics, as obtained from SPSS, are shown in
Appendix D. Some of the more relevant descriptive statistics are reviewed in the following
paragraphs.
Of the 202 acceptable survey responses received, 38% were females and 62% were from
males. Seventy-six percent of the participants reported their age as between 18-24 years old,
while 11% reported being 30 years of age or older. The average age of the respondents was
estimated to be 25 years. Because ethnicity was not found to be a significant demographic factor
in recent related studies, it was not included as a demographic variable in this study. Ninety-one
percent of the respondents identified themselves as U.S. citizens, while the percents of reported
permanent residents and international students were 2.5 and 6.9, respectively.
Sixty-three percent of the respondents stated that their highest completed level of
education was at the high school level, while 20% have earned at least a bachelor’s degree.
Forty-two percent of respondents reported having less than one year of work experience, while
17% stated that they have at least six years of work experience. Regarding annual household
income, one-third of the respondents reported $20,000 or less, while 37% of the respondents
reported annual household incomes of $60,000 or more.
Regarding computer experience, all participants reported having at least one year, while
62% stated that they have at least 10 years of experience. All of the respondents reported a fairly
regular use of information technology, such as email, instant messaging, and social networking
websites, and all stated that they had access to the Internet in at least one location.
Ninety-five percent of those surveyed claimed that they use email at least a few times a
week, while 77% claimed the same frequency of use of social networking sites, and 60% claimed
the same frequency of use for instant messaging. Sixty-five percent of the respondents reported
purchasing a product or service online at least a few times a month, while 76% of the participants
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reported rarely or never obtaining a government service online or completing a government
transaction online.
Regarding political party affiliation, 28% reported being registered as Democrats, 30%
reported as Republicans, and 6% reported being affiliated with other parties. The remaining 36%
reported not being affiliated with any political party.
Fifty-five percent of those surveyed reported participating in some form of government
election within the last year, while 34% responded that they had never voted. Fourteen percent
indicated that they had used an absentee ballot to vote at least once in the past.
Reliability Analysis
The reliability of all constructs was evaluated using Cronbach’s alpha (Cronbach, 1970),
and the results are shown in Table 5.1. Those scales achieving a value above 0.70 indicate an
acceptable reliability of the construct.
The only construct that did not achieve an acceptable reliability according to Cronbach’s
alpha was the Internet experience construct. Numerous attempts were made to try to identify a
reliable construct to assess Internet experience. These included the use of variables related to the
expressed importance and frequency of email, instant messaging, and social networking use, as
well as variables assessing the number of years of personal computer experience, the frequency of
online purchases, and the frequency of obtaining government information and services online.
In spite of these efforts, no reliable construct for assessing Internet experience was
identified. These results are consistent with the tests of variables related to Internet experience as
evaluated in the section on hypothesis testing, which is found later in this chapter.
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Several attempts were made to evaluate construct validity using factor analysis, however
the results were inconclusive. Recommendations are made in the final chapter of this paper
regarding the use of factor analysis to reevaluate and verify the cross loading of factors in future
work involving the general voting population.
Testing of Demographic and Other Characteristics
Regression analysis was used to evaluate the significance of demographic and other
characteristics. For this analysis, the variables were entered as independent variables and the
intent to vote online at the overall level was used as the dependent variable.
As a result of this analysis, two of the 21 independent variables were found to be
significant. These were the importance of the use of email for communication, which was
subsequently chosen for use in the main model as a measure of Internet experience, and the most
Table 5.1: Reliability Analysis.
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recent election in which a voter participated, which was subsequently chosen for use in the main
model as a measure of voting experience.
Repeating this regression analysis for each of the remaining three levels of government
(local, state, and federal) yielded slightly different results. However, the same two independent
variables were found to be the most significant overall. Appendix E shows the results of this
regression analysis, including ANOVA values, coefficients and excluded variables at the overall
level of government.
Testing of Model Significance
Multiple regression analysis was used to test the overall model at the 95% level of
significance. No problems were identified with respect to the assumptions of multivariate normal
distribution, independence of errors, or equality of variance.
The overall model was evaluated at each of the four levels of government being assessed
(overall, local, state, and federal). The condensed results of this testing of model significance are
summarized in Table 5.2. Complete listwise and stepwise regression results at each of the four
levels of government are provided in Appendices F through M.
The models analyzed at each level of government were found to be significant using all
seven constructs and 22 independent variables. In order to more clearly understand the
significance of the individual constructs, each model was optimized to a single independent
variable predictor, the results of which also appear in Table 5.2. When optimized to a single
predictor, the most significant variable at the overall, local, and state government levels was the
enjoyment of voting online, which is one of the compatibility construct variables. The most
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significant predictor at the federal level of government was the preference of a user to vote
online, which is one of the relative advantage construct variables.
The model representing the overall level of government was found to be significant (F =
17.34, p = 0.000), and the significance of each variable was tested using the forward selection
approach of stepwise regression analysis. The model explains 68% of the variation in a user’s
willingness to vote online at the overall level of government. The most significant variable at the
overall level of government is a variable from the compatibility construct that a potential voter
would enjoy using the Internet to vote. Fifty-four percent of the variation in a user’s willingness
to vote online at the overall level of government is explained by the model using this single
variable.
Table 5.2: Testing of Model Significance.
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The model was also found to be significant (F = 6.25, p = 0.000) at the local level of
government, and the significance of each variable was tested using the forward selection method
of analysis. Forty-three percent of the variation in a user’s willingness to vote online at the local
level is explained by the model. Similar to the model at the overall level of government, the most
significant variable at the local level of government is the compatibility variable that a potential
voter would enjoy using the Internet to vote. Thirty-three percent of the variation in a user’s
willingness to vote online at the local level of government is explained by the model using this
single variable.
Similarly, the model was found to be significant (F = 9.88, p = 0.000) at the state level of
government, and the significance of each variable was tested using the forward method of
selection analysis. Nearly 55% of the variation in a user’s willingness to vote online at the state
level is explained by the model. Like the models at the overall and local levels of government,
the most significant variable determined by the model at the state level of government is the
compatibility variable that a potential voter would enjoy using the Internet to vote. When
optimized to this single independent variable, nearly 45% of the variation in a user’s willingness
to vote online at the state level of government is explained by the model.
The model was found to be significant (F = 13.85, p = 0.000) at the federal level of
government. Sixty-three percent of the variation in a user’s willingness to vote online at the
federal level is explained by the model. Using the forward method of selection analysis, the most
significant variable is the preference that a potential voter would have to vote online as compared
to traditional voting methods. When optimized to this single independent variable from the
relative advantage construct, nearly 52% of the variation in a user’s willingness to vote online at
the federal level of government is explained by the model.
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In addition to the significant variables mentioned earlier, two other variables from the
relative advantage construct are found to be consistently significant across the models
representing all four levels of government. These included a potential voter’s likelihood to vote if
the process is available online and the perception that the use of Internet voting would enhance
one’s voting effectiveness. Overall, the variables from the relative advantage construct are the
most consistently significant of all of the independent variables. This result is interesting in that
it differs from results of recent studies in which higher levels of relative advantage do not appear
to increase the intention of citizens to vote online.
Hypothesis Testing
After evaluating the models representing each of the four levels of government, multiple
regression analysis was used to test the set of hypotheses at the 95% level of significance for the
overall level of government. Again, no violations of multivariate normal distribution,
independence of errors or equality of variance were identified. The results of the hypothesis
testing for H1 through H4 are shown in Table 5.3, while the results of the hypothesis testing for
H5 is shown in Table 5.4. Since hypotheses H2 through H5 were developed specifying direction,
the results reported for those hypotheses were obtained using one-tail tests. Hypothesis H1 (age)
does not specify direction therefore the reported results for H1 were obtained using a two-tail test.
In order for the results to be consistent with those of previous related studies, the
variables in each hypothesis were tested individually, rather than providing a complete listwise
regression analysis containing all variables. The results pertaining to each individual hypothesis
are described in the following paragraphs.
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H1: Age of Potential Voters vs. Intent to Vote Online
The age of a potential voter is not found to be significantly related to the expressed intent
to participate in online elections at the overall level of government. Given the reported digital
divide with respect to age, this was not an expected outcome. However, the limited age range of
the population of respondents in this study may have been an important factor. Further aspects of
this research with respect to age are discussed in the next chapter as part of the section on
limitations of the study, as well as in the section on recommendations for future research which is
found in the final chapter.
Table 5.3: Hypothesis Testing of H1, H2, H3, and H4.
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H2: Internet Experience vs. Intent to Vote Online
Higher levels of Internet or online experience, as measured by the perceived importance
of a respondent to communicate by email, are associated with increased intentions to adopt online
voting at the overall level of government.
In assessing the level of online experience of potential voters, the use of a summed index
of variables was considered. Those variables assessed included the number of years of
experience using a personal computer, the convenience of access to the Internet by a user, the
expressed importance and frequency of communicating by email, instant messaging, and social
networking, the frequency with which online purchases are made, and the frequency with which
respondents reported obtaining government services or information online.
As mentioned earlier in the summary of the reliability analysis, no suitable index was
identified for use in assessing Internet experience. As a result, for this study it was decided to use
the only significant variable related to online experience, namely the expressed importance of
one’s use of email for communication. Using this email communication variable as the
independent variable and the overall intent of a potential voter to vote online as the dependent
variable, the significant results are obtained as shown in Table 5.3.
Similar to the attempts described in the earlier section on reliability analysis, attempts
were made to use other variables, such as the frequency of email usage and the frequency of past
online purchases, individually and in combination with the email preference variable to try to
achieve significant results for online experience. However, these attempts yielded no significant
results. Aspects of this study regarding online experience are discussed in the next chapter as part
of the section on limitations of the study, as well as in the section on recommendations for future
research which is found in the final chapter.
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H3: Voting Experience vs. Intent to Vote Online
Higher levels of voting experience, as measured by the time frame of the most recent
election in which respondents participated, are not associated with increased intentions to use
online voting at the overall level of government. Two other possible variables were considered
for use in assessing the level of voting experience in this study. These included the age at which
respondents registered to vote and the specific type of election in which a user most recently
participated.
Since the variable indicating the time frame for the most recent election in which a
potential voter participated was the only significant variable related to voting experience, this
variable was chosen as the measure of voting experience for this study. Using this variable as the
independent variable and the overall intent of a potential voter to vote online as the dependent
variable, the results fail to achieve significance at the 95 percent level, as shown in Table 5.3.
As a result, the intent of a potential voter to vote online is not expected to be affected by
the level of one’s voting experience, and a potential voter with prior voting experience is no more
likely to vote online than one who has never voted. In spite of several attempts, no other related
variables were found to improve the significance of the results with respect to voting experience
at the overall level of government. Further aspects of this study regarding voting experience are
discussed in the next chapter as part of the section on limitations of the study, as well as in the
section on recommendations for future research which is found in the final chapter.
H4: Trust of Internet Technology vs. Intent to Vote Online
A higher level of trust in Internet technology by potential voters is associated with
increased intentions to use an online voting system at the overall level of government. This trust
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was measured using the following three variables in combination: one’s trust in the exchange of
information over the Internet, one’s belief that information exchanged over the Internet is secure,
and one’s belief that the use of the Internet is reliable.
The collective use of these three Internet trust variables as independent variables
produced significant results at the overall level of government, and each of the three variables
yielded significant results when considered individually. These results are shown in Table 5.3.
The reported results were somewhat expected since they are consistent with results from other
recent studies, and since the ongoing threat of information security breaches from a variety of
sources remains one of the greatest concerns in society with respect to the use of information
technologies.
H5: Trust of Government vs. Intent to Vote Online (Multiple Levels)
Because of the nature of this hypothesis, it was tested separately at each of the four levels
of government being evaluated, namely the overall, local, state, and federal levels. A higher level
of trust of government by potential voters is associated with increased intentions to use an online
voting system. These results are found to be significant only at the overall and federal levels of
government, and are not found to be significant at the local and state levels of government.
The level of trust of government was assessed using the process- and outcome-based
variables as described earlier. These include the dual perceptions of respondents that the
government is: “doing what is fair,” and “doing what is right.” The results of the testing of
hypothesis H5 are shown in Table 5.4, and are described in the following paragraphs. Since
hypothesis H5 was developed specifying direction, the results reported for this hypothesis were
obtained using one-tail tests.
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At the overall level of government, the results are significant when considering both trust
variables in combination, but not significant when considering the perception that government
“does what is fair” (process-based) individually. The results are significant at the overall level of
government with respect to the perception that the government “does what is right” (outcome-
based) when considering this variable individually.
As shown in Table 5.4, the combined results are not significant at the local and state
levels of government when evaluating the process- and outcome-based variables measuring the
trust of government by a potential voter. However, at the local level of government the results
are significant with respect to the perception that the government “does what is fair” (process-
based) when considering this variable individually.
When tested at the federal level of government, the results are found to be significant
when considering both government trust variables in combination as well as when considering the
outcome-based and process-based trust variables individually.
Table 5.4: Hypothesis Testing of H5.
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These results at the overall and federal levels of government were somewhat expected
due in part to widely publicized problems associated with traditional voting processes in recent
presidential elections. However, the results obtained with respect to the local and state levels of
government were not expected. The focus group interviews were helpful in providing further
insight into the lack of significant results observed at the local and state levels of government
with respect to trust of government. Those results are summarized in the following section.
Results from Focus Group Interviews
The focus group interviews provided useful information that supplemented the data
collected in the survey process. This section summarizes the qualitative data obtained from the
focus group interviews and makes comparisons to the quantitative results presented in the
preceding sections.
Each focus group included participants with a range of voting experience, although a
slight majority had never voted. Of those participants who had voted recently, all voted in the
most recent general election.
The majority of focus group participants stated that they would be more likely to vote
online if Internet voting were an available option. Those who stated that they would not vote
online were mainly concerned with questions about the potential security of online voting
processes. However, many of the participants did not consider online security to be a major
concern with respect to online voting. Interestingly, all but one participant stated that they would
be more likely to vote online in local elections if Internet voting were available. Even those
participants who had concerns about online security issues expressed the likelihood that they
would vote online locally.
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All but one focus group participant stated that they would prefer online voting compared
to traditional voting methods and every participant agreed that online voter registration for new
voters would be a good idea. Some participants expressed concerns about citizens who might not
have access to computers or to the Internet being able to vote online. Those participants
suggested the possibility of voting by telephone or having some local or regional polling stations
available for use by those without convenient online access.
With respect to advantages related to online voting, all participants agreed that being able
to vote online would provide citizens with significant advantages. These included convenience,
flexibility, time savings, ease of voting for the elderly or for those with disabilities, and providing
citizens with more information related to voting prior to the voting process.
Several participants expressed their opinions about perceived disadvantages of online
voting, which would need to be addressed if online voting were to be successfully adopted.
These included having adequate security, dealing with unreliable Internet connections, addressing
voter identification issues, minimizing the risk of identity theft, and having the system provide
confirmation of one’s voting choices. While discussing disadvantages, several participants noted
that existing voting processes are already subject to a variety of potential problems, including
registration fraud, voting machine malfunctions, and issues involving human error.
With respect to online experience, all participants reported having at least ten years of
computer use and belonging to at least one social networking site. About half of the participants
claimed to belong to multiple social networking sites. Similarly, every focus group participant
reported frequent participation in computer-related or online activities during their free time.
Regarding the area of trust of government, all but two of the interview participants stated
that they did not trust government. Related concerns included the encroachment of government
on personal freedom, the increasing size, scope, and cost of government at all levels, and the
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escalating growth of budget deficits that appear to characterize government, mainly at the state
and federal levels. Participants expressed apprehension about the perceived failure of elected
representatives in government to adequately address the concerns of citizens on major policy and
legislative efforts. There were also concerns expressed about whether government was doing
enough to keep citizens and the country safe. Finally, there was a consensus among the focus
group participants that state and federal levels of government appear to be less trustworthy
compared to local government entities.
It is interesting to consider what the qualitative data reveals about the quantitative results
and related research. As one example, the opinions expressed about trust of government are
consistent with recent research findings in which citizens tend to have a higher level of trust in
government at the local level. The higher level of trust in local government expressed by the
participants was consistent in both the qualitative and quantitative results. This may explain why
participants expressed a higher likelihood to vote online at the local level of government.
Another area where the qualitative data and results helped to add more meaning to the
quantitative results was in the area of online user experience. The fact that all focus group
participants self-reported a relatively high level of online experience may explain why no reliable
index of Internet experience was identified. Future research in the area will need to address this
issue, as further discussed in the following chapter.
Overall, the analysis and results of this study provide useful input regarding the potential
future development and use of Internet voting systems in this country. The study has raised
important issues that need to be adequately addressed in order for further research on this subject
to be successful. The final chapter summarizes the results of this study, identifies study
limitations, offers suggested steps that can be taken to insure continuing progress in this area of
research in the future, and considers the overall significance of the study.
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Chapter 6
Summary and Conclusions
This empirical study has explored and investigated a variety of factors that influence the
intentions of potential voters to use Internet voting systems within government in this country.
These factors include voter perceptions about government, voting, and the use of online
technologies, as well as demographic and other user characteristics.
In general, a better understanding of these factors can help to support future public policy
and administrative decisions related to government processes and voting methods. More
specifically, this type of research adds to the existing foundation of research related to e-voting
processes, which can help to improve the design of existing and future voting systems as well as
to identify ways to increase the level of voter participation of the public in elections.
This chapter summarizes the results of the research performed in this study and these
results are compared to those of recent studies involving e-government initiatives. A summary of
limitations is presented as well as a series of suggestions for continuing future related research
involving the general voting population in this country. Finally the significance of the study is
evaluated, including the contributions that it makes to the current body of knowledge related to e-
government and e-voting processes.
In evaluating the overall significance of this study, a range of contexts and viewpoints are
considered. Using a narrower context, this research aims to support the ongoing development of
e-government initiatives and e-voting processes. Within a broader context, this study is related to
reform movements and other approaches involving managerial, policy, and governance issues
within the overall field of public administration. Whether considering this study in a narrow or
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broad context, it is hoped that this work, and similar research, will lead to better ways for
government to perform the important functions related to providing for its citizens.
Summary of Research Results
This study aims to provide support for the proper design and implementation of future
Internet voting systems by helping to identify factors and characteristics that may determine the
extent to which online voting systems are successfully implemented. In support of this objective,
the study introduces an Internet voting conceptual model that builds on recent e-government
models and extends these models to consider online voting at multiple levels of government,
including overall, local, state, and federal levels. The Internet voting conceptual model includes a
range of factors and related characteristics that can motivate citizens to participate in the use of
Internet or online voting systems.
The study finds specific factors that are significant indicators for the use of online voting
methods. These include factors related to the categories of perceived relative advantage,
trustworthiness, and compatibility. The study identifies significant variables from the relative
advantage construct, including voter preferences to vote online as compared to using regular
voting methods, the likelihood of voters to vote online if the process were available, and the
perception that the Internet would enhance voter effectiveness in achieving voting goals and
objectives. The study also finds that one of the compatibility factors, namely a user’s enjoyment
of voting online, is significant in determining the likelihood of potential voters to vote online.
Since relative advantage is the degree to which an online voting system is perceived as
being better than traditional methods of voting, government agencies can improve the potential
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participation rate in future online voting systems by making them convenient for voters with
respect to time and location compared to other traditional voting methods.
Although this study did not find a wide range of variables from the perceived ease of use
and compatibility constructs to be as significant as found in recent studies, the nature of the
design process of an online system, such as an e-commerce website or an e-voting system,
requires that principles of good usability should be followed if users are to enjoy and benefit from
their use of a system. This includes the need for designers of e-voting systems to insure that the
systems make it easy for voters to complete desired transactions. Government agencies can
improve the level of e-government service usage by making the adoption of online services as
seamless and as natural as possible (Carter and Belanger, 2005).
This study finds that factors of perceived trustworthiness of potential voters in Internet
technology and in government are significant. These results are noteworthy in that they are
consistent with most related studies performed in recent years. Although perceptions of trust are
not easy to change, government entities at all levels can reassure citizens of the reliability of
online systems, as well as provide accurate and dependable support when needed.
Several demographic and user characteristics are evaluated by hypothesis testing. These
include voter age, Internet experience, and voter experience. Perceived levels of trust of Internet
technology and of government are also evaluated by hypothesis testing. Regarding hypothesis
testing, the study finds that perceived Internet experience (H2), and trust in Internet technology
(H4) are significant indicators of the willingness of potential voters to vote online. However,
voter age (H1) and voter experience (H2) are not found to be significant indicators of the
expressed intent of voters to participate in Internet voting.
Regarding trust in government (H5), mixed results are obtained in the study. The
expressed trust in government, as measured by the combined perceptions that government
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“does what is fair” (process-based) and “does what is right” (outcome-based), is found to be a
significant indicator of potential voters to vote online at both the overall and federal levels of
government. At the local and state levels, however, the expressed trust in government is not
found to be a significant indicator of the likelihood of potential voters to vote online.
With a few minor exceptions noted earlier, the overall results of this study are consistent
with results obtained from prior research. The observed differences in the results of this study
and those of other recent studies may be due, at least in part, to several important limitations that
are summarized in the following section.
Limitations of Study
As an exploratory study, this research builds on the foundation for future research
involving the intentions of voters in the general voting public to use Internet voting systems.
There are several important limitations that are observed in this exploratory stage. First, the
chosen population is limited to undergraduate and graduate college students. In spite of
expanding the population of students to include graduate students, the average age of those
surveyed is approximately twenty-five years old, which is less than the average age of the overall
voting public in this country. Since the turnout of younger voters is typically low compared to
the general population, this limitation due to the chosen sample population needs to be addressed
in future work.
Although the data collected from this sample of students is considered to be at least
somewhat typical of data that would be obtained from an overall student population, the set of
demographics of the chosen student population is clearly different from that of the general voting
population. Since the majority of voters in most elections are not college students, other factors
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such as level of education, online experience, and work experience would vary significantly from
the student population used in this study. In addition to the limited population from a
demographic standpoint, the sample size is not as large as originally desired.
With respect to the data analysis, several limitations are also observed. Although
multiple attempts were made to further evaluate construct validity using factor analysis, the
results were inconclusive. This may have been related to the limited sample size, or it may have
been the result of inadequate evaluation of the findings from the factor analysis methods.
Because of these issues, the results of this study are limited in their direct usefulness, and
the findings are not fully generalizable to the overall voting public. However, as an exploratory
study these limitations and results can be useful to develop new hypotheses and to refine the
Internet voting conceptual model to achieve more generalizable results in the future. A series of
steps needed for this process to be successful is outlined in the following section.
Suggestions for Future Research
There are a variety of suggested steps that can be taken to build on the important results
obtained from this study and to support further research related to Internet voting systems. Future
studies should include a larger sample size. This may help to support additional model testing
and analysis, such as the expanded use of factor analysis. As indicated earlier, a more diverse
population sample in terms of age, ethnicity, economic background, experience and other factors,
would likely increase the generalizability of the findings.
Although ethnicity was not included as a demographic factor in this study, it is often
considered as an important component of the digital divide. As such, future studies should
include ethnicity as a demographic variable.
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Enhanced data analysis methods may help to better evaluate and verify any cross loading
of factors used in the model. This could help to address several of the limitations that have been
previously identified, and may allow more relevant conclusions to be made with respect to voter
age and other characteristics.
In addition to overcoming limitations due to the sample population, future related work
should accomplish several other important objectives, such as considering better ways to address
the use of voting systems at the various levels of government, reevaluating the specific constructs
used in the model, and using factor analysis to corroborate results. Some of the measures used to
assess several of the characteristics of a voter can also be improved. For example, an index could
be developed to better assess a potential voter’s level of online experience. As described earlier,
a reliable index of online experience could not be identified. A more diverse population sample
may help with the identification of this index in the future. Likewise, a more effective way to
assess and evaluate the level of voter experience could be implemented.
In spite of the identified limitations, this study effectively tests the validity of the stated
hypotheses and provides a useful initial evaluation of the Internet voting conceptual model. In
this regard, the study helps to assess key relationships between variables and can help to identify
design or methodological changes needed to improve the model and to enable a more effective
application of it in future work involving the voting public. The flexibility of the model allows it
to be modified as needed for use in further research involving a more diverse population sample.
With respect to the Internet voting conceptual model, it was decided to use a total of
seven constructs by combining the DoI complexity construct and the TAM perceived ease of use
construct, which were considered to be equivalent based on previous studies. Future testing using
this model could include both of these constructs and the results could be reported using all eight
constructs. This would allow any issues related to multicollinearity to be evaluated as part of the
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analysis, which could be a useful confirmation as to whether or not the complexity and perceived
ease of use constructs are equivalent.
Several other issues can be evaluated as part of future, related research. For example, the
citizen status of participants needs to be reconsidered. Although in this study it was decided to
include responses from participants who declared that they were not U.S. citizens, this decision
can be reevaluated in the future. Since non-citizens may not be allowed to participate in certain
government elections, their intentions to vote online may be affected by their citizen status.
Another issue that could be more fully explored involves the differences in the
perceptions at multiple levels of government, especially those involving the perceptions of
trustworthiness of government. These perceptions of trust appear to vary widely with respect to
the level of government being considered. The results from this study can be linked with other
studies that focus more specifically on issues related to trust in government. This may help to
identify a preferred way to proceed with respect to the use of multiple levels of government.
Since e-government initiatives at various levels of government may have different purposes and
objectives, and since the level of trust in government has been found to vary significantly with
respect to the level of government considered, continuing to include multiple levels of
government in future research would seem to be useful.
An interesting and related issue that could be explored in future research involves the
impact of changes in social interaction on voters due to the replacement of traditional voting
methods with online voting systems. Since online voting systems and other electronic means of
interaction involve new modes of communication, cultural and social interaction changes may
result. This type of research can relate to changes in language and social interaction that may
result from the current trend toward an increasing use of online social networks.
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Finally, it is important to note that this study considers the potential intent of users to
participate in a nonexistent Internet voting system. It would be both interesting and useful to be
able to gather user opinions before and after the use of an actual online voting system to see if
and how the online voting process might change user intentions or perceptions.
Alvarez and Hall (2004) conclude that there is no way to know whether the use of
Internet voting can be successful unless real Internet voting systems are tested, preferably in
small-scale trials that would allow an adequate scientific evaluation to be performed. Ongoing
research in this area could explore and compare specific types of Internet voting methods. As
discussed earlier, these may include remote access voting systems versus on-site systems. This
type of comparison could help to determine the most effective way for government to implement
Internet voting in the future.
The next section evaluates the overall significance and contributions of this research
using a variety of contexts and viewpoints.
Research Significance and Contributions
The research performed in this study is related to a rapidly developing field of study that
is occurring at all levels within the public sector, namely the extent to which information
technologies and electronic delivery methods can and should be used to improve how government
identifies and meets the needs of citizens. The use of information technologies continues to
change the way that government provides information and services to its constituents.
As described earlier, a wide range of traditional governmental processes are currently
being modernized to take advantage of the potential benefits of the use of information
technologies. Along with these potential benefits however, come potential risks. It is important
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for those working in the public sector to understand the benefits and risks associated with new
technological initiatives, as well as the factors that may determine the degree to which users
choose to adopt initiatives, such as Internet voting systems.
Recent e-government initiatives have had a relatively narrow focus on improving the
quality and efficiency of public services by making them available via electronic channels.
Considering this narrow focus, this type of research can provide effective support for the ongoing
development of e-government initiatives and e-voting processes. As discussed in the first
chapter, this involves the design and implementation of Internet voting systems as well as
identifying ways to use the Internet to increase voter participation and to promote more
deliberative democracy.
The implementation of new e-voting methods in recent years has already changed the
way in which votes are cast and counted. As progress continues in the field of e-voting
technologies, government entities are increasingly considering the use of Internet voting for
elections, either exclusively or in combination with traditional methods of voting, such as in-
person polling or mail-in voting (Kim and Nevo, 2008).
As a subset of e-voting, Internet voting has the potential to play a vital role in the
modernization and redesign of electoral processes, as well as to improve the interaction between
citizens and their government. However, in order to achieve this potential, Internet voting needs
to be properly implemented through the use of reliable and secure information communication
methods (Curran and Nichols, 2005).
Traditional voting processes continue to confront a variety of potential problems. These
include, as examples, the use of outdated and unreliable equipment, a lack of standardized voting
machines and processes, inadequate access to voting locations by citizens in the military or those
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who are overseas or have disabilities, difficulties in recounting votes, and voter intimidation and
harassment.
As an alternative to traditional voting methods, Internet voting has the potential to
address these types of problems by providing a wide range of possible benefits and improvements
to voting processes, as reported earlier. For example, as a result of state and federal election
officials trying to find faster and better ways to handle the ballots of overseas and military voters,
nearly three million of these voters from at least 33 states will be permitted to cast ballots over the
Internet by e-mail or fax starting in the November 2010 elections (Urbina, 2010). In spite of
concerns that have been raised by some cybersecurity experts and election officials, the election
of November 2010 will be the first in which Internet voting has played a major role in this
country by improving voting accessibility to a large number of voters.
In addition to enhancing the viability of Internet voting and modernizing traditional
electoral processes, the results of this study can be useful in the broader context of public
administration. E-voting has the potential to play a key part in a future transition to a modernized
public administration, due to its important role as a subset of the broader areas of e-politics, e-
government, and e-democracy, as described earlier in this paper. Considering e-voting processes
in this broader context, the first chapter identified several ways in which this research can relate
to advances within the overall field of public administration.
One of these areas of impact includes the process of policy development in the field of
public policy. As discussed in the first chapter, citizens may be more apt to express their
opinions regarding policy alternatives if given the option to do so using online voting methods or
forums. Likewise, the policy implementation stage could benefit from input provided by citizens
using various applications of electronic technologies.
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With respect to the relationship between e-governance and policy issues, Nordfors,
Ericson, Lindell and Lapidus (2009) describe the need for e-governance to support three major
policy goals for overall public governance. These include efficiency, or the need to develop
savings for taxpayers; effectiveness, or the search for quality services for citizens and other
consumers of public services; and good governance for the constituents as citizens and voters.
Others areas of impact within the field of public administration include the “new public
service” and “new governance” movements, which are extensions of the field of public
management. As discussed in the first chapter, this may include the development of a reform-
oriented, citizen-centered framework, which focuses on democratic values and empowering
citizens to improve public processes. Thus, the use of new technologies to empower and engage
citizens, such as online voting systems and forums, can be a bridge between the current model of
democracy and the vision of a new public service.
There are a variety of areas in which improvements would be needed in order to achieve
the vision of a truly modernized public sector in this country. For example, Nordfors, et al.
(2009) identify several areas of attention in order to accomplish this purpose. First, government
processes much become more open to participation by constituents. This highlights the
importance of knowledge management and an increasing but effective use of information
technologies in support of public administration and democratic processes.
Second, government must become better at understanding and satisfying the needs of
constituents to motivate them and increase the likelihood of their participation in the use of e-
government initiatives. There are many ways that government at all levels can accomplish this
important task.
Finally, governments need to work more collaboratively in networks. It is expected that
networks and partnerships will need to play an increasing role in both the provision of e-
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government services and in democratic processes. If constituents are to become more actively
involved in e-government initiatives, new and innovative collaborations must be developed in
order to effectively connect public, private, and non-profit organizations (Nordfors, et al, 2009).
The results from this study can help to accomplish all three of these objectives. As stated
earlier, the use of Internet voting can help to increase the level of participation in government
processes. Likewise, research of this type can help to identify the needs of constituents so that
government can better understand and satisfy those needs. Finally, as a subset of the use of ICTs
in government, a better understanding of Internet voting systems may help to develop systems in
the future that enable better collaboration among networks that are involved in the public sector.
An important characteristic of the field of public administration related to areas of
potential reform is the changing nature of the field. Currently, public administration is in a period
of reform due to a variety of issues in a rapidly changing environment. These include
globalization, politics, e-government, accountability, networks, contracts, participatory
democracy, relationships between private, governmental, and non-profit sectors, and the
pervasiveness of the use of information technologies. These concepts are redefining the
significance of public entities and how they relate to other aspects of the global environment.
The role of the manager is being re-evaluated, as well as relationships between the private sector,
the government sector, and the non-profit sector.
This rapidly changing environment means that current trends in society may lead to
significant changes in the nature and impact that e-government initiatives will have on our society
in the future. As an example, Nordfors, et al. (2009) studied recent trends in the European Union
(EU) and concluded that EU countries will undergo increased cultural and religious diversity, an
aging population, and changing patterns in how people live, work, and communicate. Although
there are many societal and governmental differences between the EU and the U.S., there are also
100
many similarities between the current economic, political, and societal changes occurring in this
country and those that are occurring in Europe.
Nordfors, et al. (2009) hypothesize that these future societal and economic changes will
necessitate the development and use of new public services, as well as innovative ways to provide
existing services. The authors envision the possible scenario in which the use of e-government
tools is greatly expanded in coming years to strengthen democracy by increasing the participation
of citizens in a wide range of public decision-making processes.
If this occurs, one can foresee the evolution of a future vision of public administration in
this country that is restructured through the use of e-government initiatives. As a result, e-
government processes would be at the center of a modernized public administration framework in
which technology is used as a strategic tool to reform structures, processes, regulations,
competence, and cultures with the goals of improving administration and increasing public value
(Nordfors, et al., 2009).
In conclusion, it is hoped and expected that continued work on verifiable research related
to technology adoption and acceptance will have a positive impact on the development and
implementation of effective public policies and new administrative processes in the coming years.
Thus, the research performed in this study, as well as future related research, can play an
important role in supporting a smooth transition to a full and effective implementation of Internet
voting in this country. In the broader context, this research may help to provide a bridge between
the current model of democracy and the vision of a new and more effective public service.
101
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http://www.votersunite.org/info/E-VotingIn2006Mid-Term.pdf
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West, D. M. (2005). Digital government: Technology and public sector performance. Princeton,
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attitudes. Public Administration Review, 64(1), 15-27.
117
APPENDIX A
Internet Voting Recruitment Script
As college students, you are eligible to vote in U.S. elections if you are a U.S. citizen 18
years of age or older and if you have completed the appropriate voter registration
procedures. The majority of voting in U.S. elections at the local, state, and federal levels
takes place in person at specific polling locations. However, technology is advancing to
the point at which citizens will likely be able to cast their vote online in the future using
Internet voting methods. In order to learn more about your opinions and perceptions of
factors related to online voting, we are asking you to participate in a research study. This
study is designed to identify the specific factors that may have an impact on the decision
to participate in an election using Internet voting methods. This information may help to
increase voter participation as well as assist in the development of future Internet voting
systems. There are no discomforts or risks associated with participation.
Participants are asked to complete an online survey of approximately 50 questions. The
survey will be accessed via a link from within Penn State’s ANGEL environment. The
survey responses will be recorded confidentially. No identifying information will be
recorded. Participation in this research study will require about 15-20 minutes of your
time. This research project has been approved by the Office for Research Protections at
the Pennsylvania State University.
You must be 18 years of age to participate and your participation is voluntary. The choice
as to whether or not to participate will not have any effect on grading in this course or on
your final grades. By completing the survey, you give your explicit consent to
participate. You have the right to decline to answer specific questions. Any future
analysis and publication of collected data will exclude any personal identifying
information. Your confidentiality will be safe to the degree permitted by the technology
used. However, no guarantee can be made regarding the possible interception by any
third parties of data sent over the Internet.
If you are willing to participate in the research project described above, please login to
your ANGEL course section where you will be directed to click on the link entitled
“Internet Voting” in order to begin the survey.
118
APPENDIX B
Annotated Survey Instrument
Issues Related to the Use of Internet Voting Date: _____________
Thank you for participating in this study. The study is designed to investigate perceptions
related to Internet voting methods, as well as to explore the level of online experience of potential
voters. Answering the questions below should take only a few minutes. All information will be
held strictly confidential, and your responses will remain anonymous. Thank you again for taking
the time to respond to this survey. If you have any questions or concerns, please contact the
research director at [email protected] or 717-948-6639.
Demographic Information:
1. What is your highest level of completed education? (choices: Less than high school, High
school diploma, Associates degree or junior college, Bachelors degree, Graduate degree)
2. How many years of full-time work experience do you have? (choices: < one, 1-5, 6-10, > 10)
3. What is your age? (choices: 18-24, 25-29, 30-39, 40 or older)
4. What is your gender? (choices: Female, Male)
5. What is your approximate annual household income? (choices: Less than $20,000, $20,000-
$39,999, $40,000-$59,999, $60,000 or more)
6. Identify your current status. (choices: US Citizen, Permanent Resident, International Student
or Visitor, Other)
Voter Registration and Behavior:
7. In what political party are you registered? (choices: Democratic, Republican, None, Other)
8. At what age did you register to vote? (choices: 18-24, 25-29, 30-39, 40 or older, never)
9. I would be more likely to vote had I been automatically pre-registered, instead of having to
register myself. (choices: 1-5 scale - Strongly Disagree to Neutral to Strongly Agree)
10. Registration for voting should be available online.
(choices: 1-5 scale - Strongly Disagree to Neutral to Strongly Agree)
11. When did you most recently vote? (choices: < one year, 1-3 years, > 3 years, never)
119
Voting Intentions by Level of Government:
12. In general, I would be more likely to vote in an election if I could vote over the Internet.
(choices: 1-5 scale - Strongly Disagree to Neutral to Strongly Agree)
13. I would be more likely to vote in a LOCAL election if I could vote over the Internet.
(choices: 1-5 scale - Strongly Disagree to Neutral to Strongly Agree)
14. I would be more likely to vote in a STATE election if I could vote over the Internet.
(choices: 1-5 scale - Strongly Disagree to Neutral to Strongly Agree)
15. I would be more likely to vote in a NATIONAL election if I could vote over the Internet.
(choices: 1-5 scale - Strongly Disagree to Neutral to Strongly Agree)
Other Voter Behavior:
16. What type of election did you vote in most recently? (choices: Local, State, National, None)
17. What type of election did you vote in most recently? (choices: Primary, General, Don’t
know)
18. Have you ever voted using an absentee ballot? (choices: Yes, No)
19. Voting online would reduce the need to use an absentee ballot.
(choices: 1-5 scale - Strongly Disagree to Neutral to Strongly Agree)
Technology Usage Information:
20. How many years have you been using a computer? (choices: < one, 1-5, 6-10, > 10)
21. Identify where you have convenient access to the Internet (check all that apply). (choices: At
work, At home, At school, Elsewhere)
22. E-mail is an important way for me to communicate.
(choices: 1-5 scale - Strongly Disagree to Neutral to Strongly Agree)
23. Instant messaging is an important way for me to communicate.
(choices: 1-5 scale - Strongly Disagree to Neutral to Strongly Agree)
24. Social networking, such as the use of Facebook or MySpace, is an important way for me to
communicate. (choices: 1-5 scale – Strongly Disagree to Neutral to Strongly Agree)
25. Identify the frequency with which you purchase products or services over the Internet.
(choices: One or more times a day, A few times a week, A few times a month, Rarely or
never)
26. Identify the frequency with which you obtain government information over the Internet.
(choices: One or more times a day, A few times a week, A few times a month, Rarely or
never)
27. Identify the frequency with which you obtain a government service over the Internet.
(choices: One or more times a day, A few times a week, A few times a month, Rarely or
never)
28. Identify the frequency with which you use email. (choices: One or more times a day, A few
times a week, A few times a month, Rarely or never)
29. Identify the frequency with which you use instant messaging. (choices: One or more times a
day, A few times a week, A few times a month, Rarely or never)
30. Identify the frequency with which you use social networking. (choices: One or more times a
day, A few times a week, A few times a month, Rarely or never)
120
Perceptions Related to Internet Use and Internet Voting:
For each of the statements below, please indicate your level of agreement with the following
statements by identifying your preferred choice. [Each question is followed by a Likert scale of 1
to 5 (Strongly Disagree to Disagree to Neutral to Agree to Strongly Agree).]
Compatibility
31. Using the Internet fits well with the way that I like to obtain services.
32. Being able to vote online would fit my style.
33. I would enjoy using the Internet to vote in elections.
Complexity
34. Learning to use the Internet to obtain voter information is easy for me.
35. Interacting with the Internet to obtain voter information is clear to me.
36. Obtaining government services online would be easy for me.
37. I believe that using the Internet to vote would be easy for me.
Image
38. People who use the Internet to vote would be more popular with their peers.
39. People who use the Internet to vote would be more highly respected.
40. Being able to vote over the Internet would be a status symbol to me.
Perceived Usefulness
41. Voting over the Internet would save me time.
42. Being able to vote online would be useful to me.
43. I believe that voting online would be convenient for me.
Relative Advantage
44. Using the Internet to vote would enhance my voting effectiveness.
45. I would prefer to vote online compared to regular voting methods.
46. I believe that voting over the Internet would be better than traditional voting.
47. I would be more likely to vote if the process would be available online.
121
Trust of Internet Technology
48. I believe that you can trust the exchange of information over the Internet.
49. I believe that information exchanged over the Internet is secure.
50. I believe that use of the Internet is reliable.
Trust of Government
51. I believe that the government is trustworthy.
52. I believe that traditional voting processes are fair and trustworthy.
53. How much of the time do you think you can trust the LOCAL government to make
decisions in a fair way? (choices: Almost never, Once in a while, About half of the time,
Frequently, Almost always)
54. How much of the time do you think you can trust the STATE government to make
decisions in a fair way? (choices: Almost never, Once in a while, About half of the time,
Frequently, Almost always)
55. How much of the time do you think you can trust the FEDERAL government to make
decisions in a fair way? (choices: Almost never, Once in a while, About half of the time,
Frequently, Almost always)
56. How much of the time do you think you can trust the LOCAL government to do what is
right? (choices: Almost never, Once in a while, About half of the time, Frequently, Almost
always)
57. How much of the time do you think you can trust the STATE government to do what is
right? (choices: Almost never, Once in a while, About half of the time, Frequently, Almost
always)
58. How much of the time do you think you can trust the FEDERAL government to do what
is right? (choices: Almost never, Once in a while, About half of the time, Frequently,
Almost always)
Use Intentions
59. I would use the Internet to obtain a product or service.
60. I would use the Internet to obtain a government service.
61. I would use the Internet for voter registration.
62. I would use the Internet for voting.
122
APPENDIX C
Focus Group Questions
1. When was the last time that you voted in a government election? At what level (local,
state, national)?
2. Would you be more likely to participate in elections if you could vote over the Internet?
3. Would the type of election (local, state, national) affect your willingness to vote online?
4. What advantages might you expect to gain if you were able to vote online?
5. What disadvantages might you expect to face if you were able to vote online?
6. Approximately when was the first time that you ever used a computer?
7. Do you currently belong to one (or more) social networking sites?
8. Do you often participate in computer-related or online activities during your free time?
9. Do you trust the government? If so, at what level (local, state, national)?
10. Are there any aspects of government that concern you?
11. Would you prefer to vote online as compared to traditional voting methods?
12. Do you think that online voter registration for new voters would be a good idea?
123
APPENDIX D
Tables of Descriptive Statistics
Highest Year of School Completed
Frequency Percent Valid Percent
Cumulative
Percent
Valid High school diploma 128 63.4 63.4 63.4
Associates degrees or junior
college
33 16.3 16.3 79.7
Bachelors degree 32 15.8 15.8 95.5
Graduate degree 9 4.5 4.5 100.0
Total 202 100.0 100.0
Number of Years of Work Experience
Frequency Percent Valid Percent
Cumulative
Percent
Valid Less than 1 year 85 42.1 42.1 42.1
1 - 5 years 83 41.1 41.1 83.2
6 - 10 years 21 10.4 10.4 93.6
More than 10 years 13 6.4 6.4 100.0
Total 202 100.0 100.0
124
Age of Respondent
Frequency Percent Valid Percent
Cumulative
Percent
Valid 18 - 24 years old 153 75.7 75.7 75.7
25 - 29 years old 27 13.4 13.4 89.1
30 - 39 years old 14 6.9 6.9 96.0
40 years or older 8 4.0 4.0 100.0
Total 202 100.0 100.0
Gender of Respondent
Frequency Percent Valid Percent
Cumulative
Percent
Valid Female 76 37.6 37.6 37.6
Male 126 62.4 62.4 100.0
Total 202 100.0 100.0
Annual Household Income
Frequency Percent Valid Percent
Cumulative
Percent
Valid LT $20000 67 33.2 33.2 33.2
$20000-39999 29 14.4 14.4 47.5
$40000-59999 31 15.3 15.3 62.9
$60000 or more 75 37.1 37.1 100.0
Total 202 100.0 100.0
125
Status of Respondent
Frequency Percent Valid Percent
Cumulative
Percent
Valid US Citizen 183 90.6 90.6 90.6
Permanent Resident 5 2.5 2.5 93.1
International Student or
Visitor
14 6.9 6.9 100.0
Total 202 100.0 100.0
Most Recent Vote
Frequency Percent Valid Percent
Cumulative
Percent
Valid Within last year 112 55.4 55.4 55.4
1 - 3 years ago 18 8.9 8.9 64.4
More than 3 years ago 3 1.5 1.5 65.8
Never 69 34.2 34.2 100.0
Total 202 100.0 100.0
Ever Voted by Absentee Ballot
Frequency Percent Valid Percent
Cumulative
Percent
Valid No 173 85.6 85.6 85.6
Yes 29 14.4 14.4 100.0
Total 202 100.0 100.0
126
Number of Years of Computer Experience
Frequency Percent Valid Percent
Cumulative
Percent
Valid 1 - 5 years 9 4.5 4.5 4.5
6 - 10 years 68 33.7 33.7 38.1
More than 10 years 125 61.9 61.9 100.0
Total 202 100.0 100.0
Access to Internet
Frequency Percent Valid Percent
Cumulative
Percent
Valid School only 1 .5 .5 .5
Work and school 1 .5 .5 1.0
Home and school 65 32.2 32.2 33.2
Work, home and school 83 41.1 41.1 74.3
Work, home, school and
elsewhere
52 25.7 25.7 100.0
Total 202 100.0 100.0
Political Party Affiliation
Frequency Percent Valid Percent
Cumulative
Percent
Valid Democratic 56 27.7 27.7 27.7
Republican 61 30.2 30.2 57.9
None 73 36.1 36.1 94.1
Other 12 5.9 5.9 100.0
Total 202 100.0 100.0
127
Frequency of Purchasing Products Online
Frequency Percent Valid Percent
Cumulative
Percent
Valid One or more times a day 4 2.0 2.0 2.0
A few times a week 11 5.4 5.4 7.4
A few times a month 116 57.4 57.4 64.9
Rarely or never 71 35.1 35.1 100.0
Total 202 100.0 100.0
Frequency of Obtaining Government Info. Online
Frequency Percent Valid Percent
Cumulative
Percent
Valid One or more times a day 5 2.5 2.5 2.5
A few times a week 27 13.4 13.4 15.8
A few times a month 65 32.2 32.2 48.0
Rarely or never 105 52.0 52.0 100.0
Total 202 100.0 100.0
Most Recent Election Type Voted In - Geography
Frequency Percent Valid Percent
Cumulative
Percent
Valid Local 16 7.9 7.9 7.9
State 3 1.5 1.5 9.4
National 77 38.1 38.1 47.5
Local, state, and national 37 18.3 18.3 65.8
None 69 34.2 34.2 100.0
Total 202 100.0 100.0
128
Most Recent Election Type Voted In - Coverage
Frequency Percent Valid Percent
Cumulative
Percent
Valid Primary 48 23.8 23.8 23.8
General 63 31.2 31.2 55.0
Dont Know 91 45.0 45.0 100.0
Total 202 100.0 100.0
Frequency of Email Use
Frequency Percent Valid Percent
Cumulative
Percent
Valid One or more times a day 157 77.7 77.7 77.7
A few times a week 35 17.3 17.3 95.0
A few times a month 5 2.5 2.5 97.5
Rarely or never 5 2.5 2.5 100.0
Total 202 100.0 100.0
Frequency of Instant Messaging Use
Frequency Percent Valid Percent
Cumulative
Percent
Valid One or more times a day 76 37.6 37.6 37.6
A few times a week 45 22.3 22.3 59.9
A few times a month 24 11.9 11.9 71.8
Rarely or never 57 28.2 28.2 100.0
Total 202 100.0 100.0
129
Frequency of Social Networking Use
Frequency Percent Valid Percent
Cumulative
Percent
Valid One or more times a day 124 61.4 61.4 61.4
A few times a week 32 15.8 15.8 77.2
A few times a month 20 9.9 9.9 87.1
Rarely or never 26 12.9 12.9 100.0
Total 202 100.0 100.0
130
APPENDIX E
Testing of Demographic and Other Characteristics
ANOVAc
Model Sum of Squares df Mean Square F Sig.
2 Regression 14.209 2 7.105 4.932 .008b
Residual 286.687 199 1.441
Total 300.896 201
b. Predictors: (Constant), Email is Important Way to Communicate, Most Recent Election Type
Voted In - Geography
c. Dependent Variable: Likely to Vote at the Overall Level if Internet Voting Available
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
2 (Constant) 1.896 .495 3.833 .000
Email is Important Way
to Communicate
.219 .092 .165 2.376 .018
Most Recent Election
Type Voted In -
Geography
.163 .072 .158 2.268 .024
a. Dependent Variable: Likely to Vote at the Overall Level if Internet Voting Available
131
Excluded Variablesa
Model Beta In t Sig.
Partial
Correlation
Collinearity
Statistics
Tolerance
2 Highest Year of School Completed .017b .237 .813 .017 .935
Number of Years of Work Experience .012b .166 .868 .012 .936
Age of Respondent -.008b -.109 .913 -.008 .923
Gender of Respondent -.085b -1.225 .222 -.087 .995
Annual Household Income .033b .458 .647 .033 .942
Status of Respondent .014b .193 .847 .014 .977
Political Party Affiliation .015b .213 .832 .015 .920
Age When Registered .030b .365 .715 .026 .727
Most Recent Vote .043b .415 .678 .030 .454
Number of Years of Computer Exper. .045b .651 .515 .046 .994
Access to Internet .059b .847 .398 .060 .974
Instant Message is Imp. Way to Comm. -.069b -.969 .334 -.069 .934
Social Network is Imp. Way to Comm. -.015b -.216 .829 -.015 .965
Freq. of Purchasing Products Online -.050b -.727 .468 -.052 1.000
Freq. of Obtaining Gov’t. Info. Online .030b .434 .664 .031 .999
Freq. of Obtaining Gov’t. Serv. Online -.060b -.859 .392 -.061 .998
Frequency of Email Use .015b .197 .844 .014 .782
Frequency of Instant Messaging Use .078b 1.112 .267 .079 .975
Frequency of Social Networking Use .004b .060 .952 .004 .982
132
APPENDIX F
Listwise Regression Results – Overall Level of Government
Model Summary
Model R
R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .825a .681 .641 .733 .681 17.343 22 179 .000
a. Predictors: (Constant), Traditional Voting Processes are Fair and Trustworthy, Voting on the Internet
Would be Useful to Me, People Using Internet to Vote are More Respected, Obtaining Govt Services Online
Would be Easy, Internet Use Fits Well With My Obtaining Services, Info Exchanged Over Internet is Secure,
The Government is Trustworthy, Learning to Use Internet for Voting Info Is Easy, Use of the Internet is
Reliable, Internet Voting Would Enhance Voting Effectiveness, People Using Internet to Vote are More
Popular, Voting on the Internet Would Save Me Time, Using Internet to Vote Would be Easy, Would be More
Likely to Vote if Available Online, Interacting With Internet for Voting Info Is Clear, Voting Online Would be
Better than Traditional Voting, Voting on the Internet Would be Convenient for Me, Online Voting Would Fit
My Style, Can Trust the Exchange of Information on Internet, Voting on the Internet Would be Status
Symbol, Would Enjoy Internet Voting for Elections, Would Prefer to Vote Online vs Regular Voting
133
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 204.811 22 9.310 17.343 .000a
Residual 96.085 179 .537
Total 300.896 201
a. Predictors: (Constant), Traditional Voting Processes are Fair and Trustworthy, Voting on the
Internet Would be Useful to Me, People Using Internet to Vote are More Respected, Obtaining
Govt Services Online Would be Easy, Internet Use Fits Well With My Obtaining Services, Info
Exchanged Over Internet is Secure, The Government is Trustworthy, Learning to Use Internet for
Voting Info Is Easy, Use of the Internet is Reliable, Internet Voting Would Enhance Voting
Effectiveness, People Using Internet to Vote are More Popular, Voting on the Internet Would Save
Me Time, Using Internet to Vote Would be Easy, Would be More Likely to Vote if Available Online,
Interacting With Internet for Voting Info Is Clear, Voting Online Would be Better than Traditional
Voting, Voting on the Internet Would be Convenient for Me, Online Voting Would Fit My Style, Can
Trust the Exchange of Information on Internet, Voting on the Internet Would be Status Symbol,
Would Enjoy Internet Voting for Elections, Would Prefer to Vote Online vs Regular Voting
b. Dependent Variable: Likely to Vote if Internet Voting Available
134
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Correlations
Collinearity
Statistics
B
Std.
Error Beta
Zero-
order Partial Part
Toleran
ce VIF
1 (Constant) -.383 .400 -.957 .340
Internet Use Fits Well
With My Obtaining
Services
.099 .065 .077 1.512 .132 .393 .112 .064 .689 1.452
Online Voting Would Fit
My Style
.045 .102 .041 .438 .662 .689 .033 .018 .206 4.858
Would Enjoy Internet
Voting for Elections
.347 .106 .332 3.279 .001 .737 .238 .139 .174 5.756
Internet Voting Would
Enhance Voting
Effectiveness
.169 .071 .146 2.370 .019 .605 .174 .100 .470 2.129
Would Prefer to Vote
Online vs Regular
Voting
.049 .110 .048 .443 .658 .736 .033 .019 .152 6.568
Voting Online Would be
Better than Traditional
Voting
.132 .087 .134 1.507 .134 .701 .112 .064 .225 4.453
Would be More Likely to
Vote if Available Online
.263 .075 .260 3.532 .001 .704 .255 .149 .328 3.048
People Using Internet to
Vote are More Popular
-.081 .087 -.067 -.935 .351 .225 -.070 -.039 .350 2.859
People Using Internet to
Vote are More
Respected
.087 .096 .067 .905 .366 .313 .068 .038 .325 3.072
Voting on the Internet
Would be Status
Symbol
-.038 .118 -.028 -.322 .748 .302 -.024 -.014 .231 4.323
135
Learning to Use Internet
for Voting Info Is Easy
.075 .096 .055 .787 .432 .336 .059 .033 .362 2.760
Interacting With Internet
for Voting Info Is Clear
-.166 .092 -.130 -1.806 .073 .356 -.134 -.076 .343 2.913
Obtaining Govt
Services Online Would
be Easy
.112 .088 .078 1.277 .203 .330 .095 .054 .480 2.083
Using Internet to Vote
Would be Easy
-.074 .092 -.057 -.803 .423 .452 -.060 -.034 .352 2.844
Voting on the Internet
Would Save Me Time
.065 .096 .043 .678 .499 .535 .051 .029 .438 2.281
Voting on the Internet
Would be Useful to Me
-.011 .096 -.010 -.117 .907 .620 -.009 -.005 .259 3.860
Voting on the Internet
Would be Convenient
for Me
.030 .107 .022 .279 .780 .588 .021 .012 .280 3.571
Can Trust the
Exchange of
Information on Internet
-.063 .087 -.059 -.723 .470 .438 -.054 -.031 .268 3.730
Info Exchanged Over
Internet is Secure
-.032 .093 -.028 -.340 .734 .445 -.025 -.014 .265 3.780
Use of the Internet is
Reliable
.005 .071 .004 .069 .945 .464 .005 .003 .503 1.989
The Government is
Trustworthy
.062 .064 .054 .966 .335 .242 .072 .041 .566 1.768
Traditional Voting
Processes are Fair and
Trustworthy
-.011 .066 -.009 -.165 .869 .023 -.012 -.007 .610 1.640
a. Dependent Variable: Likely to Vote if Internet Voting Available
136
APPENDIX G
Stepwise Regression Results – Overall Level of Government
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change F Change df1 df2
Sig. F
Change
1 .737a .543 .541 .829 .543 237.957 1 200 .000
2 .795b .631 .628 .747 .088 47.480 1 199 .000
3 .805c .648 .643 .731 .017 9.570 1 198 .002
a. Predictors: (Constant), Would Enjoy Internet Voting for Elections
b. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Would be More Likely to Vote if
Available Online
c. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Would be More Likely to Vote if
Available Online, Internet Voting Would Enhance Voting Effectiveness
137
ANOVAd
Model Sum of Squares df Mean Square F Sig.
1 Regression 163.487 1 163.487 237.957 .000a
Residual 137.409 200 .687
Total 300.896 201
2 Regression 189.957 2 94.978 170.369 .000b
Residual 110.939 199 .557
Total 300.896 201
3 Regression 195.071 3 65.024 121.661 .000c
Residual 105.825 198 .534
Total 300.896 201
a. Predictors: (Constant), Would Enjoy Internet Voting for Elections
b. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Would be More Likely to Vote
if Available Online
c. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Would be More Likely to Vote
if Available Online, Internet Voting Would Enhance Voting Effectiveness
d. Dependent Variable: Likely to Vote if Internet Voting Available
138
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Correlations
Collinearity
Statistics
B Std. Error Beta
Zero-
order Partial Part Tolerance VIF
1 (Constant) .634 .189 3.350 .001
Would Enjoy Internet
Voting for Elections
.769 .050 .737 15.426 .000 .737 .737 .737 1.000 1.000
2 (Constant) .220 .181 1.218 .225
Would Enjoy Internet
Voting for Elections
.506 .059 .485 8.573 .000 .737 .519 .369 .580 1.725
Would be More Likely
to Vote if Available
Online
.394 .057 .390 6.891 .000 .704 .439 .297 .580 1.725
3 (Constant) .041 .186 .221 .825
Would Enjoy Internet
Voting for Elections
.444 .061 .425 7.253 .000 .737 .458 .306 .517 1.933
Would be More Likely
to Vote if Available
Online
.335 .059 .331 5.655 .000 .704 .373 .238 .519 1.927
Internet Voting Would
Enhance Voting
Effectiveness
.196 .063 .169 3.093 .002 .605 .215 .130 .596 1.678
a. Dependent Variable: Likely to Vote if Internet Voting Available
139
APPENDIX H
Listwise Regression Results – Local Level of Government
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change F Change df1 df2
Sig. F
Change
1 .659a .434 .365 .914 .434 6.251 22 179 .000
a. Predictors: (Constant), Can Trust Local Govt to Do What is Right, Would be More Likely to Vote if
Available Online, Learning to Use Internet for Voting Info Is Easy, People Using Internet to Vote are More
Popular, Internet Use Fits Well With My Obtaining Services, Use of the Internet is Reliable, Voting on the
Internet Would Save Me Time, Obtaining Govt Services Online Would be Easy, Internet Voting Would
Enhance Voting Effectiveness, Info Exchanged Over Internet is Secure, People Using Internet to Vote are
More Respected, Using Internet to Vote Would be Easy, Interacting With Internet for Voting Info Is Clear,
Voting Online Would be Better than Traditional Voting, Voting on the Internet Would be Convenient for Me,
Can Trust Local Govt to Make Fair Decisions, Online Voting Would Fit My Style, Can Trust the Exchange of
Information on Internet, Voting on the Internet Would be Useful to Me, Voting on the Internet Would be
Status Symbol, Would Enjoy Internet Voting for Elections, Would Prefer to Vote Online vs Regular Voting
140
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 114.850 22 5.220 6.251 .000a
Residual 149.487 179 .835
Total 264.337 201
a. Predictors: (Constant), Can Trust Local Govt to Do What is Right, Would be More Likely to Vote
if Available Online, Learning to Use Internet for Voting Info Is Easy, People Using Internet to Vote
are More Popular, Internet Use Fits Well With My Obtaining Services, Use of the Internet is
Reliable, Voting on the Internet Would Save Me Time, Obtaining Govt Services Online Would be
Easy, Internet Voting Would Enhance Voting Effectiveness, Info Exchanged Over Internet is
Secure, People Using Internet to Vote are More Respected, Using Internet to Vote Would be Easy,
Interacting With Internet for Voting Info Is Clear, Voting Online Would be Better than Traditional
Voting, Voting on the Internet Would be Convenient for Me, Can Trust Local Govt to Make Fair
Decisions, Online Voting Would Fit My Style, Can Trust the Exchange of Information on Internet,
Voting on the Internet Would be Useful to Me, Voting on the Internet Would be Status Symbol,
Would Enjoy Internet Voting for Elections, Would Prefer to Vote Online vs Regular Voting
b. Dependent Variable: Likely to Vote Local if Internet Voting Available
141
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Correlations
Collinearity
Statistics
B
Std.
Error Beta
Zero-
order Partial Part
Toleran
ce VIF
1 (Constant) .297 .489 .607 .545
Internet Use Fits Well
With My Obtaining
Services
.127 .082 .106 1.546 .124 .364 .115 .087 .678 1.475
Online Voting Would Fit
My Style
.029 .127 .028 .227 .821 .524 .017 .013 .207 4.842
Would Enjoy Internet
Voting for Elections
.429 .131 .438 3.285 .001 .577 .238 .185 .177 5.637
Internet Voting Would
Enhance Voting
Effectiveness
.053 .089 .049 .594 .553 .382 .044 .033 .473 2.113
Would Prefer to Vote
Online vs Regular
Voting
-.214 .137 -.225 -1.563 .120 .479 -.116 -.088 .152 6.587
Voting Online Would be
Better than Traditional
Voting
.130 .109 .142 1.191 .235 .478 .089 .067 .223 4.493
Would be More Likely to
Vote if Available Online
.100 .092 .106 1.084 .280 .439 .081 .061 .332 3.015
People Using Internet to
Vote are More Popular
-.195 .108 -.171 -1.797 .074 .073 -.133 -.101 .350 2.857
People Using Internet to
Vote are More
Respected
.131 .120 .108 1.095 .275 .173 .082 .062 .327 3.060
Voting on the Internet
Would be Status
Symbol
-.046 .147 -.036 -.310 .757 .153 -.023 -.017 .230 4.344
142
Learning to Use Internet
for Voting Info Is Easy
.314 .119 .245 2.626 .009 .378 .193 .148 .362 2.763
Interacting With Internet
for Voting Info Is Clear
-.232 .116 -.194 -2.005 .046 .306 -.148 -.113 .337 2.972
Obtaining Govt
Services Online Would
be Easy
.070 .109 .052 .638 .524 .300 .048 .036 .479 2.089
Using Internet to Vote
Would be Easy
-.086 .115 -.071 -.748 .455 .390 -.056 -.042 .355 2.820
Voting on the Internet
Would Save Me Time
.130 .120 .092 1.080 .282 .457 .080 .061 .432 2.316
Voting on the Internet
Would be Useful to Me
.005 .121 .005 .043 .966 .472 .003 .002 .257 3.888
Voting on the Internet
Would be Convenient
for Me
.096 .133 .077 .722 .471 .483 .054 .041 .280 3.573
Can Trust the
Exchange of
Information on Internet
.056 .108 .056 .517 .606 .284 .039 .029 .271 3.692
Info Exchanged Over
Internet is Secure
-.129 .116 -.121 -1.110 .269 .276 -.083 -.062 .267 3.743
Use of the Internet is
Reliable
.042 .089 .038 .479 .633 .342 .036 .027 .500 1.999
Can Trust Local Govt to
Make Fair Decisions
.046 .117 .039 .394 .694 .131 .029 .022 .327 3.061
Can Trust Local Govt to
Do What is Right
-.005 .121 -.004 -.042 .967 .099 -.003 -.002 .331 3.018
a. Dependent Variable: Likely to Vote Local if Internet Voting Available
143
APPENDIX I
Stepwise Regression Results – Local Level of Government
Model Summary
Model R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change F Change df1 df2
Sig. F
Change
1 .577a .333 .329 .939 .333 99.744 1 200 .000
2 .596b .355 .348 .926 .022 6.746 1 199 .010
3 .609c .371 .362 .916 .016 5.171 1 198 .024
a. Predictors: (Constant), Would Enjoy Internet Voting for Elections
b. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Voting on the Internet Would Save Me
Time
c. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Voting on the Internet Would Save Me
Time, Internet Use Fits Well With My Obtaining Services
144
ANOVAd
Model Sum of Squares df Mean Square F Sig.
1 Regression 87.962 1 87.962 99.744 .000a
Residual 176.375 200 .882
Total 264.337 201
2 Regression 93.745 2 46.873 54.678 .000b
Residual 170.592 199 .857
Total 264.337 201
3 Regression 98.087 3 32.696 38.940 .000c
Residual 166.250 198 .840
Total 264.337 201
a. Predictors: (Constant), Would Enjoy Internet Voting for Elections
b. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Voting on the Internet Would
Save Me Time
c. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Voting on the Internet Would
Save Me Time, Internet Use Fits Well With My Obtaining Services
d. Dependent Variable: Likely to Vote Local if Internet Voting Available
145
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Correlations
Collinearity
Statistics
B Std. Error Beta
Zero-
order Partial Part Tolerance VIF
1 (Constant) 1.607 .214 7.494 .000
Would Enjoy Internet
Voting for Elections
.564 .057 .577 9.987 .000 .577 .577 .577 1.000 1.000
2 (Constant) .911 .341 2.672 .008
Would Enjoy Internet
Voting for Elections
.460 .069 .471 6.710 .000 .577 .430 .382 .659 1.516
Voting on the Internet
Would Save Me Time
.256 .099 .182 2.597 .010 .457 .181 .148 .659 1.516
3 (Constant) .489 .385 1.270 .205
Would Enjoy Internet
Voting for Elections
.415 .071 .424 5.861 .000 .577 .384 .330 .607 1.648
Voting on the Internet
Would Save Me Time
.232 .098 .165 2.359 .019 .457 .165 .133 .651 1.535
Internet Use Fits Well
With My Obtaining
Services
.169 .074 .141 2.274 .024 .364 .160 .128 .828 1.208
a. Dependent Variable: Likely to Vote Local if Internet Voting Available
146
APPENDIX J
Listwise Regression Results – State Level of Government
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change F Change df1 df2
Sig. F
Change
1 .741a .548 .493 .786 .548 9.882 22 179 .000
a. Predictors: (Constant), Can Trust State Govt to Do What is Right, Would be More Likely to Vote if
Available Online, Obtaining Govt Services Online Would be Easy, People Using Internet to Vote are More
Popular, Internet Use Fits Well With My Obtaining Services, Use of the Internet is Reliable, Voting on the
Internet Would Save Me Time, Learning to Use Internet for Voting Info Is Easy, Internet Voting Would
Enhance Voting Effectiveness, Info Exchanged Over Internet is Secure, People Using Internet to Vote are
More Respected, Using Internet to Vote Would be Easy, Interacting With Internet for Voting Info Is Clear,
Voting on the Internet Would be Convenient for Me, Voting Online Would be Better than Traditional Voting,
Can Trust State Govt to Make Fair Decisions, Online Voting Would Fit My Style, Can Trust the Exchange of
Information on Internet, Voting on the Internet Would be Useful to Me, Voting on the Internet Would be
Status Symbol, Would Enjoy Internet Voting for Elections, Would Prefer to Vote Online vs Regular Voting
147
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 134.211 22 6.101 9.882 .000a
Residual 110.502 179 .617
Total 244.713 201
a. Predictors: (Constant), Can Trust State Govt to Do What is Right, Would be More Likely to Vote
if Available Online, Obtaining Govt Services Online Would be Easy, People Using Internet to Vote
are More Popular, Internet Use Fits Well With My Obtaining Services, Use of the Internet is
Reliable, Voting on the Internet Would Save Me Time, Learning to Use Internet for Voting Info Is
Easy, Internet Voting Would Enhance Voting Effectiveness, Info Exchanged Over Internet is
Secure, People Using Internet to Vote are More Respected, Using Internet to Vote Would be Easy,
Interacting With Internet for Voting Info Is Clear, Voting on the Internet Would be Convenient for
Me, Voting Online Would be Better than Traditional Voting, Can Trust State Govt to Make Fair
Decisions, Online Voting Would Fit My Style, Can Trust the Exchange of Information on Internet,
Voting on the Internet Would be Useful to Me, Voting on the Internet Would be Status Symbol,
Would Enjoy Internet Voting for Elections, Would Prefer to Vote Online vs Regular Voting
b. Dependent Variable: Likely to Vote State if Internet Voting Available
148
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Correlations
Collinearity
Statistics
B
Std.
Error Beta
Zero-
order Partial Part Toler. VIF
1 (Constant) -.021 .428 -.048 .962
Internet Use Fits Well
With My Obtaining
Services
.007 .070 .006 .094 .926 .334 .007 .005 .680 1.470
Online Voting Would Fit
My Style
.116 .109 .118 1.066 .288 .635 .079 .054 .207 4.823
Would Enjoy Internet
Voting for Elections
.376 .112 .399 3.347 .001 .669 .243 .168 .178 5.633
Internet Voting Would
Enhance Voting
Effectiveness
.103 .076 .099 1.350 .179 .491 .100 .068 .473 2.114
Would Prefer to Vote
Online vs Regular
Voting
-.061 .117 -.067 -.521 .603 .600 -.039 -.026 .154 6.480
Voting Online Would be
Better than Traditional
Voting
-.028 .095 -.031 -.291 .771 .554 -.022 -.015 .220 4.553
Would be More Likely to
Vote if Available Online
.189 .080 .207 2.376 .019 .571 .175 .119 .331 3.017
People Using Internet to
Vote are More Popular
-.133 .093 -.122 -1.436 .153 .200 -.107 -.072 .351 2.848
People Using Internet to
Vote are More
Respected
.099 .104 .085 .958 .340 .302 .071 .048 .321 3.115
Voting on the Internet
Would be Status
Symbol
.073 .127 .061 .579 .563 .301 .043 .029 .230 4.350
149
Learning to Use Internet
for Voting Info Is Easy
.317 .103 .258 3.087 .002 .413 .225 .155 .362 2.763
Interacting With Internet
for Voting Info Is Clear
-.182 .100 -.159 -1.831 .069 .362 -.136 -.092 .335 2.986
Obtaining Govt
Services Online Would
be Easy
.035 .094 .027 .370 .712 .311 .028 .019 .478 2.093
Using Internet to Vote
Would be Easy
-.084 .099 -.072 -.852 .396 .435 -.064 -.043 .354 2.828
Voting on the Internet
Would Save Me Time
.024 .103 .018 .232 .817 .485 .017 .012 .433 2.307
Voting on the Internet
Would be Useful to Me
-.019 .104 -.018 -.186 .852 .548 -.014 -.009 .257 3.884
Voting on the Internet
Would be Convenient
for Me
.127 .114 .105 1.115 .266 .551 .083 .056 .282 3.546
Can Trust the
Exchange of
Information on Internet
-.029 .093 -.031 -.316 .752 .357 -.024 -.016 .270 3.697
Info Exchanged Over
Internet is Secure
-.056 .100 -.054 -.558 .578 .368 -.042 -.028 .266 3.761
Use of the Internet is
Reliable
.047 .075 .044 .621 .535 .408 .046 .031 .510 1.961
Can Trust State Govt to
Make Fair Decisions
.140 .102 .119 1.366 .174 .086 .102 .069 .332 3.008
Can Trust State Govt to
Do What is Right
-.049 .100 -.044 -.493 .623 .085 -.037 -.025 .320 3.125
a. Dependent Variable: Likely to Vote State if Internet Voting Available
150
APPENDIX K
Stepwise Regression Results – State Level of Government
Model Summary
Model R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change F Change df1 df2
Sig. F
Change
1 .669a .448 .445 .822 .448 162.146 1 200 .000
2 .693b .480 .475 .800 .032 12.394 1 199 .001
3 .706c .499 .491 .787 .019 7.383 1 198 .007
a. Predictors: (Constant), Would Enjoy Internet Voting for Elections
b. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Would be More Likely to Vote if
Available Online
c. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Would be More Likely to Vote if
Available Online, Learning to Use Internet for Voting Info Is Easy
151
ANOVAd
Model Sum of Squares df Mean Square F Sig.
1 Regression 109.567 1 109.567 162.146 .000a
Residual 135.146 200 .676
Total 244.713 201
2 Regression 117.490 2 58.745 91.889 .000b
Residual 127.222 199 .639
Total 244.713 201
3 Regression 122.064 3 40.688 65.685 .000c
Residual 122.649 198 .619
Total 244.713 201
a. Predictors: (Constant), Would Enjoy Internet Voting for Elections
b. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Would be More Likely to Vote
if Available Online
c. Predictors: (Constant), Would Enjoy Internet Voting for Elections, Would be More Likely to Vote
if Available Online, Learning to Use Internet for Voting Info Is Easy
d. Dependent Variable: Likely to Vote State if Internet Voting Available
152
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Correlations
Collinearity
Statistics
B Std. Error Beta
Zero-
order Partial Part Tolerance VIF
1 (Constant) 1.321 .188 7.037 .000
Would Enjoy Internet
Voting for Elections
.630 .049 .669 12.734 .000 .669 .669 .669 1.000 1.000
2 (Constant) 1.094 .194 5.654 .000
Would Enjoy Internet
Voting for Elections
.486 .063 .516 7.687 .000 .669 .478 .393 .580 1.725
Would be More Likely
to Vote if Available
Online
.215 .061 .236 3.520 .001 .571 .242 .180 .580 1.725
3 (Constant) .587 .267 2.201 .029
Would Enjoy Internet
Voting for Elections
.418 .067 .444 6.248 .000 .669 .406 .314 .500 1.999
Would be More Likely
to Vote if Available
Online
.221 .060 .243 3.670 .000 .571 .252 .185 .579 1.727
Learning to Use
Internet for Voting
Info Is Easy
.188 .069 .153 2.717 .007 .413 .190 .137 .804 1.245
a. Dependent Variable: Likely to Vote State if Internet Voting Available
153
APPENDIX L
Listwise Regression Results – Federal Level of Government
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change F Change df1 df2
Sig. F
Change
1 .794a .630 .585 .796 .630 13.853 22 179 .000
a. Predictors: (Constant), Can Trust State Federal to Do What is Right, Interacting With Internet for Voting
Info Is Clear, People Using Internet to Vote are More Respected, Use of the Internet is Reliable, Internet
Use Fits Well With My Obtaining Services, Voting on the Internet Would Save Me Time, Can Trust the
Exchange of Information on Internet, People Using Internet to Vote are More Popular, Obtaining Govt
Services Online Would be Easy, Internet Voting Would Enhance Voting Effectiveness, Using Internet to
Vote Would be Easy, Would be More Likely to Vote if Available Online, Learning to Use Internet for Voting
Info Is Easy, Voting Online Would be Better than Traditional Voting, Voting on the Internet Would be
Convenient for Me, Can Trust Federal Govt to Make Fair Decisions, Online Voting Would Fit My Style, Info
Exchanged Over Internet is Secure, Voting on the Internet Would be Useful to Me, Voting on the Internet
Would be Status Symbol, Would Enjoy Internet Voting for Elections, Would Prefer to Vote Online vs Regular
Voting
154
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 193.165 22 8.780 13.853 .000a
Residual 113.454 179 .634
Total 306.619 201
a. Predictors: (Constant), Can Trust State Federal to Do What is Right, Interacting With Internet for
Voting Info Is Clear, People Using Internet to Vote are More Respected, Use of the Internet is
Reliable, Internet Use Fits Well With My Obtaining Services, Voting on the Internet Would Save Me
Time, Can Trust the Exchange of Information on Internet, People Using Internet to Vote are More
Popular, Obtaining Govt Services Online Would be Easy, Internet Voting Would Enhance Voting
Effectiveness, Using Internet to Vote Would be Easy, Would be More Likely to Vote if Available
Online, Learning to Use Internet for Voting Info Is Easy, Voting Online Would be Better than
Traditional Voting, Voting on the Internet Would be Convenient for Me, Can Trust Federal Govt to
Make Fair Decisions, Online Voting Would Fit My Style, Info Exchanged Over Internet is Secure,
Voting on the Internet Would be Useful to Me, Voting on the Internet Would be Status Symbol,
Would Enjoy Internet Voting for Elections, Would Prefer to Vote Online vs Regular Voting
b. Dependent Variable: Likely to Vote National if Internet Voting Available
155
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Correlations
Collinearity
Statistics
B
Std.
Error Beta
Zero-
order Partial Part Toler. VIF
1 (Constant) -.255 .427 -.598 .551
Internet Use Fits Well
With My Obtaining
Services
.112 .072 .086 1.560 .121 .367 .116 .071 .677 1.477
Online Voting Would Fit
My Style
-.004 .110 -.003 -.034 .973 .624 -.003 -.002 .207 4.822
Would Enjoy Internet
Voting for Elections
.187 .114 .177 1.635 .104 .665 .121 .074 .176 5.687
Internet Voting Would
Enhance Voting
Effectiveness
.136 .078 .116 1.752 .082 .562 .130 .080 .471 2.125
Would Prefer to Vote
Online vs Regular
Voting
.252 .119 .247 2.124 .035 .720 .157 .097 .153 6.533
Voting Online Would be
Better than Traditional
Voting
.143 .095 .145 1.504 .134 .677 .112 .068 .222 4.505
Would be More Likely to
Vote if Available Online
.282 .081 .277 3.505 .001 .686 .253 .159 .332 3.012
People Using Internet to
Vote are More Popular
.010 .095 .008 .110 .912 .242 .008 .005 .348 2.871
People Using Internet to
Vote are More
Respected
.175 .105 .133 1.669 .097 .334 .124 .076 .324 3.085
Voting on the Internet
Would be Status
Symbol
-.141 .128 -.104 -1.103 .272 .294 -.082 -.050 .231 4.327
156
Learning to Use Internet
for Voting Info Is Easy
.175 .104 .127 1.679 .095 .328 .124 .076 .362 2.759
Interacting With Internet
for Voting Info Is Clear
-.146 .100 -.114 -1.459 .146 .325 -.108 -.066 .341 2.936
Obtaining Govt
Services Online Would
be Easy
-.002 .096 -.001 -.016 .987 .261 -.001 .000 .475 2.105
Using Internet to Vote
Would be Easy
-.065 .100 -.050 -.654 .514 .398 -.049 -.030 .355 2.817
Voting on the Internet
Would Save Me Time
.058 .104 .038 .558 .578 .484 .042 .025 .439 2.277
Voting on the Internet
Would be Useful to Me
-.041 .104 -.035 -.396 .693 .566 -.030 -.018 .265 3.773
Voting on the Internet
Would be Convenient
for Me
-.023 .116 -.017 -.198 .843 .521 -.015 -.009 .280 3.572
Can Trust the
Exchange of
Information on Internet
-.216 .094 -.201 -2.302 .022 .374 -.170 -.105 .271 3.690
Info Exchanged Over
Internet is Secure
.057 .101 .049 .558 .577 .412 .042 .025 .266 3.753
Use of the Internet is
Reliable
.012 .076 .010 .164 .870 .433 .012 .007 .515 1.942
Can Trust Federal Govt
to Make Fair Decisions
.026 .100 .022 .261 .795 .163 .019 .012 .298 3.352
Can Trust State Federal
to Do What is Right
.054 .100 .044 .536 .593 .184 .040 .024 .304 3.289
a. Dependent Variable: Likely to Vote National if Internet Voting Available
157
APPENDIX M
Stepwise Regression Results – Federal Level of Government
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change F Change df1 df2
Sig. F
Change
1 .720a .519 .517 .859 .519 215.891 1 200 .000
2 .747b .559 .554 .825 .039 17.790 1 199 .000
3 .758c .575 .568 .812 .016 7.493 1 198 .007
4 .764d .584 .576 .805 .009 4.495 1 197 .035
a. Predictors: (Constant), Would Prefer to Vote Online vs Regular Voting
b. Predictors: (Constant), Would Prefer to Vote Online vs Regular Voting, Would be More Likely to Vote if
Available Online
c. Predictors: (Constant), Would Prefer to Vote Online vs Regular Voting, Would be More Likely to Vote if
Available Online, Would Enjoy Internet Voting for Elections
d. Predictors: (Constant), Would Prefer to Vote Online vs Regular Voting, Would be More Likely to Vote if
Available Online, Would Enjoy Internet Voting for Elections, Internet Use Fits Well With My Obtaining
Services
158
ANOVAe
Model Sum of Squares df Mean Square F Sig.
1 Regression 159.167 1 159.167 215.891 .000a
Residual 147.452 200 .737
Total 306.619 201
2 Regression 171.267 2 85.634 125.903 .000b
Residual 135.351 199 .680
Total 306.619 201
3 Regression 176.203 3 58.734 89.172 .000c
Residual 130.416 198 .659
Total 306.619 201
4 Regression 179.112 4 44.778 69.183 .000d
Residual 127.507 197 .647
Total 306.619 201
a. Predictors: (Constant), Would Prefer to Vote Online vs Regular Voting
b. Predictors: (Constant), Would Prefer to Vote Online vs Regular Voting, Would be More Likely to
Vote if Available Online
c. Predictors: (Constant), Would Prefer to Vote Online vs Regular Voting, Would be More Likely to
Vote if Available Online, Would Enjoy Internet Voting for Elections
d. Predictors: (Constant), Would Prefer to Vote Online vs Regular Voting, Would be More Likely to
Vote if Available Online, Would Enjoy Internet Voting for Elections, Internet Use Fits Well With My
Obtaining Services
e. Dependent Variable: Likely to Vote National if Internet Voting Available
159
Coefficientsa
Model
Unstandardized
Coefficients
Standard
Coeff.
t Sig.
Correlations
Collinearity
Statistics
B Std. Error Beta
Zero-
order Partial Part
Toleranc
e VIF
1 (Constant) .832 .182 4.561 .000
Would Prefer to Vote
Online vs Regular Voting
.736 .050 .720 14.693 .000 .720 .720 .720 1.000 1.000
2 (Constant) .578 .185 3.118 .002
Would Prefer to Vote
Online vs Regular Voting
.484 .077 .474 6.302 .000 .720 .408 .297 .393 2.545
Would be More Likely to
Vote if Available Online
.324 .077 .317 4.218 .000 .686 .286 .199 .393 2.545
3 (Constant) .377 .197 1.920 .056
Would Prefer to Vote
Online vs Regular Voting
.306 .100 .300 3.072 .002 .720 .213 .142 .226 4.427
Would be More Likely to
Vote if Available Online
.317 .076 .310 4.191 .000 .686 .285 .194 .392 2.548
Would Enjoy Internet
Voting for Elections
.232 .085 .220 2.737 .007 .665 .191 .127 .333 2.999
4 (Constant) -.016 .269 -.061 .952
Would Prefer to Vote
Online vs Regular Voting
.305 .099 .298 3.086 .002 .720 .215 .142 .226 4.427
Would be More Likely to
Vote if Available Online
.313 .075 .307 4.181 .000 .686 .285 .192 .392 2.549
Would Enjoy Internet
Voting for Elections
.190 .086 .180 2.203 .029 .665 .155 .101 .316 3.165
Internet Use Fits Well
With My Obtaining
Services
.138 .065 .106 2.120 .035 .367 .149 .097 .837 1.194
a. Dependent Variable: Likely to Vote National if Internet Voting Available
VITA
David P. Kitlan
Education:
Doctor of Philosophy – Public Administration
2010 The Pennsylvania State University Harrisburg, PA
Master of Information Systems
2002 The Pennsylvania State University Harrisburg, PA
Master of Business Administration
1992 The Pennsylvania State University Harrisburg, PA
Master of Engineering Sciences
1986 The Pennsylvania State University Harrisburg, PA
Bachelor of Science – Mechanical Engineering
1979 The Pennsylvania State University State College, PA
Recent Publications and Presentations:
Stone, J., & Kitlan, D. P. (2009). Factors impacting student perceptions of computing and
CIS majors. Journal of Computing Sciences in Colleges, 3(25), 8.
Hoffman, M., Kitlan, D., Stone, J., & Vance, D. (2008). Cultural, sociological and
experiential challenges for CIS education. Panel discussion at the Northeastern
conference of the Annual Consortium for Computing Sciences in Colleges (CCSCNE),
Staten Island, NY, April 11-12.
Joseph, R, & Kitlan, D. (2008). An examination of factors affecting multilevel e-voting.
Annual meeting of the Northeast Decision Science Institute (NEDSI), Brooklyn, NY,
March 28-30.
Joseph, R., & Kitlan, D. (2006). Key issues in egovernment and public administration. In
G. D. Garson & M. Khosrow-Pour (Eds.), Handbook of research on public information
technology (pp.1-11). Hershey, PA: IGI Global.
Professional Affiliations:
American Society for Public Administration
Public Administration Theory Network
American Society of Mechanical Engineers
Beta Gamma Sigma (International Business Honor Society)
Sigma Iota Epsilon (Business Management Honor Society)
