FACTORS INFLUENCING THE LEARNING PERFORMANCE
OF u-LEARNING SYSTEMS
SangHee Lee and DongMan Lee
Kyungpook National University, Daegu, South Korea
Keywords: u-Learning systems, context awareness, pervasive connective, Performance of Learning, learner’s
interactivity.
Abstract: This study examines the factors that are associated with user satisfaction of ubiquitous learning (u-
Learning), where in four major factors are identified that influence interaction and learning performance. A
survey of 226 u-Learning users was conducted and the data collected was used to test theoretically expected
relationships. To verify the research model, the validity through the model’s factor and reliability analyses
was inspected. The results of the analyses, by LISREL, are as follows. First, the ubiquitous characteristics
such as pervasive connectivity and context awareness had significant influence on the effectiveness of the u-
Learning systems. Second, the learner's characteristics such as academic motivation and flow played an
important role in the effectiveness of u-Learning systems. Third, the learner's interaction factors had an
important influence regarding the performance of u-Learning systems.
1 INTRODUCTION
With the revolution in computing and wireless
network technology, the advanced knowledge and
information in the 21
st
century have introduced a
ubiquitous computing environment. Since the
ubiquitous technology was developed in 2003, the
development of information and communication in
the 21
st
century has continued to transform the
information paradigm line with the computerization
that was enabled in the 80’s, and the information and
knowledge age in the 90’s (Ha, et. al, 2002). The
major ubiquitous computing technologies include
sensing, network, interface, interaction, security-
privacy, hardware platform embedded software, and
application programs (The Electronic Times, 2005).
The services produced by them are u-
communication, u-information, u-situation
recognition, u-intelligence service, etc. (KERIS,
2006). In the ubiquitous society, the development of
ubiquitous computing technology leads to changes
in politics, economy, and culture, and enables
building up a new social structure (National
Information Society Agency, 2005).
In light of this new technology, it is time to
consider defining the capabilities that this ubiquitous
society requires. Also, it is necessary to know how
education must be done accordingly in the
forthcoming ubiquitous era and how to set up its
vision and practical strategies to accomplish it. The
possibility of ubiquitous learning (u-Learning) as a
future educational technology increases with
educational efforts for detecting social changes
caused technological development and for nurturing
talented people for the next generation that allows a
learner to study freely regardless of time and place
as long as he/she is in a ubiquitous learning. This
can be achieved through an improved learner-
oriented education environment or daily space as
well as the traditional schools into a learning place.
Ubiquitous computing technology can be used to
minimize this problem. Also, it could maximize the
effectiveness of teaching and learning by upgrading
information technology-based facilities to support
ubiquitous technology.
And by using u-Learning systems, it could
encourage the development and use of electronic
textbooks. It is impossible for paper-based textbooks,
which have usability and economical efficiency, to
be substitutes to electronic based textbooks in a
short time.
In Korea, there have been diverse studies on the
vision and strategies of the future of education today
(Koh & Kim, 2005; Kim & Kwon, 2006). The
Department of Information Systems, Education and
Human Resources in particular has actively studied
these issues by applying new methods in public
education. One of the most recent efforts was to test
a u-Learning education at some of the experimental
schools and continue the studies on the teaching-
learning strategies (MOE, 2005). Also, an
alternative teaching-learning model for u-Learning
has been developed (Seo, et al., 2006).
352
Lee S. and Lee D. (2008).
FACTORS INFLUENCING THE LEARNING PERFORMANCE OF u-LEARNING SYSTEMS.
In Proceedings of the Tenth International Conference on Enterprise Information Systems - HCI, pages 352-358
DOI: 10.5220/0001689203520358
Copyright
c
 SciTePress

The purpose of this study is to suggest the major
agendas of u-Learning environment in order to
nurture capable men equipped with these abilities.
2 THEORETICAL
BACKGROUND
2.1 Analyzing the Effectiveness of
u-Learning Characteristics
2.1.1 Pervasive Connectivity
Pervasive connectivity is the ability to consume
local, broadband access to worldwide information
networks, services, and individuals anytime and
anywhere they are convenient. Information
connectivity is the ability to access and retrieve
information from the web. Unlike messaging, this
requires a mobile device to maintain a real-time
connection to the Internet. At a minimum, wireless
browsers should become commonplace before this
phase takes off (Kalakota, et al., 2002).
Real time messaging must also be able to cross-
connect channels (Kalakota, et al., 2002). And
location-based applications capitalize on the mobile
network instructor’s knowledge of where the
educator is at any given time.
2.1.2 Context Awareness
Context is used to automate and facilitate the
communication, collaboration, and coordination
among people, for example, by forwarding incoming
phone calls to a person’s current location, by
determining the communication media for which a
person can be contacted at a given point in time or
automatically choosing the most suitable
communication media for which a person can be
contacted at a given point in time or automatically
choosing the most suitable communication channel
in a given situation.
A context consists of the set of circumstances
and conditions that can be spatial, temporal,
situational, personal, social, cultural, ecological, etc.
(Gershenson, 2002). Context, also, is a powerful and
longstanding concept in learning theory and human-
computer interaction. Interaction with computation
is by explicit acts, making communication much
more efficient. Thus, by carefully embedding
computing into the context of our life activities, it
can serve us with minimal effort on our part.
Communication can not only be effortless, but it can
also fit naturally into our ongoing activities. Pushing
this further, the activities, and the computation
becomes invisible.
2.1.3 Learning Motivation
Motivation by definition is the degree of the choices
people make and the degree of effort they will exert
(Keller, 1983). Several theories have provided
theoretical frameworks for understanding motivation
(Pintrich & Schunk, 1996). Among different
constructs on motivation, continuing motivation and
intrinsic motivation are the most significant for
instructional theory and research (Kinzie, 1990).
Intrinsic motivation is defined as the motivation to
engage in an activity “for its inherent satisfactions
rather than for some separable consequence” (Ryan
& Deci, 2000). Theories of motivation and empirical
evidence have suggested several sources of intrinsic
motivation. Some motivational researchers posit that
activities providing learners with a sense of control
over their academic outcomes may enhance intrinsic
motivation (Pintrich & Schunk, 1996). Lepper and
Hodell (1989) have identified challenge, curiosity,
control, and fantasy as primary characteristics of
tasks that promote intrinsic motivation.
Continuing motivation is the type of intrinsic
motivation most directly concerned with education
and it reflects an individual’s willingness to learn
(Maeher, 1976). Studies have been done on how to
improve learner motivation. Some theorists contend
that the primary reward for the learner is the activity
itself; thus, continuing motivation is facilitated by an
intrinsic interest in the activity (Condry & Chambers,
1978).
It is important to review past studies on
motivational issues in computer-assisted instruction
and distance education settings, since motivational
features that encountered these settings are similar to
those in Web-based instruction (Song, 2000). Kinzie
(1990) argues that intrinsic and continuing
motivation are important components in computer-
based instruction.
Learning has been done on motivational
influences in online settings. Several research
studies suggest that motivation to learn via a
particular medium is influenced by the learner’s
beliefs about his own ability and the difficulty level
of the task, rather than by the medium per se (Clark,
1994). In addition, Keller (1999) posits that learner
support is important for motivation learners in Web-
based instruction.
2.1.4 Flow
The flow theory becomes one of the most important
frameworks in the internet research arena. Hoffman
FACTORS INFLUENCING THE LEARNING PERFORMANCE OF u-LEARNING SYSTEMS
353

and Novak proposed a hierarchical flow model
showing the antecedents and outcomes of flow and
the relationship among these variables in the hyper-
media computer circumstances (Hoffman, et al.,
1996). This model was further tested after their
initial research (Novak, et al., 2000).
In their paper, Hoffman and Novak explained that
the balance of challenge and skill leads to the flow
which means the positive optimal state of mind
(Hoffman, et al., 1996). An imbalance between
challenge and skill, leads to negative states of mind
like anxiety, boredom, apathy (Csikszentmihalyi, et
al., 1988). Almost all research on the flow 4-channel
model has been focusing on flow, the positive state
of mind (Ellis, 1994; Mathwick, et al., 2004).
However, it also needs to examine the formation of
the negative states of minds and their outcomes.
Flow researchers explain play or playfulness as
antecedents or the early state of flow. However, play
has been regarded as a distinct concept from flow in the
flow literatures (Hoffman, et al., 1996; Novak,
Hoffman, et al., 2000). Mathwick and Rigdon
discovered the influences of challenge and skill on
play; they also observed the influence of play on web-
loyalty and brand loyalty (Mathwick, et al., 2004).
This paper attempts to analyze the relationships
among the state of mind, the skill of play, the
challenge, and interest for learning.
2.2 Moderating factors
2.2.1 Learner’s Interactivity
Interactivity is another area of instructional method,
and there have been a few studies to make
comprehensive instructional models for interactivity
in Web or hypertext environments. Choi (1999)
investigated the effects of instructional strategies for
interactivity in Web-based instruction. Lim (1999)
proposed a set of systematic prescriptions for
designing interactive Web-based instruction. Kim
(1998) explored strategies for interaction in
hypertext systems. Besides motivation and
interactivity, a few instructional design models have
been studied for other instructional methods. For
example, a design model for learner control was
suggested by Chung (1994).
2.3 Analyze the Performance of
u-Learning with Regard to the
Learners’ Perspective
2.3.1 Self-Directed Learning
The present study investigated learners of self-
directed learning courses of answer aforementioned
research questions. Here, self-directed learning
courses refer to courses delivered via the Web in
which learners go through instructional materials
delivered via the Web at their own pace without the
presence of an instructor. Learners can participate in
u-Learning in various contexts, yet the self-directed
u-Learning format is the focus of this study because
it is a primary instructional format in training
settings for learners (Driscoll, 2002; Galvin, 2002).
2.3.2 Problem-Solving
In a knowledge-information based society, problem-
solving capabilities are very important in utilizing
knowledge and information in problem situations.
Many research and studies on the learning
effectiveness of problem-solving learning, are, in
general, categorized into two parts: One is about
cognitive aspects which deal mostly with the issues
on improvement of learning acquisition, critical
thinking, and problem-solving skills; the other is
about affective aspects including learning motivation,
and self-confidence as a learner (DeVries et al.,
1994).
Recently, one of the main streams of
instructional methods has involves problem solving.
Problem solving, which is any goal-directed
sequence of cognitive operations and mental
processes, is the most important cognitive activity
within an educational goal setting (Jonassen, 2000;
Phye, 2001). Research on the effectiveness of
problem solving has been examined, thus most
problem solving is conducted and designed to
develop competence in solving problems via
collaborative efforts.
2.3.3 Satisfaction
An important step that is typically required prior to
implementing e-Learning is the selection of a
suitable learning management system
(Govindasamy, 2002). Like any other information
system, the success of learning management stems
largely from user satisfaction (Bharati, 2003;
DeLone and McLean, 1992; Doll and Torkzadeh,
1992; Seddon, 1997) and other such factors. Stokes
(2001) indicated that the issue of learner satisfaction,
within the digital environment, is very important.
Thus, satisfaction with u-Learning is most
frequently used as one of many indicators of
effectiveness in u-Learning. The questionnaire to
measure learners’ satisfaction with u-Learning was
interchangeable with research purpose or the
intentions of the research institution.
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354

3 THE METHODOLOGY
This study examined the relationship between the
mandatory adoption of ubiquitous information
technology and the performance of e-Learning or u-
Learning. Fig.1 shows the proposed model and
hypotheses.
Recently, interest in the usage of ubiquitous
applications in the learning environment has
increased. Previous research mainly provided
general overviews of technical concepts and issues
that were based on evidence, thus less attention was
being paid to the empirical validation of the
relationship between ubiquitous technology
accepting and e-Learning market performance.
Several research efforts in the area of e-Learning
apply the technology acceptance model (TAM) to
the sales force setting on user’s Satisfaction,
however, these studies focus on volitional
technology acceptance, thus in general they fail to
consider the unique features of mandatory u-
Learning learners.
Figure 1: The proposed model and hypotheses.
1
H
: Pervasive Connectivity has a positive effect on
Learner’s Interactivity
2
H
: Context Awareness has a positive effect on
Learner’s Interactivity
3
H
: Learning Motivation has a positive effect on
Learner’s Interactivity
4
H
: Flow has a positive effect on Learner’s
Interactivity
5
H
: Learner’s Interactivity has a positive effect on
Self-directed Learning
6
H
: Learner’s Interactivity has a positive effect on
Problem-Solving
7
H
: Learner’s Interactivity has a positive effect on
Learner’s Satisfaction
4 RESEARCH DESIGN
4.1 Sample and Data Collection
In order to derive the preference structure, a survey
was carried out. Data was gathered from students
experienced u-Learning at a large university in
Daegu of South Korea.
The subjects for this study were u-Learning
learners who had fully experienced u-Learning
interactivity. For this study, a survey (face-to face)
was conducted.
Table 1: Demographics profile of the respondents.
Item Category
Frequency
(people)
Percent
(%)
Male 123 54.4
Gender
Female 113 45.6
15-25 years 76 33.6
26-35 years 94 41.6
36-45 years 32 14.2
Age
> 46 years 24 10.6
Undergraduate
students
156 69.0
Company
employees
45 19.9
Occupation
Etc. 21 9.3
High school
graduates
23 10.2
Attending
universities
101 44.7
Bachelors
degree
65 29.8
Education
Advanced
degree
37 16.4
Learn the
foreign
language
112 49.6
Acquiring the
degree
25 11.1
Acquiring the
license
72 31.9
Goal for the
u-Learning
Etc. 17 7.5
Total 226 100
A total of 226 valid samples were returned.
Among the 266 respondents, 123 were male (54.4%)
and 113 were female (45.6%); 76 were 15-25 years
(33.6%), 94 were 26-35 years (41.6%), 32 were 36-
45 years (14.2%) and 24 were 46 years and over
(10.6%); 156 were student s(69.0%), 45 were
workers (19.9%), 21 were Etc. (9.3%); As the
degree of scholarship were 23 were high school
graduates (10.2%), 101 were attending universities
(44.7%), 65 were masters (29.8%), 37 had a master
degree or higher (16.4%);
FACTORS INFLUENCING THE LEARNING PERFORMANCE OF u-LEARNING SYSTEMS
355

As for the goal of u-Learning 112 were taking it
foreign language (49.6%), 25 were trying to
academic degree acquire (11.1%), 72 were trying to
acquire a licence degree (31.9%), 17 were others.
The demographic profile of the respondents is
shown in Table 1.
4.1.1 Measures
Table 2: The results of reliability and variance analysis
(n=226).
Variables
Cronbach
Alpha
No.
of
Items
Mean S.D
Pervasive
Connectivity
0.880 3 4.084 1.557
Context
Awareness
0.876 3 4.220 1.406
Learning
Motivation
0.879 3 4.423 1.267
Flow 0.885 3 4.491 1.525
Learner’s
Interactivity
0.877 4 3.983 1.522
Self-directed
Learning
0.876 2 4.559 1.270
Problem
Solving
0.876 2 4.508 1.425
Satisfaction 0.877 2 4.457 1.140
The survey instruments were developed based on
measures from the literature that were modified via
preliminary interviews with selected u-Learning
learners. Participants indicated their level of
agreement or disagreement using a seven-point
Likert scale.
The results of a reliability analysis of the
research variables are shown in Table 2. Internal
consistency, as measured by Chronbach’s alpha for
all variables, ranges between 0.876 and 0.885.
5 DATA ANALYSIS AND
RESULTS
The empirical analysis was analyzed with
significance of p<0.10. SPSS 12.1 and LISREL 8.70
were used for the empirical analysis. The empirical
analysis procedure used in the study is shown below
(figure 2. and table 3).
The hypothesis was tested by using LISREL 8.70.
According to the results from the structural equation
model of variance presented in Table 1. and Fig 2.
Partial support is given for Hypotheses H1, H2,
H3, H4, H5, H7 in a significant positive association
between individual differences and the effectiveness
of the u-Learning systems. However Hypotheses H6
was rejected.
Figure 2: Analysis of the research model results.
Table 3: Results of the Hypotheses Test.
Hypothesized Path
Standardized
Coefficient
t-value Result
1
H
Pervasive Connectivity
Æ Learner’s Interactivity
0.373** 4.636 +
2
H
Context Awareness
Æ Learner’s Interactivity
0.352** 4.186 +
3
H
Learning Motivation
Æ Learner’s Interactivity
0.402*** 5.557 +
4
H
Flow
Æ Learner’s Interactivity
0.295** 4.628 +
5
H
Learner’s Interactivity
Æ Self-directed Learning
0.508** 5.565 +
6
H
Learner’s Interactivity Æ
Problem Solving
-0.071*** -0.645 -
7
H
Learner’s Interactivity Æ
Satisfaction
0.416** 4.485 +
(p; *<0.01, **<0.05, ***<0.001)
In this study the results show that the learner's
interactivity factors had an influence on the
performance of the u-Learning system. Data from a
survey of 226 u-Learning users were used to test the
research model. Also, the validity of the model
through factors, and analyze. The results of data
analysis by LISREL are as follows. First, ubiquitous
characteristics, pervasive connectivity and context
awareness had a significant influence on the
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356

effectiveness of u-Learning systems. Second,
learner's characteristics, academic motivation and
flow played an important role in the effectiveness of
u-Learning. Finally, learner's interactivity factors
had positive influence on the performance of the u-
Learning systems.
6 CONCLUSION AND
DISCUSSION
Under the ubiquitous computing environment, the
learner can use any device to exchange information
with anyone anytime and anywhere. Based on the
characteristics of ubiquity, the area of u-Learning
systems has developed by an increase in
interactivity; however, there are some crucial aspects
needed for the effective launch of u-Learning. There
has been little empirical research regarding
information systems conducted up to now.
Like other empirical studies, this study has several
limitations. First, the questionnaire approach is not
free of subjectivity from the respondent. Second, the
generalizability of our study is subject to debate.
Finally, our study was conducted with a snapshot
research approach. More effort is needed to evaluate
the validity of our findings.
REFERENCES
Balawati, T., “Increasing student persistence in Indonesian
post-secondary distance education”,
Distance
Education
, Vol. 19, No. 1, 1998, pp. 60-71.
BenMoussa, C., “Workers on the move: New
opportunities through mobile commerce”,
UKAIS
Conference
, Vol. 16, 2003, pp. 22-23.
Biner, P. M., R. S. Dean, and A. E. Mellinger, “Factors
underlying distance learner satisfaction with
televised college-level courses”,
The American
Journal of Distance Education
, Vol. 8, 1994. pp.60-
71.
Burgoon, J. K., J. A. Bonito, Bengtsson B., A. Ramirez Jr.,
N. E. Dunbar and N. Miczo, “Testing the
interactivity model: Communication process Partner
assessments and the quality of collaborative work”,
Journal of Management Information Systems, Vol.
16, No. 3, 2000, pp. 33-56.
Chute, A. G., M. M. Thompson and B. W. Hancock,
The
McGraw-Hill handbook of distance learning,
New
York: McGraw-Hill, 1999.
Csikszenntmihalyi, M., Optimal experience:
Psychological of flow in consciousness.
Cambridge
University Press, 1988.
Coldeway, D. O., “Methodological issues in distance
educational research”,
The American Journal of
Distance Education,
Vol. 5, No 2, 1988, pp. 47-52.
Coutaz, J., J. L. Crowley, S. Dobson and D. Garlan,
“Context is Key”, Communications of the ACM, Vol.
48, No. 3, 2005, pp. 49-53.
Chen, L. D., “Consumer Acceptance of Virtual Stores: A
Theoretical Model and Critical Success Factors for
Virtual Stores”,
Doctoral Thesis, The University of
Memphis, 2000.
Eager, J. F., R. D. Blackwell, and P. W. Miniard,
Customer Behavior, 6th Ed., Chicago: The Dryden
Press, 1990.
Fortin, D. R. and R. R. Dholakia, “Interactivity and
vividness effects on social presence and involvement
with a web-based advertisement”, Journal of
Business Research
, Vol. 58, 2005, pp. 387-396.
Ghani, J. A., R. Supnick and P. Rooney, “The Experience
of Flow in Computer Mediated and in Face-to-Face
Groups”, in 12th internet,
Conference on
Information Systems
, 1991, pp. 16-18.
Gibson. P. M., “Generalized doubly stochastic and
permutation matrices over a ring”, Linear Algebra
Appl.
, Vol. 30, 1980, pp. 101-107.
Harasim, L.,
Online education: Perspectives on a new
environment,
In L. Harasim, eds., New York:
Praeger Publisher, 1990.
Hair, J. F., R. E. Anderson, R. L. Tatham and W. C. Black,
Multivariate data analysis(4th ed.). Englewood
Cliffs.: Prentice-Hall, 1995.
Heeter, C., Implications of New Interactive Technologies
for Conceptualizing Communication, in Media Use
in the Information Age: Emerging Patterns of
Adoption and Computer Use
, J. L. Salvaggio and J.
Bryant, eds., Hillsdale: Lawrence Erlbaum
Associates, 1989.
Hicks, W. D. and R. J. Klimosky, “Entry into Training
Outcomes: A Field Experiment”,
Academy of
Management Journal
, Vol. 30, 1987, pp. 542-552.
Hoffman, D. L. and T. P. Novak, “Marketing in
Hypermedia Computer-Mediated Environments:
Conceptual Foundations”,
Journal of Marketing, Vol.
60, 1996, pp. 50-68.
Hult, G. T. and D. Ketchen, “Does market orientation
matter?: a test of the relationship between positional
advantage and performance”,
Strategic Management
Journal,
Vol. 22, No. 9, 2001, pp. 899-906.
Jarvenpaa, S. J. and P. A. Todd, “Consumer reactions to
eletronic shopping on the world wide web”,
International Journal of Electronic Commerce, Vol.
1, No. 2, 1997, pp. 59-88.
Jonassen, D., “Instructional design models for well-
structured and ill-structured problem solving
learning outcomes”,
ETR&D, Vol. 45, No. 1, 1997.
Junglas, I. A. and R. T. Watson, “U-Commerce: An
Experimental Investigation of Ubiquity and
Uniqueness”,
24th ICIS, 2003, pp. 414-426.
Kalakota, R. and M. Robinson,
M Business, McGraw-Hill
Company, 2002.
Kannan, P., A. Chang and A. Whinston, “Wireless
Commerce”,
Marketing Issues and Possibilities,
34th HICCS,
Vol. 9, No. 9, 2001, pp. 15-21.
Khan, B. H., Factors to consider when evaluating a web-
based instruction course; A survey, Khan, B. H.
FACTORS INFLUENCING THE LEARNING PERFORMANCE OF u-LEARNING SYSTEMS
357

(Ed.), Web-based Instruction, Educational
Technology Publications
, 1997.
Kenny, D. and J. Marshall, “Contextual Marketing”,
Harvard Business Review, Vol. 78, No. 6, 2000, pp.
119-125.
Knowles, M. S.,
Self-directed learning: A guide for
learners and teachers,
Englewood: Prentice Hall,
1975.
Lim C. P., “Object of the activity systems as a major
barrier to the creative use of ICT in schools”,
Australian Journal of Educational Technology, Vol.
17, No. 3, 2001, pp. 295-312.
Lyytinen, K. and Y. Yoo, “Issues and Challenges in
Ubiquitous Computing”,
Communications of the
ACM,
Vol. 45, No. 12, 2002, pp. 63-65.
McMillan. S. J. and J. S. Hwang, “Measures of Perceived
Interactivity; An Exploration of the Role of
Direction of Communication, User Control and
Time in Shaping Perception of Interactivity”,
Journal of Advertising, Vol. 31, No. 3, 2002, pp. 41-
54.
Moore, M. G., “Three types of interaction”, American
Journal of Distance Education,
Vol. 3, No. 2, 1989,
pp. 1-6.
Moore, M. G., and G. Kearsley, Distance Education.
Belmont: Wadsworth Publishing Company,
1996.
Newhagen, J. E. and S. Rafaeli, “Why communication
researchers should study the Internet: A dialogue”,
Journal of Communication, Vol. 46, 1996, pp. 4-13.
Novak, T. P., D. L. Hoffman, and Y. F. Young,
“Measuring the Customer Experience in Online
Environments: A Structural Modeling Approach”,
Marketing Science, Vol. 19, No. 1, 2000, pp. 22-42.
Rafaeli, S., “Interactivity: From New Media to
Communication in Advancing Communication
Science”,
Sage Annual Review of Communication
Research
, Vol.16, Sage Publications, 1988, pp. 110-
134.
Rogers, E. M., Communication Technology; The New
Media in Society
, New York; The Free Press, 1986.
Srinivassan, S., A. Rolph, and P. Kishore, “Customer
loyalty in e-Commerce: an exploration of its
antecedents and consequences”,
Journal of Retailing,
Vol. 78, 2002, pp. 41-50.
Steuer, J., “Defining Virtual Reality; Dimensions
Determining Telepresence”,
Journal of
Communication,
Vol. 42, No. 4, 1992, pp. 73-93.
Sukpanich, N. and L. Chen, “Antecedents of Desirable
Consumer Behaviors in Electronic Commerce”,
Association of Information Systems Conference,
1999.
Treffinger, D. J., S. G. Isakesen and K. B. Dorval,
Creative Problem Solving: An Overview, In M.A.
Runco. (eds.). Norwood: Albex, 1994.
Vygotsky, L. S. Mind in Society: The Development of
Higher Psychological Processes. Cambridge, Mass:
Haevard University Press, 1978.
Wu, G., “The Mediating Role of Perceived Interactivity in
the Effect of Actual Interactivity on Attitude toward
the Web-site”,
Journal of Interactive Advertising,
Vol. 5, No. 2, 2005, pp. 55-72.
Yuping, L. and L. J. Shrum, “What Is Interactivity and Is
It Always Such a Good Thing? Implications of
Definition, Person, and Situation for the Influence of
Interactivity on Advertising Effectiveness”, Journal
of Advertising,
Vol. 31, No. 4, 2002, pp. 53-64.
ICEIS 2008 - International Conference on Enterprise Information Systems
358