Understanding Continuance Intention in E-Learning Community
Khet Khet Oo Tha
Yu Xiaohui
HT060973J
HT060997W
Abstract. The continuous growth of e-learning community has drawn a lot of attention from
IS researchers. Based on irretrievable investments and IS continuance model, we propose a
theoretical model employing reputation, trust, social ties, perceived usefulness and
satisfaction to explain individuals’ continuance intention in an e-learning community. The
research model was tested with the current users of an e-learning community (Integrated
Virtual Learning Environment). Reputation, social ties are found to be significant, while trust,
perceived usefulness and satisfaction was not. Implications for theory and practice are drawn
and discussed.
1. Introduction
With the rapid development of the technology and moving learning material into the web
environment, e-learning becomes popular and plays an important role in education area.
However, there is not a specific pedagogical approach in the context of e-learning; mostly
individuals need to learn by themselves. Unfortunately this kind of learning mechanism
restricts learners’ exchanges, influencing the quality of learning effects.
E-learning community refers to a group of individuals engaged intentionally and collectively
in the transaction or transformation of knowledge which is fully or partially delivered,
enabled, or mediated by electronic means (Kowch and Schwier 1997). E-learning community
provides a useful platform for learners to exchange knowledge, share good practices with
each other, thereby enhancing individuals’ learning effectiveness (Hardaker and Smith 2002).
Universities and Schools have initiated e-learning communities to encourage new forms of
learning, where technology is used for more active, interactive and collaborative learning,
whether in a distance learning or classroom-based environment (Teo et al 2003). IT makes
possible an entirely new environment and experience of learning that goes well beyond the
classrooms, curricula, and text-based formats to which we are accustomed.
However, like any other information system, the success of e-learning depends largely on
user satisfaction and other factors that will eventually increase users’ intention to continue
using it.
Past research has found that most electronic communities are facing the problems with
retaining members and motivating them for active participation (Sangwan 2005). In the case
of e-learning community, what factors affect learners to continue to use is the most important
issue concerning the sustainability of the virtual learning environment. Therefore, we intend
to address the following research question in this paper:
What are the key factors that influence individuals’ continuance intention in an elearning community?
Unfortunately, only few researches have been examining continuance intention in e-learning
context. Most of today’s studies on e-learning are focused on the technology issues, such as
developing e-learning approaches and mediums to improve the effectiveness of virtual
learning environment. In order to fill this research gap, we will develop a theoretical research
model based on irretrievable investments, and IS continuance model to determine the factors
influencing the member’s intention to continue in e-learning communities.
This paper is organized as follows: Section 2 reviews the key literature on e-learning
community, knowledge sharing and continuance behavior; Section 3 discusses the theoretical
foundations used for this study based on sunk cost theory and IS continuance model; Section
4 details the research methodology used in this study; Section 5 describes the statistical
techniques and presents the results of hypothesis testing and Section 6 discusses the findings,
draws implications for further research and practice followed by the conclusion.
2. Review of Related Work
E-learning community is an important part of virtual learning environment. Virtual learning
environment (VLE) is designed to facilitate students’ learning activities, along with the
provision of content and resources required to help make the activities successful.
It brings benefits for both educators and students. For educators, it can help them accumulate
knowledge, know what the problem among students, thereby improving the teaching quality.
For students, it provides a good platform to them to communicate with each other, and
enhance learning through collaboration. Students also cultivate intimate relationships among
themselves—something that may not be easily done in the physical world (Teo et al 2003).
These benefits enhance an individual’s self-worth, sense of belonging and overall level of
happiness. In the reality, students tend to accept this e-learning community easily and share
knowledge to it due to enthusiasm, but fail to sustain on it. So it is important to understand
what factors influence students’ continuance intention to use e-learning community.
Based on a comprehensive literature review, we found that literatures related with our
research question could be categorized into the following two groups. First, factors affect
individual’s willingness to share knowledge, such as costs and benefits, incentive systems,
extrinsic and intrinsic motivation, organization climate, and management championship (e.g.,
Bock and Kim, 2002; Bock et al., 2005; Kankanhalli et al., 2005; Wasko and Faraj, 2005).
Second, some researches deal with IS continuance behavior. Continuance is not entirely an
alien concept in IS research. Studying IS continuance is of an organizational interest since IS
continuance at the individual user level is central to the survival of the organizations
(Bhattacherjee 2001). Information system success depends on whether users are willing to
continue to use it. A lot of attentions have been paid on information system continuance in
recent years (e.g., Bhattacherjee 2001; Hsu et al. 2004; Tiwana and Bush 2005).
Bhattacherjee (2001) developed an IS continuance model in line with the expectation
confirmation theory and presented a new set of variables specific to the IS post adoption,
arguing there are substantive differences between adoption and continuance behavior.
Researchers have done some work to examine continuance behavior in various contexts. Teo
et al (2003) evaluate the effects of information accessibility and community adaptivity
features on the sustainability of virtual learning community; He and Wei (2006) draw upon
the user acceptance theory, continuance of IS and social capital theory to examine the
knowledge management systems continuance in organizations; Hsu et al (2004) examines
motivational factors influencing one’s intention to continue using WWW application based
on the theory of social cognitive theory and expectancy disconfirmation theory; Employing
sunk cost theory, Tiwana and Bush (2005) investigate the continuance in expertise-sharing
networks; Based on IS success model of DeLone and McLean (2002), Clay et al (2005)
examined the factors affecting the loyal use of knowledge management systems; Chen (2007)
addressed the importance of contextual factors and technological factors on the continuance
intention in professional communities.
Despite the importance of virtual learning environment, little is understood as to what factors
are critical to the sustainability of e-learning community. In order to fill this gap, we draw on
irretrievable investment theory and IS continuance model to examine the individuals’
continuance intention in an e-learning community.
3. Conceptual Foundation and Hypothesis
Sunk cost theory states that individuals incline to persist in activities in which they have
already invested considerable irrecoverable resources (P88, Tiwana et al 2005). Irretrievable
investments refer to any irrecoverable resources that are invested previously such as time,
effort, attention by using the virtual community. Based on a four-year observational study
and empirical research, Tiwana et al (2005) point out that reputation and relational capital are
significant factors influencing individuals’ continuance intention to engage in expertisesharing networks.
Expectations-confirmation theory posits that expectations, coupled with perceived
performance affect post-purchase satisfaction, thus leading to repurchase intention.
IS continuance refers to the behavior patterns reflecting continued use of a particular
IS. Bhattacherjee (2001) suggests that the IS users’ continuance decision is similar
to consumers’ repurchase decision and adapts Expectation-Confirmation Theory
(ECT) to theorize and validate that intention to IS continuance is strongly predicted
by user’s satisfaction, with perceived usefulness as a second predictor. Many
researches provide evidence that satisfaction is a significant factor influencing continuance
behavior (Ives et al., 1983; Delone and McLean, 1992).
Based on the above two theories, we proposed a theoretical research model as shown in
Figure 1.
Reputation
H1
H2
Trust
Continuance
Intention
H3
Social Ties
Perceived
Usefulness
H4
H6
H5
Satisfaction
Expected
Rewards
Figure1- Research model
Reputation
Reputation refers to an individual user’s recognition as a valuable member among the peers
of the virtual community (Tiwana and Bush 2003). There are three important elements
underlying this conceptualization: 1) it is a characteristic ascribed to an individual by others
in the virtual community; 2) it is a measure of an individual’s status compared to others in the
community; 3) it is socially constructed over time (Tiwana and Bush 2003).
Because building reputation takes both time and effort, individual users of an e-learning
community value their reputation which can make it easy to access other users’ knowledge,
increase credibility of oneself, they exhibit reputation-preserving behavior, thereafter are
likely to continue sharing knowledge. It has been reported that users in an expertise-sharing
network system intend to safeguard their reputation by continuing participating in it (Tiwana
and Bush 2003). Therefore, we hypothesize that:
H1: Individual user’s reputation among peer users of e-learning community is positively
associated with their continuance intention to use e-learning community.
Trust
Trust is the belief that another individual makes efforts to fulfill commitments, is honest, and
does not seek to take unfair advantage of opportunities (Quigley et al 2007). Trust has been
found to facilitate knowledge sharing in a variety of contexts. Since e-learning community is
a tool for people to exchange knowledge, trust is a prerequisite for people to share knowledge
with each other. In order to access other individuals’ useful knowledge, especially tacit
knowledge, individuals must cultivate trust between himself/herself and the other members.
In order to build trust, individuals might devote time and effort to share useful knowledge
with members which is not irretrievable in an e-learning community. According to sunk cost
theory, individuals tend to sustain engagement which they have invested in irretrievable
investment. Therefore, we hypothesize that:
H2: Trust among peer users of e-learning community is positively associated with their
continuance intention to use e-learning community.
Social Ties
Theories of collective action and social capital propose that social ties, which are
created through the social interactions between individuals in a virtual community,
are important predictors of collective action. If there are strong ties between
members, collective action is relatively easy to achieve (Wasko and Faraj 2005).
Individuals who have strong social ties with the members in a virtual community are
likely to develop the habit of cooperation. In an e-learning community individuals need to
build social ties in order to access other’s knowledge. Thus, they are likely to exhibit
continuance intention after making value-enhancing irretrievable investments. Hence,
we hypothesize that
H3: Social ties among peer users of e-learning community are positively associated with their
continuance intention to use e-learning community.
Perceived Usefulness of the system
In the context of e-learning community, perceived usefulness is the degree to which a person
believes that a particular system will enhance his or her job performance (i.e., by reducing the
time to accomplish a task or providing timely information). According to TAM, usefulness of
the system is an important construct influencing user’s intention to adopt a system.
Although the usefulness- intention was originally developed in an adoption context, it is
likely to hold true since people intend to pursue instrumental behavior independent of timing.
It is showed that usefulness can significantly influence individual user’s continuance
adoption of an information system (Bhattacherjee 2001). Besides, empirical studies show
that usefulness impacts attitude substantively and consistently during both pre-acceptance and
post-acceptance stages of IS use. Perceived usefulness is the most salient ex-post expectation
influencing users’ post-acceptance satisfaction (Bhattacherjee 2001). Therefore, we
hypothesize that
H4: Perceived usefulness is positively related to individual user’s continuance intention to
use the e-learning community.
H5: Perceived usefulness is positively related to individual user’s satisfaction with the use of
e-learning community.
Satisfaction
Satisfaction is defined as an emotional affect toward the outcome of using an e-learning
community. According to IS continuance model, satisfaction is the key determinant of IS
continuance use. Individual users who contribute knowledge to an e-learning community can
be motivated when their needs (expectation) are satisfied. Based on empirical findings,
Bhattacherjee (2001) showed that there is a positive association between satisfaction and IS
continuance intention. Therefore, we hypothesis that
H6: Individual user’s satisfaction with the e-learning community is positively related with
their continuance intention to use it.
Anticipated Extrinsic Rewards
Many researchers show that anticipated extrinsic rewards could affect individuals’ intention
to share knowledge (e.g. Ewing et al 2001; Bock et al 2005). It is plausible to assume that
anticipated extrinsic rewards can also influence subsequent continuance decisions. In the
context of e-learning community, learners can expect assessment in terms of scores for their
performance.
4. Research Methodology
The survey methodology is used to collect data for empirically testing the research hypothesis.
This methodology is chosen because it aims to enhance the generalizability of results (Dooley,
2001). The unit of analysis in our research is the individual user of the IVLE (Integrated
Virtual Learning Environment) at National University of Singapore.
4.1
Scale Development
It is recommended that researchers should reuse existing quality questions designed by others
for the same construct (Xu et al 2006). To test our research model, the seven constructs were
measured using multiple-item scales that were drawn from pre-validated measures in IS or
KM literature and reworded appropriately for our study.
To ensure that items reflect the intended construct, content validity should be checked first.
Assume that all the possible manifestations of a construct collectively form the population of
questions, content validity is the degree that the questions used in survey for a construct
provide a representative coverage of the population. The questions we used were to a large
degree the rephrasing of different aspects of a construct as defined in the literature. This
provided the basis for content validity. In addition, we request one professor of IS
department, NUS to review the phrasing of the questions to ensure that the questions had at
least face validity. Minor changes were made based on the feedback.
Satisfaction items were measured using seven point semantic differential scales. All the other
remaining scale items used seven-point Likert scales that ranged from 1 (strongly agree) to 7
(strongly disagree). The survey instrument and sources are shown in Appendix.
The scales measuring the reputation were adapted from Constant et al. (1996) using three
items. Trust was assessed with items adapted to reflect an individual's beliefs in other
members' non-opportunistic behavior, truthfulness, and behavior consistency, following prior
studies (Tsai & Ghoshal, 1998; Mcknight et al., 2002). Items for measuring social interaction
ties focus on close relationships, time spent in interacting, and frequent communication with
other members, similar to those applied by Tsai and Ghoshal (1998). Perceived usefulness
items were adapted from Davis et al. (1989) six-item perceived usefulness scales. We
modified it and used seven-point Likert type. Satisfaction items were measured using
Bhattacherjee’s (2001) four-item semantic differential scale with bipolar anchors.
Continuance intention was measured by the items adapted from Bhattacherjee (2001). The
initial items measured respondents’ intention to continue e-learning as opposed to using any
alternate method, such as traditional learning environment.
The fourth item assessed
respondents’ overall discontinuance intention (worded negatively to control for potential
common-method bias). Reward item measured as a control variable was self-developed in the
context of e-learning community.
Table 1 Formal Definitions of Constructs
Construct
Reputation
Trust
Social Ties
Satisfaction
Perceived Usefulness
Continuance Intention
Reward
4.2
Operational Definition
Online user’s recognition as a valuable member by the peer group of users
in an e-learning community
The belief in the good intent, competence and reliability of others with
respect to engaging in an e-learning community
The degree of intensity of user’s connection with others in an e-learning
community
Online user’s affect with the e-learning experience
Online user’s perception of the expected benefits using the e-learning
community
Online user’s intention to continue using the e-learning community
The degree to which one believes that one can have extrinsic incentives
due to engagement in the e-learning community.
Data Collection
A pilot test is carried out to verify the instrument. We collect a convenience sample from the
class of Quantitative Analysis and some peers from the research labs at School of Computing.
After receiving all available questionnaires, cronbach’s alpha and factor analyses are
conducted to confirm the stability and validity of all the constructs in the questionnaire (Teo
et al., 2003).
Main test is conducted in order to validate our research model. A convenience sample is used
for our research. The sample includes 84 graduate students from School of Computing. First,
we conduct a survey in e-government class. Students in this class are encouraged to use IVLE
to share knowledge with each other. Each respondent was paid S$7 for the participation. 49
questionnaires are obtained from this class and all are valid. We also send the questionnaire
to other students who have the experience using IVLE. We get another 35 valid responses.
This study recruits student subjects since the context of the research is e-learning community.
Our subjects (SOC students) have similar profile on exchanging knowledge through IVLE as
a means of learning mechanism; hence we can expect that the result of this study will provide
adequate predictability and generalizability to determine the factors influencing the
continuance intention to use the e-learning community.
5. Data Analysis and Results
Data analysis is carried out in accordance with a two-stage methodology – the measurement
model and the structural model (McDonald and Ho, 2002) using LISREL.
The first step in the data analysis is to establish the convergent and discriminant validity of
the constructs. We test the measurement model using Principal Components Analysis (PCA)
and Confirmatory Factor Analysis (CFA). In the second step, the structural model is
examined based on the cleansed measurement model.
5.1. Pilot Study
In order to verify the questionnaires, exploratory factor analysis (Nannally & Bernstein, 1994)
was conducted to test the convergent and discriminant validity of the instrument based on the
84 questionnaires.
We first performed exploratory factor analysis using the data on all items for the various
construct scales. As shown in the table, the analysis revealed seven underlying factors with
eigenvalue greater than 1.0, corresponding to the seven main constructs in our study. All
constructs explain 77.94% of the total variance. The overall factor solution has an acceptable
loading pattern as each item and the intended construct correlation is greater than 0.5 and the
unintended construct correlation is less than 0.4 thus satisfying both convergent validity and
discriminant validity. All the items showed appropriate validity and are kept for the main
study. Table 2 reports the principal component analysis results with Varimax rotation using
SPSS15.0.
5.2. Main study
Evaluating the Measurement Model
The purpose of measurement modeling is to further ensure instrument quality. Confirmatory
factor analysis is used to test the relationship for measurement model. In this method, items
are expected to be highly correlated with the intended construct only. Otherwise, the
instrument needs to be adjusted.
Table 2 – Factor loading table for exploratory factor analysis of pilot data
Construct
Items
1
Social Ties
Perceived
Usefulness
Trust
Reputation
Continuance
Intention
Satisfaction
Reward
2
Component
3
4
5
6
.756
.087
.115
.267
.059
.072
Ties1
.800
.132
.023
.039
.095
.215
Ties2
.776
.150
.213
.038
.142
.122
Ties3
.822
.018
.163
.014
.138
.162
Ties4
.060
.876
.139
.082
.253
.118
PU1
.181
.869
.181
.179
.148
.103
PU2
.134
.879
.177
.222
.189
.182
PU3
.188
.280
.642
.203
.235
-.136
Trust1
.177
.062
.795
.235
-.018
.036
Trust2
-.054
.047
.699
.171
.310
.207
Trust3
.217
.240
.815
-.080
.029
.139
Trust4
.111
.002
.064
.796
.243
.251
Rep1
.084
.201
.191
.854
.165
.046
Rep2
.127
.313
.157
.796
.156
-.013
Rep3
.232
.261
.310
.296
.687
.092
Cont1
.332
.263
-.055
.174
.541
.118
Cont2
.347
.057
.022
.335
.740
.258
Cont3
-.041
.321
.225
.096
.703
.144
Cont4
.233
.079
.127
.013
.213
.805
Sat1
.260
.432
.258
.220
.056
.539
Sat2
.226
.202
-.014
.156
.119
.806
Sat3
-.021
.000
.045
.098
.058
.040
Rew1
8.114
2.197
1.835
1.629
1.206
1.133
Eigenvalue
36.883 9.985
8.340
7.403
5.484
5.149
Variance %
36.883
46.869
55.209
62.612
68.095
73.244
Cumulative Variance %
7
-.065
.049
.048
-.068
-.025
.045
-.012
.037
.110
-.307
.059
.199
.037
-.099
-.152
.203
.163
-.044
.020
-.325
.083
.937
1.012
4.493
77.737
If the following requirements are satisfied, the convergent validity of the items is considered
to be confirmed. First, the construct-item correlation should be significant. Second, for an
item, the average variance extracted (AVE) by the latent factor should be greater than 0.5,
which means that a construct should explain more than 50% of the item variance. Third,
items of the same construct should be highly correlated. To measure such correlations, two
measures- composite factor reliability (CFR) and Cronbach’s alpha (α) are required to be
greater than 0.7. Table 2 reports the convergent validity for our sample using LISREL v8.5.
All criteria are satisfied.
One method to confirm discriminant validity is to check that inter-construct correlation is less
than the square root of AVE. The underlying rationale is that an item should be better
explained by its intended construct than by some other constructs (Xu et al 2006). Table 3
shows the correlation among constructs. It shows that discriminant validity is satisfied.
Besides, we also check the collinearity between the constructs, it shows that there is no
multicollnearity between constructs.
Finally, we check the goodness-of-fit of the overall CFA model. Table 4 lists all the fitting
indices we have examined. It shows that GFI and AGFI are near to 0.8, although less than 0.9;
NFI is near to 0.9; all the other indices satisfy the criterion. However, as some researchers
suggest that the sample size could influence the GFI, so we accept the value of GFI and rely
on other indices to measure the model fit. From the results, we can see that model fitting
indices are satisfactory.
Table 3 - Convergent Validity of Measurement Model
Construct
Item
Std
Loading
T-value
AVE
α
Trust
Trust1
0.70
6.77
0.52
0.861
Factor
Composite
Reliability
0.81
Trust2
0.74
7.18
Trust3
0.66
6.27
Trust4
0.78
7.73
Rep1
0.78
8.06
0.69
0.809
0.76
Rep2
0.93
10.54
Rep3
0.78
8.18
Ties1
0.72
7.22
0.60
0.855
0.86
Ties2
0.78
7.99
Ties3
0.80
8.32
Ties3
0.80
8.25
Sat1
0.67
6.37
0.55
0.780
0.67
Sat2
0.80
8.01
Sat3
0.74
7.15
Continuance Cont1
0.87
9.45
0.54
0.82
0.82
Intention
Cont2
0.62
6.13
Cont3
0.80
8.44
Cont4
0.63
6.17
Perceived
Pu1
0.88
10.07
0.87
0.950
0.85
usefulness
Pu2
0.92
10.84
Pu3
0.99
12.55
Reputation
Social Ties
Satisfaction
Table 4 - Correlations and Square Root of AVE
Rep
Trust
Ties
Sat
Cont
PU
Mean
13.64
18.99
14.05
14.26
18.24
13.05
S.D.
4.097
3.639
4.734
2.528
4.032
3.580
Min.
1
1
1
1
1
1
Max.
7
7
7
7
7
7
Rep
0.83
0.45
0.32
0.30
0.63
0.5
Trust
Ties
Sat
Cont
PU
0.72
0.45
0.31
0.54
0.50
0.77
0.23
0.51
0.37
0.74
0.52
0.61
0.73
0.61
0.93
Table 5 - Model Fit Indices for Measurement Model
Model Fit Indices
Results
Recommended Value
x 2 df
1.56
<3
GFI
0.76
>0.9
NFI
0.88
>0.9
CFI
0.95
>0.9
RMSEA
0.082
<0.1
NNFI
0.94
>0.9
AGFI
0.69
>0.9
IFI
0.95
>0.9
Evaluating the Structural Model
Before we test the hypothesis, we must insure that the model fits the data well. A few model
fitting indices are used to measure model fitting. The goodness-of-fit index of 0.76 and AGFI
were not satisfactory. However, since researchers suggest that when the sample size is less
than 200, the goodness of fit index might reject a good model (Bearden et al 1982). Therefore,
we relied on other indices to measure the fitness of the model. NFI is close to the cutoff value
(0.9). All the other indices were above the acceptance value .Table 4 shows the model fit
indices for the structural model. It shows the model generally fit the data.
Hypothesis testing was done in LISREL by creating an equation model. First, control
variables are excluded from the model. Figure 2 summarizes the result of hypothesis testing.
Three of the six hypotheses were supported. Reputation and social ties show a positive effect
on continuance intention. Consequently, Hypothesis 1 and 3 is supported empirically. While
trust and perceived usefulness are not significant factors of continuance intention. So
hypothesis 2 and 4 are not supported. However, perceived usefulness exhibits a positive and
strong effect on continuance intention, so hypothesis 5 is supported. Satisfaction was not
significant at p=0.05 level but significant at p=0.1 level.
Table 6 - Model Fit Indices for the Structural Model
Model fit indices
Results
Recommended value
x 2 df
1.56
<3
GFI
0.76
>0.9
NFI
0.88
>0.9
CFI
0.95
>0.9
RMSEA
0.082
<0.1
NNFI
0.94
>0.9
AGFI
0.69
>0.9
IFI
0.95
>0.9
Reputation
0.36
(p=0.002)
0.13
(p=0.29)
Trust
Continuance
Intention
R 2  0.63
0.24
(p=0.03)
Social Ties
Perceived
Usefulness
0.13
(p=0.33)
0.61
(p=0.000)
0.23
(p=0.08)
Satisfaction
Expected
Rewards
 2 =276.03, df = 177, p= 0.0000
Figure 2 - Standardized LISREL Solution for Hypothesis Testing
Finally, we add expected rewards as control variable to the model. It was not significant
(   0.06, p  0.51 ). There are no significant changes to the main factors.
6. Discussion and Implications
6.1.
Summary of Data Analysis
This study helps us to understand continuance intention in e-learning community. Based on
irretrievable investment theory and IS continuance model, we examine effects of reputation,
social ties, trust, perceived usefulness and expected rewards on the continuance intention.
Exploratory factor analysis and confirmatory factor analysis show that all our constructs
satisfy discriminant validity and convergent validity. The study shows that reputation, social
ties are significant factors affecting continuance intention. Besides, perceived usefulness
positively related with users’ satisfaction.
Contrary to our expectation, trust does not have a significant impact on continuance intention.
One possible explanation is that individuals are willing to continue to share knowledge with
other members due to close social ties and reputation, without necessarily trusting other
members in the e-learning community. Another possible explanation is that trust is not
important in less risky knowledge sharing activity like e-learning community (Chiu et al
2006). Coleman (1990) argued that only in risky situations individuals do individuals need
trust.
Contrary to our expectation, perceived usefulness does not have significant impact on
continuance intention. One plausible explanation is that perceived usefulness is a premium
factor. Users may not have a keen expectation of it. Another explanation is that users may
more value their devoted time and effort to the e-learning community without caring the
usefulness of the community. As researchers show that community is a place not just for
knowledge sharing, it is also a place for users to develop social ties with each other (Teo et al
2003).
Satisfaction is also not considered as significant factor in our study. One reasonable
explanation is that our sample size is not enough. Since this hypothesis is supported at p= 0.1
level but not at p= 0.05 level. Another explanation is that students may regard the e-learning
community as a social network. They value their reputation and social ties developed in the
form of active participation without concerning whether they are satisfied or not.
6.2.
Limitations
There are several limitations in our study. First, our sample size is small. This is also the
reason why we have a relatively low GFI (0.76). Second, we did not use random sampling.
Third, we use the virtual learning environment which is used to facilitate learning in actual
class. Since this kind of e-learning community might be different from distant learning in
which members of e-learning community never meet each other in reality. So whether this
finding can be generalized to all types of e-learning community is unclear.
6.3.
Implications
The findings of the study have various implications for research as well as for practice.
For research, a strong, positive relationship between individual users’ reputation and
continuance intention suggests that individual users who develop a reputation among peer
users of a system are less likely to discontinue its use. Building a reputation requires
investments of time and effort in the form of active participation in an e-learning community,
and such reputation provides future benefits to an individual user. If an individual with a
developed reputation discontinues use of a system, these associated benefits no longer remain
available. The theoretical implication of this finding is that individuals value their perceived
reputation among peer users of an e-learning community.
The results of this study show that social ties are an important variable for continuing elearning participation. This is consistent with Wasko and Faraj’s ((2005) and Kankanhalli et
al.’s (2005) findings that social capital plays an important role underlying online knowledge
exchange. Further research is suggested to examine other dimension of social capital such as
norms, identification in e-learning users’ continuance intentions. Also, an extension of the
current model is encouraged to investigate whether e-learning community members’
continuance intentions can lead to actual future action of continued participation.
Our results offer several tactical implications for designers of e-learning community. The key
insight for practice offered by our findings is that user perceptions influence continuance
intention.
First, incorporating reputation tracking mechanisms into the design of systems is one way to
reinforce continued use of the system. These mechanisms should be persistent and visible
system-enabled indicators that track an individual user’s reputation over time. System
designers should consider incorporating publicly visible cues such as: number of user posts,
length of membership, and membership status. This also requires providing users
comparative feedback on their participation, and usage; or by comparing them to their peer
users.
Second, a system should create an environment for positive and active knowledge-based
communications by increasing user awareness of the relational portfolios that develop
through the use of the system. For example, system features such as buddy lists, personal
messaging, and rating or reviewing other’s contributions allow users to develop closer ties
with each other. In turn, such features reduce the likelihood that users will transfer to other
similar systems because they are more aware of their relationships with other users of the
current system.
7. Conclusion
The continuance behavior of individuals is critical to the success of an e-learning community.
The motivation of this study was to understand how individuals’ continuance behavior can be
improved in e-learning community. Irretrievable investments, and IS continuance model are
used to examine the key factors influencing continued sharing behavior. Findings show
reputation and social ties have strong effects on continuance intention. This draws attention to
the roles of reputation and social ties in continuance intention, offering some suggestion for
designers of e-learning community.
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Appendix 1
Table Operationalisation of Constructs
Construct
Item Wording
1.
Reputation
2.
3.
1.
2.
Trust
3.
4.
1.
2.
Social Ties
3.
4.
1.
Perceived
Usefulness
2.
3.
1.
Satisfaction
2.
3.
1.
2.
Continuance
Intention
3.
4.
Reward
1.
Source
I earn recognition from others by participating in the
forum.
I feel that participation in the forum improves my
status among the members.
I enjoy a reputation for posting useful information/
knowledge in the forum.
To what extent do you personally believe that your
relationship with others is mutually trusting?
I spend a lot of time developing trust with other
members
To what extent do you personally believe that the
other members think you are honest with them when
sharing knowledge?
To what extent do you personally believe that other
members trust your ability to share useful
information with them ?
I maintain close social relationships with some
members in the forum.
I spend a lot of time interacting with some members
in the forum.
I know some members in the forum on a personal
level.
I have frequent communication with some members
in the forum.
Using this forum enables me to accomplish study
related tasks more quickly.
Using this forum improves my performance for the
study.
Using this forum enhances the effectiveness of my
study.
I feel ____ about my overall experience using this
forum.
Participating in this forum makes me feel ____.
As a contributor, how do you feel about your
experience using this forum?
Adapted from Constant et al.
(1996)
I plan to continue using this forum to learn about
new knowledge.
My intentions are to continue using this forum than
using any alternative means.
I will continue using this forum to exchange
knowledge with other members.
If I could, I would like to discontinue my use of this
forum.
I will receive some marks for assessment in return
for my knowledge sharing.
Adapted and extended from
Bhattacherjee (2001)
Adapted from Tsai and Ghoshal
(1998)
Adapted from Tsai and Ghoshal
(1998)
Adapted from Davis et al. (1989)
Adapted from Bhattacherjee’s
(2001)
Self-developed
Appendix 2
Co rrel ati o n s
rep1
rep1
rep2
rep3
t rust1
t rust2
t rust3
t rust4
t ies1
t ies2
t ies3
t ies4
sat1
sat2
sat3
cont1
cont2
cont3
cont4
pu1
pu2
pu3
rew1
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
Pearson Correlation
Sig. (2-t ailed)
N
1
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*. Correlat ion is signif icant at the 0. 05 level (2-tailed).
rep3
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. 368**
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. 219*
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. 241*
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84
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84
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84
. 370**
. 001
84
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1
84
Appendix 3
E-learning Community Survey
Purpose: There is a forum related with each module in IVLE. The purpose of this survey is
to investigate people’s behavior in the forum.
Instructions: Please indicate the extent to which the following statements describe your
personal experience in using the forum in IVLE. This questionnaire will take you less than 10
minutes to complete. The data collected from the survey will be kept CONFIDENTIAL.
Thank you very much for your participation.
1. I earn recognition from others
Strongly Disagree
by participating in the forum.
1
2. I feel that participation in the
Strongly Disagree
forum improves my status
1
2
2
Neutral
3
4
Strongly Agree
5
Neutral
3
4
6
7
Strongly Agree
5
6
7
among the members.
3. I enjoy a reputation for
Strongly Disagree
posting useful information/
1
2
Neutral
3
4
Strongly Agree
5
6
7
knowledge in the forum.
4. To what extent do you
Very little
personally believe that your
1
Neutral
2
3
4
To a very great extent
5
6
7
relationship with others is
mutually trusting?
5. I spend a lot of time
Very little
developing trust with other
1
Neutral
2
3
4
To a very great extent
5
6
7
members.
6. To what extent do you
Very little
personally believe that the other
1
members think you are honest
with them when sharing
knowledge?
Neutral
2
3
4
To a very great extent
5
6
7
7. To what extent do you
Very little
personally believe that other
1
Neutral
2
3
4
To a very great extent
5
6
7
members trust your ability to
share useful information with
them ?
8. I maintain close social
Strongly Disagree
relationships with some
1
2
Neutral
3
4
Strongly Agree
5
6
7
members in the forum.
9. I spend a lot of time
Strongly Disagree
interacting with some members
1
2
Neutral
3
4
Strongly Agree
5
6
7
in the forum.
10. I know some members in the
Strongly Disagree
forum on a personal level.
1
11. I have frequent
Strongly Disagree
communication with some
1
2
2
Neutral
3
4
Strongly Agree
5
Neutral
3
4
6
7
Strongly Agree
5
6
7
members in the forum.
12. I feel ____ about my overall
Very Dissatisfied
experience using this forum.
1
13. Participating in this forum
Very Displeased
makes me feel ____.
1
14. As a contributor, how do you
Very Frustrated
feel about your experience using
1
2
2
2
Neutral
3
4
Very Satisfied
5
Neutral
3
4
4
7
Very Pleased
5
Neutral
3
6
6
7
Very Contended
5
6
7
this forum?
15. I plan to continue using this
Strongly Disagree
forum to learn about new
1
2
Neutral
3
4
Strongly Agree
5
6
7
knowledge
16. My intentions are to
Strongly Disagree
continue using this forum than
1
2
Neutral
3
4
Strongly Agree
5
6
7
using any alternative means.
17. I will continue using this
Strongly Disagree
forum to exchange knowledge
1
with other members.
2
Neutral
3
4
Strongly Agree
5
6
7
18. If I could, I would like to
Strongly Disagree
discontinue my use of this
1
2
Neutral
3
4
Strongly Agree
5
6
7
forum.
19. Using this forum enables me
Strongly Disagree
to accomplish study related
1
2
Neutral
3
4
Strongly Agree
5
6
7
tasks more quickly.
20. Using this forum improves
Strongly Disagree
my performance for the study.
1
21. Using this forum enhances
Strongly Disagree
the effectiveness of my study.
1
22. I will receive some marks for
Strongly Disagree
assessment in return for my
1
2
2
2
Neutral
3
4
Strongly Agree
5
Neutral
3
4
4
knowledge sharing.
Your Personal Information
Name:
Gender:
Thank you for your participation!
7
Strongly Agree
5
Neutral
3
6
6
7
Strongly Agree
5
6
7