Challenges facing students to adopting the blackboard system: The case study of the University of Ha’il in Saudi Arabia

This study seeks to identify the challenges facing students through the adoption of a blackboard system at the University of Ha’il in the KSA. Though the blackboard system has an effective role in the educational context, the adoption of the learning system in educational institutes is still in its infancy. However, students face various types of challenges that could affect their adoption and usage of the blackboard system. The previous researches on blackboard systems produced general rules, and studied a lot of common factors, did not consider the specific human factors. This study uses a quantitative research method. An online survey questionnaire was employed for data collection. For data analysis, SPSS was used for descriptive analysis; Structural equation modeling using AMOS software was applied. The results confirmed that the user’s LMS experience leaves a positive effect on the perception of the usefulness of the Blackboard system, but not computer anxiety. In addition, user’s LMS experience has a moderating effect on the relationship between self-efficacy and their perception of the usefulness of the Blackboard system. This moderating effect reflects that the more student’s LMS experience is the more affected their computer self-efficacy to perceive the Blackboard system more useful. This study produces theoretical and practical implications, and recommendations for the University of Ha’il should move forward with a learning platform.


Introduction
*Many opportunities and inventions have arisen within the university learning context because of the recent developments in Information and Communication Technologies (ICTs). The efficient and effective use of ICTs offers new-fangled learning opportunities for learners (Öztürk, 2019). One of such technologies is Electronic-Learning (Elearning), which is a Web-based learning platform containing practical frameworks offered for supporting both lecturers and learners. Knowledge can be shared through E-learning, using numerous tools and systems such as discussion groups, Internet, Intranet, email, chat, blogs, wikis, etc. Elearning facilitates the delivery process of learning and teaching. E-learning offers different services which can be adjusted in conformity with the experience, requirements, knowledge, and competency of the students (AlKurdi et al., 2020). However, the value of E-learning would not be exploited unless learners use the system. Therefore, research should focus on the learners' perception of e-learning (Muniasamy et al., 2014).
In educational contexts, many online activities can be achieved using Learning Management Systems (LMSs). Blackboard is one of the main widespread LMSs adopted by universities across the globe (AlMeajel and Sharadgah, 2018). However, students are facing various types of challenges that could influence their intention to use and adopt blackboard. For example, Contrado (2016) reported that the infrastructure reliability, lack of students' awareness and readiness, lack of support, and ease of access to the technology are the main challenges inhibiting the adoption of the blackboard in universities. Blackboard systems are linked to technology; thus, it is essential for deliverers to understand the users' attitudes toward such systems. This can provide a good understanding of the way blackboard approaches can be developed and offered. Blackboards need to be made more accessible and manageable for learners to handle such challenges.
Universities seeking higher motivational and competitive environments that convoy technology evolution; whilst, many challenges could inhibit the blackboard adoption. Merely offering a blackboard learning system does not guarantee that it would be accepted and used by students, nor not promise its learning success. Though the energetic role of the blackboard in the educational context has been confirmed, its adoption is still in its infancy (Shin and Kang, 2015;Han and Shin, 2016). That is, the blackboard system was adopted within many universities, but the successful adoption of such a system can be determined only by the university student's adoption. The blackboard reaches the success level of adoption only if students consider it as a slice of their routine learning process.
There are two levels of technology adoption that might be examined, i.e. at both the individual and organizational levels. This study concentrated on the individual level (AlKurdi et al., 2020). The importance of individual factors in e-learning was confirmed by recent researches. For example, Salloum et al. (2019) developed a comprehensive E-Learning acceptance model on a literature review comprising 120 significant published studies for the last 12 years. Their model classified factors into two groups: system characteristics, and individual factors (i.e., computer self-efficacy, subjective/social norm, perceived enjoyment). AlMeajel and Sharadgah, (2018) called for discovering what limits the use of the blackboard system; also Shin and Kang (2015) reported the need for further studies to determine challenges facing students' acceptance of blackboard. A recent study by AlMeajel and Sharadgah (2018) conducted a study exploring challenges to using the blackboard system at King Saud University in Saudi Arabia. They ranked the student challenges category the third among other types of challenges. Also, they classified students' lack of skills on the use of blackboard as a top challenge. This was confirmed by Margaryan et al. (2011). Therefore, the research question required to be answered is, what are the individual factor challenges facing students to adopting the blackboard system from different perspectives?

A review of blackboard adoption in universities in Saudi Arabia
Though the technology infrastructure is advanced, E-Learning systems are still in the early stages of adoption in developing countries (Jawad and Hassan, 2015). Mtebe (2015) asserted that the LMS has mostly failed adoption in developing countries because of inefficient and limited utilization and engagement among students, but it has successfully been adopted in developed countries (Abdel-Jaber, 2017). The literature reported reasons from different perspectives. For example, some studies (Kaliisa and Picard, 2017;Asiimwe and Gronlund, 2015;Binyamin et al., 2019) have stated that the technological infrastructure challenges are the critical issues on the LMS adoption, although most of them had positive attitudes towards LMS as reported by Asiimwe and Gronlund (2015). Similar findings were reported by Kaliisa and Picard (2017), who reviewed E-Learning literature in universities and concluded that the core limitations were associated with technological infrastructure issues, such as lack of internet accessibility. In Saudi Arabia, (Binyamin et al., 2019) reported that content quality, system navigation, ease of access, system interactivity, learning support, instructional assessment, and system learnability were significant to affect the use of LMS in Saudi higher education.
LMS has been commonly adopted in higher educational contexts worldwide, including Saudi Arabia. In Saudi Arabia, 87% of universities have adopted LMS where blackboard is the leading system. However, the users' engagement of LMS in Saudi Arabia is limited. Previous studies (Alharbi and Drew, 2014) have reported that students in Saudi universities use E-learning ineffectively. In addition, most LMS research in Saudi Arabia investigated technical usability, functions of LMS, and users' attitude toward the system (Binyamin et al., 2018).
Alenezi and Karim (2010) have surveyed 402 governmental university students through the extended Technology Acceptance Model (TAM). They discovered that the effect of enjoyment, computer anxiety, and computer self-efficacy were significant predictors of E-learning, while the Internet experience was not. An early study conducted by Alharbi and Drew (2014) stressed that researchers have disregarded using TAM to examine LMS in the educational context of Saudi Arabia. AlMeajel and Sharadgah (2018) conducted a study exploring challenges to using the blackboard system at King Saud University in Saudi Arabia, the students' challenges category was among those challenges. Also, Muniasamy et al. (2014) used the TAM by evaluating the relationships among perceived ease of use, perceived usefulness, attitude towards using, and behavioral intention to use the E-learning in King Khalid University in Saudi Arabia. Binyamin et al. (2018) determined the use of blackboard among university students in King Abdulaziz University in Saudi Arabia by extending the TAM model. They reported that students' computer self-efficacy can positively affect their perceived ease of use, but not perceived usefulness. In the same way, Binyamin et al. (2017) has extended TAM to discover the determining factors affecting students' use of LMS in the King Abdulaziz University in Saudi Arabia. They found that students' prior experience of LMS, satisfaction, social influence, and computer selfefficacy leave a positive influence on the use of LMS; they added that the TAM is valid to understand the students' use of LMS in Saudi Arabia. Yet, Binyamin et al. (2018) affirmed that further studies should employ TAM to evaluate Saudi students' adoption of LMS.
Other studies have investigated the factors affecting faculty member's intent to use e-learning systems using TAM, some of such is the study by AlMeajel and Sharadgah (2018) about the blackboard system at King Saud University, also Ahmed (2016) in Najran University in Saudi Arabia; and Alsuwailem (2018) about E-Learning in the King Faisal University in Saudi Arabia, and the study conducted by Alharbi and Drew (2014) about LMS at Shaqra University in Saudi Arabia. They are all confirmed the significant effect and correlation of the TAM core factors (e.g., perceived ease of use, perceived usefulness, and attitude) alongside the external factors added (e.g., prior experience), where several challenges were facing academic staff reported as well. If this the case for academic staff, it is expected that students are facing more challenges and this emerging the need to determine their perceptions indeed.
Other studies such as Alshammari et al. (2016) claimed that the predictive factors are self-factors, such as self-efficacy and enjoyment. These factors were commonly measured in different contexts, and their effects were verified. Similarly, Han and Shin (2016) found that psychological determinants (i.e., self-efficacy, innovativeness, perceived usefulness, and perceived ease of LMS use) shape students' perception are significant to affect their LMS acceptance. No doubt, different countries, settings, and universities likely face different challenges (Mtebe, 2015). The serious challenge of blackboard system implementation is determined by addressing students' perceptions (Alkhaldi and Abualkishik, 2019). As LMS can grow students' performance in their courses, hence, LMS acceptance and usage are vital (Mtebe, 2015). Unfortunately, according to the recent study by Alkhaldi and Abualkishik (2019) about the University of Ha'il in Saudi Arabia, the limited number of students actively accept and use the blackboard system. Therefore, the success of blackboard system adoption must be examined from the students' perspective in Saudi Arabia, as asserted by Al-Hujran et al. (2014). To this end, this study aims to answer the following two research questions. What are the challenges that affect students' intention to adopt the blackboard system at the University of Ha'il in Saudi Arabia? How do users perceive such challenges? To fill this gap, this study examined the potential challenges that might affect the students' intention to adopt the blackboard system at the University of Ha'il in Saudi Arabia.

Literature review
A recent study by Alkurdi et al. (2020), argued as to why students prefer an e-learning system could include their perceptions associated with their skills, as well as their capacity to be engaged in independent learning. Abdullah et al. (2016) showed that the best predictor of student's perceived ease of use of e-Learning is experienced, followed by enjoyment, selfefficacy through TAM. Hanif et al. (2018) extended the TAM to examine students' use of e-learning systems by digital learners (those who have higher levels of computer literacy and experience) . These emerging the importance of determining the computer literacy and experience in e-learning. AlKurdi et al. (2020) studied E-Learning in the United Arab of Emirates (UAE) using TAM. They reported that E-learning computer self-efficacy, computer anxiety, social influence, enjoyment, system interactivity, technical support, perceived usefulness, perceived ease of use, attitude, and behavioral intention to use E-Learning are predictive to change users' decision to implement e-learning system. AlKurdi et al. (2020) added that TAM is a suitable theory to understand the acceptance of E-Learning from a user's perspective. Alokluk (2018) discussed the usage of the blackboard system among students. The results showed that the more students work together with their peers via the blackboard system tend to use it frequently than those who didn't. Other studies such as Badwelan et al. (2016), reported that performance expectancy, effort expectancy, lecturers' influence, personal innovativeness, and self-management of learning were substantial variables that can influence behavioral intention to use the MLS. Salloum et al. (2019) developed a comprehensive e-learning acceptance model through extending the TAM after conducting the e-learning acceptance on literature review comprising of 120 significant published studies for the last 12 years. Their model classified factors into two groups: system characteristics, and individual factors. As the current study focuses on users (i.e., students), the individual factors are the interesting factors, these are computer self-efficacy, subjective/social norm, perceived enjoyment, and computer playfulness were the most common external factors of TAM associated with an individual. Afterward, the model was tested in five different universities in the UAE. The results showed that computer self-efficacy and computer playfulness have a significant impact on perceived ease of use of E-Learning system, and perceived enjoyment and accessibility were found to have a positive influence on perceived ease of use and perceived usefulness of E-Learning system. Depends on the above, several studies conducted empirical examination to discover users' challenges of acceptance of e-learning systems from different perspectives and with no focused view. Some studies determined factors that influence students, others, academics. Most studies have studied E-learning acceptance in general, or from Mobile-Learning (Mlearning) (i.e., mobile services), limited research has focused on the blackboard (i.e., LMS). Some studies have a cover boundless area of research, which covers various natural factors or domains, these are technical use and infrastructure, and social influencing (e.g., subjective norms, social influence, lecturers' influence), economic influencing, and system characteristics. As Salloum et al. (2019) comprehensive E-Learning classified factors into two groups: System characteristics, and individual factors. The current study argues that examining the individual, (i.e., knowledge about abilities, human limitations, and other human characteristics) from students' perspective need to be measured to provide a better understanding of the LMS acceptance, as their acceptance is a serious challenge and attaining the success of blackboard system implementation (Alkhaldi and Abualkishik, 2019).

Conceptual model and hypotheses
There are many models that have been extended or used to examine the users' acceptance of technologies. TAM is one of the most common models that has been used to explain the acceptance of various technologies and was empirically tested in various E-learning studies (AlKurdi et al., 2020;Ramírez Anormaliza et al., 2016;Yoon, 2016;Mohammadi, 2015), and specifically in the context of Saudi Arabia (Alenezi and Karim, 2010; Muniasamy et al., 2014;Binyamin et al., 2018). AlKurdi et al. (2020) concluded that adding users' satisfaction factor to their E-learning model to determine the users' adoption of E-Learning; TAM would be understood more sufficiently in higher education, and the role played by the faculty members in the users' adoption of E-Learning system should be determined by future studies. TAM clarifies the user's acceptance of the technologies by assessing the relationship between technologies and users. TAM is one of the acceptance models that succeed in combining factors from psychological and technical constructs aspects; however, the majority of other acceptance models have failed in this (Binyamin et al., 2017).
A systematic literature review on TAM in an educational context was conducted by Granić and Marangunić (2019) have discovered trends and various gaps in existing knowledge. First, TAM application in the educational context lack in addressing the diversity of learning domains, learning technologies, and types of users. Second, provide evidence on the predictive validity of the models in an educational context based on TAM. Third, TAM is a leading credible model for facilitating the examination of various technological distributions in an educational context. Ultimately, the core determinants of TAM (i.e., perceived ease of use and perceived usefulness) have been proven to be antecedent variables that can affect acceptance of learning technologies.
The applicability of TAM is resulting from three reasons. First, TAM focuses on users' beliefs, as it takes users' perspective as a base in considering the success of technology acceptance. Second, TAM has the flexibility to examine various types of external factors to determine the use of different technologies. Finally, TAM has been used widely to examine the acceptance of many technologies (Yoon, 2016). Alharbi and Drew (2014) stated that researchers have ignored using TAM to estimate the acceptance of LMS in the context of Saudi Arabia. Binyamin et al. (2017) added that TAM is valid to understand the students' use of LMS in Saudi Arabia. Yet, Binyamin et al. (2018) called for more theoretical research, which is based on TAM to evaluate Saudi students' adoption of LMS.
A conceptual model of BLMS acceptance on the basis of TAM is conceptualized and measured in the current study, based on previous literature that extended TAM in the educational context.

Computer self-efficacy
Self-efficacy is "the degree to which an individual believes that he or she has the ability to perform a specific task/job using the computer." The literature of e-learning has reported a significant effect of computer self-efficacy on user's perception of usefulness (Haryanto and Kultsum, 2016;Chang et al., 2017;Fathema et al., 2015;Tran, 2016;Kanwal and Rehman, 2017), and positive influence on the perception of the usefulness of LMS (Binyamin et al., 2017).
Differently, Salloum et al. (2019) found no significant effect. Previous studies on e-learning also generated a significant effect of users' computer selfefficacy on their perception of ease of e-learning use (Fathema et al., 2015;Alia, 2017;Boateng et al., 2016;Mahmodi, 2017;Abdullah et al., 2016), whilst, Hanif et al. (2018) reported that its non-significance. In Saudi Arabia, Binyamin et al. (2017; found that the King Abdulaziz University students' computer self-efficacy leaves a positive influence on the ease of use of LMS. Hence, the current study is synthesized the following hypotheses: H1a: Computer self-efficacy has a positive effect on the users' perception of the usefulness of the Blackboard system. H1b: Computer self-efficacy has a positive effect on the users' perception of ease of Blackboard system use.

Computer anxiety
Computer anxiety is an individual's fear when she/he is faced with the possibility of using computers (AlKurdi et al., 2020). The e-learning literature has reported that users' computer anxiety has a significant effect on their perception of usefulness (Chang et al., 2017;Kanwal and Rehman, 2017;Al-Gahtani, 2016). In the same direction, users' computer anxiety has a significant effect on the users' perception of ease of use (Chang et al., 2017;Kanwal and Rehman, 2017;Al-Gahtani, 2016;Alenezi and Karim, 2010). Therefore, the following hypotheses are proposed: H2a: Computer anxiety has a positive effect on the users' perception of the usefulness of the Blackboard system. H2b: Computer anxiety has a positive effect on the users' perception of ease of Blackboard system use.

Self-management
Self-management of learning was defined by Badwelan et al. (2016) as "the extent to which an individual feels sufficiently self-disciplined to engage in autonomous learning". Self-management of learning is the level to involve in self-governed learning. The related works give a jumbled view of the role of "self-management" in Mobile learning adoption, with positive outcomes obtained by Liu et al. (2008) and negative results by Lowenthal (2010). For this reason, it's very essential to explain the role of the self-management factor in E-learning adoption. In Saudi Arabia, Alkhaldi and Abualkishik (2019), and Badwelan et al. (2016) users' ability to self-management learning has a significant impact on the desire to adopt the m-learning system. Masrek and Samadi (2017) mentioned that much researches were reported a positive result of selfmanagement of learning on the intention to adopt mlearning. Upon that, researchers hypothesize that: H3a: Self-management has a positive effect on the users' perception of the usefulness of the Blackboard system. H3b: Self-management has a positive effect on the users' perception of ease of Blackboard system use.

Personal innovativeness
Personal innovativeness is an individual's willingness to try out any new information technology (Badwelan et al., 2016). Turan et al. (2015) observed a gap in the integration of ICTs acceptance and personal issues, such as personal innovativeness. They proposed a model that extends earlier research about the relationship between ICTs acceptance and personal innovativeness. Turan et al. (2015) assumed that highly innovative people accept new ICTs easily; also highly innovative people might turn in considerable useful as using the ICTs. Han and Shin (2016) found a significant positive impact of students' innovativeness on students' adoption of MLMSs in Korea. Similar findings were reported by Badwelan et al. (2016), direct effect on students' behavioral intention to use M-learning in Saudi Arabia. However, Turan et al. (2015) added that the results of innovativeness are depended on systems use and whether it is easy to use or not. Upon that, researchers hypothesize that: H4a: Personal innovativeness has a positive effect on the users' perception of the usefulness of the Blackboard system. H4b: Personal innovativeness has a positive effect on the users' perception of ease of Blackboard system use.

System interactivity
System interactivity refers to "the interactions between faculty and students, and the collaboration in learning that results from these interactions" (Abbad et al., 2009). In previous E-Learning research, the significant influence of system interactivity on perceived usefulness was confirmed (Abbad et al., 2009;Moreno et al., 2017) and particularly in blackboard . Also, previous E-Learning studies have proved that system interactivity has a significant effect on users' perception of ease of E-Learning use (Abbad et al., 2009;Moreno et al., 2017), and particularly in blackboard . Thus, this study hypothesized the following: H5a: System interactivity has a positive effect on the users' perception of the usefulness of the Blackboard system. H5b: System interactivity has a positive effect on the users' perception of ease of Blackboard system use.

Enjoyment
Enjoyment is "the extent to which the activity of using a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from system use" (Alharbi and Drew, 2014). Sarrab et al. (2017) confirmed the significance of users' enjoyment in various MLMs. Prior studies (Chang et al., 2017;Abdullah et al., 2016) have reported that enjoyment has a significant effect on the perceived usefulness of e-learning practically in Saudi Arabia (Al-Gahtani, 2016). The same findings were reported by several studies (Chang et al., 2017;Kanwal and Rehman, 2017;Elkaseh et al., 2015;Abdullah et al., 2016).
H6a: Enjoyment has a positive effect on the users' perception of the usefulness of the Blackboard system. H6b: Enjoyment has a positive effect on the users' perception of ease of Blackboard system use.

Lack of technical support
Technical support refers to people helping the users of the computer, using online support and hotlines, machine-readable support knowledge bases, automated telephone voice systems, and other assistance facilities (AlKurdi et al., 2020). An early study conducted by Louw et al. (2009) to examine practices of distribution of tasks and ICTs resources among students from South African universities. They concluded that the students, in most cases, are not resistant to the adoption of ICTs, but feel constrained by practical issues such as the lack of support. According to Boshielo (2014), blackboard systems present barriers that diminish access to resources like lack of support for students who are residing outside the campus, and lack of online support and training for blackboard-learn systems for students.
Several e-learning studies produced findings of the significant impact of the technical support on users' perception of usefulness (Abbad et al., 2009;Alia, 2017;Sánchez et al., 2013). In the same way, some studies found that e-learning studies found that technical support is significant to increase users' perception of ease of use (Alia, 2017;Sánchez et al., 2013). Therefore, the following hypothesis is formulated: H7a: Lack of technical support has a positive effect on the users' perception of the usefulness of the Blackboard system. H7b: Lack of technical support has a positive effect on the users' perception of ease of Blackboard system use. Abdullah et al. (2016) showed that the salient predictor of student's perceived ease of use of elearning is their experience of use. In Saudi Arabia, Binyamin et al. (2017) found that the King Abdulaziz University students' prior experience of LMS leaves a positive influence on the use of LMS. Past online learning experience shapes the perceived usefulness of online learning programs which motivates intentions for using online learning resources (Asiimwe and Gronlund, 2015). Alharbi and Drew (2014) reported that there is no support of the hypotheses stating that LMS usage experience negatively influences the non-user group's perceived ease of use, usefulness, and user's intention to use LMS. They reported that inexperienced users indicated a higher degree of positivity towards LMS adoption. Hanif et al. (2018) examined students' use of e-learning by digital learners (those who have higher levels of computer literacy and experience). They found that selfefficacy does not influence the perceived ease of elearning system use. This finding is contrary to the findings reported by Chang et al. (2014) and Al-Gahtani (2016). Hanif et al. (2018) clarified these findings' inconsistency by the fact that students in these studies have high system experience and computer efficacy.

Users' LMS experience
H8a: Users' LMS experience has a positive moderating effect on the relationship between users' self-efficacy and their perception of the usefulness of the blackboard system. H8b: Users' LMS experience has a positive moderating effect on the relationship between users' self-efficacy and their perception of ease of blackboard system use.

TAMs' factors
From the TAM standpoint, the adoption of technology is decided mainly to the extent to which users' perceptions of usefulness, as well as, perceptions ease of use. These factors were synthesized to determine users' behavioral intention to use a technology, which then can decide their actual technology use (Davis, 1989). Several e-learning studies have reported the significant effect of perceived ease of use and perceived usefulness on the users' behavioral intention to use E-learning, either directly or indirectly (Haryanto and Kultsum, 2016;Kanwal and Rehman, 2017;Boateng et al., 2016;Mahmodi, 2017;Alharbi and Drew, 2014). A significant positive effect of users' perceived usefulness on their intention to use the E-learning system was also reported by Haryanto and Kultsum (2016) and Boateng et al. (2016), no effect reported by Hanif et al. (2018). It has been also shown by many studies (Haryanto and Kultsum, 2016;Boateng et al., 2016;Mahmodi, 2017;Alharbi and Drew, 2014) that attitude has a direct effect on behavioral intention. Therefore, to determine the way the students' attitude impacts their use of e-learning system, some studies in e-learning reported that behavioral intention to use can affect the actual system use of e-learning (Fathema et al., 2015;Abbad et al., 2009;Mahmodi, 2017;Al-Gahtani, 2016). As a result, this study has the assumption of the hypotheses specified below: H9: Perceived ease of use has a positive effect on the Perceived Usefulness of the Blackboard system. H10a: Perceived ease of use has a positive effect on attitude towards the use of the Blackboard system. H10b: Perceived usefulness has a positive effect on the attitude towards the use of the Blackboard system. H11a: Perceived ease of use has a positive effect on the behavioral intention to use the Blackboard system. H11b: Perceived usefulness has a positive effect on the behavioral intention to use the Blackboard system. H12: Attitude towards use has a positive effect on the behavioral intention to use the Blackboard system. H13: Behavioral intention to use has a positive effect on the actual use of the Blackboard system. The proposed research model (Fig. 1) shown below is formulated from the hypotheses specified above, keeping in view the extended TAM model for Blackboard system acceptance by students.

Methodology
A quantitative method using a survey questionnaire technique with closed questions was used for data collection. The questionnaire was deemed necessary to facilitate the respondents' understanding in answering the questionnaire questions (Sekaran and Bougie, 2016), to test the hypotheses formulated in the research model of the study. The survey questionnaire was employed to collect data from students at the University of Ha'il in Saudi Arabia, who are accessible to the blackboard system. This study conducted a cross-sectional survey for data collection, as user perceptions need a long period to change.

Measurement
The questionnaire consists of questions with two types of response options: Multiple choice and a 5point Likert-type scale. It begins with multiple choice questions about demographics, followed by questions formulated to test the hypotheses. The questions used to assess variables were adapted from previous studies. Some modifications were made to adapt these questions for use in the current study, as the questions were initially formulated in English. As this study was conducted in Saudi Arabia, it was necessary to translate the questionnaire into Arabic (see Appendix A, Table A1). Prior to the survey, a pilot study was conducted to assure the reliability and validity of the questionnaire. An expert panel evaluated the instrument's validity. The reliability of measures is acceptable when Cronbach's alpha value is greater than 0.7 for the entire instrument (Sekaran and Bougie, 2016). As such, the survey instrument demonstrated enough reliability and validity to be distributed to participants. The questionnaire is shown in Appendix A,  Fig. 1: The proposed research model

Data collection and sampling
A random stratified sample was targeted at certain respondents (i.e., students at the University of Ha'il in Saudi Arabia). An online-based questionnaire was conducted using Google forms and distributed via WhatsApp to reach a greater number of students. As a result, 427 responses were received, only 370 were usable responses, and thus submitted for further data analysis.

Data analysis
SPSS version 17 and structural equation modeling (SEM) using AMOS 16 were used to analyze the data. The SPSS software was used to obtain the demographic profile of the respondents, descriptive statistics, reliability testing of scales, and exploratory factor analysis. AMOS was used for the measurements and structural models, including testing hypotheses.

Demographic profile of respondents
As shown in Table 1, females were dominant (61.9%) of the sample. Most of the respondents were Bachelor's degree students (56.8%), because the majority of the University of Ha'il students in this program level, followed by diploma and master respectively. The respondent's sample was distributed normally according to their majors (humanist, applied, natural, and medical science (30.5%, 29.2%, 21.9%, 18.4%) respectively. Most of the responses were from students who were registered from 2016 to 2019, (80.5%), according to students' priority the respondents have ranked ascendingly. Respondents' experience of using blackboard was concentrated in the middle range of experience, (39.7%, 35.9%) were from 1 to years of experience, and less than 1 year, only (5.1%) have not used, and (19.2%) were experts. 32.2% of the sample were reported that they using a blackboard system via smartphone or tablet, only (7.6%) via computer, the rest (60.3%) via both.

Descriptive statistics and reliability
Cronbach's Alpha values were all reliable (0.7 or above) except for the self-learning management was 0.69. Therefore, the item SLM1 was removed from the data set to increase the Cronbach's Alpha value to 0.74. Cronbach's Alpha value for technical support was 0.78, which is within acceptable reliability. Cronbach's Alpha values for computer self-efficacy, computer anxiety, personal innovativeness, system interactivity, perceived ease of use, intention to use blackboard system, and actual use of blackboard were 0.89, 0.80, 0.81, 0.84, 0.87, 0.88, and 0.81 respectively, are within the preferable level of reliability. Cronbach's Alpha values for enjoyment, perceived usefulness, and attitude were 0.90, 0.92, and 0.93 respectively, reflects excellent reliability. Consequently, all the variables are reliable, as Cronbach's Alpha value of every single variable is more than 0.7.

Exploratory factor analysis
For testing constructs validity, the Varimax rotation method was executed. The Kaiser-Meyer-Olkin (KMO) is 0.95, reflects the acceptable value, and Bartlett's Test of Sphericity (BTS) was 0.00, which is a significant value (>0.05). This guarantees the appropriateness of EFA analysis and thus the significance of the sample. Therefore, EFA is considerable for this study.
The results show that no items with low loading (<0.50) commonalities extraction were detected, as ranged from 0.506 to 0.839 and thus removed from the analysis. The rotated component matrix shows several double loading items (i.e., CSE4, SI2, ENJ1, ENJ2, ENJ3, PEU1, PEU2, PEU3, and INT1) were detected (i.e., computer self-efficacy 4, system interactivity 2, enjoyment 1, enjoyment 2, enjoyment 3, perceived ease of use 1, enjoyment 2, enjoyment 3, and intention to use blackboard system 1), and removed. The variables enjoyment and perceived ease of use were not submitted for further analysis as all of its items were removed. The other variables are valid and thus were submitted for further data analysis.

Measurement model using CFA
The CFA was used to provide empirical evidence for the fit of the whole model and its construct validity. The results show that all the indicators were loaded significantly on their factors. As shown in Table 2, the AVE values were all above 0.5, ranging from 0.55 to 0.82, as suggested by Bagozzi and Yi (1991). The standardized factor loadings were all significant and higher than the recommended value (<0.5). The discriminant validity was verified for some latent variables since the AVE for each construct was lower than all the squared correlations among any two constructs except for CSE, CA, and PU. Thus, only these three latent variables are submitted for further analysis. Table 3 shows the results of the measurement model fit evaluation, reflecting that the whole model was fit.

Structural model
The structural model was examined through testing the proposed model using AMOS 16.0 software. To estimate fit indices with all the parameters, the maximum likelihood method was operated. The results showed that normed x2 was significant 1.55, which reflects a good level. The RMSEA was 0.04, which is within an excellent fitness level. The GFI, IFI, TLI, and CFI were 0.95, 0.98, 0.96, and 0.98, respectively which is greater than the threshold of 0.95, reflects an excellent fit. The AGFI was 0.86, this reflects an acceptable fit. Therefore, the hypothesized structural model fills the criteria of fit for the data.
As shown in Table 4, H1a has posited that users' computer self-efficacy will positively influence their perception of the usefulness of the Blackboard system. In other words, the students' computer selfefficacy could increase their perceptions of the usefulness of the Blackboard system. As shown in Table 5, as expected in H8a, the user's LMS experience leaves a positive moderating effect on the relationship between self-efficacy and their perception of the usefulness of the Blackboard system. This moderating effect reflects that the more student's LMS experience is the more affected of their computer self-efficacy to perceive the blackboard system more useful, since the coefficient for the paths were less than 0.05, with a coefficient estimate of 0.57, 0.70, 0.78, and 0.96, respectively, for those students have no LMS experience to those experts.

Discussion and implications
A handful of studies were examined students' use of LMS. This study comes to investigate the challenges facing students in adopting the Blackboard system at the University of Ha'il from the perspectives of students. This study aims to discover what predictive factors can inhibit learner's proper utilization of the Blackboard system by examining the association between the students' actual use of the Blackboard system and external variables were reviewed by related studies in the literature, these are computer self-efficacy, computer anxiety, selfmanagement, personal innovativeness, system interactivity, enjoyment, lack of technical support, and users' LMS experience. Therefore, this study is one of a few types of research to investigate the influence of factors on students' adoption and use of LMS in the context of Saudi Arabia.
This study enriches the literature by reviewing, comparing, and analyzing the related previous studies. This study provided evidence of the reliability of TAM in investigating the acceptance of E-learning, through the use of Cronbach's alpha measure, this is consistent with the findings reported by AlKurdi et al. (2020) and Ramírez Anormaliza et al. (2016). The Findings demonstrate the applicability of the TAM constructs in Saudi Arabia, this supporting the findings reported by (Binyamin et al., 2017;Alharbi and Drew, 2014).
Several factors were eliminated for being not valid, and not submitted for hypothesis testing. A justification could be that examination of the effect of such factors on the users' perception of ease of blackboard use, as well as, their usefulness, not in general as previous studies. Another reason that the respondents' sample probably misunderstood the meaning of the factors' questionnaire questions. This requires further investigation. The results prove that Saudi students' perceive self-efficacy can positively affect their perception of the usefulness of the blackboard system. As previous studies on elearning were reported a significant effect of users' computer self-efficacy on their perception of ease of e-learning use, not investigated the same relationship on the perception of usefulness in Saudi Arabia, such as Hanif et al. (2018) reported that selfefficacy has non-significance. Also, Binyamin et al. (2017; found that King Abdulaziz University students' computer self-efficacy leaves a positive influence on the ease of use of LMS. This study is the first to prove such a hypothesis in Saudi Arabia. These results are consistent with what was reported by Haryanto and Kultsum (2016), Chang et al. (2017), Fathema et al. (2015), Tran (2016), and Kanwal and Rehman, (2017); also the positive influence on the user's perception of the usefulness of LMS (Binyamin et al., 2017).
Also, this study is the first to investigate the effect of self-efficacy on students' perception of usefulness by considering the moderating effect of their systems experience, particularly in Saudi Arabia. As suggested by Hanif et al. (2018), who studied elearning by digital learners (those who have higher levels of computer literacy and experience), and found that self-efficacy does not influence perceived ease of e-learning system use. It's worth mentioning that, the findings reported by Hanif et al. (2018) were contrary to findings reported by Al-Gahtani (2016). This study discovered that the more student's LMS experience is more affected by their computer self-efficacy to perceive the Blackboard system more useful. Such an issue should be taken into account by decision-makers in academic institutes.

Conclusion, limitations, and recommendations
The study has some limitations. The sample of the experiment was dominated by female students' respondents (61.9%). Furthermore, the sample was taken from students at the University of Ha'il. Consequently, further studies might expand the sample to contain more male students; alongside students from different universities in Saudi Arabia. This study has extended the TAM. Other researchers are advised to employ other recent technology acceptance models after the required justification. This study has focused on individual factors from the student's perspective. Next researchers are advised to study individual factors from a different perspective, as stated by Salloum et al. (2019) and Sarrab et al. (2017), the social influence is significant to be investigated in e-learning acceptance, the same was reported by AlKurdi et al. (2020) and Binyamin et al. (2017) in Saudi Arabia. Finally, this study investigated only the perception of students. Later, teachers and administrators can be added to the scope of the study. Therefore, the impact of lecturer influence should be explored by the next studies as stated by Badwelan et al. (2016). The produced research model can be employed in different institutes, countries, and technologies. This study called for more research aiming to address the effect of student's LMS experience on their level of selfefficacy perception towards blackboard usefulness, as these have not been clarified by the existing research on E-Learning.
Appendix A: Study questionnaire Table A1: Questionnaire Computer Self-efficacy I feel confident in the utilization of the blackboard system even when no one is there for assistance. Salloum et al. (2019) I have sufficient skills to use the blackboard system. I feel confident when using the blackboard system even if I have only the online instructions. I feel confident when using the online learning content in the blackboard system. Computer Anxiety I feel apprehensive about using computers. Saadé and Kira (2009) It scares me to think that I could cause the computer to destroy a large amount of information by hitting the wrong key. I hesitate to use a computer for fear of making mistakes I cannot correct. Self-Management of Learning When it comes to learning and studying, I am a self-directed person.
Badwelan et al. System Interactivity The blackboard system enables interactive communication between instructors and students. Moreno et al. (2017) The blackboard system enables interactive communication among students. The communicational tools in the blackboard system are effective (e-mail, chat, etc). Enjoyment My imagination is stimulated by using the blackboard system Salloum et al.
The blackboard system environment is enjoyable. The use of the blackboard system arouses my curiosity.
Lack of Technical Support The plate-form provides assistance when you have technical problems Sánchez et al. (2013) The technical support staff is available for consultation at any time There is a designated person that I can call or email when I need help with using the blackboard system. Moreno et al. (2017) Perceived Ease of Use There are clarity and understanding in my interaction with the blackboard system. Salloum et al. (2019) The blackboard system is easy to use for me. Interacting with the blackboard system does not require a lot of my mental effort.
Perceived Usefulness The blackboard system enhances my learning performance.
Salloum et al.
My productivity is elevated through the utilization of the blackboard system in my study. Using the blackboard system enhances my learning effectiveness. Attitude towards use Blackboard System I like the idea of using a blackboard System Alharbi and Drew (2014) Using a blackboard is a good idea The blackboard system provides an attractive learning environment Salloum et al. (2019) Intention to use Blackboard System I intend to use the blackboard system for educational purposes Alkhaldi and Abualkishik (2019) I will prefer the blackboard system over another medium of learning I will recommend other colleagues to use blackboard system Actual Use of Blackboard I use the blackboard system frequently. Salloum et al. (2019) I use the blackboard on a daily basis.