# Modeling the Social Factors Affecting Students’ Satisfaction with Online Learning: A Structural Equation Modeling Approach

Authors: Abdulhameed Rakan Alenezi
Journal: Education Research International
 (2022)

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## Abstract

While online learning has always faced criticism regarding social issues such as the lack of engagement, interaction, and communication, the COVID-19 social distancing has increased the public’s concern and criticism. Therefore, the purpose of this research was to investigate and model the social factors affecting students’ satisfaction with online learning. The proposed research model was constructed based on the extensive review of relevant literature to determine the critical social factors examined and validated in this research. The data were collected from a total of 258 students using a quantitative method approach. A structural equation modeling technique was utilized in analyzing the obtained data. The findings of the study reveal that all proposed social factors namely social presence, social interaction, social space, social identity, social influence, and social support were found to significantly affect the students’ satisfaction with online learning. The examined factors account for about 56% of the total variance in students’ satisfaction. Several suggestions and recommendations are provided in line with study limitations.


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## Body

## 1. Introduction

Online learning has risen in popularity in recent years, as technology has been incorporated into all levels of education and training. Consequently, scholars have been looking for methods to enhance its efficacy [1]. Online learning has been marketed as being less expensive and more convenient than conventional educational settings and offering more possibilities for students to complete their education. Learning is considered to be a social process [2], and online learning involves all of the social elements and factors at the heart of the learning process [3, 4]. Social presence, interaction, space, and connectivity are all considered important determinants of online learning success [3, 5]. Among the most significant disadvantages of virtual learning is the fact that instructors and students are separated geographically [6, 7]. As a consequence, students may feel cut off from their instructors and other students on a social level.To solve this problem and improve students’ educational experiences, virtual learning platforms and learning management systems (LMS) should include rich materials that promote adaptive social engagement with instructors and other students. This study is based on the idea that online learning is essentially social and that social contact with instructors and other students is critical in motivating students to learn and enhancing their satisfaction with online courses [8, 9]. Online learning and teaching are complex activities with several success indicators. One of the indicators used to assess the quality and effectiveness of online learning is student satisfaction.There have been multiple student satisfaction studies related to online learning, focusing on areas such as learner-learner, learner-instructor, and learner-content interaction [10], student engagement [11], faculty satisfaction [12], social presence [8], and overall student satisfaction [13]. Thus, this study will try to examine the critical social factors that could influence the student’ satisfaction with online learning.


### 1.1. Problem Statement

Due to the COVID-19 outbreak, the majority of educational institutions were forced to shift and moved towards online learning [14]. The social distancing caused by the pandemic has resulted in a reduction in several social elements such as interactivity, engagement, and support, which considered significant keys in influencing students’ satisfaction [15, 16]. Furthermore, in both face-to-face and virtual learning, social elements play a key role in assuring student retention and satisfaction [17]. However, there is limited research regarding the effects of social factors on students’ satisfaction with online learning during the COVID-19 pandemic [18]. Therefore, the objective of this study was to contribute to the limited existing body of literature by investigating and modeling the social factors affecting students’ satisfaction with online learning.


## 1.1. Problem Statement

Due to the COVID-19 outbreak, the majority of educational institutions were forced to shift and moved towards online learning [14]. The social distancing caused by the pandemic has resulted in a reduction in several social elements such as interactivity, engagement, and support, which considered significant keys in influencing students’ satisfaction [15, 16]. Furthermore, in both face-to-face and virtual learning, social elements play a key role in assuring student retention and satisfaction [17]. However, there is limited research regarding the effects of social factors on students’ satisfaction with online learning during the COVID-19 pandemic [18]. Therefore, the objective of this study was to contribute to the limited existing body of literature by investigating and modeling the social factors affecting students’ satisfaction with online learning.


## 2. Literature Review

### 2.1. Online Learning Satisfaction

Online learning is used as a complement to face-to-face learning. Some benefits of online learning include simple access to information, appropriate content distribution, content standardization, customized teaching, self-pacing, interaction, and convenience. Although online learning is beneficial to many learners, student satisfaction is required for a successful and effective educational experience. Student satisfaction is an attitude formed as a result of an individual’s evaluation of the educational experience, services, and facilities provided [19]. In the context of online learning, satisfaction relies on a wide range of factors, including communication, student participation in virtual conversations, flexibility, workload, technical support, instructional methods, and feedback [20]. Interacting with people, engaging in activities, and receiving feedback help students gain knowledge in a social environment [21].Several studies have compared student satisfaction and academic outcomes in face-to-face and online courses [22]. However, the results are inconclusive. According to several studies, online learning is exciting for students and produces better learning results than conventional face-to-face learning [23, 24]. Some other studies found no changes [25], while yet another subset of research shows that online education has a low level of engagement [26]. The factors affecting student satisfaction can be divided into three categories: instructor, interactivity, and technology [27]. While the three factors are interrelated, social factors fall under the category of interactivity. Interaction and technology have impacted student satisfaction, requiring instructors to possess appropriate techno-pedagogical abilities and put in additional effort to engage learners online [28]. Despite the inconclusive findings of the comparison between virtual learning and face-to-face learning outcomes, satisfaction has been found to be positively correlated with academic performance and student engagement.


### 2.2. Online Learning Satisfaction and the Importance of Social Factors

It is a fundamental human desire to form social bonds. This need also applies to online environments. Individuals seek more than just information; they want to feel connected, supported, and validated even when they are online [29]. Similarly, social elements are present in online learning, and social interactions are essential to the online learning process [4]. Social factors that contribute to student satisfaction include social interaction, social identity, social space, social influence, social presence, and social support [5, 9, 30–32].The works of [33] state that in order for e-learning to succeed and produce effective results, student satisfaction is crucial. Furthermore, Rubin et al. [34] expand on earlier research on the community of inquiry [35], highlighting the importance of social, cognitive, and instructional involvement in student learning and satisfaction. The characteristics of learning management systems (LMS) have a substantial impact on community perceptions, according to the inquiry framework. In a similar study, Mahmood et al. [36] found that instructor participation is an essential element in how students perceive virtual learning.The interaction component is important in both face-to-face and online learning modalities [37]. Numerous studies show that in every virtual learning environment, the quantity and quality of student interactions are highly linked to student satisfaction [38–40]. Researchers have discovered, however, that demographic and cultural variables have an impact on the design of appropriate online learning interaction techniques [41]. Social presence is also a significant social factor in virtual learning, as it fosters comfort and emotional ties among students [42] and has been positively linked with learning outcomes and student satisfaction [43]. All these social factors are intertwined and manifest themselves in online learning in social interactions and social presence [44].


### 2.3. Social Presence (SP)

There are ongoing debates regarding what social presence means and how the term is used. In our case, social presence can be defined as how students perceive and build a feeling of connection with teachers, peers, and staff in a virtual environment, through their contact with and the assistance they get from professors and administrative personnel. Online social presence may range from primary contact between people or groups through emails, text messages, and announcement boards to more sophisticated, supervised group work. Engagement techniques may also be used in online teaching and learning to improve social presence and learner retention in online courses [9, 45, 46]. These may include providing more opportunities for social interaction in the class, providing timely feedback, allowing access to everyone, and providing self-assessment opportunities.Early research on social presence found that students’ emotional connections are essential. In virtual learning settings, social presence is a significant determinant of satisfaction and perceived learning [47]. Humor, facial expressions, gestures, tone and speed of verbal expressions, salutations, group allusions, acceptance, and guidance aid student satisfaction and perceived learning. How instructors portray themselves in online classes, how they speak with students, their tones, their emotional displays, and the quality of their interactions with students all add to the sense of social presence [13].To bring social presence to learning, teachers and students must connect to stimulate critical inquiry and guarantee inclusiveness [48]. According to Kehrwald [49], interactions alone do not characterize social presence among students. Still, social presence may be measured using relational presence and the ability to establish connections and sustain ongoing meaningful engagement.


### 2.4. Social Interaction (SI)

The term “social interaction” refers to the interaction that happens between students and instructors when instructors implement strategies to foster interpersonal encouragement and social inclusion [50], as cited in [22]. Lonn et al. [51] outlined three types of social interaction: learner-learner, learner-content, and learner-instructor. Learner-to-learner interactions take place in a virtual setting, whether or not teachers are present [52]. When students are accessing information via various resources including the social media and Web-based courses, their engagement and academic performance perception will be improved [53, 54]. When an instructor shares data, provides suitable support, elucidates misapprehensions, and escalates student enthusiasm, it is called learner-instructor interaction [22]. These three types of social interaction are important in determining student satisfaction. When different types of interaction are used in the learning environment, satisfaction with the learning experience increases [55]. Multiple forms of interaction may be established by adding extracurricular activities to the academic program.While student-student interaction is essential for online student satisfaction, the frequency, quality, and timeliness of student-instructor contact are the most significant predictors of student satisfaction [56]. In a study of 120 online graduate nursing students, Thurmond et al. [57] concluded that getting quick feedback from the instructor, choosing evaluation methods, and knowing the instructor all contributed to student satisfaction; these results are consistent with the works of [58]. Researchers found that individuals who gave the most favorable responses about knowing their instructors reported actively engaging in Web-based conversations more frequently.These findings confirm the importance of encouraging student-instructor interaction to promote active learning. Similarly, in a quantitative study of 186 online graduates, the study [59] discovered a significant statistical link between instructor feedback on completed assignments and learning outcomes, as measured by student satisfaction and overall grades. These results stress the importance of student-instructor contact in student performance and further highlight the importance of satisfaction in online learning.


### 2.5. Social Space (SS)

Social space is the network of social connections among members of a group. It includes groupings of values and norms, rules and responsibilities, beliefs, and aspirations [60]. Social space is also referred to as social climate, online atmosphere, and social environment. Social space benefits from affective aspects of interpersonal relationships in a group, such as group cohesiveness, respect, trust, and belonging [60]. Due to the trust and feeling of community among members, social space affects social interaction, resulting in possibilities for critical discussion where open speech is neither insulting nor destructive. The free flow of information encourages members’ devotion to the group’s objectives and thus increases overall satisfaction. In conclusion, as Kreijns et al. [60] explain, “a good social space inside the group leads to a positive social climate/online atmosphere.”The three concepts of social space, sociability, and social presence are interconnected and do not function independently. They affect how social interaction in groups is created and sustained when they work together. According to the research of [61], low sociability in a group has a negative impact on the formation of social space. That is to say, although the two categories involve different aspects of group social interactions, sociability contributes to social space [62].During asynchronous online conversations, the community members are thought to affect the development of social space [63]. Members may choose to follow the group’s objectives or use the learning environment’s benefits (i.e., sociability). However, the components of social spaces that influence social space impressions are unclear. Most social presence scholars, unaware of the social space and sociability concepts, use social presence theory, which combines the three aspects into a single “social presence” concept.


### 2.6. Social Identity (SID)

The social identity comprises self-categorization theory [64] and social identity theory [65]. Social identity can be defined as a person’s self-concept concerning their social group membership [65]. Individuals self-identify as members of several social groups or classes [66, 67]. Individuals use categories to organize and place themselves in their social environments [68], a social and relative approach that leads to in-group and out-group identification [69].The study of [70] empirically investigated the relationship between students’ social identities and their performance in online learning and found that online learning performance affects students’ social identities. Their research also emphasizes that students’ social identities should be improved to enhance online learning performance and satisfaction.Social identification increases in-group homogeneity [71], which improves social ties inside a group, as well as individual students’ commitment to studying, school performance, and satisfaction with their curriculum and organization. Students who achieve their learning goals are likely to be satisfied with their program and institution [72]. Considering that learning is an identity experience that affects an individual and what he or she can achieve, learning and social identification are intimately connected [73].Students arrive at college with an academic self-concept, or a belief in their own academic competence. Learners who achieved high-grade point averages in high school tend to have a great academic self-construct, linked to outstanding goal achievement [74]. Students who enroll in postgraduate schools with a strong track record of work experience, for example, as a junior or midrange manager, have similar social identity traits [75]. Seeing oneself as a “proven” manager may form a significant part of one’s personality, influencing one’s interactions with students and instructors.


### 2.7. Social Influence (SIN)

Social influence is defined as the process through which the presence or activity of others influences someone’s beliefs, attitudes, or conduct. The four elements of social influence include compliance, obedience, conformity, and minority influence. Fishbein and Ajzen [76] developed the theory of reasoned action (TRA) as a model for explaining how social standards of conformity, identification, and compliance may influence conduct. They named these social effects “subjective norms” and showed that, when coupled with individually held beliefs, they could be utilized to predict behavior. Students’ subjective norms are the influences they feel from teachers, other students, or important individuals in the learning environment to adopt the system.The subjective norm construct indicates that students are influenced by others in their usage and appropriation of the virtual learning system [77]. The study of [78] found that subjective norms affect the frequency with which students utilize ICT in education, based on previous research on the roles of peers and teachers in fostering effective virtual learning. Subjective criteria set by superiors (e.g., parents or employers) have been shown to influence a person’s choice to participate in an online program [79]. According to the research of [80], the participation of the instructor and social influence among students impact course involvement, student motivation, educational achievement, and views regarding virtual learning platforms.Certain studies show that peers are important in influencing technology adoption and use pattern habits for e-learning. The research of [81] examined how peer pressure affects academic achievement and perceptions of online learning. Shin’s research did not discover a link between performance and peer influence. However, more recent research indicates that students who feel a strong sense of belonging among their fellow students are more satisfied and more likely to continue with virtual learning [82].


### 2.8. Social Support (SSP)

Social support is a three-part construct with many different aspects. It is defined as an activity in which individuals experience, perceive, and express emotional concern, instrumental help, or knowledge via human interaction [83]. The study of [84] described it as “interaction with others that provides students with insight and good learning experience.” Demaray et al. [85] defined social support as “information, appraisal, and emotional support that comes from a variety of sources, including instructors, parents, friends, and colleagues that improves student satisfaction.” Social support is among the most significant and vital areas of intermediate studies. It is a critical component often utilized in socio-educational and sociopsychological research [86].The study of Bean [87] believes that close friends and colleagues facilitate social integration into educational institutions. The study of [88] stated that social support improves interpersonal and intergroup relationships. Student satisfaction is positively linked to social support from classmates or relatives [89]. According to [90], students with strong social lives display higher levels of satisfaction.When students are incorporated into the social environment on campus, their quality of life improves. The research of [91] feels that involving students in various social activities helps them build a positive mindset and supports learning. A lack of social support may make it more difficult to speak for oneself, maintain autonomy, and establish and sustain relationships, all of which can have a major impact on a person’s life and learning. Social support for online learners can be achieved through both student-student interactions and student-instructor interactions [30]. Therefore, institutions must play a critical role in building their social lives and exercising social integration as they study and grow.


### 2.9. The Influence of Social Factors in Successful Online Learning

Social factors are often interrelated with instructor support. Students’ attributes, technical support, and virtual course design all influence online learning. Tunison and Noonan [92] examined high school students’ initial encounters with online courses and found that they saw their online experiences as inspiring and liberating. Although most of the students regarded their instructors as the ultimate source of knowledge, they felt a newfound sense of independence in virtual learning. Students enjoyed their online experiences as a result of this positive impression, which led to favorable outcomes.Other studies identify student autonomy as a defining feature of virtual learning [93, 94]. Rice [95] found that students found remote learning challenging and alienating: “Even if students are highly driven and self-directed, they may still find the experience lonely, challenging, and disappointing in a distance education environment.” The following characteristics of virtual learners impact their learning: communication values, beliefs, and emotions; dedication to group procedures; and interaction abilities [96]. According to Li et al. [97], student characteristics seem to have a significant role in students’ satisfaction in online learning.In addition to the factors mentioned earlier, virtual course design impacts student success in online learning [98], since it affects students’ engagement and involvement in courses. Thus, the virtual classroom’s design and the students’ characteristics influence the students’ learning in the virtual classroom, with the three variables influencing students’ involvement in the classroom and their views of learning in it. The social atmosphere in online learning has an impact on students’ involvement in the virtual classroom and their perceptions of their learning in this setting [44]. At the same time, interactions with the instructor and with classmates have an impact on the social environment [99]. Hence, the aforementioned social factors have been proven to be significant and influential in the online learning environment. Therefore, this study aimed to investigate and model the social factors that could affect students’ satisfaction with online learning.


## 2.1. Online Learning Satisfaction

Online learning is used as a complement to face-to-face learning. Some benefits of online learning include simple access to information, appropriate content distribution, content standardization, customized teaching, self-pacing, interaction, and convenience. Although online learning is beneficial to many learners, student satisfaction is required for a successful and effective educational experience. Student satisfaction is an attitude formed as a result of an individual’s evaluation of the educational experience, services, and facilities provided [19]. In the context of online learning, satisfaction relies on a wide range of factors, including communication, student participation in virtual conversations, flexibility, workload, technical support, instructional methods, and feedback [20]. Interacting with people, engaging in activities, and receiving feedback help students gain knowledge in a social environment [21].Several studies have compared student satisfaction and academic outcomes in face-to-face and online courses [22]. However, the results are inconclusive. According to several studies, online learning is exciting for students and produces better learning results than conventional face-to-face learning [23, 24]. Some other studies found no changes [25], while yet another subset of research shows that online education has a low level of engagement [26]. The factors affecting student satisfaction can be divided into three categories: instructor, interactivity, and technology [27]. While the three factors are interrelated, social factors fall under the category of interactivity. Interaction and technology have impacted student satisfaction, requiring instructors to possess appropriate techno-pedagogical abilities and put in additional effort to engage learners online [28]. Despite the inconclusive findings of the comparison between virtual learning and face-to-face learning outcomes, satisfaction has been found to be positively correlated with academic performance and student engagement.


## 2.2. Online Learning Satisfaction and the Importance of Social Factors

It is a fundamental human desire to form social bonds. This need also applies to online environments. Individuals seek more than just information; they want to feel connected, supported, and validated even when they are online [29]. Similarly, social elements are present in online learning, and social interactions are essential to the online learning process [4]. Social factors that contribute to student satisfaction include social interaction, social identity, social space, social influence, social presence, and social support [5, 9, 30–32].The works of [33] state that in order for e-learning to succeed and produce effective results, student satisfaction is crucial. Furthermore, Rubin et al. [34] expand on earlier research on the community of inquiry [35], highlighting the importance of social, cognitive, and instructional involvement in student learning and satisfaction. The characteristics of learning management systems (LMS) have a substantial impact on community perceptions, according to the inquiry framework. In a similar study, Mahmood et al. [36] found that instructor participation is an essential element in how students perceive virtual learning.The interaction component is important in both face-to-face and online learning modalities [37]. Numerous studies show that in every virtual learning environment, the quantity and quality of student interactions are highly linked to student satisfaction [38–40]. Researchers have discovered, however, that demographic and cultural variables have an impact on the design of appropriate online learning interaction techniques [41]. Social presence is also a significant social factor in virtual learning, as it fosters comfort and emotional ties among students [42] and has been positively linked with learning outcomes and student satisfaction [43]. All these social factors are intertwined and manifest themselves in online learning in social interactions and social presence [44].


## 2.3. Social Presence (SP)

There are ongoing debates regarding what social presence means and how the term is used. In our case, social presence can be defined as how students perceive and build a feeling of connection with teachers, peers, and staff in a virtual environment, through their contact with and the assistance they get from professors and administrative personnel. Online social presence may range from primary contact between people or groups through emails, text messages, and announcement boards to more sophisticated, supervised group work. Engagement techniques may also be used in online teaching and learning to improve social presence and learner retention in online courses [9, 45, 46]. These may include providing more opportunities for social interaction in the class, providing timely feedback, allowing access to everyone, and providing self-assessment opportunities.Early research on social presence found that students’ emotional connections are essential. In virtual learning settings, social presence is a significant determinant of satisfaction and perceived learning [47]. Humor, facial expressions, gestures, tone and speed of verbal expressions, salutations, group allusions, acceptance, and guidance aid student satisfaction and perceived learning. How instructors portray themselves in online classes, how they speak with students, their tones, their emotional displays, and the quality of their interactions with students all add to the sense of social presence [13].To bring social presence to learning, teachers and students must connect to stimulate critical inquiry and guarantee inclusiveness [48]. According to Kehrwald [49], interactions alone do not characterize social presence among students. Still, social presence may be measured using relational presence and the ability to establish connections and sustain ongoing meaningful engagement.


## 2.4. Social Interaction (SI)

The term “social interaction” refers to the interaction that happens between students and instructors when instructors implement strategies to foster interpersonal encouragement and social inclusion [50], as cited in [22]. Lonn et al. [51] outlined three types of social interaction: learner-learner, learner-content, and learner-instructor. Learner-to-learner interactions take place in a virtual setting, whether or not teachers are present [52]. When students are accessing information via various resources including the social media and Web-based courses, their engagement and academic performance perception will be improved [53, 54]. When an instructor shares data, provides suitable support, elucidates misapprehensions, and escalates student enthusiasm, it is called learner-instructor interaction [22]. These three types of social interaction are important in determining student satisfaction. When different types of interaction are used in the learning environment, satisfaction with the learning experience increases [55]. Multiple forms of interaction may be established by adding extracurricular activities to the academic program.While student-student interaction is essential for online student satisfaction, the frequency, quality, and timeliness of student-instructor contact are the most significant predictors of student satisfaction [56]. In a study of 120 online graduate nursing students, Thurmond et al. [57] concluded that getting quick feedback from the instructor, choosing evaluation methods, and knowing the instructor all contributed to student satisfaction; these results are consistent with the works of [58]. Researchers found that individuals who gave the most favorable responses about knowing their instructors reported actively engaging in Web-based conversations more frequently.These findings confirm the importance of encouraging student-instructor interaction to promote active learning. Similarly, in a quantitative study of 186 online graduates, the study [59] discovered a significant statistical link between instructor feedback on completed assignments and learning outcomes, as measured by student satisfaction and overall grades. These results stress the importance of student-instructor contact in student performance and further highlight the importance of satisfaction in online learning.


## 2.5. Social Space (SS)

Social space is the network of social connections among members of a group. It includes groupings of values and norms, rules and responsibilities, beliefs, and aspirations [60]. Social space is also referred to as social climate, online atmosphere, and social environment. Social space benefits from affective aspects of interpersonal relationships in a group, such as group cohesiveness, respect, trust, and belonging [60]. Due to the trust and feeling of community among members, social space affects social interaction, resulting in possibilities for critical discussion where open speech is neither insulting nor destructive. The free flow of information encourages members’ devotion to the group’s objectives and thus increases overall satisfaction. In conclusion, as Kreijns et al. [60] explain, “a good social space inside the group leads to a positive social climate/online atmosphere.”The three concepts of social space, sociability, and social presence are interconnected and do not function independently. They affect how social interaction in groups is created and sustained when they work together. According to the research of [61], low sociability in a group has a negative impact on the formation of social space. That is to say, although the two categories involve different aspects of group social interactions, sociability contributes to social space [62].During asynchronous online conversations, the community members are thought to affect the development of social space [63]. Members may choose to follow the group’s objectives or use the learning environment’s benefits (i.e., sociability). However, the components of social spaces that influence social space impressions are unclear. Most social presence scholars, unaware of the social space and sociability concepts, use social presence theory, which combines the three aspects into a single “social presence” concept.


## 2.6. Social Identity (SID)

The social identity comprises self-categorization theory [64] and social identity theory [65]. Social identity can be defined as a person’s self-concept concerning their social group membership [65]. Individuals self-identify as members of several social groups or classes [66, 67]. Individuals use categories to organize and place themselves in their social environments [68], a social and relative approach that leads to in-group and out-group identification [69].The study of [70] empirically investigated the relationship between students’ social identities and their performance in online learning and found that online learning performance affects students’ social identities. Their research also emphasizes that students’ social identities should be improved to enhance online learning performance and satisfaction.Social identification increases in-group homogeneity [71], which improves social ties inside a group, as well as individual students’ commitment to studying, school performance, and satisfaction with their curriculum and organization. Students who achieve their learning goals are likely to be satisfied with their program and institution [72]. Considering that learning is an identity experience that affects an individual and what he or she can achieve, learning and social identification are intimately connected [73].Students arrive at college with an academic self-concept, or a belief in their own academic competence. Learners who achieved high-grade point averages in high school tend to have a great academic self-construct, linked to outstanding goal achievement [74]. Students who enroll in postgraduate schools with a strong track record of work experience, for example, as a junior or midrange manager, have similar social identity traits [75]. Seeing oneself as a “proven” manager may form a significant part of one’s personality, influencing one’s interactions with students and instructors.


## 2.7. Social Influence (SIN)

Social influence is defined as the process through which the presence or activity of others influences someone’s beliefs, attitudes, or conduct. The four elements of social influence include compliance, obedience, conformity, and minority influence. Fishbein and Ajzen [76] developed the theory of reasoned action (TRA) as a model for explaining how social standards of conformity, identification, and compliance may influence conduct. They named these social effects “subjective norms” and showed that, when coupled with individually held beliefs, they could be utilized to predict behavior. Students’ subjective norms are the influences they feel from teachers, other students, or important individuals in the learning environment to adopt the system.The subjective norm construct indicates that students are influenced by others in their usage and appropriation of the virtual learning system [77]. The study of [78] found that subjective norms affect the frequency with which students utilize ICT in education, based on previous research on the roles of peers and teachers in fostering effective virtual learning. Subjective criteria set by superiors (e.g., parents or employers) have been shown to influence a person’s choice to participate in an online program [79]. According to the research of [80], the participation of the instructor and social influence among students impact course involvement, student motivation, educational achievement, and views regarding virtual learning platforms.Certain studies show that peers are important in influencing technology adoption and use pattern habits for e-learning. The research of [81] examined how peer pressure affects academic achievement and perceptions of online learning. Shin’s research did not discover a link between performance and peer influence. However, more recent research indicates that students who feel a strong sense of belonging among their fellow students are more satisfied and more likely to continue with virtual learning [82].


## 2.8. Social Support (SSP)

Social support is a three-part construct with many different aspects. It is defined as an activity in which individuals experience, perceive, and express emotional concern, instrumental help, or knowledge via human interaction [83]. The study of [84] described it as “interaction with others that provides students with insight and good learning experience.” Demaray et al. [85] defined social support as “information, appraisal, and emotional support that comes from a variety of sources, including instructors, parents, friends, and colleagues that improves student satisfaction.” Social support is among the most significant and vital areas of intermediate studies. It is a critical component often utilized in socio-educational and sociopsychological research [86].The study of Bean [87] believes that close friends and colleagues facilitate social integration into educational institutions. The study of [88] stated that social support improves interpersonal and intergroup relationships. Student satisfaction is positively linked to social support from classmates or relatives [89]. According to [90], students with strong social lives display higher levels of satisfaction.When students are incorporated into the social environment on campus, their quality of life improves. The research of [91] feels that involving students in various social activities helps them build a positive mindset and supports learning. A lack of social support may make it more difficult to speak for oneself, maintain autonomy, and establish and sustain relationships, all of which can have a major impact on a person’s life and learning. Social support for online learners can be achieved through both student-student interactions and student-instructor interactions [30]. Therefore, institutions must play a critical role in building their social lives and exercising social integration as they study and grow.


## 2.9. The Influence of Social Factors in Successful Online Learning

Social factors are often interrelated with instructor support. Students’ attributes, technical support, and virtual course design all influence online learning. Tunison and Noonan [92] examined high school students’ initial encounters with online courses and found that they saw their online experiences as inspiring and liberating. Although most of the students regarded their instructors as the ultimate source of knowledge, they felt a newfound sense of independence in virtual learning. Students enjoyed their online experiences as a result of this positive impression, which led to favorable outcomes.Other studies identify student autonomy as a defining feature of virtual learning [93, 94]. Rice [95] found that students found remote learning challenging and alienating: “Even if students are highly driven and self-directed, they may still find the experience lonely, challenging, and disappointing in a distance education environment.” The following characteristics of virtual learners impact their learning: communication values, beliefs, and emotions; dedication to group procedures; and interaction abilities [96]. According to Li et al. [97], student characteristics seem to have a significant role in students’ satisfaction in online learning.In addition to the factors mentioned earlier, virtual course design impacts student success in online learning [98], since it affects students’ engagement and involvement in courses. Thus, the virtual classroom’s design and the students’ characteristics influence the students’ learning in the virtual classroom, with the three variables influencing students’ involvement in the classroom and their views of learning in it. The social atmosphere in online learning has an impact on students’ involvement in the virtual classroom and their perceptions of their learning in this setting [44]. At the same time, interactions with the instructor and with classmates have an impact on the social environment [99]. Hence, the aforementioned social factors have been proven to be significant and influential in the online learning environment. Therefore, this study aimed to investigate and model the social factors that could affect students’ satisfaction with online learning.


## 3. Research Methodology

The main objective of this study was to investigate and model the social factors affecting students’ satisfaction with online learning. Therefore, the research question was “Could the proposed social factors affect students’ satisfaction with online learning?”. Based on the extensive literature regarding social factors and related social theories, the proposed factors—namely social presence (SP), social interaction (SI), social space (SS), social identity (SI), social influence (SIN), and social support (SSP)—were investigated and incorporated into the proposed research model to assess students’ satisfaction (SAT) with online learning.


### 3.1. Research Model and Hypotheses

To achieve the research objective, a research model was developed including the six independent factors mentioned above. These factors’ direct influence on students’ satisfaction was investigated, as depicted in Figure1. Based on this research model and the proposed social factors, the following assumptions were developed:(i)
H1: social presence is significantly related to students’ satisfaction with online learning.(ii)
H2: social interaction is significantly related to students’ satisfaction with online learning.(iii)
H3: social space is significantly related to students’ satisfaction with online learning.(iv)
H4: social identity is significantly related to students’ satisfaction with online learning.(v)
H5: social influence is significantly related to students’ satisfaction with online learning.(vi)
H6: social support is significantly related to students’ satisfaction with online learning.Figure 1
Proposed research model.


### 3.2. Research Design and Sampling

Due to the nature of research, a quantitative method approach was applied. A survey was the main tool for collecting the research data. The research was conducted at Jouf University during the second semester of the 2020-2021 academic year. This research employs the chain-referral sampling technique to obtain the required sample. A total of 258 responses were obtained after the elimination of uncompleted questionnaires, with a response rate of 90.8%.


### 3.3. Research Questionnaire Development

It was important to provide the respondents with a clear understanding of the research before organizing the survey questions. Therefore, the questionnaire included the title and purpose of this research. To determine whether the instruments were appropriate to measure the investigated dimensions, face and content validity were assessed and the questionnaire was sent to three instructional technology experts. As most respondents spoke Arabic as their first language, the original English version of the questionnaire had to be translated into Arabic using the “back translation” technique to achieve clarity and meaning similarity. There were two main sections of questionnaire: one section focused on demographic items, and the other focused on the social factors being investigated—namely social presence (SP), social interaction (SI), social space (SS), social identity (SID), social influence (SIN), social support (SSP), and student satisfaction (SAT). Table1 shows the research factors, related items, and adapted sources.Table 1
Research factors, items, and adapted sources.
ConstructsNo. of itemsAdapted sourcesSocial presence (SP)7[9]Social interaction (SI)5[31]Social space (SS)6[60]Social identity (SID)4[32]Social influence (SIN)3[30]Social support (SSP)3[30]Satisfaction (SAT)9[100]Total37


## 3.1. Research Model and Hypotheses

To achieve the research objective, a research model was developed including the six independent factors mentioned above. These factors’ direct influence on students’ satisfaction was investigated, as depicted in Figure1. Based on this research model and the proposed social factors, the following assumptions were developed:(i)
H1: social presence is significantly related to students’ satisfaction with online learning.(ii)
H2: social interaction is significantly related to students’ satisfaction with online learning.(iii)
H3: social space is significantly related to students’ satisfaction with online learning.(iv)
H4: social identity is significantly related to students’ satisfaction with online learning.(v)
H5: social influence is significantly related to students’ satisfaction with online learning.(vi)
H6: social support is significantly related to students’ satisfaction with online learning.Figure 1
Proposed research model.


## 3.2. Research Design and Sampling

Due to the nature of research, a quantitative method approach was applied. A survey was the main tool for collecting the research data. The research was conducted at Jouf University during the second semester of the 2020-2021 academic year. This research employs the chain-referral sampling technique to obtain the required sample. A total of 258 responses were obtained after the elimination of uncompleted questionnaires, with a response rate of 90.8%.


## 3.3. Research Questionnaire Development

It was important to provide the respondents with a clear understanding of the research before organizing the survey questions. Therefore, the questionnaire included the title and purpose of this research. To determine whether the instruments were appropriate to measure the investigated dimensions, face and content validity were assessed and the questionnaire was sent to three instructional technology experts. As most respondents spoke Arabic as their first language, the original English version of the questionnaire had to be translated into Arabic using the “back translation” technique to achieve clarity and meaning similarity. There were two main sections of questionnaire: one section focused on demographic items, and the other focused on the social factors being investigated—namely social presence (SP), social interaction (SI), social space (SS), social identity (SID), social influence (SIN), social support (SSP), and student satisfaction (SAT). Table1 shows the research factors, related items, and adapted sources.Table 1
Research factors, items, and adapted sources.
ConstructsNo. of itemsAdapted sourcesSocial presence (SP)7[9]Social interaction (SI)5[31]Social space (SS)6[60]Social identity (SID)4[32]Social influence (SIN)3[30]Social support (SSP)3[30]Satisfaction (SAT)9[100]Total37


## 4. Data Analysis and Discussion

The data were analyzed using Analysis of Moment Structures (AMOS 26).


### 4.1. Profile of Respondents

The research participants included 285 students attending Jouf University during the second semester of the 2020-2021 academic year. 42.2% of the respondents were male, while 57.4% were female. Table2 shows the full frequencies and percentages of the respondents’ profiles.Table 2
The frequency and percentage of the respondents’ profiles.
ProfileFrequencyPercentageGenderM10942.2%F14957.8%CollegeScience colleges6324.4%Medical and health colleges2710.5%Humanity and management college7328.3%Education college9034.9%Community college51.9Total258100%


### 4.2. Descriptive Statistics

An examination of the descriptive statistics of the data showed that all of the items’ means were above the midpoint of scale. Using the maximum-likelihood estimation, the data were examined for multivariate and univariate normality using skewness and kurtosis indexes and Mardia’s coefficient. The results show that all of the items’ skewness and Kurtosis statistics are between the ordinary distribution ±2, where the Kurtosis ranges from −0.37 to 0.81 and skewness from 0.36 to 0.67 [101]. The multivariate normality test of the data also shows normal distribution; Mardia’s coefficient is 167.06, which is less than the acceptable cutoff value [102]. Therefore, the requirements of multivariate and univariate normality are met, with no significant violations observed.


### 4.3. Test of the Measurement Model

To assess the measurement model, confirmatory factor analysis (CFA) was performed with AMOS 26, using the maximum-likelihood estimation technique. Figure2 shows the diagram of confirmatory factor analysis (CFA).Figure 2
Confirmatory factor analysis.The aim of conducting CFA is to examine composite reliability (CR), convergent validity, discriminant validity, and standardized factor loadings along with the goodness-of-fit indices of the measurement model. Table3 shows the results of confirmatory factor analysis (CFA).Table 3
Results of confirmatory factor analysis (n = 258).
FactorItemsU.ES.E.Standardized factor loadingComposite reliability (CR)Convergent validity (AVE)PSocial presenceSP51.0000.9610.940.69SP71.0120.0230.981∗∗∗SP60.9980.0250.965∗∗∗SP20.7500.0470.723∗∗∗SP40.7500.0500.699∗∗∗SP30.6630.0500.683∗∗∗SP10.7070.0510.670∗∗∗Social spaceSS31.0000.9320.920.66SS40.9480.0460.855∗∗∗SS20.9060.0460.836∗∗∗SS50.8250.0490.771∗∗∗SS10.8600.0500.780∗∗∗SS60.7110.0520.694∗∗∗Social interactionSI21.0000.9740.900.77SI30.9890.0230.968∗∗∗SI50.9480.0310.910∗∗∗SI10.8560.0420.806∗∗∗SI40.7260.0480.703∗∗∗Social identitySID21.0000.9870.910.73SID30.9740.0270.948∗∗∗SID40.7820.0450.749∗∗∗SID10.7380.0480.704∗∗∗Social supportSSP21.0000.9150.810.60SSP30.7610.0680.706∗∗∗SSP10.7860.0780.675∗∗∗Social influenceSIN21.0000.7100.770.53SIN10.9670.1060.716∗∗∗SIN31.0750.1220.752∗∗∗∗p<0.05; ∗∗p<0.01; ∗∗∗.p<0.001.The results in Table3 indicate that the measurement model unstandardized and standardized estimates for all items are significant at the p<0.001 confidence level. The standardized factor loadings range from .65 to .98, which is higher than 0.50, the level recommended by [103]. The composite reliability (CR) of all items ranges from 0.77 to 0.94, exceeding the acceptable cutoff level of 0.70; this indicates adequate reliability based on the recommendation of Peterson and Kim (2013). The convergent validity was assessed using the average variance extracted (AVE). The results indicate that the AVE ranges from 0.53 to 0.77, which is above the level of 0.5 recommended by [104]. To assess discriminant validity, the square root of the average variance extracted (AVE) for each examined factor was compared with its correlation to all other factors. Table 4 indicates the discriminant validity results for the measurement model. The results seem to be satisfactory, as the results of the square roots of the AVEs are above the inter-construct correlation in the correlation matrix [103]. Therefore, the proposed factors in the measurement model are acceptable for further analysis.Table 4
Discriminant validity for the measurement model (n = 258).
FactorsSSPSPSSSINSIDSISSP0.773SP0.1790.833SS0.0670.5810.815SIN0.1280.4560.5260.878SID0.3230.3890.4050.4780.856SI0.3060.1510.2790.1950.5480.726∗p<0.05; ∗∗p<0.01.∗∗∗p<0.001.As illustrated in Table5, the results of the goodness-of-fit indices of the measurement model indicate that the chi-square/degree of freedom (CMIN/DF) is 1.758, which is less than the acceptable cutoff limit of 3 [103]. The goodness-of-fit index (GFI) and comparative fit index (CFI), which are .927 and .954, respectively, are both above the acceptable limit of 0.90. Similarly, the standardized root-mean-square residual (SRMR) is .065, which is below the acceptable maximum of .08. Additionally, the root-mean-square error of approximation (RMSEA) is .059, which is considered acceptable [101, 103]. Overall, the goodness of fit of the measurement model is confirmed, paving the way for additional structural model analysis.Table 5
Goodness-of-fit indices of the measurement model.
Goodness-of--fit indices of the measurement modelχ2CMIN/DFNFIGFICFISRMRRMSEA578.468∗∗∗1.758 (<3.00)0.914 (>0.90)0.923(>90)0.961(>0.90)0.061(<0.08)0.054(<0.08)Note:∗∗∗.p<0.001.


### 4.4. Test of the Structural Model and Hypotheses

To examine the path analysis of the structural model, the goodness-of-fit indices were inspected using criteria similar to that applied in testing the measurement model [101, 102]. Table6 indicates that the structural model indices have a good fit and have achieved the acceptable cutoff level to be considered satisfactory (χ2 = 599.891 p<0.001, CMIN/DF = 2.153, NFI = 0.911, GFI = 0.942, CFI = 0.943, SRMR = 0.053, RMSEA = 0.063). Therefore, the path coefficient analysis was performed to examine the significance of the proposed hypotheses. Figure 3 illustrates the path coefficient analysis of the proposed hypotheses in the structural model.Table 6
Goodness-of-fit indices of the structural model.
Goodness-of-fit indices of the structural modelχ2CMIN/DFNFIGFICFISRMRRMSEA599.891∗∗∗2.153 (<3.00)0.911 (>0.90)0.942(>0.90)0.943(>0.90)0.053(<0.08)0.063(<0.08)Note:∗∗∗.p<0.001.Figure 3
Path coefficient analysis of proposed hypotheses.The results of testing the hypotheses, as shown in Figure3 and Table 7, reveal that all proposed hypotheses were significantly and positively related to students’ satisfaction. The proposed model accounted about 56% of the total variance in students’ satisfaction. Interestingly, social interaction is the factor most significantly related to students’ satisfaction with online learning (β = 0.382, p<0.001). However, social presence is also significantly related to students’ satisfaction with online learning (β = 0.356, p<0.001), as are social support (β = 0.325, p<0.001), social space (β = 0.279, p<0.001), social influence (β = 0.243, p<0.001), and social identity (β = 0.203, p<0.001). Therefore, the examined hypotheses were all considered statistically significant towards students’ satisfaction with online learning and were thus accepted.Table 7
Results for the testing hypotheses of structural model.
HPath of hypothesesSRβURS.E.C.R.PHypotheses’ resultsH1SAT<---SP0.3560.2170.0258.548∗∗∗H1: SupportedH2SAT<---SI0.3820.2460.0279.187∗∗∗H2: SupportedH3SAT<---SS0.2790.1530.0236.696∗∗∗H3: SupportedH4SAT<---SID0.2030.1150.0244.874∗∗∗H4: SupportedH5SAT<---SIN0.2430.1240.0215.831∗∗∗H5: SupportedH5SAT<---SSP0.3250.2240.0297.810∗∗∗H6: SupportedSRβ: standardized regression; UR: unstandardized regression; SE: standard error.∗∗∗p<0.001.


## 4.1. Profile of Respondents

The research participants included 285 students attending Jouf University during the second semester of the 2020-2021 academic year. 42.2% of the respondents were male, while 57.4% were female. Table2 shows the full frequencies and percentages of the respondents’ profiles.Table 2
The frequency and percentage of the respondents’ profiles.
ProfileFrequencyPercentageGenderM10942.2%F14957.8%CollegeScience colleges6324.4%Medical and health colleges2710.5%Humanity and management college7328.3%Education college9034.9%Community college51.9Total258100%


## 4.2. Descriptive Statistics

An examination of the descriptive statistics of the data showed that all of the items’ means were above the midpoint of scale. Using the maximum-likelihood estimation, the data were examined for multivariate and univariate normality using skewness and kurtosis indexes and Mardia’s coefficient. The results show that all of the items’ skewness and Kurtosis statistics are between the ordinary distribution ±2, where the Kurtosis ranges from −0.37 to 0.81 and skewness from 0.36 to 0.67 [101]. The multivariate normality test of the data also shows normal distribution; Mardia’s coefficient is 167.06, which is less than the acceptable cutoff value [102]. Therefore, the requirements of multivariate and univariate normality are met, with no significant violations observed.


## 4.3. Test of the Measurement Model

To assess the measurement model, confirmatory factor analysis (CFA) was performed with AMOS 26, using the maximum-likelihood estimation technique. Figure2 shows the diagram of confirmatory factor analysis (CFA).Figure 2
Confirmatory factor analysis.The aim of conducting CFA is to examine composite reliability (CR), convergent validity, discriminant validity, and standardized factor loadings along with the goodness-of-fit indices of the measurement model. Table3 shows the results of confirmatory factor analysis (CFA).Table 3
Results of confirmatory factor analysis (n = 258).
FactorItemsU.ES.E.Standardized factor loadingComposite reliability (CR)Convergent validity (AVE)PSocial presenceSP51.0000.9610.940.69SP71.0120.0230.981∗∗∗SP60.9980.0250.965∗∗∗SP20.7500.0470.723∗∗∗SP40.7500.0500.699∗∗∗SP30.6630.0500.683∗∗∗SP10.7070.0510.670∗∗∗Social spaceSS31.0000.9320.920.66SS40.9480.0460.855∗∗∗SS20.9060.0460.836∗∗∗SS50.8250.0490.771∗∗∗SS10.8600.0500.780∗∗∗SS60.7110.0520.694∗∗∗Social interactionSI21.0000.9740.900.77SI30.9890.0230.968∗∗∗SI50.9480.0310.910∗∗∗SI10.8560.0420.806∗∗∗SI40.7260.0480.703∗∗∗Social identitySID21.0000.9870.910.73SID30.9740.0270.948∗∗∗SID40.7820.0450.749∗∗∗SID10.7380.0480.704∗∗∗Social supportSSP21.0000.9150.810.60SSP30.7610.0680.706∗∗∗SSP10.7860.0780.675∗∗∗Social influenceSIN21.0000.7100.770.53SIN10.9670.1060.716∗∗∗SIN31.0750.1220.752∗∗∗∗p<0.05; ∗∗p<0.01; ∗∗∗.p<0.001.The results in Table3 indicate that the measurement model unstandardized and standardized estimates for all items are significant at the p<0.001 confidence level. The standardized factor loadings range from .65 to .98, which is higher than 0.50, the level recommended by [103]. The composite reliability (CR) of all items ranges from 0.77 to 0.94, exceeding the acceptable cutoff level of 0.70; this indicates adequate reliability based on the recommendation of Peterson and Kim (2013). The convergent validity was assessed using the average variance extracted (AVE). The results indicate that the AVE ranges from 0.53 to 0.77, which is above the level of 0.5 recommended by [104]. To assess discriminant validity, the square root of the average variance extracted (AVE) for each examined factor was compared with its correlation to all other factors. Table 4 indicates the discriminant validity results for the measurement model. The results seem to be satisfactory, as the results of the square roots of the AVEs are above the inter-construct correlation in the correlation matrix [103]. Therefore, the proposed factors in the measurement model are acceptable for further analysis.Table 4
Discriminant validity for the measurement model (n = 258).
FactorsSSPSPSSSINSIDSISSP0.773SP0.1790.833SS0.0670.5810.815SIN0.1280.4560.5260.878SID0.3230.3890.4050.4780.856SI0.3060.1510.2790.1950.5480.726∗p<0.05; ∗∗p<0.01.∗∗∗p<0.001.As illustrated in Table5, the results of the goodness-of-fit indices of the measurement model indicate that the chi-square/degree of freedom (CMIN/DF) is 1.758, which is less than the acceptable cutoff limit of 3 [103]. The goodness-of-fit index (GFI) and comparative fit index (CFI), which are .927 and .954, respectively, are both above the acceptable limit of 0.90. Similarly, the standardized root-mean-square residual (SRMR) is .065, which is below the acceptable maximum of .08. Additionally, the root-mean-square error of approximation (RMSEA) is .059, which is considered acceptable [101, 103]. Overall, the goodness of fit of the measurement model is confirmed, paving the way for additional structural model analysis.Table 5
Goodness-of-fit indices of the measurement model.
Goodness-of--fit indices of the measurement modelχ2CMIN/DFNFIGFICFISRMRRMSEA578.468∗∗∗1.758 (<3.00)0.914 (>0.90)0.923(>90)0.961(>0.90)0.061(<0.08)0.054(<0.08)Note:∗∗∗.p<0.001.


## 4.4. Test of the Structural Model and Hypotheses

To examine the path analysis of the structural model, the goodness-of-fit indices were inspected using criteria similar to that applied in testing the measurement model [101, 102]. Table6 indicates that the structural model indices have a good fit and have achieved the acceptable cutoff level to be considered satisfactory (χ2 = 599.891 p<0.001, CMIN/DF = 2.153, NFI = 0.911, GFI = 0.942, CFI = 0.943, SRMR = 0.053, RMSEA = 0.063). Therefore, the path coefficient analysis was performed to examine the significance of the proposed hypotheses. Figure 3 illustrates the path coefficient analysis of the proposed hypotheses in the structural model.Table 6
Goodness-of-fit indices of the structural model.
Goodness-of-fit indices of the structural modelχ2CMIN/DFNFIGFICFISRMRRMSEA599.891∗∗∗2.153 (<3.00)0.911 (>0.90)0.942(>0.90)0.943(>0.90)0.053(<0.08)0.063(<0.08)Note:∗∗∗.p<0.001.Figure 3
Path coefficient analysis of proposed hypotheses.The results of testing the hypotheses, as shown in Figure3 and Table 7, reveal that all proposed hypotheses were significantly and positively related to students’ satisfaction. The proposed model accounted about 56% of the total variance in students’ satisfaction. Interestingly, social interaction is the factor most significantly related to students’ satisfaction with online learning (β = 0.382, p<0.001). However, social presence is also significantly related to students’ satisfaction with online learning (β = 0.356, p<0.001), as are social support (β = 0.325, p<0.001), social space (β = 0.279, p<0.001), social influence (β = 0.243, p<0.001), and social identity (β = 0.203, p<0.001). Therefore, the examined hypotheses were all considered statistically significant towards students’ satisfaction with online learning and were thus accepted.Table 7
Results for the testing hypotheses of structural model.
HPath of hypothesesSRβURS.E.C.R.PHypotheses’ resultsH1SAT<---SP0.3560.2170.0258.548∗∗∗H1: SupportedH2SAT<---SI0.3820.2460.0279.187∗∗∗H2: SupportedH3SAT<---SS0.2790.1530.0236.696∗∗∗H3: SupportedH4SAT<---SID0.2030.1150.0244.874∗∗∗H4: SupportedH5SAT<---SIN0.2430.1240.0215.831∗∗∗H5: SupportedH5SAT<---SSP0.3250.2240.0297.810∗∗∗H6: SupportedSRβ: standardized regression; UR: unstandardized regression; SE: standard error.∗∗∗p<0.001.


## 5. Discussion

The main aim of this study is to investigate and model the social factors affecting students’ satisfaction with online learning. The results of study reveal that social interaction is the factor that contributes most significantly to students’ satisfaction. The findings related to the effects of social interaction are consistent with the findings of previous studies [3, 5, 22, 52, 55, 56, 58]. The result of this study also shows that social presence is significantly related to students’ satisfaction with online learning. Based on previous research, social presence was expected to be the biggest contributing factor; it was not, although it was significantly related to students’ satisfaction to a degree that is consistent with other studies [9, 13, 45, 46, 48]. As seen in other studies [30, 88, 89], social support and social influence significantly influenced students’ satisfaction. The findings were consistent with other recent research [80, 82]. Social influence is theoretically related to subjective norms, which have an important relationship with students’ satisfaction in an online environment. Furthermore, social space was also significantly related to students’ satisfaction. This result confirms Kreijns et al.’s study [60], which stated that “a good social space inside the group leads to a positive social climate/online atmosphere,” and supports the results of their recent study [63]. Finally, while social identity shows less of a contribution to students’ satisfaction with online learning, it is still significantly related; this finding is consistent with other studies discussing social identity in the learning environment [69, 70, 72].


## 6. Study Implications

In respect of theoretical implications, the study result revealed that the examined social factors have explained about 56% of the total variance of students’ satisfaction. Thus, examined further social factors are needed by integrating different models and theories related to social context in online learning setting. Furthermore, future research might examine indirect, mediating, and moderation influence of proposed examined factors in this study and other related factors.On the subject of practical implications, the results indicated that the examined social factors namely social presence, social interaction, social space, social identity, social influence, and social support have shown that significant influence on the students’ satisfaction needs to be taken into consideration with the development of online learning. Therefore, the online course designers need to design interactive content that promotes the social interaction and engagement, which would ultimately help the educators prevent the negative absence of social elements in online environment. Furthermore, the administrators of e-learning systems should activate all the features that encourage social communication and engagement between instructors and students to reduce social isolation in the online atmosphere. Course designers, administrators, and instructors should take into consideration the importance of social factors when they design and manage online courses to provide a better online learning experience and increase satisfaction and performance.


## 7. Conclusion and Limitations

The study findings shed light on the effects of the proposed social factors on students’ satisfaction with online learning. The proposed factors namely social presence, social interaction, social space, social identity, social influence, and social support were found to significantly affect students’ satisfaction with online learning. Therefore, social factors are vital to maintain students’ satisfaction in an online learning setting. The COVID-19 pandemic has deprived students of face-to-face learning and ultimately reduced the social elements that are considered a significant part of the learning process. Online learning will continue to expand, and therefore, instructors must provide enrichment in online courses with materials that promote social engagement and interaction.The study has some limitations that need to be addressed in order to provide clear direction for future research. The study is limited by the size of the sample and quantitative methodology. It is also limited by the number of examined social factors, which explain only about 56% of the total variance of students’ satisfaction. Hence, in future research, the proposed factors and instrumentation can be used in studies focused on more universities, either public or private. Researchers could examine additional social factors that may influence students’ satisfaction with online learning using qualitative or triangulation methods and ultimately increase the proposed model variance and understanding of students’ satisfaction with online learning.



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*Source: 2594221-2022-01-21.xml*