Information Technology Reference
In-Depth Information
was age 65 or above. By 2015 it is estimated that
14.9% of the North American population will be
in this age bracket. By 2030, this segment of the
population will nearly double, with over 20%
of the entire population over the age of 65 (U.S.
Census Bureau, 2002). The graying of America
will have a dramatic impact on the workforce,
retirement age, healthcare, and elderly support
services. This segment of the population will
also be a lucrative market with low debt, higher
disposable incomes, and additional leisure time
due to retirement and reduced family commit-
ments. The increased influence of seniors will
require marketers to pay more attention to this
segment of the population (Burnett, 1991; Moschis
& Mathur, 1993; Schiffman & Sherman, 1991).
What is not clear is whether older adults will be
active participants in the online community.
Unlike prior generations, older Americans are
extremely active and more likely to learn new
skills. Perhaps contrary to the common percep-
tion, there has been a dramatic increase in the
use of technology by seniors. A national survey
done by the American Association of Retired
People (AARP) found that 81% of the participating
computer users used the Internet. The participants
spent an average of five hours per week using
e-mail and an additional nine hours using the
Internet. Fifty one percent used the Internet to
comparison shop and 39% had made purchases
online (AARP, 2000). From January 2002 to 2003,
AARP had a 70% increase in new and renewing
memberships processed online (Kelleher, 2003).
According to Nielson//NetRatings (2003), older
Americans are the fastest growing segment us-
ing high-speed Internet access. The number of
American aged 55-64 accessing the Internet via
cable, DSL, ISDN, or other high-speed connec-
tions surged 78% from 2001 to 2002 (Nielson//
NetRatings, 2003).
Researchers have explored some aspects of
technology use by the elderly, including the psy-
chological benefits of using the computer for com-
munication and learning (Billipp, 2001; Ogozalek,
1991) and the effectiveness of computer training
(Groves, 1990; Marquie, Jourdan-Boddaert, &
Huet, 2002; Temple & Gavillet, 1990). There has
also been a considerable amount written with
regard to the elderly using technology to enable
continued independent living (Finn, 1997). For
example, robotics can be used to assist in routine
household chores such as opening jars and artificial
intelligence applications can include health moni-
toring (Raymond, 2002; Shellenbarger, 2002),
and memory aids (Eisenberg, 2001). Research on
the antecedents to online shopping participation
by older adults is limited. Given the growth of
this demographic and the attention marketers are
placing on them as consumers, exploration of the
motivators and barriers to electronic commerce
participation by older adults is a critical area of
examination.
model
The acceptance of new technologies has long
been an area of inquiry in the MIS literature.
The acceptance of personal computer applica-
tions (Doll, Hendrickson, & Deng, 1998; Henry
& Martinko, 1997; Igbaria, Guimaraes, & Davis,
1995), telemedicine (Hu, Chau, Sheng, & Tam,
1999), e-mail (Karahanna & Straub, 1999), bro-
ker workstations (Lucas & Spitler, 1999) and the
WWW (Lederer, Maupin, Senca, & Zhuang,
2000; Lin & Lu, 2000; Moon & Kim, 2001) are
a few examples of technologies that have been
investigated. The model most widely used is the
Technology Acceptance Model (TAM), developed
by Davis (1989). TAM was specifically developed
to measure the determinants of computer usage.
The model states that perceived usefulness and
perceived ease of use impact attitude towards use,
which impacts behavioral intentions, which in turn
impacts actual usage. As other researchers have
done (Adams, Nelson, & Todd, 1992; Davis, 1989;
Gefen, Karahanna, & Straub, 2003) this research
drops the intermediate variable, attitudes towards
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