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Key factors to instructors’ satisfaction of learning management systems in blended learning Kamla Ali Al-Busaidi Hafedh Al-Shihi Published online: 17 November 2011 Ó Springer Science+Business Media, LLC 2011 Abstract Learning Management System (LMS) enables institutions to administer their educational resources, and support their traditional classroom education and distance education. LMS survives through instructors’ continuous use, which may be to a great extent associated with their satisfaction of the LMS. Consequently, this study examined the key factors that influence the instructors’ satisfaction of LMS in blended learning, and how this satisfaction is related to their intention to continu- ously use LMS in blended learning and purely for distance education. These investigated factors are related to instructors’ individual characteristics (computer anxiety, technology experience and personal innovativeness), LMS characteristics (system quality, information quality and service quality), and organizational char- acteristics (management support, incentives policy and training). The findings indicated that computer anxiety, personal innovativeness, system quality, infor- mation quality, management support, incentives policy and training are key factors to instructors’ satisfaction of LMS in blended learning. Furthermore, instructors’ satisfaction is a significant determinant of their continuous intention to use LMS in blended learning, and their intention to purely use LMS for distance education. Keywords Learning management system Á e-learning Á Instructors’ satisfaction Á Critical factors to LMS Á Blended learning Introduction Learning Management Systems (LMSs) and e-learning have become an essential tool for stakeholders in education and training. The global market for e-learning reached US$27.1 billion in 2009 and revenues will reach $49.6 billion by 2014 K. A. Al-Busaidi (&) Á H. Al-Shihi Sultan Qaboos University, P.O. Box: 20, 123 Al-Khod, Muscat, Oman e-mail: [email protected] 123 J Comput High Educ (2012) 24:18–39 DOI 10.1007/s12528-011-9051-x

Key factors to instructors’ satisfaction of learning management systems in blended learning

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Page 1: Key factors to instructors’ satisfaction of learning management systems in blended learning

Key factors to instructors’ satisfaction of learningmanagement systems in blended learning

Kamla Ali Al-Busaidi • Hafedh Al-Shihi

Published online: 17 November 2011

� Springer Science+Business Media, LLC 2011

Abstract Learning Management System (LMS) enables institutions to administer

their educational resources, and support their traditional classroom education and

distance education. LMS survives through instructors’ continuous use, which may

be to a great extent associated with their satisfaction of the LMS. Consequently, this

study examined the key factors that influence the instructors’ satisfaction of LMS in

blended learning, and how this satisfaction is related to their intention to continu-

ously use LMS in blended learning and purely for distance education. These

investigated factors are related to instructors’ individual characteristics (computer

anxiety, technology experience and personal innovativeness), LMS characteristics

(system quality, information quality and service quality), and organizational char-

acteristics (management support, incentives policy and training). The findings

indicated that computer anxiety, personal innovativeness, system quality, infor-

mation quality, management support, incentives policy and training are key factors

to instructors’ satisfaction of LMS in blended learning. Furthermore, instructors’

satisfaction is a significant determinant of their continuous intention to use LMS in

blended learning, and their intention to purely use LMS for distance education.

Keywords Learning management system � e-learning � Instructors’ satisfaction �Critical factors to LMS � Blended learning

Introduction

Learning Management Systems (LMSs) and e-learning have become an essential

tool for stakeholders in education and training. The global market for e-learning

reached US$27.1 billion in 2009 and revenues will reach $49.6 billion by 2014

K. A. Al-Busaidi (&) � H. Al-Shihi

Sultan Qaboos University, P.O. Box: 20, 123 Al-Khod, Muscat, Oman

e-mail: [email protected]

123

J Comput High Educ (2012) 24:18–39

DOI 10.1007/s12528-011-9051-x

Page 2: Key factors to instructors’ satisfaction of learning management systems in blended learning

(Ambient Insight Research 2011). A number of top universities around the world

have adopted LMS to enhance the instructional process (Browne et al. 2006;

Hawkins and Rudy 2007; National Center for Educational Statistics 2003). More

than 90% of all participating academic institutions in the US are adopting LMS

(Hawkins and Rudy 2007). Similarly, almost 95% of participating institutions in the

UK have adopted LMS (Browne et al. 2006). In Middle East and Africa, the demand

for e-learning products is growing by a 5-year compound annual growth rate of

more than 10% from 2009 to 2014 (Ambient Insight Research 2011).

Users’ satisfaction of an information system is critical to its continuous success.

Similarly for LMS, its success to a great extent depends on instructors’ satisfaction

of the system. Assessing individual users’ acceptance of the e-learning systems is a

‘‘basic marketing element’’ (Kelly and Bauer 2004). A survey of more than 800

instructors at 35 LMS-adopting institutions found that very few instructors use LMS

tools for assessing student learning or promoting community (Woods et al. 2004).

Prior studies also found that instructors’ adoption of LMS may be limited by fear of

technology and lack of time (Yueh and Hsu 2008). Therefore, instructors’

satisfaction of LMS is crucial and should be carefully studied to ensure successful

LMS deployment. LMS survive through instructors’ continuous use, which may be

to great extent linked to their satisfaction of the LMS.

Consequently, the objective of this study is to investigate the key factors

contributing to instructors’ satisfaction of LMS use in blended learning environ-

ment. These factors can be categorized as instructors’ individual characteristics

(computer anxiety, technology experience and personal innovativeness), LMS

characteristics (system quality, information quality and service quality), and

organizational characteristics (management support, incentives policy and training).

In addition, the study also assesses how instructors’ satisfaction of LMS use in

blended learning is related to their continuous intention to LMS use in blended

learning, and their intention to purely use LMS for distance education. Several

organizations initiate their LMS adoption by using them in blended learning

environment, to elevate the risks of a complete pure LMS use for distance

education. The following sections discuss the background literature, research

framework and methodology, analysis and results, and the conclusion.

Literature review

LMS and benefits

According to the World Bank (2010), LMS is a software package that automatically

administers education and trains human resources. It is the use of a Web-based

communication, collaboration, learning, knowledge transfer, and training to add

value to learners and businesses (Kelly and Bauer 2004). LMS supports e-learning

activities such as presenting information, managing course materials, and collecting

and evaluating student (Yueh and Hsu 2008). Several terminologies also are used to

refer to LMS such as Course Management Systems (CMS) and Learning Content

Management Systems (LCMS) (Yueh and Hsu 2008), and Computer-assisted

Key factors to instructors’ satisfaction of learning 19

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Learning (CAL), Computer-based Learning (CBL), and Online Learning (Chan

et al. 2008). LMS can be used to support traditional face-to-face classroom learning

(blended learning); or it can be used to build a virtual classroom where learning is

done purely fully online for distance learning (Rainer et al. 2007). Also, LMS

applications have become an important tool in business for training purposes.

Approximately 20% of all training in large US corporations is delivered through

LMS (Reiser 2002).

There are several LMS applications in the market. The most popular LMS used at

academic organizations in the US is Blackboard (Falvo and Johnson 2007).

Blackboard has course management features that support integration with student

databases (Knecht and Reid 2009); it enables course delivery, content management

and community engagement (Blackboard 2011). Other commercial LMS solutions

are eCollege and Learn.com. Moodle, a free open-source software package, is

sometimes preferred over Blackboard (Beatty and Ulasewicz 2006). Moodle has

been adopted by many universities, schools, companies and independent teachers

around the world (Moodle 2011). ETutor, Claroline, eFront and Joomla are also

open-source applications.

LMS applications offer instructors several functionalities that benefits and

contribute to teaching process (Burniske and Monke 2001). Course management

tools, group chat and discussion, assignment submission, and course assessment are

the primary tools in LMS (Yueh and Hsu 2008). In addition, LMS helps instructors

provide learners with educational materials and track their participation and

assessments (Falvo and Johnson 2007). LMS solutions also aim to increase interest

in learning and teaching among learners and instructors, respectively (Mahdizadeh

et al. 2008). Furthermore, LMS enhances teaching process efficiency and result in

cost-savings (Aczel et al. 2008; Naidu 2006).

Prior studies on LMS

Users’ satisfaction of LMS, as any other information system, is critical to their

continuous use (DeLone and McLean 2003). There are a number of studies that

have empirically investigated the learners’ acceptance, use and/or satisfaction of

LMS such as Klobas and McGill (2010), Liaw et al. (2007), Pituch and Lee (2006),

Raaij and Schepers (2008), Roca et al. (2006), Sun et al. (2008), and Wu et al.

(2006). However, limited quantitative studies have investigated instructors’

acceptance, use and/or satisfaction of LMS. In the LMS context, researchers have

studied LMS acceptance and success, from instructors’ perspective, in various ways.

Liaw et al. (2007) assessed factors influencing learners’ and instructors’ behavioral

intention to use e-learning, which is influenced by perceived usefulness, perceived

self-efficacy, and perceived enjoyment. Ball and Levy (2008) investigated the

impact of instructor’s individual characteristics on instructors’ intention to use

LMS. Teo (2009) assessed the instructor’s perceived usefulness of LMS and

perceived ease of use. However, user satisfaction of an information system is critical

to its continuous use and resulted benefits (DeLone and McLean 1992, 2003).

Moreover, key factors that might impact the instructors’ adoption of LMS can be

related to their individual characteristics (Ball and Levy 2008; Liaw et al. 2007;

20 K. A. Al-Busaidi, H. Al-Shihi

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Raaij and Schepers 2008; Teo 2009), LMS characteristics (Pituch and Lee 2006;

Roca et al. 2006), and organization characteristics (Sumner and Hostetler 1999).

Al-Busaidi and Al-Shihi (2010) developed a theoretical model of the determinants

of instructors’ acceptance of LMS.

None of these studies, however, empirically investigated the direct impact of

instructors’ characteristics, LMS characteristics, and/or organizational characteris-

tics on instructors’ satisfaction. User satisfaction is an important indicator of IS

success (DeLone and McLean 2003). In addition assessing the impacts of

organization characteristics along with instructors’ characteristics and LMS

characteristics on instructors’ satisfaction is vital.

Instructor characteristics

The adoption and satisfaction of LMS may, critically, be determined by the

characteristics of its users. Several users characteristics have been recommended

and investigated as determinants of technology acceptance. In the context of

e-learning, few studies have investigated the impact of instructor dimensions on

LMS acceptance. Ball and Levy (2008) investigated the impact of self-efficacy,

computer anxiety, and technology experience on instructor intention to use

emerging learning experience in a small private university in the US and found

that self-efficacy was the only major determinant of instructor intention. Teo (2009)

found that computer self-efficacy directly impacts pre-service teacher’s perceived

usefulness, perceived ease of use, and behavioral intention in Singapore. Liaw et al.

(2007) found that perceived self-efficacy determines instructor behavioral intention

to use e-learning in Taiwan. Albirini (2006) investigated the perception of school

teachers of the use of ICT in education in Syria, and the results highlighted the

importance of teachers’ vision of technology, their experiences with it, and the

cultural conditions on their attitudes toward technology. Mahdizadeh et al. (2008)

found that a teacher’s previous experience with e-learning environments and ease of

use explain teachers’ perception of the usefulness of e-learning environments and

their actual use of these environments. Instructor innovativeness is important to the

satisfaction of e-learning (Raaij and Schepers 2008).

LMS characteristics

LMS characteristics may, to a great extent, impact the instructor’s satisfaction of

LMS. DeLone and McLean (2003) proposed that the major characteristics of any

information system include system quality, information quality, and service support

quality. System quality of LMS was found to be significant on instructors’ perceived

usefulness, perceived enjoyment, and perceived self-efficacy, which consequently

affect their intention to use the system (Liaw et al. 2007).

In the e-learning context, few studies have examined the general quality of the

technology or a specific dimension. For instance, from instructors’ and learners’

perspective, Liaw et al. (2007) investigated the impact of e-learning systems’

general quality on perceived usefulness, perceived enjoyment, and perceived self-

efficacy, which consequently affect their intention to use the system in the

Key factors to instructors’ satisfaction of learning 21

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classroom, and found it significant. Albirini (2006) indicates that instructor vision of

technology impacts their attitudes toward the use of ICT in education. Two

significant studies on the impact of technology on user acceptance of LMS are

Pituch and Lee’s (2006) and Roca et al.’s (2006), but they are from the learner’s

perspective. Roca et al. (2006) investigated learner’s perception of system quality

from three dimensions (system quality, information quality, and service quality).

They found that a learner’s perceived system factors (system quality, information

quality, and service quality) directly affected e-learning satisfaction and intention to

use and indirectly their perceived usefulness. Pituch and Lee (2006) examined the

impact of system quality from three dimensions: the system’s functionality,

interactivity, and response.

As indicated, limited studies provide a detailed examination of the influence of

the three dimensions (system quality, information quality, service quality) of LMS

on instructor satisfaction. This study integrates these three dimensions of LMS on

the instructors’ satisfaction.

Organization characteristics

An organization’s characteristics can have a major impact on the behaviors of its

employees, including their use and satisfaction of any technology including LMS.

Corporate culture plays a major role in the success of any project. Culture is defined

as ‘‘the way we do things around here’’ (Schein 1985, p. 12). Cultural values outline

an organization’s customs and practices, which consequently influence employees’

behaviors such as LMS use and satisfaction. Organizational norms are considered a

reason for the use or resistance of a technology (such as LMS) in education (Lin

et al. 2010). In the e-learning context, management support, incentives, and training

are some organization’s characteristics that might be relevant to employees’

satisfaction of LMS.

Very limited empirical studies capture the impact of organization’s character-

istics on the acceptance and satisfaction of LMS. In the e-learning context, senior

management support, incentives and training are important for the instructor’s

adoption of e-learning. Senior managers should identify the alignment of e-learning

with the department and university curriculum and highlight its importance (Sumner

and Hostetler 1999). Motivators or incentives for instructors can be imposed by

having technology use as a factor in a nomination for teaching award, promotion,

and tenure (Sumner and Hostetler 1999). Finally, facilitating conditions including

training indirectly affect teachers’ acceptance of technology in education (Teo

2009).

Instructors’ satisfaction of LMS

Framework development

The objective of this study was to investigate the impact of instructor’s individual

characteristics, LMS characteristics, and organization characteristics on instructor

22 K. A. Al-Busaidi, H. Al-Shihi

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satisfaction of LMS in blended learning, and consequently, on their continuous use in

blended learning and pure use intention for distance learning. Limited studies have

investigated this integrated examination of instructors’ satisfaction of LMS. Specifi-

cally, this study assessed the impact of individual characteristics (computer anxiety,

technology experience and personal innovativeness), LMS characteristics (system

quality, information quality, and service quality), and organization characteristics

(management support, incentives policy and training) on instructor satisfaction. The

impact of instructor self efficacy was also initially considered as part of instructors’

characteristics, but was dropped from the analysis because of low reliability and validity

of the construct in this study. Figure 1 illustrates this study model.

Instructor individual characteristics hypotheses

Computer anxiety hypothesis

Computer anxiety is an important factor for the acceptance of the technology (Ball

and Levy 2008; Piccoli et al. 2001; Raaij and Schepers 2008; Sun et al. 2008).

Fig. 1 A model of critical factors to instructors’ satisfaction of LMS in blended learning and its link totheir continuous intention to use LMS in blended learning and intention to use LMS purely for distanceeducation

Key factors to instructors’ satisfaction of learning 23

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Computer anxiety is ‘‘the fear or apprehension felt by individuals when they used

computers, or when they considered the possibility of computer utilization’’

(Simonson et al. 1987, p. 238). The user’s acceptance of LMS and their perceived

satisfaction may negatively be affected by their fear of computers (Piccoli et al.

2001). The literature has different empirical evidence of the impact of computer

anxiety. Ball and Levy (2008) did not detect a significant link between computer

anxiety and instructors’ intention to use the e-learning. On the other hand, Sun et al.

(2008) found that computer anxiety significantly impacts the learners’ perceived

satisfaction of e-learning. Therefore:

Hypothesis 1 Instructors’ computer anxiety will be negatively associated with

their satisfaction of LMS.

Technology experience hypothesis

User experience with the technology plays a role in the acceptance of the

technology (Thompson et al. 2006; Venkatesh and Davis 2000). A user’s experience

with technology is his/her exposure to the technology along with the skills and

abilities that are gained through using a technology (Thompson et al. 2006). Thus,

instructors’ acceptance of LMS can be impacted by their technology experience.

Even though Ball and Levy’s (2008) empirical study found no significant impact of

technology experience on instructors’ intention to use LMS, researchers theoret-

ically emphasized its importance. Wan et al. (2007)underlined the importance of

technology experience on the learning processes and learning outcomes. Similarly,

Mahdizadeh et al. (2008) suggested that instructors’ prior experience with

e-learning may explain their perception of the usefulness of e-learning environments

and their actual use. Therefore:

Hypothesis 2 The instructor’s experience with the use of technology will be

positively associated with their satisfaction of LMS.

Personal innovativeness hypothesis

Personal innovativeness is another issue that may be critical factor on instructor

satisfaction of LMS. Personal innovativeness in information technology context is

an individual’s attitude reflecting a tendency to experiment with and to adopt new

information technologies independently of the communicated experience of others;

‘‘Being used to adapting to new systems and processes might reveal the usefulness

and ease of use more quickly to an innovative person than to a non-innovative

person’’ (Schillewaert et al. 2005, p. 843). Instructors’ innovativeness is important

to the satisfaction of e-learning (Raaij and Schepers 2008). Therefore,

Hypothesis 3 The instructor’s personal innovativeness will be positively associ-

ated with their satisfaction of LMS.

24 K. A. Al-Busaidi, H. Al-Shihi

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LMS characteristics hypotheses

System quality hypothesis

System quality is a critical factor to user satisfaction of any technology, including

LMS. The impact of system quality on technology acceptance, use or satisfaction

has been highlighted by several researchers such as Bailey and Pearson (1983),

DeLone and McLean (1992), and Seddon (1997). Instructor satisfaction of LMS

may be impacted to a great extent by system quality. The more functionalities,

interactivity, and response of LMS, the better its acceptance and utilization (Pituch

and Lee 2006). Several characteristics of e-learning system have a significant

impact on e-learning acceptance. Such system characteristics are system’s reliability

(Wan et al. 2007; Webster and Hackley 1997), accessibility (Wan et al. 2007), and

functionality, interactivity, and response (Pituch and Lee 2006). Instructor vision of

technology impacts their attitudes toward the use of ICT in education (Albirini

2006). Therefore:

Hypothesis 4 LMS system quality will be positively associated with the

instructor’s satisfaction of LMS.

Information quality hypothesis

Information quality can be significant for instructor satisfaction of LMS. Informa-

tion quality is the perceived output produced by the system. It is related to

information accuracy, relevance, timeliness, sufficiency, completeness, understand-

ability, format, and accessibility, which are important for the success of information

technology (Bailey and Pearson 1983; Seddon 1997). Limited research investigated

the impact of information quality on instructor satisfaction of LMS. From a

learner’s perspective, Roca et al. (2006) found that information quality was directly

significant for learner’s satisfaction and indirectly for perceived usefulness. They

assessed information quality of LMS by indicators related to relevance, timeliness,

sufficiency, accuracy, clarity, and format, Similarly, Lee (2006) found content

quality was significant for learner’s perceived usefulness. Consequently:

Hypothesis 5 LMS information quality will be positively associated with the

instructor’s satisfaction of LMS.

Service quality hypothesis

Service quality can be an important factor to instructor satisfaction of LMS. It is

related to the quality of support services provided to the system’s end-users. Service

quality is related to reliability, responsiveness, assurance, tangibles, and empathy of

support service (Parasuraman et al. 1988; Kettinger and Lee 1994). Limited studies

have investigated the impact of service quality on LMS adoption and success from

instructors’ perspective. Thus:

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Hypothesis 6 LMS service quality will be positively associated with instructor’s

satisfaction of LMS.

Organization characteristics hypotheses

Management support hypothesis

Management support can be a key factor for the instructors’ satisfaction of LMS.

Senior managers’ open approval of LMS promotes instructor adoption and

acceptance of LMS. Managers may support LMS adoption by encouraging

instructors to adopt it and clearly identify how it is aligned with the university

vision. There is very limited empirical research on the impact of management

support on instructor satisfaction of LMS. However, Sumner and Hostetler (1999)

indicated that senior managers should clearly identify the goal of LMS for the

university curriculum to encourage instructors to adopt and use the system.

Managers are acknowledged as a high authority (Ali 1990). Igbaria (1990) found

that management support of end-users significantly improves computer use.

Similarly, Teo (2009) found that administrative support, is a facilitating condition,

and indirectly affect instructors’ acceptance of technology in education.

Consequently:

Hypothesis 7 Management support will be positively associated with the

instructor’s satisfaction of LMS.

Incentives policy hypothesis

Incentives may have an impact on instructor satisfaction of LMS. Incentives are

important factors to encourage instructors to integrate LMS in their teaching.

Incentives can be in form of ‘‘non-trivial’’ monetary and non-monetary incentives.

There is an absence of research on the impact of incentives on LMS acceptance. In

e-learning context, incentives for instructors can be enforced by using the LMS as a

factor in nomination for a teaching award, promotion, and tenure (Sumner and

Hostetler 1999). Incentive policies may drive instructors’ use and satisfaction of

LMS. Therefore:

Hypothesis 8 An incentive policy will be positively associated with instructor’s

satisfaction of LMS.

Training hypothesis

Training may improves instructor adoption of LMS as it illustrates its potential

usefulness, and encourages its use in teaching. Limited research has investigated the

impact of training on instructor satisfaction of LMS. Training may include

workshops, online tutorials, courses, and seminars (Sumner and Hostetler 1999).

Teo (2009) found that facilitating conditions, including training, indirectly affect

teachers’ acceptance of technology in education. Thus:

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Hypothesis 9 Training will be positively associated with the instructor’s

satisfaction of LMS.

Usage and future intention hypotheses

Continuous blended learning intention hypothesis

The intention to use the technology is significantly determined by user acceptance:

perceived ease of use and perceived usefulness (Venkatesh and Davis 2000). The

higher the instructor satisfaction with LMS, the more likely it is that they will

continue to use it. Hayashi et al. (2004) indicated that continuous intention to

e-learning use is determined by perceived usefulness and satisfaction. Thus:

Hypothesis 10 The instructor satisfaction of LMS will be positively associated

with their intention to continuously use LMS in blended learning.

Pure use intention hypothesis

Some organizations begin their LMS adoption in blended learning environment (as

a supplementary tool to traditional classroom teaching), hoping that this supple-

mentary adoption will eventually promote the pure full use of LMS for distance

education. Instructors’ satisfaction of LMS in blended learning may have an

important impact on their future intention to use LMS purely for distance education.

When instructors realize the benefits of LMS and become satisfied with its use in

blended learning, they are more likely to adopt it purely for distance education.

Perceived usefulness of a technology is significant factor for their intention to use it

(Venkatesh and Davis 2000). Pituch and Lee (2006) found that perceived usefulness

and supplementary use were significant factors for learner use of e-learning for

distance education. Thus:

Hypothesis 11 The instructor satisfaction of LMS in blended learning will be

positively associated with their intention to purely use LMS for distance education.

Methodology

Participants

The study’s sample included 82 instructors from Sultan Qaboos University (SQU) in

Oman. SQU adopted WebCT application initially, but later switched to the open-

source Moodle application. Instructors could voluntarily adopt LMS to supplement

their traditional classes (blended learning).

The majority of participants (62%) were male, whereas 38% were female.

Participants included assistant lecturers (5% of them), lecturers (27%), assistant

professors (50%), associate professors (13%), and full professors (5%). Eight

percent of the participants were in their 20s, 26% were in their 30s, 16% in their 40s,

and 32% were 50 or over. Almost 44% had less than 6 years of work experience,

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30% had less than 11 years, 16% had less than 16 years, 7% had less than 21 years,

and 2% had more than 20 years. When asked to rate their computer skills, 71%

reported above average computer skills, 23%, about average, and only 6% reported

below average skills. The majority, 59%, had used the LMS for classes for 3 years

or more; 30% have used it for 1–2 years; and 11% have used it for less than 1 year.

Research questionnaire

The data were collected electronically. An invitation to participate in the

questionnaire, including the website link of the questionnaire, was posted on the

Moodle login webpage for a month. An email invitation was also sent to 646

instructors who were registered with the Moodle application. This list of instructors

was obtained from the Moodle support team. Instructors were asked to complete the

study questionnaire either online or using an attached MS Word document. A

reminder was sent 2 weeks after the initial invitation. However, only 82 instructors

(12.69% of invited instructors) completed the questionnaire. Most of the instructors

completed the questionnaire online (95%).

The questionnaire included several measures of the study’s constructs, along with

demographic questions (e.g., gender, age, degree, LMS usage experience, work

experience, and job title). Construct measurements items were phrased according to

a five-point Likert-type scale (1 = strongly disagree; 2 = disagree; 3 = Neutral;

4 = agree and 5 = strongly agree). To statistically evaluate the study framework,

39 indicators were used. Tables 1 and 2 show the total indicators used for each

construct. The LMS characteristic constructs (system quality, information quality,

and service quality) were adopted and modified from Pituch and Lee (2006) and

Roca et al. (2006). Individual characteristics constructs (computer anxiety and

technology experience) were adopted from Ball and Levy (2008); while the personal

innovativeness construct was adopted from (Raaij and Schepers 2008). Organiza-

tional characteristics’ constructs (management support, incentives, and training)

were self-developed, based on Sumner and Hostetler (1999). The user satisfaction

construct was adopted from Sun et al. (2008), and continuous blended learning and

pure LMS intention were adopted and modified from Pituch and Lee (2006).

Data analysis & results

PLS analysis methodology

PLS-Graph 3.0 software was used for data analysis. PLS (partial least square) is a

variance-based structural equation model (SEM) technique that allows path analysis

of models with latent variables (Chin 1998, 2001). The PLS approach is a variance-

based SEM that assists researchers in obtaining determinate values of latent

variables for predictive purposes. The PLS does that by minimizing the residual

variances of all dependent variables rather than using the model to explain the co-

variation of all indicators (Chin 1998; Chin and Newsted 1999). Thus, the model

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Table 1 Independent constructs measures and loadings

Construct measures Loading

Computer anxiety

I believe that working with computers is very difficult 0.8717

Computers make me feel uncomfortable 0.9493

I get a sinking feeling when I think of trying to use a computer 0.8961

Technology experience

I feel confident using the e-learning system 0.7617

I feel confident downloading/uploading necessary materials from the Internet 0.8460

I feel confident using online communication tools 0.6333

Personal innovativeness

I like to experiment with new information technologies 0.6713

Among my peers, I am usually the first to try out new information technologies 0.9735

System quality

The system offers flexibility in teaching as to time and place 0.7046

The system offers multimedia (audio, video, and text) types of course content 0.7225

The response time of the system is reasonable 0.7017

The system enables interactive communication between instructor and students 0.8190

Information quality

The information provided by the system is relevant for my job 0.8537

The information in the system is very good 0.9060

The information from the e-learning system is up-to-date 0.8457

The information provided by the system is complete 0.8186

Service quality

The system support services give me prompt service 0.8485

The system support services have convenient operating hours 0.8388

The system support services are reliable 0.8859

The system support services are easy to communicate with 0.8769

Management support

Senior administrators strongly support the use of e-learning system 0.8811

I get support by department chair or dean on my use of e-learning system 0.8253

My mangers highlight the importance of e-learning system on my curriculum 0.8624

Senior administrators clearly identify the importance of e-learning to the curriculum 0.7517

Incentives

The use of e-learning is a factor in the nomination for teaching award 0.9396

The use of e-learning system is a factor in determining promotion 0.9620

The use of e-learning system is a factor in annual elevation of teaching 0.9685

Training

I receive training workshops on how to use e-learning tools 0.8015

I receive on-line manuals on how to use e-learning tools 0.7993

I receive seminars on the use of e-learning tools 0.8761

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paths are estimated based on the ability to minimize the residual variances of the

dependent variables.

For this study, PLS SEM was selected over the traditional covariance-based SEM

mainly because it requires a smaller sample size than covariance-based SEM. With

PLS, the rule of thumb suggests that the sample-size is to equal 5–10 times the

larger of the following: (1) the scale with the largest number of formative indicators

(scales with reflective indicators can be ignored), or (2) the largest number of

structural paths directed at a particular construct in the structural model (Chin and

Newsted 1999). In reflective mode indicators, 82 sample-size satisfies the second

rule; it is between 45 (5 times 9 paths) and 90 (10 times 9 paths). Reflective mode

was selected for this study rather than formative mode because reflective indicators

are appropriate for explanation and prediction studies (Chin and Newsted 1999).

In reflective mode, all the indicators of a specific construct measure the same

underlying construct, hence they all correlate. On the other hand, in formative

mode, indicators are not considered to be correlated; indicators are assumed to be

causing rather than being caused by the latent construct.

The PLS algorithm uses an iterative process for the estimation of weights and

latent variables scores. The process almost converges to a stable set of weight

estimates. The evaluation of the model is based on (1) the assessment of the model

measurements by assessing their validity, reliability, and discriminant validity, (2)

the analysis of the paths of the structural model (Chin 1998). Tables 1 and 2 show

the independent and dependent construct measures and loading respectively.

Constructs validity and reliability

The applicability of adopted measurements was empirically assessed by two

criteria: reliability and validity. Reliability refers to the consistency of the measures

(indicators) of a specific latent variable; while, validity refers to how well the

Table 2 Dependant constructs measures and loadings

Construct measures Loading

User satisfaction (SAT)

I am satisfied with the performance of the e-learning system 0.8078

I am pleased with the experience of using the e-learning system 0.9133

My decision to use the e-learning system was a wise one 0.8684

Continuous intention to LMS use in blended learning (CUI)

I will frequently use e-learning system to do a teaching task 0.8743

I will use e-learning system on regular basis to supplement my classes in the future 0.8645

I will always try to use the e-learning system to do a teaching task whenever it has a useful

feature

0.8917

Intention to pure LMS use (PUI)

I plan to teach purely online courses for distance learners 0.9393

I will use e-learning system to teach purely online courses 0.9594

I plan to teach purely online courses in as many occasions as possible 0.9304

30 K. A. Al-Busaidi, H. Al-Shihi

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concept is defined by the measures (Crano and Brewer 2002; Hair et al. 1998). With

PLS, in the reflective mode, loading can determine the amount of variance in that

indicator that the prospective latent variable is able to account for. The reliability of

the measurements was evaluated by internal consistency reliability, and the validity

was measured by the average variance extracted (AVE), which refers to the amount

of variance a latent construct captures from its indicators. AVE was developed by

Fornell and Larcker (1981) to assess construct validity. The recommended level for

internal consistency reliability is at least 0.70, and is at least 0.50 for AVE (Chin

1998). Tables 1 and 2 show the model constructs’ measurements and loading.

Table 3 shows that the study constructs’ reliability and AVE are above the

recommended levels for all the constructs.

To achieve the discriminant validity of the constructs, Fornell and Larcker (1981)

suggest that the square root of AVE of each construct should exceed the correlations

shared between the constructs and other constructs in the model. The discriminant

validity is used to ensure the differences among constructs (Barclay et al. 1995;

Chin 1998). Table 4 shows that the model constructs satisfy that rule, as the square

root of the AVE (on the diagonal) was greater than the correlations with other

constructs. Thus, all the model constructs have a satisfactory discriminant validity

construct.

Model evaluation and paths analysis

With PLS, R-square (R2) values are used to evaluate the predictive relevance of a

structural model for the dependent latent constructs, and the path coefficients are

used to assess the effects of the independent constructs (Chin 1998). The

significance of the model paths was assessed based on their t values. Only paths’

coefficients with significance level (a) of at least 0.05 were considered significant.

Table 5 shows the R2 values of the endogenous dependent constructs. The

analysis indicated that the model explains 47.1% of variance in the instructor

Table 3 Constructs reliability and validity

Construct Total items Reliability AVE

Computer anxiety (CA) 3 0.932 0.821

Technology experience (TE) 3 0.794 0.566

Personal innovativeness (PI) 2 0.818 0.699

System quality (SQ) 4 0.827 0.545

Information quality (IQ) 4 0.917 0.734

Service quality (SvQ) 4 0.921 0.744

Management support (MS) 4 0.899 0.692

Incentives (IN) 3 0.970 0.915

Training (TR) 3 0.866 0.683

User satisfaction (SAT) 3 0.898 0.747

Continuous supplementary use intention (CUI) 3 0.909 0.769

Pure use intention (PUI) 3 0.960 0.889

Key factors to instructors’ satisfaction of learning 31

123

Page 15: Key factors to instructors’ satisfaction of learning management systems in blended learning

Ta

ble

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32 K. A. Al-Busaidi, H. Al-Shihi

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Page 16: Key factors to instructors’ satisfaction of learning management systems in blended learning

satisfaction of LMS in blended learning. The analysis also showed that instructor

satisfaction of LMS in blended learning explains 58.4% of variance in their

intention to continuously use LMS in blended learning, and 12.6% of their intention

to use LMS purely for distance education.

Table 5 also shows the paths coefficients analysis between the exogenous

independent constructs (instructors characteristics, LMS characteristics, and orga-

nization characteristics) and the endogenous dependent construct (instructors’

satisfaction of LMS in blended learning), and, consequently, intention (continuous

LMS use in blended learning, and LMS pure use for distance education).

The analysis showed that most of the instructor characteristics, the LMS

characteristics and the organization characteristics to some extent have impact on

the instructor’s satisfaction of LMS in blended learning. First, instructor computer

anxiety negatively impacts satisfaction of LMS (b = - 0.3058, p \ 0.0005); thus

Hypothesis 1 was supported. Second, the impact of instructor experience with the

technology did not have a significant effect on their satisfaction of LMS

(b = 0.0587, p \ 0.20); thus Hypothesis 2 was not supported. Third, instructor

personal innovativeness positively impacted their satisfaction of LMS (b = 0.2371,

p \ 0.001); thus, Hypothesis 3 was supported. Fourth, system quality significantly

impacted instructor satisfaction of LMS (b = 0.1808, p \ 0.025); thus, Hypothesis

4 was supported. Fifth, information quality significantly impacted instructor

satisfaction of LMS (b = 0.2371, p \ 0.001); thus, Hypothesis 5 was supported.

Sixth, service quality did not significantly impact instructor satisfaction of LMS

(b = 0.0398, p \ 0.25); thus Hypothesis 6 was not supported. Seventh, manage-

ment support significantly impacted instructor satisfaction of LMS (b = 0.1272,

p \ 0.025); thus, Hypothesis 7 was supported. Eighth, incentives policy signifi-

cantly impacted instructor satisfaction of LMS (b = 0.1476, p \ 0.01); thus,

Hypothesis 8 was supported. Ninth, training significantly impacted the instructor

Table 5 Model evaluation and paths analysis

Dependent construct Path Beta (b) p value Hypothesis

Satisfaction (SAT) (R2 = 0.471) CA ? SAT -0.3058 \0.0005 H1: supported

TE ? SAT 0.0587 \0.20 H2: not

supported

PI ? SAT 0.1115 \0.025 H3: supported

SQ ? SAT 0.1808 \0.025 H4: supported

IQ ? SAT 0.2371 \0.001 H5: supported

SvQ ? SAT 0.0398 \0.25 H6: not

supported

MS ? SAT 0.1272 \0.025 H7: supported

IN ? SAT 0.1476 \0.01 H8: supported

TR ? SAT 0.2046 \0.001 H9: supported

Continuous supplementary use intention (CUI)

(R2 = 0.584)

SAT ? CUI 0.7693 \0.0005 H10:

supported

Pure use intention (PUI) (R2 = 0.126) SAT ? PUI 0.3592 \0.0005 H11 supported

Key factors to instructors’ satisfaction of learning 33

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satisfaction of LMS (b = 0.2046, p \ 0.001); thus, Hypothesis 9 was supported. In

addition, instructor satisfaction of LMS in blended learning significantly impacted

their intention to continuously use LMS in blended learning (b = 0.7693,

p \ 0.0005), and their intention to purely use LMS for distance education

(b = 0.3592 p \ 0.0005); thus, Hypothesis 10 and Hypothesis 11 respectively

were supported. Figure 2 illustrates these significant factors to instructors’

satisfaction of LMS.

Discussion

LMS offers several benefits for academic and training organizations. LMS can be

used to efficiently support distance education, supplement traditional teaching and

capture educational materials for future reuse. This study examined the impact of

instructor characteristics (computer anxiety, technology experience and personal

innovativeness), LMS characteristics (system quality, information quality, and

service quality), and organization characteristics (management support, incentives,

and training) on instructor satisfaction of LMS in blended learning, and,

Fig. 2 Significant factors to instructors’ satisfaction of LMS in blended learning and its link to theircontinuous intention to use LMS in blended learning and intention to use LMS purely for distanceeducation

34 K. A. Al-Busaidi, H. Al-Shihi

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consequently, their future intention of using LMS in blended learning and in pure

e-learning for distance education. Findings suggested that individual instructor

characteristics, LMS characteristics, and organization characteristics have impact

instructor satisfaction of LMS in blended learning.

The study found that instructor computer anxiety negatively impacts satisfaction

of LMS. The study found that instructor computer anxiety was the key factor

influencing instructor satisfaction of LMS. Empirical studies have shown mixed

impacts of computer anxiety on LMS adoption (perceived ease of use, perceived

usefulness and satisfaction). These mixed results could be linked to computer

literacy or cultural issues. Nevertheless, organizations need to investigate the causes

of computer anxiety in order to eliminate it and consequently improve the adoption

of LMS in their organizations. Second, even though qualitative research has

suggested that technology experience might contribute to the LMS adoption and

satisfaction, this study was unable to find a significant impact on instructor

satisfaction, which is consistent with Ball and Levy’s (2008) findings. Third, the

current study found that instructor personal innovativeness is another positive key

factor to satisfaction of LMS in blended learning, which was consistent with Raaij

and Schepers (2008). Thus, improving instructor personal innovativeness could

improve satisfaction of LMS.

The study found system quality and information quality as key factors to

instructor satisfaction of LMS. This finding was consistent with Roca et al. (2006)

study on learner satisfaction of e-learning. For a successful deployment of LMS

organizations should ensure that system is with high functionalities and has quality

information. This study was unable to detect a significant impact of service quality

on instructor satisfaction as found by Roca et al. (2006). This study, compared to

Roca et al.’s study on learners, investigated additional factors. Thus, service quality

may be an important factor, but it is not a key factor to instructor satisfaction of

LMS compared to the other factors.

Management support, incentives policy and training were found to be key factors

to instructor satisfaction of LMS. Prior qualitative research has suggested these

findings; there were few quantitative studies that have reported this impact on LMS

satisfaction. Support of the LMS initiative and encouragement to use the system by

organizations is important. Senior managers should also integrate LMS use in their

incentives policy such as a factor in nomination for a teaching award, promotion,

and tenure. Finally, the organization should provide sufficient training to instructors;

this training program can be in form of workshops, on-line manuals or/and

seminars.

Finally, the study found that instructor satisfaction of LMS is a key determinant

of their continuous use of LMS in blended learning. The study also found that

instructor satisfaction of LMS in blended learning is a key determinant of their

intention to purely use LMS for distance education. Few studies have examined the

link between instructor use of LMS in blended learning to their intention of pure

e-learning. This study found that organizations that were not ready for pure

e-learning, that the use of LMS in blended learning was a valuable option to prepare

organizations and instructors to complete digital transformation through the use of

LMS purely for distance education.

Key factors to instructors’ satisfaction of learning 35

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This study offered some implications for researchers and practitioners. The study

has showed that individual characteristics, LMS characteristics, and organization

characteristics were key factors to instructor satisfaction of LMS in blended

learning, and that instructor satisfaction of LMS was a significant in impacting their

future intentions for blended learning or pure e-learning. In addition, organizations

should deploy high quality LMS’ (in terms of system quality and information

quality) systems to encourage instructor adoption and use. Finally, to improve

instructor satisfaction of LMS in blended learning, organizational senior managers

should support its deployment and integrate LMS use in their incentives policy.

Limitations and future research

This study had few limitations First, the sample was from one academic institution

in Oman; more research can be conducted in several organizations in different

countries to improve the generalization of the findings. Second, the response rate

(12.69%) of invited participants was low; other effective invitation methods (e.g.

post mail, phone calls etc.) could be utilized to enhance response rate. Third, the

adopted measurements of self-efficacy were of low reliability and validity; new

measurements should be developed to improve its reliability and validity across

different countries. Moreover, future research may also examine in detail the

outcomes of LMS for instructors, and the critical factors to instructors’ satisfaction

of full e-learning and the critical factors to organizations’ deployment of LMS.

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Author Biographies

Kamla Ali Al-Busaidi is an Assistant Professor of information systems at Sultan Qaboos University in

Oman. She received her PhD in management information systems from Claremont Graduate University in

California in 2005. Her research interests include knowledge management systems, learning management

systems, decision support systems and the deployment of information and communication technologies in

Arab countries. She has published in several conference proceedings, book chapters and journals,

including International Journal of Knowledge Management, Journal of Global Information Technology

Management and International Journal of Global Management Studies. She also served as a reviewer for

several international conference proceedings, books and journals.

Hafedh Al-Shihi is an Assistant Professor at the College of Commerce and Economics in Sultan Qaboos

University. He obtained his PhD from Victoria University, Australia in 2006. He is a member of the

Association of Information Systems (AIS) and the Special Interest Group on Electronic Government

(SIGeGov). He is also a Research Associate in the Center for International Corporate Governance

Research at Victoria University, Australia. He has several publications on e-government/m-government,

e-learning and technology adoption and dissemination and has attended several local and international

conferences.

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