Factors to Instructors Satisfaction of Learning Management Systems

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Learning management systems (LMS) enable institutions to administer their educational resources, support their traditional classroom education and distance education. LMS survive through instructors ' continuous use, which may be to 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 continuously 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 organization's characteristics (management support, incentives policy and training). The findings indicated that computer anxiety, personal innovativeness, system quality, information 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.

INTRODUCTION

Learning Management Systems (LMS) and e-learning have become lately the hard sell among stakeholders in education and training. A number of top universities around the world have adopted LMS for instructors and students to enhance the educational process (Hawkins & Rudy 2007; Browne et al., 2006; National Center for Educational Statistics, 2003). More than 90 percent of all participating academic institutions in the US are adopting LMS (Hawkins & Rudy, 2007). Similarly, almost 95 percent of participating institutions in the UK have adopted LMS (Browne et al., 2006).

Users' satisfaction of an information system is critical to its continuous success. Likewise for a LMS, its success to a great extent depends on instructors' satisfaction of the system. Evaluating individual users' acceptance and use of the e-learning systems is a "basic marketing element" (Kelly & Bauer, 2004). Instructors may not fully utilize all the features, even when LMS are well in place; a survey of more than 800 instructors at 35 LMS-adopting institutions found that very few instructors use LMS tools for assessing students or promoting community (Woods et al., 2004). Research also indicated that fear of technology and lack of time may limit instructors' adoption of LMS (Yueh & Hsu, 2008). Instructors' needs and capabilities should thoroughly be investigated when deploying LMS applications (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 environment. 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 organization's characteristics (management support, incentives policy and training). Investigating the non-technical factors is important to promote the adoption and diffusion of LMS initiatives (Albirini, 2006; ElTartoussi, 2009). 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.

Background

Learning Management Systems & Benefits

According to the World Bank (2010), a 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 & Bauer, 2004). In particular, a LMS is an Internet application that aims to support education and training activities (Cavus and Momani, 2009) and provides a platform to support e-learning activities (Yueh & Hsu, 2008). Course Management Systems (CMS) and Learning Content Management Systems (LCMS) are sometimes used to indicate LMS (Yueh & Hsu, 2008); other related terms are Computer-assisted Learning (CAL), Computer-based Learning (CBL), and Online Learning (Chan, 2008). It should be noted, however, that LMS applications are not unique to educational institutions; even public and private organizations use such systems for training purposes.

Many LMS applications are available. The most popular LMS used at colleges and universities in the US is Blackboard followed by WebCT, which was acquired by Blackboard, Inc. in 2006 (Falvo & Johnson, 2007). Other LMS solutions are Moodle, ATutor, Learn.com, Joomla, and Krawler. LMS applications offer instructors several functionalities that benefits and contribute to teaching process. Course management tools, group chat and discussion, assignment submission, and course assessment are the primary tools in LMS (Yueh & Hsu; 2008). In addition, LMS help instructors provide learners with educational materials and track their participation and assessments (Falvo & Johnson, 2007). More technically sophisticated LMS features include maintaining office hours online, creating student groups, and assigning online projects to groups, according to Yildirim et al. (2004). Also, Ceraulo (2005) indicated that ePortfolios is a key feature in some LMS applications, which enable instructors to maintain student submissions throughout the course (i.e., tests, assignments, projects). LMS solutions aim also to increase interest in learning and teaching among learners and instructors, respectively (Mahdizadeh et al., 2008). Furthermore LMS enhance teaching process efficiency and result in cost-savings (Aczel et al., 2008).

Prior Studies on LMS

LMS have been adopted by academic and training institutions to support their distance education and/or supplement their traditional way of teaching (Rainer et al., 2007). Users' satisfaction of LMS, as any other information system, is critical to their continuous success (DeLone & McLean, 2003). There are a number of studies that have investigated the learners' acceptance, use and/or satisfaction of LMS such as Arbaugh(2000), Pituch and Lee(2006), Roca et al. (2006), Liaw et al(2007), Raaij and Schepers (2008) , 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 teachers' perceived usefulness of LMS and perceived ease of use. However, users' satisfaction of an information system is critical to its continuous use and resulted benefits (DeLone & McLean, 2003). Moreover, key factors that might impact the instructors' adoption of LMS can be related to their individual characteristics (Ball & Levy, 2008; Liaw et al., 2007; Raaij & Schepers, 2008; Teo, 2009), LMS characteristics (Pituch & Lee, 2006; Roca et al., 2006) and organization characteristics (Sumner & Hostetler, 1999).

None of these studies, however, investigated the direct impact of instructors' characteristics, LMS' characteristics, and/or an organization's characteristics on instructors' satisfaction. User satisfaction is an important indicator of IS success (DeLone & 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, to a great extent, be determined by the characteristics of its users. Several dimensions of users' characteristics have been proposed and investigated as determinants of technology acceptance. In the context of e-learning, few studies have investigated the impact of instructors' dimensions on LMS acceptance. Ball and Levy (2008) investigated the impact of self-efficacy, computer anxiety, and technology experience on instructors' 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 instructors' intention. Teo (2009) found that computer self-efficacy directly impacts pre-service teachers' perceived usefulness, perceived ease of use, and behavioral intention in Singapore. Liaw et al. (2007) found that perceived self-efficacy determines instructors' 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, Biemans, and Mulder (2008) found that teachers' 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. Instructors' innovativeness is important to the satisfaction of e-learning (Raaij & Schepers, 2008)

LMS Characteristics

The characteristics of LMS may have a great impact on the instructor's acceptance and use of LMS. Characteristics of any information system, including LMS, may be related to system, information, and service support quality as classified by DeLone and McLean (2003). E-learning systems' quality was found to be significant on the instructors' perceived usefulness, perceived enjoyment, and perceived self-efficacy, which consequently affect their intention to use the system in the classroom (Liaw et al., 2007).

In the e-learning context, few studies have examined the general quality of technology or 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 classroom, and found it significant. Albirini (2006) indicates that instructors' vision of technology impacts their attitudes toward the use of ICT in education. Two significant studies on the impact of technology on users' acceptance of LMS are Pituch and Lee's (2006) and Roca et al.'s (2006), but they are from the learners' perspective. Roca et al. (2006) investigated learners' perceived system quality from three dimensions (system quality, information quality, and service quality). They found that learners' perceived system factors (system quality, information quality, and service quality) directly affect their 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 instructors' satisfaction. This study integrates these three dimensions of LMS on the instructors' satisfaction.

Organization Characteristics

An organization's characteristics play a major role in the behaviors of its employees, including the acceptance use and satisfaction of any technology such as LMS. Corporate culture plays a key role in the success of any project. Schein defines culture as "the way we do things around here" (1985, p. 12). Cultural values shape an organization's norms and practices, which consequently influence employees' behaviors such as LMS utilization. Some of an organization's characteristics that might be relevant to the utilization of LMS are management support, incentives, and training.

There is a lack of empirical studies that capture the influence of organization factors on the acceptance and use of LMS generally. In the e-learning context, senior management support and the alignment of e-learning with the department and university curriculum are important for its adoption (Sumner & Hostetler, 1999). Motivators are also an important factor for instructors' acceptance to integrate the technology in teaching. Motivators or incentives for instructors can be enforced by having the use of the technology as a factor in a nomination for teaching award, promotion, and tenure (Sumner & Hostetler, 1999). Finally, training end-users is important, and can be in form of workshops, online tutorials, courses, and seminars. In addition, Teo (2009) found that facilitating conditions, measured by technical support, training, and administrative support, indirectly affect teachers' acceptance of technology in education.

Instructors' SATISFACTION OF LMS

Framework Development

This study aimed to examine the impact of instructor's individual characteristics, LMS' characteristics, and organization's characteristics on instructors' satisfaction of LMS in blended learning, and consequently, on their continuous use in blended learning and pure use intention for distance learning. As indicated, few studies have examined this integrated investigation of instructors' LMS acceptance and usage. This study assessed the individual characteristics based on instructors' computer anxiety, technology experience and personal innovativeness, LMS characteristics based on system, information, and service quality; and organizational characteristics based on management support, incentives policy and training. The impact of instructors' self efficacy was also initially considered as part of instructors' characteristics, but was dropped out after the analysis because of low reliability and validity of the construct in this study. Figure 1 illustrates this study model.

Figure 1: Instructors LMS Acceptance and Use Model

Instructor Individual Characteristics Hypotheses

Computer Anxiety Hypothesis

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). Computer anxiety is an important factor for the acceptance of the technology (Ball & Levy, 2008; Piccoli et al., 2001; Raaij & Schepers, 2008; Sun et al., 2008). Fear of computers may negatively affect the acceptance of LMS and the user's perceived satisfaction (Piccoli et al., 2001). Empirical evidence of the impact of computer anxiety was mixed. Ball and Levy (2008) did not detect a significant link between computer anxiety and instructors' intention to use the e-learning; however, Sun et al.(2008) found that computer anxiety significantly impacts the learners' perceived satisfaction of e-learning, and Raaij and Schepers (2008) found the computer anxiety impacts the learner's perceived ease of use of e-learning. Therefore we hypothesized that:

Hypothesis 1: Instructors' computer anxiety is negatively associated with their satisfaction of LMS.

Technology Experience Hypothesis

Users' experience with the technology (EUT) also plays a major role in the acceptance of technology (Venkatesh & Davis, 2000; Thompson et al., 2006). An individual's EUT is his/her exposure to the technology as well as the skills and abilities that are gained through using a technology (Thompson et al., 2006). Therefore, EUT may impact instructors' acceptance of LMS for their classes. Although empirical quantitative research, such as that of Ball and Levy (2008), found no significant impact of EUT on instructors' intention to use LMS, researchers Sumner and Hostetler (1999) indicated that current level of computer skills and extent of use of computing skills in teaching are important for instructors' acceptance of ICT in education. Likewise, Wan et al. (2007) highlighted the importance of technology experience on the learning processes and, consequently, learning outcomes. 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 we hypothesized:

Hypothesis 2: The instructor's experience with the use of technology is positively associated with their satisfaction of LMS.

Personal innovativeness Hypothesis

Personal innovativeness is another issue that may be critical factor on instructors' satisfaction of LMS. Personal innovativeness in information technology context means person's attitude reflecting his 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). Instructors' innovativeness is important to the satisfaction of e-learning (Raaij & Schepers, 2008)

Hypothesis 3: The instructor's personal innovativeness is positively associated with their satisfaction of LMS.

LMS Characteristics Hypotheses

System Quality Hypothesis

System quality is essential for the user's satisfaction of any technology, including LMS. Researchers, such as DeLone and McLean (2003), and Seddon (1997) highlighted the impact of system quality on technology acceptance, use or satisfaction and have introduced several ways to measure it. Instructors' acceptance of LMS may be determined to a great extent by system quality. The more functionalities, interactivity, and response of LMS, the better is its acceptance and utilization (Pituch & Lee, 2006). Quantitative empirical studies found a significant impact of system characteristics on e-learning acceptance: reliability (Wan et al., 2007; Webster & Hackley, 1997), accessibility (Wan et al., 2007), and system functionality, interactivity, and response (Pituch & Lee, 2006). Albirini (2006) indicated that instructors' vision of technology impacts their attitudes toward the use of ICT in education. Therefore, we hypothesized that:

Hypothesis 4: LMS system quality is positively associated with the instructor's satisfaction of LMS.

Information Quality Hypothesis

Information quality is also important for instructors' satisfaction of LMS, and refers to the perceived output produced by the system. Information quality with great accuracy, relevance, timeliness, sufficiency, completeness, understandability, format, and accessibility are important for the success of an information technology (Seddon, 1997). There is a lack of research on the impact of information quality on instructors' satisfaction of LMS. Some research was conducted from the learners' perspective. Roca et al. (2006) measured information quality of LMS by indicators related to relevance, timeliness, sufficiency, accuracy, clarity, and format, and proved that information quality was directly significant for learners' satisfaction and indirectly for perceived usefulness. Likewise, Lee (2006) found content quality was significant for learners' perceived usefulness. Consequently, we hypothesize that:

Hypothesis 5: LMS information quality is positively associated with the instructor's satisfaction of LMS.

Service Quality Hypothesis

Service quality refers to the quality of support services provided to the system's end-users. Instructors' acceptance of LMS may be related to the quality of the support services. Common measurements of service quality are tangibles, reliability, responsiveness, assurance, and empathy (Parasuraman et al., 1988; Kettinger & Lee, 1994). Few studies have investigated the impact of service quality on LMS adoption and success. For instance, Roca et al. (2006) assessed service quality by indicators related to responsiveness, reliability, and empathy, and confirmed its direct significance on learners' satisfaction and indirect significance of perceived usefulness in the e-learning context. Thus, we hypothesized that:

Hypothesis 6: LMS service quality is positively associated with instructor's satisfaction of LMS.

Organization Characteristics Hypotheses

Management Support Hypothesis

Management support is a key factor for the acceptance of any organizational initiative. Senior managers' open approval and endorsement of LMS adoption promote instructors' adoption and acceptance of LMS. Managers may support an LMS by encouraging instructors to adopt it and identify a clear vision of the objective of the LMS and how it is aligned with the university vision. Little research has investigated the impact of management support on instructors' acceptance of LMS. However, in the e-learning context, senior managers should clearly identify the goal of LMS for the university curriculum (Sumner & Hostetler, 1999). This managers' support assures instructors that using LMS is part of the organization's culture and is useful and encourages them to adopt and use the system. Managers are recognized as a high authority (Ali, 1990); thus, instructors' adoption and acceptance of LMS may be associated with the endorsement of their senior managers. Management support of end-users significantly improves computer usage (Igbaria, 1990). Facilitating conditions, including administrative support, indirectly affect teachers' acceptance of technology in education (Teo, 2009). Consequently, we hypothesized that:

Hypothesis 7: Management support is positively associated with the instructor's satisfaction of LMS.

Incentives Policy Hypothesis

Motivators, in terms of incentives, are important factors to encourage instructors to integrate LMS in their teaching. Incentives can be "non-trivial" monetary and non-monetary incentives. E-learning research lacks the assessment of incentives on LMS acceptance. Motivators or incentives for instructors can be enforced by using the LMS as a factor in nomination for a teaching award, promotion, and tenure (Sumner & Hostetler, 1999). These incentives' policies push instructors to adopt and utilize LMS for their teaching. Therefore, we hypothesized that:

Hypothesis 8: An incentive policy is positively associated with instructor's satisfaction of LMS.

Training Hypothesis

Providing end-users with training is important, as training improves instructors' adoption of LMS and illustrates its potential usefulness, and encourages its use in teaching. Limited research has investigated the impact of training on instructors' satisfaction of LMS. Training can be in form of workshops, online tutorials, courses, and seminars (Sumner & Hostetler, 1999). Facilitating conditions, including training, indirectly affect teachers' acceptance of technology in education (Teo, 2009). Thus, we hypothesized:

Hypothesis 9: Training is 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 users' perceived ease of use and perceived usefulness (Venkatesh & Davis, 2000). The higher the instructors' perceived usefulness of LMS, and actual use, the more likely it is that they will continue to use it. Continuous intention to e-learning use is determined by perceived usefulness and satisfaction (Hyashi et al., 2004). Thus, we hypothesized:

Hypothesis 10: The instructors' satisfaction of LMS is positively associated with their intention to continuously use LMS in blended learning.

Pure Use Intention hypothesis

Many organizations begin their LMS adoption as a supplementary tool to traditional classroom teaching, hoping that this supplementary adoption will eventually promote the pure use of LMS for distance education. Perceived ease of use, perceived usefulness, and actual use may have an important impact on continuous intention for supplementary use and intention for pure use of the LMS for education. When instructors believe that LMS is useful, and can be utilized for supplementary purposes, they are more likely to adopt it purely for distance education. The perceived usefulness of a technology is found to be significant determinant of the intention to use the technology (Venkatesh & Davis, 2000). Perceived usefulness and supplementary use are significant determinants of learners' use of e-learning for distance education (Pituch & Lee, 2006). Thus we hypothesized:

Hypothesis 11: The instructors' satisfaction of LMS in blended learning is positively associated with their intention to purely use LMS for distance education.

METHODOLOGY

Participants' Profile

This study included 82 instructors from Oman. Instructors can voluntarily adopt Moodle LMS to supplement their traditional classes.

The instructors were from different colleges in the university and with different demographics. About 62 percent of them were male and 38 percent were female. About 5 percent of them were assistant lecturers, 27 percent were lecturers, 50 percent were assistant professors, 13 percent were associate professors, and 5 percent were full professors. The instructors' age varied from 20s to above 50s: about 8 percent were in their 20s, 26 percent were in their 30s, 16 percent in their 40s, and 32 percent were 50 or over. Almost 44 percent had less than six years of work experience, 30 percent had less than 11 years, 16 percent had less than 16 years, 7 percent had less than 21 years, and 2 percent had more than 20 years. Most indicated that their computer skills were above average. Almost 71 percent have above average computer skills; 23 percent, about average; and only 6 percent were below average. The majority, about 59 percent, has used the LMS for classes for three years or more; 30 percent have used it for one to two years; and 11 percent have used it for less than one year.

Research Questionnaire

The questionnaire was distributed to SQU instructors. An invitation email was sent to instructors to complete the study questionnaire either online or on an attached MS Word document. A reminder was sent two weeks after the initial invitation. Most of the instructors filled the questionnaire online (about 95 percent of them).

The questionnaire included the constructs to be measured for quantitative analysis, 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 scale (1= strongly disagree; 2=disagree; 3=Neutral; 4= agree and 5=strongly agree). To statistically evaluate the study framework, 28 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 Roca et al. (2006) and Pituch and Lee (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 & Schepers, 2008). Organizational 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 according to Pituch and Lee (2006).

DATA ANALYSIS & RESULTS

PLS Analysis Methodology

Data was analyzed by PLS-Graph 3.0 software. PLS (partial least square) is a variance-based structural equation model (SEM) technique that allows path analysis of models with latent variables (Chin, 1998). 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 variance 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 paths are estimated based on the ability to minimize the residual variances of the dependent variables. 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). Table 1 and Table 2 show the independent and dependant constructs' measures and loading respectively.

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

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

Constructs Validity and Reliability

The reliability and the validity are two criteria used by researchers to evaluate the applicability of their measurements to their investigated model. Reliability refers to the consistency of the measures (indicators) of a specific latent variable; whereas, validity refers to how well the concept is defined by the measures (Hair et al., 1998). With PLS, 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 variable 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.

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

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 (Chin, 1998). Table 4 shows that the model constructs satisfy that rule, as the square root of the AVE (on the diagonal) is greater than the correlations with other constructs. Thus, all the model constructs have a satisfactory discriminant validity construct.

Table 4: Construct' Correlations and Discriminant Validity

Construct

CA

TE

PI

SQ

IQ

SvQ

MS

IN

TR

SAT

CUI

PUI

Computer Anxiety

(CA)

0.906

Technology Experience

(TE)

-0.153

0.752

Personal Innovativeness

(PI)

-0.295

0.551

0.836

System Quality

(SQ)

-0.092

0.159

0.260

0.738

Information Quality

(IQ)

-0.078

0.179

0.209

0.633

0.857

Service Quality

(SvQ)

-0.027

0.056

0.128

0.472

0.689

0.863

Management Support

(MS)

0.199

-0.174

0.125

0.298

0.226

0.229

0.832

Incentives

(IN)

0.227

-0.224

-0.106

0.158

0.124

0.142

0.530

0.957

Training

(TR)

0.020

0.003

0.165

0.271

0.348

0.353

0.241

0.297

0.826

User Satisfaction

(SAT)

-0.338

0.182

0.333

0.491

0.497

0.324

0.226

0.209

0.388

0.864

Continuous supplementary Use Intention (CUI)

-0.329

0.373

0.493

0.488

0.365

0.191

0.173

0.163

0.340

0.764

0.877

Pure Use Intention

(PUI)

0.008

0.123

0.374

0.103

0.054

-0.026

0.072

0.094

0.113

0.355

0.435

0.943

Model Evaluation and Paths Analysis

With PLS, R-square values are used to evaluate the predictive relevance of a structural model for the dependent latent variables, and the path coefficients are used to assess the effects of the independent variables (Chin, 1998). The significance of the model paths was assessed based on their t-values.

Table 5: Model Evaluation & Paths Analysis

Path

Beta

(β)

p-value

Hypothesis

CASAT

- 0.3058

< 0.0005

H1: supported

TESAT

0.0587

> 0.5

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

> 0.5

H6: not supported

MSSAT

0.1272

> 0.5

H7: supported

INSAT

0.1476

< 0.01

H8: supported

TRSAT

0.2046

< 0.001

H9: supported

SATCUI

0.7693

< 0.0005

H10: supported

SATPUI

0.3592

< 0.0005

H11 supported

Table 5 shows the R2 values of the endogenous dependent constructs. The analysis indicated that the model explains 47.1 percent of variance in the instructors' satisfaction of LMS in blended learning. The analysis also showed that instructors' satisfaction of LMS in blended learning explains 58.4 percent 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's characteristics, and organization's 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's characteristics, the LMS's characteristics and the organization's characteristics to some extent have impact on the instructor's satisfaction of LMS in blended learning. First, instructors' computer anxiety negatively impacts their satisfaction of LMS (Beta -β = - 0.3058, p < 0.0005); thus hypothesis 1 is supported. Second, the impact of instructors' experience with the technology is not significant on their satisfaction of LMS (0.0587, p > 0.05); thus hypothesis 2 is not supported. Third, instructors' personal innovativeness positively impacts their satisfaction of LMS (β = 0.2371, p< 0.001); thus, hypothesis 3 is supported. Fourth, system quality significantly impacts instructors' satisfaction of LMS (β = 0.1808, p < 0.025); thus, hypothesis 4 is supported. Fifth, information quality significantly impacts instructors' satisfaction of LMS (β =0.2371, p< 0.001); thus, hypothesis 5 is supported. Sixth, service quality is not significant on instructors' satisfaction of LMS (β = 0.0398, p > 0.05); thus hypothesis 6 is not supported. Seventh, management support significantly impacts instructor's satisfaction of LMS (β = 0.1272, p < 0.025); thus, hypothesis 7 is supported. Eight, incentives policy significantly impacts instructors' satisfaction of LMS (β = 0.1476, p< 0.01); thus, hypothesis 8 is supported. Ninth, training significantly impacts the instructors' satisfaction of LMS (β = 0.2046, p < 0.001); thus, hypothesis 9 is supported. In addition, instructors' satisfaction of LMS in blended learning significantly impacts their intention to continuously use LMS in blended learning (β = 0.7693, p< 0.0005), and their intention to purely use LMS for distance education (β = 0.3592 p< 0.0005); thus, hypothesis 10 and hypothesis 11 respectively are supported.

DISCUSSION & CONCLUSIONS

Discussion of Findings and Implications

LMS include several tools that provide academic and training institutions an efficient and effective means to support distance education and supplement their traditional teaching. Moreover, LMS enable these institutions to capture their educational materials and preserve them for future reuse. This study examined the impact of instructors' characteristics (computer anxiety, technology experience and personal innovativeness); LMS' characteristics (system quality, information quality, and service quality); and an organization's characteristics (management support, incentives, and training) on instructors' satisfaction of LMS in blended learning, and, consequently, their future intention of using LMS in blended learning and in pure e-learning for distance education. The results showed that instructor' individual characteristics, LMS' characteristics, and organization's characteristics have various impacts on instructors' satisfaction of LMS in blended learning.

Regarding the instructors' individual characteristics, the study, first, found that instructors' computer anxiety negatively impacts their satisfaction of LMS. In fact, the study showed that instructors' computer anxiety is the main key factor influencing instructors' satisfaction of LMS. As indicated earlier, empirical studies showed mixed impacts of computer anxiety on LMS adoption (perceived ease of use, perceived usefulness and satisfaction). These mixed results might be linked to computer literacy or cultural issues. Nevertheless, organizations need to investigate the causes of individuals' 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 individuals' technology experience might contribute to the LMS adoption and satisfaction, this empirical study was unable to find a significant impact of this factor on instructors' satisfaction, which is consistent with Ball and Levy's (2008) empirical study. Third, the study found that instructors' personal innovativeness is another positive key factor to their satisfaction of LMS in blended learning, which is consistent with (Raaij & Schepers, 2008). Thus, improving instructors' personal innovativeness will improve their satisfaction of LMS.

Concerning the LMS's characteristics, the study found system quality and information quality are also positive key factors to instructors' satisfaction of LMS. This finding is consistent with Roca and his colleagues' (2006) empirical study on learners' satisfaction of e-learning. Therefore, for a successful deployment of LMS organizations should ensure that system is with high functionalities and contains good information quality. Unfortunately this study was unable to detect a significant impact of service quality on instructors' satisfaction inconsistent with Roca and his colleagues' (2006) finding. This study, compared to Roca and his colleagues' study on learners, investigated more factors.

Concerning the organization's characteristics, the study significantly found that management support, incentives policy and training are key factors to instructors' satisfaction of LMS. Even though much qualitative research has suggested this issue, not much empirical quantitative research has asserted this impact on LMS satisfaction. Thus, organizations and their senior managers should constantly support the LMS initiative and encourage instructors' use. 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, senior managers 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 instructors' satisfaction of LMS is a key determinant of their continuous use of LMS in blended learning. The study also found that instructors' 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 instructors' use of LMS in blended learning to their intention of pure e-learning. Thus, this study showed organizations that are not ready for pure e-learning, that the use of LMS in blended learning is a valuable option to prepare organizations and instructors to complete digital transformation through the use of LMS purely for distance education.

In conclusion, LMS is promising for developing countries, as they provide tools to efficiently build human resources. This study offered significant findings for researchers and practitioners. The study has demonstrated that individual characteristics, LMS's characteristics and organization's characteristics are key factors to instructors' satisfaction of LMS in blended learning, and that instructors satisfaction of LMS is significant factor on their future intentions for blended learning or pure e-learning. Thus, this study provided useful insights for practitioners (instructors and academic institutions). Organizations, especially in the Middle East where computer and Internet literacy is not as high as in developed countries, should provide training to lessen instructor's computer anxiety, and consequently improve their satisfaction of LMS. In addition, organizations should adopt high-quality LMS (in terms of system quality and information quality) to promote their adoption and use by instructors. Furthermore, management support and incentives are important to improve instructors' satisfaction of LMS in blended learning.

Limitations and Future Research

This study has 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 study assessed LMS usage from instructors' perspective; further research may assess it from learners' perspective. Third, this study was unable to assess the impact of self-efficacy; new measurements might be developed to improve its reliability and validity across different countries. Moreover, future research could also examine in detail the benefits of LMS for instructors and the critical factors influencing organizations' deployment of LMS.

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