Factors Affecting University Instructors’ Continuance Intention to

Use Learning Management Systems: The Blackboard System Case

Samar Mouakket¹ and Anissa M. Bettayeb²

¹Department of Management Information Systems, College of Business Administration, University of Sharjah,

Sharjah, U.A.E.

²Information Technology Center, University of Sharjah, Sharjah, U.A.E.

Keywords: The Expectation-Confirmation Model, Learning, Management Systems, University Instructors, Training,

User-Interface Design, Technical Support, Computer Self-efficacy.

Abstract: Although academic institutions have invested heavily in Learning Management Systems (LMS) to support

e-learning platform, few studies have examined the factors affecting their usage, particularly by university

instructors. To fill this research gap, this study proposes a framework based on the expectation-confirmation

model (ECM) to examine the influence of several critical independent factors related to organizational,

technological and individual characteristics on university instructors’ perceived usefulness of Blackboard

system as one well-known LMS, which in turn will affect their continuance intention to use this technology.

Data was gathered from 158 university instructors at a university in the United Arab Emirates (UAE).

Structural equation modeling technique was used to validate the causal relationships between the different

variables.

1 INTRODUCTION

Academic institutions around the world are investing

heavily in various Learning Management Systems

(LMS) to deliver and manage e-learning services

(Cheng, 2014; Caputi and Garrido, 2015). LMS,

such as Web Course Tools (WebCT) and

Blackboard system, are among the most commonly

used types of e-learning systems for both students

and instructors in academic institutions (Sun, 2008;

Liaw, 2008; Cheng, 2014). The focus of previous

research has been on the student’s perception of

LMS (Yi and Hwang, 2003; Ngai et al., 2007;

Limayem and Cheung, 2008; Paechter et al., 2010;

Tarhini et al., 2013; Chang, 2013; Liaw and Huang,

2013; Chen, 2014) with less emphasis on the

instructor’s attitude (Sørebø et al., 2009; Al-Busaidi

and Al-Shihi, 2012). This study contributes to the

literature by developing a model for the post-

adoption context, based on the expectation-

confirmation model (ECM, Bhattacherjee, 2001), to

investigate the factors affecting instructors’

continuance usage intention of Blackboard system as

one well-known LMS in academic institutions in the

United Arab Emirates (UAE). In this study, the

constructs of the ECM have been selected based on

their widespread use and relevance to the LMS

context. Hence, we have adopted the following

constructs from ECM: perceived usefulness and

continuance intention.

In this study, we propose a framework which

provides a comprehensive view of the critical factors

that influence university instructors’ perceived

usefulness of LMS and consequently continuance

intention to use this technology. According to our

framework, these critical factors are related to the

following characteristics: individual, organizational

and technological. We suggest that the individual

characteristics include computer self-efficacy (Ball

and Levy, 2008; Sawang et al., 2013; Chen, 2014),

the organizational characteristics include technical

support and training (Sumner and Hostetler, 1999;

Bradford and Florin, 2003; Al-Busaidi and Al-Shihi,

2012), and the technological characteristics include

user-interface design (Jeong, 2011; Chen, 2014). We

believe that taking into consideration these different

characteristics will provide us with a more complete

picture of LMS adoption and usage by university

instructors.

This study is organized as the following. After

the introduction in section 1, the research model and

hypotheses are proposed in section 2. The research

Mouakket, S. and Bettayeb, A.

Factors Affecting University Instructors’ Continuance Intention to Use Learning Management Systems: The Blackboard System Case.

In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 2, pages 215-222

ISBN: 978-989-758-187-8

215

method used in this study is described in section 3.

The results of the collected data analyzed using

structural equation modeling (SEM) are reported in

section 4. Section 5, discusses the findings of the

study, and finally section 6 presents the implications

and venues for further research.

2 RESEARCH MODEL

This research investigates the influence of the

following characteristics: organizational

characteristics (training and technical support),

technological characteristics (user-interface design),

and individual characteristics (computer self-

efficacy) on perceived usefulness, which in turn will

affect university instructors’ continuance intention to

use LMS. Figure 1 presents the research model.

Figure 1: Research model.

Continuance intention is the degree to which an

individual is willing to continue using an

information system (IS) in the future and to

recommend it to others (Chang, 2013). Perceived

usefulness refers to an individual’s perception that

the usage of IS will improve work performance

(Davis, 1989). Previous studies have found that the

extent to which users perceive an information

system to be useful positively affect their

continuance intention (Bhattacherjee, 2001; Lin et

al., 2005; Limayem et al., 2007, Hoehle et al., 2011).

Within LMS context, prior studies have found

that perceived usefulness significantly influences

continuance intention to use LMS among university

students (Limayem and Cheung, 2008; Lee, 2010).

Drawing on the findings of previous studies, we

hypothesize that as instructors feel the usefulness of

the Blackboard system, their intention to continue

using it will increase. Thus, we postulate the

following:

H1: Perceived Usefulness has a Positive Effect on

the Continuance Intention to use Blackboard

System. Training is considered one of the

organizational factors which can influence the

success of IS implementation (Bradford and Florin,

2003). It is a process needed to obtain IS skills

required to perform specific tasks (Nelson and

Cheney, 1987). Because of the increased use of

information systems in the educational field,

academic institutions need to provide adequate

training programs for their instructors on the use of

new IS (Lareki et al., 2010). Training programs can

be effective for improving the level of utilization of

LMS and for enabling users to obtain the benefits of

this technology (Randeree and Narwani, 2009). Prior

research has reported that training influences

technology acceptance indirectly through its

influence on perceived usefulness (Amoroso and

Cheney, 1991; Igbaria et al., 1997). Similarly, within

LMS context, we postulate that training offered to

university instructors will influence their perceived

usefulness:

H2: Training has a Positive Effect on the

Perceived Usefulness of Blackboard System.

Technical support is another organizational

characteristic which refers to answering questions

regarding information systems usage and offering

support to users when requested by expert

individuals in help desk and information technology

center (Ngai et al., 2007; Bhattacherjee and Hikmet,

2008). Prior studies have shown that technical

support is a key factor influencing attitude of

instructors and students (Williams, 2002). Thus, lack

of technical support will make teachers frustrated

with the technology which may discourage them

from using it, whereas providing appropriate

technical support to teachers will help them to

integrate new technologies easily into their teaching

(Tong and Triniada, 2005). Previous studies have

found that technical support positively influences

perceived usefulness of LMS (Ngai et al., 2007).

Drawing on prior studies, we suggest that having

technical support will enhance the usefulness of

Blackboard system, thus the following hypothesis is

proposed:

H3: Technical Support has a Positive Effect on

the Perceived Usefulness of Blackboard System.

A good menu design with control tool bars will

enable the functions of a system to be easily

accessible to the user, thus enhancing its perceived

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216

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usefulness (Cho et al., 2009). Several studies have

examined the impact of user-interface design on user

attitude towards IS (Jeong, 2011; Cyr et al., 2006).

Within LMS context, a good user-interface design is

an important factor for supporting user acceptance

and usage of e-learning services (Cho et al., 2009).

In this study, we hypothesize that user-interface

design of Blackboard system enable university

instructors to achieve their goals effectively which

would help them to enhance the usefulness of the

system. Thus, the following hypothesis is suggested:

H4: User-interface Design has a Positive Effect on

the Perceived Usefulness of Blackboard System.

Computer self-efficacy refers to the individual’s

ability to use a computer to perform a specific task

(Compeau and Higgins, 1995). Prior studies suggest

that computer self-efficacy is a significant

determinant of an individual’s decision to use

computers through perceived usefulness (Agarwal et

al., 2000). Similarity, in this study, we argue that

instructors who have higher computer self-efficacy

will recognize the usefulness and value of

Blackboard system. Accordingly, the following

hypothesis will be tested:

H5: Computer Self-efficacy has a Positive Effect

on the Perceived Usefulness of Blackboard

System.

3 RESEARCH METHODOLOGY

3.1 Data Collection Procedures and

Sample

In order to empirically assess the proposed model

and hypotheses, we have conducted a paper-based

survey method as well as an online survey method to

university instructors who use Blackboard system on

a voluntary basis in one well-known university in

the United Arab Emirates. Since our study focuses

on users during the post-adoption stage of LMS, we

have targeted university instructors who used

Blackboard system before, and they were assured

that anonymity would be maintained. The study is

conducted in three steps. First, the questionnaire is

developed in English language and translated to

Arabic language since the teaching method in the

university is both in English and Arabic. Two

English university instructors who are experts in

translation have examined the questionnaire and

made suggestions about the clarity of the translated

items. Second, the questionnaire is pilot-tested with

5 randomly selected university instructors in the

university. Based on the feedback from the pilot test,

the questionnaire is refined and a revised final

questionnaire has been developed. Third, a paper-

based questionnaire is self-administered by the

researchers to university instructors in different

colleges who volunteered to participate in this

survey. Some university instructors have helped the

researchers by distributing the questionnaires among

their colleagues and later collecting them before

giving them back to the researchers. The

questionnaire is also distributed online and faculty

members are encouraged to complete it and send it

via email to the researchers.

The researchers distributed 200 questionnaires,

and they received 115 questionnaires back. 7

questionnaires were eliminated due to missing

values and wrong data provided, making the number

of completed questionnaires 108. To increase the

response rate, a second round of follow-up was

carried out by the researchers themselves making the

number of returned questionnaire 167. After

checking the questionnaires for completeness and

any missing values, 9 questionnaires were

eliminated. The final number of valid responses was

158. Approximately 74% of the respondents are

male while 25.9% are female. 20.9% of the

respondents are between 30 and 39, while 79.1% are

40 and above. 79.1% of the respondents are Arabs

while 20.9% are non-Arab. The highest number of

respondents is from the engineering College, 20.3%,

while the lowest numbers is from the Fine Art

College, 3.8%. 48.7% of the respondents are

assistant professors, while 10.1% of the respondents

are full professors. The number of respondents who

teach in English is 70.9%, while the number of

respondents who teach in Arabic is 29.1%. Finally,

27.2% of the respondents have between 1 and years

of teaching experience, while 33.5% of the

respondents have more than 15 years of teaching

experience. 34.8% of the respondents spend more

than 120 minutes on the Internet daily. 58.9% of the

respondents spend 30 minutes or less daily using

Blackboard system.

3.2 Measurement Items

The questionnaire is divided into two main parts.

The first part consists of 9 items. It contains

demographic data about the university instructors

(gender, age, nationality, college, job rank, teaching

experience, frequency of Internet usage, and

frequency of Blackboard system usage). The second

part, which consisted of 27 items to assess the

proposed seven constructs, is measured using a five-

Factors Affecting University Instructors’ Continuance Intention to Use Learning Management Systems: The Blackboard System Case

217

point Likert scale ranging from 1-strongly agree to

5-strongly disagree, with the mid-point (3)

representing the state of unsure or neutral.

Measurement items in the survey are adapted

from prior studies to fit the context of learning

management systems. Items of organizational

characteristics which include training (3 items) and

technical support (4 items) are adapted from Al-

Busaidi and Al-Shihi (2012) and Ngai et al., (2007).

Items of technological characteristics which include

user-interface design (5 items) are adapted from Cho

et al., (2009) and Liu et al., (2010). Items of

individual characteristics which include computer

self-efficacy (6 items) is adapted from Chatzoglou et

al. (2009), Lee et al., (2009) and Chiu and Wang

(2008). Perceived usefulness (5 items) is adapted

from Yoon and Kim (2007) and Sørebø and Sørebø

(2009). Continuance intention (4 items) is adapted

from Chiu et al., (2005), Kim (2010) and Lee

(2010). The items of each variable are listed in the

appendix.

4 DATA ANALYSIS AND

RESULTS

The data has been analyzed as the following. The

first step involves analyzing the measurement model

to establish the reliability and validity of the

measures while the second step tests the structural

relationships of the model. SPSS has been used to

analyze the demographic data and to evaluate the

Cronbach’s alpha. AMOS has been used to conduct

structural equation modeling (SEM) to examine our

measurement model and then to test the structural

model.

To test the measurement models of our model,

we have examined the following: (1) factor loading

for each item, (2) reliability of measures and (3)

composite reliability and average variance extracted.

First, this study has conducted a confirmatory factor

analysis (CFA) to examine if the measurement items

of each construct are loaded as predicted on their

respective constructs. Based on the recommended

values provided in the literature, a factor should

have at least two items and each item factor loading

should be greater than 0.40 (Hair et al., 1998). As a

result of the CFA, two items from computer self-

efficacy were dropped due to low factor loading,

while the factor loading of the remaining items in

this study ranged between 0.592 and 0.898. The

items and their factor loading are listed in the

Appendix.

Second, the reliability of each measurement scale

is computed by applying the Cronbach’s alpha. The

reliability coefficients range from 0.745 to 0.937

(See Appendix), which is higher than the

recommended level of 0.70 suggested in the

literature (Nunnally and Bernstein, 1994; Hair et al.,

2006). Third, the values of composite reliability

(CR) have exceeded 0.70 and the average variance

extracted (AVE) is higher than the recommended

value of 0.50 (Fornell and Larcker, 1981). Thus, we

can conclude that the scales used in this study are

both reliable and valid.

This study has used five goodness-of-fit indices

to investigate the goodness-of-fit of the

measurement model and then the structural model.

According to researchers, the value of Chi²/ Degree

of freedom (df) should be less than 5.0 (Bentler and

Bonett, 1980). Comparative fit index (CFI), normed

fit index (NFI), and incremental fit index (IFI)

should be 0.90 and above (Hair et al. 2006). Root

mean square error of approximation (RMSEA)

should not exceed 0.10 (Anderson and Gerbing,

1988). All the values of the measurement model

used in this research have been above the

recommended values by researchers, with the

exception of the value of NFI which is slightly low.

The results are as the following:

Chi²/df=638.524/238=2.683, CFI=0.903,

NFI=0.891, IFI=0.902 and RMSEA=0.090.

Figure 2: Results of hypotheses tests (***p<0.001, dotted

line=Not significant).

The results of the structural model have been

very close to the measurement model which

provides evidence that the structural model fits the

observed data well. Thus, we proceed to examine the

hypothesized relationships within the model. As can

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be shown in figure 2, all the path coefficients are

significant in our structural model, supporting all

hypotheses with the exception of H5. The results

show that the characteristics related to the

organizational and technological factors positively

influence perceived usefulness, while the

characteristics related to the individual factors have

no impact on perceived usefulness.

5 DISCUSSION

This study investigates university instructors’

continuance intention to use Blackboard system as a

learning management system to support e-learning

platform in the UAE. Our study is motivated by the

need to examine a “university instructor”

perspective, which has not been highly investigated

in the literature. We believe that the results of the

study will further offer scholars and researchers

some insights of the influence of the proposed

factors on motivating university instructors’

continuance intention to use leaning management

systems.

Our findings indicate that all the hypotheses

related to the direct relation between perceived

usefulness are supported with the exception of the

influence of computer self-efficacy. First, we have

found that the technological characteristics,

represented by user-interface design, influence

perceived usefulness of Blackboard system. Second,

we have found that having good technical support

and proper training, as factors of organizational

characteristics, can increase the feeling of the

benefits of Blackboard system, suggesting that

regular training and offering technical support to

users will allow them to become familiar with LMS

and consequently realize the benefits of these

technologies. Our results are consistent with

previous research, which has reported that technical

support and user-interface significantly determine

perceived usefulness within e-learning context (Cho

et al., 2009). Our findings also draw attention to the

importance of the technological factors in

influencing the perceived usefulness of Blackboard

system and, eventually increase continuance

intention to use this technology by university

instructors. Thus, we suggest that a good Blackboard

system user-interface design which is user friendly

and simple will allow university instructors to feel

the benefits of this technology, and motivate them to

have continuance intention to use it. Our result

contradicts with the finding of Hong et al., (2011)

who have found that interface design has no

influence on perceived usefulness of digital archives

system among users in Taiwan.

On the other hand, our findings have indicated

that users with individual characteristics represented

by high computer self-efficacy does not positive

influence of perceived usefulness of Blackboard

system. This suggests that high computer-self

efficacy does not necessarily enable university

instructors to perceive Blackboard system as useful.

Hence, further research is needed to validate the

influence of this characteristic and its link to

technology continuance adoption of LMS.

As for the relation between perceived usefulness

and continuance intention, our finding shows that

users who feel the benefits of the Blackboard will

have positive intention to continue use it. Our results

are consistent with the study of Sørebø and Sørebø

(2009) which has found that university teachers’

continuance intention toward e-learning technology

in connection with on-site courses is influenced by

perceived usefulness of these technologies.

Similarly, Cho et al., (2009) have found that

perceived usefulness determine university students’

continuance intention to use e-learning tools in Hong

Kong.

6 IMPLICATIONS AND

SUGGESTIONS FOR FUTURE

RESEARCH

Although prior studies have investigated the post-

adoption of management learning systems from a

student perspective, there has been little research

examining post-adoptive continuance intention from

a university instructor perspective. This study

attempts to fill this gap and thus its results have

several theoretical and practical implications.

From a theoretical perspective, the results of this

study suggest that our proposed model provides a

better understanding of the factors influencing

instructors’ decision to continue using Blackboard

system. In fact, to our knowledge this is one of the

few empirical studies which have investigated the

factors influencing post-adoptive intention towards

Blackboard system for university instructors. We

believe that our results will encourage further

research to apply our model to other management

learning systems, such as WebCT.

From a practical perspective, our research reports

that perceived usefulness positively influence

continuance intention towards Blackboard system.

Hence, universities should organize seminars and

Factors Affecting University Instructors’ Continuance Intention to Use Learning Management Systems: The Blackboard System Case

219

workshops to explain the benefits of Blackboard

system and familiarize the instructors with any

updates of the system which can be useful in order

to motivate them to have continuance intention to

use it. As for the exogenous factors influencing

perceived usefulness, this study recommends

considering the organizational and the technological

characteristics to emphasize the usefulness and the

benefits of Blackboard system.

In terms of user-interface Design, our findings

have revealed that when Blackboard system design

is developed in a more user-friendly manner, users

will be able to perceive its benefits and be satisfied

with it, which will eventually encourage them to

continue using it. Thus, web developers of LMS

should consider developing user-friendly systems so

that their customers will feel comfortable with the

features of the website, which will influence their

decision to continue using it. Also, having a good

user-interface will allow the users to obtain the

benefits of the system, which will also encourage

them to consider using it again.

In terms of training, universities using LMS are

encouraged to provide additional flexible and

voluntary training sessions for the instructors’

personal development, so that they can be familiar

with the capabilities and the benefits of these

technologies. Furthermore, universities can offer

instructors personalized online training sessions

which will be customized according to their

individual needs. Finally, in terms of technical

support, universities can offer instructors various

methods which will allow them to ask questions

about any technical problem they encounter while

using the LMS, such as online chatting as well as

direct phone number or email. Effective technical

support will help instructors to become comfortable

with the LMS which will lead to their understanding

of the system’s benefits.

Although this study has provided valuable

findings, it has several limitations. First, this study

examines the influence of several factors on

continuance intentions to use Blackboard system.

We propose investigating the influence of those

factors on other LMS to enhance the generalizability

of our findings. Second, this research has been

conducted in a university in the UAE, where using

Blackboard system is voluntary. Thus, further

research is needed to test our model in other

academic institutions where using Blackboard

system is mandatory. Third, this study has

investigated the influence of certain variables on

user continuance intention to use Blackboard

system. Future research can incorporate other

factors, such as computer anxiety and subjective

norms, to investigate their effects. Furthermore,

future research could take into consideration

investigating the role of individual differences, such

as gender and personality traits. Finally, this study

has investigated the influence of critical factors

which are related to the individual, organizational

and technological characteristics on perceived

usefulness. Further research can examine the

influence of other characteristics such as

environmental characteristics.

ACKNOWLEDGEMENTS

The framework in this study is a modified model

adopted from the second author’s thesis in the

fulfillment of Master of Science degree in

Information Technology Management. The modified

framework has been applied to a larger sample size

and utilized different statistical tools to analyze the

data. Consequently the results are different.

REFERENCES

Agarwal, R., Sambamurthy, V., Stair, R. M., 2000.

Research report: The evolving relationship between

general and specific computer self-efficacy—An

empirical assessment, Information Systems Research,

Vol. 11, No. 4, pp. 418–430.

Al-Busaidi, K. L., Al-Shihi, H., 2012. Key factors to

instructors’ satisfaction of learning management

systems in blended learning, Journal of Computing in

Higher Education, Vol. 24, No. 1, pp. 18–39.

Amoroso, D. L., Cheney, P. H., 1991. Testing a causal

model of end-user application effectiveness, Journal

of Management Information Systems, Vol. 8, No. 1,

pp. 63–90.

Anderson, J. C., Gerbing, D. W., 1988. Structural equation

modeling in practice: A review and recommended

two-step approach, Psychological Bulletin, Vol. 103,

No. 3, pp. 411-423.

Ball, D. M., Levy, Y., 2008. Emerging Educational

Technology: Assessing the Factors that Influence

Instructors’ Acceptance in Information Systems and

Other Classroom, Journal of Information Systems

Education, Vol. 9, No. 4, pp. 431-444.

Bentler, P. M., Bonett, D. G., 1980. Significance tests and

goodness of fit in the analysis of covariance structure,

Psychological Bulletin, Vol. 88, No. 3, pp. 588–606.

Bhattacherjee, A., 2001. Understanding information

systems continuance: An expectation-confirmation

model, MIS Quarterly, Vol. 25, No. 3, pp. 351-370.

Bhattacherjee, A., Hikmet, N., 2008. Reconceptualizing

organizational support and its effect on information

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

220



technology usage: evidence from the health care

sector, Journal of Computer Information Systems, Vol.

48, 4, pp. 69-76.

Bradford, M., Florin, J., 2003. Examining the role of

innovation diffusion factors on the implementation

success of enterprise resource planning systems,

International Journal of Accounting Information

Systems, Vol. 4, No. 3, pp. 205-225.

Caputi, V., Garrido, A., 2015. Student-oriented planning

of e-learning contents for Moodle, Journal of Network

and Computer Applications, Vol. 53, pp. 115–127.

Chang, C-C., 2013. Exploring the determinants of e-

learning systems continuance intention in academic

libraries, Library Management, Vol. 34, No. 1/2, pp.

40-55.

Chatzoglou, P. D., Sarigiannidis, L., Vraimaki, E.,

Diamantidis, A., 2009. Investigating Greek

employees’ intention to use web-based training,

Computers & Education, Vol. 53, No. 3, pp. 877–889.

Chen, Y-C, 2014. An empirical examination of factors

affecting college students’ proactive stickiness with a

web-based English learning environment, Computers

in Human Behavior, Vol. 31, pp. 159–171.

Cheng, Y-M, 2014. Roles of interactivity and usage

experience in e-learning acceptance: a longitudinal

study, International Journal of Web Information

Systems, Vol. 10, No. 1 pp. 2 – 23.

Chiu, C. M., Hsu, M. H., Sun, S. Y., Lin, T. C., Sun, P. C.,

2005. Usability, quality, value and e-learning

continuance decisions, Computers & Education, Vol.

45, No. 4, pp. 399–416.

Chiu, C-M., Wang, E. T. G., 2008. Understanding Web-

based learning continuance intention: The role of

subjective task value, Information & Management,

Vol. 45, No. 3, pp. 194–201.

Cho, V., Cheng, T.C. E., Lai, M.W. J., 2009. The role of

perceived user-interface design in continued usage

intention of self-paced e-learning tool, Computers &

Education, Vol. 53, No. 2, pp. 216–227.

Compeau, D. R., Higgins, C. A., 1995. Computer Self-

Efficacy: Development of a Measure and Initial Test,

MIS Quarterly, Vol. 19, No. 2, pp. 189-211.

Cyr, D., Head, M., Ivanov, A., 2006. Design aesthetics

leading to m-loyalty in mobile commerce, Information

& Management, Vol. 43, No. 8, pp. 950-963.

Davis, F. D., 1989. Perceived usefulness, perceived ease

of use and user acceptance of information technology,

MIS Quarterly, Vol. 13, No. 3, pp. 319–340.

Fornell, C., Larcker, D. 1981. Structural equation models

with unobservable variables and measurement error,

Journal of Marketing Research, Vol. 18, No. 1, pp.

39–50.

Hair, J. F., Anderson, R. E., Tatham, R. L. and Black, W.

C. (1998), Multivariate data analysis with readings

(5th ed.), New York, NY: Macmillan.

Hair, J., Black, W., Babin, B., Anderson, R., Tatham, R.

L., 2006. Multivariate Data Analysis, Pearson

Education: Harlow, 6

edition.

Hoehle, H., Sid Huff, S., Goode, S., 2011. The role of

continuous trust in information systems continuance,

Journal of Computer Information Systems, Vol. 52,

No. 4, pp. 1-9.

Hong, J-Ch, Hwang, M-Y, Hsu, H-F, Wong, W-T, Chen,

W-Y, 2011. Applying the technology acceptance

model in a study of the factors affecting usage of the

Taiwan digital archives system, Computers &

Education, Vol. 57, No. 3, pp. 2086–2094.

Igbaria, M., Guimaraes, T., Davis, G., Zinatelli, N., Cragg,

P., Cavaye, L. M., 1997. Computing acceptance

factors in small firms: A structural equation model,

MIS Quarterly, Vol. 21, No. 3, pp. 279–305.

Jeong, H., 2011. An investigation of user perceptions and

behavioral intention towards the e-library, Library

Collections, Acquisitions, & Technical Services, Vol.

35, No. 2, pp. 45–60.

Kim, B., 2010. An empirical investigation of mobile data

service continuance: Incorporating the theory of

planned behavior into the expectation–confirmation

model, Expert Systems with Applications, Vol. 37, No.

10, pp. 7033–7039.

Lareki, A., Martínez de Morentin, J. I., Amenabar, N.,

2010. Towards an efficient training of university

faculty on ICTs”, Computers & Education, Vol. 54,

No. 2, pp. 491–497.

Lee, H., Choi, S. Y., Kang, Y. S., 2009. Formation of e-

satisfaction and repurchase intention: Moderating roles

of computer self-efficacy and computer anxiety,

Expert Systems with Applications, Vol. 36, No. 4, pp.

7848–7859.

Lee, M-C, 2010. Explaining and predicting users’

continuance intention toward e-learning: An extension

of the expectation–confirmation model, Computers &

Education, Vol. 54, No. 2, pp. 506-516.

Liaw, S-S, 2008. Investigating students’ perceived

satisfaction, behavioral intention, and effectiveness of

e-learning: A case study of the Blackboard system,

Computers & Education, Vol. 51, pp. 864–873.

Liaw, S-S, Huang, H-M., 2013. Perceived satisfaction,

perceived usefulness and interactive learning

environments as predictors to self-regulation in e-

learning environments, Computers & Education, Vol.

60, pp. 14–24.

Limayem, M., Hirt, S.G., Cheung, C.M.K., 2007. How

habit limits the predictive power of intention: The case

of information systems continuance, MIS Quarterly,

Vol. 31, No. 4, pp. 705-737.

Limayem, M., Cheung, C.M.K., 2008. Understanding

information systems continuance: The case of Internet-

based learning technologies”, Information &

Management, Vol. 45, pp. 227–232.

Lin, C.S., Wu, S., Tsai, R.J., 2005. Integrating perceived

playfulness into expectation-confirmation model for

web portal context, Information & Management, Vol.

42, No. 5, pp. 683-693.

Liu, I-F, Chen, M. C., Sun, Y. S., Wible, D., Kuo, C-H,

2010. Extending the TAM model to explore the factors

that affect Intention to use an online learning

community, Computers & Education, Vol. 54, pp.

600–610.

Ngai, E.W.T., Poon, J.K.L., Chan, Y.H.C., 2007.

Factors Affecting University Instructors’ Continuance Intention to Use Learning Management Systems: The Blackboard System Case

221

Empirical examination of the adoption of WebCT

using TAM, Computers & Education, Vol. 48, No. 2,

pp. 250-267.

Nelson, R. R., Cheney, P., 1987. Training End Users: An

Exploratory Study, MIS Quarterly, Vol. 11, No. 4, pp.

547-559.

Nunnally, J. C., Bernstein, I. H., 1994. Psychometric

theory, McGraw-Hill, New York, 3rd edition.

Paechter, M., Maier, B., Macher, D., 2010. Students’

expectations of, and experiences in e-learning: Their

relation to learning achievements and course

satisfaction, Computers & Education, Vol. 54, No. 1,

pp. 222–229.

Randeree, K., Narwani, A., 2009. Managing Change in

Higher Education: An Exploration of the Role of

Training in ICT Enabled Institutions in the United

Arab Emirates, The International Journal of Learning,

Vol. 16, No. 4, pp. 447-456.

Sørebø, A. M., Sørebø, Ø., 2009. Understanding e-

learning satisfaction in the context of university

teachers”, International Journal of Humanities and

Social Sciences, Vol. 3, No. 4, pp. 309-312.

Sumner, M., Hostetler, D., 1999. Factors influence the

adoption of technology in teaching, Journal of

Computer Information Systems, Vol. 40, No. 1, pp.

81–87.

Sun, P-C, Tsai, R. J., Finger, G., Chen, Y-Y, Yeh, D.,

2008. What drives a successful e-Learning? An

empirical investigation of the critical factors

influencing learner satisfaction, Computers &

Education, Vol. 50, No. 4, pp. 1183–1202.

Sawang, S., Newton, C., Jamieson, K. 2013. Increasing

learners’ satisfaction/intention to adopt more e-

learning, Education + Training, Vol. 55, No. 1, pp. 83

– 105.

Tarhini, A., Honea, K., Liua, X., 2013. User acceptance

towards web-based learning systems: investigating the

role of social, organizational and individual factors in

European higher education, Procedia Computer

Science, Vol. 17, pp. 189 – 197.

Tong, K.P., Triniada, S.G., 2005. Conditions and

constraints of sustainable innovative pedagogical

practices using technology, Journal of International

Electronic for leadership in learning, Vol. 9, No. 3,

pp. 1-27.

Williams, P., 2002. The learning Web: the development,

implementation and evaluation of Internet-based

undergraduate materials for the teaching of key skills,

Active Learning in Higher Education, Vol. 3, No. 1,

pp. 40–53.

Yi, M. Y., Hwang, Y, 2003. Predicting the use of web-

based information systems: self-efficacy, enjoyment,

learning goal orientation, and the technology

acceptance model, International Journal of Human-

Computer Studies, Vol. 59, No. 4, pp. 431–449.

Yoon, C., Kim, S., 2007. Convenience and TAM in a

ubiquitous computing environment: The case of

wireless LAN, Electronic Commerce Research and

Applications, Vol. 6, No. 1, pp. 102–112.

APPENDIX

Note: Two items were dropped from computer self-

efficacy due to low factor loading.

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