continuance intention of e-learning platform

206
International Journal of Electronic Business Management, Vol. 8, No. 3, pp.206-215 (2010)
CONTINUANCE INTENTION OF E-LEARNING PLATFORM:
TOWARD AN INTEGRATED MODEL
Cheng-Hsun Ho
Department of Information Technology and Management
Shih Chien University
Taipei (10462), Taiwan
ABSTRACT
Researchers have expended considerable effort in constructing theoretical models, such as
the technology acceptance model (TAM), expectation confirmation model (ECM), and
cognitive model (COGM), that explain and predict user behavior in information system
acceptance and continuance. This study combines these three models and two central
constructs - satisfaction and attitude - into an integrated model. Additionally, this study
integrates the motivation perspective, and the self-determination model (SDM). Although
information system continuance theory has garnered significant attention in previous
research, this study is the first study to examine the motivational factors affecting the
synthesized model, which is a combination of the TAM, ECM, COGM, and SDM.
Analytical results demonstrate that the four models explain and predict e-learning
continuance intention.
Keywords: Technology Acceptance Model, Expectation Confirmation Model, Cognitive
Model, Self-determination Model, E-learning
*
1. INTRODUCTION
The knowledge economy has fostered a
learning revolution. In this era of knowledge
competition, “continuous learning” is essential. The
ability of an enterprise to acquire and apply
knowledge is the key to its success. Hence, in the
foreseeable business environment, winners will be
those firms that effectively exploit learning resources
and are dedicated to continuous learning [3].
Adoption of information systems (ISs) is an
important issue in information management. Previous
researchers, who have explored the factors and
mechanisms associated with user acceptance and
adoption of ISs, established related theoretical
foundations. The Technology Acceptance Model
(TAM) [11] is one of the most common models
applied in research investigating IS adoption and
usage. Many models that are extensions of the TAM
have also been developed and applied widely [30].
Bhattacherjee [5] developed and applied the
Expectation Confirmation Model (ECM) to examine
IS continuance. The ECM was based on the
Consumer
Satisfaction/Dissatisfaction
Model
(CS/DM) [37], a model originally applied in
marketing to investigate consumer repurchase
behavior. By comparison, the TAM is focused more
on user acceptance and adoption of an IS in the early
stage of implementation. The primary tasks of the
*
Corresponding author: [email protected]
ECM are to evaluate continuance intention of users
and their loyalty to an IS. While the TAM is
extensively applied to investigate initial IS
acceptance of users, the ECM is adopted by an
increasing number of studies of user post-acceptance
behavior and IS continuance [34].
A comparison of the theoretical structures and
practical applications of the TAM and ECM identifies
three principal dissimilarities. First, studies applying
the TAM to examine continuance and post-acceptance
behavior [19,27,47,50] place relatively more
emphasis on variables affecting initial acceptance.
Conversely, studies using the ECM focus primarily
on retention and loyalty, arguing that these factors are
central to long-term IS survival and continuance [5].
Second, while the TAM posits that behavioral
intention is affected by attitude, the ECM assumes
that IS continuance is affected most by user
satisfaction. Many studies have noted that satisfaction
and attitude are conceptually different dimensions
[37,51]. Third, the TAM assumes that perceived
usefulness (PU) and perceived ease of use (PEOU)
influence user attitude and behavioral intention. The
two beliefs are strongly associated with explicit
expectations [1,13]. Hence, the TAM measures user
expectation at a single time point to explain and
predict behavioral intention. Based on the CS/DM,
the ECM assumes user satisfaction is strongly
correlated with disconfirmation, which is the gap
between
initial
expectation
and
perceived
performance.
C. H. Ho: Continuance Intention of e-Learning Platform
Oliver [37] developed a cognitive model
(COGM) using both attitude and satisfaction as
intention antecedents. In the COGM, satisfaction is
an antecedent of attitudinal post-exposure. Whether
one can use attitude, satisfaction, and related
variables to develop an integrated model that explains
user continuance intention to use technology is a
primary issue explored in this study.
In the information management field, many
researchers have demonstrated that both extrinsic and
intrinsic motivations influence user acceptance of
information technology [25,46,53]. This study
identified important antecedents of e-learning
continuance and explored how motivation affects user
continuous usage of e-learning platforms using the
self-determination model (SDM), a model rooted in
social psychology. According to Deci and Ryan [14],
motivation is determined by social factors, and these
social factors in turn influence one’s self-determined
motivation through autonomy, competence, and
relatedness. That is the need for autonomy,
competence, and relatedness positively influences
self-determined motivation. This study integrates the
SDM into the proposed model to explain and predict
users’ continuous usage of e-learning platforms.
2. LITERATURE REVIEW
2.1 Technology Acceptance Mode
The TAM is based on the Theory of Reasoned
Action (TRA) [17]. This model uses two perceptions
related to information technology, namely, perceived
usefulness. Liao et al. [34] determined that the TAM
has garnered considerable attention from scholars in
ISs field for at least three reasons. First, the TAM was
built upon a psychological foundation [7,50]. Second,
the TAM is parsimonious; that is, it can be applied
easily to identify] success factors for ISs [55]. Third,
the applicability and reliability of the TAM have been
tested empirically in different settings and using
different samples [55].
The TAM was originally developed to predict
user acceptance of a new technology in the initial
introduction stage. The model views “short-term
interaction with a new system” as a prototype trial or
pre-adoption trial [13]. This view has been applied to
explain user acceptance of word processing programs
[13], graphical systems [54], virtual operating
systems [52], and data search systems [56]. Notably,
many TAM-based studies assume continuance is as
an extension of acceptance behavior [5]. Some
scholars utilized the TAM to validate user adoption
intention. However, the users studied had already
adopted and are using a new information technology
[27,50]. For instance, Taylor and Todd [50]
investigated student usage of a student computing
information resource facility, which was already used
by study participants. Davis [12] focused on the
207
adoption of e-mail systems and word processors by
IBM employees who had used these systems for some
time. Konana and Balasubramanian [29] constructed
a TAM-based technology adoption model for
investors with online investment experience. In all of
these research contexts, participants were experienced
users, not inexperienced users. In fact, these studies
investigated “continuance intention.” Although these
studies clearly distinguished between adoption and
continuance intention, their use of the TAM
demonstrated the possibility of using it to predict user
continuance decisions. Therefore, this study uses the
TAM as a theoretical basis for predicting e-learning
platform continuance.
2.2 Cognitive Model
Many studies have elucidated the difference
between attitude and satisfaction. Although some
studies argued that attitude is synonymous with
satisfaction [31], most agreed that these are s
conceptions. Attitude is one’s perceptual evaluation
of a product or service, while satisfaction is one’s
post-purchase evaluation of a product/service [6].
Hunt [24] noted that attitude is an outgrowth of
emotions, referring to the degree to which one is
pleased or displeased with a product or service;
satisfaction is an assessment of emotions induced by
product or service performance. Oliver [37] argued
that attitude gradually transforms into pre-adoption
expectations and subsequent evaluation of
experience. Hence, if attitude continually influences
and transcends previous experiences, the effects of
satisfaction
will
become
transient
and
experience-specific.
Oliver [37] developed a cognitive model of
satisfaction decisions. This model was employed to
develop the ECM and extensively applied to evaluate
consumer satisfaction and post-purchase behavior.
The COGM defines satisfaction as a function of
expectation and disconfirmation, and expectation is
an attitude antecedent. The COGM clearly describes
the cognitive process of how these variables affect
ultimate success and sustainability of a product or
service during different adoption stages. The model
asserts that the function of initial attitude (t1) consists
of expectation (t1); the function of post-exposure
attitude (t2) consists of attitude (t1) and satisfaction
(t2); the function of disconfirmation (t2) is measured
by disconfirmation of perceived performance; the
function of satisfaction (t2) consists of expectation
(t1) and disconfirmation (t2); and the function of
intention (t2) consists of intention (t1), attitude (t2),
and satisfaction (t2).
2.3 Expectation Confirmation Model
The CS/DM describes the process starting from
formulating product expectations before use to
evaluating product performance after use [37].
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International Journal of Electronic Business Management, Vol. 8, No. 3 (2010)
Bhattacherjee [5] applied this model to investigate IS
continuance and developed the ECM. The ECM
posits that user IS continuance decisions are very
similar to consumer repurchase decisions.
Bhattacherjee [5] claimed that the CS/DM is
controversial and unreasonable in some situations.
First, the CS/DM does not account for the fact that
users may alter their expectations of a product or
service after using, and such change influences the
subsequent cognitive processes of users. This
suggests that the initial expectations consumers have
for a product or service may be colored by first-hand
experience with that product or service. In other
words, consumers adjust their expectations via reality.
Hence, the initial expectations and post-consumption
(ex post) expectations of consumers may differ.
Furthermore, controversies and conflicts may
arise when conceptualizing the satisfaction construct
in the CS/DM. For example, some scholars view
satisfaction, attitude, and emotion as conceptually the
same because all are emotion-based responses [31].
However, Oliver [37] demonstrated that a clear
distinction can be made between satisfaction and
attitude. Satisfaction is one’s transient emotional
response to a product or service, while attitude is a
persistent and long-term emotion-based response.
Further, satisfaction may exist before a product or
service is adopted. Studies using the CS/DM have
proposed widely varying definitions for expectation.
Some viewed expectation as a consumer’s belief in
the characteristics of a product or service and used it
as a performance predictor [58]. However, others
viewed expectation as an individual belief or sum of
beliefs [38].
To effectively predict and explain IS
continuance behavior, Bhattacherjee [5] modified the
CS/DM, such that it fit the IS usage context. The
post-acceptance
model
of
IS
continuance
Bhattacherjee proposed has the following features.
(1) The proposed model focuses only on
post-acceptance variables because the effects of
pre-acceptance variables are already captured within
the confirmation and satisfaction constructs. (2) The
CS/DM examines only the effects of initial
expectations, although user expectations may change
over time. Hence, the proposed continuance model
also considers the effects of ex post expectation. (3)
Ex post expectation is represented in the proposed
model by PU. This representation is consistent with
the expectation definition in the CS/DM as individual
beliefs or sum of beliefs, because PU is a cognitive
belief that is important to IS use, as suggested by the
TAM [12]. Perceived performance is removed from
the ECM because disconfirmation in the CS/DM is
defined as the difference between initial expectation
and perceived performance, and the effects of
perceived performance are captured within the
confirmation and satisfaction constructs.
2.4 Self Determination Model
The SDM [14] is based on cognitive evaluation
theory. The SDM stresses that achievement of a goal
relies on satisfaction of three basic psychological
needs—the needs of autonomy, competence, and
relatedness [15]. From the perspective of its
applicability, these needs in the SDM allow
researchers to identify the social context that
motivates an individual most and increases an
individual’s self-determined motivation by satisfying
the needs of autonomy, competence, and relatedness
[16].
Deci and Ryan [16] demonstrated that an
individual becomes increasingly self-determined and
self-motivated when these three fundamental needs
are satisfied. A lack of satisfaction for any need may
cause negative effects [15]. Empirical studies of an
individual’s psychological needs using the SDM
focused mostly on the needs of autonomy and
competence [21,32,40] and seldom addressed
relatedness. Markus and Kitayama [36] determined
that people in different cultures have strikingly
different concepts of the self. Individuals in Western
cultures value independence. The Western world
typically views individuals as autonomous and
expressive of their uniqueness [36]. Conversely,
individuals in non-Western cultures, particularly
those in Asian cultures, are typically interdependent.
Instead of being independent of society, these
individuals want to be interrelated and maintain
affinity in interpersonal relationships [23].
The SDM assumesthat the needs of autonomy,
competence, and relatedness are generally satisfied in
any culture or developmental stage. One’s
self-determined motivation forms through satisfaction
of these three needs. One will feel increasingly
self-motivated and self-determined when all three
needs are satisfied.
3. METHODOLOGY
3.1 Research Framework
This study used satisfaction and attitude as the
main variables and integrated variables from other
models to create a novel model that explains user
continuance intention to use e-learning platforms. The
research framework and hypotheses were developed
based on the TAM, ECM, COGM, and SDM.
Autonomy
H1
Confirmation
H7
H2
H3
Competence
H4
Relatedness
Motivation
H14
H13
Continuance
Intention
H10
H9
Perceived H12
Easy of Use
Belief
Satisfaction
H8
Perceived
Usefulness
H11
H5
H6
H15
Attitude
Attitude
Figure 1: Research framework
Intension
C. H. Ho: Continuance Intention of e-Learning Platform
3.2 Hypotheses
According to Ryan and Deci [44], autonomy,
competence, and relatedness are important factors
that increase one’s intrinsic and extrinsic motivation,
which in turn enhance one’s job performance. The
effects of autonomy and positively related manifest
variables have been validated. In the research domain
of ISs, Karahanna and Straub [26] proposed that
social factors can influence one’s IS usage through
PEOU and PU. Roca and Gagné [43] viewed
autonomy, competence, and relatedness as exogenous
variables positively related to PU in their study of IS
continuance. The following hypothesis is based on
this study.
H1: User-perceived autonomy positively affects PU
of an e-learning platform.
Sørebø et al. [48] argued that the sense of
competence of e-learning platform users is the degree
to which they desire to self-regulate. Bandura [2]
argued that self-efficacy is not associated with certain
skills, but rather user assessments regarding goal
users believe they can accomplish. Roca and Gagné
[43] demonstrated that this concept is similar to the
need for competence in the SDM. In IS research,
computer self-efficacy is defined as the capability
perceived by an individual to apply his or her
computer skills to accomplish a given task. Many
studies have demonstrated that computer self-efficacy
is positively related to PEOU and PU [8,9,10].
According to Sørebø et al. [48], e-learning platform
users will use an e-learning platform when they feel
competent. Hence, the degree of competence affects
user confirmation of the e-learning platform, i.e., the
gap between initial expectation and perceived
performance. The following hypotheses are based on
these studies.
H2: User-perceived competence positively affects
attitude toward an e-learning platform.
H3: User-perceived competence positively affects
PU of an e-learning platform.
H4: User-perceived competence positively affects
PEOU of an e-learning platform.
Although the SDM states that needs of
autonomy and competence are most important
motivation antecedents, relatedness also plays an
important role [15]. Deci and Ryan [15] asserted that
individuals can be influenced by people related to
them when participating in activities that are
uninteresting. Hence, relatedness is an embodiment of
social effects. Subjective norms in the IS domain
have been utilized to measure social effects. The
findings acquired by Venkatesh and Davis [55]
confirmed that subjective norms influence PU.
Therefore, the following hypothesis is proposed.
H5: User-perceived relatedness positively affects
the PU of an e-learning platform.
209
According to Bhattacherjee [5], a positive
relationship exists between confirmation and PU.
Cognitive dissonance theory also assumes users may
have cognitive inconsistency or anxiety when
disconfirmation of their pre-acceptance perception of
PU exists. The rationale is that users typically attempt
to adjust their perception of pre-acceptance
usefulness, such that it is consistent with
post-acceptance reality. That is confirmation
enhances PU and reduces the occurrence of
disconfirmation. In the CS/DM developed by Oliver
[37], performance can transform into satisfaction
through the confirmation process. Bhattacherjee [5]
also demonstrated that confirmation influences
satisfaction. Thus, the following hypotheses are
proposed.
H6: User-perceived confirmation positively affects
user satisfaction with an e-learning platform
H7: User-perceived confirmation positively affects
the PU of an e-learning platform.
Perceived usefulness, which is derived from
“expectation” and “perceived performance” in the
ECM, includes pre-acceptance PU (t1) and
post-acceptance PU (t2). According to Bhattacherjee
[5], users may adjust their pre-acceptance
expectations to post-acceptance expectations, which
is PU in the ECM, and PU further affects satisfaction.
The TAM proposes that as the degree to which a user
believes an IS is helpful for his or her job increases,
the degree of positivity toward continuance increases.
Bhattacherjee [5] also demonstrated that PU
positively
influences
continuance
intention.
Therefore, the following hypotheses are proposed.
H8: User-perceived usefulness of an e-learning
platform positively affects satisfaction with the
platform.
H9: User-perceived usefulness of an e-learning
platform positively affects attitude toward using
the platform.
H10: User-perceived usefulness of an e-learning
platform positively affects continuance
intention.
Many studies have demonstrated that PEOU is
positively associated with PU [12,22,25,35]. Findings
obtained by Lederer et al. [33], who discussed the
effects of PU and PEOU on attitude toward using
job-related websites, suggested that PU positively
affects user attitude toward website usage. We infer
that PEOU positively affects PU and user attitude
and, thus, the following hypotheses are proposed.
H11: User-perceived usefulness of an e-learning
platform positively affects PU of that platform.
H12: User-perceived usefulness of an e-learning
platform positively affects attitude toward using
that platform.
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International Journal of Electronic Business Management, Vol. 8, No. 3 (2010)
According to the ECM [5], user IS continuance
intention is primarily determined by satisfaction
related to use of that IS. A high degree of satisfaction
can lead to a high degree of continuance intention.
Westbrook [57] determined that satisfaction is
frequently considered a mediator variable of
post-purchase behavior, and pre-choice product belief
affects post-choice perception and repurchase
intention. Oliver [37] confirmed that consumer
satisfaction influences future behavioral intentions
through attitude and directly affects repurchase
intention. Hence, we infer that user-perceived
satisfaction with an e-learning platform positively
affects attitude and continuance intention.
H13: User-perceived satisfaction with an e-learning
platform positively affects attitude toward using
that e-learning platform.
H14: User-perceived satisfaction with an e-learning
platform positively affects continuance
intention.
study selected a well-known, high-traffic website for
online forums, the PPT Bulletin Board System (BBS),
to recruit volunteers. To increase the response rate,
this study informed all participants they had a chance
to win a USB storage stick worth US$30. Survey
information was published on the PTT, the largest
online BBS in Taiwan. Since it was established in
1995, PTT has become the largest Chinese BBS
worldwide. In total, 815 questionnaires were
obtained. As 106 questionnaires were incomplete or
subjects lacked e-learning experience, 709 valid
questionnaires were obtained. Of all respondents,
55% were female, 35.5% were in the 21–30 age
group, and 31.9% were in the of 31–40 age group,
68% had a college or university degree, and 44.3%
had <1 year’s experience with e-learning platforms.
Both Robey [42] and Swanson [49] determined
that attitude affects technology usage. That behavior
is affected by attitude is a view widely shared by
many behavior researchers [41]. According to the
TAM [13], PU and PEOU are utilized to measure an
individual’s attitude toward adopting a technology,
and attitude further influences adoption intention.
Therefore, we hypothesize that user attitude
positively affects continuance intention.
H15: User-perceived attitude toward using an
e-learning platform affects continuance
intention.
The reliability and validity of the measurement
instrument was assessed using reliability and
convergent validity criteria. Instrument reliability was
established by calculating the composite reliability to
measure construct reliability. All values exceeded the
recommended level of 0.5 [18].
Convergent and discriminant validity of the
model were also determined using the procedure
described by Fornell and Larcker [18]. This study
conducted confirmatory factor analysis to test the
convergent validity of each construct; analytical
results indicate that most items had factor loadings
exceeding 0.7. For satisfactory discriminant validity,
the square root of the average variance extracted
(AVE) for a construct should be greater than that of
the correlation between the construct and other
constructs. The measurement model had a high
degree of reliability and convergent and discriminant
validity (Table 1).
3.3 Measurement
Based on the scales developed by Kasser et al.
[28], this study developed 21 items to measure the
autonomy, competence, and relatedness constructs.
Three items, based on the work by Bhattacherjee [5],
were constructed to measure the confirmation
construct. Four items were developed based on the
scales used by Davis et al. [12] for measuring the PU
construct. Four items, also designed according those
used by Davis et al. [13], were used to measure the
PEOU construct. Based on scales introduced by
Bhattacherjee [5], four items were developed to
measure the satisfaction construct. According to
Davis et al. [13], four items were constructed to
measure the attitude construct. Three items,
developed according to the work by Bhattacherjee
[5], were utilized to measure the continuance
intention construct. Responses to all 21 items were on
a seven-point Likert scale, ranging from 1 for
“strongly disagree” to 7 for “strongly agree.”
3.4 Data Collection
The questionnaire was administered online to
people who have used an e-learning platform. This
4. DATA ANALYSIS AND
RESULTS
Table 1: Summary of latent variables
1. AUT
2. COM
3. REL
4. CON
5. PU
6. PEOU
7. SAT
8. A
9. CI
STDDEV
MEAN
CR
1.
2.
3.
1
2
3
4
5
6
7
8
9
0.787
0.708
0.383
0.650
0.756
0.632
0.685
0.669
0.709
0.777
5.816
0.830
0.749
0.346
0.675
0.704
0.712
0.715
0.692
0.704
0.755
5.803
0.792
0.709
0.434
0.458
0.429
0.400
0.389
0.338
0.893
4.767
0.744
0.831
0.752
0.692
0.755
0.655
0.648
0.840
5.531
0.870
0.824
0.653
0.755
0.736
0.727
0.775
5.764
0.864
0.715
0.650
0.687
0.668
0.786
5.610
0.757
0.883
0.786
0.792
0.767
5.842
0.914
0.883
0.785
0.794
5.988
0.914
0.875
0.788
6.018
0.867
Diagonal elements are the square root of average variance
extracted. These values must exceed inter-construct
correlations for adequate discriminant validity.
All correlations are significant at the 0.001 level (two-tailed).
AUT, autonomy; COM, competence; REL, relatedness; CON,
confirmation; PU, perceived usefulness; PEOU, perceived
ease of use; SAT, satisfaction; A, attitude; CI, continuance
intention; CR, composite reliability.
C. H. Ho: Continuance Intention of e-Learning Platform
The structural model was tested using AMOS, a
software package designed to apply the structural
equations model approach during path analysis. To
assess how well the model represented data, this
study evaluated the following six goodness-of-fit
indices: the Normed chi-square (NC); goodness-of-fit
index (GFI); adjusted goodness-of-fit index (AGFI);
normal fit index (NFI); comparative fit index (CFI);
and, the root mean square error of approximation
(RMSEA). Analytical results demonstrate that the
research model had a good fit to data. The NC was
4.57 (should be <5) [4], the remaining four indices
were GFI=0.90, AGFI=0.86, NFI=0.92, CFI=0.92,
and RMSEA=0.07. The AGFI should be >0.8 [45],
the GFI, NFI, and CFI should be >0.9, and the
RMSEA should be <0.1 [20]. Therefore, we conclude
that
the
goodness-of-fit
indices
exceeded
recommended levels, suggesting that the research
model had a good fit to data.
Figure 2 shows the standardized path
coefficients for the research model. Most paths were
significant in the expected direction. The exception
was the path connecting PEOU with PU. Analytical
results indicate that autonomy was positively
correlated with PU; thus, H1 (β=0.36, p<0.001)
holds. Consistent with H2 (β=0.75, p<0.001), H3
(β=0.16, p<0.001), and H4 (β=0.74, p<0.001),
competence was positively correlated with
confirmation, PU, and PEOU. Additionally,
relatedness was positively related to PU; thus, H5
(β=0.09, p<0.001) holds. Confirmation was positively
correlated with both PU and satisfaction; thus, H6
(β=0.39, p<0.001) and H7 (β=0.32, p<0.001) holds.
As expected, H8 (β=0.43, p<0.001), H9 (β=0.18,
p<0.001), and H10 (β=0.25, p<0.001) were also
supported; that is. PU was associated with
satisfaction, attitude, and continuance intention.
Contrary to predictions, H11 (β=0.03, p=0.32), stating
that PEOU is positively related to PU, was not
supported by data. Hypothesis 12, positing that
PEOU positively influences attitude, was supported.
The hypothesized effects of satisfaction on attitude
and continuance intention were confirmed, thereby
supporting H13 (β=0.47, p<0.001) and H14 (β=0.39,
p<0.001), respectively. As proposed in H15 (β=0.35,
p<0.001), continuance intention positively affected
attitude. The R2 values for confirmation (0.46), PU
(0.69), PEOU (0.51), satisfaction (0.63), attitude
(0.66), and continuance intention (0.69) were high.
Autonomy
.36***
.75***
.16***
Competence
.74***
Relatedness
.09***
Confirmation
.39***
Satisfaction
R 2=0.46
.32***
Perceived
usefulness
R2=0.63
.43***
.47***
.18***
R 2=0.69
.03
Perceived
easy of use
R 2=0.51
.39***
Continuance
intention
R2=0.69
.25***
.24***
Attitude
.35***
R2=0.66
Figure 2: The SEM analysis of the research model
211
5. CONCLUSION AND
IMPLICATIONS
The first goal of this study was to develop a
novel integrated model that characterizes e-learning
continuance intention. Thus, this study integrated the
TAM, ECM, and COGM. These three models have
different suppositions about underlying constructs
dictating user behavior. Generally, analytical results
indicate that all constructs have excellent explanatory
power. Notably, PU was the major antecedent of user
satisfaction, attitude and continuance intention.
Likewise, satisfaction and attitude significantly and
positively affected continuance intention. User
satisfaction was formulated as a direct function of
confirmation. Although satisfaction had a somewhat
transient effect on intention, it can still result in
rejection or unwillingness to continuously use an
e-learning platform. The success of an e-learning
platform is also determined by user attitude. In
theory,
attitude
is
derived
from
user
post-expectations, which are consequences of
cognitive dissonance and assimilation. The lack of a
significant relationship between PEOU with PU is in
conflict with findings obtained by Davis [12].
However, no previous studies integrated the TAM,
ECM, COGM, and SDM. The correlation between
PEOU and PU was relatively strong (r=0.653) (Table
1). However, when autonomy, competence, and
relatedness are controlled, this correlation decreases
to approximately nil. One possible explanation may
be the differences in stages of the adoption lifecycle
[34]. Can it be that different use experiences have the
potential to neutralize the impact from each other?
This post hoc explanation may be used as a basis for
future research.
Managers and trainers must take note of the
finding that both satisfaction and attitude are
important determinants of usage intention.
Satisfaction is considered a more transient factor than
attitude because it is the outcome of an evaluation of
pre-consumption attitude and, therefore, is
experience-specific. Attitude is defined as the
outcome of an individual’s overall evaluation of a
product or service. Therefore, attitude is more
enduring, transcending all prior experiences. The
factors affecting satisfaction and attitude differ and
must be focused on by management to ensure
e-learning platform success. Accordingly, platform
design and training programs must emphasize
different components to enhance both user
satisfaction and attitude.
The second goal of this study was to examine
the ability of the SDM to explain the role of
motivation to continue using an e-learning platform.
As expected, findings support all of five hypotheses,
H1–H5, related to the SDM in the extended
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e-learning continuance model. Among the three basic
psychological needs, competence is the most
important for the extent of confirmation. The reason
may be that competence can make expectations
increasingly realistic and usage increasingly efficient.
When realistic expectations meet efficient usage, a
high level of confirmation may be a likely outcome.
This study provides support for the critical roles of
user autonomy, competence, and relatedness.
Analytical results demonstrate that these three needs,
autonomy, competence and relatedness, are
significant determinants of e-learning continuance
variables.
From a theoretical perspective, this study takes
an initial step toward extending and validating the
e-learning continuance model using the SDM, TAM,
ECM, and COGM. This study investigated a limited
number of variables to elucidate e-learning platform
continuance intention, and successfully integrated
four models. Future research is required to fully
elucidate the roles of other important variables, such
as subjective norm, that improve or undermine user
continuance intention.
From a managerial perspective, findings have
important implications for managing e-learning
platforms. Managers should promote autonomy,
competence and relatedness for users to increase
continuance intention for an e-learning platform. User
interest, effort, and learning performance will
increase. Most importantly, user competence is
critical to user confirmation, PU, and PEOU,
indicating that user training and support are required
investments for e-learning platforms.
The limitations of the study include those
commonly associated with surveys. Pinsonneault and
Kraemer [39] identified several weaknesses in
survey-based studies. While proposing an integrated
model for e-learning continuance, this model requires
further testing and validation Future investigators can
evaluate the proposed model in similar and different
contexts.
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ABOUT THE AUTHOR
Cheng-Hsun Ho is a lecturer in the Department of
Information Technology and Management at Shih
Chien University, Taiwan. He received his Ph.D.
degree in Business Administration at National Taipei
University.
(Received May 2010, revised August 2010, accepted
September 2010)
C. H. Ho: Continuance Intention of e-Learning Platform
數位學習平台持續使用意圖之研究-整合模型觀點
何政勳
實踐大學資訊科技與管理學系
台北市大直街 70 號
摘要
在知識經濟時代中，「持續學習」已成為重要的成功關鍵之一。企業持續地對知識的
吸收與應用之能力已成為經營成功與否的關鍵，真正的贏家將是善用學習資源持續進
行學習者。資訊系統的採用在資訊管理領域中已發展成為重要的研究議題之一，先前
研究者對於使用者採用的決策因素及機制已有廣泛地探索及解釋，並建立了重要的理
論基礎。研究者認為採用的決策流程會影響資訊系統使用的成功與否，所提出的資訊
系統持續使用模型研究，便指出資訊系統最終的成功要件中，瞭解使用者的長期持續
使用比第一次的使用更加重要。本研究主要目的為整合資訊系統持續使用模型，包含
TAM、ECM及COGM等，並採納社會學派動機理論的SDM，彙整出一個新的數位學
習持續使用模型。其次，進一步探討不同使用階段的使用者對於持續使用決策之差
異。透過實證調查，並以結構方程模式技術分析，新的整合模型獲得支持；此外不同
使用階段對使用者在持續使用決策上有差異，亦獲實證支持。本研究提出的模型，藉
由動機、信念、態度至行為意圖之間關係的實證與解釋，可以提供實務界於推行數位
學習平台時，規劃適當的管理政策及措施。特別是指使用者在不同的數位學習平台使
用階段，使用者對於數位學習平台的使用決策會隨著實際使用經驗改變而產生變化。
因此，在不同的使用階段採用相同或錯誤的管理戰術，將可能導致負面的結果，或是
降低數位學習平台的有效性。本研究可供企業規劃不同使用階段之數位學習管理戰術
時，作為參考。
關鍵詞：科技接受模型、期望確認模型、認知模型、自我決定模型、數位學習
（*聯絡人：[email protected]）
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