Richness Versus Parsimony Antecedents of Technology Adoption Model 15
5 Discussion
A fair comparison of models or theories includes careful empirical design, operation-
alization and measurement [10]. The research design in this study was undertaken in
the same e-learning context and using the same respondents to measure the constructs
of TAM and PCI model. The findings of this study provide a preliminary test of the
viability of the two research models within the context of e-learning websites. Ana-
lytical results indicate that the PCI constructs explain slightly more variance (0.9%) in
users’ intentions of continued use than the TAM antecedents. Both the PCI and TAM
perceived constructs are highly reliable, and have considerable prediction power in
terms of exploring a user’s continuing intention to use e-learning websites. However,
the TAM model has fewer measurement items (12) than the sort-form PCI instru-
ments (25). The TAM model places fewer strains on respondents and researchers than
PCI model.
The results of TAM model demonstrate that the perceived usefulness construct
plays an important role in predicting users’ intentions of continued use, while the
perceived ease-of-use has a significant impact on it. Conversely, the PCI results report
that while relative advantage construct plays a critical role in explaining the intentions
of continued use, trialability and compatiability constructs are also significant. Hence,
teachers or marketing staff can try to enhance the innovation perception of trialability
and compatiability, in addition to the perception of relative advantage, to raise the
continued use of e-learning websites. The study also adds to the literature on compar-
ing performance of TAM versus PCI, using data gathered in a naturally occurring and
field-based adoption process.
References
1. Bahreininejad, A.: E-learning and associated issues in Iran. International Journal of Dis-
tance Education Technologies 4(4), 1–4 (2006)
2. Agarwal, R., Harahanna, E.: Time flies when you’re having fun: cognitive absorption and
beliefs about information technology usage. MIS Quarterly 24(4), 665–694 (2000)
3. Agarwal, R., Prasad, J.: The role of innovation characteristics and perceived voluntariness
in the acceptance of information technologies. Decision Sciences 28(3), 557–582 (1997)

4. Brown, A.: Learning from a distance. Journal of Property Management 71(4), 42–45
(2006)
5. Al-Gahtani, S.S., King, M.: Attitudes satisfaction and usage: factors contributing to each
in the acceptance of information technology. Behaviour & Information Technology 18(4),
277–297 (1999)
6. Wixom, B.H., Todd, P.A.: A Theoretical Integration of User Satisfaction and Technology
Acceptance. Information Systems Research 16(1), 85–102 (2005)
7. Barclay, D.W., Higgins, C.A., Thompson, R.: The Partial Least Squares (PLS) approach to
causal modeling: Personal computer adoption and use as an illustration. Tech. Stud. 2(2),
285–309 (1995)
8. Chin, P.R.: Newsted: Structural equation modeling analysis with small samples using par-
tial least squares. Statistical Strategies for Small Sample Research (1998)
16 H L. Liao and H P. Lu
9. Plouffe, C.R., Hulland, J.S., Vandenbosch, M.: Research report: richness versus parsimony
in modeling technology adoption decisions–understanding merchant adoption of a smart
card-based payment system. Information systems research 12(2), 208–222 (2001)
10. Cooper, W.H., Richardson, A.J.: Unfair comparisons. J. Appl. Psych. 71(2), 179–184
(1986)
11. Douglas, D.E., Van Der Vyver, G.: Effectiveness of e-learning course materials for learn-
ing database management systems: an experimental investigation. Journal of Computer In-
formation Systems 44(4), 41–48 (2004)
12. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of informa-
tion technology. MIS Quarterly 13(3), 319–340 (1989)
13. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User Acceptance of Computer Technology: A
Comparison of Two Theoretical Model. Management Science 35(8), 982–1003 (1989)
14. Davis, F.D.: User Acceptance of Information Technology System Characteristics, user
perceptions and behavioral impacts. International Journal of Man-Machine Studies 38(3),
475–487 (1993)
15. Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable vari-
ables and measurement error. Journal Marketing Research 18, 39–50 (1981)

16. Lin, H F., Lee, G G.: Effects of socio-technical factors on organizational intention to en-
courage knowledge sharing. Management Decision 44(1), 74–88 (2006)
17. Huang, E.: The acceptance of women centric websites. Journal of Computer Information
Systems 45(4), 75–83 (2005)
18. Hulland, J.: Use of Partial Least Squares (PLS) in strategic management research: A re-
view of four recent studies. Strategic Management Journal 20(2), 195–204
19. Jieun, Y., Ha, I., Choi, M., Rho, J.: Extending the TAM for a t-commerce. Information and
Management 42(7), 965 (2005)
20. Koufaris, M.: Applying the technology acceptance model and flow theory to online con-
sumer behaviour. Information Systems Research 13(2), 205–223 (2002)
21. Lee, M.K.O., Cheung, C.M.K., Chen, Z.: Acceptance of Internet-based learning medium:
the role of extrinsic and intrinsic motivation. Information and Management 42(8), 1095
(2005)
22. Lu, J., Yu, C.S., Liu, C.: Facilitating conditions, wireless trust and adoption intention.
Journal of Computer Information Systems 46(1), 17–24 (2005)
23. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of
adopting an information technology innovation. Information Systems 2(3), 192–222
(1991)
24. Palvia, S.C.: Effectiveness of Asynchronous and Synchronous Modes for Learning Com-
puter Software for Endusers: an Experimental Investigation. Journal of Computer Informa-
tion Systems 41(2) (2000)
25. Nunnally, J.C.: Psychometric Theory. McGraw-Hill, New York (1978)
26. Fretty, P.: Go the distance. PM Network 20(9), 16–21 (2006)
27. Rogers, E.M.: Diffusion of innovation. The Free Press, New York (1983)
28. Rogers, E.M.: Diffusion on Innovations. The Free Press, New York (1995)
29. Seyal, A.H., Rahim, M., Rahman, M.N.: Determinants of academic use of the Internet: A
structural equation model. Behaviour Information Technology 21(1), 71–86 (2002)
30. Mackay, S., Stockport, G.J.: Blended learning, classroom and e-learning. The Business
Review 5(1), 82–88 (2006)
31. Tornatzky, L., Fleischer, M.: The Processes of Technological Innovation. Lexington

Books, New York (1990)
Richness Versus Parsimony Antecedents of Technology Adoption Model 17
32. Tornatzky, L.J., Klein, K.J.: Innovation characteristics and innovation adoption-
implementation: A meta-analysis of findings. IEEE Transactions on Engineering Man-
agement 29(1), 28–45 (1982)
33. Van Slyke, C., Belanger, F., Comunale: Factors influencing the adoption of web-based
shopping the impact of trust. Database for Advances in Information Systems 35(2), 32–46
(2004)
34. Van Slyke, C., Lou, H., Day, J.: The impact of perceived innovation characteristics on in-
tention to use groupware. Information Resources Management Journal 15(1), 5–12 (2002)
35. Venkatesh, V., Davis, F.D.: A model of the antecedents of perceived ease of use: devel-
opment and test. Decision Sciences 27, 451–481 (1996)
36. Venkatesh, V., Speier, C., Morris, M.G.: User acceptance enablers in individual decision
making about technology toward an integrated model. Decision Sciences 33(2), 297–316
(2002)
37. Ilie, V., Van Slyke, C., Green, G., Lou, H.: Gender differences in perceptions and use of
communication technologies: a diffusion of innovation approach. Information Resources
Management Journal 18(3), 13–31 (2005)
38. Yi, Y., Wu, Z., Tung, L.L.: How individual differences influence technology usage behav-
iour? Toward an integrated framework. Journal of Computer Information Systems 46(2),
52–63 (2005)

F. Li et al. (Eds.): ICWL 2008, LNCS 5145, pp. 18–26, 2008.
© Springer-Verlag Berlin Heidelberg 2008
Exploring a Computer–Assisted Managing System with
Competence Indicators in Taiwan
Yen-Shou Lai
1
, Hung-Hsu Tsai
2

, Yuan-Hou Chang
1
, and Pao-Ta Yu
1

1
Dept. of Computer Science and Information Engineering,
National Chung Cheng University, Chiayi, Taiwan 621
2
Dept. of Information Management, National Formosa University,
Huwei, Yulin, Taiwan 632
{lys,cyh,csipty}@cs.ccu.edu.tw,
Abstract. Grade 1-9 Curriculum connects elementary school and junior high
school in Taiwan with Competence Indicators (CIs). CIs are the references for
editing teaching materials, designing instruction, planning and implementing
evaluation. Teachers can follow CIs while designing the teaching materials or
offering supplement teaching materials. In order to allow the instructors to ac-
quire suitable teaching materials from Internet and the students to access to the
learning materials suitable for them from Internet, this study proposes a com-
puter-assisted learning system which uses a clustering strategy to systematically
access instructional materials according to learning map and CIs, so that correct
learning components can be found efficiently and learning sequence can be de-
signed effectively. This study takes numbers and quantities of mathematics at
third grade students of elementary school as an example, then further investigate
the changes of 32 students’ mathematics achievement after learning. This study
conclusion is that the students can enhance the learning effects by learning on
their own.
Keywords: Grade 1-9 Curriculum, Competence Indicators (CIs), Learning
strategies.
1 Introduction

In recent years, “Grade 1-9 Curriculum” is a key issue of educational revolution in
Taiwan. Grade 1-9 Curriculum aims at bridging the students’ learning gap, enhancing
non linear-and-circular characteristics of learning on the curriculum design [1], and
emphasizing the cultivation of students’ portable capacity instead of the pure memory of
knowledge [2] [3]. “Competence Indicators” means to transform the capacity items the
students should possess into quantitative measure for observation and evaluation in
order to assess the students’ learning performance [4] [5]. CIs can be divided into de-
tailed subitems. Based on detailed subitems of CIs, the teachers can draw learning ob-
jectives, edit teaching materials, design activities, and implement evaluation. Currently,
various versions of textbooks are used in elementary schools in Taiwan. Although
learning units in textbooks are associated with a CI or a set of CIs, it is inefficient to
collect a set of learning units associated with CIs teachers or learners give. In other
Exploring a Computer-Assisted Managing System with Competence Indicators 19
words, teachers or students have to manually search for the learning units indexed by
CIs in textbooks. This manner is time-consuming and causes collect incomplete learning
units for given CIs.
Nowadays, e-Learning is a rapid growing trend [6]. Large amount of teaching ma-
terials can be readily access through WWW over Internet. However, too many websites
and connections lead to the users’ information overload. In order to find out useful
materials they want, they usually should spend plenty of time to evaluate, screen, and
choose related materials on Internet [7]. It is not easy to search for the proper teaching
materials for learners, especially for pupils [8] [9]. The reason is that most instructional
materials on the Internet are not associated with CIs. Therefore, this paper proposes a
computer-assisted learning management (CALM) system which structurally manages
learning materials with CIs. Students can use the system to avoid losing their direction
or inappropriately ending the courses. Additionally, questionnaire survey is also con-
ducted to validate the system to comply with the requirement on curriculum schedule in
elementary school. Furthermore, in order to investigate learning effects of using the
system for student, a quasi-experiment is designed to assess effects. In the
quasi-experiment, the CALM system provides a set of course units in Mathematics for

the concept of “number” and “quantity”.
The rest of the paper is organized as follows. Section 2 describes the CALM system.
Section 3 shows the experiment. Finally, results and conclusions are drew in Section 4
and Section 5, respectively.
2 Grade 1-9 Curriculum Learning System
2.1 System Structure
Based on CIs, the system uses detailed subitems of CIs at different grades for classi-
fication of knowledge uses and further categories different learning and evaluation
components. The system is divided into two segments, the teacher and the student. The
teacher can upload and download the instrumental elements based on the CIs, and the
student can make progress in the learning activity based on both the instrumental
course and self-ability. The system is the WBI (Web-based Instruction) system pro-
viding the teachers access teaching components and the students’ self-learning. In this
system, the students can surf learning components and receive the tests. If the students
cannot pass the evaluation of certain detailed subitems of indicator at different grades,
they can surf the learning components again in the system for learning activities in
order to find out the tips for passing the evaluation. Fig. 1 illustrates the proposed
system architecture.
2.2 CIs and the Index of Knowledge Map
Ordinary searching engine does not provide “teaching components” information de-
signed on learning perspective. Using a set of information attributes to describe data
content, so that the users can manage and search the resources [10]. The searching
effect is better than ordinary one if the learning component information is divided into
different categories by field, subject, and learning stage in Grade 1-9 Curriculum.