PREDICTING THE USER ACCEPTANCE OF PERSONALIZED
INFORMATION SYSTEMS: CASE MEDICAL PORTAL
Seppo Pahnila
Department of Information Processing Science, University of Oulu, 90014, Oulu, Finland
Keywords: Personalization, personalized Web information systems, technology acceptance model, external factors.
Abstract: This paper describes ongoing research, which focuses on the effect of attitudes and intentions in the use of
personalized Web Information Systems (WIS). By applying the widely used Technology Acceptance Model
(TAM), the theory of planned behavior, innovation diffusion theory and self-efficacy theory, we take an
extended view of the factors explaining the individual acceptance and usage of newly emerging
personalized Web Information Systems. Many features of personalized WIS differ from the “traditional”
information systems, and we believe that this research will shed new light on the research into the
acceptance of personalized WIS.
1 INTRODUCTION
Mathieson (1991) states that information systems
can be effective and improve organizational
performance only if they are used. All technical
advances and improvements are negligible if users
do not want to use IT. Therefore it is important to
understand how people decide to use or not to use
given systems. This may vary depending on the
system, the people and the context (Mathieson
1991).
As the Internet is growing all the time, easy
access to relevant information and management of
information space have become major issues for
users. Users can get lost when navigating in
information space, or they might not find what they
are looking for (Brusilovsky 1996a). Personalization
tries to reduce the users’ workload and respond to
their individual needs by providing them with
tailored information, services and products. From
the business point of view, personalization has the
bonus of drawing the users’ attention to themselves,
their products and services. Personalized Web IS can
be a powerful tool for businesses, as well as for
organizations, to set them apart from their
competitors. Brusilovsky (1996b) defines
personalization as a new research direction in the
field of user- adaptive systems aiming to increase
functionality of hypermedia. In this study
personalization is defined as the interaction between
users and service providers aiming to offer a user-
friendly Web experience, based on the individual
user’s preferences, background and previous
behavior.
According to Davis (1989), perceived usefulness
and perceived ease of use are especially important
determinants, which have an effect on whether the
users accept or reject information technology. The
determinants, perceived usefulness and ease of use,
represent the beliefs that lead to user acceptance of
information technology (Lederer 1998). Davis
(1989) defines perceived usefulness, as “the degree
to which a person believes that using a particular
system would enhance his or her job performance.”
This definition emphasizes the belief that systems
will improve the user’s job performance without
involving salary raises, promotions, bonuses or other
rewards. Perceived ease of use refers to “the degree
to which a person believes that using a particular
system would be free of effort” (Davis 1989). TAM
claims that individuals would use computers if they
envisage positive results (outcomes). Thus, by
focusing on the preceding factors of usage, -
perceived ease of use and perceived usefulness -,
TAM pays less attention to external factors, (social
influence) individuals’ believe in their own
capabilities to cope with the task ahead (Igbaria and
Iivari 1995). In the analysis of the self-efficacy
theory Bandura (1977) distinguishes two
expectations, efficacy expectations and outcome
expectations. Efficacy expectations mean that one
195
Pahnila S. (2004).
PREDICTING THE USER ACCEPTANCE OF PERSONALIZED INFORMATION SYSTEMS: CASE MEDICAL PORTAL.
In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 195-202
DOI: 10.5220/0002630101950202
Copyright
c
 SciTePress

can successfully execute the behavior required to
produce the outcomes. Outcome expectations refer
to a person’s judgment that a given behavior will
lead to certain outcomes (Bandura 1977; Compeau
and Higgins 1995). Bandura (1977) argues that both
efficacy expectations and outcome expectations are
linked together and have an effect on outcomes.
The theory of planned behavior (TPB) focuses on
predicting intentions and explaining human behavior
(control beliefs) (Ajzen 1991). Intention to perform
a given behavior is the central determinant of TPB.
According to Ajzen (1991), “Intentions are assumed
to capture the motivational factors that influence
behavior; they are indicators of how hard people are
willing to try, or how much of an effort they are
planning to exert, in order to perform the behavior.”
Adopting new ideas and social practices and
spreading them within a society, or from on society
to another, is an important element in explaining
personal and social changes (Bandura 1986).
Innovation diffusion theory depicts the process of
spreading ideas, practices and issues related to
adopting or rejecting new technology (Rogers 1995).
Although TAM is widely used for explaining the
acceptance of information systems, there are some
limitations, which have shown up in prior studies:
 user data is collected mainly from students
in a university environment, not in a “real
environment”;
 the tools under study, like word processing
software, spreadsheet software or e-
commerce and internet – are general rather
than specific;
 they are mostly confined to the US.
This research tries to address these limitations
and aims to be more specific by identifying a
specific behavior pattern in a specific personalized
WIS (medical portal) within a specific context (real
environment). By applying the TAM, TBP,
innovation diffusion theory and self-efficacy theory
this research will take an extended view of both the
internal and external factors, that explain user
acceptance of personalized WIS.
2 RESEARCH QUESTION AND
CONTEXT
This ongoing research is a continuation of the
development work in a completed OWLA-project
(OWLA 2002) where we developed a new approach
for producing personalized services. In the current
research the portal -a personalized medical portal,
here “mediport.com” - is a free information channel
for doctors and medical personnel. The main
objective of the portal is to provide access to a
special field information and to facilitate the flow of
information. Personalization in a medical portal is
designed for certain particular groups with varying
duties and preferences. For example, the portal
offers up-to-date information (scientific-, medical-
and research information) related to the users’ work,
and information related to different social activities.
Up-to-date information, its immediate availability to
the users, easy acceptance, and the quality of the
information content are priority features in terms of
the potential users’ responsibilities. It could be
vitally important, for example, for the doctors or
surgeons to get the latest information related to
drugs, diseases or methods of treatment.
As a starting point for this research it was carried out
surveys in OWLA-project between 2000-2002.
According to the preliminary surveys, the potential
users were interested in personalized services but
they were not interested in implementing the
personalization themselves for different reasons.
Basically, the motivation and topic of this research
activity arise from the findings of the OWLA-
project.
At present there is a lot of research interest in the
area of personalized systems. Experimental results
confirm that even the minimal use of adaptive
hypermedia - based on user modeling - can improve
the degree of user satisfaction at low cost (Strachan
et al. 1997; Billsus et al. 2002). However, more
research is needed to improve our understanding
related to personalization (Straub and Watson 1991)
and on the factors that are related the acceptance and
use of personalized WIS. Alike it is important to pay
more attention to the factors, which may concern
and cause fear against the usage of personalized
WIS. This research even more emphasizes the “user
center” view of personalization, which focuses on
identifying factors affecting attitudes and intentions
to use personalized information systems. The
primary research question is: what are the
acceptance factors that affect intentions and actual
use of personalized hypermedia systems? In order to
answer this research question, we must also consider
two additional sub questions as follows: (1) what is
the significance of the intentions and what are the
most important intentions that affect use of
personalized WIS? (2) what is the role of predictive
factors of intentions in predicting usage of
personalized WIS?
ICEIS 2004 - HUMAN-COMPUTER INTERACTION
196

3 FACTORS OF THEORETICAL
MODEL OF IT USAGE
3.1 Background to personalized web
systems
Personalized adaptive hypermedia systems - or here
personalized web information systems - are always
based on user models (Brusilovsky 1996b). One of
the first who used the notion of a user model was
Elaine Rich (1979). She suggested that one “major
problem to be confronted in the quest for a
sympathetic computer system, besides that of man-
machine communication, is the question of the
system’s understanding of its users, their goals,
knowledge, preferences, etc. In order to deal
effectively with its users a system must have a
model of them” (Rich 1979).
The success and advantages of personalized
systems depend on user data. It is therefore
necessary to have access to sufficient, relevant and
updated data about the user. It is essential to
discover the users’ real needs and collect adequate
information about the users. The problem here is that
the users’ needs may vary depending on several
factors such as time, location, cultural background,
expertise, knowledge and cognitive skills. In
practice, the system’s knowledge about the users and
its ability to build an accurate user profile/model
increases the more the user interacts with the system.
On the other hand, when the systems supply
information about users issues of privacy are raised.
Users like to control what kind of data is collected,
for what purposes and for what purposes their data
are processed (Kobsa 2001). It is obvious that users
have a need to control the information a system has
about them. Users’ lack of knowledge about how
their individual information is collected, how it is
used or processed, presents a potential barrier for the
intention to use this kind of information in IS.
3.2 Need for control
A need for control is a new determinant, which
refers to the users’ unwillingness to provide
individual information without knowing how the
information will be used. Information about the
users is an absolute condition for an adaptive
hypermedia system. Access to the user data can be
accomplished in many ways, it can be requested
directly from the users or it can be done in the
background without the knowledge of the users. The
more the system can supply user information, the
better it can help build an accurate profile of the
users’ behavior. Some people may be willing to give
individual information, but for others it can be an
obstacle and may turn visitors away from sites.
Information can be supplied by the user or by the
system both explicitly and implicitly. For example
when the user logs into the system for the first time,
the system may explicitly ask the user to fill in a
registration form. Collecting information ‘behind the
curtain’ may track the users’ browsing behavior on
an ongoing basis. These implicit and explicit ways
of data gathering may make use of information, that
is private by nature, which goes against the users’
right to privacy.
Individuals’ desire for control is an attempt to
master their environment. They are keen to master
their own acts and to know the causes and
consequences of their own and others’ acts (Baronas
and Louis 1988). Basically, individuals are not
willing to accept that they do not have control.
When user visit web site that require registration,
some may give false registration information and
some may leave the web site with concerns about
their privacy (Kobsa 2001). Users want to control
what kind of data is collected, for what purposes,
how long data is recorded for, how and for what
purposes their data is processed (Kobsa 2001; Kobsa
2002). Therefore it appears reasonable to
hypothesize:
H1. The need for control has a significant influence
on attitudes towards personalized WIS.
3.3 Self-efficacy and perceived
behavioral control
In a computer environment, two kinds of control can
be identified: people can control their own beliefs
and behavior, and they can control their
environment. They want to control different
resources such as time, money, and so on; and/or
they want to control information. Self-efficacy
emphasizes the individual’s ability, or judgment of
their capabilities to cope with the task ahead
(Bandura 1977). The self-efficacy theory suggests
that, if organizations can increase employees’ self-
efficacy, judgment about their abilities to cope
successfully with the tasks ahead, this can improve
their efficiency. Self-efficacy could have a positive
impact on acceptance because users feel more
comfortable with the computers and the use of
computers (Venkatesh and Davis 1996). Moreover
self-efficacy can be a strong and significant
predictor of use over a lengthy time period, even
though users have gained more experience
(Compeau 1999). In short, self-efficacy deals with
both the individual’s beliefs in their own capabilities
PREDICTING THE USER ACCEPTANCE OF PERSONALIZED INFORMATION SYSTEMS: CASE MEDICAL
PORTAL.
197

to use computers and their own behavior (Bandura
1977).
The primary objective of the theory of planned
behavior is to predict intentions and explain human
behavior (control beliefs) (Ajzen 1991). Perceived
behavioral control (PBC) is a determinant of TBP
construction. It refers to barriers in behavioural
performance. PBC is compatible with Bandura’s
(1977; 1982) concept of perceived self-efficacy.
PBC reflects perceptions of internal and external
constraints on behavior, for example with reference
to skills, self-respect, time, opportunities, co-
operation with others and resources needed to use
the system. It can be applied, together with
behavioral intention, to predict behavioral
performance (to use personalized WIS). The more
resources and opportunities individuals believe they
possess, and the fewer obstacles they expect, the
stronger their perceived control over their behavior
(Ajzen 1991).
To sum up, it is likely that the individual with
higher behavioral control should also have a stronger
intention to perform (Ajzen 1991). Internal factors
of perceived behavioral control refer both to the
individual’s beliefs about having enough resources
such as time and money. External factors refer to
self-efficacy, the individual’s self-confidence about
his or her ability to perform an activity. Moreover,
past experiences, positive or negative have an effect
on the perceived behavioral control (Bandura 1977;
Bandura 1982; Ajzen 1991). Thus:
H2. Computer self-efficacy will have a positive
influence on the perceived behavioral control.
H3. Perceived behavioral control will have a
significant influence on behavioral intentions to use
personalized IS.
H4. Perceived behavioral control will have a
significant influence on actual use of personalized
IS.
3.4 Computer anxiety
Computer anxiety refers to computer users’
apprehension and resistance to computer use of
technology (here, resistance to personalized WIS). It
describes the individual’s internal fear or phobia of
making mistakes or causing damage when using
computers (Sievert et al. 1988). Self-efficacy affects
both emotional reactions and behavior (here,
computer usage). This concerns anxiety especially.
The self-efficacy theory suggests that the
relationship is reciprocal. In other words, the higher
the computer anxiety, the lower the computer self-
efficacy. Furthermore, increasing computer self-
efficacy level may lead to decreasing computer
anxiety (Bandura 1977). Igbaria and Chakrabarti
found a significant correlation between computer
anxiety and attitudes towards computers (Igbaria and
Chakrabarti 1990). Instead they found no significant
correlation between education/age, and computer
anxiety and attitudes toward microcomputers. Data
for the research was gathered from questionnaires to
MBA students. Thatcher and Perrew (2002) carried
out research on the effect of anxiety to self-efficacy.
Their findings indicate that computer anxiety has a
significant negative effect on computer self-efficacy.
Their sample consisted of university students, as in
the Igbaria and Chakrabarti study. According to the
literature, the users’ psychological state may affect
the adoption of technology. Thus:
H5. Computer anxiety has a negative influence on
self-efficacy.
H6. Computer anxiety has a negative influence on
attitudes to use personalized WIS.
3.5 Perceived ease of use and
usefulness
TAM asserts that two beliefs - perceived usefulness
and perceived ease of use - are relevant determinants
and have a significant impact on computer
acceptance behavior (Davis 1989). The theoretical
background to Davis’s TAM model is based on
Fishbein and Ajzen’s (1975) Theory of Reasoned
Action (TRA), and is especially tailored for
modeling user acceptance of information systems.
Davis (1989) defines perceived usefulness as “the
degree to which a person believes that using a
particular system would enhance his or her job
performance”. This definition emphasizes the user
belief that systems will improve a user’s job
performance without the need for salary raises,
promotions, bonuses or other rewards. Perceived
ease of use refers to “the degree to which a person
believes that using a particular system would be free
of effort.” Davis (1989) asserts that an application
perceived to be easier to use than another is more
likely to be accepted by users. Although expertise is
not a variable of TAM, it may be represented
indirectly via ease of use, since a person with high
expertise may feel a system is easier to use than a
person with low expertise (Mathieson and Chin
2001).
Self-efficacy research supports Davis’s perceived
ease of use definition. The self-efficacy theory
distinguishes two types of expectations, self-efficacy
expectations and outcome expectations. Outcome
ICEIS 2004 - HUMAN-COMPUTER INTERACTION
198

expectations are similar to perceived usefulness
(Davis 1989; Venkatesh and Davis 1996). Venkatesh
and Davis showed in their empirical research that
self-efficacy is associated with ease of use and it has
a significant impact on users’ perceptions of ease of
use of computer system (Venkatesh and Davis
1996). By applying TAM relationships in the
personalized WIS context:
H7. Perceived ease of use has a positive influence
on the perceived usefulness of personalized WIS.
H8. Perceived ease of use has a positive influence
on the behavioral intention to use personalized WIS.
H9. Perceived usefulness has a positive influence on
the behavioral intention to use personalized WIS.
H10. Perceived usefulness has a positive influence
on the attitude to use personalized WIS.
H11. Attitudes towards usage will have a positive
influence on the behavioral intention to use
personalized WIS.
H12. Behavioral intentions to use personalized WIS
have a positive influence on actual use of
personalized WIS.
3.6 Information Quality
User information satisfaction is widely accepted
among researchers as the most significant criterion
for measuring IS success. User information
satisfaction factors are difficult to define as much as
they are also difficult to measure. Therefore, there is
a significant discrepancy in the different measures
(DeLone and MacLean 1992). Whether the system is
good or bad depends on how the user feels about the
system. If the users do not rely on the system and its
information, their behavior against the system can be
negative. Although success is not necessarily
dependent on the technical quality of the system
(Ives et al. 1983), it is obvious that if the system
cannot provide the needed information the user will
feel dissatisfied and will leave the site. DeLone and
MacLean (1992) have identified the following six
different features for information success: system
quality, use, user satisfaction, individual impact,
organizational impact and information quality.
Information quality was seen as one key determinant
for identifying the factors, which may affect the
success of information systems. Previous research
has developed numerous measures of information
quality and identified varying constructs.
Information quality examines user satisfaction of the
usefulness of the provided information. Larcker and
Lessig (1980) developed a measure consisting of
two dimensions, namely, perceived importance of
information and perceived usefulness of
information. Perceived importance of information
identifies factors such as relevance, informativeness,
meaningfulness, importance, helpfulness and
significance. Perceived usefulness consists of factors
such as unambiguity, clarity and readability.
McKinney et al. (2002) examined web-customer
satisfaction by separating web site quality from
information quality and from systems quality.
In personalized web information systems,
information is provided by the system based on the
user information recorded in the user profile. This
provides tailored information content which is
dependent on the users’ preferences and their web
behavior. It is important to find out if the provided
information really reflects the needs of user.
Furthermore, understanding the factors that affect
information quality satisfaction will provide
significant information for developing better
personalization methods and techniques. The
information quality of Web IS is included in our
construction of terms of perceived ease of use and
information quality.
H13. The information quality of personalized IS has
a positive influence on the perceived usefulness.
3.7 Compatibility
Understanding how individuals adopt new ideas and
social practices, and spread them within a society or
from one society to another, are important elements
in explaining personal and social changes (Bandura
1986). The process of spreading ideas and practices
has been examined extensively by Rogers (Rogers
1995) in the book “Diffusion of Innovations”.
Diffusion of innovation can be seen as a social
process, in which subjectively perceived information
about a new idea is communicated. According to
Rogers diffusion can be defined as “a process by
which an innovation is communicated through
certain channels over time among the members of
the system” (Rogers 1995). This definition indicates
that diffusion can be seen as communication in a
social context, spreading messages, which are
concerned with new ideas, practices, or devices.
Web information systems, systems that are linked
and related systems of entities providing access to
knowledge as a communication mechanism, provide
a unique channel for disseminating information
(Scharl 2000). Simultaneously they offer the
advantages of mass media channels, organizational
channels and personal channels. In the course of
PREDICTING THE USER ACCEPTANCE OF PERSONALIZED INFORMATION SYSTEMS: CASE MEDICAL
PORTAL.
199

time, users will lose consciousness of the medium
and, instead of a document or computer screen, will
recognize the power of the text itself (Scharl 2000).
Rogers (1995) categorizes the characteristics, which
could promote IT usage and the decisions to adopt
new technology into the following five attributes:
relative advantage, compatibility, complexity,
trialability, observability. Compatibility of the
innovation (here, personalized WIS) indicates “the
degree to which an innovation is perceived as
consistent with the existent values, past experiences,
and needs of potential adopters” (Rogers 1995).
According to Tornatzky and Klein (1982), there are
three attributes of innovation; relative advantage,
complexity and compatibility, which are related to
innovation adoption and IT usage. Chen et al. (2002)
found in their research that compatibility correlates
significantly with the perceived usefulness. Their
finding is consistent with Moore and Benbasat’s
(1991) finding. Moore and Benbasat suggest that the
relationship of compatibility to relative advantage is
significant. When we consider Taylor and Todd’s
(1995) suggestion that relative advantage - “the
degree to which an innovation is perceived as better
than the idea it supersedes” (Rogers 1995) - is
compatible with the determinant perceived
usefulness of TAM’s construct, we can then
conclude in the personalized WIS context that:
H14. Compatibility will have a positive influence on
perceived usefulness of the personalized IS.
The factors in the research model are presented in
Figure 1.
4 METHODOLOGY
Our intention is to execute a field study by using a
quantitative approach based on Web questionnaire.
The number of potential users of the given portal is
about 10000. Developing the instruments for
measuring each of the factors of the presented model
is based on the prior research and the literature
adapted to the IS technologies and organizations.
According to Straub (1989) and Boudreau et al.
(2001) using validated and tested questions will
improve the reliability of the findings.
Scale items perceived ease of use, perceived
usefulness, attitude and behavioral intention are
based on the Davis’ (1989) studies. These will be
measured using seven point scales. Measuring actual
use is also based on the Davis’ (1989)
Computer self-efficacy will be measured by
using item scale, developed by Compeau and
Higgins (1995). Items to measure information
quality are based on the questionnaire items
developed by Larcker and Lessig (1980). Computer
anxiety will be measured by using seven point scale
based on the research of Igbaria and Chakrabarti
(1990). Perceived behavioral control will be
measured by using the item scale developed by
Taylor and Todd (1995). Items to measure for
compatibility are based on the scales developed by
Moore and Benbasat (1991). Need for control items
will be generated by asking respondents their
concern in giving demographic information to a
system, their concern in filling out registration forms
and their concern about how their individual
information is collected, used and processed.
After completing the questionnaire depicted above,
we will perform a pilot test in a real portal
environment with the focus group consisting of 7-10
portal users. Definite responses of the field study
will be collected in a database for statistical analysis.
5 CONTRIBUTIONS
Our focus is to study the acceptance factors that may
affect intentions and actual use of personalized web
information system. Although many empirical
studies have been carried out into the field of
technology acceptance, we feel that the issues
described in this paper need further examination.
Many features of personalized web information
systems differ from the “traditional” information
systems, and we believe that this research will shed
light on the research into the acceptance of
personalized WIS.
Understanding users’ psychological and
behavioral incentives, their attitudes and intentions
to use IS, will be a step towards increasing the
acceptance of personalized WIS. Moreover,
identifying the factors for predicting and explaining
system use are important and have a high practical
value for practitioners, researchers and IS managers
in general.
The main contributions of our future work will
be:
 to provide useful information to IS
practitioners, researchers and IS managers
studying the voluntary adoption of specific
personalized WIS
 to shed light on possible individual barriers
to the use of personalized WIS
 to find out the significance of the factors
presented in our model with regard to the
acceptance of personalized WIS
 to help organizations understand why a
particular system may be unacceptable and
may need some corrective work.
ICEIS 2004 - HUMAN-COMPUTER INTERACTION
200

Perceived information quality of
personalized Web IS
Behavioral
Intention
Actual Use
Attitude
Perceived
Usefulness
Perceived
Ease of Use
Self-efficacy
Perceived
Behavioral Control
Compatibility
Information
quality
Anxiety
Need for Control
H1
H2
H3
H5
H4
H6
H7
H8
H9
H10
H11
H12
H13
H14
Figure 1: Research model.
6 CONCLUSIONS
This research has attempted to identify users’
perceptions when they come into contact with the
system, which is based on the use of individual
information, and users’ prior net behavior when
providing tailored information and experiences.
Our next step is to test presented model and later
carry out the field study. The field study is based
on questionnaires located on the web. Answers will
be collected in a database for future analysis. Our
target group will consist of the users of a
personalized medical portal.
REFERENCES
Ajzen I (1991) The Theory of Planned Behavior.
Organizational Behavior and Human Decision
Processes 50(2): 179-211.
Bandura A (1977) Self-Efficacy: Toward a Unifying
Theory of Behaviour Change. Psychological Review
84(2): 191-215.
Bandura A (1982) Self-Efficacy Mechanism in Human
Agency. American Psychologist 37(2): 122-147.
Bandura A (1986) Social Foundations of Thought and
Action. A Social Cognitive Theory. New Jersey,
Prentice Hall. Englewood Cliffs.
Baronas A & Louis M (1988) Restoring a Sense of
Control During Implementation: How User
Involvement Leads to System Acceptance. MIS
Quarterly 12(1): 111-124.
Billsus D, Brunk CA, Evans C, Gladish B & Pazzani M
(2002) Adaptive Interfaces for Ubiquitous Web
Access. Communication of ACM 45(5): 34-38.
Boudreau M-C, Gefen D, Straub DW (2001) Validation
in Information Systems Research: A State-of-the-Art
Assessment. MIS Quarterly 25(1): 1-16.
Brusilovsky P (1996a) Adaptive hypermedia: An Attemp
to Analyze and Generalize. In Multimedia,
Hypermedia and Virtual Reality, Lecture Notes in
PREDICTING THE USER ACCEPTANCE OF PERSONALIZED INFORMATION SYSTEMS: CASE MEDICAL
PORTAL.
201

Computer Science 1077, Eds. Brusilovsky, P.,
Kommers, P., Streitz, N. Springer Verlag: 288-304.
Brusilovsky P (1996b) Methods and Techniques of
Adaptive Hypermedia. User Modeling and User
Adapted Interaction. 6: 87-129
Chen L-d, Gillenson ML & Sherrell DL (2002) Enticing
Online Consumers: an Extended Technology
Acceptance Perspective. Information & Management
39(8): 705-719.
Compeau R, D & Higgins A, C (1995) Computer Self-
Efficacy:Development of a Measure and Initial Test.
MIS Quarterly 19(2): 189-211.
Compeau R, Deborah, Higgins, A, Christopher, Huff,
Sidd (1999) Social Cognitive Theory and Individual
Reactions to Computing Technology: a Longitudinal
Study. MIS Quarterly 23(2): 145-158.
Davis DF (1989) Perceived Usefulness, Perceived Ease
of Use, and User Acceptance of Information
Technology. MIS Quarterly 13(3): 319-340.
DeLone W & MacLean E (1992) Information Systems
Success: The Quest for the Dependent Variable.
Information Systems Research 3(1): 60-95.
Fishbein M & Ajzen I (1975) Belief, Attitude, Intention
and Behavior: An Introduction to Theory and
Research. MA, Addison-Wesley.
Igbaria M & Chakrabarti A (1990) Computer Anxiety
and Attitudes Towards Microcomputer Use.
Behaviour & Information Technology 9(3): 229-241.
Igbaria M & Iivari J (1995) The Effects of Self-efficacy
on Computer Usage. Omega. The International
Journal of Management Science. 23(6): 587-605.
Ives B, Olson MH & Baroudi JJ (1983) The
Measurement of User Information Satisfaction.
Communications of ACM 26(10): 785-793.
Kobsa A (2001) Tailoring Privacy to User's Needs
(Invited keynote). User modeling: 8th International
Conference, Berlin-Heidelberg, Springer Verlag:
303-313.
Kobsa A (2002) Personalized Hypermedia and
International Privacy. Communication of ACM
45(5): 64-67.
Larcker DF & Lessig VP (1980) Perceived Usefulness of
Information: a Psychometric Examination. Decision
Sciences 11(1): 121-134.
Lederer L, Albert., Maupin, J, Donna., Sena, P, Mark.,
Zhuang (1998) The Role of Ease of Use, Usefulness
and Attitude in the Prediction of World Wide Web
Usage. Conference on Computer Personnel
Research, Boston, Massachusetts, United States:
195-204.
Mathieson K (1991) Predicting User Intentions:
Comparing the Technology Acceptance Model with
the Theory of Planned Behavior. Information
Systems Research 2(3): 173-191.
Mathieson K & Chin WW (2001) Extending the
Technology Acceptance Model: The Influence of
Perceived User Resources. The Data Base for
Advances in Information Systems 32(3): 86-112.
McKinney V, Yoon K & Zahedi FM (2002) The
Measuement of Web-customer Satisfaction: An
Expectation and Disconfirmation Approach.
Information Systems Research 13(3): 296-315.
Moore GC & Benbasath I (1991) Development of an
Instrument to Measure the Perception of Adopting
an Information Technology Innovation. Information
Systems Research 2(3): 191-222.
OWLA (2002) Open Web/Wireless Adaptive
Hypermedia Applications [online]. Oulu, University
of Oulu. Available from: http://owla.oulu.fi/
[Accessed 25 september 2003]
Rich E (1979) Building and Exploiting User Models.
Department of Computer Science. Pittsburg,
Canegie-Mellon University: 193.
Rogers EM (1995) Diffusion of Innovations. New York,
The Free Press.
Scharl A (2000) Evolutionary Web Development. J. P.
Ray, J. P. Thomas and J. Kuljis, Springer-Verlag.
Sievert ME, Albritton RL, Roper P & Clayton N (1988)
Investigating Computer Anxiety in an Academic
Library. Information Technology and Libraries. 7(9):
243-252.
Strachan L, Anderson J, Sneesby M & Evans M (1997)
Pragmatic User Modeling in A Commercial
Software System. Sixth International Conference on
User Modeling (UM-97), Chia Laguna, Italy,
Springer: 189-200.
Straub DW (1989) Validating Instruments in MIS
Research. MIS Quarterly 13(2): 147-169.
Straub DW & Watson RT (2001) Research
Commentary: Transformational Issues in
Researching IS and Net-Enabled Organizations.
Information Systems Research: 12(4): 337-345
Taylor S & Todd PA (1995) Understanding Information
Technology Usage: A Test of Competing Models.
Information Systems Research 6(2): 144-176.
Thatcher JB & Perrewé PL (2002) An Empirical
Examination of Individual Traits as Antecedents to
Computer Anxiety and Computer Self-efficacy. MIS
Quarterly 26(4): 381-396.
Tornatzky LG & Klein KJ (1982) Innovation
Characteristics and Innovation Adoption-
Implementation: A Meta-analysis of Findings. IEEE
Transactions on Engineering Management 29(1): 28-
45.
Venkatesh V & Davis DF (1996) A Model of the
Antecedents of Perceived Ease of Use: Development
and Test. Decision Sciences 27(3): 451-481.
ICEIS 2004 - HUMAN-COMPUTER INTERACTION
202