FACTORS INFLUENCING TAXPAYERS’ INTENTION TO ADOPT
FREE ELECTRONIC TAX FILING (FREE FILE)
Alex P. Barrios
B.A., California State University, Sacramento, 2003
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
COMMUNICATION STUDIES
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2010
© 2010
Alex P. Barrios
ALL RIGHTS RESERVED
ii
FACTORS INFLUENCING TAXPAYERS’ INTENTION TO ADOPT
FREE ELECTRONIC TAX FILING (FREE FILE)
A Thesis
by
Alex P. Barrios
Approved by:
__________________________________, Committee Chair
Dr. Barbara O’Connor
__________________________________, Second Reader
Dr. Diego Bonilla
__________________________________, Third Reader
Dr. Elaine Gale
____________________________
Date
iii
Student: Alex P. Barrios
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator
Dr. Mark Williams
Department of Communication Studies
iv
___________________
Date
Abstract
of
FACTORS INFLUENCING TAXPAYERS’ INTENTION TO ADOPT
FREE ELECTRONIC TAX FILING (FREE FILE)
by
Alex P. Barrios
This thesis studied the factors influencing taxpayers’ intention to adopt free internet tax
filing services, referred to by the Internal Revenue Service as free electronic tax filing
(Free File). Focusing on how the digital divide can explain why there is a knowledge gap
between internet users who utilize online programs and those who do not, this study drew
upon previous diffusions of innovations research to examine whether relative advantage,
compatibility with internet experience, complexity, and perceived risk influenced
taxpayer’s intention to adopt Free File. Information was gathered through the
administration of a survey to a convenience sample of internet users at 2 free income tax
preparation events that took place on February 6th and February 13th, 2010 in
Sacramento, California. Each participant was provided an informational brochure which
explained what Free File is and participants were pre-screened to ensure they were
internet users and eligible for Free File based on their annual household income (less than
$56,000). The data was analyzed using multiple regression analysis and measured
participants’ intention to adopt based on their perceptions of Free File. The results
showed that relative advantage, compatibility with internet experience and perceived risk
were significant predictors of intention to adopt. Perceived complexity was not a
v
significant predictor. Almost half of the participants were not previously aware of Free
File and the findings suggest that lack of awareness of the benefits of Free File is an
inhibitor to adoption. Some of the limitations of the study were that participants were not
able to test the program on a trial basis, only intention to adopt was measured (not actual
adoption), and there are some theoretical issues with the statistical method used to
perform the analysis (multiple regression).
_______________________, Committee Chair
Dr. Barbara O’Connor
_______________________
Date
vi
TABLE OF CONTENTS
Page
List of Tables ..................................................................................................................... ix
List of Figures ......................................................................................................................x
Chapter
1. INTRODUCTION ..........................................................................................................1
The Digital Divide Issue ......................................................................................... 2
Relevance of the Current Study .............................................................................. 4
2. LITERATURE REVIEW ...............................................................................................7
Electronic File (E-File) and Free File ..................................................................... 7
Importance of Free File ......................................................................................... 11
Diffusion of Innovations ....................................................................................... 12
Relative Advantage ............................................................................................... 16
Compatibility ........................................................................................................ 20
Complexity............................................................................................................ 26
Perceived Risk ...................................................................................................... 29
Research Question and Hypothesis ....................................................................... 32
3. METHODS ...................................................................................................................34
Research Design.................................................................................................... 34
Sample................................................................................................................... 37
Data Collection ..................................................................................................... 39
Data Analysis ........................................................................................................ 40
vii
4. RESULTS .....................................................................................................................42
5. DISCUSSION ...............................................................................................................45
6. CONCLUSION ............................................................................................................ 53
Implications........................................................................................................... 53
Limitations of the Study........................................................................................ 55
Suggestions for Future Research ......................................................................... 56
Summary………………………………………………………………………... 59
Appendix A. Free File Questionnaire ...............................................................................62
Appendix B. Sample Brochure .........................................................................................67
Appendix C. Script for Surveyors ....................................................................................68
Appendix D. IRS Publication 4821 ..................................................................................69
Appendix E. Statistical Results.........................................................................................70
References ..........................................................................................................................85
viii
LIST OF TABLES
Page
1.
Table 3.1 Operational Defintions of Constructs .....................................................37
ix
LIST OF FIGURES
Page
1.
Figure 2.1. Federal Individual Income Tax Preparation and Filing Data ...............8
x
1
Chapter 1
INTRODUCTION
As the internet becomes more popular, a number of industries and government
agencies are switching to online services. For example, the banking and grocery
shopping industries that have traditionally conducted business in a face-to-face setting are
now offering customers the ability to conduct transactions online. Companies in each of
these industries are investing money to develop websites that offer their clients the
convenience of being able to shop or bank from home or work at any time of the day.
Although internet services in many industries are becoming increasingly popular,
will websites offering free internet tax filing services, also known as Free File, be just as
popular among consumers? As a result of the E-Government Act of 2002, there are
already a number of government agencies and programs that are offering services online
(Festa, 2002). According to the Pew Internet Research Center, 82% of internet users
(61% of all adults) have used the internet to access a government service or visit a
government website (Smith, 2010). The federal government is trying to increase the
number of taxpayers who electronically file their tax returns because it will save money
and provide benefits to society (United States General Accounting Office, 2002).
Although internet tax filing services have been around for over a decade, the fact
is that most Americans are still not adopting them (Internal Revenue Service, 2008). The
low adoption rates are of particular concern to Congress who asked the Internal Revenue
Service (IRS) to significantly increase the percentage of Americans who electronically
file. Achieving this goal is important to federal and state governments because they can
2
lower their operating costs and paper usage if more filers submit their returns
electronically.
The Digital Divide Issue
The low adoption rates for Free File are also of concern to communication studies
scholars who are concerned with the digital divide. The digital divide is the gap between
people with effective access to digital and information technology and those with very
limited or no access at all (Umar, 2010). Another definition of the digital divide by
Rogers (2003) is “the gap that exists between individuals advantaged by the Internet and
those individuals relatively disadvantaged by the Internet” (p. 468).
The digital divide is an important concept in the current study because free online
tax filing programs were developed to help middle and lower income people who do not
necessarily have the resources to pay for a private tax preparer. These people can save
money by taking advantage of Free File. Furthermore, increased adoption rates will have
a positive consequence on the government who will also save money on the cost of
processing tax returns. Those are the intended individual and societal benefits of this
program. However, the Pew Research Center study found that most people who use
government services and information online have higher incomes and education levels.
Those with lower incomes and education levels are less likely to use the internet to access
government services (Smith 2010).
The potential benefits of Free File have been recognized by leading organizations
in the field. In fact, in 2009, the IRS Free File program received the Software and
Information Industry Association’s (SIIA) Special Award for Innovation in Public
3
Service. SIIA President Ken Wasch said, “The program provides the highest level of
service to the public in delivering free electronic government services to those in need,
while encouraging innovation in the private sector” (Bramlet, 2009, ¶ 2).
However, simply having this technology available does not automatically yield
benefits. Adoption rates must increase for any benefits to be reaped. Wasch’s 2009
comment that the program delivered a service to those in need is not necessarily an
accurate statement for everybody (more on Free File adoption rates will be discussed in
chapter 2), as the Pew Research Center points out. When it comes to Free File internet
tax filing programs, the scope of the digital divide goes beyond just making sure that
most people have access to high speed internet. Users would ideally need access to high
speed internet so they can utilize all of Free File’s features. For example, high speed
internet allows a user to quickly download a copy of their return using adobe acrobat
reader. People must also have the knowledge and ability to use Free File, as well as the
proper equipment and an understanding of how the applications can help them.
Otherwise, access alone is not the issue.
According to Madden (2009), 79% of American adults are now online. With a
large majority of the population already online, other issues, such as who utilizes the
internet and how it is being utilized, are important in understanding the adoption of
internet tax filing. Simply having access to the internet and using it does not assure that
people are going to utilize it to its full potential. Rogers (2003) predicted this would be
one of the issues associated with the digital divide. That is why Rogers viewed the
digital divide as more than just an issue of access. Rogers wrote:
4
The access divide is replaced by a learning-divide (in which certain individuals
lack the skills of computer and/or Internet use), a content-divide (in which less
educated individuals may not be able to comprehend the content of Web sites
created by highly educated individuals), and other types of divides. (p. 469)
Rogers (2003) predicted that access alone is not the issue. The learning divide
and the content divide also inhibit people from adopting innovations. In the case of Free
File, low adoption rates may not necessarily be an issue of access as the current study
will show. For example, among non-Internet users in California, only one in five are
interested in using the Internet, stating lack of interest as the main reason for their nonuse (Baldassare, et al. 2009). Lack of interest results because applications and content do
not relate to peoples’ needs. Another issue is affordability. It costs more money to have
broadband. It also costs more to invest in a computer and software needed to operate
these programs. According to a study by the California Public Utilities Commission, lack
of access, affordability, and relevant applications and content are all explanations for why
gaps in broadband usage exist (Bradshaw 2006).
Relevance of the Current Study
There is no question that the rate of Free File adoption among lower income
taxpayers must increase for the technology to benefit society. Public and private
investments have been made to develop Free File programs offered at reduced or no cost
to taxpayers who earn annual household incomes of less than $56,000. Failure to
increase the adoption rate among potential users means taxpayers who could otherwise be
filing their taxes online for free, will instead spend money to hire a tax preparer or will do
5
it themselves and risk failing to claim all of their credits. The fees private tax preparers
charge usually cost the taxpayer a significant amount of their refund. The more a
taxpayer spends for tax preparation services, the less money that will end up in their
pockets. Saving money is particularly important to lower income families—the same
populations that Free File services were developed to serve.
Hence, the purpose of this study is to identify the factors that will influence
taxpayers’ intention to adopt Free File. Learning this information will help increase
adoption rates. It will also help explain why current adoption rates are low. Although the
IRS conducted very basic research on electronic tax filing (e-file) and Free File (i.e., user
surveys and statistical data), no further research has been conducted in this area.
However, there are many studies that examine the factors that influence the
adoption of other online services, such as banking and grocery shopping. Many of these
studies have been centered on Everett Rogers’ diffusions of innovations theory.
Specifically, the attributes of relative advantage, compatibility, and complexity have been
significant predictors of adoption. The current study examined whether findings of
diffusion studies related to online grocery shopping and online banking were related and
helpful in explaining the factors that influence attitudes toward adoption of Free File
internet tax filing services.
The current study was conducted in Sacramento, California and participants were
recruited at two Volunteer Income Tax Assistance (VITA) sites—Grant High School and
Charles Jones Career and Education Center. To encourage an adequate sample size,
taxpayers who agreed to participate in the study were entered into a raffle to win a $100
6
cash prize. As a Senior Field Representative for State Assemblymember Dave Jones, I
was responsible for coordinating outreach and promoting the two tax events to the
general public.
The results of this study may not be generalizable as the sample population was
limited to a convenience sample of taxpayers who attended the tax workshops. All
participants earned an annual household income of $56,000 or less. Because the purpose
of the study is to understand the factors that influence adoption among taxpayers who are
eligible for Free File services, using a convenience sample of VITA clients was
appropriate to ensure the sample meet this criterion.
The remainder of this study includes a literature review of relevant research,
followed by a proposed methodology section, and attachments (example questionnaire,
instructions for surveyors, and a sample promotional brochure). Key terms used
throughout the remainder of the paper include electronic tax filing (also referred to in the
research as e-file, internet tax filing or online tax filing), Free File, diffusion of
innovations theory, relative advantage, compatibility, complexity, and perceived risk.
7
Chapter 2
LITERATURE REVIEW
The topics to be discussed in the literature include electronic file (e-file), Free
File, the importance of Free File, diffusion of innovations research (specifically, the
attributes of relative advantage, compatibility, and complexity), and perceived risk.
Research and information related to these topics will be highlighted in this section.
Electronic File (E-File) and Free File
Online tax filing, or e-file, services have increased in popularity over the last 10
years. E-file is the electronic filing of a tax return to the IRS. For taxpayers who earn an
annual income of less than $56,000, there is a special IRS program called “Free File.”
The Free File program provides free federal income tax preparation and electronic tax
filing services for eligible taxpayers through a partnership between the Internal Revenue
Service (IRS) and Free File Alliance, LLC—a group of private sector tax software
companies (IRS, 2009). All participants in the study were eligible for Free File based on
their earnings. According to the IRS (2009), some of the benefits of Free File are:

Free tax preparation and e-filing if your adjusted gross income was $56,000 or
less in 2009.

The use of simple questions and inserting your answers on the correct forms.

The ability to file your taxes any hour of the day or night.

Getting a faster refund—in as little as 10 days with direct deposit.

Knowing that your return is safe and secure.

Saving paper.
8
Perhaps the most important government document related to electronic filing is the IRS’s
2008 “Advancing E-File Study Phase 1 Report” (see Figure 2.1).
Figure 2.1.
Federal individual income tax preparation and filing data (as of tax year 2006) (IRS,
2008, p. 1).
According to the IRS (2008) report, Congress set a goal of achieving an 80%
e-file rate by 2007. The report is
a major effort to collect, synthesize, taxpayer and preparer behaviors, related
programs and effort, and options for expansion – to help the IRS validate and
launch future studies, research, and other activities to meet the congressionally set
goal of an 80% e-file rate. (IRS, 2008, p. 1)
According to the IRS (2008) report, “there are a number of ways that tax returns
can be prepared and filed using paper or electronic means” (p. 1). Figure 2.1 simplifies
the complex array of choices into three main combinations of preparation and filing for
individual tax returns:

Prepared on a computer and filed electronically through e-file.
9

Prepared on a computer, then printed and filed on paper. (The IRS calls these
types of filers V-Coders.)

Prepared on paper and filed on paper (IRS, 2008).
The IRS e-file program began in 1986 and in its first year, 25,000 returns were
submitted. Electronic filing has grown substantially since then. According to the IRS
study, over 87,000,000 (about 60%) of individual tax returns were e-filed in the 2008
filing season. However, the 60% figure quoted in the study does not necessarily reflect
the number of taxpayers who actually self-prepared and electronically filed their tax
returns. Many of those returns were prepared by a third party tax preparer or company
and then electronically filed on the taxpayer’s behalf. Under those circumstances, the
taxpayer is paying the tax preparer or company a preparation and filing fee. This means
the taxpayer is not taking advantage of a Free File service and is spending more than is
necessary to file their taxes.
To assess the number of taxpayers who self-prepare their taxes on a computer and
electronically file on their own behalf, a simple three-step calculation using the figures
represented in the data must be performed. This calculation revealed that the actual
number of self-prepared and e-filed returns was approximately 17%. This approximate
estimate was generated using IRS data shown in Figure 2.1 and performing the following
calculations:
1. The approximate percentage of taxpayers who self-prepared their return using
a computer: 0.89 (% of total computer-generated returns) x 37 (% of total
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self-prepared returns) = 33 (approximate % of self-prepared/computergenerated returns).
2. The percentage of e-filed returns that were computer-generated: 0.58 (% of
total e-filed returns) x 89 (% of total computer-generated returns) = 52
(approximate % of e-filed returns that were computer-generated).
3. Determining the percentage of self-prepared e-file returns: 0.52 (approximate
% of e-filed returns that were computer-generated) x 33 (approximate % of
self-prepared/computer generated returns) = 17 (% of self prepared e-file).
According to the results of the calculation, approximately eight of 10 taxpayers are not
preparing their own tax return and e-filing. That is a fairly low adoption rate considering
that e-file has been around since 1997. Although the results of the 2008 IRS survey help
to provide basic statistics for e-file users, the study did not indicate what percentage of
users had qualified or filed their return using a Free File service.
To better understand the characteristics of Free File users, the IRS released the
“2008 Free File Survey” report. A telephone survey was conducted and the sample
consisted of 1,802 users who had free filed in the 2008 tax filing season. The results
showed that 92% of total users cited convenience as a reason for choosing Free File.
Other reasons cited by users as key factors in choosing Free File were cost (79%) and
ease of use (65%). The survey also found that 92% of users indicated they intend to use
Free File again the following year. Another significant finding was that 97% of Free File
users in the survey indicated they were very confident or somewhat confident that the
information provided during the Free File process was secure.
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Importance of Free File
There are significant societal benefits for increasing the adoption of Free File
services to income eligible taxpayers. First, the government can save operating and paper
costs. The more taxpayers who submit their returns electronically, the more money the
government will save. In a 2002 report to the House of Representatives’ Subcommittee
on Oversight, Committee on Ways and Means, the United States General Accounting
Office wrote:
In a March 2000 study prepared for IRS, a national consulting firm presented a
range of cost-reduction estimates depending on changes in several variables, such
as the number of returns filed electronically, and assuming several operational
changes, such as making additional business forms available for electronic filing.
Using 1999 expenditures as the baseline, the consultant’s annual cost-reduction
estimates ranged from $27 million to a “best case” of $243 million starting in
2007. (p. 2)
If the cost savings estimate is correct, the overall beneficiaries of e-file adoption would
be taxpayers. If the IRS can save money on what it costs to process tax returns, then
those tax dollars can be diverted to other programs.
Second, individuals who use Free File would benefit economically. Aside from
the convenience of being able to file their taxes at any time from home or work, they
would also be saving money. A 2008 report by the Department of the Treasury titled
“Many Taxpayers Who Obtain Refund Anticipation Loans Could Benefit from Free Tax
Preparation Services” highlighted the importance of increasing adoption of free tax
12
preparation services such as Free File among lower income populations. The report
highlighted:
Millions of taxpayers borrow against all or part of their expected tax refunds to
receive their money more quickly. This is accomplished through short-term loans
that cost taxpayer’s fees and interest payments. Many of these taxpayers are
eligible for free tax preparation services offered by the Internal Revenue Service
(IRS) and its partners. (p. 2)
When low income taxpayers who could otherwise be self-preparing and filing
their taxes for free are losing significant amounts of their tax refund to refund anticipation
loans and tax preparer fees, it diminishes legislative and congressional intent to subsidize
the incomes of these families through the implementation of tax credits such as the
Earned Income Tax Credit (EITC) and Child Tax Credit. The fact that technology to help
connect lower income families to these programs exists but is not being utilized furthers
the argument in support of government funding for broadband expansion to lower income
communities. However, until there is research to help explain the factors that will
increase adoption of Free File and outreach strategies developed based on these findings,
these trends will most likely continue.
Diffusion of Innovations
Findings from the IRS (2008) Free File survey indicate that two of the most
significant reasons for choosing Free File were convenience and ease of use. These
factors derive from diffusion of innovations theory—the driving theory behind this study.
Diffusion research centers on the conditions that increase or decrease the likelihood a
13
new idea, product, or practice will be adopted by members of a given culture (Rogers,
2003).
According to Rogers (2003), the process of making a decision about an
innovation is described as the innovation-decision process. This process describes how
an individual passes from gaining initial knowledge of an innovation, to forming an
attitude toward the innovation, to making a decision to adopt or reject, to implementation
of the new idea, and to confirmation of this decision. This process consists of “a series of
choices and actions over time through which an individual or a system evaluates a new
idea and decides whether or not to incorporate the innovation into ongoing practice”
(p. 168).
The innovation-decision process is a five step process. The five steps are:
1. Knowledge—An individual is exposed to an innovation’s existence and gains
an understanding of how it functions.
2. Persuasion—The individual forms a favorable or unfavorable attitude toward
the innovation.
3. Decision—The individual engages in activities that lead to a choice to adopt
or reject an innovation.
4. Implementation—The individual puts the innovation to use.
5. Confirmation—The individual decides to adopt or reject the innovation.
(Rogers, 2003, pp. 171-189)
This study took place at the persuasion stage of the innovation-decision process.
The persuasion stage is critical because it is the stage where attitude formation occurs.
14
The persuasion stage is where an individual forms a favorable or unfavorable attitude
toward an innovation. According to Rogers (2003):
Such perceived attributes of an innovation as its relative advantage, compatibility,
and complexity are especially important at this stage. In developing a favorable
or unfavorable attitude toward an innovation, an individual may mentally apply
the new idea to his or her present or anticipated future situation before deciding
whether or not to try it. (p. 175)
Therefore, if an innovation is to eventually be adopted, having a potential adopter
develop a favorable attitude toward the innovation is an important first step. Findings
from the Public Policy Institute of California in collaboration with the California
Emerging Technology Fund show that when it comes to attitude, more than 80% of
Californians are at least somewhat comfortable using modern technology (Baldassare, et
al. 2009). Therefore, it shall be assumed that most internet users in this study will be
comfortable using Free File.
As Rogers (2003) stated, relative advantage, compatibility, and complexity are
especially important at the persuasion stage. Therefore, the subsequent literature review
will provide a summary of relevant research findings related to these attributes. Another
attribute which will be covered in the literature is perceived risk, which is also a relevant
construct in the study.
In the next few paragraphs, a summary of the literature related to each of the
aforementioned constructs will be reviewed within the context of performing online
transactions. There are no diffusions studies of internet tax filing, or for that matter, any
15
academic research studies in this area, so the corresponding review of literature related to
relative advantage, compatibility, and complexity will be based on diffusions research of
other related online services. In particular, research in the area of online grocery
shopping and online banking will be provided. Online banking and online grocery
shopping services were chosen as parallel because of the characteristics they share with
internet tax filing services and how those characteristics relate to the attributes of relative
advantage, compatibility, complexity, and perceived risk. In particular, online grocery
shopping and online banking services share the following characteristics with internet tax
filing services:

Users enjoy the convenience of being able to perform transactions online from
home, work, or wherever is convenient at a reduced cost and at any time of
the day (relative advantage).

Users of online banking and online grocery shopping must be familiar with
the internet and usually have previous experience performing similar
transactions online (compatibility).

Users must have the ability and skill to perform transactions online in order to
use these services (complexity).

Users are required to release private information or make this information
available over the internet in order to use these services (perceived risk).
Although internet tax filing services are distinct from online banking and online
grocery shopping services, the characteristics they share are adequately similar.
Therefore, research findings for these services will be used to help explain the potential
16
factors that may influence the adoption of internet tax filing services. The next few
sections will provide information related to research findings based on diffusion of
innovations theory (or similar theories) and how attributes derived from this theory
(relative advantage, compatibility, complexity, and perceived risk) have helped to predict
individual adoption patterns of online banking and online grocery shopping.
Relative Advantage
The first key construct in this study is relative advantage. Rogers (2003) defined
relative advantage as “the degree to which an innovation is perceived as being better than
the idea it supersedes” (p. 15). Furthermore, “the degree of relative advantage is often
expressed as economic profitability, as conveying social prestige, or in other ways”
(p. 229). The more an individual perceives an innovation provides them with a relative
advantage, the more likely they are to adopt it. The results of the 2008 IRS Free File
study found support for that concept as 92% of users described convenience as a reason
for using Free File and 79% mentioned cost as a reason for adoption as well. Past
literature has also shown that perceived relative advantage characteristics are
significantly and positively related to the adoption of new innovations (Holak &
Lehmann, 1990).
Verhoef and Langerak (2001) conducted a study to identify the factors that led to
consumers’ adoption of electronic grocery shopping in the Netherlands. Perceived
relative advantage was defined as “the degree to which consumers perceive electronic
grocery shopping to be superior to in-store shopping” (p. 277). Relative advantage was
measured using a three-item scale consisting of shopper excitement, time savings, and not
17
having to be dependent on store hours. Self-administered questionnaires were mailed to
over 2,500 households. From that sampling frame, 415 surveys were mailed back and
used in the final study. Analyzing the data using correlation tests, the researchers
concluded “perceived relative advantage was positively related to intention to adopt
electronic grocery shopping” (p. 282).
A number of other studies, particularly in the area of online banking, also
concluded that relative advantage, or similar factors, are positively related to adoption or
intention to adopt. Sathye (1999) conducted a study of Australian banking customers’
perceptions of online banking. Sathye implemented a mail survey that provided a sample
size of 589 business and personal banking clients. The data showed that 68% of these
banking clients were not clear about the benefits and added value that internet banking
offers. An even more revealing statistic displayed in the results section of Sathye’s study
is that 81% of the non-adopters surveyed indicated they were unaware of the benefits of
online banking. The high number of Australian banking clients who failed to find
relative advantage in online banking may help to explain why, in 1996, only 1% of
banking transactions in Australia were being conducted online (Ernst & Young, 1996).
Tan and Thompson (2000) researched the attitudinal, social, and perceived
behavioral control factors that influenced adoption of internet banking. Using a web
survey as their instrument, they received responses from 454 banking customers in
Singapore. Relative advantage was defined in terms of how much advantage and
convenience it offered to consumers and was measured on a six-item scale in terms of
how much easier it made conducting bank transactions, greater control of finances, ability
18
to manage more efficiently, convenience, effectiveness, and usefulness. Processing the
data using regression analysis, Tan and Thompson found support for their hypothesis:
“The greater the perceived relative advantage of using internet banking services, the more
likely that internet banking will be adopted” (p. 9). Although the results were limited to
internet users in Singapore, the study provides support for the positive relationship that
exists between relative advantage and intention to adopt online banking.
Ekin and Polatoglu (2001) conducted an exploratory investigation of Turkish
consumers’ acceptance of internet banking by investigating the factors that influence
customers’ acceptance of internet banking services. An email survey was sent out to
select customers of Garanti Bank in Turkey to “explore the actions they were likely to
take and investigate their satisfaction level regarding the use of IB services” (p. 156).
Relative advantage was defined in terms of price, convenience, and performance. The
researchers achieved a sample size of 114 participants and, after performing factor
analysis, created the savings dimension that consisted of “the cost savings, time savings,
and self-service characteristics of internet banking services” (p. 159). The results
indicate that early adopters and frequent users of internet banking services were very
satisfied in the cost, time savings, and self-service characteristics of internet banking and
were likely to make positive recommendations about the service to their peers. The
study’s findings provide further support that characteristics of relative advantage (cost
savings, time savings, and self-service characteristics) are positively related to adoption.
In other online banking-related research, Cunningham and Gerrard (2003)
performed a qualitative study of consumers in Singapore. After interviewing eight
19
adopters of internet banking and eight non-adopters of internet banking, they found that
both adopters and non-adopters thought the service was convenient. However, “more
adopters found it convenient than non-adopters” (p. 23). Hamilton, Hewer, and Howcraft
(2002) surveyed a cross section of 286 consumers in a study of consumer attitude and the
usage and adoption of home-based banking in the United Kingdom. The authors
concluded that 70% of banking customers believed the lower fees offered by internet
banking were a fairly important, important, very important, or extremely important factor
in encouraging adoption of internet banking. Furthermore, approximately 73% of the
customers also found improved service quality and time savings as important factors in
encouraging adoption. Finally, in a comparison of adopters and non-adopters of internet
banking, Kwon, Lee, and Schumann (2005) found current adopters of internet banking
“appear to perceive convenience and quick service as important attributes, compared to
persistent non-adopters” (p. 431) and their hypothesis that “consumer perceptions of
convenience as an important attribute will be positively associated with their adoption
levels of internet banking” (p. 418) was fully supported. The findings provide further
evidence to support that relative advantage (convenience, cost savings, better service
quality, time savings, and quicker service) is positively related to adoption, or is a factor
in encouraging adoption of internet banking.
The findings suggest that perceived relative advantage in the adoption of online
banking and online grocery shopping does in fact consist of the perceived benefits of the
service. Therefore, for the purpose of this study, relative advantage is defined as the
degree to which taxpayers will perceive Free File to be a more beneficial way to file their
20
taxes. This construct is operationally defined in terms of ease, control, efficiency,
convenience, usefulness, and time savings. The operational definitions are also provided
in Table 3.1.
Compatibility
Another important construct in this study is compatibility. Rogers (2003) defined
compatibility as “the degree to which an innovation is perceived as consistent with the
existing values, past experiences, and needs of potential adopters” (p. 240). Because
online tax filing requires the user to have enough skill to be able to fill out online forms
and respond to questions prompted by the program, users should have some past
experience using the internet. Given the nature of internet tax filing, past experience
using the internet is perhaps the greatest indicator of compatibility for the proposed study.
Data results from the 2008 IRS Free File Survey also provided support for this concept as
92% of Free File users indicated they intend to use Free File again in the future. In other
words, those who used Free File will continue to use it as a result of their previous
experience. This section provides a summary of research related to compatibility and
internet experience as it relates to online banking and grocery services adoption. The
more an innovation falls in line with a person’s beliefs and values, the more likely they
will intend to adopt it. Although a number of studies have provided empirical support for
the positive correlation between compatibility and adoption, results vary. Some of these
findings are highlighted in this section.
21
Langerak and Verhoef (2001) found perceived compatibility is “related positively
to the intention to adopt electronic grocery shopping” (p. 282). On the other hand, Kwon
et al. (2005) found:
Adoption factors such as compatibility . . . did not differ among current adopters
and prospective adopters . . . [but] the odds of being a prospective adopter over a
persistent non-adopter increase 1.44 times when a consumer uses the computer
heavily for his/her work compared to light use of the computer for work. (p.432)
Cunningham and Gerrard (2003) found “adopters of internet banking perceived the selfservice technology to be more compatible to them” (p. 24) and also “some non-adopters
would have low levels of PC skills and this may strongly influence their decision to not
apply for an internet bank account” (p. 24). On the other hand, Sathye (1999) found
“only 32% of customers cite resistance to change as a reason for non-adoption of online
banking” (p. 331).
Other findings indicate there is a positive correlation between adoption, previous
internet experience, and other forms of compatibility. Hansen, Jensen, and Solgaard
(2004) conducted a study based on “the theory of reasoned action and theory of planned
behavior in predicting online grocery buying intention” (p. 539). Online surveys were
self-administered and a sample population of 1,022 Danish consumers and 1,058 Swedish
consumers were included in the study. The researchers tested whether “a consumer’s
intention to perform a certain behavior may be influenced by the normative social beliefs
held by the consumer” (p. 540). This concept was operationally defined and measured by
the level of agreement with the following statements: “(1) electronic shopping of
22
groceries is attractive to me in my daily life, and, (2) buying groceries via the Internet is
well-suited to the way in which I normally shop groceries” (p. 543). Analyzing the data
using a two-step approach (factor analysis and then a linear regression test) Hansen et al.
found that social normative influence could be “of high importance to a consumer when
considering online grocery buying” (p. 547).
Previous studies also found evidence that non adopters of electronic mediums
were resistant to change. For example, Hamilton, Hewer, and Howcraft (2002) found
“wealthier and older respondents placed particular emphasis on face-to-face contact [and
were also] less inclined to use the telephone or internet banking” (p. 117). The findings
were consistent with early research on technology in financial services focused on the
adoption of ATMs that “revealed that a significant factor for non-use, especially among
older consumers, was the preference for conducting financial affairs through a human
teller” (Zeithaml & Gilly, 1987; Kwan, 1991).
Karjaluoto, Mattila, and Pento (2002) researched “the effect of different factors
affecting attitude formation towards internet banking in Finland” (p. 261). They studied
four factors related to compatibility: prior computer and technology experience, personal
banking experience, and reference group influence. Personal computer and technology
experience were defined as “the use of PC’s, the Internet, and email” (p. 262). Personal
banking experience was defined as “customer satisfaction/dissatisfaction of the delivery
channel in use, on the one hand, and intention to change banking behavior, on the other
hand” (p. 263). Reference group influence was defined as “an extensive construct,
referring to all the people who influence on consumer behavior” (p. 263). Karjaluoto et
23
al. conducted a mail survey of 1,067 individual bank customers in Finland. Analyzing
the data using factor analysis, they found “prior computer experience, prior technology
experience, personal banking experience, and reference group affect attitude towards
online banking as well as online banking usage” (p. 266). Their results indicate there is a
positive correlation between elements of compatibility and attitude towards online
banking, with prior internet/computer experience serving as a key measure of
compatibility.
Lin, Tang, Wang, and Wang (2003) examined the effect of computer selfefficacy, among other variables, in relation to intention to use internet banking.
Computer self-efficacy was defined as “the judgment of one’s ability to use a computer”
(p. 506). They conducted 123 telephone interviews with a sample representative of a
cross-section of the Taiwanese population. Factor analysis was used to test their research
model and results showed that computer self-efficacy’s “total effect on behavioral
intention is positive” (p. 514). The Lin et al. study was limited because it did not
measure actual behavior.
Laforet and Li (2005) studied consumers’ attitudes of online and mobile banking
in China. A convenience sample consisting of 128 participants from six major cities in
China were interviewed and t-tests were used to analyze the relationship between the
different variables. The research findings revealed “non-users tend to rate that they had
no prior computer experience while the opposite was true with users” (p. 374). The
results provided further evidence to support that prior computer experience is related to
adoption and, once again, is a factor in measuring compatibility.
24
Lee and Lee (2001) examined the relationship between internet purchasing
experience and the likelihood to adopt internet banking. They investigated whether
consumers’ decisions to adopt internet banking was need-based or skill-based. Internet
purchase behavior was operationally defined based on two factors: “(1) How many times
the respondent had purchased products through the Internet in the last 12 months, and (2)
How willing the respondent was to provide credit card and purchase information through
WWW/e-mail” (p. 106). An internet survey was administered to 1,600 participants and
the data was analyzed using a structural equation modeling approach followed by a set of
regression analyses. Results showed “previous internet purchase behavior positively
affected consumers’ likelihood of adopting internet banking” (p. 107). Their results
confirmed previous findings that prior experience with technologies may influence
consumers’ future adoption of similar technologies (Dabholkar 1992; Dickerson &
Gentry, 1983; Hirschman 1980).
Black, Ennew, Lockett, and Winklhofer (2001) conducted a qualitative
examination of important determinants in the adoption decisions of consumers of internet
financial services. Using focus group interviews to garner detailed responses from 12
participants who had previously purchased some type of goods or services over the
internet, 12 who had purchased goods or services (including financial services) over the
internet, and 12 who had not, they found significant differences between the three groups.
In particular, “the degree to which an innovative channel such as the internet is
compatible with the individual’s past experiences and values appears to have a significant
impact on willingness to adopt” (p. 395).
25
Tan and Thompson (2002) operationally defined internet experience as a
combination of internet usage (span of use, frequency of use, intensity of use, and
diversity of use) and internet skill level. Their results found support for the hypothesis
that “the greater the experience with using the Internet, the more likely that internet
banking will be adopted” (p. 10). Tan and Thompson’s findings are consistent with the
previous studies cited in this section. However, their study was unique in the sense that it
provided internet experience as a broader construct comprised of internet use and skill,
whereas other studies separated the two and provided different operational definitions for
similar constructs (Cunningham & Gerrard, 200; Karjaluoto et al., 2002; Laforet & Li,
2005; Lee & Lee, 2001; Lin et al., 2003). Furthermore, Tan and Thompson were the only
researchers to group internet experience along with compatibility in the formation and
measurement of a new construct, compatibility with internet experience. This construct is
similar to that of Kwon et al.’s (2005) definition of compatibility which was
“compatibility with the internet and computer technologies” (p. 428). The findings of
this study also found a positive association between prior internet experience and
adoption level of internet banking.
Most prior studies (with the exception of Tan and Thompson, 2002) categorized
internet experience and compatibility as separate constructs. However, relevant literature
(Gerrard & Cunningham, 2003; Karjaluoto et al., 2002; Laforet & Li, 2005; Lee & Lee,
2001; Lin et al., 2003; Tan & Thompson, 2000) provided support for the assertion that
studies examining compatibility in online settings utterly rely on prior internet experience
as a key factor in measuring the construct. For this study, the relationship between
26
compatibility and previous internet experience is significant enough that it merits special
consideration.
Elgarah, Hightower, Johnson, and Van Slyke (2008) warned researchers not to
“mechanistically reuse” (p. XX) scales when conducting diffusions research (particularly
in relation to relative advantage and compatibility) because “mechanistically reusing
scales developed for a particular context may lead to a variety of undesirable effects” (p.
50). Elgarah et al.’s concerns should be taken into consideration because elements of
compatibility measured in previous diffusions studies may not necessarily match with
what is intended to be measured in the current study. In striving for accuracy and to
avoid taking a one size fits all approach, a constricted version of the compatibility
construct is proposed for the current study. The construct will be termed compatibility
with internet experience and is adapted from Tan and Thompson (2003).
For this study, compatibility with internet experience is defined as a taxpayer’s
previous internet use and their level of skill in performing online functions. The
operational definition of this construct consists of span of use, frequency, intensity,
diversity, skill, and knowledge of using the internet. The operational definition is also
provided in the methodology section of this paper.
Complexity
Rogers defined (2003) complexity as “the degree to which an innovation is
perceived as relatively difficult to understand and use” (p. 257). The more difficult a user
perceives an innovation to be, the less likely they are to adopt. Data results from the
2008 IRS Free File Survey also supported this concept, as ease of use was cited by 65%
27
of users as a reason for adoption. A number of research studies have established a
negative correlation between complexity and adoption.
Langerak and Verhoef (2001) defined perceived complexity as “the degree to
which consumers find electronic grocery shopping difficult to understand” (p. 277).
They measured perceived complexity using a five-item scale consisting of statements
related to complexity, level of difficulty ordering products, how problematic it is to
compare products, how hard it is to find products, and complexity in relation to not being
able to feel and see products. Results were that “perceived complexity is related
negatively to the intention of consumers to adopt electronic grocery shopping” (p. 282).
Hansen et al. (2004) measured perceived behavioral control as a determinant of
behavioral intention. Perceived behavioral control was conceptualized as “the
consumer’s subjective belief about how difficult it will be for that consumer to generate
the behavior in question” (p. 541). It was measured using a five-item scale consisting of
statements related to complexity, difficulty finding products, difficulty ordering products,
a reverse-coded item, and difficulty receiving groceries purchased over the internet.
Results showed that perceived behavioral control “was only slightly found to affect
buying intention“ (p. 547).
Other studies measured complexity in terms of user-friendliness and perceived
ease of use. Chow, Luk, and Wan (2004) investigated the factors influencing customers’
adoption of banking channels in Hong Kong. User-friendliness was measured in terms of
ease of use and clarity of service instructions. Data was gathered through the
administration of a telephone and mail survey. A sample size of 314 bank customers was
28
obtained. The data was analyzed using factor analysis and correlation tests were run to
test the relationships between the remaining variables. Chow et al. found:
Beliefs about the convenience of internet banking were not correlated with its
adoption. . . . What really mattered were the other three attributes of the channel
(i.e., informativeness, user-friendliness, and assurance). These are the areas in
which channel managers have to work hard. (p. 270)
Their findings showed that complexity was a greater factor in determining adoption than
other attributes.
Lin et al. (2003) defined perceived ease of use as “the extent to which a person
believes that using a particular system will be free of effort” (p. 503). It was measured
using a four-item scale consisting of statements related to whether internet banking was
clear and understandable, whether it was easy to learn, whether it would be easy to
become skillful, and whether it would be easy to use. Lin et al. found “perceived ease of
use exerting a stronger influence than both perceived usefulness and perceived
credibility” (p. 513).
Other online banking research studies also concluded that there were negative
relationships between complexity and adoption. Ekin and Polatoglu (2001) and
Cunningham and Gerrard (2003) both found support for negative correlations between a
user’s level of perceived complexity and their level of adoption.
For this study, complexity will be defined as the level of difficulty the taxpayer
perceives it will be to use Free File. Because internet tax filing requires more of a user’s
time and the user is prompted with more questions, it can be argued that it is more
29
complex than online banking and online grocery shopping. Therefore, it will be
necessary to operationally define complexity accordingly. The operational definition of
this construct consists of mental effort, difficulty, simplicity, and complexity. The
operation definition is also provided in the chapter 3.
Perceived Risk
Perceived risk has been previously defined as “a consumers’ uncertainty about
decision outcomes and possible negative consequences associated with a particular
choice” (Dowling & Staelin, 1994). The more risk an individual perceives, the less likely
they are to adopt an innovation. Perceived risk is not one of the original attributes of
Rogers’ (2003) diffusion of innovations theory. Bauer (1960), Webster (1969), and
Ostlund (1974) introduced risk as an additional dimension in diffusion and adoption.
Research shows that a negative correlation exists between perceived risk and adoption,
making it a relevant construct in this study. A review of the literature revealed that
previous studies related to perceived risk identified security concerns as a main factor in
defining and measuring the construct.
Rotchanakitumnuai and Speece (2003) conducted a qualitative research study of
corporate banking clients in Thailand. Seven adopters of internet banking and eight non
adopters of internet banking were interviewed using a face-to-face technique. After
performing a content analysis of the interviews, they concluded that internet banking
users perceived lower levels of concerns with security, transaction reliability, and trust of
the bank than did non internet banking users. Rotchanakitumnuai and Speece’s findings
matched those of Sathye (1999), who found that security concerns were a barrier to the
30
adoption of online banking, but differed from Gerrard and Cunningham (2003) who
found security concerns of online banking high in both adopters and non adopters in their
study of Singapore banking clients. Hamilton et al. (2002) defined perceived risk as
focusing “upon the importance of trust and the security of different delivery channels” (p.
113). Their survey found that 84% of customers found security either fairly to extremely
important, while approximately 83% of customers thought fear of the likelihood of errors
as important factors in discouraging adoption. Hamilton et al. defined perceived risk as
focusing “upon the importance of trust and the security of different delivery channels” (p.
113). Kwon et al. (2005) defined perceived risk as “associated with consumer
uncertainty about decision outcomes and possible negative consequences associated with
a particular choice” (p. 419). They measured it using a three-item scale consisting of “the
size of the service provider (in terms of assets), familiarity with the provider, and
transaction security. The importance of each attribute was recorded on a five-point Likert
scale” (p. 424). They found “transaction security was found to be an important service
attribute, differentiating persistent non adopters and prospective adopters from adopters”
(p. 428). In measuring the attitude of consumers in China, Laforet and Li (2005) found
“among the security concerns, hackers and fraud were identified as the main concerns for
not using online banking services” (p. 373). Lin et al. (2003) identified a new construct,
perceived credibility, as a “new factor that reflects the user’s security and privacy
concerns in the acceptance of Internet banking” (p. 501). Perceived credibility was
measured using a two-item scale consisting of whether customers believed internet
banking would divulge personal information and whether they felt secure in conducting
31
banking transactions online. Their study found perceived credibility was a predictor of
adoption and concluded that compared to other online banking research based on trust or
perceived risk, perceived credibility had “the higher ability to predict and explain the
intention of users to adopt Internet banking” (p. 514). Chow et al. (2005) measured
assurance in terms of security of customer information and accuracy of transaction
information and found bank customers believed internet banking provided the least
assurance among four banking channels (branch banking, ATM, telephone, and internet).
Meanwhile, Ekin and Polatoglu (2001) characterized perceived risk within a “reliability
dimension [which included] financial, physical, or social risks associated with trying an
innovation” (p. 161). They measured it testing customers’ perceptions of online
banking’s reliability, security, and whether privacy was maintained. They found
“customers who use internet banking for the longest time or who use more of its services,
find internet banking to be very reliable” (p. 162). Tan and Thompson (2000) measured
risk using a three-item scale consisting of confidence over security aspects, concern about
internet banking transactions being known to others, and whether internet banking
transactions can be tampered with by others. The same conclusions as other studies were
reached—the lower the perceived risk of using internet banking, the more likely that
internet banking will be adopted.
For the current study, perceived risk will be defined as how secure the taxpayer
feels about Free File. It is operationally defined in terms of security, fear of knowledge
known to others, and tampering. The operational definition is also provided in Table 3.1.
32
Research Question and Hypothesis
Based on the review of the literature and relevant research findings, the
following research question is presented:
RQ: What are the factors influencing taxpayers’ intentions to adopt Free File?
Hypotheses based on the research question and relevant findings are also
proposed in relation to each of the attributes examined in the literature review. First,
because the research findings found relative advantage to be positively related to
adoption of internet banking and grocery shopping, the following hypothesis is
proposed:
H1: Internet users with higher levels of perceived relative advantage will be
more likely to adopt Free File.
Second, most of the research findings in the literature review found compatibility,
specifically, compatibility with internet experience, to be positively related to adoption of
internet banking and grocery shopping. Therefore, the second hypothesis is:
H2: Internet users with higher levels of perceived compatibility with internet
experience will be more likely to adopt Free File.
Third, the research found that complexity was negatively related to adoption of
internet banking and grocery shopping. Therefore, the third hypothesis is:
H3: Internet users with higher levels of perceived complexity will be less likely
to adopt Free File.
Finally, perceived risk was also found to be negatively related to adoption of
internet banking and grocery shopping. Therefore it is presumed:
33
H4: Internet users with higher levels of perceived risk will be less likely to adopt
Free File.
34
Chapter 3
METHODS
This section provides a description of the methodology used to test the
hypotheses. An explanation will be provided for the research design, sampling process,
collection and tabulation of data, and the procedures used to analyze the data.
Research Design
A quantitative research design was used to test each of the four hypotheses
outlined at the end of chapter 2. Each hypothesis was tested using multiple regression
analysis to measure the size and direction of the correlation between the dependent
variable (intention to adopt) and the independent variables (relative advantage,
compatibility with internet experience, complexity, and perceived risk). This method of
analyzing data was chosen for this study because, according to Wright (1979), “the
multiple regression model allows us to examine the effects of several variables
simultaneously on the dependent variable” (p. 147). Because the objective of this study
was to test the relationships between relative advantage, compatibility with internet
experience, complexity, perceived risk, and each participant’s intention to adopt Free
File, the multiple regression test seemed fitting. However, there were some limitations
associated with using a multiple regression approach for this study (more about this in the
limitations section).
The scales developed to measure relative advantage, compatibility with internet
experience, complexity, and perceived risk were adapted, for the most part, from Tan and
Thompson (2000). Tan and Thompson conducted a similar study using multiple
35
regression analysis to measure the relationship between these same variables and
adoption of internet banking. Their study tested the relationship between adoption of
internet banking and three factors—attitude, subjective norms, and perceived behavioral
control. Within the attitude factor, Tan and Thompson tested the correlation between
relative advantage, compatibility with internet experience, complexity, perceived risk,
and adoption of internet banking. They found that individuals who felt online banking
provided a relative advantage were more likely to adopt it. They also found that
compatibility with internet experience and lower levels of perceived risk were also more
likely to predict adoption. On the other hand, the hypothesis that lower levels of
perceived complexity would lead to a larger likelihood of adopting internet banking, was
not found to be a significant predictor.
The research design for the Tan and Thompson (2000) study was based on
diffusion of innovations theory and tested the relationship between each variable and its
relationship to the adoption of online banking. Therefore, it serves as a good model for
the current study.
Using parts of the Tan and Thompson (2000) research design, scales for this study
were adapted for relative advantage, compatibility with internet experience, and
perceived risk. For the complexity scale, only one item was adapted from Tan and
Thompson. The rest of the complexity scale consisted of four new items created
specifically for this study. The four new items to measure complexity were developed
because internet tax filing programs require more time and effort to operate than online
banking programs. The complexity scale items used by Tan and Thompson to measure
36
complexity in online banking were not used for this study because the way the questions
were phrased would have required the participant to have actually used the program in
order to make an assessment about the level of complexity experienced. For example,
one of the items in the Tan and Thompson scale asked participants to respond to the
following item: “Using internet banking can be frustrating” (p. 17). This question could
only be asked in retrospect. The current study asked participants to answer questions
based on their perception of complexity, so a question framed in such a way that requires
a person to answer in retrospect is not appropriate. Therefore, new scale items were
created that tested how the perceived levels of complexity would be associated with
intention to adopt. The three new complexity scale items measured participants’
perceived levels of complexity, simplicity, and difficulty to operate. The fourth item was
a reverse coded item. The new complexity scale is shown in Table 3.1. A new scale was
also developed to measure intention to adopt and is also shown in Table 3.1.
The resulting seven-item scale for relative advantage (adapted from Tan &
Thompson, 2000), 13-item scale for compatibility with internet experience (adapted from
Tan & Thompson, 2000), five-item scale for complexity (one item adapted from Tan &
Thompson, 2000; the other four items are new), three-item scale for perceived risk
(adapted from Tan & Thompson, 2000), and five-item scale for intention to adopt (newly
created) are shown in the questionnaire (see Appendix A). Operational definitions of the
constructs are provided in Table 3.1.
37
Table 3.1
Operational Definitions of Constructs
Construct
Dimensions
Scale Derived From
Relative advantage
ease, control, efficiency,
convenience, usefulness,
time savings
Tan & Thompson (2000) (6
items)
Made for this study (1 item)
Compatibility with
internet experience
span of use, frequency,
intensity, diversity, skill,
knowledge
Tan & Thompson (2000) (8
items)
Complexity
mental effort, difficulty,
simplicity, complexity
Tan & Thompson (2000) (1
item)
Made for this study (4
items, 1 reverse-coded item)
Perceived risk
security, fear of knowledge
known to others, tampering
Tan & Thompson (2000) (1
item, reverse coded)
Made for this study (2
items)
Intention to adopt
likely, planning on using,
suspect will use, probably
use, not use (reverse)
Made for this study (5
items, 1 reverse-coded)
In addition to the scale items, the questionnaire also asked participants to provide
demographic data regarding whether they knew how to use the internet, their age, sex,
income, education level, primary language, and whether they were previously aware of
Free File.
Sample
A convenience sample was used to collect the data. Participants were recruited at
two free tax assistance workshops that took place on February 6, 2010 at the Charles A.
38
Jones Career and Education Center and February 13, 2010 at Grant High School in
Sacramento, California. Only people who indicated they knew how to use the internet
were eligible to participate in the study (this criterion will be discussed in detail later in
chapter 3). These tax assistance workshops were hosted by the Volunteer Income Tax
Assistance (VITA) program. The VITA program provides free tax help for low or
middle income individuals in Sacramento who earn less than $56,000 a year. The tax
events were organized by the Sacramento Coalition for Working Families and
Assemblyman Dave Jones. I chose these two sites to conduct the study because they
were the closest VITA sites in the area and the people in attendance fit the description of
the type of person I wanted to study (taxpayers earning less than $56,000).
Assemblyman Dave Jones created the coalition in 2005 when he learned millions
in federal tax refunds went unclaimed by Sacramento county residents (Kim, 2010). The
coalition is made up of a number of community partners whose mission is to connect low
and moderate income people to services that help them achieve self-sufficiency.
Having a large turnout at the tax events was important so that there would be
enough people in attendance to ensure an adequate number of participants in the study.
As a member of Assemblyman Jones’ staff, I was responsible for organizing the events
and conducting outreach to promote the events to the community. Therefore, to ensure
many people would attend the events, I mobilized coalition members to distribute flyers
to their networks (see Appendix B for a sample brochure). Over 250,000 promotional
flyers were distributed in six different languages. The result was that both VITA events
were well-attended and yielded a high number of people from which to draw a sample. A
39
combined total of 250 people attended these two events. Four other tax events took place
after February 13th, but, due to academic deadlines, people attending those events were
not asked to participate in this study. Approximately $80,000 was raised from both
private and public sources to fund outreach for these events and accompanying VITA
sites. Together, over 12,000 people in the Sacramento area were served by a VITA site.
Data Collection
Of those who attended the February 6th and February 13th events, only internet
users who were non adopters of Free File were asked to participate in the study. Only
internet users were sought because a person must know how to use the internet in order to
use Free File. To ensure adequate participation from this targeted demographic, I, and
two volunteer surveyors (both trained by me to administer the questionnaire), approached
taxpayers at the workshop and asked if they knew how to use the internet (see Appendix
C for the script for surveyors). If the person replied “yes” to the question about whether
they knew how to use the internet, they were informed about the study being conducted
and asked if they wanted to participate. Those who did not know how to use the internet
were not asked to participate. Those who knew how to use the internet and agreed to
participate were provided a participant consent form and asked to fill it out. Upon
completing the consent form, they were provided with IRS Publication 4821 (see
Appendix D) that explained Free File. It was important to make sure every participant
knew what Free File was so that lack of awareness would not theoretically be a factor in
determining whether or not the participant would intend to adopt Free File. Rogers
(2003) wrote, “an innovation typically comes with such questions as ‘What is the
40
innovation?’ ‘How does it work?’ and ‘Why does it work?’” (p. 172). In this case, if a
participant did not know what Free File was, then it would not have been possible to
accurately measure their intention to adopt, because lack of awareness would have been a
determining factor. Without understanding what Free File is or how it works, the
participant would not have had all of the information needed to answer the questionnaire.
Therefore, this study measured participants’ intention to adopt Free File based on the
information they read in IRS Publication 4821.
Data Analysis
Overall, 104 people participated in the study. All of the data collected from the
questionnaires was placed in a Statistical Package for the Social Sciences (SPSS) data
file. Each item on the questionnaire was entered separately and once all the data was
entered into SPSS, all like scale items were combined. The resulting scales were
previous internet use (UseInternet), Free File awareness (FFAwareness), sex, education
level (Education), age, primary language (language), relative advantage (RelAdvantage),
complexity, perceived risk (Risk), compatibility with internet experience (IntExperience),
and intention to adopt Free File (Intent).
The reliability of the scales was tested using Cronbach’s alpha. The reliability
value of each scale was as follows: relative advantage = .958, complexity = .790,
perceived risk = .721, compatibility with internet experience = .873, and intention to
adopt = .788. To further enhance the reliability of the scales, one item was removed from
each of the following scales: complexity, risk, and intention to adopt. Removing these
items increased the Cronbach’s alpha value for each of these scales to the following:
41
complexity = .828, risk = .973, and intention to adopt = .971. Increasing the Cronbach’s
alpha value for each of these scales improved their internal consistency. Results were
then run for both the original scales and the improved scales (see chapter 4).
42
Chapter 4
RESULTS
The descriptive data were as follows: All participants indicated they knew how to
use the internet. The average age was approximately 38 years, with the age range being
between 17 and 67 years. Males comprised 36.5% of the participants and 63.5% were
female. Approximately 4% had an education level less than high school, 22% indicated
high school as the highest level of education they had completed, 48% had some college,
and 26% had a college degree or higher. Approximately 82% of participants indicated
English was their primary language, while approximately 18% responded that their
primary language was a language other than English. Approximately 53% of participants
indicated they were previously aware of Free File, while 47% were not previously aware
of Free File.
Three of the four hypotheses (H1, H2, and H4) were shown to be consistent with
the data after analyzing the data under both the original and improved scales. One
hypothesis (H3) was not shown to be a significant predictor of adoption. The final results
of the data can be found in Appendix E.
The first hypothesis (H1: internet users with higher levels of perceived relative
advantage will be more likely to adopt Free File) was shown to be significant at the .005
level (relationship is significant when p < .05) when running the first test on the original
scale. The second test (with the improved scale for intent) was also shown to be
significant (p = .00). These results are consistent with previous research that shows a
43
positive correlation between relative advantage and adoption (Ekin & Polatoglu, 2001;
Langerak & Verhoef, 2001; Tan & Thompson, 2000).
The second hypothesis (H2: internet users with higher levels of perceived
compatibility with internet experience will be more likely to adopt Free File) was shown
to be significant at the .008 level under the original scale. Under the improved scale, the
relationship was once again shown to be significant as p = .011. These results are also
consistent with previous research showing a positive correlation between compatibility
and adoption (Black et al., 2001; Karjaluoto et al., 2002; Laforet & Li, 2005; Langerak &
Verhoef, 2001; Lee & Lee, 2001; Lin et al., 2003; Tan & Thompson, 2000).
The third hypothesis (H3: internet users with higher levels of perceived
complexity will be less likely to adopt Free File) was not shown to be significant under
both the original and improved scales (p = .979 for the original scale, p = .319 for the
improved scale). Tan and Thompson (2000) also found that there was not a significant
relationship between complexity and adoption in their study of internet banking. An
explanation as to why this might be the case will be discussed in chapter 5.
The fourth hypothesis (H4: internet users with higher levels of perceived risk will
be less likely to adopt Free File) was shown to be significant at the .007 level under the
original scale. The relationship was also shown to be significant .024 level under the
improved scale. These results are also consistent with previous data which shows a
negative correlation between these two variables (Ekin & Polatoglu, 2001; Gerard &
Cunningham, 2003; Hamilton, et al., 2002; Kwon et al., 2005; Laforet & Li, 2005; Lin et
44
al., 2003; Chow et al., 2005; Rotchanakitumnuai & Speece, 2003; Tan & Thompson,
2000).
In summary, all scales were deemed reliable. Relative advantage was shown to
be positively correlated with intention to adopt. Compatibility with internet experience
was also positively correlated with intention to adopt. Perceived risk was negatively
correlated with intention to adopt. There was no significant relationship between
complexity and intention to adopt. These results mean that for the most part, attributes
pertaining to the diffusion of innovations theory predicted adoption behavior for Free File
(or at least intention to adopt).
45
Chapter 5
DISCUSSION
The results showed that relative advantage, perceived risk, and compatibility with
internet experience were all significant predictors of intention to adopt Free File. For the
most part, the results of this study are consistent with the results of previous diffusion of
innovations studies related to online banking and online grocery shopping (Black, et al.,
2001; Ekin & Polatoglu, 2001; Gerard & Cunningham, 2003; Hamilton, et al., 2002;
Karjaluoto et al., 2002; Kwon et al., 2005; Laforet & Li, 2005; Langerak & Verhoef,
2001; Lee & Lee, 2001; Lin et al., 2003; Chow et al., 2005; Rotchanakitumnuai &
Speece, 2003; Tan & Thompson, 2000). The similarities in results can be explained in
most part because of the characteristics these online programs share with each other
(convenience, familiarity with the internet, ability to perform transactions online and
required to release private information over the internet).
One of the major findings of the current study is that despite only using an
informational brochure to describe the program, similar conclusions were reached when
testing the relationship between adoption and relative advantage, compatibility, and risk.
It is important to note that the current study did not measure actual adoption behavior.
The conclusions drawn about likelihood to adopt were all based on participants’
perceptions of Free File based on the informational brochure they were provided. The
relationships between each variable are based solely on perception, not actual usage or
adoption. Therefore, it may be possible for future diffusions studies to measure adoption
behavior (or at least intention to adopt) without actually having to provide participants
46
with access to the online program. This is one implication of the current study.
On the other hand, the results for the complexity variable were not necessarily
consistent with most research findings of online banking and online grocery shopping
(Cunningham & Gerrard, 2003; Ekin & Polatoglu, 2001; Langerak & Verhoef, 2001).
Previous studies of online banking and grocery shopping found that there was a negative
relationship between the level of complexity and adoption rates. The more complex users
thought the programs were, the less likely they were to adopt them. In the current study,
this relationship was not found to be significant. There may be several possible
explanations why in this study, complexity was not found to be negatively related to
intention to adopt.
First, this study did not actually require participants to try using the Free File
program and it did not test for actual adoption. What this study measured was participant
perceptions of Free File based on the informational material provided in IRS Publication
4821. Therefore, participants may not have had a good sense of exactly how complex the
program was to operate. In contrast, most of the previous online banking studies asked
participants for their attitudes toward online banking in retrospect (after they had used the
program). Therefore, the responses in the current study may have differed if they had
actually had the opportunity to use the Free File program.
Second, the IRS informational brochure promoting Free File (IRS Publication
4821) is written in a way that it could have persuaded the reader to think the program is
easier to use than it actually is. Publication 4821 states that Free File “is a very user
friendly and simple way to prepare and e-file your own federal tax return” (p. 1). The
47
language in this brochure used to describe Free File makes it appear very easy to use.
This language may have influenced participants’ perceptions. Again, without actually
logging on to the program and trying it out, each participant could not accurately assess
what their ability to use the program really was.
Third, it may be that the education level of most participants was a factor in why
Free File was not perceived as complex to use. Approximately 74% of participants had
achieved some level of college or higher. The sample population was highly educated, so
their experience with computers and their level of comfort in using computers may have
been higher than it would have been among a less educated population. Furthermore,
their perception about how easy it is to use Free File may have been significantly biased
as they most likely have experience using other types of internet programs and may have
felt this program would be easy to use as well. The final results may have been different
if the participants in this study had been less educated and not as familiar with how to use
web-based programs.
The higher education level of participants in the current study (almost 75% had
some college experience or more) was not something I had expected. For the most part,
when working with a population that is eligible for VITA’s free services, I would have
expected a less educated sample. Those who are eligible for VITA earn lower wages
than the average college educated person. A possible explanation for why the VITA
clients were more educated than expected is because of the current bad economy and high
unemployment rate. More college educated people than usual are losing their jobs or
being furloughed as a result of the bad economy. This could explain why the sample
48
population was more educated than I had originally anticipated.
My observations lead me to believe there are two main reasons why Free File is
not being used more by the public. One reason is that many people still do not use the
internet or feel comfortable filing their taxes using the internet. Another reason is that
even among those who do use the internet, there are still many who are not aware of Free
File or its benefits. As was described in the Public Utilities Commission study, a possible
explanation for the broadband gap and/or adoption of web programs such as Free File, is
that users have to be able to relate to, and find value in the applications and content of a
website (Bradshaw 2006). If they do not find value in the content or cannot use the
applications, they will not adopt it. In the next few paragraphs, I will discuss these two
issues.
First, more than half of those who received assistance at the VITA sites did not
fill out the questionnaire claiming they did not use the internet. Of the 250 people who
received tax assistance between the two events, only 104 filled out the questionnaire.
Only those who claimed they knew how to use the internet were asked to fill out the
questionnaire. The majority of taxpayers at these events did not participate, meaning that
they were not internet users. They would not be able to use Free File to do their taxes
simply because they would not have the knowledge of the application or the interest.
This is consistent with the findings of Bradshaw’s Public Utilities Commission report
which highlighted that the “broadband gap” can be explained by one of the three
following broad categories: “(i) issues concerning access, (ii) issues concerning
affordability, and (iii) issues concerning applications and content” (p.1).
49
I did not keep track of demographic information for people who were not internet
users and could not participate in the study. However, my observation and estimate is
that most people who did not participate were either Latino and did not speak English, or
they were elderly. Although the observations are purely anecdotal, they support findings
from the Public Utilities Commission study which found that Non-English speakers and
those over the age of 65 are less likely to have broadband in the home. The study found
that only 13% of those who over the age of 65 used broadband in the home (Bradshaw,
2006). Meanwhile, the Public Policy Institute study found that only 61% of Latinos are
using the Internet in California (Baldassare, et al., 2009). Therefore, it is not surprising
that most of the taxpayers at the events who did not use the internet, also happened to be
elderly or Latino.
The aforementioned observation helps explain why Free File adoption is not
widespread, especially among the populations it is intending to target. If a person is not
able to use the internet or a computer, then there is no way they can do their own taxes
using Free File. Although the results of this study show that potential users are more
likely to adopt Free File if they perceive value in its attributes, the conclusions drawn are
only limited to those who know how to use the internet. The results of this study are
therefore not applicable to people who do not use the internet, are not comfortable with
their ability to use the internet, or do not have access to it.
Second, there are still many people who do not know about Free File. Given that
the participants in this study were a highly educated group of internet users, the fact that
more than 46% of them were not previously aware of Free File is alarming. This statistic
50
reveals that Free File has not been promoted heavily enough to its targeted audience.
Furthermore, although 53% of participants were previously aware of Free File, they were
obviously not persuaded to use it to file their taxes this year. Free File could have been
more heavily advertised, and the advertisements could have done a better job of
communicating the benefits of the service and explaining the security of the program.
The makers of Free File should address both of these issues if they want to increase
adoption rates among internet users.
Finally, there are factors other than internet experience and lack of awareness
preventing people from using Free File. For example, some participants who indicated
they would not use Free File in the future manually wrote on their questionnaires that
they would rather have “a tax expert” do their taxes for them. Although this observation
is purely anecdotal and was not measured, it appears to be a relevant comment and was
repeated several times by participants who chose to write comments at the end of their
questionnaire. For many of these people, simply knowing that someone with expert
knowledge is preparing their tax return is the kind of reassurance they need when filing
taxes – they simply do not trust their own ability to do taxes. The self-efficacy of each
participant in relation to their own perceived knowledge of how to do their taxes is not a
factor that was measured in this study. Because Free File requires more knowledge and
time to use than other online programs such as online banking and grocery shopping,
one’s perception of their own ability to do taxes (even with assistance from an online
program) could be a significant factor in determining whether a person chooses to adopt
Free File and this should be measured in future studies.
51
From a personal perspective, these results helped me develop a better
understanding of what factors influence a person’s willingness to adopt Free File. In my
five years working with the VITA program, a common assumption was that people do
not use Free File simply because they find it too complicated to use. However, the results
of this study do not necessarily show that assumption to be entirely correct. To the
contrary, people who participated in this study seemed very open to the idea of using Free
File based on its relative advantages and how it appears to be compatible with their
lifestyle. Complexity did not appear to be an issue. Among internet users who believed
themselves capable, lack of awareness of the program and not understanding its benefits
seemed to be better explanations of why Free File was not previously adopted.
Another observation I came across when conducting this study is that many of the
people we approached who did not know how to use the internet were non-English
speakers. Of those that did not speak English, the majority spoke Spanish. Although I
was able to communicate with them in Spanish and ask them if they knew how to use the
internet, the response was overwhelmingly “no.” There were approximately 12 Spanishspeaking clients at each site and almost all of them did not participate in the study
because they claimed they did not know how to use the internet. There were also many
seniors who did not participate for the same reason.
This final observation highlights a bigger issue—the digital divide. The digital
divide is more prevalent among those who do not speak English and the elderly, as was
discovered when conducting research for this study. Online programs like Free File
promise great societal benefits to low income families (many of which do not speak
52
English). However, these programs are not necessarily effective because the
communities that could benefit from them in many cases do not have the technical
competence to go online and use the programs. Until we close the digital divide, there
will always be a group of people who will miss out on the benefits of online programs
such as Free File. Therefore, it would make sense for the makers Free File and the
government to invest in programs that help teach people how to use computers and the
internet—particularly in lower income communities and those where English is not a first
language.
53
Chapter 6
CONCLUSION
The results showed that relative advantage, compatibility with internet
experience, and perceived risk were all significant predictors of intention to adopt Free
File. With the exception of complexity, these results are consistent with the findings of
previous studies related to online banking and online grocery shopping (Black, et al.,
2001; Ekin & Polatoglu, 2001; Gerard & Cunningham, 2003; Hamilton, et al., 2002;
Karjaluoto et al., 2002; Kwon et al., 2005; Laforet & Li, 2005; Langerak & Verhoef,
2001; Lee & Lee, 2001; Lin et al., 2003; Chow et al., 2005; Rotchanakitumnuai &
Speece, 2003; Tan & Thompson, 2000). Anecdotal evidence suggests that non English
speakers (primarily Latinos) and the elderly were the least likely to participate in the
study because they lacked the ability and skills to use the internet. Of the 250 people
who attended the tax workshops, only 104 people said they were internet users and
participated in the study. This low participation rate suggests that lower income people
(at least those earning under the VITA guideline maximum of $45,000) are less likely to
be internet users.
Implications
The implications of this study provide some empirical data to explain the factors
that influence the adoption of Free File. The results of this study (assuming there are no
violations of the assumptions of linear regression models) indicate that relative
advantage, compatibility with internet experience, and perceived risk are all significant
predictors of adoption. Knowing this information is valuable to advocates of Free File
54
because it helps them understand what attributes of Free File will persuade more
members of the public to adopt the program (assuming they are internet users). If
adoption rates increase, it is assumed taxpayer’s will save money and so will the
government.
Furthermore, the results provide evidence to support Rogers’ (2003) theory that
the digital divide goes beyond the issue of access. Because 46% of participants in this
survey did not previously know about Free File, the results speak volumes about the
general lack of awareness of Free File internet tax filing programs and how that
influences adoption. As Rogers predicted, new technologies are plagued by more than
just the digital divide. The knowledge divide and content divide are now becoming
significant issues inhibiting adoption as well. As advocates of Free File continue
working to improve adoption rates, they cannot ignore these critical issues. Furthermore,
the fact that many people continue using in person tax preparation services such as VITA
should be taken into consideration. If adoption rates for internet tax filing do not
improve, then it is important that the government not squander resources by investing
more than is necessary for these programs. Doing so could promote unintended
consequences such as diverting resources away from programs such as VITA which
actually do provide a good service to low income individuals and families and do not
require any level of skill or equipment.
It has already been revealed in this study that Free File has a problem promoting
and advertising itself to internet users (46% of internet users in this study did not know
what Free File was). But this study has revealed another problem with Free File – The
55
fact that the populations Free File can help most (the elderly, non English speakers, lower
income individuals, etc.), are less likely to be internet users. This is a problem with Free
File that should be of great concern to both communication scholars who study the digital
divide, the makers of Free File programs, and the government. Some of these issues
could be resolved by making simple changes such as developing content in other
languages or providing free telephone customer service operators who could help internet
users less confident in their ability to navigate the program.
Limitations of the Study
As with all studies, this study had limitations. First, the current study only
measured intention to adopt and not actual adoption behavior. The taxpayers surveyed
had already completed this year’s tax return at the tax workshop, so measuring whether
or not they will actually adopt Free File for the next tax season would require a
longitudinal study. It would not be known until the taxpayer completes next year’s tax
return in 2011, whether they actually used Free File. Not being able to measure actual
adoption behavior is a big limitation in this study.
Second, this study measured a participant’s intention to adopt based on the
information he or she received in IRS Publication 4821. The information provided in this
publication is limited and may not provide the taxpayer with all of the information
needed to make a decision about whether to adopt. The publication is informational only.
Participants were not given the opportunity to use the technology and test it out for
themselves. Had the participants been able to use the technology on a trial basis, the
results may have been different. Using the technology or providing tutorials for
56
participants was not an option in the current study because there were not enough
computers available on site for participants to undergo a tutorial. Future studies should
provide participants with a hands-on tutorial of the program and then test attitudes
regarding the program after using the technology.
Third, the results of the current study may not be generalizable because
participants were only from Sacramento. This study should be viewed as a convenience
sample case study of taxpayers living in Sacramento and not reflective of the general
population. Conclusions were drawn without performing an analysis of the linear
regression residuals; therefore, not verifying if the assumptions for this specific statistical
test were violated.
Suggestions for Future Research
Future studies should measure the attitudes of taxpayers in multiple parts of the
state or country to determine if attitudes are similar across a larger cross section of the
population. The current study was limited to Sacramento because due to time constraints
and limited resources, travel and/or coordination with VITA sites in different parts of the
state was not possible. Although Sacramento is a very diverse city and the Capitol of one
of the most diverse states in the Country, future studies should seek participation from
multiple parts of the state and/or country if the results are to be considered generalizable.
Future studies should also attempt to achieve a sample population of internet users
who are less educated and compare those results with the results of the current study
(which had an unusually high number of participants with some college experience or
higher). Because those who participated in the current study tended to be more educated,
57
there may be differences between the behavior of this population and the behavior of
participants with lower levels of education. Future studies should assess whether there
are differences in how these subgroups perceive Free File. The challenge may be that it
will be harder to find taxpayers who meet the criteria and are also competent enough to
use internet programs and Free File. This brings up another issue for future research –
that researchers should keep track of which taxpayers indicate they are able to use the
internet and which are not. Knowing what the demographics are of the non internet users
who get help at VITA sites might reveal new information such as whether language
barriers or lack of knowledge are the reasons why they are not going online to file.
One of the major draw backs of this study was that it did not measure whether a
participant actually adopted Free File. As mentioned in the limitations section, this
would require the researcher to track the participant’s behavior during the next tax season
when they will once again file taxes. Therefore, future researchers should consider
conducting a longitudinal study that measures actual behavioral patterns over a longer
period of time. For example, the researcher could assess whether the participant intends
to adopt Free File and then check with them the following year to find out if they
followed through with their intent. Future researchers could even take the study a step
further by checking in with the participants annually to find out whether they continue to
use Free File on an annual basis.
It should also be studied whether adjustments to content of Free File sites would
help to increase adoption. If Free File websites were written in Spanish or other
languages, it might increase usage. Therefore, future research should explore whether
58
translating material or changing website content will help get non English-speaking
internet users to visit and use the site. In addition to content changes, the makers of Free
File should study whether adding additional support services such as a 24-hour customer
service phone line would help get more people online using the program. One of the
advantages of online banking is that most banks offer a toll free customer service line
where users can ask questions if they are confused. Free File does not offer this kind of
additional assistance to its users. Future studies should assess whether adding features
such as a 24 hour customer service line would increase adoption rates.
The current study did not give participants the opportunity to use the Free File
program on a trial basis. Therefore, future studies should measure the relationship of the
predictor variables only after participants have tried the program on a trial basis.
Participants should test the program first, and then answer questions related to their
attitudes and perceptions of it. Conducting a study in this fashion will provide
researchers with a more accurate representation of participants’ attitudes toward Free File
and whether they will actually adopt it.
Finally, future researchers should consider using a qualitative approach.
Conducting a qualitative study could help identify other variables that may influence the
adoption of Free File. Since a quantitative approach only allows researchers to test for
specific variables, a qualitative study may shed light on other issues that were not taken
into consideration by the current study. For example, in this study, one participant wrote
a note on their survey stating that they would not use free file because they did not trust
their own ability to do taxes and wanted a professional to do it for them. This is an
59
example of a factor that may influence adoption of Free File but could not be captured by
quantitative nature of the current study. A qualitative approach would allow the
researcher to have more flexibility to identify the factors, perceptions, and attitudes that
influence adoption.
Summary
This study provided both an insight into what factors influence current internet
users to use Free File, while identifying potential reasons why non internet users would
not be able to use it. Among the reasons why internet users would choose to adopt Free
File in the future, perceived relative advantage, compatibility with internet experience,
and a sense that the program is secure, were all factors that influenced intention to adopt.
Among the reasons why people would not use Free File, are lack of awareness and lack
of ability to use the internet.
Free File faces two major obstacles moving forward. First, a large quantity of the
population it intends to serve is not capable of utilizing the service because they either do
not know how to use the internet, do not have access to it, cannot afford it, or do not have
the necessary equipment. These potential users will either not be interested in going
online or will not feel that they are capable of using the website or its applications. In
some cases (as in the case of non English speakers), people will just not be able to
identify with or understand the content. These are serious problems and significant
barriers to adoption.
Second, even among internet users, many of them do not know about Free File,
what it does, or how it would benefit them. Although the government has made it a goal
60
to increase adoption of Free File, and all online government services, the general
population has not necessarily followed because not everyone is able to. Some people
just do not use the internet for these purposes or are unaware that they can.
The solution would appear to be that the government needs to invest more
resources and conduct more outreach in order to explain to the public what the benefits
are of e-government programs like Free File. More resources must also be invested into
helping people get wired, connected, and trained on how to utilize these resources online.
Outreach in multiple languages will be important to increase adoption rates among those
who do not speak English. But outreach is not enough. Published content at every level
(websites, applications, instruction manuals, etc.) must be translated if we are going to
get serious about helping non English speakers get online.
The current study demonstrates that once people reach the point where they feel
competent enough with their ability to use the internet, persuading them to use these
programs is simply a matter of communicating the benefits. However, until the digital
divide closes and there are no longer technology haves and have not’s, programs like
Free File will clearly not benefit much of the population it intends to serve. In the
meantime, this study provides further evidence to support the argument that our
government must make closing the digital divide, on all fronts, a top domestic policy
priority. Closing the digital divide must be a priority if our state and nation are truly
motivated to provide government services to everyone who could stand to benefit from
these services.
61
APPENDICES
62
APPENDIX A
Free File Questionnaire
Participant Instructions (Part 1):
For questions 1 – 8 please circle the number that pertains to you or answer with the
information requested.
1. Do you use the internet?
1 – Yes
2 – No
2. Before today, were you aware of Free File?
1 – Yes
2 – No
3. What is your sex?
1 – Male
2 – Female
4. What is the highest level of education you have completed?
1 – Less than high school
2 – High school
3 – Some college
4 – College or higher
5. What is your age?
6. What is your primary first language?
Participant Instructions (Part 2):
For questions 9 - 35, please circle the number that best represents how you feel about
each statement.
7. Free File would make it easier for me to prepare my own tax return (Relative
Advantage 1)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
8. Free File would give me greater control over preparation of my tax return (Relative
Advantage 2)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
63
9. Free File would allow me to prepare my tax return more efficiently (Relative
Advantage 3)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
10. Free File would be a more convenient way to prepare my tax return (Relative
Advantage 4)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
11. Free File would allow me to prepare my tax return more effectively (Relative
Advantage 5)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
12. Free File would be a more useful way to prepare my tax return (Relative Advantage
6)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
13. Using Free File to prepare my tax return would save me time (Relative Advantage 7)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
14. Using Free File would require a lot of mental effort (Complexity 1)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
15. Using Free File would be difficult for me to operate (Complexity 2)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
16. Free File would be easy for me to operate (Complexity 3 - Reverse)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
17. Free File would not be simple for me to use (Complexity 4)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
18. Using Free File would be complex for me (Complexity 5)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
64
4 = Agree
5 = Strongly Agree
19. I am confident over the security aspects of Free File (Perceived risk 1 - Reverse)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
20. I fear that the information I submit through Free File will be known to others
(Perceived risk 2)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
21. I fear that the information I submit through Free File will be tampered with by others
(Perceived risk 3)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
22. How long have you been using the internet? (Internet experience 1 – Span of usage)
1 = Less than a month
2 = 1 to 6 months
3 = 6 months – 1 year
4 = 1 year to 2 years 5 = More than 2 yrs
23. On average, how frequently do you use the internet? (Internet experience 2 Frequency)
1 = Never/Almost Never 2 = About once a month
3 = A few times a month
few times a week 5 = At least once a day
4=A
24. I use the internet to perform complex tasks such as online banking or online
shopping (Internet exp. 3 – Intensity of usage)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
25. Please indicate the extent to which you use the Internet to perform the following
tasks: (Internet experience 4 – Diversity)
Gathering information
1 = Not at all
4 = Often
2 = Rarely
3 = Sometimes
5 = To a great extent
Communication (e.g. email, cyberchat)
1 = Not at all
2 = Rarely
3 = Sometimes
4 = Often
5 = To a great extent
Downloading software
1 = Not at all
4 = Often
2 = Rarely
3 = Sometimes
5 = To a great extent
65
Shopping
1 = Not at all
4 = Often
2 = Rarely
3 = Sometimes
5 = To a great extent
Searching for job
1 = Not at all
4 = Often
2 = Rarely
3 = Sometimes
5 = To a great extent
Swapping or selling stuff
1 = Not at all
2 = Rarely
3 = Sometimes
4 = Often
5 = To a great extent
26. I am very skilled at using the Internet (Internet experience 5)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
27. I consider myself knowledgeable about good search techniques on the Internet
(Internet Experience 6)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
28. I know less about using the Internet than most users (Internet Experience 7– Reverse)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
29. I know how to find what I want on the internet using a search engine (Internet
Experience 8)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
30. I will likely use Free File to file my taxes next year? (Intention to adopt 1)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
31. I plan on using Free File to file my taxes next year (Intention to adopt 2)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
32. I suspect that I will use Free File to file my taxes next year (Intention to adopt 3)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
33. It will probably use Free File to file my taxes next year (Intention to adopt 4)
66
1 = Strongly Disagree
4 = Agree
2 = Disagree
5 = Strongly Agree
3 = Neither Agree nor Disagree
34. I will not use Free File to file my taxes next year (Intention to adopt 5 – Reverse)
1 = Strongly Disagree
2 = Disagree
3 = Neither Agree nor Disagree
4 = Agree
5 = Strongly Agree
35. If you do not intend to use Free File next year, please provide your reasons for why
you will not use it.
67
APPENDIX B
Sample Brochure
68
APPENDIX C
Script for Surveyors
Instructions
You will be asking people for their permission to participate in a study measuring
taxpayer attitudes of Free File services. Filling out the survey is optional. When
soliciting participants for the study, please use the following script:
YOU SAY:
“Hi, my name is _____________. I’m here today conducting a study of taxpayers’
attitudes of free electronic tax filing preparation services. Do you use the internet?
(WAIT FOR RESPONSE)”
If response is NO  Do not administer questionnaire. Move on to next person.
If response is YES, YOU SAY  “Ok. If you complete a questionnaire today, you will
be entered into a raffle contest with a chance to win a cash prize of $100. Would you like
to fill out the questionnaire?
If response is NO  Thank them for their time and move on to next person.
If response is YES  “Great. Before we get started on the questionnaire, I would
like to provide you with a few details about the study. The study is being
conducted by Alex Barrios, a graduate research student at California State
University, Sacramento. It is an academic study of taxpayers’ attitudes of
electronic tax filing services. To participate in the study, you will fill out this
questionnaire (Hand them the questionnaire). It will take approximately three to
five minutes of your time today. Before you fill out the questionnaire, please look
through the following brochure (Hand them IRS Publication 4821). Also, I will
need you to read and sign this consent form (Hand them the consent form and
have them fill it out and return it before filling out the questionnaire).
** WHEN THE PARTICIPANT IS DONE FILLING OUT THE QUESTIONNAIRE,
PLEASE REVIEW IT TO MAKE SURE THEY FILLED OUT EACH QUESTION. IF
THEY MISSED A QUESTION, PLEASE ASK THEM TO FILL IT OUT. IF THE
PARTICIPANT HAS ANY QUESTIONS FOR THE RESEARCHER, PLEASE
CONTACT ALEX IMMEDIATELY. THANKS!
69
APPENDIX D
IRS Publication 4821
70
APPENDIX E
Statistical Results
Frequencies
Statistics
UseInternet
N
Valid
Missing
FFAwareness
Sex
Education
language
104
103
104
104
103
0
1
0
0
1
Frequency Table
UseInternet
Cumulative
Frequency
Valid
Yes
Percent
104
Valid Percent
100.0
Percent
100.0
100.0
FFAwareness
Cumulative
Frequency
Valid
Total
Valid Percent
Percent
Yes
55
52.9
53.4
53.4
No
48
46.2
46.6
100.0
103
99.0
100.0
1
1.0
104
100.0
Total
Missing
Percent
System
Sex
71
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
Male
38
36.5
36.5
36.5
Female
66
63.5
63.5
100.0
104
100.0
100.0
Total
Education
Cumulative
Frequency
Valid
less than high school
Percent
Valid Percent
Percent
4
3.8
3.8
3.8
high school
23
22.1
22.1
26.0
some college
50
48.1
48.1
74.0
college or higher
27
26.0
26.0
100.0
104
100.0
100.0
Total
Language
Cumulative
Frequency
Valid
Missing
Percent
Valid Percent
Percent
English
85
81.7
82.5
82.5
other
18
17.3
17.5
100.0
Total
103
99.0
100.0
1
1.0
104
100.0
System
Total
Descriptives
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
72
Age
99
17.00
67.00
37.8788
14.21574
RelAdvantage
103
1.00
5.00
3.5908
.91189
Complexity
101
1.00
4.40
2.6752
.70646
Risk
101
1.33
4.67
2.8251
.72356
99
1.62
5.00
3.6628
.71721
103
1.00
5.00
3.2058
.70430
IntExperience
Intent
Valid N (listwise)
91
Reliability
Scale: ALL VARIABLES
Case Processing Summary
N
Cases
Valid
Excludeda
Total
%
103
99.0
1
1.0
104
100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's
Alpha
N of Items
.958
7
Item-Total Statistics
Cronbach's
Scale Mean if
Item Deleted
Scale Variance if Corrected ItemItem Deleted
Total Correlation
Alpha if Item
Deleted
73
ra1
21.5534
30.367
.799
.956
ra2
21.5049
29.958
.884
.949
ra3
21.6505
28.975
.899
.948
ra4
21.3689
30.039
.873
.950
ra5
21.6990
29.781
.885
.949
ra6
21.5728
30.424
.880
.950
ra7
21.4660
31.467
.762
.959
Reliability
Scale: ALL VARIABLES
Case Processing Summary
N
Cases
Valid
Excludeda
Total
%
101
97.1
3
2.9
104
100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's
Alpha
N of Items
.790
5
Item-Total Statistics
Cronbach's
Scale Mean if
Item Deleted
Scale Variance if Corrected ItemItem Deleted
Total Correlation
Alpha if Item
Deleted
c1
10.2376
8.583
.511
.769
c2
10.8812
7.546
.766
.683
74
c3
10.8911
9.538
.323
.828
c4
10.6337
8.334
.606
.739
c5
10.8614
8.021
.678
.715
Reliability
Scale: ALL VARIABLES
Case Processing Summary
N
Cases
Valid
Excludeda
Total
%
101
97.1
3
2.9
104
100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's
Alpha
N of Items
.721
3
Item-Total Statistics
Cronbach's
Scale Mean if
Item Deleted
Scale Variance if Corrected ItemItem Deleted
Total Correlation
Alpha if Item
Deleted
r1
5.7228
3.582
.177
.973
r2
5.5842
1.785
.768
.309
r3
5.6436
1.792
.794
.274
Reliability
75
Scale: ALL VARIABLES
Case Processing Summary
N
Cases
Valid
Excludeda
Total
%
99
95.2
5
4.8
104
100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's
Alpha
N of Items
.873
13
Item-Total Statistics
Cronbach's
Scale Mean if
Item Deleted
Scale Variance if Corrected ItemItem Deleted
Total Correlation
Alpha if Item
Deleted
i1
42.8788
78.638
.547
.865
i2
43.3333
74.816
.563
.863
i3
43.9293
71.964
.594
.861
i4
43.5354
73.272
.665
.857
i5
43.7374
71.665
.660
.857
i6
44.7273
75.506
.510
.866
i7
44.8990
70.582
.628
.859
i8
44.3131
74.360
.432
.873
i9
45.6364
76.499
.415
.872
i10
43.6465
74.353
.660
.858
76
i11
43.4848
74.946
.728
.857
i12
43.8384
81.565
.203
.882
i13
43.4343
74.983
.718
.857
Reliability
Scale: ALL VARIABLES
Case Processing Summary
N
Cases
Valid
Excludeda
Total
%
103
99.0
1
1.0
104
100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's
Alpha
N of Items
.788
5
Item-Total Statistics
Cronbach's
Scale Mean if
Item Deleted
Scale Variance if Corrected ItemItem Deleted
Total Correlation
Alpha if Item
Deleted
in1
12.6505
6.857
.891
.628
in2
12.6311
6.921
.885
.632
in3
12.6311
7.137
.855
.646
in4
12.6019
7.222
.843
.651
in5
13.6019
13.654
-.308
.971
77
Regression
Variables Entered/Removed
Variables
Model
1
Variables Entered
Removed
Risk,
Method
. Enter
RelAdvantage,
IntExperience,
Complexitya
a. All requested variables entered.
Model Summary
Std. Error of the
Model
R
R Square
.477a
1
Adjusted R Square
.228
Estimate
.194
.65111
a. Predictors: (Constant), Risk, RelAdvantage, IntExperience, Complexity
ANOVAb
Model
1
Sum of Squares
df
Mean Square
Regression
11.371
4
2.843
Residual
38.579
91
.424
Total
49.950
95
a. Predictors: (Constant), Risk, RelAdvantage, IntExperience, Complexity
b. Dependent Variable: Intent
F
Sig.
6.706
.000a
78
Coefficientsa
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Std. Error
2.270
.580
RelAdvantage
.210
.073
IntExperience
.255
Complexity
Risk
Coefficients
Beta
t
3.915
.000
.270
2.867
.005
.095
.255
2.691
.008
-.003
.103
-.003
-.027
.979
-.267
.096
-.267
-2.778
.007
a. Dependent Variable: Intent
RELIABILITY AND RESULTS FOR IMPROVED SCALES
(COMPLEXITY, RISK, INTENTION TO ADOPT)
Scale: Reliability Complex
Case Processing Summary
N
Cases
Valid
%
101
97.1
3
2.9
104
100.0
Excludeda
Total
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Sig.
79
Cronbach's Alpha
Based on
Standardized
Cronbach's Alpha
Items
.828
N of Items
.828
4
Item Statistics
Mean
Std. Deviation
N
c1
3.1386
.98010
101
c2
2.4950
.95524
101
c4
2.7426
.93438
101
c5
2.5149
.93396
101
Inter-Item Correlation Matrix
c1
c2
c4
c5
c1
1.000
.524
.443
.511
c2
.524
1.000
.637
.608
c4
.443
.637
1.000
.554
c5
.511
.608
.554
1.000
Item-Total Statistics
Scale Mean if
Scale Variance if
Corrected Item-
Item Deleted
Item Deleted
Total Correlation
Squared Multiple Cronbach's Alpha
Correlation
if Item Deleted
c1
7.7525
5.848
.576
.340
.818
c2
8.3960
5.422
.720
.529
.751
c4
8.1485
5.748
.651
.455
.784
c5
8.3762
5.677
.672
.454
.774
Scale Statistics
80
Mean
Variance
10.8911
Std. Deviation
9.538
N of Items
3.08837
4
Scale: Reliability Risk
Case Processing Summary
N
Cases
Valid
%
101
97.1
3
2.9
104
100.0
Excludeda
Total
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's Alpha
Based on
Standardized
Cronbach's Alpha
Items
.973
N of Items
.973
2
Item Statistics
Mean
Std. Deviation
N
r2
2.8911
.96851
101
r3
2.8317
.94941
101
Inter-Item Correlation Matrix
r2
r3
81
r2
1.000
.948
r3
.948
1.000
Item-Total Statistics
Scale Mean if
Scale Variance if
Corrected Item-
Item Deleted
Item Deleted
Total Correlation
Squared Multiple Cronbach's Alpha if
Correlation
Item Deleted
r2
2.8317
.901
.948
.898
.a
r3
2.8911
.938
.948
.898
.a
a. The value is negative due to a negative average covariance among items. This violates reliability
model assumptions. You may want to check item codings.
Scale Statistics
Mean
Variance
5.7228
Std. Deviation
3.582
N of Items
1.89272
2
Scale: Reliability Intent
Case Processing Summary
N
Cases
Valid
%
103
99.0
1
1.0
104
100.0
Excludeda
Total
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's Alpha
Based on
Standardized
Cronbach's Alpha
Items
N of Items
82
Reliability Statistics
Cronbach's Alpha
Based on
Standardized
Cronbach's Alpha
Items
.971
N of Items
.971
4
Item Statistics
Mean
Std. Deviation
N
in1
3.3786
.98136
103
in2
3.3981
.97357
103
in3
3.3981
.95322
103
in4
3.4272
.94560
103
Inter-Item Correlation Matrix
in1
in2
in3
in4
in1
1.000
.939
.875
.912
in2
.939
1.000
.873
.868
in3
.875
.873
1.000
.886
in4
.912
.868
.886
1.000
Item-Total Statistics
Scale Mean if
Scale Variance if
Corrected Item-
Item Deleted
Item Deleted
Total Correlation
Squared Multiple Cronbach's Alpha
Correlation
if Item Deleted
in1
10.2233
7.567
.949
.920
.955
in2
10.2039
7.693
.928
.893
.961
in3
10.2039
7.889
.907
.830
.967
in4
10.1748
7.871
.921
.866
.963
83
Scale Statistics
Mean
Variance
13.6019
Std. Deviation
13.654
N of Items
3.69509
4
Regression Results – Improved Scales
Variables Entered/Removedb
Model
1
Variables Entered Variables Removed
IntExp, Risk,
RelAdv,
Method
. Enter
Complexa
a. All requested variables entered.
b. Dependent Variable: Intent
Model Summary
Std. Error of the
Model
R
R Square
.507a
1
Adjusted R Square
.257
Estimate
.225
3.32613
a. Predictors: (Constant), IntExp, Risk, RelAdv, Complex
ANOVAb
Model
1
Sum of Squares
Regression
Df
Mean Square
349.089
4
87.272
Residual
1006.745
91
11.063
Total
1355.833
95
F
Sig.
7.889
.000a
a. Predictors: (Constant), IntExp, Risk, RelAdv, Complex
b. Dependent Variable: Intent
Coefficientsa
Standardized
Model
Unstandardized Coefficients
Coefficients
t
Sig.
84
B
1
(Constant)
Std. Error
7.479
2.660
.212
.055
Complex
-.122
Risk
RelAdv
IntExp
Beta
2.812
.006
.366
3.869
.000
.122
-.095
-1.001
.319
-.456
.198
-.225
-2.299
.024
.097
.037
.242
2.602
.011
85
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