INFORMATION ADOPTION IN AN ONLINE DISCUSSION
FORUM
Christy M. K. Cheung
Department of Finance and Decision Sciences, Hong Kong Baptist University, Hong Kong
Matthew K. O. Lee
Department of Information Systems, City University of Hong Kong, Hong Kong
Keywords: Online discussion forum, information adoption, information usefulness, satisfaction, information systems
continuance.
Abstract: This paper introduces the research model of user intention to continue adopting information in an online
discussion forum. The model is built upon the information adoption model and the IS continuance model.
Since the focus of this study is on continuance intention, the model takes user evaluation process into
account and includes some important constructs (satisfaction, information quality, source credibility, and
information usefulness) to explain the continuance behavior. An online survey was conducted and a total of
315 completed questionnaires were collected. Among the 315 respondents, 144 respondents have adopted
the information in an online discussion forum. The research model explains 60% of the variance. The results
also provide strong support for the existing theoretical links, as well as for those newly hypothesized in this
study.
1 INTRODUCTION
With the advanced Internet development, people can
interact and exchange information with other people
in online social spaces, like instant messaging,
blogs, online discussion forums, newsgroups, chat
rooms, wikipedia, youtube, and all social networking
sites. This is an interesting phenomenon as the
contents in the communities are collaboratively
created and shared by members who are not limited
by physical or temporal constraints.
The success of a virtual community depends
primarily on whether members are willing to
continue to use the community, as well as to share
and adopt knowledge. A review study (Lee et al.
2003a) however showed that there are very few
studies related to user behaviors in virtual
communities in information systems (IS) literature.
In order to gain a better understanding of
continuance of virtual communities, this study
attempts to examine user behaviors in virtual
communities, in particular, user intention to continue
adopting information in an online discussion forum.
The rest of this paper is organized as below. The
next section provides a review on the literature
related to information adoption and information
systems continuance. The third section describes the
research model and hypotheses. Then the research
methodology is described. The last section
summarizes the findings and discusses the
implications for both research and practice.
2 LITERATURE REVIEW
Online discussion forum is one of the earliest and
most popular technologies for collaborative
knowledge creation and sharing (Wagner and
Bolloju 2005). In the context of online discussion
forums, usage behaviour involves both knowledge
sharing (e.g., posting questions and answers,
experience sharing etc.) and knowledge adoption
(e.g., reading messages, seeking information, using
knowledge from the forum, etc.), and the
sustainability of an online discussion forum depend
on both the supply and demand of knowledge. In
this study, the focus is on continuance adoption of
322
M. K. Cheung C. and K. O. Lee M. (2007).
INFORMATION ADOPTION IN AN ONLINE DISCUSSION FORUM.
In Proceedings of the Second International Conference on e-Business, pages 322-328
DOI: 10.5220/0002110703220328
Copyright
c
 SciTePress

information in an online discussion forum. Both the
Information Adoption Model and the Information
Continuance Model are reviewed in this section.
2.1 Information Adoption Model
In the past two decades, there are plenty of studies
on the adoption of information systems/technologies
(Lee et al., 2003b, Legris et al., 2003). Adoption
theories describe the processes people will face
when they decide to perform an action or activity for
the first time they receive the ideas, information, or
technologies. The theories also suggest that people
form intentions to adopt a technology based on their
beliefs about the consequence of adoption and their
valuation of these consequences. Applying this
concept in the adoption of information, Sussman and
Siegal (2003) proposed the Information Adoption
Model and explained information adoption in terms
of information usefulness, source credibility and
argument quality.
2.2 Information Continuance Model
The Information Systems Continuance Model
(Bhattacherjee 2001) is built on expectation
confirmation theory and suggested that IS
continuance relates satisfaction and perceived
usefulness to the degree in which user expectation
about an information system is confirmed.
Expectation provides a baseline level to evaluate the
actual performance of an information system and
confirmation in turn determines satisfaction. The IS
Continuance Model has been receiving a lot of
attention in recent IS research (e.g., Hong et al.,
2006, Lin et al. 2005, Thong et al. 2006). The model
however is too generic and may not provide enough
insight to explain user intention to continue adopting
knowledge in an online discussion forum. Therefore,
this study attempts to use both the Information
Adoption Model and IS Continuance Model to
explain the continuance of information adoption.
3 RESEARCH MODEL
Figure 1 depicts the research model of intention to
continue adopting information in an online
discussion forum. The model is basically an
extension of the Information Adoption Model in the
continuance stage. The model postulates that
information quality and source credibility are the
factors affecting user perception on the usefulness of
information, as well as user satisfaction with the
information in an online discussion forum.
Information usefulness and user satisfaction in turn
affect user intention to continue adopting
information in an online discussion forum.
Information
Usefulness
Information
Quality
Satisfaction
Source
Credibility
Intention to
Continue
Adopting
Information
H1
H4
H3
H2
H7
H6
H5
H8
Information
Usefulness
Information
Quality
Satisfaction
Source
Credibility
Intention to
Continue
Adopting
Information
H1
H4
H3
H2
H7
H6
H5
H8
Figure 1: Research Model.
In this section, the key components of the research
model and their interrelationships are addressed.
3.1 Intention to Continue Adopting
Information
Information adoption refers to using the information
(messages) in an online discussion forum. Since the
focus is on continuance behavior, a similar
conceptualization of IS continuance intention is
used. Intention to continue adopting information is
defined as the likelihood a user will continue
adopting and using information from an online
discussion forum.
3.2 Satisfaction and Information
Usefulness
Applying the IS continuance model to the domain of
information usage in an online discussion forum,
information usefulness and user satisfaction with the
information in an online discussion forum are
proposed to be important factors determining
continuance behavior.
User satisfaction is one of the most important
measures of information systems success (Seddon et
al. 1999, Mahmood et al. 2000, Rai et al. 2002,
DeLone and McLean 1992, 2003, Zviran and Erlich
2003). A high level of user satisfaction is associated
with enhanced IS continuance (Bhattacherjee 2001)
and improved user performance (Gelderman 1998).
In this study, user satisfaction refers to the affective
response to user evaluation on the information in an
online discussion forum. The hypothesis is:
INFORMATION ADOPTION IN AN ONLINE DISCUSSION FORUM
323

H1: User satisfaction positively affects intention to
continue adopting information in an online
discussion forum.
The IS continuance model (Bhattacherjee 2001) also
suggests that perceived usefulness of an information
system is an important factor of IS continuance
intention. Bhattacherjee (2001) argued that human
tendencies for pursuing instrumental behaviors are
independent of the timing or stage of behavior. This
argument is supported by a number of studies that
attempted to compare the determinants of IS usage
at both pre-adoption and post-adoption stages (Davis
and Venkatesh 2004, Karahanna et al. 1999).
Perceived usefulness is found significant to user
affect across different stages of IS use. In the
adoption model, perceived usefulness is the primary
motivator of user attitude toward IS use. In the post-
adoption (continuance) model, perceived usefulness
is a key factor of user satisfaction with IS use.
Applying this conceptualization in the context of
online discussion forum, and it is believed that if
users find the information in the forum is useful,
they will have a higher tendency to continue using
the forum. Similar to the IS continuance model,
information usefulness is expected to enhance user
satisfaction with the information in an online
discussion forum. Therefore, the hypotheses are:
H2: Information usefulness positively affects
intention to continue adopting information in an
online discussion forum.
H3: Information usefulness positively affects user
satisfaction with an online discussion forum.
3.3 Information Quality
High information quality has long been found
associated with system use, user satisfaction, and net
benefits (DeLone and McLean 1992, 2003). Turban
and Gehrke (2000) urged that the quality of the web
content determines whether potential customers will
be attracted to or driven away from the website.
Janda et al. (2002) and Szymanski and Hise (2000)
suggested that information quality is a strong
determinant of consumer satisfaction with Internet
shopping. In the context of online discussion forum,
argument quality (i.e., information quality) is
identified as the extent to which users think that
information is relevant, timely, accurate and
complete. It reflects the features of the content
contained in a message. The quality of information
in an online discussion forum is expected to
determine user satisfaction with the information. The
information adoption model also suggests that
information quality affects information usefulness of
an online discussion forum. Therefore, the
hypotheses are:
H4: Perceived information quality positively affects
user satisfaction with an online discussion forum.
H5: Perceived information quality positively affects
information usefulness of an online discussion
forum.
3.4 Source Credibility
Source credibility represents the informational
authority, and it serves as the informational indicator
when people cannot distinguish good messages from
bad ones (Sussman and Siegal 2003). If users find
that contributors are trustworthy, participants tend to
consider the information useful and credible, just
like readers believe the news published in authorized
newspapers valuable and convincing (Donath 1999).
In recent years, source credibility is found to be an
important determinant of information usefulness and
user satisfaction (Sussman and Siegal 2003;
Bhattacherjee and Sanford 2004). Further, source
cue is the heuristic judgment of information quality
(Rieh et al. 1998). That means information from
identified trustworthy experts will be perceived to be
of high quality in the context of online discussion
forums.
H6: Source credibility positively affects user
satisfaction with an online discussion forum.
H7: Source credibility positively affects information
usefulness of an online discussion forum.
Information (message) is the basic component of an
online discussion forum. Users evaluate their
experiences with the online discussion forum on the
information itself and the source. Since it is not
about the initial adoption of information in an online
discussion forum, users already have some
experiences with the forum, and they are able to
evaluate both the information itself and the source
credibility. It is also believed that if users find the
source has high credibility, there is a higher chance
that they think the information has higher quality.
That means, perceived quality of the information is
partly determined by the credibility of the source
(i.e., who writes the messages). Therefore, the
hypothesis is:
H8: Source credibility positively affects perceived
information quality of an online discussion forum.
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4 METHODOLOGY
The research model was empirically tested in a real
virtual community, Hong Kong Education City
(www.hkedcity.net). Hong Kong Education City
(HKed City) is a leading and one-stop education
portal with a vision to build Hong Kong into a
learning city. Details about the measures, data
collection method, and survey responses are
discussed in the following sections.
4.1 Measures
The measures of the constructs in the current study
are listed in Appendix A. A multi-item approach is
used. That means each construct is measured by a
few items for construct validity and reliability. A
slider scale is used in this study and provides a
continuous scale from 0 to 100 or -50 to 50 (See
Figure 2). Respondents can either click or drag the
slider to indicate their preference point.
Figure 2: The Slider Scale.
4.2 Data Collection
The target respondents of this study were the
teachers who have used the HKed City. In order to
reach the respondents, an invitation email with the
URL to the online questionnaire was sent to both
primary and secondary school teachers. To increase
the response rate, an incentive of three USB flash
drives and thirty book coupons were offered as
lucky draw prizes. The reminder emails were also
sent a few weeks after the first invitation email.
4.3 Survey Response
A total of 315 responses were collected in this study
and 144 of them have adoption information in the
online discussion forum of HKed City. Among the
respondents, 55% were male and 45% were female.
About 15% were aged 21-30 and only 5% were aged
51 or above. 72% were secondary school teachers
and 28% were primary school teachers, and around
15% had more than 20 years teaching experiences.
In terms of the usage behavior in the virtual
community (HKed City), over 50% had less than 2-
year experience with the virtual community, and
25% respondents used it every week. The
nonresponse error estimation was conducted and we
did not find the error exists in this study.
5 DATA ANALYSIS
Following the two-step analytical procedures (Hair
et al. 1998), the measurement model is first
examined and then the structural model is assessed.
5.1 Measurement Model
Convergent validity, which indicates the extent to
which the items of a scale that are theoretically
related to each other should be related in reality, was
examined using the composite reliability (CR) and
the average variance extracted (AVE). The critical
values for CR and AVE are 0.7 and 0.5 respectively
(Fornell and Larcker 1981). As shown in Table 1, all
CR and AVE values meet the recommended
thresholds and all item loadings are higher than 0.70.
Table 1: Psychometric Properties of Measures.
Construct/Item Loading
Information Adoption (CR: 0.97, AVE: 0.95)
IA1 0.97
IA2 0.97
Information Usefulness (CR: 0.98, AVE: 0.93)
IU1 0.97
IU2 0.97
IU3 0.96
Information Quality (CR: 0.98, AVE: 0.76)
IQ1 0.88
IQ2 0.91
IQ3 0.88
IQ4 0.80
Source Credibility (CR: 0.98, AVE: 0.88)
SC1 0.88
SC2 0.94
SC3 0.94
SC4 0.95
Satisfaction (CR: 0.97, AVE: 0.87)
SAT1 0.94
SAT2 0.93
SAT3 0.95
SAT4 0.92
Note: CR – Composite Reliability, AVE – Average
Variance Extracted
INFORMATION ADOPTION IN AN ONLINE DISCUSSION FORUM
325

Discriminant validity is the extent to which the
measure is not a reflection of some other variable. It
is indicated by low correlations between the measure
of interest and the measure of other constructs
(Fornell and Larcker 1981). Evidence about
discriminant validity can be demonstrated when the
squared root of the average variance extracted for
each construct higher than the correlations between
it and all other constructs. Table 2 shows that the
squared root of average variance extracted for each
construct is greater than the correlations between the
constructs and all other constructs. The results
suggest that an adequate discriminant validity of the
measures.
Table 2: Discriminity Validity of Measures.
Construct IA IU SAT IQ SC
Information
Adoption (IA)
0.97
Information
Usefulness (IU)
0.75 0.96
Satisfaction
(SAT)
0.73 0.88 0.93
Information
Quality (IQ)
0.81 0.89 0.87 0.87
Source
Credibility (SC)
0.73 0.85 0.89 0.89 0.94
5.2 Structural Model
Figure 3 presents the overall explanatory power,
estimated path coefficients (all significant paths are
indicated with asterisks), and associated t-value of
the paths of the research model. Test of significance
of all paths were performed using the bootstrap
resampling procedure.
Information
Usefulness
Information
Quality
Satisfaction
Source
Credibility
Intention to
Continue
Adopting
Information
0.89***
0.66***
0.27***
0.46***
0.16
0.35***
0.48***
0.32**
R
2
=0.60
R
2
=0.85
R
2
=0.81R
2
=0.79
Information
Usefulness
Information
Quality
Satisfaction
Source
Credibility
Intention to
Continue
Adopting
Information
0.89***
0.66***
0.27***
0.46***
0.16
0.35***
0.48***
0.32**
R
2
=0.60
R
2
=0.85
R
2
=0.81R
2
=0.79
Figure 3: PLS Result.
The research model accounts for 60 percent of the
variation in “Intention to Continue Adopting
Information in an Online Discussion Forum”, the
exogenous variables explain 85 percent of the
variation in “Satisfaction with the information in an
Online Discussion Forum”, 81 percent of the
variation in “Information Usefulness”, and 79
percent of the variation in “Information Quality”.
All the structural paths in the research model are
found to be statistically significant, except the path
from information quality to satisfaction. The
strength of the path provides some insights into the
relationships among the model’s constructs. Both
information usefulness and satisfaction have a strong
and significant impact on Intention to continue
adopting information in an online discussion forum,
with path coefficients at 0.48 and 0.32 respectively.
In turn, information usefulness is significantly
affected by information quality (β=0.66, t=9.68) and
source credibility (β=0.27, t=3.83). Satisfaction is
determined by information usefulness (β=0.35,
t=4.08) and source credibility (β=0.46, t=4.05).
Finally, source credibility exhibits a very strong
impact on information quality, with path coefficient
at 0.89.
6 CONCLUSION
The research model seeks to explain user intention
to continue adopting information in an online
discussion forum. The results show that all path
coefficients are found statistically significant except
the path from information quality to user satisfaction.
The findings are supported by Bhattacherjee’s(2001)
IS continuance model. Surprisingly, information
quality does not have any direct effect on user
satisfaction. Information quality only affects user
satisfaction indirectly through information
usefulness. In other words, users’ affective
responses were not based on the quality of the
messages in the online discussion forum directly, but
instead were based on how useful they find the
messages. The result is contrary to conventional
prediction as per IS success model (DeLone and
McLean 2003). One possible explanation is that
most Internet users today suffer from information
overload. Rheingold (2000) argued that in the
information age, most people are suffered from “too
much information available and few effective filters
for sifting the key data that are useful and
interesting to us as individuals”. People tend to use
heuristic evaluation policies that do not consider
simultaneously the values of all information, and
they focus only on the “useful” information. In
consumer research, Jacoby (1994) also argued that
consumers tend not to be overloaded as they tend to
be selective in the amount and nature of information
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they process. In this case, it is probable that users
focus more on information usefulness instead of
information quality, as they tend to minimize their
evaluation process in the online environment.
In interpreting the results of this study, one must
pay attention to a number of limitations. The first
bias might have been introduced by the omission of
important variables. The theoretical model accounts
for 60% of the variance in continuance intention and
this suggests that some important predictors may be
missing. A second threat to validity may be common
method bias, as this study only uses one single
questionnaire to measure all constructs included. A
third potential bias is related to the sample frame and
response rate. Compared with the number of emails
that are sent, the number of responses is relatively
low. There are a few reasons that lead to the
relatively low response rate in this study: (1) The
sample frame complied in this study is relatively
large as it contains both users and non-users of Hong
Kong Education City. (2) The invitation is sent in
mid May. It is still the academic period and most
teachers are very busy with their work. (3) The
length of the questionnaire is a bit too long. Past
research demonstrated that survey length is
negatively related to the response rate. (4)
Respondents may be being oversurveyed. There is
an increase in the number of requests of online
survey, and this may be the reason of lower response
rate. (5) Similarly, there is an increase in unsolicited
emails to Internet users. Information overload causes
them to develop ways for dealing with emails (e.g.,
using filtering software) and discourage them from
reading unsolicited emails. (6) Respondents may
have a perception that the chance of winning the
lucky draw prize is low. The incentive may not be
attractive enough to draw their interest to participate
in this study.
Finally, care must be taken when extrapolating
the findings to other types of virtual communities.
This study represents one type of professional group
where the participants usually share some common
interests, background, and goals to participate and
collectively contribute to the professional
knowledge. It would be interesting to compare this
finding with the studies in other types of virtual
communities in future research.
REFERENCES
Bagozzi, R. P., & Dholakia, U. M. (2002). Intentional
Social Action in Virtual Communities. Journal of
Interactive Marketing, 16(2), 2-21.
Bhattacherjee, A. (2001). Understanding Information
Systems Continuance: An Expectation Confirmation
Model. MIS Quarterly, 25(3), 351-370.
Bhattacherjee, A., & Premakumar, G. (2004).
Understanding Changes in Belief and Attitude toward
Information Technology Usage: A Theoretical Model
and Longitudinal Test. MIS Quarterly, 28(2), 351-370.
Davis, F. D., & Venkatesh, V. (2004). Toward
Preprototype User Acceptance Testing of New
Information Systems: Implications for Software
Project Management. IEEE Transactions on
Engineering Management, 51(1), 31-46.
DeLone, W. H., & McLean, E. R. (1992). Information
System Success: The Quest for the Dependent
Variable. Information Systems Research, 3(1), 60-95.
DeLone, W. H., & McLean, E. R. (2003). The DeLone
and McLean Model of Information Systems Success:
A Ten-year Update. Journal of Management
Information Systems, 19(4), 9-30.
Donath, J. S. (1999). Identity and Deception in the Virtual
Community. Communities In Cyberspace (M.A. Smith
and P. Kollock (eds.) ed.). New York: Routledge.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural
Equation Models with Unobservable Variables and
Measurement Error. Journal of Marketing Research,
18, 39-50.
Gelderman, M. (1998). The Relationship Between User
Satisfaction, Usafe of Information Systems and
Performance. Information & Management, 34, 11-18.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W.
C. (1998). Multivariate Data Analysis, 5th Ed.
Hong, S., J. Y. L. Thong, & K. Y. Tam. (2006).
Understanding continued information technology
usage behavior: A comparison of three models in the
context of mobile internet. Decision Support Systems,
42, 1819-1834.
Jacoby, J. (1984). Perspectives on Information Overload.
Journal of Consumer Research, 10, 432-435.
Janda, S., Trocchia, P. J., & Gwinner, K. P. (2002).
Consumer Perceptions of Internet Retail Service
Quality. Journal of Service Industry Management,
13(5), 412-431.
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999).
Information Technology Adoption across Time: A
Cross-Sectional Comparison of Pre-Adoption and
Post-Adoption Beliefs. MIS Quarterly, 23(2), 183-213.
Lee, F. S. L., Vogel, D., & Limayem, M. (2003a). Virtual
Community Informatics: A Review And Research
Agenda. Journal of Information Technology Theory
and Application, 5(1), 47-61.
Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y.
(2003b). AIMQ: A Methodology for Information
Quality Assessment. Information & Management, 40,
133-146.
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003c). The
technology acceptance model: Past, present, and
future. Communications of the Association for
Information Systems (CAIS), 12.
Legris, P., Ingham, J., & Collerette, P. (2003). Why do
people use information technology? A critical review
INFORMATION ADOPTION IN AN ONLINE DISCUSSION FORUM
327

of the technology acceptance model. Information &
Management, 40(3), 191-204.
Lin, C. S., S. Wu, & Tsai, R. J. (2005). Integrating
perceived playfulness into expectation-confirmation
model for web portal context. Information &
Management, 42(5), 683-693.
Mahmood, M. A., Burn, J. M., Gemoets, L. A., & Jacquez,
C. (2000). Variables Affecting Information
Technology End-User Satisfaction: A Meta-Analysis
of the Empirical Literature. Int. J. Human-Computer
Studies, 52, 751-771.
Rai, A., Sandra S, L., & Welker, R. B. (2002). Assessing
the Validity of IS Success Models: An Empirical Test
and Theoretical Analysis. Information System
Research, 13(1), 50-69.
Rheingold, H. (2000). The Virtual Community:
Homesteading on the Electronic Frontier. Boston:
MIT Press.
Rieh, S. Y., & Belkin, N. J. (1998). Understanding
judgment of information quality and cognitive
authority in the WWW. Paper presented at the
Proceedings of the ASIS Annual Meeting.
Seddon, P. B., Staples, S., Patnayakuni, R., & Bowtell, M.
(1999). Dimensions of information systems success.
Communications of the Association for Information
Systems (CAIS), 2(20).
Sussman, S. W., & Siegal, W. S. (2003). Informational
Influence in Organizations: An Integrated Approach to
Knowledge Adoption. Information Systems Research,
14(1), 47-65.
Szymanski, D. M., & Hise, R. T. (2000). E-Satisfaction:
An Initial Examination. Journal of Retailing, 76(3),
309-322.
Thong, J. Y. L., Hong, S.-J., & Tam, K. Y. (2006). The
effects of post-adoption beliefs on the expectation-
confirmation model for information technology
continuance. International Journal of Human-
Computer Studies, 64, 799-810.
Turban, E., & Gehrke, D. (2000). Determinants of E-
Commerce Website. Human Systems Management, 19,
111-120.
Wagner, C., & Bolloju, N. (2005). Supporting Knowledge
Management in Organizations with Conversational
Technologies: Discussion Forums, Weblogs, and
Wikis. Journal of Database Management, 16(2), I.
Zviran, M., & Erlich, Z. (2003). Measuring IS User
Satisfaction: Review and Implications.
Communications of the Associations for Information
Systems, 12(3).
APPENDIX
Appendix A: Measures
Information Adoption
IA1 Please express the degree to which
you might intend to continue
adopting knowledge in Teachers'
Channel in the next few weeks.
IA2 I intend to continue adopting
knowledge in Teachers' Channel in
the next few weeks.
Modified
from
Bagozzi
and
Dholakia
2002
Information Usefulness
The information in the discussion forum of Teachers' Channel
is
IU1 (Not Valuable/ Valuable)
IU2 (Uninformative/ Informative)
IU3 (Harmful/ Helpful)
Sussman
and Siegal
2003
Information Quality
Based on your experience of using the discussion forum of
Teachers' Channel, please provide your evaluation on the
quality of information in terms of the following features, as in:
“The information in the discussion forum is ____________”.
IQ1 (Irrelevant/ Relevant)
IQ2 (Inappropriate/ Very Appropriate)
IQ3 (Inapplicable/ Very Applicable)
IQ4 (Out-dated/ Current)
Lee et al.
2003c
Source Credibility
Based on your experience of using the discussion forum of
Teachers' Channel, please provide your evaluation on the
people who write messages in terms of the following features.
SC
1
(Not Very Knowledgeable/ Very
Knowledgeable)
SC
2
(Not Experts/ Experts)
SC
3
(Arrogant/ Modest)
SC
4
(Unlikable/ Likable)
Sussman
and Siegal
2003
Satisfaction
How do you feel about the information in the discussion forum
of Teachers' Channel?
SAT
1
(Strongly Dissatisfied/ Strongly
Satisfied)
SAT
2
(Strongly Displeased/ Strongly
Pleased)
SAT
3
(Strongly Frustrated/ Strongly
Contented)
SAT
4
(Absolutely Terrible/ Absolutely
Delighted)
Bhattache
rjee 2001
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