Cognitive style and training method impacts

Computers & Education 37 (2001) 11–25
www.elsevier.com/locate/compedu

Influences of cognitive style and training method on training
effectiveness
Huey-Wen Chou *
Department of Information Management, National Central University, 38, Wu-Chuan Li, Chungli,
Taiwan 32054, People’s Republic of China
Received 1 April 2000; accepted 1 February 2001

Abstract
The present study compares the relative effects of cognitive style and training method on learners’ computer self-efficacy and learning performance by a field experiment. The purpose was to determine which
training method could be best utilized in computer-related training while taking trainees’ cognitive style
into account. The significant two- and three-way interactions indicate the critical roles that personal
characteristics and situation factors play as joint determinants of behavior. Gender did significantly moderate the effects of training method on performance and self-efficacy. The cognitive by training method
effects were most significant for female participants. This finding suggests that to assist individuals taking
IT-related training courses, the contingency effects of gender, cognitive style, training approach, and
training objective should be taken into account. # 2001 Elsevier Science Ltd. All rights reserved.
Keywords: Country-specific developments; Gender studies; Improving classroom teaching; Secondary education

As information technology (IT) becomes more critical to business operation, computer literacy
becomes a requisite for employees. Although training has been widely recognized as a critical
contributor to successful systems implementation and end user computing (Grover & Teng,
1994), the broad diversity of individual differences among potential trainees should be taken into
account when developing the training program (Chou & Wang, 1999; Liu & Reed, 1994). In
addition, while many approaches for developing computer skills are available, little research has
systematically examined the relative merits of various training programs. The current study
addresses these concerns.
This paper focuses on three objectives: (1) to develop a conceptual model to evaluate how
training method and cognitive style influence learning performance and computer self-efficacy; (2)
* Corresponding author. Tel.: +886-3-426-7256; fax: +886-3-425-4604.
E-mail address: hwchou@im.mgt.ncu.edu.tw (H.-W. Chou).
0360-1315/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved.
PII: S0360-1315(01)00028-8

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H.-W. Chou / Computers & Education 37 (2001) 11–25

to investigate whether individuals with different cognitive styles perform differently in two training conditions, that is, assessing the feasibility of a contingency approach to training on learning
performance and on computer self-efficacy; and (3) to investigate the gender-moderating effects
on the causal relationship between cognitive style and/or training method, and learning performance and computer self-efficacy.
The remainder of this paper is organized as follows. Section 1 reviews the literature on the
variables included in the proposed model, followed by the theoretical framework and hypotheses
for the model. Section 2 presents the research methodology including participants, experimental
design, procedure, and training intervention. Section 3 describes the data analysis and corresponding results. Section 4 provides summaries of the study findings, study limitations, and
recommendations for future research.

1. Literature review
1.1. Self-efficacy
Self-efficacy emanates from the Social Cognitive Theory (SCT; Bandura, 1986) and is defined
as the beliefs about one’s ability to perform a particular behavior, which affects choices about
which behaviors to undertake, and the effort and persistence exerted in the face of obstacles to the
performance of those behaviors. Self-efficacy perceptions influence an individual’s actual ability
to perform the behavior. Gist, Schwoerer, and Rosen (1989) and Gist, Stevens, and Bavetta
(1991) confirmed a positive relationship between self-efficacy and achievement. They also suggested that initial computer self-efficacy moderated the effect of training method on training
outcome. Self-efficacy was treated as both a dependent variable and an independent variable in
the computer-training literature.
1.1.1. Self-efficacy as an independent variable
Igbaria and Iivari’s (1995) study found that self-efficacy had both direct and indirect effects on
computer usage. Compeau, Higgins, and Huff (1999) conducted a longitudinal study to test an
SCT-based model, which indicated a significant relationship between self-efficacy and computer
usage. Coffin and MacIntyre (1999) found that self-efficacy had a significant effect on learning
performance in a programming course. Compeau and Higgins (1995) found that computer selfefficacy exerted a strong influence on performance in learning Lotus 1-2-3 and WordPerfect.
Christoph, Schoenfeld, and Tansky’s (1998) study on the influence of self-efficacy on multimediabased training receptiveness indicated that training effectiveness was determined partly by the
trainees’ self-efficacy.
1.1.2. Self-efficacy as a dependent variable
Self-efficacy was also treated as a dependent variable in the literature. Martocchio (1994) found
age and outcome expectation significantly affect self-efficacy. Torkzadeh and Koufteros (1994)
found a significant training effect on self-efficacy. In Compeau and Higgins’ (1995) study, they
confirmed that the behavior-modeling approach was more effective for training in Lotus 1-2-3
and resulted in a higher computer self-efficacy.


13

The main purpose of the present study was to determine whether different training methods
and/or participants’ cognitive style would impact their computer learning performance and
computer self-efficacy. Self-efficacy was therefore employed as a dependent variable.
1.2. Training methods
Many training formats are reviewed in the computer training literature, such as lecture,
exploration-based, tutorial, and computer-assisted instruction (CAI). Nevertheless, little
research has systematically examined the relative merits of various training programs. Studies
suggested that the choice of training method has consequences for the degree of learning
(Webster & Martocchio, 1993) as well as for self-efficacy (Compeau & Higgins, 1995; Gist et al.,
1989).
In the present study, the instruction-based method was chosen because it represents a more
traditional approach and is appropriate for almost all training needs. Behavior modeling was
included because of the work of Baldwin (1992), Compeau and Higgins (1995), Gist et al. (1989),
Olfman and Mandviwalla (1994), Simon and Werner (1996), and Simon, Grover, Teng, and
Whitcomb (1996) on computer training.
The instruction-based method offers a traditional approach that is appropriate for almost all
training. This approach teaches primarily by lecture and follows a deductive way to learning,
where learners proceed from general rules to specific examples (Chou & Wang, 2001). Overall, the
literature suggests that the instructional technique is superior for retention of information.
Behavior modeling is a training process developed in the 1970s for building an individual’s skill
in the context of managerial skill training. This task-focused method involves a visual observation
of the behaviors of a model performing a task. Learners then imitate and extend the model’s
behavior in practice and experimentation to master the task. The behavior-modeling method
employs an inductive approach that teaches first by hands-on demonstrations, followed by complementary lectures. Bandura (1986) suggested that by observing someone performing the target
behavior, the participants’ self-efficacy would be raised.
In a field experiment, Gist et al. (1989) compared alternative training methods on self-efficacy
and computer software learning. In the behavior-modeling group, video modeling served as the
principal means of instruction, whereas in the tutorial training group, a one-on-one interactive
tutorial instruction on the computer monitor was the primary means of instruction. After each
step was demonstrated, the trainees in the behavior-modeling group were asked to execute the
step. On the other hand, the participants in the tutorial condition were only told what to do; they
were not asked to execute specific tasks. The results indicate the behavior-modeling approach
relative to a computer-aided instructional approach yielded higher self-efficacy and performance
scores in learning computer software.
Compeau and Higgins (1995) examined the relative effectiveness of behavior-modeling and
traditional lecture-based programs on the learners’ performance and self-efficacy. They found
that the behavior-modeling approach was more effective than the lecture-based approach in
learning Lotus 1-2-3. Chou and Wang (2001) confirmed the superiority of the behavior-modeling
approach to the instruction-based approach on computer self-efficacy and learning performance.
Simon et al. (1996) and Simon and Werner (1996) compared three training techniques —
instruction, self-paced, and behavior modelin — with a no-training control group. Findings of

14


both studies indicate that the behavior-modeling approach generates the highest scores in cognitive learning, skill demonstration, and satisfaction with the computer system.
H1. Participants in the behavior modeling group will score significantly higher on learning

performance and computer self- efficacy measures than participants in the instruction-based
group.
1.3. Cognitive style
Cognitive styles define learner traits. The cognitive style refers to the way individuals collect,
analyze, evaluate, and interpret data (Harrison & Rainer, 1992). The study of individual differences in cognition and their impact on learning and instruction has long been a focus in cognitive
psychology and education. Witkin’s (1964) research on how people separate one factor from the
total visual field has become the field-independence/dependence theory, which is also one of the
most extensively researched cognitive styles.
Witkin, Moore, Goodenough, and Cox (1977) defined field independence as ‘‘the extent to
which a person perceives part of a field as discrete from the surrounding field as a whole, rather
than embedded in the field; or . . . the extent to which the person perceives analytically’’ (p. 7).
According to Witkin and Goodenough (1981), field-independent (FI) people are more likely to do
well with numbers, science, and problem-solving tasks. They tend to analytically approach a
problem and perceive a particular and relevant item in a field of distracting items. On the other
hand, field-dependent (FD) people tend to be better at recalling such social information as conversations and relationships. They prefer to approach a problem in a more global way and are
capable of perceiving the total picture in a situation.
The practical implications of FI/FD cognitive style for education have indicated that the individuals’ different cognitive styles bear direct impact upon their achievement performance (Tina´
jero & Paramo, 1997; Wieseman, Portis, & Simpson, 1992). Witkin and Goodenough (1981)
suggested that FI relates to academic progress in some specialized courses. Several studies have
suggested that more FI students tend to academically outperform FD students. For instance,
´
Paramo and Tinajero’s (1990) study showed that FI people tend to outperform FD people in
overall school performance. Moore and Dwyer (1992) conducted a study that examined the effect
of B&W- and color-coding of information. They found that FI students, on the whole, scored
significantly higher than FD students in both visually and verbally oriented tests. In addition,
significant performance differences were found between FI and FD students in the B&W-coded
treatment but not in the color-coded treatment. The results also showed that external support
could be important for FD people in processing information. Liu and Reed (1994) found that
participants of different cognitive styles employed different learning strategies in accomplishing
the same task in a hypermedia environment.
H2. Participants with FI cognitive style will score significantly higher on learning performance

and computer self-efficacy measures than participants with FD cognitive style.
In addition to the direct effects of cognitive style on achievement performance, the relationship
between cognitive style and training approach has been studied. Studies that have attempted to


15

match situational factors to learning styles have only inconsistently found any benefits for learning (Knight, Halpin, & Halpin, 1992; Snow & Swanson, 1992). However, the search for such
aptitude-treatment interaction goes on, and a few studies have found positive effects for programs
that adapt instruction to an individual’s learning style (Chou & Wang, 1999; Lipsky, 1989).
Lipsky (1989) found FD participants performed better with the outlining technique, and FI students performed better with the mapping technique. The above results confirmed that one
instructional method suitable for FI learners might not necessarily be beneficial for FD learners.
Learners with different cognitive styles pursue quite different ways of learning.
Snow (1991) proposed that learners differ profoundly in what they do in learning and in their
success in any particular learning situation. The present study employs the interactional psychology perspective that emphasizes the importance of considering both the person characteristics (cognitive style) and the situation factor (training method) as joint eterminants of behavior
(Lee, Kim, & Lee, 1995).
H3. FD participants will score significantly higher on learning performance and computer

self-efficacy with the behavior-modeling method, whereas FI participants will score significantly higher on learning performance and computer self-efficacy with the instruction-based
approach.
1.4. Gender
Harrison and Rainer (1992) found that male gender is associated with higher computer skill. In
a meta-analysis, Whiteley (1997) found men and boys exhibited higher computer self-efficacy than
did women and girls. The author claimed gender differences appear in attitude towards computers, knowledge about computers, and skills to work with computers. Gender was introduced in
this study because it may contribute to the understanding of self-efficacy exerted in improving the
training benefits of computer skills (Rattanapian & Gibbs, 1995, p. 60).
Research in instructional psychology has demonstrated that individual characteristics could
call for one or the other method of instruction. The moderating variable is a kind of independent
variable that has a significant contributory or contingent effect on the independent-dependent
relationship. In the present study, gender is proposed to be a moderating variable that moderates the effects of cognitive style and/or training method on learning performance and computer
self-efficacy. In addition, the interaction effect of cognitive style and training method was
proposed.
H4. FI cognitive style will have stronger positive effect on learning performance and computer

self-efficacy for male participants than for female participants.
H5. The behavior-modeling method has a stronger positive effect on learning performance and

computer self-efficacy for male students than for female students.
H6. FI cognitive style will have stronger positive effect on learning performance and computer

self-efficacy for male participants in the behavior-modeling group than for male participants in
the instruction-based group, or female participants in either training condition.

16


1.5. Conceptual research model
Fig. 1 presents the research model. Four constructs are included in the model. Cognitive style
and training method each are posited to directly impact self-efficacy and learning performance.
Gender is posited to moderate the causal effects of cognitive style and/or training method on selfefficacy and learning performance. Two training methods, instruction-based and behavior-modeling, are developed. Cognitive style is classified as field dependent and field independent.
2. Research methodology
2.1. Participants
To obtain permission for conducting the field experiment, the researcher contacted local senior
high schools in a northern city of Taiwan. All of the school principals expressed their willingness
to participate. One school that had a computer classroom equipped with 55 Pentium-level networked PCs was chosen for the experiment. Two classes of 10th graders were randomly chosen
and assigned to one of the two training conditions. The number of participants in each class was
53 and 55, respectively. Twenty-four participants either did not complete the entire training process or had missing data and were therefore dropped from the final statistical analysis. The two
groups were comparable in terms of gender distribution (approximately 57 and 63% male, and
43% and 37% female for each class, respectively) and past performance (P=0.790 and P=0.821
for last semester’s math and Chinese grades, respectively).
2.2. Experimental design
Due to practical limitations, a field experiment, instead of a laboratory experiment, was conducted to test the hypotheses. The experiment is a factorial design with training method and

Fig. 1. Conceptual research model.


17

cognitive style as independent variables, and learning performance and computer self-efficacy as
dependent variables. The present study proposes that the training method and cognitive style will
have differential effects on self-efficacy and learning performance. That is, the research employs
the interactional psychology perspective that emphasizes the importance of considering both the
personal characteristics (cognitive style) and the situation factor (training method) as joint
determinants of behavior (Lee et al., 1995).
2.3. Training material
Three segments of training materials were developed based on a commercialized reference book
for WWW homepage design (Horton, Taylor, Ignacio, & Hoft, 1996). The first segment included
introduction of Netscape Composer, primary attributes, and document background. The second
segment contained paragraph definition and image insertion. The final segment included hyperlink and table manipulation. Each segment was designed for a 1-h training program.
2.4. Procedure
The three-session workshop was held on three consequent weeks, with 2 h in each session.
At the beginning of the first session, all participants were given a background questionnaire, a
computer self-efficacy scale (CSE; Murphy, Coover, & Owen, 1989), and a cognitive style inventory, the Group Embedded Figures Test (GEFT; Witkin, Oltman, Raskin, & Karp, 1971). A
lecture on the basics of computing concepts, introduction of Netscape Composer, primary attributes, and document background was delivered thereafter.
In the remaining two sessions, the manipulation was introduced. Each of the sessions began
with 60 min of instruction in a specific training approach, followed by 30 min of practice, and
concluding with a 30-min performance test. All training was conducted using a single instructor,
providing continuity throughout all training sessions. The instructor followed a fixed outline, but
questions were answered as they arose. Participants were told that their performance on TEST1
and TEST2 would contribute to their course final grades. Following the manipulation, a CSE test
was given again after the third session.
2.5. Treatment intervention
Uniform content of information was provided to both training groups in the same computer
room. The training intervention was implemented through the following procedure. The two
training approaches differ in the presentation sequence, instructional philosophy, and instructional media.
The instruction-based training method employs a deductive approach in the lecture format. At
each session, the instructor began by briefly outlining the key learning points, followed by
descriptions on general knowledge, which included concepts, general rules, and command features. Next, procedural knowledge and the accompanying examples were presented step-by-step
using computer-driven transparencies as the primary instructional media.
In the behavior-modeling condition, the instructor started with specific examples and proceeded to general rules, which is termed an inductive approach. From their computer monitors,

18


participants observed how the instructor (model) demonstrated examples and executed corresponding step-by-step procedures on the computer. Next, the instructor gave a lecture on general
knowledge, followed by a summary on key learning points. The computer-driven demonstration
was the principal instructional media.
2.6. Measures
Cognitive style and computer self-efficacy were assessed by two inventories: the Group
Embedded Figures Test (GEFT; Witkin et al., 1971) and the Computer Self-efficacy scale (CSE,
Murphy et al., 1989). Two self-developed tests, TEST1 and TEST2, including hands-on and
objective questions to test procedural and general knowledge, were self-developed to evaluate
learning performance.
2.6.1. GEFT
The GEFT, comprised of 18 complex figures, measures the cognitive style ‘‘field independence’’. Within each complex figure is embedded a previously seen simple figure. The participants’ task was to locate the embedded figures that were hidden with each of the complex figures.
People who were successful at disembedding the hidden figures were considered to be more fieldindependent. A score was obtained by counting the number of correctly traced embedded figures.
Scores range from 0 to 18, with higher scores indicating more field-independence. The GEFT
grand mean score was employed as the cutting point for FD/FI group classification.
2.6.2. Computer self-efficacy
The CSE self-reported scale was translated and slightly modified to include questions associated with working in a networked environment. This five-point Likert-type scale inventory,
including 32 items, was administered twice to the participants, before and after the experiment.
CSE.net, defined as the difference between the pre- and post-experiment CSE scores (CSE.pre and
CSE.post), is the change in expectation for performance due to the treatment effects. The range of
possible total scores for CSE.pre and CSE.post is between 32 and 160, with higher scores indicating more computer self-efficacy. Positive CSE.net scores indicate higher computer self-efficacy
due to the experiment.
2.6.3. Learning performance
Two learning performance tests, TEST1 and TEST2, were administered during the last 30 min
of the second and third sessions, respectively. Both tests were self-developed and included objective questions for testing general knowledge and skill-based tasks for testing procedural knowledge. The range of possible total scores for each test is between 0 and 100, with higher scores
indicating better learning performance. Examples of the objective questions and skill-based tasks
for TEST1 and TEST2 include the following:
1.TEST1: Name the basic edit functions of FrontPage Express, explain the meaning of ‘‘attributes’’
(objective questions), and set the text format as directed (skill-based task).
2. TEST2: Explain the functions of hyperlink (objective question), and insert figures and tables as
requested (skill-based task).

19


2.7. Statistical techniques
t Tests, correlation analysis, and the analysis of covariance (ANCOVA) procedure were used to
analyze data. The ANCOVA procedure was used to remove potential sources of bias from the
experiment so that unbiased estimates of treatment effects could be obtained. In the present
study, subjects’ pre-experiment computer self-efficacy, chosen as the covariate, represents one of
the potential sources that must be partitioned out to purify the effects of the experimental variables, the training method and learning style.
The moderating variable, gender, is included because it may have a significant contributory or
contingent effect on the independent-dependent relationship. In the present study, it is posited
that training method and/or cognitive style will have different effects on learning performance
and computer self-efficacy, depending on the subject’s gender.

3. Results
Reliability measures for GEFT, CSE.pre, CSE.post, CSE.net, TEST1, and TEST2 were assessed. The Cronbach a coefficients are 0.75, 0.95, 0.93, 0.85, 0.81, and 0.82, respectively, with all
>0.70. Table 1 provides a correlation matrix for all variables. The matrix displays the strength of
association for the entire group as well as for the individual treatment group.
3.1. Correlation analysis
The significant correlations between TEST1 and TEST2, and among CSE.pre, CSE.post, and
CSE.net proved the validity of those measures. Compared with TEST1, TEST2 has stronger
association with CSE.pre and CSE.post, which implies that people who performed better in
Table 1
Correlation matrix among studied variablesa
Variable

1.

2.

1. TEST1
2. TEST2

–
0.587**

3.

4.

5.

–

0.614**(0.603**)

3. CSE.pre

0.188

0.782**

0.049(0.181)

708**(0.800**)

À0.086

0.072

À0.163

488**

À0.085(0.149)

À0.333**(À0.144)

0.430**(0.478**)

0.080

0.137

0.280**

0.190

À0.091

À0.020(0.173)
a

0.004

0.029(À0.141)

6. GEFT

0.118(0.102)

008(0.011)

5. CSE.net

0.161

0.031(0.109)

4. CSE.post

0.066

0.044(0.3066

0.299**(0.3346*)

0.198(0.276*)

À0.118(À0.034)

–

*)

–
–

The first row is for the entire group, whereas the second row is for instruction group and behavior modeling group
(in parentheses).
*P< 0.05.
**P< 0.01.

20


TEST2 would have higher perception on their computer efficacy. CSE.pre and CSE.post are
strongly correlated with GEFT, which suggests that people who are more field-independent tend
to develop higher computer self-efficacy. An opposite correlation pattern was found between the
two training methods in the following variable sets.
3.1.1. CSE.net and TEST2
The directions of the correlation between the CSE.net and TEST2 in two treatment groups
were opposite (r=0.149 and À=0.085 for behavior-modeling and instruction-based, respectively), although neither was significant. This suggested that in the behavior-modeling group,
students who performed better in TEST2 also scored higher on CSE.net, whereas the instructionbased group students who performed better in TEST2 showed smaller gains in CSE.net.
3.1.2. GEFT and TEST1, TEST2
GEFT has a higher association with TEST1 and TEST2 in the behavior-modeling group
(r=0.173 and r=0.306) but is much weaker in the instruction group (r=À0.020 and r=0.044),
which implies that in the behavior-modeling group, the more field-independent participants performed better in both tests. On the other hand, no significant difference was found between different learning style participants in the instruction-based group.
3.2. Main effects study
The three-way ANCOVA technique was employed to examine whether different training
methods and personal traits would generate significant differences in learning performance and
computer self-efficacy change. The effects of gender on TEST1 [F (1,75)=4.29, MSE=890.1,
P<0.05], and of training method on TEST2 [F (1,75)=5.51, MSEs=609.7, P<0.05) and CSE.net (F (1, 75)=3.29, MSE=647.8, P<0.05] were significant. These partially supported the propositions displayed in the previous section.
Since some main effects were found to be significant, t-tests on the outcome measures by
training condition, gender, and cognitive style were conducted. Table 2 displays gender by training method subgroup means in TEST1, TEST2, and CSE.net. Participants with GEFT scores
greater than 12.32 (grand means) were assigned to the FI group, whereas the remaining participants were assigned to the FD group.
Overall, the behavior-modeling method results in better learning outcomes and FI participants
performed better than FD participants. Male participants obtained higher scores in TEST2,
CSE.pre, and CSE.post, and female participants performed better in TEST1 and CSE.net.
Table 2
Gender by treatment subgroup means
Male

Female

CSE.net
Behavior modeling
Instruction-based

TEST1

TEST2

CSE.net

TEST1

TEST2

12.03
À1.82

58.55
67.50

55.97
56.36

9.71
11.88

80.29
67.50

68.71
37.13


21

3.2.1. Training method effects
Participants in the behavior-modeling group performed consistently better in all of the studied
variables. The differences in TEST2 (t (1,82)=À2.24, P<0.05) and CSE.post were significant
[t (1,82)=À3.34, P<0.00]. The significant differences in CSE.pre [t (1,82)=À2.35, P<0.05]
indicated pre-existing class differences in computer self-efficacy. Nevertheless, the significant difference in CSE.net [t (1,82)=À2.19, P<0.05] implies that participants in the behavior-modeling
group obtained a much higher computer self-efficacy score change than participants in the
instruction-based group. The superiority of behavior modeling in computer learning performance
and computer self-efficacy partially supported H1.
3.2.2. Gender effects
Gender effects were found in all studied variables. Male participants scored significantly higher
on CSE.pre [t (1,82)=4.66, P< 0.00] and CSE.post [t (1,82)=3.12, {P <0.00] whereas female
participants scored higher on CSE.net and TEST1. This suggests that the female participants
learned better due to the experiment, whereas male participants in general rated significantly
higher in computer self-efficacy.
3.2.3. Cognitive style differences
The difference was significant on CSE.pre [t (1, 82)=À2.53, P<0.01] and on CSE.post
[t (1, 82)=À1.98, P<0.05]. This finding is consistent with the literature that suggested people
with FI cognitive style in general tend to academically outperform FD people (Tinajero &
´
Paramo, 1997; Wieseman et al., 1992). Thus, H2 was partially supported.
3.3. Interaction effects study
3.3.1. Two-way interaction
Cognitive style by training method interaction effect was found insignificant on any of the three
dependent variables. Therefore, H3 was not supported. The hypothesized moderating effects of
gender were significant on the relationships between training method and TEST1 [F (1,75)=3.35,
P<0.05], TEST2 [F (1,75)=9.04, P<0.00], and CSE.net [F (1,75)=3.30, P<0.05]. Participants
of different gender would favor different training methods, depending on the training objective.
H5 then was supported.
Concerning TEST1 and TEST2, male participants preferred instruction-based, whereas female
participants favored behavior-modeling conditions. On the other hand, when self-efficacy is the
training objective, instruction-based is more appropriate for female participants, and behavior
modeling is the better approach for male learners.
ANCOVA results showed that the other gender-moderating effects on the causal relation
between cognitive style and dependent variables were not significant. Therefore, H4 was not
supported.
3.3.2. Three-way interaction
The significant three-way interaction effects of gender by training method by cognitive
style on TEST1 [F (1,75)=3.966, P<0.05] partially supported H6. Table 3 provides subgroup
means.

22


Table 3
Gender by treatment by cognitive style subgroup meansa
Male

Female

FD

FI

FD

FI

Instruction-based

n=8
0.88
72.5(61.38)

n=14
À3.36
64.64(53.50)

n=8
6.75
55.0(33.50)

n=8 17.0 80.0(40.75)

Behavior-modeling

n=13
12.69
51.92(50.54)

n=16
11.50
63.94(60.37)

n=8
14.13
82.5(68.75)

n=9 5.78 78.33(68.67)

a
The second row represents group means of CSE.net; the third row represents group means of TEST1 and TEST2
(in parentheses).

Regarding CSE.net, FI male participants preferred the behavior-modeling approach, whereas
their female counterparts favored the instruction-based condition. On the other hand, FD male
participants performed better on TEST1 and TEST2 in the instruction-based condition, whereas
their female counterparts preferred the behavior-modeling method. In addition, the cognitive by
training method interaction effects were most evident for female participants in CSE.net. Female
FI participants preferred the instruction-based condition, whereas FD participants favored the
behavior-modeling approach.

4. Conclusions
The study results showed that while the behavior-modeling training method is superior with
respect to learning performance and computer self-efficacy, the significant two- and three-way
interactions indicate the critical roles that personal characteristics and situation factors play as
joint determinants of behavior. This finding confirms the contingency effects of gender, cognitive
style, training approach, and training objectives on behavior.
Gender did significantly moderate the effects of training method on performance and selfefficacy, as hypothesized. Concerning TEST1 and TEST2, male participants benefited more
from the instruction-based condition, and female participants learned better in the behaviormodeling condition. Nevertheless, the combined effects of gender and training method on computer self-efficacy were reversed, that is, female participants preferred the instruction-based condition, whereas male participants were more suited to the behavior-modeling condition.
Cognitive style by training method interaction effect was not significant on any of the three
dependent variables.
Regarding the three-way interaction, gender did significantly moderate the effects of training
method on TEST1. The three-way interaction indicates that, regarding CSE.net, FI male participants preferred the behavior-modeling approach, whereas their female counterparts favored the
instruction-based condition. On the other hand, FD male participants performed better on
TEST1 and TEST2 in the instruction-based condition, whereas their female counterparts


23

preferred the behavior-modeling method. The cognitive by training method effects were
most significant for female participants in CSE.net. Female FI participants preferred the
instruction-based condition, whereas female FD participants favored the behavior-modeling
approach.
4.1. Limitations
Although the results of this research were clear-cut, the study is not without limitations. Specific weaknesses include the artificial nature of a laboratory setting, the use of students instead of
employed individuals, and a relatively small sample. Although some evidence exists for the
external validity of laboratory research and participant populations, caution is warranted when
applying these results in different settings (Goldstein & Musicante, 1986).
4.2. Recommendations for future research
Recommendations for future research come from the present findings. First, the moderating
influence of software differences on learning outcomes is worth exploring. Another avenue of
research is to understand some aspects of the model that were not supported. Finally, the effects
of different types of cognitive style, as well as other individual differences, on training outcomes is
worth examining. As with any scientific finding, replication is needed in different settings and with
diverse populations. In particular, it would be helpful to test the hypotheses in an organizational
setting to increase the external validity.

References
Baldwin, T. T. (1992). Effects of alternative modeling strategies on outcomes of interpersonal-skills training. Journal of
Applied Psychology, 77(2), 147–154.
Bandura, A. (1986). Social foundations of thought and action: a social cognitive theory. Englewood Cliffs, NJ: PrenticeHall.
Chou, H. W., & Wang, Y. F. (1999). The effects of learning style and training method on computer attitude and performance in WWW page design training. Journal of Educational Computing Research, 21(3), 323–342.
Chou, H. W., & Wang, T. B. (2001). The influence of learning style and training method on self-efficacy and learning
performance in WWW homepage design training. International Journal of Information Management, in press.
Christoph, R. T., Schoenfeld Jr., G. A., & Tansky, J. W. (1998). Overcoming barriers to training utilizing technology:
the influence of self-efficacy factors on multimedia-based training receptiveness. Human Resource Development
Quarterly, 9(1), 25–38.
Coffin, R. J., & MacIntyre, P. D. (1999). Motivational influences on computer-related affect states. Computers in
Human Behavior, 15, 549–569.
Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills.
Information Systems Research, 6(2), 118–143.
Compeau, D. R., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing
technology: A longitudinal study. MIS Quarterly, 23(2), 145–158.
Gist, M. E., Schwoerer, C., & Rosen, B. (1989). Effects of alternative training methods on self-efficacy and performance in computer software training. Journal of Applied Psychology, 74, 884–891.
Gist, M. E., Stevens, C. K., & Bavetta, A. G. (1991). Effects of self-efficacy and post training intervention on the
acquisition and maintenance of complex interpersonal skills. Personnel Psychology, 44, 837–861.

24


Goldstein, I. L., & Musicante, G. R. (1986). The applicability of a training transfer model to issues concerning rater
training. In E. A. Locke, Generalizing from laboratory to field setting (pp. 83–98). Lexington, MA: Lexington.
Grover, V., & Teng, T. C. (1994). Facilitating the implementation of customer-based inter-organizational systems: an
empirical analysis of innovation and support factors. Information Systems Journal, 4, 61–89.
Harrison, A. W., & Rainer, R. K. Jr. (1992). The influence of individual differences on skill in end-user computing.
Journal of Management Information Systems, 9(1), 93–111.
Horton, W. L., Taylor, A. L., Ignacio, A. & Hoft, N. L. (1996). The web page design cookbook (pp. 31–50). New York:
John Wiley & Sons.
Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605.
Knight, C. B., Halpin, G. & Halpin, G. (1997). The effects of learning environment accommodations on the achievement of second graders. Research in the Schools, 3(2), 9–14.
Lee, S. M., Kim, Y. R., & Lee, J. (1995). An empirical study of the relationships among end-user information systems
acceptance, training, and effectiveness. Journal of Management Information Systems, 12(2), 189–202.
Lipsky, S. A. (1989). Effect of field independence/dependence on two textbook notetaking techniques. (ERIC Document Reproduction Service No. ED 311 983).
Liu, M., & Reed, W. M. (1994). The relationship between the learning strategies and learning styles in a hypermedia
environment. Computers in Human Behavior, 10(4), 419–434.
Martocchio, J. J. (1994). Effects of conceptions of ability on anxiety, self-efficacy, and learning in training. Journal of
Applied Psychology, 79(6), 819–825.
Moore, D. M. & Dwyer, F. M. (1992). Effects of color coding on cognitive style. Paper presented at the annual meeting
of the Eastern Education Research Association, Hilton Head, SC.
Murphy, C. A., Coover, D., & Owen, S. V. (1989). Development and validation of the computer self-efficacy scale.
Educational and Psychological Measurement, 49, 893–899.
Olfman, L., & Mandviwalla, M. (1994). Conceptual versus procedural software training for graphical user interfaces: a
longitudinal field experiment. Management Information Systems Quarterly, 18, 405–426.
´
Paramo, M. F., & Tinajero, C. (1990). Field dependence/independence and performance in school: an argument
against neutrality of cognitive style. Perceptual and Motor Skills, 70, 1079–1087.
Rattanapian, V., & Gibbs, W. (1995). Computerized drill and practice: design options and learner characteristics.
International Journal of Instructional Media, 22(1), 59–77.
Simon, S. J., Grover, V., Teng, J. T. C., & Whitcomb, K. (1996). The relationship of information system training
methods and cognitive ability to end-user satisfaction, comprehension, and skill transfer: a longitudinal field study.
Information Systems Research, 7(4), 466–490.
Simon, S. J., & Werner, J. M. (1996). Computer training through behavior modeling, self-paced, and instructional
approaches: a field experiment. Journal of Applied Psychology, 81(6), 648–659.
Snow, R. E. (1991). Aptitude-treatment interaction as a framework for research on individual differences in psychotherapy. Journal of Consulting and Clinical Psychology, 59(2), 205–216.
Snow, R. E., & Swanson, J. (1992). Instructional psychology: aptitude, adaptation, and assessment. Annual Review of
Psychology, 43, 583–626.
´
Tinajero, C., & Paramo, M. F. (1997). Field dependence-independence and academic achievement: a re-examination of
their relationship. British Journal of Educational Psychology, 67, 199–212.
Torkzadeh, G., & Koufteros, X. (1994). Factorial validity of a computer self-efficacy scale and the impact of computer
training. Educational & Psychological Measurement, 54(3), 813–821.
Webster, J., & Martocchio, J. J. (1993). Turning work into play: implications for microcomputer software training.
Journal of Management, 19(1), 127–146.
Wieseman, R. A., Portis, S. C., & Simpson, F. M. (1992). An analysis of the relationship between cognitive styles and
grades: new perspectives on success or failure of preservice education majors. College Student Journal, 26(4), 512–517.
Witkin, H. A. (1964). Origins of cognitive style. In C. Scheerer, Cognition: theory, research, promise (pp. 172–205). New
York: Harper and Row.
Witkin, H. A., & Goodenough, D. R. (1981). Cognitive style: essence and origins. New York: International Universities
Press.


25

Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field-dependent and field-independent cognitive styles and their educational implications. Review of Educational Research, 47, 1–64.
Witkin, H. A., Oltman, P., Raskin, E., & Karp, S. (1971). A manual for the embedded figures test. Palo Alto, CA:
Consulting Psychologist Press.
Whiteley Jr., B. E. (1997). Gender differences in computer-related attitudes and behavior: a meta-analysis. Computers
in Human Behavior, 13(1), 1–22.

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