Investigating Student Satisfaction and Adoption of Technology-Enhanced Learning to Improve Educational Outcomes in Saudi Higher Education

Abstract

The current tendency in education is to deliver high-quality instruction with the use of technology in order to increase students’ global competitiveness. Currently, few empirical studies in the literature examine the significance and implications of technology-enhanced learning (TEL) in Saudi higher education. Therefore, the current study aims to develop a new model by examining the impact of a number of variables, including students’ perceived support, virtual social skills, subjective norms, information quality, subjective interest, and resource availability, on students’ self-efficacy and students’ perceived benefits of TEL enrolled in various public and private educational institutions in Saudi Arabia. The method is based on partial least squares structural equation modeling (PLS-SEM). A survey question on the idea of students’ self-efficacy and students’ perceived benefits of TEL was used as the main method of data collection, and 274 valid responses from undergraduate and graduate students at King Faisal University, particularly those who have been using TEL for at least a year, were obtained. The results of the student self-efficacy survey show that TEL adoption and student satisfaction are both positively impacted. The finding of this study was that all of the TEL characteristics were significantly and favorably mediated by perceptions of the TEL benefits. Student satisfaction is influenced by students’ perceptions of support, virtual social skills, subjective norms, informational quality, subjective interest, resource accessibility, and TEL uptake by students.

1. Introduction

TEL, which is learning facilitated by information and communications technology (ICT) resources, is being used more frequently in higher education [1,2], and has established itself as a standard and essential method of instruction for college students [3]. ICT, however, increases the demands on time, information, and skills and some people find it difficult to adapt to technological development and are uneasy around various technologies. Thus, societies place a high emphasis on higher education because it creates graduates who are knowledgeable and skilled [4]. Due to increased accountability in this sector, educational sustainability has recently attracted a lot of research attention [4,5]. Researchers have realized the value of utilizing technologies to improve students’ learning and more effectively accomplish educational objectives as technology is increasingly incorporated into educational practice [6,7]. Therefore, TEL, or the integration of technology into the learning process, has become an important tactic for raising educational standards [8]. The effectiveness of students’ learning in the context of TEL is greatly influenced by how they make use of such technologies, as different technologies are extending access to a variety of educational resources and providing for learners’ individualized and self-directed learning [9].

Online education is a significant example of TEL [10,11] and was frequently used as a last resort following the COVID-19 epidemic [12]. However, according to studies, students who participated in online learning sustainability during the pandemic encountered a number of technological difficulties, including a lack of necessary technical knowledge and access restrictions [13,14]. Moreover, some students mentioned a lack of motivation, unpleasant feelings, and trouble focusing [13,15]. Such difficulties may negatively affect students’ online learning, emphasizing the need to support their education, emotional health, and digital use [12,14].

The impact of TEL on performance [12,14,16,17] and the development of thinking skills [18,19] has been examined in earlier publications. In addition, according to academic research, TEL is not appropriate for students with diverse cognitive styles [20], because the instructor’s methods may result in improperly matched learning styles [21]. Therefore, it is important to pay attention to the possibility of TEL utilization in the future, particularly to support learning. TEL’s benefits are constantly changing because they are essential to the creation of futuristic evaluation [22,23].

As a result, the study and use of TEL have gained acceptance in Germany [24], where institutions have started using it heavily in an effort to lessen the effects of COVID-19 [25]. Although TEL has been used in a similar way outside of Europe, little research has been conducted on the subject. Yet, little is understood about the variables that might influence whether TEL is accepted or rejected, particularly when it comes to human affective characteristics like motivation, sentiments, and perceived efficacy [26]. According to COVID-19, university students have started taking classes online or through open distance learning. The adoption of a different teaching strategy became essential. Yet, it is still completely unknown what criteria are appropriate for incorporating TEL that suit the motivations of the learners [26]. Students are the primary end-users of TEL, despite the fact that a wider range of mindsets are required to minimize prejudice in this context [27].

Therefore, it is important to comprehend the quality and viability of instruction during the COVID-19 pandemic from the perspective of the students. By identifying and researching the elements influencing university students’ self-efficacy and perception of the benefits of using technologically enhanced learning sustainability, the current study fills a research gap in this area. According to [28], the self-efficacy idea was first presented. One of the variables thought to affect students’ acceptance of TEL is self-efficacy. Students’ decision-making, motivational, and cognitive processes are influenced by their level of self-efficacy, which has an impact on their academic behaviors, such as their capacity to manage their learning and master academic tasks [29]. A self-efficacy method makes a similar prediction that prior experience will affect students’ perceptions of the benefit and performance impact [30,31]. One of the often-used exogenous factors for the technological acceptance model is self-efficacy [32]. According to the available research, self-efficacy is thought to be the primary variable affecting how learners use technology, such as learning management systems and mobile learning [32,33].

Using TEL is advantageous for students who comprehend the consequences of self-efficacy. It is unknown, though, how self-efficacy from the standpoint of TEL might influence the adoption and use of TEL, particularly in the wake of the recent epidemic. On the other hand, according to the self-efficacy hypothesis developed by [34], self-efficacy and students’ perceived benefits are crucial affective factors in humans when it comes to absorbing new technologies. A decade later, the theory was adopted into education after playing a significant role in academic studies [35]. An earlier study found a connection between motivation and technology acceptance [34]. Yet, it is still unclear what the consequences of motivation from the core ideas of self-determination theory will be for the acceptance of TEL.

By incorporating many psychological variables, such as motivation and self-efficacy, into an improved version of the self-efficacy theory, this study intends to fill in any gaps before conducting an empirical test utilizing modeling approaches to examine how well TEL is received. The theoretical framework will be further examined together with external variables and self-efficacy theory.

Numerous theoretical and practical consequences are drawn from this study. Firstly, this is the first study that, to our knowledge, incorporates the full range of self-efficacy theory into the external variables. Prior studies, on the other hand, have solely focused on the original form of self-efficacy theory by focusing solely on the fundamental psychological needs of autonomy, competence, and relatedness. The second part of this study looks at how TEL has advanced the use of self-efficacy in the present. Research on self-efficacy that goes beyond the incorporation of computer self-efficacy with external variables has become scarce, as technology has advanced.

As the model has been criticized for being overly simplistic and there are few studies on the acceptability of TEL based on the self-efficacy theory, this study modified the self-efficacy theory to integrate a wider perspective on psychological aspects. To sum up, the usage of TEL applications in universities is impacted by these activities. It will be known what influences student acceptance and how students’ learning might be psychologically and technologically assisted.

The following are the research questions: RQ1. What are the variables that affect students’ adoption intention and satisfaction with TEL in Saudi higher education? RQ2. What hypothesis exists between the components of students’ self-efficacy and perceived TEL benefits? RQ3. What are the variables that act as a mediator between the independent variables and students’ intention to use TEL and satisfaction in Saudi higher education?

Technology-Enhanced Learning in Sustainable Education

TEL is increasingly gaining acceptance as a strategy for delivering sustainable education because it has the potential to revolutionize the way we teach [36]. The phrase TEL can refer to a wide range of programs, resources, and technological advancements created to fit the various learning preferences of students as well as encourage lifelong learning and active student participation. Learning that is supported by technology fosters creativity, gives students a sense of accomplishment, and challenges them to think beyond the box [37]. In this context, it is acknowledged that the utilization of information technology in education, particularly video-based materials, has a bright future, particularly now that the Internet has taken over as the primary medium for information exchange, especially among young people [38]. The ability of the web and social networking platforms to store and handle enormous volumes of information in many formats gives students access to more learning tools and materials.

According to [39], refer to this as pedagogy 2.0, which gives students greater opportunities for involvement, personalization, and productivity. Because of this, the vast majority of knowledge is not only easily accessible to students but is also shared and edited in an open, collaborative setting. Consequently, it is possible to argue that modern technologies like the web and social networking sites have led to a paradigm shift in education, where students are no longer taught by an instructor but rather, are exposed to the rest of the world. Also, it gives students the opportunity to improve their academic learning through teamwork and knowledge co-construction [40,41]. While efficiency and convenience gains for both students and teachers may be a side effect, TEL is not just about finding the proper mix of tools or increasing access to learning. Prior to selecting the right tools, TEL must first consider methodology and learning culture. Technology integration into education is dependent on, a reflection of, and requires precise philosophical and educational foundations since, in the absence of such foundations, it may represent faulty or contradictory knowledge [42].

According to [37], who conducted a recent study to identify the function of digital technologies in education, video-based learning materials allow students to interact with a variety of freely available learning resources, promoting the growth of a self-learning environment and sustainability. Jenkins and Walsh [17] discovered that regular exposure to a video intended to promote critical thinking on patients’ responsibilities improved senior nursing students’ problem-solving abilities. Additional research suggests that teachers are less likely than students to use social media for instruction and that they see it as a disruptive tool that does not promote student learning [43,44,45]. According to these studies, some academics may not have taken advantage of the opportunity to use education 2.0 technology to improve their methodology, delivery of materials, and evaluation.

2. Research Model and Hypotheses Development

This study is especially interested in investigating the precursors’ self-efficacy and students’ perceptions of the advantages of TEL in this setting. As a result, we suggested that the self-efficacy theory and extrinsic factors (students’ perceived support, virtual social skills, subjective norm, information quality, subjective interest, and available resources) serve as the antecedents of students’ self-efficacy and their perceptions of the advantages of TEL. The academic literature on the study variables is included in the section below, along with an association chart for each factor. Student satisfaction and student acceptance of TEL are offered as the results of self-efficacy and students’ perceived benefits from TEL. Our study model with hypotheses is depicted in Figure 1.

2.1. Students’ Perceived Support

According to the ecological viewpoint, students’ social environments have a big impact on them [46]. This viewpoint provides a method for comprehending the connection between social assistance and academic outcomes for students [47]. Students who receive social support feel confident and secure, which enables them to handle intellectual problems more effectively [48]. The social capital hypothesis contends that social networks’ inherent resources help people accomplish a variety of objectives [49]. In order to increase their academic performance, those who have more support networks are better positioned since they are more socially engaged in their university academic contexts and better able to build supportive networks [50]. According to a number of studies, students who considered their social support to be higher reported improved participation [50,51], and educational adjustments [52,53]. According to a one-year retrospective study by [54], social support is a key predictor of academic success for university students. By meta-analyzing 109 papers, Robbins et al. [55] have verified the beneficial relationship between peer support and college students’ overall academic performance (GPA). Furthermore, we propose that social encouragement is positively related to students’ perceptions of the advantages of TEL as well as their own sense of self-efficacy [48]. Students’ overall experiences and their views of the advantages offered by technology-mediated learning are greatly influenced by effective support mechanisms, which include technical assistance, instructional guidance, and peer interactions [48,50]. Hence, we put up the following theories:

H1. 

Students’ perceived support has a significant effect on the students’ self-efficacy.

H2. 

Students’ perceived support has a significant effect on the students’ perceived benefits.

2.2. Virtual Social Skills (VSS)

The behaviors that “result in pleasant social interactions” and “include both the verbal and nonverbal behaviors necessary for successful interpersonal interaction” are referred to as social skills [56]. Social constructivist theory holds that learners need to actively participate in their external interactions because social context has a significant impact on learning [57]. Faculty and peers are significant learning resources for students in the context of e-learning [58]. The intricacy of e-learning sustainability, nevertheless, makes it more challenging for students to interact with their professors, peers, and friends and necessitates using various techniques to forge connections. It is suggested that because they are accustomed to the norms and techniques, students who have engaged in online socialization will be able to interact with classmates and professors in TEL sustainability more successfully [58,59]. A student who has a high level of digital self-efficacy, for example, is accustomed to and adept at engaging in online sociability and may use emoticons or animations to speak with classmates or a teacher. They might also receive more feedback than students who have a poor level of digital self-efficacy [58,59]. These tactics help the students obtain good results while having an impact on the TEL’s effectiveness. Hence, we put up the following theories:

H3. 

Virtual social skills have a significant effect on the students’ self-efficacy.

H4. 

Virtual social skills have a significant effect on the students’ perceived benefits.

2.3. Subjective Norm (SN)

Subjective norms are the impacts left by peer groups and colleagues on a member of a society’s decision-making process. Subjective norms are connected to the impact of societal groupings and peers on a member of that society’s decision-making [60]. Subjective norm was defined as a person’s perceptions of engaging in a particular conduct as impacted by significant others [61]. According to previous empirical evidence [62,63], the subjective norm may be able to explain students’ intention to use mobile technology. However, according to the study [64], there was no significant relationship between behavioral intention and subjective standards when social learning systems were used. Some scholars [65] claim that, depending on the circumstances, the effect of social influence on the adoption of technology varies and is complex. Instead of focusing on their own feelings and views, people are prone to use a system in some circumstances to comply with the demands of others [66]. According to [67], facilitating conditions and perceptions of the usefulness and simplicity of an e-learning system are significantly correlated. Students are more likely to perceive the benefits of TEL themselves when they believe that there is a favorable subjective norm in place where peers and teachers support it [61]. As a result, the following hypotheses are generated:

H5. 

Subjective norm has a significant effect on the students’ self-efficacy.

H6. 

Subjective norm has a significant effect on the students’ perceived benefits.

2.4. Information Quality

Using e-learning to seek out knowledge that may be crucial for learning and that is updated to make it simpler for the learner to understand is known as information quality (IQ) [68,69]. The phrase “users’ belief in the quality of information offered on a website” is another way to describe information quality [70], or how well the learner is informed over the online service interface in a complete, accurate, and timely manner [71]. Previous studies on e-learning discovered a substantial relationship between perceived ease of use and information quality [72,73,74]. Moreover, earlier studies discovered a favorable correlation between IQ and the perceived utility of e-learning systems [75]. Students’ perceptions of the advantages of information quality in technologically enhanced learning environments are closely related. The learning process is improved overall when accurate, pertinent, and trustworthy information is provided [73]. In order to help students succeed academically and develop their digital literacy abilities, educational institutions can have a positive influence on how they view the advantages of using technology for learning by placing a high priority on information quality [76,77]. On the other hand, when students are exposed to unreliable, difficult, and erroneous information through technology-based learning, their intent to adapt decreases. As a result, the following theories were developed:

H7. 

Information quality has a significant effect on the students’ self-efficacy.

H8. 

Information quality has a significant effect on the students’ perceived benefits.

2.5. Subjective Interest (SIN)

Topic interest indicates how involved students are in the TEL context, which helps them perform better academically. According to Singh [78], learners are more likely to use more or less of a subject utilizing TEL at any one time depending on their interest in the subject and relevant study materials (TEL contents and textbooks). Also, the desire to accept technology-based learning is increased by learners’ interest [79,80,81]. Students’ participation, attitudes, and results in technologically enhanced learning are shaped by a dynamic trio that includes subjective interest, self-efficacy, and perceived rewards [78]. A strong sense of success, active participation, and meaningful interactions with digital resources are all encouraged by a good link between these variables. Hence, the following theories were created:

H9. 

Subjective interest has a significant effect on the students’ self-efficacy.

H10. 

Subjective interest has a significant effect on the students’ perceived benefits.

2.6. Resource Availability (RA)

The research claims that organizational resilience requires financial, technological, and social resources, especially in the initial stages of prediction [82]. A wide range of readily available materials form the basis for quick and appropriate responses in challenging circumstances [83,84]. When a crisis develops, the coping phase of the disaster management process necessitates both human and financial resources [85]. In order to deal with the switch to remote learning, researchers [86] discovered that maintaining financial reserves was essential during the closure of Malaysian schools, as shown by the country’s well-established information technology (IT) infrastructure [87]. Similar to this, the three main elements of available resources are physical assets, human assets, and material assets [88]. Resources are not always readily available in classrooms, according to earlier research on the availability of educational resources [87,88]. The lack of available resources has raised serious concerns among educators. Learning is a complicated process, according to [89,90], involving the interaction of students’ motivation, the infrastructural environment, educational resources, teaching methods, and curriculum requirements. In TEL contexts, students’ perceptions of the benefits and outcomes are greatly influenced by resource accessibility and self-efficacy [87,90]. Understanding the relationship between self-efficacy and resource accessibility offers important insights into students’ involvement and views of the advantages of technology-assisted learning. Hence, the following theories were created:

H11. 

Resource availability has a significant effect on the students’ self-efficacy.

H12. 

Resource availability has a significant effect on the students’ perceived benefits.

2.7. Students’ Self-Efficacy

A person’s view of their capacity to perform to an achievement standard or to reach their intended outcome is referred to as self-efficacy, and this impression is dependent on their assessment of their prior outcomes [28]. Online learning self-efficacy is the phrase used to describe the idea of distance learning. It is founded on the fusion of two ideas. The first is e-learning self, which refers to a user’s proficiency with a technology and their confidence in their ability to do so effectively and efficiently. The other is Internet self-efficacy, which would be defined similarly by the user’s proficiency at utilizing the Internet and their confidence in their ability to do it effectively. Hence, a user’s real capability as well as their impression of their ability to use digital technology successfully and effectively in the learning process can be described as their level of online learning self-efficacy [91]. Also, it is important to standardize teaching materials and boost students’ confidence in their ability to use cutting-edge learning techniques while coming up with implementation strategies for online learning [92]. Prior research has demonstrated that the idea of online learning self-efficacy affects how well online learning strategies work out [92,93], and how successfully online learning is implemented [94]. A holistic approach to education is crucial because of the connections between students’ self-efficacy, their perception of the advantages of TEL, and their satisfaction [91,93]. Students are more likely to participate actively and effectively in technologically enhanced learning environments when they feel secure in their ability to use technology for learning, perceive major benefits from it, and feel satisfied with their efforts and results [93,94]. Consequently, we tested the following hypotheses:

H13. 

Students’ self-efficacy has a significant effect on the students’ perceived benefits.

H14. 

Students’ self-efficacy has a significant effect on the students’ satisfaction.

H15. 

Students’ self-efficacy has a significant effect on the students’ adoption.

2.8. Students’ Perceived Benefits to TEL

The extent to which a student believes that the use of TEL will be advantageous for his or her study in regard to duration, effort, and cost is referred to as perceived benefits. According to Bennett and Bennett [95], teachers’ ability to compare new innovations to those already in use and to discuss both the advantages and disadvantages of newly adopted technologies has a direct impact on how much benefit students perceive from them. TEL is important for a variety of reasons [96,97]. First off, TEL can offer a variety of advantages for both students and universities [98]. It aids universities in saving money on large infrastructure investments for teaching and learning [99]. Secondly, TEL aids universities in their efforts to digitize themselves and contribute to the development of a “digital learning society” that uses Internet-based technology to provide knowledge and education to students anytime and anywhere [100]. Thirdly, TEL makes it easier for institutions to integrate their offerings into higher education sustainability on a worldwide scale [101]. There are many chances for online learning outside of one country thanks to international cooperation and connections in the sphere of education. The advantages and successful outcomes that students believe TEL has to offer are included in the perceived benefits. These advantages may include easier access to materials, interactive learning opportunities, greater engagement, more adaptability in the learning process, and possibly better academic results [97,101]. As a result, we put forward the following theories:

H16. 

Students’ perceived benefits have a significant effect on the students’ satisfaction.

H17. 

Students’ perceived benefits have a significant effect on the students’ adoption.

2.9. Student Satisfaction (SS)

Sweeney and Ingram [102] describe student satisfaction as the joy and success they experience in learning sustainability. Students’ satisfaction is also influenced by a number of elements, including teachers’ expertise and performance, a supportive educational experience, effective communication, participation in the teaching–learning process, and the institution’s worth and reputation [103]. In addition to being one of the most important concepts in the marketing literature to apply to the setting of online education [104], satisfaction has also been proven to have the greatest impact on user performance expectations, trailing only perceived usefulness [105]. In addition, satisfaction mediates the relationship between the adoption of TEL and perceived self-efficacy [106]. The sustained engagement, perseverance, and usage intentions of learners with digital systems as a whole and e-learning platforms in particular have been proven to be significantly influenced by learner satisfaction [107,108]. For instance, in their analysis of student social media usage, [108] discovered a significant direct relationship between student happiness and continued use intentions. Furthermore, [106] discovered that learners’ intentions to continue using TEL in their evaluation of TEL’s continuous use intention were strongly predicted by their level of pleasure. In light of the available research, the following hypothesis is put forth in this study:

H18. 

Students’ satisfaction has a significant effect on the students’ adoption.

2.10. Students’ Adoption of TEL

When students say they plan to adopt TEL, they mean they want to employ learning technology to improve their academic achievement. The use of technology-based learning techniques is thought to fill up learners’ knowledge gaps and enhance learning results. According to Fishbein and Ajzen [109], intention is a person’s subjective probability of carrying out a specific task. EL includes a variety of ICT-based methods, including websites, mobile and web apps, learning management systems, YouTube, and other such platforms. Technology-based learning may be advantageous for students, teachers, and professionals because it eliminates the need for physical limitations [110,111]. TEL has provided a new way for teachers and students to participate in the learning process as one of the benefits of information technology development [106]. Using digital media, such as the Internet or local computer networks, to improve educational and instructional activities is performed through the employment of an installed application [112]. TEL is an institution that uses IT to support the educational process [113]. Students’ cognitive skills are enhanced by using a TEL system, which enables them to understand concepts using distributed resources. TEL enables a new way for kids to study, according to [114], by utilizing electronic media and devices like cell phones, tablets, and computers as a tool to expand communication accessibility.

3. Research Methodology

As the main statistical programs for this work, we used IBM SPSS and Smart-PLS 3.3.3 to cover measure construction, convergent measurement validity, discriminant measurement validity, and structural model investigation. The hypotheses were evaluated using quantitative survey methods, and conclusions regarding the research objective were developed. Self-efficacy questionnaires were circulated to gather empirical data. There were three sections in the questionnaire. In Section 1, 45 items were used to measure students’ self-efficacy, perceived benefits, satisfaction, and adoption of TEL. Nine items were also taken out to evaluate the adoption of TEL. The second segment contained 45 items that assessed students’ self-efficacy (SE), perceived benefits (SPBs), satisfaction (SS), and adoption of TEL. Nine constructs were also extracted from this section. Thirdly, as stated by Hair et al. [115], the proposed conceptual model for the adoption of TEL in educational institutions was experimentally evaluated using structural equation modeling. The items were rated using a five-point Likert scale, with 1 representing “strongly disagree” and 5 representing “strongly agree.” In order to evaluate the data and make suggestions about the relevance of the study’s findings, appropriate statistical tests were used. A total of 274 students, including local and international undergraduate, master’s, and PhD students, were chosen at random from the students’ universities. The survey asked participants about their opinions on three topics: self-efficacy (SE), perceived benefits for students (SPBs), and student uptake of TEL.

3.1. Demographic Profile of Learners

The sample size used in this study is adequate to capture Saudi Arabian students’ perceptions of the adoption of TEL. In total, 22 participants were eliminated because of incomplete questionnaires. A total of 274 surveys, or 92.5%, of the total 296 that were distributed were returned by respondents. A quantitative method and a questionnaire survey were both used in this study. Self-administrated questionnaires were given out to King Faisal University students between April and May 2023 in order to gather the data, see (Appendix A). Additional information on the participants is shown in

Table 1. Demographic profile.

Demographic	Description	N	%
Gender	Male	197	71.9
	Female	77	28.1
Age	18–20	56	20.4
	21–24	63	23.0
	25–29	101	36.9
	30–34	35	12.8
	35 and above	19	6.9
Specialization	Social Science	110	40.1
	Humanities	88	32.1
	Medical Science	76	27.7

. Thus, information from a total of 274 surveys was evaluated using SPSS. According to Table 1’s demographic data, of the sample responses, 197 (71.9%) are male students, and the remaining 77 (28.1%) are female students. The age range of 25 to 29 years is where the majority of students (72.4%) fall. Also, 101 (36.6%) of the university’s students are local and international, and 51.5% of the students are Indian. Demographic determinants of specialty included 110 respondents (40.1%) from the social sciences, 88 respondents (32.1%) from the humanities, and 76 respondents (27.7%) from the medical sciences.

3.2. Measurement Instrument and Analysis

The measurement scales for the components were developed for this study based on widely used validity and reliability data from other investigations. The sample questionnaire was modified to include basic demographic information, including the respondents’ age, gender, and level of education, as well as questionnaire items measuring the components SPS, VSS, SN, IQ, SI, RA, SE, TEL, SS, and SAT. Students’ perceived benefits of TEL (SPBs) was modified from survey items [116]; VSS was modified from survey items [117,118]; SN was modified from survey items [119]; IQ was adapted from survey items [120]; SI was modified from survey items [78]; RA was modified from survey items [121]; SE was modified from survey items [119]; SS was adapted from survey items [120,122]; and TEL questions were modified to address how students adopted from [123,124]. Smart-PLS3 was used to implement PLSSEM procedures for data analysis. Moreover, measurements and factor structure were evaluated using Smart-PLS 3.3 software. During their computation in the measurement model, the validity and reliability of the data were evaluated. The values should be 0.500. The authors reported both convergent and discriminant validity for the reliability of the data by average variance extraction (AVE). Based on the computations of the Fornell–Larcker criterion, cross-loadings, and discriminant validity, the issue of discriminant validity was addressed. Internal reliability and reliability testing were performed in the meantime to report on the data’s dependability. Two methods for assessing reliability were Cronbach’s alpha coefficient (CA) and composite reliability (CR); both values needed to be higher than 0.700. We reported the importance of the link for the assessment model using the path coefficient, t-value, and p-value.

4. Results and Analysis

4.1. Measurement Model

Four evaluations of the PLS-SEM measurement models were supported by Hair et al. [115] and included convergent validity, discriminant validity, loadings of indicators, and dependability of internal consistency.

4.2. Reflective Indicator Loading

According to Hair et al. [115], the loadings of the reflective indicators attained in SEM should be greater than 0.700. According to the computation, all loadings exceeded 0.700. Subjective interest (SI), SI 2, 0.974, had the highest loading, and students’ perceived support (SPS), SPS 1, 0.808, had the lowest loading. Indicators for the following data analysis process totaled 46, including

Table 2. Construct items, load, CR, and AVE.

Construct	Items	IL	CA	CR	AVE
Students’ perceived support (SPS)	SPS_1	0.808	0.883	0.914	0.680
	SPS_2	0.838
	SPS_3	0.819
	SPS_4	0.843
	SPS_5	0.815
Virtual social skills (VSS)	VSS_1	0.886	0.921	0.944	0.809
	VSS_2	0.910
	VSS_3	0.922
	VSS_4	0.879
Subjective norm (SN)	SN_1	0.853	0.932	0.948	0.786
	SN_2	0.891
	SN_3	0.906
	SN_4	0.918
	SN_5	0.862
Information quality (IQ)	IQ_1	0.832	0.924	0.943	0.768
	IQ_2	0.875
	IQ_3	0.917
	IQ_4	0.882
	IQ_5	0.874
Subjective interest (SI)	SI_1	0.944	0.967	0.975	0.908
	SI_2	0.974
	SI_3	0.954
	SI_4	0.938
Resource availability (RA)	RA_1	0.962	0.951	0.965	0.873
	RA_2	0.881
	RA_3	0.941
	RA_4	0.951
Self-efficacy (SE)	SE_1	0.811	0.902	0.927	0.718
	SE_2	0.851
	SE_3	0.884
	SE_4	0.862
	SE_5	0.826
Students’ perceived benefits (SPBs)	SPB_1	0.805	0.917	0.938	0.753
	SPB_2	0.836
	SPB_3	0.923
	SPB_4	0.908
	SPB_5	0.861
Student satisfaction (SS)	SS_1	0.812	0.914	0.936	0.745
	SS_2	0.855
	SS_3	0.879
	SS_4	0.883
	SS_5	0.883
Students’ adoption of TEL (SAT)	SAT_1	0.826	0.939	0.954	0.806
	SAT_2	0.820
	SAT_3	0.940
	SAT_4	0.959
	SAT_5	0.934

.

4.3. Internal Consistency Reliability (ICR)

ICR was put in place to assess how consistently outcomes varied across indicators. Cronbach’s alpha coefficient (CA) and composite reliability (CR) values were reported for the current methodology. ICR values should range from 0 to 1. Values for the composite reliability (CR) and Cronbach’s alpha coefficient (CA) should be greater than 0.7 [115]. Table 2 displays the composite reliability and Cronbach’s alpha coefficient (CR) data. All of the constructs’ composite reliability (CR) and Cronbach’s alpha coefficient (CA) values are satisfactory and above the necessary level; the students’ felt support factor had a CR of 0.914, and the effectiveness factor had a CA and CR of 0.924 and 0.943, respectively.

4.4. Convergent Validity

According to Hair et al. [115], convergent validity is connected to construct validity; tests using the same or a similar construct should have a strong correlation. In this work, the average extracted variance was used to evaluate and report the convergent validity (AVE). The AVE Hair et al. calculation was performed using Smart-PLS3. The algorithm requires that AVE values have a value of 0.500 or above, explaining at least 50% of the variance (Table 2). All constructs identified by the computation show AVE values higher than 0.500 or explain more than 50% of the variation. The AVE for students’ acceptance of TEL was 0.806; the AVE for virtual social skills was 0.809 (see Table 2).

4.5. Discriminant Validity

The degree to which a concept differs empirically from other constructs is known as discriminant validity. In order to test the discriminant validity of this study, the Fornell–Larcker criterion, cross-loadings, and HTMT were all applied. A construct’s variance should be lower than others’ AVE [125], according to the Fornell–Larcker criterion. The values of the shared variances for each construct are greater than the construct, as seen in

Table 3. Fornell–Larcker criterion.

	IQ	RA	SE	SS	SPB	SAT	SPS	SI	SN	VSS
Information quality	0.877
Resource availability	0.802	0.934
Self-efficacy	0.770	0.621	0.847
Student satisfaction	0.652	0.515	0.645	0.863
Students’ perceived benefits	0.667	0.580	0.535	0.545	0.868
Students’ adoption of TEL	0.507	0.409	0.325	0.737	0.511	0.898
Students’ perceived support	0.547	0.639	0.662	0.427	0.755	0.570	0.825
Subjective interest	0.552	0.254	0.627	0.544	0.633	0.581	0.629	0.953
Subjective norm	0.800	0.608	0.803	0.547	0.455	0.600	0.476	0.566	0.886
Virtual social skills	0.543	0.520	0.661	0.866	0.556	0.546	0.538	0.493	0.515	0.899

. For instance, the value of overall satisfaction (0.863) exceeds all of its common variances, including self-efficacy (0.645), availability of resources (0.515), and information quality (0.652). The Fornell–Larcker criterion served as the foundation for establishing the discriminant validity. Moreover, if an indication loading on a concept is higher than its loadings, discriminant validity will manifest [115]. The loadings for all indicators are shown in Table 2. Table 2 contains a detailed report on all loading computations.

Discriminant validity will also show up when the HTMT is higher than 0.900, as shown in

Table 4. Heterotrait–monotrait ratio for discriminant validity.

	IQ	RA	SE	SS	SPB	SAT	SPS	SI	SN	VSS
Information quality
Resource availability	0.643
Self-efficacy	0.553	0.745
Student satisfaction	0.510	0.438	0.708
Students’ perceived benefits	0.403	0.699	0.588	0.691
Students’ adoption of TEL	0.529	0.426	0.653	0.584	0.680
Students’ perceived support	0.598	0.559	0.513	0.584	0.834	0.797
Subjective interest	0.455	0.464	0.430	0.663	0.439	0.597	0.753
Subjective norm	0.461	0.656	0.576	0.592	0.491	0.319	0.522	0.666
Virtual social skills	0.386	0.439	0.620	0.542	0.604	0.658	0.482	0.407	0.754

. A HTMT value greater than 0.900 denotes a lack of discriminant validity [115]. The HTMT examination confirmed the discriminant validity since, as shown in

Table 5. Variance inflation factor (VIF < 3).

	SE	SS	SPB	SAT
Information quality	2.316		1.603
Resource availability	1.209		1.232
Self-efficacy		1.402	2.228	1.865
Student satisfaction				1.891
Students’ perceived benefits		1.402		1.550
Students’ adoption of TEL
Students’ perceived support	1.698		1.785
Subjective interest	1.255		1.290
Subjective norm	2.916		2.252
Virtual social skills	1.678		2.182

, all HTMTs are below 0.900 and significantly differ from 1. The relationship between the subjective norm and students’ adoption of TEL shows the lowest HTMT (0.319), but the relationship between students’ perceived support and students’ perceived benefits shows a higher HTMT value (0.834). Student satisfaction and self-efficacy (both 0.708), resource availability, and information quality are the other HTMT values that emerged from the algorithm (both 0.643). Table 4 provides more information on the HTMT results in greater depth.

4.6. Structural Model and Collinearity

The structural model’s computation includes evaluating the model’s predicting abilities. However, the collinearity value should be acknowledged by providing the variance inflation factor (VIF) results before presenting the structural model. Particularly, the collinearity of the predictor sets was investigated (Hair et al. [115]), as well as the adoption of TEL by students and the use of students’ perceived benefits as a predictor of student satisfaction. Self-efficacy, as well as the perceived benefits of the students, are predicted by resource availability (Table 5). VIF readings should be less than three; those greater than three are frequently thought to have multicollinearity issues. All of the VIFs are less than three, according to the data analysis’s findings. For instance, Table 5 shows that the VIF values for students’ perceived support as a predictor of self-efficacy and students’ reported benefits were 1.698 and 1.785, respectively.

Regression analysis’s output, the determination coefficient (R2), is defined as the percentage of the predictor variables that the exogenous variable might be able to predict. It evaluates the precision of a proposed model for prediction. The square of the correlation among two constructs is used to calculate it. On the R2 scale, which ranges from 0 to 1, a higher value denotes a higher level of R2. A value of 0.25 is regarded as a weak value, 0.50 as noteworthy, and 0.75 as important (Hair et al., [115]). Based on the results of the investigation, the R² result is shown in

Table 6. The determination coefficient (R2).

	R Square	R Square Adjusted
Self-efficacy	0.839	0.836
Student satisfaction	0.471	0.467
Students’ perceived benefits	0.688	0.68
Students’ adoption of TEL	0.181	0.172

. Students’ acceptance of TEL, student satisfaction (0.471, average), perceived benefits (0.688, high), self-efficacy (0.839, high), and students’ adoption of TEL (0.181) are all factors that show a positive R² result. Table 6 displays the outcomes.

4.7. Hypothesis Testing

All of the hypotheses, including the one that states that there is “no subjective norm between student groups for students’ perceived benefits,” are true, as shown in

Table 7. Hypotheses testing.

H	Factors	β	T-Value	p Values
H1	Students’ perceived support -> Self-efficacy	0.118	3.298	0.001
H2	Students’ perceived support -> Students’ perceived benefits	0.622	11.343	0.000
H3	Virtual social skills -> Self-efficacy	0.285	6.538	0.000
H4	Virtual social skills -> Students’ perceived benefits	0.227	4.716	0.000
H5	Subjective norm -> Self-efficacy	0.232	4.359	0.000
H6	Subjective norm -> Students’ perceived benefits	0.025	0.420	0.675
H7	Information quality -> Self-efficacy	0.606	10.193	0.000
H8	Information quality -> Students’ perceived benefits	0.304	2.886	0.004
H9	Subjective interest -> Self-efficacy	0.076	2.068	0.039
H10	Subjective interest ----> Students’ perceived benefits	0.090	2.077	0.038
H11	Resource availability----> Self-efficacy	0.062	2.344	0.019
H12	Resource availability ----> Students’ perceived benefits	0.141	2.960	0.003
H13	Self-efficacy ----> Students’ perceived benefits	0.391	3.605	0.000
H14	Self-efficacy ----> Student satisfaction	0.495	7.534	0.000
H15	Self-efficacy ----> Students’ adoption of TEL	0.577	9.567	0.000
H16	Students’ perceived benefits ----> Student satisfaction	0.280	4.487	0.000
H17	Students’ perceived benefits ----> Students’ adoption of TEL	0.195	3.221	0.001
H18	Student satisfaction ----> Students’ adoption of TEL	0.229	3.523	0.000

and Figure 2 and Figure 3. According to the current sample, there are no subjective norms (H6) among student groups that would cause students to perceive the benefits of adopting TEL (β = 0.025, t = 0.420). The hypotheses of students’ perceived support (H1 and H2) on self-efficacy (β = 0.118, t = 3.298) and students’ perceived benefits (β = 0.622, t = 11.343) were shown to be positively and significantly related to students’ adoption of TEL for technology-enhanced learning in higher education. Virtual social skills were significant determinants of self-efficacy (H3) and students’ perceived benefits (H4) (H3, β = 0.285, t = 6.538, and H4, β = 0.227, t = 4.716, respectively), thereby supporting H3 and H4. Subjective norm (H5) was shown to be positively and significantly related to students’ self-efficacy for adopting TEL in higher education (β = 0.232, t = 4.359). Therefore, H5 is supported. As a result, information quality (H7 and H8) is also found to be positively and significantly related to self-efficacy (β = 0.606, t = 10.193) and students’ perceived benefits (β = 0.304, t = 2.886). Therefore, H7 and H8 were supported. Moreover, subjective interest is positively and significantly associated with students’ self-efficacy (H9, β = 0.076, t = 2.068) and students’ perceived benefits (H10, β = 0.090, t = 2.077), supporting hypotheses 9 and 10. The outcomes suggest that resource availability (H11 and H12) on students’ adoption of TEL was shown to be positively and significantly related to self-efficacy (β = 0.062, t = 2.344) and students’ perceived benefits (β = 0.141, t = 2.960) for adopting TEL in higher education. Therefore, H11 and H12 were supported. The hypotheses of self-efficacy on students’ adoption of TEL were shown to be positively and significantly related to students’ perceived benefits (H13) (β = 0.391, t = 3.605), student satisfaction (H14) (β = 0.495, t = 7.534), and students’ adoption of TEL for technology-enhanced learning in higher education (H15) (β = 0.577, t = 9.567). Therefore, H13–H15 were supported. As a result, students’ perceived benefits are positively associated with student satisfaction (H16) (β = 0.280, t = 4.487) and students’ adoption of TEL (H17) (β = 0.195, t = 3.221) for TEL in higher education. Therefore, H16 and H17 were supported. Finally, student satisfaction (H18) was shown to be positively and significantly related to students’ adoption of TEL for technology-enhanced learning in higher education (β = 0.229, t = 3.523). Therefore, H18 is supported.

Each of the ten variables’ indirect effects is shown for each in

Table 8. Indirect effects of the research model.

	Student Satisfaction	Students’ Perceived Benefits	Students’ Adoption of TEL
Information quality	0.281	0.237	0.298
Resource availability	0.477	0.324	0.414
Self-efficacy	0.209		0.315
Students’ perceived benefits			0.464
Students’ perceived support	0.303	0.446	0.398
Subjective interest	0.271	0.330	0.404
Subjective norm	0.134	0.091	0.091
Virtual social skills	0.335	0.411	0.344

, and the sum of these indirect effects shows the overall effect. In accordance with Cohen’s (1988) classification, an effect-size value beyond 0.5 is regarded as high, 0.5 to 0.3 is moderate, 0.3 to 0.1 is modest, and values smaller than 0.1 are deemed insubstantial. In terms of indirect impacts, the structural model suggests that students’ self-efficacy and perceived benefits acted as a mediating factor between the effect of information quality and their adoption of TEL. Notably, with matching total effect values of 0.298, 0.281, and 0.237, respectively, students’ adoption of TEL, satisfaction, and perceived benefits emerged as the primary factors influencing information quality. Table 8 provides specific values for the combined effects of these determinants across all variables.

5. Discussion and Implications

One of the first studies to look at TEL for sustainability education in Saudi Arabia used self-efficacy with external variables, while in this study, TEL is used as an independent factor. Thus, this study develops 18 hypotheses and 9 factors that affect TEL. Self-efficacy and students’ perceptions of the benefits of TEL are significantly influenced by their perceived support, virtual social skills, subjective norm, information quality, subjective interest, and resource accessibility (see Figure 2). Self-efficacy and students’ perceptions of the benefits of TEL had an impact on student satisfaction and their adoption of it. As a result, the conclusions corroborated the theories that had been put forth and the design of the study technique. According to this study, students’ perceptions of support for TEL positively influence their self-efficacy and their perceptions of its benefits (H1 and H2), and as a result, students’ perceptions of support for these systems appear to be crucial success factors. Self-efficacy and students’ perceived benefits use perceived support characteristics, if they are there, to increase acceptance of TEL. Hence, increasing the perceived benefits and self-efficacy of students while also enhancing those that already exist could directly increase student happiness and the uptake of TEL. This supports the conclusions reached in [126,127,128,129]. The results of the test of the hypotheses show that students’ perceptions of the benefits of TEL and self-efficacy were positively correlated and significantly influenced by virtual social skills (H3 and H4). These results are in line with those of earlier investigations (e.g., [130,131]). The H5 hypothesis test findings showed that subjective norms of TEL have considerably and favorably influenced students’ perceptions of the benefits and self-efficacy, and the hypothesis is accepted. The outcome was discovered to be in line with earlier study results [119,132].

Consequently, it can be claimed that students are motivated to use online learning as a medium by subjective standards in the form of societal endorsement from friends, family, the sustainability, peer groups, and others. Also, it can create a solidly positive perception of the sources of adoption in the future. This study’s findings disproved the subjective norms element and provided evidence for hypothesis (H6), which states that students’ perceptions of benefits were unaffected by subjective norms. However, these findings did not agree with those of other studies [119,132,133]. The ISSM’s information quality constructs (H7 and H8) have been shown to be significant in the integrated model as well as when independently verified to predict tertiary students’ self-efficacy and students’ perceived benefits from TEL. This strongly suggests that both constructs will be significant in future research on TEL. The ISSM proposed by DeLone and McLean [134] has received widespread validation in investigations of IS acceptances [135,136]. These results are in line with those of earlier investigations (e.g., [135,136]).

The findings of the hypothesis tests (H9 and H10) showed that students’ perceptions of TEL’s perceived subject interest considerably and favorably influenced their self-efficacy and their perceptions of the program’s advantages, and the hypothesis was found to be true. Similar results from earlier research investigations have also been discovered in [81,137,138]. This suggests that if a subject is of exceptional interest to students, it will encourage them to pursue online learning in a good and substantial way. Consequently, it can be inferred that students prefer online learning when they have a strong desire to learn a subject and find it to be a useful tool for finding and utilizing learning resources [81,137,138].

The results of the eleventh and twelfth (H11 and H12) hypotheses tests showed that the hypotheses are accepted and that students’ perceptions of TEL’s perceived resource availability and benefits have a significant and favorable impact on their self-efficacy. The results are discovered to be in line with earlier research findings [121,139,140,141]. So, it can be claimed that students will be more likely to adopt online learning if there are more resources available for it. The likelihood that students will accept new technology and other materials for fostering a supportive learning environment is significantly influenced by their availability [121,140,141].

The results of this study show that SE (H13–H15) increases student satisfaction, student acceptance of TEL, and student belief in the benefits of TEL. According to Abdullah et al.’s research [142], SE has a favorable effect on students’ perceptions of the benefits of TEL, student satisfaction, and students’ adoption of the practice. As a result, the findings of this study and that investigation are in agreement. The beneficial and substantial effects of SE are comparable to those reported by earlier studies [143,144,145]. Lastly, this study’s results strongly support the choice to utilize TEL made by students, verifying hypothesis number 18 (H18), which claims that this choice has a positive influence on students’ acceptance of TEL for educational sustainability in higher education. This is in line with earlier research by [120,146], who discovered that student happiness affects students’ acceptance of TEL for educational sustainability.

5.1. Implications

The implementation of self-efficacy and students’ perceived benefits, together with the new components introduced to the model, are supported by this study in the setting of Saudi Arabia. This study’s findings also give legislators, experts, developers, and designers practical advice on how to effectively integrate TEL. They also provide a deeper knowledge of external influences. The university administration must firstly provide the necessary infrastructure for TEL and assess the readiness of the student body to adopt it. Secondly, those in charge of making decisions and managing TEL in Saudi higher education institutions must concentrate on the elements that can help students accept this approach, which in turn affects how well teachers perform, how effectively students learn, and how much they value it. Thirdly, the results of this study demonstrate the importance of outside variables in students’ adoption of TEL. So, it is important to establish the sustainability of TEL for the students. As a result, it is important to assess and develop students’ perceptions of support, and all university students should have access to computer labs that are equipped with the necessary tools for TEL. Fourthly, training programs should be designed to support students’ perceptions of their own self-efficacy and the benefits of TEL, as doing so would increase their positive satisfaction and adoption of the practice. Fifthly, the empirical findings of this study may help stakeholders make informed judgments on the acceptance of TEL, namely in support of its deployment in the Saudi Arabian context and other contexts that are comparable.

5.2. Limitation

This study’s findings, while highly intriguing and crucial in characterizing how students reacted to e-learning systems, also suggested certain limits. Firstly, since this study was only intended for students, it would be impossible to compare student and teacher analyses without taking into account the reactions of the instructors. This point should be taken into account in future studies. The model also predicts users’ perceptions and intentions for a specific point in time because it is cross-sectional. Because it is probable that people’s perceptions and preferences would alter as they gained more experience over time, it is advised that more research be conducted using the longitudinal survey. Fourthly, because the sample was drawn from a university with a narrow focus, it was vital to take into account larger populations with varied income levels, educational attainment, racial and ethnic composition, and psychological orientations. There is a chance to extrapolate the research findings to the broader Saudi higher education context when the selection is highly representative. Lastly, because the current study concentrated on a single university in Saudi Arabia, the findings can only be applied to public universities, not private ones.

6. Conclusions

The findings of this study confirm that students’ perceived support, virtual social skills, information quality, subjectivity, availability of resources, and self-efficacy, which influence their adoption of TEL, are successful factors. The findings also showed that students’ perceptions of support, virtual social skills, subjectivity, norm, information quality, subjectivity, interest, and resource availability, which heighten students’ perceptions of benefits from TEL, influence their satisfaction with and implementation of TEL. The results also showed that students’ perceptions of the benefits of TEL, as well as their contentment with and adoption of it, are favorably influenced by self-efficacy. The results also supported the use of self-efficacy in conjunction with external variables to examine students’ self-efficacy and perceived benefits of TEL in order to promote students’ satisfaction with and acceptance of TEL in higher education. Overall, students’ perceptions of the benefits of TEL and their sense of self-efficacy improve their learning activities, peer interaction, and the sharing and exchanging of knowledge. Future research should be repeated in provinces with sustainability different from Saudi Arabia and should take these disadvantages into further consideration.