Complexities, Challenges, and Opportunities of Mobile Learning: A Case Study at the University of Jordan

Abstract

With the increasing popularity of mobile learning in educational settings, the use of social networking sites (SNSs) as a tool for remote learning has become increasingly prevalent. However, the negative aspects of mobile learning through SNSs have not been extensively explored by scholars. Therefore, in this paper, we aim to investigate the impact of social overload, information overload, life invasion, and privacy invasion on students’ technostress and exhaustion and the resulting reduced intention to use SNSs for mobile learning. We also aim to investigate the impact of social overload, information overload, life invasion, and privacy invasion on students’ technostress and exhaustion and the resulting reduced intention to use SNSs for mobile learning. Data were collected through an online survey from 648 voluntary participants in Jordanian universities. The SOR framework provided a theoretical foundation for understanding the impact of SNSs on mobile learning. Through this study, we found information overload and life invasion were significantly related to higher levels of technostress and exhaustion among students. This suggests that, when students feel overwhelmed by excessive information or when their personal lives are invaded by SNS use, they experience increased technostress and exhaustion. Moreover, the study revealed that technostress and exhaustion were positively associated with a reduced intention to use SNSs for mobile learning. This indicates students who experience higher levels of technostress and exhaustion are less likely to engage with SNSs as a platform for their mobile learning needs. In contrast, we did not find a significant relationship between social overload and technostress, suggesting the amount of social interaction on SNSs may not directly contribute to students’ technostress levels. Additionally, we observed no significant relationship between privacy invasion and exhaustion, indicating privacy concerns may not directly contribute to students’ feelings of exhaustion in the context of mobile learning through SNSs. Regarding practical implications, we thus suggest the importance of establishing norms and rules to protect students’ privacy and prevent overburdening them with excessive SNS use. The SOR framework provides a theoretical foundation for understanding the impact of SNSs on mobile learning, and future researchers could benefit from its application.

1. Introduction

Since their introduction in the field of learning, mobile devices have played a crucial role in enhancing education [1,2,3,4]. However, the significance of mobile learning increased even further during the COVID-19 pandemic, when curfews and lockdowns were imposed in many countries. The pandemic made electronic learning (e-learning) a necessity to ensure the continuity of the educational process. Governments, ministries of education, and educational institutions worldwide had to swiftly adapt to distance learning, and, for many students, particularly in developing countries, smartphones became their only means of accessing education due to the affordability factor [5]. This presented several advantages to both students and educators, such as the flexibility of learning, anytime and anywhere, as well as personalised educational experiences [6]. However, having numerous students enrolled in online programmes resulted in accessibility challenges because of the lack of internet services and smart mobile devices in some students’ areas [7]. These circumstances raised concerns about the quality of education experienced during social distancing.

The main focus of the current study is to explore the difficulties and challenges faced by students engaging in mobile learning. Special attention will be given to the psychological effects of mobile learning on students’ stress and anxiety levels, as well as how these psychological reactions affect the online learning process. Specifically, through this study, we aim to investigate how anxiety and stress related to mobile learning contribute to a reduced intention to use these technologies, and we examine the existing literature on the various challenges faced by students when utilising internet-based learning.

Studies have shown mere exposure to smart technologies and devices has increased users’ stress levels since their initial use. Mheidly et al. [8] argued regular use of telecommunication technologies, as in online learning, can lead to mental burnout, including feelings of exhaustion, cynicism, and a lack of accomplishment. Irawan et al. [9] addressed the psychological impacts of online learning during the COVID-19 pandemic and found students experienced boredom after two weeks of studying online. They also discovered that some students faced financial anxiety due to limited access to the internet and that mood changes were significant due to the number of assignments. Similarly, Maulana [10] found higher education students experienced psychological impacts from online learning, resulting in high levels of depression, anxiety, and stress among a considerable percentage of students. Sundarasen et al. [11] investigated the stressors affecting students’ anxiety levels while studying online during the pandemic and identified financial constraints, remote online teaching, and uncertainty about the future as significant factors.

In Jordan, e-learning technology was first introduced in 2002 by the Arab Open University, making Jordan an IT hub in the Middle East [12]. As in many countries, Jordan also turned to internet-facilitated distance learning during the COVID-19 pandemic. Educational institutions had to adapt their learning systems to facilitate distance learning, resulting in changes to study plans, rescheduled start dates, and deadlines, among other adjustments [13]. Almarabeh et al. [14] examined the acceptance and challenges faced by University of Jordan students when using the e-learning platform. While they acknowledged that e-learning presented a revolutionary approach to learning, they also identified the challenges faced by instructors and students in using the system. These challenges encompassed areas such as ‘technical difficulties, access to computers, English competency, need for face-to-face interaction, level of awareness, computer literacy, resistance to change, student assistance, and privacy and security’ (p. 1004).

Williams [15] argued that, although social networking sites (SNSs) were originally created for social activities, they exhibited potential for educational purposes, indicating their successful integration into pedagogy. Jong et al. [16] proposed that social media sites like Facebook are more effective for educational interactions and information sharing compared to the LMS known as Blackboard. Sadowski et al. [17] suggested utilising SNSs for educational purposes mitigates the effects of social isolation during online learning. A literature search revealed a limited number of studies investigating the use of SNSs in mobile learning, but the reduced intention to use mobile learning has received little attention.

The existing literature on mobile learning primarily focuses on theoretical frameworks, technological advancements, and pedagogical approaches. Although these studies have provided valuable insights, there is a need for a comprehensive exploration of the practical complexities that arise when implementing mobile learning in a university setting. The University of Jordan is a prestigious educational institution in Jordan and serves as a representative example of the country’s education system. However, limited research has specifically examined the challenges and opportunities associated with mobile learning at the University of Jordan, highlighting the significance of investigating this unique context and identifying potential solutions. Hence, the objective of our study was to identify and empirically investigate the variables that influence university students’ reduced intention to use mobile learning via SNSs. By comparing our study’s findings with previous research conducted on students from different cultural backgrounds, as exemplified by Loh et al. [18], we can gain a more comprehensive understanding of the factors influencing the intention to use mobile learning through SNSs and address the gaps in the current literature.

2. Literature Review

The literature review is structured into three sections: The first section explores the literature on mobile learning, encompassing its evolution, development, and utilisation as a continuous process over time. The second section introduces the SOR framework, discussing its origins and components as they pertain to the present study. Finally, the last section provides a brief overview of the reduced intention to use mobile learning resulting from the stimuli and organisms discussed earlier.

2.1. Mobile Learning

The term ‘online learning’ was initially coined in 1995 to describe the use of Learning Management Systems (LMS) like Blackboard or the uploading of documents and texts online. Today, online learning encompasses various terms, such as ‘e-learning’, ‘online education’, and ‘mobile and ubiquitous learning’ [19]. Mobile learning, also known as mobile and ubiquitous learning, has emerged as one of the most popular and significant aspects of online learning due to recent technological advancements. Mobile learning can be defined as the utilisation of mobile technologies, such as smartphones and tablets, to facilitate learning anytime and anywhere [20,21,22]. Gulek et al. [23] concluded that incorporating technology as a learning tool can enhance student learning and educational outcomes. However, much of the pre-COVID-19 research on mobile learning focused on its nature, its effectiveness, and the acceptance of mobile learning devices and technologies [24,25,26,27,28]. Although a few studies have explored the relationship between mobile learning and social media, such as Hylén’s [29] argument that social media platforms support open-ended learning environments and increase mobile learning opportunities, less attention has been given to the potential drawbacks of utilising mobile learning through social media.

Loh et al. [18] conducted a study to examine the drawbacks of mobile learning through social media. Using a qualitative methodology, the researchers evaluated data from interviews with 24 undergraduate students from a university in Singapore. They found that, although mobile learning through social media offers advantages such as flexibility and convenience, it also presents drawbacks such as distractions, addiction, cyberbullying, privacy violations, and the spread of disinformation. Educators and decision makers should be aware of these potential drawbacks and take necessary precautions. Overall, the literature highlights both the potential benefits and drawbacks of mobile learning, particularly when utilising social media platforms. To successfully integrate mobile learning into education, it is crucial to understand the factors that influence students’ reduced intention to use mobile learning via SNSs.

2.2. Stimulus–Organism–Response Framework

The stimulus–organism–response (SOR) model was initially introduced to the field of psychology by Woodworth (1929) [30] as an expansion of Pavlov’s (1926) [31] stimulus–response model. The SOR framework was subsequently developed by Mehrabian and Russell (1974) [32] to examine environmental psychology. According to the SOR model, the stimuli present in an individual’s surroundings can influence their internal state, leading to positive or negative responses.

Consistent with the existing literature, we employ the SOR framework in this study, where four constructs serve as stimuli from the students’ environment, eliciting their response: (a) social overload, (b) information overload, (c) life invasion, and (d) privacy invasion. The students’ internal states manifest as technostress and exhaustion. We will delve into the stimuli and organisms further in the hypothesis development section. Ultimately, the expected response is a reduced intention to use SNSs.

2.3. Reduced Intention to Use

According to Loh et al. [18], university students employ self-preservation as an emotion-focused strategy to mitigate emotional challenges, resulting in a reduced intention to use social media for online learning. This reduced intention to use is the response to the aforementioned stimuli of social overload, information overload, life invasion, and privacy invasion, as well as the organisms of technostress and exhaustion. In recent years, several scholars have examined the intention to use technology in the context of online and distance learning. For instance, Zhu et al. [33] investigated the persistent intention to use massive open online courses, whereas Yang et al. [34] focused on the intention to continue using mobile learning. Osatuyi and Turel [35] aimed to develop a theoretical framework for exploring factors that could decrease the intention to use information systems (IS), proposing determinants such as addiction to SNSs, recognition of problematic SNS use, and perceived decline in peer SNS usage. Collectively, these authors have contributed to a better understanding of the factors influencing users’ intention to use technology in online and distance learning contexts.

Chao [36] utilised the UTAUT model to predict the factors influencing students’ behavioural intentions to use mobile learning, identifying satisfaction, trust, performance expectancy, and effort expectancy as significant factors. Similarly, Al-Rahmi et al. [37] extended the technology acceptance model to reveal that perceived usefulness and perceived ease of use positively influence students’ behavioural intentions toward online learning. These studies share the objective of investigating the factors that affect students’ behavioural intentions toward different learning modes, with both emphasising the importance of perceived usefulness and performance expectancy in predicting such intentions.

3. Hypotheses Development

3.1. Stimulus–Organism Relationship

The rise of social media platforms as a means of internet-based human interaction has had a significant impact on face-to-face socialising. This has led to a phenomenon known as ‘social displacement’, which is part of media displacement and refers to the idea that the time individuals spend on social media replaces their real-life interactions with family and friends. Social displacement is believed to have negative effects on people’s well-being [38]. However, despite the rapid growth in the number of users on each platform, there has often been a decline in the number of users who actively engage on the platform. ‘Facebook fatigue’, as described by GlobalWebIndex in 2012 [39], is an example of people losing interest in social media platforms for various reasons. Several studies have been conducted to explore the reasons behind users discontinuing their use of social networking services. The findings suggest discontinuation is a coping mechanism employed by users to deal with the exhaustion and social overload associated with these platforms [40,41].

The term ‘social overload’ was originally used by McCarthy and Saegert [42] to describe the negative effects of population crowding—long before the advent of social networking services. It referred to the challenges individuals face in maintaining and enhancing social relationships within their societies due to the increased number of people, which can have a negative impact on their well-being [39]. In the context of social media platforms, social overload refers to the stress experienced by users in managing the numerous social relationships they have online, feeling responsible for maintaining and supporting each of these relationships [40]. When users perceive their continued use of the platforms becomes overwhelming, they tend to discontinue their use. In the context of using social media platforms for mobile learning, we examined social overload as a factor influencing students’ use of these platforms during their educational journey. Consequently, we developed the following hypotheses:

H1a. 

Social overload has a significantly positive relationship with technostress.

H1b. 

Social overload has a significantly positive relationship with exhaustion.

Information overload, also known as ‘information overabundance’, ‘information fatigue’, and ‘information pollution’, among other names, has been emphasised by the global technological revolution, despite existing long before that. As a phenomenon, information overload has existed since the beginning of recorded information and can be characterised as facing a multitude and diversity of written texts and a shortage of time [43,44,45]. It can be further defined as a condition in which people are unable to process and effectively use all the communication inputs they receive, resulting in a breakdown [46].

One of the earliest recorded uses of the term information overload was in 1964 by Bertram Gross [47], an American social scientist, to refer to a condition where the amount of information input into a system is beyond its capacity for information processing [43]. Today, information overload is problematic because it kills productivity and negatively affects creativity [48]. Because we are living in an information age, it has become harder for researchers to encompass all aspects concerning information overload in each discipline. Roetzel [49] claimed we are still missing an actual review of information overload and that any prior literature offers discipline-specific reviews of the issue. Zhang et al. [50] looked into information overload and social overload’s influence on social media users’ intention to use. They concluded information and social overload influence social fatigue and intention to switch behaviours in social media, ‘namely, information relevance could moderate the overload and social fatigue positively’ (p. 228). Moreover, recent studies have looked into ways to reduce the negative effect of information overload. Kaufhold et al. [51] investigated how to mitigate information overload issues through social media alerts based on information gathering, mining, and quality, as well as through designing the trade-offs between automation and user interaction. Accordingly, we developed the following hypotheses:

H2a. 

Information overload has a significantly positive relationship with technostress.

H2b. 

Information overload has a significantly positive relationship with exhaustion.

‘Life invasion’, which is part of the techno invasion that also includes privacy invasion, mainly means technology is interfering with people’s personal lives. It also indicates there is a lack of balance between work and personal lives because a person is never out of reach when communication technology exists, anytime and anywhere. Hence, the blurred boundaries between personal and work lives facilitated by modern technology and social media would lead to eventual burnout of the users’ psychological well-being [52,53].

Social media platforms, with their ubiquitous qualities and ease of use, have encouraged companies to use them for work-related conversations. In this manner, employees would receive messages related to their jobs outside of their set time, violating their private lives [53,54]. Similarly, after the peak of the COVID-19 pandemic and the extensive need to use distance learning technologies, students’ and teachers’ personal use of social networks was interrupted because of their academic use of the same networks. Typically, they face six types of interactions when they use distant learning tools: student–student, student–teacher, student–content, teacher–teacher, teacher–content, and content–content [55]. For example, students may receive schoolwork outside school time from their teachers via social media platforms [56], blurring the lines between their private and school lives. In the same manner, teachers may also receive direct messages from their students outside their assigned office hours. Accordingly, we developed the following hypotheses:

H3a. 

Life invasion has a significantly positive relationship with technostress.

H3b. 

Life invasion has a significantly positive relationship with exhaustion.

Privacy invasion is one of the stimuli or stressors that can cause social media fatigue and one of the major concerns for social media users [57,58,59]. It refers to social media users’ feeling of anxiety regarding the possibility of their private information being violated [60]. It can also refer to any sort of compromising of a user’s information on social media platforms [61,62]. The importance of privacy for SNS users is evident in the users’ negative feelings and attitudes toward the networks when they have privacy concerns [63].

Kim et al. [64] found privacy violations play a significant role in affecting living disorders and reduced intention to use SNSs. The nature and characteristics of social media are thought to be the reason for privacy invasion. Such characteristics include anonymity, flexibility, and presentism [58]. Privacy invasion has been identified as one of the vulnerabilities accompanying the security system of social networks [65]. Moreover, examining privacy invasion is gaining more popularity following the advancement of SNSs [66]. However, privacy violation or invasion can sometimes be unintentional due to certain gaps in the platforms [63]. In a study to examine the influence of the excessive use of mobile SNSs over students’ academic performance, Cao et al. [67] found privacy invasion, alongside life invasion and techno exhaustion, affect the academic performance of students in the pre-addicted and addicted phases. As such, we developed the following hypotheses:

H4a. 

Privacy invasion has a significantly positive relationship with technostress.

H4b. 

Privacy invasion has a significantly positive relationship with exhaustion.

3.2. Organism–Response Relationship

The term ‘technostress’ refers to the psychological state experienced by users of technology and information systems (IS) when they deal with them [68,69]. The term was first used by Brod (1984) [70], who defined it as ‘a modern disease of adaptation caused by an inability to cope with the new computer technologies in a healthy manner’ (p. 16). The term was later expanded by Weil and Rosen (1997) [71] to include any negative effect on an individual’s mindset, cognitive processes, conduct, or mental well-being that arises either directly or indirectly from technology.

The use of information systems becomes a stress creator when individuals find them too demanding for an action [69]. Scholars have shown technostress has a negative impact, especially on people who need to keep up with advancements in information and communications technologies (ICT). Moreover, technostress affects people working in information technology (IT) sectors who are affected by each newly created technology [72]. Luqman et al. [73] used the concept of user exhaustion while using SNSs to support the hypothesis that the excessive use of social networking increases technostress. Hence, we developed the following hypotheses:

H5. 

Technostress has a significantly positive relationship with exhaustion.

H6. 

Technostress has a significantly positive relationship with reduced intention to use.

SNS exhaustion and technostress are considered negative harms caused by excessive use of social networking. They refer to the subjective feeling of tiredness from using technology [73]. ‘Exhaustion’ is also defined as ‘an individual’s aversive, potentially harmful, and unconscious psychological reaction to stressful situations such as perceiving social overload when using SNS’ [40] (p. 5). Exhaustion and regret are the two psychological states that Cao et al. [67] examined to determine whether there are any impacts on social media users due to information, communication, and social overload on them.

Exhaustion is thought to be induced via the employment of social media for social, cognitive, and pleasurable purposes, which may lead to users’ decision to quit the media [73]. The social demands and overload would increase the requirements of energy for social media users, which may result in SNS exhaustion [74]. In fact, any sort of overload while using social media would result in user exhaustion [40,41]. Another cause of SNS exhaustion is the interpersonal interactions and online relationships that make them feel tired [75]. Exhaustion is also one of the accompanying negative effects of information overload besides stress and anxiety [76] and a main cause of users’ intention to change their behaviour to extract themselves from the stressful situations in which they find themselves [39,77,78]. Accordingly, we developed the following hypothesis:

H7. 

Exhaustion has a significantly positive relationship with reduced intention to use.

4. Research Methodology

Empirical quantitative techniques and survey research methods are extensively employed in information and communications technology adoption and diffusion research for data collection. This preference stems from surveys’ ability to explore the relationships between variables and the construct models representing these relationships. Surveys enable researchers to reach a substantial population and gather specific data on relevant issues. Therefore, in line with these advantages, descriptive survey study methods are appropriate for achieving this study’s objective of uncovering the impact of social networking sites on students. Surveys continue to dominate as the primary research tool for descriptive research designs. We used an online survey to collect data from a sample of 648 university students to describe their experiences with SNSs and how they affect their intentions to use SNSs for remote learning.

4.1. Population and Sample

The study population comprised students from various schools at the University of Jordan who had remote lectures during the summer semester of the 2021/2022 academic year. The participants were selected as a random sample, consisting of 648 students, and were distributed across two types of schools at the University of Jordan: 290 humanities students and 358 scientific students.

4.2. Data Collection Tool

We adapted our questionnaire [18] based on a literature review of articles in the field of online and remote learning. The items of the questionnaire were written and distributed to subscales, and it contained 28 items organised on a 5-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’, in addition to some categorical variables.

Table 1. The subscales of the questionnaire with their items.

Subscales	# Questions	Question Numbers
Social Overload (SO)	6	1–6
Information Overload (IO)	4	7–10
Life Invasion (LI)	4	11–14
Privacy Invasion (PI)	3	15–17
Technostress (TS)	4	18–21
Exhaustion (EX)	3	22–24
Intention (IN)	4	25–28

illustrates this in further detail.

Validity and Reliability

We distributed the questionnaire to a panel of referees consisting of six professors from the University of Jordan. Based on their feedback, we made some minor modifications to the wording of the questionnaire items.

To assess the construct validity of the questionnaire, we utilised Pearson correlations between the subscales of the survey (see Appendix A). It can be observed that there were significant correlations among the scores on the subscales. The Pearson coefficients ranged from 0.147 to 0.804. Furthermore, we conducted an exploratory factor analysis using principal component analysis.

Table 2. Total variance explained and rotation sums of squared loadings.

Components	Eigenvalue	% of Variance	Cumulative %
1	4.517	16.133	16.133
2	2.785	9.945	26.079
3	2.773	9.905	35.984
4	2.633	9.404	45.388
5	2.530	9.035	54.422
6	2.515	8.981	63.403
7	1.169	4.174	67.578

displays the total variance explained and rotation sums of squared loadings for the extracted factors.

As noted in Table 2, seven factors explain 67.578% of the total variance; all of these factors have an eigenvalue of more than 1. Additionally, Figure 1 shows the eigenvalues of all the variables of the survey.

4.3. Data Analysis

The findings of this study are based on primary data collected from students at the University of Jordan. We used descriptive statistics, including means, standard deviations, frequencies, and percentages, to analyse the data. To ensure the validity and reliability of the survey, we conducted an exploratory factor analysis and Cronbach’s alpha reliability analysis, and we deployed structural modelling to test the research hypotheses. We used SPSS and AMOS software to obtain these results.

4.4. Ethical Considerations

We provided the participants with clear information regarding the research purpose, procedures, and potential benefits. They were also informed that their participation was voluntary, and they had the right to withdraw from the study at any time without facing any negative consequences. To ensure students’ privacy, we handled and stored the data collected in this study securely, without any intrusion into their personal information.

5. Results and Data Analysis

Are you registered in materials currently using social networking sites for teaching?

A group of 473 students (73%) were registered currently, but 175 students (27%) were not.

How did you attend the online classes?

A total of 343 students (53%) used mobile devices, 254 students (39%) used computers, and 51 students (8%) used tablets.

What social media did you use the most during the study period?

A group of 459 students (71%) used Teams, 73 students (11%) used YouTube, 46 students (7%) used Facebook, 45 students (7%) used Instagram, seven students (1%) used Zoom, five students (1%) used Twitter, and 13 students (2%) used something else.

Do you still use the phone extensively for learning purposes?

Responses showed 544 students (84%) still used the phone extensively for learning purposes, whereas 104 students (16%) did not.

How long do you use your phone for learning purposes every day?

A total of 323 students (50%) spent 2–3 hr daily, 206 students (32%) spent 4–5 hr daily, 69 students (11%) spent 6–7 hr daily, and 50 students (7%) spent more than 8 hr daily.

What is the impact of social networking sites on students’ social life?

Table 3. Frequency percentages, means, and standard deviations for social life.

Items	Strongly Disagree	Disagree	Neutral	Agree	Strongly Agree	Mean	Std. Deviation
I take a large amount of time to check on my friends’ psychological health during studying through social networking sites.	16.2%	22.5%	38.0%	13.0%	10.3%	2.79	1.172
I often help my friends solve their technology problems while studying on social media.	7.4%	17.3%	33.3%	23.6%	18.4%	3.28	1.167
I feel very responsible for how much my friends enjoy learning through social media.	22.8%	24.8%	30.9%	13.3%	8.2%	2.59	1.207
I take great care of my friends while learning through social media.	13.4%	20.2%	32.7%	21.3%	12.3%	2.99	1.203
I care a lot about my friends’ posts on social media while learning through these sites.	11.1%	16.2%	31.8%	23.0%	17.9%	3.20	1.230
I often congratulate my friends on social networking sites on their birthday because of the reminder feature on these sites, knowing I may not congratulate them if I meet them in person.	23.3%	13.9%	23.3%	17.9%	21.6%	3.01	1.455
Overall Subscale						2.98	1.24

shows the frequency percentages, means, and standard deviations for each item and on the overall domain.

Table 3 presents the mean scores for the items in the social life subscale, ranging from 2.59 to 3.28 on a scale of 5. The overall mean score for this subscale is 2.59, indicating an average level of agreement. A higher score suggests a more positive impact of social networking sites on students’ social life. Among the items, the highest mean score is observed for the statement, ‘I often help my friends solve their technology problems while studying on social media’. Conversely, the lowest mean score is associated with the statement, ‘I feel very responsible for how much my friends enjoy learning through social media’.

Regarding the impact of social networking sites on students’ cognitive load,

Table 4. Frequency percentages, means, and standard deviations for cognitive load.

Items	Strongly Disagree	Disagree	Neutral	Agree	Strongly Agree	Mean	Std. Deviation
I often feel distracted by the huge amount of information available on social media during the learning period.	7.4%	12.3%	28.9%	24.4%	27.0%	3.51	1.218
I get confused when learning through social media because of how much daily information I have to absorb.	7.4%	14.0%	31.0%	24.5%	23.0%	3.42	1.196
I feel a great burden in dealing with the information about my friends on social networking sites during the learning period through them because of the huge amount of information that exists.	12.8%	21.8%	32.4%	21.0%	12.0%	2.98	1.193
I find that only a small part of the information available on social networking sites related to the scientific subject is considered basic and important.	11.6%	17.6%	39.2%	21.8%	9.9%	3.01	1.120
Overall Subscale						3.23	1.18

shows the frequency percentages, means, and standard deviations for each item and on the overall domain.

Table 4 displays the mean scores for the items in the cognitive load subscale, ranging from 2.98 to 3.51 on a scale of 5. The overall mean score for this subscale was 3.23, indicating an average level of agreement. A higher score suggests a more positive impact of social networking sites on students’ cognitive load. Among the items, the highest mean score was observed for the statement, ‘I often feel distracted by the huge amount of information available on social media during the learning period’. Conversely, the lowest mean score was associated with the statement, ‘I feel a great burden in dealing with the information about my friends on social networking sites during the learning period through them because of the huge amount of information that exists’.

Regarding the impact of social networking sites on students’ private life,

Table 5. Frequency percentages, means, and standard deviations for private life.

Items	Strongly Disagree	Disagree	Neutral	Agree	Strongly Agree	Mean	Std. Deviation
Using a smartphone for learning through social media is blurring the boundaries between my life as a student and my private life.	9.4%	14.2%	34.3%	22.2%	19.9%	3.29	1.206
I am forced to stay in touch with my school assignments even during vacations due to using smartphone to learn through social media.	6.0%	9.0%	30.4%	25.9%	28.7%	3.62	1.162
I am forced to sacrifice weekends because of the need to constantly be aware of the latest developments on social media during the learning period.	7.9%	11.4%	26.9%	24.4%	29.5%	3.56	1.240
I feel like my private life is being invaded by the social media sites I use for education.	11.4%	14.8%	28.5%	22.4%	22.8%	3.30	1.285
Overall Subscale						3.44	1.22

shows the frequency percentages, means, and standard deviations for each item and on the overall domain.

Table 5 shows the mean score of the items in the private life subscale ranged from 3.29 to 3.62 out of 5; the overall mean score was 3.44. All these values indicate an average level of agreement. A higher score indicates a more positive impact of social networking sites on students’ private lives.

The highest mean in this table belongs to the item, ‘I am forced to stay in touch with my school assignments even during vacations due to using a smartphone to learn through social media’, whereas the lowest mean in this table belongs to the item, ‘Using a smartphone for learning through social media is blurring the boundaries between my life as a student and my private life’.

Regarding the impact of social networking sites on students’ privacy,

Table 6. Frequency percentages, means, and standard deviations for privacy.

Items	Strongly Disagree	Disagree	Neutral	Agree	Strongly Agree	Mean	Std. Deviation
Someone I didn’t know previously published my personal information on my social media accounts without my prior permission.	61.9%	11.0%	15.3%	5.6%	6.3%	1.83	1.240
Someone previously used my account picture on social media illegally.	61.9%	8.6%	16.0%	6.6%	6.8%	1.88	1.282
Someone I did not know previously stole the content of my scientific comments and opinions and republished them and affiliated with him on the social networking sites used for education.	58.5%	10.3%	17.4%	8.3%	5.4%	1.92	1.255
Overall Subscale						1.88	1.26

shows the frequency percentages, means, and standard deviations for each item and on the overall domain.

Table 6 shows the mean score of the items in the privacy subscale ranged from 1.83 to 1.92 out of 5; the overall mean score was 1.88. All these values indicate a low level of agreement. A higher score indicates a more positive impact of social networking sites on privacy.

The highest mean in this subscale belongs to the item, ‘Someone I did not know previously stole the content of my scientific comments and opinions and republished them and affiliated with him on the social networking sites used for education’, whereas the lowest mean in this subscale belongs to the item, ‘Someone I did not know previously published my personal information on my social media accounts without my prior permission’.

Regarding the impact of social networking sites on students’ technostress,

Table 7. Frequency percentages, means, and standard deviations for technostress.

Items	Strongly Disagree	Disagree	Neutral	Agree	Strongly Agree	Mean	Std. Deviation
I find myself forced to change my teaching habits to adapt to new developments on social media used for education.	9.4%	14.4%	31.8%	24.2%	20.2%	3.31	1.214
I have to sacrifice my learning time to keep up with new updates on social media used for education.	9.9%	16.5%	34.9%	23.8%	15.0%	3.17	1.170
I feel like my personal life is being invaded by social media features used for education.	16.0%	16.7%	32.9%	18.8%	15.6%	3.01	1.274
I feel threatened by people who have the latest skills to deal with social media sites used for education.	29.5%	18.8%	27.6%	14.8%	9.3%	2.56	1.300
Overall Subscale						3.01	1.24

shows the frequency percentages, means, and standard deviations for each item and on the overall domain.

Table 7 shows the mean score of the items in the technostress subscale ranged from 2.56 to 3.31 out of 5; the overall mean score was 3.01. All these values indicate an average level of agreement. A higher score indicates a more positive impact of social networking sites on technostress.

The highest mean in this subscale refers to the item, ‘I find myself forced to change my teaching habits to adapt to new developments on social media used for education’, whereas the lowest mean in this subscale refers to the item, ‘I feel threatened by people who have the latest skills to deal with social media sites used for education’.

Regarding the impact of social networking sites on students’ exhaustion,

Table 8. Frequency percentages, means, and standard deviations for each item and on the overall domain of exhaustion.

Items	Strongly Disagree	Disagree	Neutral	Agree	Strongly Agree	Mean	Std. Deviation
I feel overwhelmed by educational activities that require me to use social networking sites via smartphone.	10.3%	12.5%	29.9%	22.8%	24.4%	3.38	1.264
I am tired of using social networking sites via my smartphone to complete learning activities.	10.0%	14.4%	30.6%	21.6%	23.5%	3.34	1.259
Using social media to learn is stressful for me.	12.8%	15.3%	31.3%	21.6%	19.0%	3.19	1.268
Overall Subscale						3.30	1.26

shows the frequency percentages, means, and standard deviations for each item and on the overall domain.

Table 8 shows the mean score of the items in the exhaustion subscale ranged from 3.19 to 3.38 out of 5; the overall mean score was 3.30. All these values indicate an average level of agreement. A higher score indicates a more positive impact of social networking sites on exhaustion.

The highest mean in this table refers to the item, ‘I feel overwhelmed by educational activities that require me to use social networking sites via smartphone’, whereas the lowest mean in this table refers to the item, ‘Using social media to learn is stressful for me’.

Regarding whether students have an intention to reduce the use of social networking sites for education,

Table 9. Frequency percentages, means, and standard deviations for intention.

Items	Strongly Disagree	Disagree	Neutral	Agree	Strongly Agree	Mean	Std. Deviation
I will reduce the amount of learning via social media using a smartphone.	13.9%	20.1%	36.6%	16.4%	13.1%	2.95	1.202
I would like to have a certain period of time in which I do not use social media for smartphone education.	7.3%	10.8%	28.2%	22.7%	31.0%	3.59	1.231
I plan to stop using social media for smartphone education.	19.8%	17.6%	31.8%	16.7%	14.2%	2.88	1.300
I will no longer use social media for smartphone education.	23.9%	21.5%	28.5%	15.1%	11.0	2.68	1.288
Overall Subscale						3.03	1.26

shows the frequency percentages, means, and standard deviations for each item and on the overall domain.

Table 9 shows the mean score of the items in the intention subscale ranged from 2.68 to 3.59 out of 5; the overall mean score was 3.03. All these values indicate an average level of agreement. A higher score indicates a more positive impact of social networking sites on intention.

The highest mean in this table refers to the item, ‘I would like to have a certain period of time in which I do not use social media for smartphone education’, whereas the lowest mean in this table refers to the item, ‘I will no longer use social media for smartphone education’.

Hypotheses Testing

To test the hypotheses of the structural model, we developed testing using AMOS Software. Figure 1 shows the structural model testing according to the proposed conceptual model, and

Table 10. Hypotheses testing.

			Estimate	S.E.	C.R.	P	Remarks
TS	<---	SO	0.017	0.022	0.798	0.425	Not sig
TS	<---	IO	0.155	0.036	4.348	***	Sig
TS	<---	LI	0.544	0.032	17.239	***	Sig
TS	<---	PI	0.296	0.032	9.335	***	Sig
EX	<---	SO	−0.093	0.022	−4.305	***	Sig
EX	<---	IO	0.247	0.036	6.900	***	Sig
EX	<---	LI	0.224	0.038	5.947	***	Sig
EX	<---	TS	0.244	0.039	6.279	***	Sig
EX	<---	PI	0.026	0.033	0.790	0.430	Not sig
IN	<---	TS	0.258	0.039	6.536	***	Sig
IN	<---	EX	0.539	0.044	12.153	***	Sig

*** p < 0.001.

shows the structural estimates for testing the hypotheses according to the proposed conceptual model.

According to Figure 2 and Table 10, all the hypotheses were accepted, except for H1a and H4b, indicating technostress was influenced by information overload, life invasion, and privacy invasion. Additionally, exhaustion was affected by social overload, information overload, life invasion, and technostress. Table 10 further demonstrates intention was influenced by technostress and exhaustion. Interestingly, we did not find a significant relationship between social overload and technostress, nor between privacy invasion and exhaustion. This suggests the level of social interaction or feeling overwhelmed by SNSs does not directly contribute to technostress. Similarly, privacy violation alone does not directly lead to exhaustion. These findings imply other factors may have a more significant impact on the experience of technostress and exhaustion.

To answer the question of whether there a significant difference at α = 0.05 in students’ viewpoints about the impact of social networking sites attributed to the time they spend daily using social networking sites for education, we determined the means and standard deviations and conducted a one-way MANOVA (Multivariate Analysis of Variance).

Table 11. Means, standard deviations, and one-way MANOVA results for the effect of time on viewpoints about the impact of social networking sites.

Time	2–3 hr		4–5 hr		More than 5 hr		F-Value	Sig.
Subscales	Mean	Std. Deviation	Mean	Std. Deviation	Mean	Std. Deviation
Social	2.92	0.85	3.01	0.82	3.08	0.87	1.74	0.18
Cognitive	3.12	0.92	3.24	0.94	3.51	0.88	8.16 **	0.01
Private	3.33	1.01	3.47	0.99	3.7	1	6.06 **	0.01
Privacy	2.76	0.86	2.77	0.86	2.99	0.94	3.21 *	0.04
Stress	3.2	1.16	3.24	1.15	3.7	1.02	9.13 **	0.01
Exhaustion	3.01	1.03	2.91	1	3.28	1.01	5.07 **	0.01
Intention	3.06	0.69	3.11	0.68	3.38	0.7	9.65 **	0.01

* Significant at 0.05; ** Significant at 0.01; Note: Wilks’ Lambda = 0.954, p-value = 0.002.

presents the results.

In summary, there is a significant difference in students’ viewpoints regarding information overload, life invasion, privacy, technostress, exhaustion, and intention subscales based on the amount of time spent daily using social networking sites for education. These differences were in favour of students who spent more time daily using social networking sites for education. However, there was no significant difference observed for the social overload subscale. The significant effects observed in the aforementioned subscales suggest spending more time on SNSs for educational purposes is associated with a higher perception of information overload, life invasion, privacy concerns, technostress, exhaustion, and intention to use SNSs. This could be attributed to the fact that students who spend more time using SNSs for educational activities may engage in interactive learning, knowledge sharing, and collaborative discussions with their peers.

They might feel their personal information and online activities are more exposed, leading to concerns about privacy and a heightened perception of its impact. This perception could stem from the sharing of personal information, online behaviours, and interactions on these platforms, which carry potential risks to their privacy, such as data breaches, identity theft, or unauthorised access to their personal information. Consequently, this heightened perception of risk may contribute to information overload, constant connectivity, fear of missing out, or the pressure to maintain an online presence, all of which can lead to increased stress levels among students. Continuous engagement with online educational content, communication, and multitasking on these platforms can also be mentally and emotionally draining, potentially resulting in feelings of fatigue and exhaustion. As a result, students may develop stronger intentions to continue using social networking sites for educational purposes, indicating a positive perception of the benefits and value they derive from these platforms.

Regarding the question of whether a significant difference at α = 0.05 in students’ viewpoints about the impact of social networking sites was attributed to the device used by them to attend classes,

Table 12. Means, standard deviations, and one-way MANOVA results for the effect of device type on viewpoints about the impact of social networking sites.

Subscales	Devices	N	Mean	Std. Deviation	t-Value	Sig.
Social	Mobile or tablet	343	2.96	0.85	0.16	0.69
	Computer	305	2.99	0.84
Cognitive	Mobile or tablet	343	3.22	0.96	0.07	0.79
	Computer	305	3.24	0.90
Private	Mobile or tablet	343	3.40	1.04	1.62	0.20
	Computer	305	3.50	0.97
Privacy	Mobile or tablet	343	2.81	0.88	0.13	0.71
	Computer	305	2.79	0.88
Stress	Mobile or tablet	343	3.32	1.12	0.15	0.69
	Computer	305	3.29	1.17
Exhaustion	Mobile or tablet	343	3.03	1.03	0.03	0.86
	Computer	305	3.02	1.02
Intention	Mobile or tablet	343	3.12	0.73	0.05	0.83
	Computer	305	3.14	0.66

Note: Hotelling = 0.007, p-value = 0.601.

indicates there were no significant differences at α = 0.05 in students’ viewpoints regarding the impact of SNSs on various subscales, including social load, information overload, life invasion, privacy, technostress, exhaustion, and intention, based on the device type they use. This means that, regardless of whether students use a computer or a mobile device, their perceptions of the impact of SNSs on these aspects remain similar. This lack of significant differences can be attributed to the fact that SNS platforms are designed to be accessible and functional across various devices, including computers and mobile devices. Students are likely to have similar experiences and interactions on these platforms, regardless of the device they use. Moreover, SNSs generally provide consistent content and features across different devices. Whether students access these sites on a computer or a mobile device, they are exposed to similar types of information, engage in comparable activities, and encounter similar functionalities. The device type does not seem to have a significant influence on students’ perceptions of the impact of SNSs on different aspects. Students’ experiences and interactions on SNS platforms remain consistent, regardless of whether they use a computer or a mobile device.

Regarding whether there is a significant difference at α = 0.05 in students’ viewpoints about the impact of social networking sites attributed to the school category,

Table 13. Means, standard deviations, and one-way MANOVA results for the effect of school category on viewpoints about the impact of social networking sites.

Subscales	School Category	N	Mean	Std. Deviation	t-Value	Sig.
Social	Humanities	290	3.04	0.82	2.62	0.11
	Scientific	358	2.93	0.86
Cognitive	Humanities	290	3.22	0.93	0.01	0.92
	Scientific	358	3.23	0.93
Private	Humanities	290	3.4	1.03	1.12	0.29
	Scientific	358	3.48	0.99
Privacy	Humanities	290	2.81	0.9	0.06	0.81
	Scientific	358	2.8	0.86
Stress	Humanities	290	3.34	1.13	0.52	0.47
	Scientific	358	3.27	1.16
Exhaustion	Humanities	290	3.09	1.04	1.90	0.17
	Scientific	358	2.97	1.01
Intention	Humanities	290	3.15	0.73	0.40	0.53
	Scientific	358	3.11	0.67

Note: Hotelling = 0.016, p-value = 0.113.

indicates there were no significant differences at α = 0.05 in students’ viewpoints regarding the impact of SNSs on various subscales, based on the school category. This means that, regardless of whether students belong to different school categories, their perceptions of the impact of SNSs on social load, information overload, life invasion, privacy, technostress, exhaustion, and intention remain similar. This result may be attributed to the fact that students, regardless of their school category, may perceive similar levels of social load, information overload, life invasion, privacy concerns, technostress, exhaustion, and intention in relation to their usage of SNSs. This could be due to shared experiences among students, exposure to similar online content, or the influence of societal factors that affect all students, irrespective of their school category.

Furthermore, the educational methods, curriculum, and overall teaching approaches may not significantly differ between the two school categories in terms of incorporating or utilising social networking sites for educational purposes. This similarity in the educational environment could contribute to the convergence of students’ viewpoints regarding the impact of these platforms. In summary, the school category does not appear to have a significant influence on students’ perceptions of the impact of SNSs on different aspects. Students from different school categories tend to have similar perspectives on the impact of these platforms, potentially due to shared experiences and the similarity in the educational environment.

6. Discussion

Recently, there has been increased interest among researchers in regard to studying mobile learning, particularly due to the widespread use of online learning technologies and mobile devices, which has been further accelerated by the COVID-19 pandemic. Social media platforms have been recognised as playing a critical role in mobile learning, as highlighted by several researchers [18,29,79,80,81,82,83]. However, there has been limited research investigating the risks and dangers associated with social media in the context of mobile learning using a structured and organised approach [18]. This study was conducted to address this research gap and examine the potential dangers of mobile learning on students. The survey questions used in this study were based on the work of Loh et al. (2021), and the hypotheses were tested based on empirical findings. Overall, the results of the study supported all the hypotheses except for H1a and H4b. The following section provides a detailed discussion of the study findings.

Through H1a and H1b, we proposed that social overload has a positive relationship with technostress and exhaustion, respectively. The results regarding H1b were unexpected considering university students were generally familiar with using social media platforms even before their educational experience. This finding may be attributed to the motivations underlying the use of social media platforms. Alhabash and Ma [84] identified various use motivations for popular social media platforms, such as Facebook, Twitter, Instagram, and Snapchat, including information sharing, self-documentation, social interaction, entertainment, passing time, self-expression, medium appeal, and convenience. They found Instagram and Snapchat had higher use intensity compared to Facebook or Twitter, with entertainment being the primary motivation for their use. These platforms are less likely to be used for online learning, which aligns with the findings of the current study, where only 1% of the surveyed students reported using Instagram for mobile learning. The absence of learning as a motivation for using these platforms justifies the lack of significant outcomes related to technostress and exhaustion in this context.

This study’s findings partially contradict those of Alvarez-Risco et al. [85], who examined the influence of technostress on the academic performance of university medical students in Peru during the COVID-19 pandemic. The researchers found social overload positively influenced technostress, with a correlation coefficient of 0.557, and technostress positively influenced exhaustion, with a correlation coefficient of 0.898. However, in the current study, we found overload, specifically social overload and information overload, was associated with exhaustion, thereby providing further support for the arguments made by Nawaz et al. [86]. Nawaz et al. [86] classified overload and exhaustion as factors that contribute to users’ intentions to discontinue using SNSs. They suggested overload and exhaustion lead to regret and dissatisfaction, motivating SNS users to discontinue their usage. Therefore, the positive relationship found in the current study between these factors may support the students’ intentions to discontinue using SNSs. One student expressed this sentiment in the additional comments section of the questionnaire, stating, ‘The online classes during the distance learning period were a great academic burden and psychological pressure’.

H2a confirmed the association between information overload and technostress, which was also demonstrated by Ayyagari [87], who found a positive relationship between information overload and technostress. Ayyagari [87] proposed technostress occurs when individuals are unable to process the information provided by ICT channels, a phenomenon known as information overload. Therefore, support for this hypothesis in the current study can be attributed to the sudden exposure of students to educational information, in addition to the information overload they experience through social media channels. Consistent with the existing literature, the support for H3a and H4a in this study confirms life invasion and privacy invasion have significantly positive relationships with technostress. This result is not surprising, as SNSs and other ICT platforms are known for invading privacy and encroaching upon individuals’ personal lives due to their nature or misuse. These findings align with those of Lee et al. [88], who also found that life invasion and privacy invasion outside of official working or studying hours, in the case of university students, contribute to technostress. H6 and H7 propose that technostress and exhaustion, respectively, have significantly positive relationships with the reduced intention to use. This finding is supported by Nawaz et al. [86], who demonstrated a relationship between exhaustion and users’ intentions to discontinue using SNSs. Similarly, Maier et al. [41] argued that technostress among SNS users leads to attempts to avoid stress, resulting in intentions to discontinue usage or reduced intention to use, as noted in this study.

Our study found all four stimuli significantly affect students’ internal states and lead to reduced intention to use SNSs in one way or another. This finding is supported by the existing literature. Fu et al. [89] recognised that overload in general, including social overload and information overload, as well as exhaustion, positively influences users’ reduced intention to use or ‘use discontinuance’. Zhang et al. [50] also linked social fatigue (equivalent to exhaustion in the current study) with social overload and information overload as factors influencing the intention to switch social media behaviours. Pang et al. [90] conducted a significant study on people’s discontinued use of mobile services. They found that ‘privacy invasion and communication overload mediate the association between network externalities and mobile app discontinued use intentions’. Maier et al. [41] also identified discontinued use intention as a response to technostress and exhaustion. Finally, Joo et al. [91] explored the relationship between technostress and reduced intention to use from the perspective of secondary school teachers in South Korea, suggesting technostress significantly influenced teachers’ intentions to use technology.

Our findings support the relationship between technostress and exhaustion, highlighting that technostress contributes to the experience of exhaustion. This reinforces the understanding that exhaustion is not solely an independent phenomenon but can be influenced by technostress. These findings have significant implications for the fields of psychology, education, and ICT development, as they shed light on the conditions that contribute to the reduced intention to use mobile learning. However, our findings suggest that, in general, there is a moderate level of agreement among students regarding the impact of social networking sites across different subscales. Furthermore, the type of device used or the school category does not significantly influence students’ viewpoints on this impact across the various subscales. Overall, these findings contribute to our understanding of the relationship between technostress, exhaustion, and intention to use mobile learning. They also highlight the importance of considering factors such as social overload, information overload, and time spent on social networking sites for educational purposes when examining students’ viewpoints on the impact of social networking sites.

7. Implications

With the rapid advancement of communication technology, educational institutions worldwide have embraced online learning, a trend that has been further accelerated by the COVID-19 pandemic. Social media platforms and online communication channels have played a crucial role in facilitating effective online learning. However, although many studies have examined the risks associated with using social media platforms, there has been limited exploration of the drawbacks specifically related to mobile learning. Therefore, the findings of our study hold significant practical and theoretical implications. From a practical standpoint, our findings can assist educational institutions in evaluating the outcomes and potential negative effects of using social media platforms in the learning process. Educational stakeholders can develop strategies to mitigate the impact of the factors identified in the literature. This may involve implementing regulations for mobile learning usage and raising awareness among students and institutions about the potential drawbacks of mobile learning. For instance, setting limits on the amount and nature of information required from students through social media platforms can help mitigate life invasion and privacy invasion. Educators can also reduce social overload and information overload by controlling the quantity of educational material shared with students.

One suggested way to address students’ privacy invasion and technostress is through the use of serious game learning techniques. Fatima et al. [92] designed a serious game to educate users about the potential risks associated with sharing excessive personal information online. They assessed the effectiveness of the game in increasing privacy awareness through empirical evaluation and found it to have a positive effect on enhancing participants’ privacy awareness. By incorporating such serious games, the feelings of privacy invasion among students can be significantly reduced, potentially alleviating the negative effects of technostress and exhaustion. Additionally, students can benefit from identifying the triggers of their technostress and exhaustion while using mobile learning devices, enabling them to better manage their screen time and improve their overall quality of life. Social media companies, including those mentioned in this study and others involved in the educational process, can utilise the study’s findings to enhance their applications and improve the response to reduced intention to use.

The theoretical implications of these findings raise intriguing questions and offer new avenues for research in the field of mobile learning and the use of social media in education. Further investigation can provide extended longitudinal data and incorporate a wider geographical scope to strengthen and validate the findings. Replicating the study’s approach across other universities in Jordan, the MENA region, and globally can enhance its generalisability and accuracy. Lastly, this study contributes to the existing literature by applying the SOR (stimulus–organism–response) framework in the context of learning and ICT development, further advancing our understanding of these dynamics.

8. Limitations and Directions for Future Research

The limitations of our study can be summarised in two aspects: geographical location and timespan. However, these limitations do not undermine the study’s contribution in highlighting the negative aspects of mobile learning. In terms of geographical location, we surveyed students exclusively from the University of Jordan. Future researchers could expand the research population to include students from different regions around the world. This would enhance the generalisability of the findings and provide a more comprehensive understanding of the topic. Regarding the timespan, in this study, we focused on students who took online courses during the summer semesters of 2021 and 2022. To gain a more comprehensive perspective, future researchers could extend the timespan to include data from subsequent years following the COVID-19 pandemic. This would allow for longitudinal analysis and enable researchers to observe any potential changes or developments in the phenomenon over time. Despite these limitations, we effectively framed the dark side of mobile learning and provided valuable insights into the challenges and potential negative consequences associated with its usage.

9. Conclusions

Mobile learning emerged as a necessary tool during the pandemic to ensure educational continuity. However, the existing literature primarily focuses on theoretical frameworks, technological advancements, and pedagogical approaches, leaving a gap in the understanding of the practical complexities associated with mobile learning. Through this study, we aimed to fill this gap by investigating the dark side of mobile learning through SNSs and their effects on students’ technostress, exhaustion, and intention to use SNSs for mobile learning. By employing the stimulus–organism–response (SOR) framework and conducting an online survey with 648 voluntary participants from Jordanian universities, we obtained valuable insights.

The findings indicated factors such as information overload, life invasion, and privacy invasion significantly contribute to students’ technostress and exhaustion. Moreover, technostress and exhaustion were found to be positively associated with a reduced intention to use SNSs for mobile learning. However, we did not find a significant relationship between social overload and technostress, nor between privacy invasion and exhaustion. These findings have important implications for the research community, administrators, and educators in educational settings. Establishing norms and rules that protect students’ privacy and prevent them from being overwhelmed by excessive SNS use can create a conducive environment for mobile learning. The utilisation of the SOR framework in this study provides a solid theoretical foundation for understanding the impact of SNSs on mobile learning. This study contributes to the existing literature by offering a comprehensive and structured approach to examining the complex relationships between stimuli, organisms, and responses in the context of mobile learning and SNSs. Future researchers could build upon these findings to further explore and understand these relationships in greater detail.