85 Organizacija, V olume 58 Issue 1, February 2025 Research Papers 1 Received: 18th March 2024; Accepted: 12th June 2024 The Impact of Students’ Cybersecurity Vulnerability Behavior on E-Learning Obstacles Ibrahim Mohamed TAHA 1 , Rajaa Hussein Abd ALI 2 , Ali Abdulhassan ABBAS 3 1 Sadat Academy for Management Sciences, Tanta, Egypt, ibrahimaboalazm@gmail.com 2 University of AL-Zahraa for Women/ College of Health and medical Techniques/ Radiological Technics Department, Kerbala, Iraq, rajaa.ali@uokerbala.edu.iq, ms.rajaahussien@gmail.com 3 University of Kerbala, College of Administration and Economics, Department of Accounting, Kerbala, Iraq, fuhrer313@gmail.com, ali.abd.alhassan@uokerbala.edu.iq Background/purpose: This study examines the relationship between students’ cybersecurity vulnerability behav- ior and e-learning obstacles. With the rapid growth of online education, ensuring the security and privacy of digital platforms has become crucial. In this background, the current study is a first-of-its-kind attempt to understand the relationship between these two variables in the background of higher educational institutions in Iraq. Methods: For this study, the researchers collected data during 2023 from students aged between 19 and 25 enrolled in the University of Karbala, Iraq, using a semi-structured research questionnaire, who were selected through a ran- dom sampling method. The questionnaire comprised questions pertaining to the dimensions of both the dependent and the independent variable. A total of 350 valid responses were considered for the analysis in which PLS-SEM was conducted. Results: The outcomes revealed that the professional and human obstacles have a high association with cyber- security vulnerability behavior. The study also found that the overall obstacles have a significant effect on the cy- bersecurity vulnerability behavior. All hypotheses were verified and the outcomes confirm that there is an effective relationship between cybersecurity vulnerability behavior and e-learning obstacles Conclusion: Based on the study outcomes, the authors proposed a few recommendations for all the stakeholders of the e-learning process, such as educational institutions, governments, faculty members, students, and their parents. Though the current study has been confined to a single university in Iraq, future researchers can focus on expanding the study to other higher educational institutions so that a nationwide policy-level initiative can be brought based on the research evidence. Keywords: Cybersecurity Vulnerability Behavior, E-Learning Obstacles, Higher education, PLS-SEM, Student moti- vation, Learning behaviour DOI: 10.2478/orga-2025-0006 1 Introduction E-learning or online learning has become a popular learning method, especially in the aftermath of COVID-19 pandemic (Fauzi, 2022). E-learning brings positive impact on the success of the students in terms of their academics. However, various challenges are associated with e-learn- ing from the perspective of universities/educational insti- tutions (lack of financial and physical resources, lack of technical infrastructure, trained professionals, resistance 86 Organizacija, V olume 58 Issue 1, February 2025 Research Papers from faculty to adopt to novel training methods and so on), students/learners (unable to access internet, lack of nec- essary equipment/technical infrastructure, high chances of distraction, loss of humanly approach, disconnection with peers and instructors) and the faculty/teacher (access to internet/technical infrastructure, unable to understand the learners’ outcomes, clarify their queries etc.,(Mojarad et al., 2023) (Alhamdawee, 2023) mentioned that Iraq opted for e-learning only in the recent years, due to two-decade long political instability, internet unavailability, outdated technical infrastructure, etc., However, Iraqi institutions understood COVID-19 as an opportunity in disguise to upgrade their technical infrastructure and e-learning op- tion by leveraging the open-source and paid platforms like doodle, Google classroom and free conference call etc., In literature, the authors mentioned a variety of challenges in Iraq for e-learning adoption, one of which remains the cybersecurity issue. Cybersecurity Vulnerability Behavior (CVB) refers to actions or behaviors that increase the chances of experi- encing cyber-attack or data breach. In this behavior, the victims tend to lose their confidential data due to their weak passwords or clicking the suspicious links or attach- ments, failing to update software, or sharing sensitive in- formation (N. F. Khan et al., 2022). When students engage in e-learning platforms, they exhibit cybersecurity vulner- ability behavior and expose themselves to cybersecurity risks, including malware, ransomware, or phishing attacks (Wijayanto & Prabowo, 2020). The impact of students’ behavior exposed to cyberse- curity on e-learning obstacles can be significant, for in- stance inability to access the e-learning platforms (Mor- row, 2024). In addition to this, a compromised device of the student may be used by the attacker to gain access to the rest of the students and even the platform also. Thus, the cyberattacker may disrupt the module altogether and the institutional infrastructure as well. So, it becomes im- portant to understand the behaviour of the students with regards to cybersecurity risks. Research has shown that a lack of awareness, training, or motivation can contribute to students’ cyberattack vulnerability (Vishal Verma & Ja- nardan Pawar, 2024). In the case study published earlier (Al Shabibi & Al-Suqri, 2023), 83% students were found to have been exposed to cybersecurity threats, when they were enrolled in online learning programs during the COV- ID-19 pandemic. Further, 77% of the target population i.e., post-basic education students from Muscat, lacked aware- ness about the cybersecurity issues. Therefore, it is impor- tant for the educators and administrators to provide the stu- dents with appropriate cybersecurity education, training, and resources to reduce the cybersecurity vulnerability be- havior and ensure the security and continuity of e-learning activities (Abbas, 2020; Kumar et al., 2022). E-learning obstacles are of different types such as lec- turer-related (desire for change, understanding and knowl- edge about the technology, sufficient training, technical support etc.,), student-related, curriculum-related and so on (Abeer, 2022). In this background, it is important to understand the relationship between cybersecurity vul- nerability behaviour of the students and the e-learning obstacles, since the researchers have mentioned it as a complex phenomenon that requires in-depth understand- ing (A. H. Ibrahim et al., 2019). Various studies have been conducted earlier focusing cybersecurity awareness among the students and faculty members in Iraq (Abdulla et al., 2023; Al-Janabi & Al-Shourbaji, 2016; Ameen et al., 2017; Nagham Oudeh Alhamdawee, 2023; Tarrad et al., 2022; Zahid et al., 2023), Iraqi private banks (Faez Hasan & Al-Ramadan, 2021), Iraqi national security (Al-Tae et al., 2022), Iraqi organizations (Khadija Hassan & Musta- fa Jawad, 2022) and so on. However, there is a lack of studies pertaining to cybersecurity vulnerability behavior and the presence of e-learning obstacles, conducted among students in University of Karbala, Iraq. Thus, the current study is a first-of-its-kind attempt in this domain within the study environment as no other study has been con- ducted so far, to the best of the authors’ knowledge. The current study outcomes will help the decision makers at the institutional level, governments and the cybersecurity organizations that fight the cyberattackers on the daily ba- sis. By understanding the relationship between these two constructs and using a multi-faceted approach, it is impor- tant to identify the most vulnerable areas so as to create awareness among students on appropriate cybersecurity practices and provide them with the tools and resources to protect their devices and data. Based on the study findings, the institutional commit- tees can set up training programs for the students, faculty and other non-teaching staff on how to combat phishing attacks, using safe and complex passwords and making sure the technical infastructure is up-to-date. By promot- ing good cyber security practices, it is possible for all the stakeholders involved in the e-learning process to reduce the cyberattack vulnerabilities and minimize the impact of their behavior on increasing obstacles to e-learning (Al- kaaf, 2023; Arul & Punidha, 2022). 2 Literature review The current section details about the studies conduct- ed in cybersecurity vulnerability behaviour and the obsta- cles faced in e-learning system in terms of electronic and physical obstacles, financial and organizational obstacles followed by professional and human obstacles. Cyberse- curity vulnerable behavior refers to actions or behaviors that can make an individual, organization, or a system highly vulnerable to cyberattacks or data breaches. Cy- bersecurity vulnerabilities are weaknesses or gaps in the security measures exploired by the cybercriminals to gain 87 Organizacija, V olume 58 Issue 1, February 2025 Research Papers unauthorized access, steal sensitive data, install malware, or disrupt operations (Ewoh & Vartiainen, 2024). Poor cybersecurity vulnerability behavior can include a wide range of actions, such as failing to update software and systems, using weak or easy-to-guess passwords, clicking on suspicious links or attachments, sharing sen- sitive information over unsecured networks or platforms, and neglecting to implement the basic security practices such as two-factor authentication and data backups (Gour- isetti et al., 2020). So, it is essential to understand and ad- dress the cybersecurity vulnerability behavior to mitigate cybersecurity risks in an effective manner. By identifying and providing a remedy for the vulnerability behavior, both individuals as well as the organizations can reduce the likelihood of successful cyberattacks and protect them- selves from potential threats. Some of the studies conduct- ed earlier pertaining to awareness levels among students about cybersecurity have been discussed herewith. Al-Sherideh (Al-Sherideh et al., 2023) analyzed the satisfaction level of the students enrolled in e-learning platform named Moodle e-learning system, in terms of data security and privacy and their opinions on the overall standard of education. The study outcomes revealed that the presence of security and cybersecurity measures posi- tively influence the increased usage of e-learning modules while the study recommended to get regular feedback and have a constant communication with the students about their experience with the e-learning portals. Thus, it is pos- sible to mitigate the security risks and also have increased engagement. (Bottyan, 2023) assessed the awareness lev- els among Dunaújváros university students in Hungary, pertaining to cybersecurity using a Personal Cyber Secu- rity Provision Scale questionnaire. The questionnaire in- volved questions regarding protection of privacy, payment information, avoiding the untrusted links, precaution and no trace of transaction history. The study found that pass- word management and performing sensitive transactions on public computers are some of the issues that need to be taken care, since the students are highly exposed to cy- berattacks. Abeer (Abeer, 2022) made an attempt to identify the most important obstacle faced by the lecturers in handling e-learning modules for the purpose of higher education. In this study, the 95 lecturers working in the Palestine Techni- cal University Kadoorie were chosen through convenient sampling method and the outcomes revealed the following challenges in the order of high to low; technological infra- structure > university-oriented > student-related > curricu- lum-related and finally lecturer-related. The study also es- tablished a moderate positive correlation among lecturer, student and the curriculum-bound challenges. In literature (Abdulla et al., 2023), the authors analyz- ed the risks involved in data attacks, focusing the Univer- sity of Sulaimani, Iraq and how far the students and faculty members are aware of social engineering attacks and cy- ber-security threats. The institution was chosen since the university’s internet users invited security risks, confiden- tiality issues and so on. Using a self-report questionnaire, the data was collected and the outcomes revealed that spear phishing is mostly used by the attackers followed by phish- ing, baiting, pretexting, quid pro quo and piggybacking, while the victims have significant knowledge about piggy- backing. Some of the reasons cited by the participants on not being aware include lack of experience, human error, lack of appropriate training, using same or shared pass- words by multiple persons in the same department, not being aware of the social engineering-based attacks, poor knowledge and so on. Cybersecurity vulnerability behav- iour includes the following concepts in a broader perspec- tive such as human factors (lack of awareness, training, etc.,), risk management (detection and mitigation of risks followed by risk management practices), threat landscape (constant evolution of threats and the increasing security vulnerabilities), compliance (lack of Standard Operating Procedures and non-adherence to security measures, cy- bersecurity audits etc) and technology (using outdated software or hardware, absence of technological infrastruc- ture) (Geogiana Buja et al., 2021; Syed, 2020; Yusif & Ha- feez-Baig, 2023). In literature (Tarrad et al., 2022), the authors consid- ered five independent variables such as information secu- rity, cybereducation, cyber-training, internet applications and creative behavior with a dependent variable named digital awareness to understand the relationship among these variables and its impact on each other. For this study, 140 school academicians from Eastern Iraq were randomly selected and the data was collected. From the study find- ings, digital awareness was found to have a positive effect on the rest of the variables. The results emphasized the importance of digital awareness while it urged the gov- ernment to introduce novel cybersecurity and information security programs within the curriculum itself. In a study conducted among graduate and undergraduate students in Iraq (Zahid et al., 2023), the authors analyzed the impact of demographic features of the individuals upon their aware- ness levels with regards to cybersecurity. For this study, the authors developed a questionnaire and collected 613 responses. Based on the data analysis outcomes, gender has a significant difference while educational level and age had no significant difference on the cybersecurity aware- ness. Alzubaidi (Alzubaidi, 2021) measured the awareness level about cybersecurity among 1,230 Saudi Arabian na- tionals aged above 18 participants, in which the authors as- sessed the level of awareness and the number of incidents, educational background, critical thinking and the absence of e-government portals for dealing cybercrime-related is- sues. The study found that half of the participants used per- sonal information to create their passwords while 32.5% had no idea about phishing attacks while 21.7% were al- ready victims of cybercrimes. 88 Organizacija, V olume 58 Issue 1, February 2025 Research Papers 3 E-learning obstacles E-learning has gained wide popularity in recent years, especially after the outbreak of the COVID-19 pandemic. Due to the outbreak, many educational institutions were forced to switch to online learning mode so as to ensure safe distance and avoid public gatherings. E-learning plat- forms are a flexible, cost-effective, and scalable way to deliver education. However, e-learning model has its own challenges for all the stakeholders involved in the institu- tion such as the decision makers in the institution, technical staff, faculty members, students and their parents (Aboru- jilah et al., 2022). . The current section details about the studies conducted earlier that defined the issues faced by learners towards e-learning and the ways to address it. In literature (Almaiah et al., 2020), the authors analyzed the critical challenges faced by 30 students, 25 faculty mem- bers and 4 e-learning experts at six universities, located in Jordan and Saudi Arabia about the primary factors that support and hinder the adoption of e-learning system. The authors identified the factors that influence the adoption of e-learning and segregated them under various aspects such as trust, system quality, cultural aspects, self-effica- cy and interest issues. On the other hand, the challenges found were financial issues, change management conflicts and the lack of technical infrastructure. Various authors (Barakat et al., 2022; Muhammad, 2022; Pandian, 2023) have summarized the obstacles found in e-learning such as technical issues (low process- ing capability, faulty or absence of power, hardware and bug issues, compatability etc.,), educational issues (differ- ent pedagogical approach, resistance to change from con- ventional classroom teaching, lack of engagement between the learner and the teacher etc.,), social issues (absence of interaction, less or no motivation, insufficient social skills etc., ), motivational issues and time-management issues. In the systematic review conducted earlier (Mohamed & Kim, 2023), the authors found technology barriers, en- gagement issues, learning interest among the learners and anxiety to perform are the challenges faced by learners in e-learning programs, enrolled in the educational insti- tutions in Middle east. In the qualitative study conducted among 10 female undergraduate students enrolled in Saudi Public universities (Abed et al., 2022), the authors ana- lyzed how far the learners are motivated and have belief towards online education and the barriers faced by them in terms of societal and religious bases. As per the study find- ings, the sudden change that occurred during COVID-19 had a heavy impact upon their learning. On the other hand, personal challenges too reduced the student’s willingness towards online education. In a study conducted at Salahaddin University, Iraq, the authors (Ameen et al., 2017) determined the challenges encountered in e-learning and their perceptions about the impact caused by e-learning system in Iraqi higher educa- tion. For this study, 300 responses were collected from the students studying in the university through convenience sampling. The findings confirm the following challenges in Iraqi higher educational institutions regarding online learning; inability to get certified, lack of electricity, bad internet connection, absence of a supportive culture and absence of knowledge about the system. Based on the re- view of literature, it can be understood that there is a lack of studies pertaining to cybersecurity vulnerability behav- iour and e-learning obstacles while no study has been con- ducted at the University of Karbala in this background. In order to fulfil this research gap, the current study aims at understanding the relationship between students’ cyberse- curity vulnerability behavior and e-learning obstacles. 4 Development of the hypotheses The current section deals with the development of the hypotheses. Cybersecurity vulnerability behavior refers to actions or inactions that increase an individual’s risk of ex- periencing a cyberattack while such actions include using weak passwords or failing to update software. Learners who use e-learning platforms for engaging in digital learn- ing programs are highly prone to experience cyberattacks. These issues, in turn, can hinder their successful e-learn- ing experience. Additionally, the learners who possess cy- bersecurity vulnerability behavior are less likely to trust e-learning platforms or feel confident about themselves on using such digital platforms in a safe and effective manner. This lack of confidence and trust can result in motivational issues and hinder their ability to engage with the platform. On this basis, the first main hypothesis has been developed for the study (Abumandour, 2022; Maatuk et al., 2022). First Hypotheses (H1): There is a significant relation- ship between cybersecurity vulnerability behavior and the obstacles to e-learning. Based on this main hypothesis, seven sub hypotheses have been framed as briefed herewith. Access to data and information is essential for effective learning in e-learning environments. In the absence of adequate access to data and information, the learning experience becomes incom- plete while it also hinders the students from achieving their educational goals. For example, it was found that students perceived access to online resources positively affected their motivation and participation in e-learning (Yeh & Tsai, 2022). Similarly, it was found that insufficient ac- cess to data and information was a significant barrier to effective e-learning in healthcare education (Al Shamari, 2022). Moreover, it was found that the lack of access to appropriate resources was one of the major obstacles to the successful adoption of e-learning in the workplace (Abdel- fattah et al., 2023). In this background, the first sub-hy- pothesis has been framed as follows. The first sub-hypothesis (H1.1): There is a significant relationship between the Behavior of Data and Informa- 89 Organizacija, V olume 58 Issue 1, February 2025 Research Papers tion Access and e-learning obstacles. Access to reliable devices and internet/network con- nectivity is critical to effective e-learning. Improper de- vice and internet/network use can lead to technical issues, power outages, and limited access to online resources, all of which can affect the student’s participation and perfor- mance. Therefore, it can be hypothesized that insufficient use of devices and internet/networks may exacerbate these e-learning obstacles, resulting in lower student engage- ment and performance (M. Khan et al., 2020). It was found that technical issues with hardware and internet/network connectivity were the most significant barriers to effective e-learning during the COVID-19 pandemic (Abeer, 2022). Similarly, it was found that insufficient use of devices and the internet/network were important factors influencing the adoption of e-learning among Jordan and Saudi Ara- bian university students (Almaiah et al., 2020). Moreover, it found that students with reliable devices and internet/ network connections were likelier to engage in e-learning activities. In this background, the second sub-hypothesis has been framed as follows. The Second sub-hypothesis (H1.2): There is a signif- icant relationship between the behavior of devices and in- ternet / network usage and e-learning obstacles. Using social media can be a distraction for e-learn- ers and can reduce their focus and concentration. Social media addiction can lead to procrastination, poor time management, and lower productivity, affecting student engagement and performance (Vishal Verma & Janardan Pawar, 2024). Therefore, it can be hypothesized that ex- cessive use of social media may exacerbate the obstacles of e-learning, leading to lower student engagement and performance. Several studies have identified the relation- ship between social media use and e-learning barriers (Ab- dulhassan Abbas & Hurajah Al Hasnawia, 2020; Sefriani et al., 2023). For example, excessive use of Facebook is associated with lower academic performance among col- lege students (N. T. Khan & Ahmed, 2018).So, was it found that social media addiction was negatively related to academic performance and time management among undergraduate students. Moreover, it was found that the use of social media was a significant predictor of procras- tination among college students (Sobaih et al., 2022). In this background, the third hypothesis has been framed as given below. The third sub-hypothesis (H1.3): There is a signifi- cant relationship between the behavior of social media and e-learning obstacles. Password security is an important aspect of eLearn- ing security. Weak or easy-to-guess passwords can result in unauthorized access while the hacked passwords can create a chaos in the e-learning environment, reducing participation (Abeer, 2022; Salman & Shahadab, 2022). Therefore, it can be hypothesized that the behavior of using weak or easy-to-guess passwords may exacerbate e-learning obstacles, leading to lower student engagement and performance Several studies have identified the rela- tionship between password security and e-learning obsta- cles (Darawsheh et al., 2023; K. Elberkawi et al., 2022; Klaib et al., 2022). For example, a study found that weak passwords were among the most common causes of se- curity breaches in e-learning environments (Khlifi, 2020). Further, it was found that technical issues and accessibility issues were the major obstacles that hinder the adoption of e-learning among adult learners. In this background, the fourth sub-hypothesis has been framed as follows. The fourth sub-hypothesis (H1.4): There is a signifi- cant relationship between the Behavior of Using Password and e-learning obstacles. As mentioned earlier, smartphone addiction can lead to procrastination, poor time management, and lower pro- ductivity, affecting student engagement and performance (Peng, 2023; C. Zhang et al., 2022). Several studies have identified the relationship between smartphone use and e-learning obstacles. For example, higher smartphone use levels were associated with lower academic performance among college students. Similarly, it was found that us- ing smartphones for non-academic purposes during class was negatively associated with a student’s GPA. Moreo- ver, smartphone addiction was negatively related to aca- demic performance among university students (J. Zhang & Zeng, 2024; Zou et al., 2022). In this background, it can be hypothesized that the excessive use of smartphones may exacerbate these e-learning obstacles, leading to low- er student engagement and performance (Sunday et al., 2021). So, the fifth sub-hypothesis has been framed as given below. The Fifth sub-hypothesis (H1.5): There is a signifi- cant relationship between the behavior of using smart- phone devices and e-learning obstacles. As mentioned earlier, weak technical infrastructure represented by outdated devices, power outage, lack of ac- cess of high-speed internet, networking issues, lack of per- manent maintenance, the lack of modern computers, and the lack of original programs remain the most important physical and electronic obstacles towards the widespread adoption of online education. The cybersecurity vulner- ability behavior affects this dimension directly based on which the following hypothesis has been developed. The Six sub-Hypotheses (H1.6): There is a significant relationship between cybersecurity vulnerability behavior and the electronic and physical obstacles In addition to the lack of technical infrastrcutrue, the organizational obstacles such as the lack of financial support, lack of support from the management, lack of equipped and modern scientific laboratories, and the weak- ness of training programs and so on. These issues tend to have an impact upon the cybersecurity vulnerability be- havior based on which the seventh hypothesis has been framed below. 90 Organizacija, V olume 58 Issue 1, February 2025 Research Papers The Seven sub-Hypotheses (H1.7): There is a signif- icant relationship between cybersecurity vulnerability be- havior and financial and organizational obstacles The lack of seasoned professionals in the organization, inexperienced faculty members, lack of basic computer education for the students, conventional teaching methods in the field of the internet and computers, the lack of spe- cialized people to maintain devices and update programs, and the absence of clear mechanisms in the employment and application of e-learning heavily affect the Cyberse- curity Vulnerability Behavior based on which the eight sub-hypothesis has been framed as given below. The Seven sub-Hypotheses (H1.8): There is a signifi- cant relationship between cybersecurity vulnerability be- havior and the professional and human obstacles 5 Methods In order to achieve the objective, a semi-structured research questionnaire was developed and the number of questions pertaining to each and every dimension of the study are quoted in table 1. Table (1) shows the dimensions of both dependent variable (E-learning obstcles ELO) and the independent variable (Cybersecurity vulnerability be- havioru – CVB) used in the current study. The respective number of questions, for the dimensions, used in the ques- tionnaire along with the source articles are shown in the table. The questionnaire developed was converted into a google form so that the responses can be easily collect- ed and used for analysis. For this study, random sampling method was followed to choose the potential respondents from a pool of students enrolled at the Faculty of Admin- istration and Economics, Department of Accounting, Uni- versity of Karbala, Iraq. The potential respondents i.e., stu- dents were given this questionnaire to respond during the study period 08th May and 19th May 2023. The respond- ents were given time and informed consent was obtained from the study participants. Out of the total 968 students, 450 students were approached to participate in the study. Based on the responses received and upon validation, 350 valid responses were considered for final analysis. Out of the final responses, 167 (47.71%) were male students and 183 (52.28%) were female students aged between 19 and 25 years. Before completing the design of the study, the researchers conducted interviews among a sample of stu- dents in this department, and majority of the respondents reported that they actually encounter numerous obstacles in the field of cybersecurity and also in e-learning, which had an impact on their performance and increased the vulnerability of their accounts to hacking. This study em- ployed the Structural Equation Modeling (SEM) approach with Partial Least Squares as an analytical tool (PLS). PLS studies psychometric traits and provides evidence for the existence or absence of associations (Bagozzi, 1981). SmartPLS 3.2.9 and SPSS 28 were used to analyze the data in this investigation in two phases. The first step measure- ment model validated the structures’ content, convergent, and discriminant validity. In the second step, the structural model and hypotheses were tested. Common Method Bias (CMB) was detected through Harman’s single-factor test; the percentage of the factor’s explained variance for the common factor (10.8%) was below the threshold of 50%, indicating the absence of this problem (MacKenzie & Pod- sakoff, 2012). Table 1: Variable, dimensions and the number of questions pertaining to the dimension in the questionnaire Variable Dimensions number of questions Type Source Cybersecurity Vul- nerability Behavior (CVB) The behavior of Data and Information Access (BDIA) 5 independent (Wijayanto & Prabowo, 2020) The behavior of Device and Internet / Network Us- age (BDIU) 4 The behavior of social media (BSM) 3 The behavior of Using Password (BUP) 5 The behavior of Using Smartphone Devices (BUSD) 4 E-Learning Obsta- cles (ELO) Electronic and physical obstacles (EPO) 4 Dependent (Abeer, 2022; A. F. Ibrahim et al., 2021) Financial and organizational obstacles (FOO) 6 Professional and human obstacles (PHO) 5 91 Organizacija, V olume 58 Issue 1, February 2025 Research Papers Itmem BUP BDIA BDIU BSM BUSD EPO PHO FOO BUP1 0.401 BUP2 0.61 BUP3 0.47 BUP4 0.467 BUP5 0.533 BDIA1 0.57 BDIA2 0.456 BDIA3 0.486 BDIA4 0.663 BDIA5 0.528 BDIU1 0.49 BDIU2 0.716 BDIU3 0.611 BDIU4 0.541 BSM1 0.587 BSM2 0.585 BSM3 0.703 BUSD1 0.533 BUSD2 0.416 BUSD3 0.655 BUSD4 0.595 EPO1 0.552 EPO2 0.658 EPO3 0.595 EPO4 0.512 PHO1 0.461 PHO2 0.652 PHO3 0.501 PHO4 0.534 PHO5 0.66 PHO6 0.649 FOO1 0.524 FOO2 0.424 FOO3 0.464 FOO4 0.7 FOO5 0.504 CR 0.622 0.675 0.683 0.659 0.637 0.67 0.751 0.656 Table 2: Measurement model assessment 92 Organizacija, V olume 58 Issue 1, February 2025 Research Papers 6 Results The current study details about the measurement model for the reflective and latent variables. Further, factor load- ings, composite reliability and discriminant validity were also utilized in this study. Further, discriminant validity is assessed through Fornell–Larcker criterion and HTMT ra- tio. In addition to this, Pearson correlation analysis was conducted after which the structure model was assessed. Finally, the hypothesis testing was conducted and the re- sults are discussed in this section along with discussion. 6.1 Measurement Model To establish the validity of the model’s constructs, the measurement model was evaluated for reflective and latent variables (see Figure 1). Factor loadings, composite relia- bility (CR), and discriminant validity were used to assess construct validity (Hair et al., 2014). Hair et al. recom- mended dropping indicators with loading below 0.40 to allow forester composite reliability (CR) (Leguina, 2015) Click or tap here to enter text.. No indicators were dropped from the model, as shown in Table (2) and Figure (1). The values of composite reliability should be greater than 0.6 (Bagozzi, 1981). These indicate that the study satisfied these requirements for convergent validity and internal consistency of the scales. Further, discriminant validity is assessed through For- nell–Larcker criterion and HTMT ratio. Fornell–Larcker criterion required that each composite A VE square root on the diagonal element be greater than the correlations be- tween the constructs (Leguina, 2015). The HTMT approach is ‘the ratio of the between-trait correlations to the within-traits correlations’. The HTMT values should be lower than 1 (Gaskin et al., 2018). The discriminant validity is established following the previous guides of the Fornell-Larcker criterion and HTMT values in tables 3 and 4. Figure 1: Measurement model assessment 93 Organizacija, V olume 58 Issue 1, February 2025 Research Papers Figure 2: Structural model assessment Table 3: Discriminant validity (Fornell-Larcker criterion) BDIA BDIU BSM BUP BUSD EPO FOO PHO BDIA 0.545 BDIU 0.351 0.596 BSM 0.123 0.181 0.627 BUP 0.214 0.213 0.204 0.501 BUSD 0.077 0.28 -0.02 0.069 0.557 EPO -0.219 -0.352 -0.181 0.069 -0.193 0.582 FOO -0.117 -0.273 -0.249 0.038 -0.14 0.19 0.532 PHO -0.388 -0.469 -0.262 -0.123 -0.159 0.328 0.321 0.582 94 Organizacija, V olume 58 Issue 1, February 2025 Research Papers Table 4: Discriminant validity (HTMT ratio) BDIA BDIU BSM BUP BUSD EPO FOO PHO BDIA BDIU 0.93 BSM 0.593 0.713 BUP 0.733 0.712 0.835 BUSD 0.618 0.767 0.578 0.631 EPO 0.685 0.975 0.648 0.643 0.683 FOO 0.629 0.788 0.98 0.575 0.8 0.656 PHO 0.72 0.957 0.824 0.494 0.529 0.713 0.673 Table 5: Descriptive statistics and multiple correlations BUP BDIA BDIU BSM BUSD EPO PHO FOO CVB ELO BUP r -- BDIA r .22 *** -- P <.001 BDIU r .19 *** .32 *** -- P <.001 <.001 BSM r .20 *** .13 * .18 *** -- P <.001 0.016 0.001 BUSD r 0.09 0.06 .26 *** -0.03 -- P 0.097 0.299 <.001 0.563 EPO r 0.06 -.20 *** -.35 *** -.19 *** -.17 *** -- P 0.242 <.001 <.001 <.001 0.001 PHO r -.12 * -.34 *** -.46 *** -.27 *** -.16 ** .32 *** -- P 0.030 <.001 <.001 <.001 0.003 <.001 FOO r 0.02 -0.06 -.23 *** -.26 *** -0.10 .18 *** .31 *** -- P 0.751 0.252 <.001 <.001 0.057 0.001 <.001 CVB r .57 *** .58 *** .71 *** .51 *** .49 *** -.31 *** -.48 *** -.23 *** -- P <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 ELO r -0.02 -.29 *** -.4 *** -.33 *** -.20 *** .73 *** .76 *** .66 *** -.48 *** -- P 0.759 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 M 3.97 3.82 4.01 3.66 3.23 3.87 3.97 3.96 3.74 3.93 SD 0.43 0.42 0.51 0.46 0.47 0.52 0.49 0.44 0.26 0.35 Skewness -0.16 0.33 -0.17 0.31 0.41 0.19 -0.01 -0.08 -0.43 0.24 Kurtosis -0.65 -0.06 -0.62 -0.35 -0.10 -0.68 -0.97 -0.62 -0.11 -0.69 r= correlation coefficient; P= P-value; M=mean; SD=standard deviation. 95 Organizacija, V olume 58 Issue 1, February 2025 Research Papers Figure 3: Visualization of the correlation matrix 6.2 Descriptive Statistics and Multiple Correlations After establishing the reliability and validity of the variables, descriptive statistics and multiple correlations were conducted between the selected constructs includ- ing the mean (M) and standard deviation (SD) as shown Table (5). The descriptive statistics for the independ- ent variable “Cybersecurity Vulnerability Behaviour” was (M=3.74,SD=0.26), and for the dependent variable, “E-Learning Obstacles” was (M=3.93,SD=0.35). Among the dimensions of the independent variable “Cybersecurity Vulnerability Behaviour”, it was found that the “BDIU” had the highest mean (M=4.01,SD=0.51) and “BUSD” had the lowest mean (M=3.23,SD=0.47). Among the dimensions of the dependent variable “E-Learning Obstacles”, it was found that the “PHO” had the highest mean (M=3.97,SD=0.49) and “EPO” had the lowest mean (M=3.87,SD=0.52). The values for Skewness between -2 to +2 and kurtosis between -7 and +7 are generally consid- ered to be acceptable to prove normal distribution (Byrne, 2016; Hair et al., 2021) Click or tap here to enter text.. The results of the normality test, shown in Table 5, infer that the values of Skewness and kurtosis for the constructs of the model were within the specified range. Pearson product-moment correlation coefficient is cal- culated to determine the strength and the direction of the relationship between the selected constructs. Correlation coefficients marked with three stars (***) are significant at 0.001, i.e., 99.9% confidence level; correlation coefficients marked with two stars (**) are significant at 0.01, i.e., 99% confidence level, coefficients marked with one star (*) are significant at 0.05, i.e., 95% confidence level, and finally, coefficients NOT marked are not significant at 0.05, i.e., P-values are greater than 0.05. Table 5 shows the matrix of Pearson correlation coefficients among all the constructs and the dimensions. A negative relationship was found be- tween the independent variable (and its dimensions) and the dependent variable (and its dimensions). However, a significant negative relationship was found between Cy- 96 Organizacija, V olume 58 Issue 1, February 2025 Research Papers bersecurity Vulnerability Behaviour and E-Learning Ob- stacles since (r(350)=-.48,P<0.001). 6.3 Assessing the Structural Model Examining the structural model includes path coeffi- cients, collinearity diagnostics, coefficient of determina- tion (R2), effect size (f²), predictive relevance (Q2), and global goodness of fit criteria. Before analyzing the struc- tural model, the collinearity among the constructs was ex- amined (table 7) using Variance Inflation Factors (VIF), and found that all the values were less than the threshold of 5 (Leguina, 2015). The results of hypothesis testing in Table 6 and Fig- ure 2 showed that Cybersecurity Vulnerability Behavior yielded a significant negative effect on E-Learning Ob- stacles since (β=-0.555,t=11.943,P<0.001,95% CI for β=[-0.632,-0.453]), consequently, the first hypothesis is confirmed. Additionally, in Table 5 and Figure 4, the di- mensions of Cybersecurity Vulnerability Behavior yielded a significant negative effect on E-Learning Obstacles as follows: Behavior of Data and Information Access (β=- 0.214,P<0.001), Behavior of Device and Internet/Network Usage (β=-0.412,P<0.001), Behavior of Social Media (β=-0.258,P<0.001), and Behavior of Using Smartphone Devices (β=-0.147,P=0.001). While Behavior of Using Passwords does not influence E-Learning Obstacles since (β=0.048,P>0.05). Figure 4: Effect of Cybersecurity Vulnerability Dimensions on E-Learning Obstacles 97 Organizacija, V olume 58 Issue 1, February 2025 Research Papers Table 6: Results of Hypothesis Testing Path B t-value P-value 95% Bias-Corrected CI Remark LB UB H1: Cybersecurity Vulnerability Behavior -> E-Learning Obstacles -0.555 11.943 <.001 -0.632 -0.453 Supported H1.1: Behavior of Data and Information Access -> E-Learning Obstacles -0.214 4.506 <.001 -0.294 -0.118 Supported H1.2: Behavior of Device and Internet / Net- work Usage -> E-Learning Obstacles -0.412 8.648 <.001 -0.506 -0.32 Supported H1.3: Behavior of Social Media -> E-Learning Obstacles -0.258 6.396 <.001 -0.337 -0.18 Supported H1.4: Behavior of Using Password -> E-Learn- ing Obstacles 0.048 0.623 0.533 -0.073 0.232 Not Support- ed H1.5: Behavior of Using Smartphone Devices -> E-Learning Obstacles -0.147 3.476 0.001 -0.224 -0.058 Supported H1.6: Cybersecurity Vulnerability Behavior -> Electronic and physical obstacles -0.421 9.128 <.001 -0.492 -0.312 Supported H1.7: Cybersecurity Vulnerability Behavior -> Financial and organizational obstacles -0.395 8.658 <.001 -0.461 -0.279 Supported H1.8: Cybersecurity Vulnerability Behavior -> Professional and human obstacles -0.581 20.03 <.001 -0.622 -0.518 Supported CI=Confidence Interval; LB=Lower Bound; UB=Upper Bound. Furthermore, in Table 5 and Figure 5, the Cyberse- curity Vulnerability Behavior construct yielded a sig- nificant negative effect on E-Learning Obstacles di- mensions as follows: Electronic and physical obstacles (β=-0.421,P<0.001), Financial and organizational obsta- cles (β=-0.395,P<0.001), and Professional and human ob- stacles (β=-0.581,P<0.001). The results in Table 7 indicate that about 31% of the variation in E-Learning Obstacles is explained by the variation in Cybersecurity Vulnerability Behavior with a high Cohen’s effect size (f2 =0.444). The effect sizes of the other hypotheses were reported and ordered in Fig- ure 6. Then, the predictive relevance was determined by assessing the Stone-Geisser’s Q2 Blindfolding, a sample reuse technique that can be used to calculate Q2 values for latent variables. The blindfolding procedure was followed and the Q2 values were calculated for the E-Learning Ob- stacles (Q2 =0.049). All the values were higher than zero, thus indicating a predictive relevance for endogenous la- tent variables in the current study’ PLS path model (Legui- na, 2015; Wetzels et al., 2009). The Goodness of Fit (GoF) was introduced by (Tenenhaus et al., 2005) as a global fit metric (Wetzels et al., 2009). The GoF criterion for determining if GoF values are too little, too moderate, or too high to be considered a globally adequate PLS model. The GOF value (0.314) was greater than 0.25, indicating moderate fit, so it can be safely con- cluded that the GoF model is good enough to be considered a sufficiently valid global PLS model. All hypotheses were verified and the outcomes confirm that there is an effective relationship between cybersecurity vulnerability behavior and e-learning obstacles. Also, there are influencing rela- tionships and varying proportions among the dimensions for each of the two variables. Figure (6) explains them in detail that are arranged according to their importance. All hypotheses were fulfilled in varying proportions, even if they were few, except for the sub-hypothesis 1.4, whose percentage was very low and was not supported. 7 Discussion The current study outcomes confirmed that the profes- sional and human obstacles have a high association with cybersecurity vulnerability behavior. This might be due to the students’ poor experience in using modern technolo- gies, and most students have no proficiency in English lan- guage and remain unfamiliar with the scientific terminol- ogy. Further, the conventional training programs too add fuel to the fire. In most of the cyberattacks, the victims are 98 Organizacija, V olume 58 Issue 1, February 2025 Research Papers either duped by a malicious portrayal or it occurs as a result of lack of cybersecurity knowledge. This finding alarms the educational institutions to develop a sense of belong- ing and awareness among the students about cybersecurity issues because it not only affects the students’ themselves, but also the entire e-learning portal users, technical infra- structure developed by the university/educational institu- tion and so on. With increasing instances of human rights violations on the internet and telecommunication modes, it is important to develop and nurture a healthy ecosystem for the online learning education system, which is possible only through the establishment of a strong, vibrant and se- cure cyber-communication environment (AbdulAmeer et al., 2022). The study also found that the overall obstacles have a significant effect on the cybersecurity vulnerability behav- ior. This finding is in line with the literature pertaining to Iraqi and other MENA countries’ educational institutions since in the aftermath of COVID-19, most educational in- stitutions started preferring hybrid mode of education due to health advisories, increasing cost of infrastructure and so on. (Hameed, 2023) listed various obstacles towards the widespread adoption of e-learning in Iraq in terms of edu- cational institutions, student learners, faculty members and so on. According to the authors, students feel isolated and becomes introvert through e-learning mode of education while they lack sufficient socialization skills, lack face- to-face interaction with faculty members and are afraid of facing the real-world scenarios. Cyberattacks pose a significant risk while the students also face difficulties in meeting the technical infrastructure requirements. The study findings emphasized the importance of using advanced devices, high-speed internet connectivity, access to uninterrupted power and the absence of cyberattacks. Because, these factors tend to affect the mindset of the students. It is important for the student to gain motivation for attention, to gain confidence on the learning outcomes, satisfied over the learning objectives and stay relevant to the job market (Yahiaoui et al., 2022). The current study findings confirm that using smart devices may come as an obstacle towards e-learning while the social media behav- ior also have an impact on digital learning outcomes. On the contrary, the quantitative study conducted among 185 Iraqi students and lecturers (Al-Malah et al., 2021), the Figure 5: Effect of Cybersecurity Vulnerability Behavior on E-Learning Obstacles Dimensions 99 Organizacija, V olume 58 Issue 1, February 2025 Research Papers Path R-Square R-Square Adjusted Q Square F-Square VIF Cut -off > 0.1 > 0 > 0.02 <5 Cybersecurity Vulnerability Behavior -> E-Learning Obstacles 0.308 0.306 0.049 0.444 1 Behavior of Data and Information Access -> E-Learning Obstacles 0.445 0.437 0.067 0.072 1.153 Behavior of Device and Internet / Network Usage -> E-Learn- ing Obstacles 0.238 1.286 Behavior of Social Media -> E-Learning Obstacles 0.114 1.051 Behavior of Using Password -> E-Learning Obstacles 0.004 1.046 Behavior of Using Smartphone Devices -> E-Learning Obsta- cles 0.036 1.089 Cybersecurity Vulnerability Behavior -> Electronic and physi- cal obstacles 0.177 0.175 0.053 0.215 1 Cybersecurity Vulnerability Behavior -> Financial and organi- zational obstacles 0.156 0.154 0.028 0.185 1 Cybersecurity Vulnerability Behavior -> Professional and human obstacles 0.337 0.335 0.102 0.509 1 Table 7: Structural model assessment Cut-off values reference: (Leguina, 2015; Wetzels et al., 2009) Figure 6: Effect sizes arranged in the order of highest to the lowest 100 Organizacija, V olume 58 Issue 1, February 2025 Research Papers authors found that the digital educational activities, when they are provided in the form of social media, can increase the attentiveness among the students and also attracts them towards the education. It also increases the self-learning motivation. Though the findings may contradict, given the circumstances, environment where the study was conduct- ed, sample population and the possible bias, it can be con- sidered as a suggestion for the future researchers to further explore in this domain. (Mohamed & Kim, 2023) recommended that the ed- ucational institutions must devise their strategies to align with remote learning in a comprehensive and a holistic manner. As per the current study findings, all the hypoth- eses have been supported (except one) based on which the authors recommend the educational institutions and the governments to focus on developing the technical in- frastructure, update the curricula as per the international standards, original applications for the computers, high- speed internet connectivity, conduct awareness programs among the students about cybersecurity issues and the ways to overcome the challenges, refresher training work- shops for the faculty members and develop strategies to meet the digital learning requirements in line with the in- ternational standards. 8 Conclusion The current study is a first-of-its-kind attempt to deter- mine the relationship between cybersecurity vulnerability behavior and the obstacles present in e-learning while the study found that the professional and human obstacles had a heavy impact on the cybersecurity vulnerability behav- iour. The findings help the educational institutions, policy makers in the governments, cybersecurity experts in the country, students, their parents, faculty members and all the stakeholders involved in imparting digital mode of ed- ucation to the students in higher educational institutions. There is an urgent need for deploying highly qualified technical staff who are specialized in English language as well as computer proficient. Advanced human-orient- ed and social engineering strategies must be framed by the educational institutions to overcome the obstacles of e-learning. Future researchers must explore the challenges faced by other university students in Iraq so that a collec- tive initiative can be taken by the educational institutions to bring a digital reform in the country. Literature Abbas, A. A. (2020). Educational Competition as a Mod- erating variable of the relationship between electron- ic management and intelligent organizations. Revista Tempos e Espaços Em Educação, 13(32), 25. https:// doi.org/10.20952/revtee.v13i32.13173 Abdelfattah, F., Al Alawi, A. M., Dahleez, K. A., & El Sa- leh, A. (2023). Reviewing the critical challenges that influence the adoption of the e-learning system in high- er educational institutions in the era of the COVID-19 pandemic. Online Information Review, 47(7), 1225– 1247. https://doi.org/10.1108/OIR-02-2022-0085 AbdulAmeer, S. A., Saleh, W. R., Hussam, R., Al-Hareeri, H., Alghazali, T., S. Mezaal, Y ., & Saeed, I. N. (2022). Cyber Security Readiness in Iraq: Role of the Human Rights Activists. International Journal of Cyber Crim- inology, 16(2), 1–14 Abdulhassan Abbas, A., & Hurajah Al Hasnawia, H. (2020). Role of Psychological Contract Breach and Violation in Generating Emotional Exhaustion: The Mediating Role of Job Procrastination. Cuadernos de Gestión. https://doi.org/ https://doi.org/10.5295/ cdg.181021aa Abdulla, R., Faraj, H., Abdullah, C., Amin, A., & Rashid, T. (2023). Analysis of Social Engineering Awareness Among Students and Lecturers. IEEE Access, PP, 1. https://doi.org/10.1109/ACCESS.2023.3311708 Abed, M. G., Abdulbaqi, R. F., & Shackelford, T. K. (2022). Saudi Arabian Students’ Beliefs about and Barriers to Online Education during the COVID-19 Pandemic. Children (Basel, Switzerland), 9(8). https:// doi.org/10.3390/children9081170 Abeer, Q. (2022). Obstacles To Effective Use Of E-Learn- ing In Higher Education From The Viewpoint Of Fac- ulty Members. Turkish Online Journal of Distance Education-TOJDE, 23(1), 144–177. https://files.eric. ed.gov/fulltext/EJ1329784.pdf Aborujilah, H. A., Al-Alawi, E., Al-Hidabi, D., & Al-Oth- mani, A. (2022). Exploring Critical Challenges and Factors Influencing E-Learning Systems Security During COVID-19 Pandemic. https://doi.org/10.1109/ ITSS-IoE56359.2022.9990935 Abumandour, E.-S. T. (2022). Applying e-learning system for engineering education–challenges and obstacles. Journal of Research in Innovative Teaching & Learn- ing, 15(2), 150–169. Al-Janabi, S., & Al-Shourbaji, I. (2016). A Study of Cy- ber Security Awareness in Educational Environment in the Middle East. Journal of Information & Knowl- edge Management, 15(01), 1650007. https://doi. org/10.1142/S0219649216500076 Al-kaaf, H. A. (2023). Machine Learning Approaches for Kids’ E-learning Monitoring BT - Kids Cybersecurity Using Computational Intelligence Techniques (W. M. S. Yafooz, H. Al-Aqrabi, A. Al-Dhaqm, & A. Emara (eds.); pp. 25–36). Springer International Publishing. https://doi.org/10.1007/978-3-031-21199-7_2 Al-Malah, D., Abbas, A., Majeed, B., & Alrikabi, H. (2021). The Influence E-Learning Platforms of Un- dergraduate Education in Iraq. International Journal 101 Organizacija, V olume 58 Issue 1, February 2025 Research Papers of Recent Contributions from Engineering Science & IT (IJES), 9, 90–99. https://doi.org/10.3991/ijes. v9i4.26995 Al-Sherideh, A. S., Maabreh, K., Maabreh, M., Mousa, M. R. Al, & Asassfeh, M. (2023). Assessing the Im- pact and Effectiveness of Cybersecurity Measures in e-Learning on Students and Educators: A Case Study. International Journal of Advanced Computer Science and Applications, 14(5). https://doi.org/10.14569/ IJACSA.2023.0140516 Al-Tae, A. K. J., Al-Dhalimi, H. A.-H., & Al- Shaibani, A. K. (2022). Relationship of Cybersecurity and the Na- tional Securityofthe Country: Iraq Case Study. Sys Rev Pharm, 11(12), 469–476. https://www.sysrevpharm. org/articles/relationship-of-cybersecurity-and-the-na- tional-security-of-the-country-iraq-case-study.pdf Al Shabibi, A. M., & Al-Suqri, M. N. (2023). Cybersecu- rity Awareness Among Students During the COVID-19 Digital Transformation of Education: A Case Study at the Muscat (Oman) Schools BT - Future Trends in Ed- ucation Post COVID-19 (H. M. K. Al Naimiy, M. Bet- tayeb, H. M. Elmehdi, & I. Shehadi (eds.); pp. 39–51). Springer Nature Singapore. Al Shamari, D. (2022). Challenges and barriers to e-learn- ing experienced by trainers and training coordinators in the Ministry of Health in Saudi Arabia during the COVID-19 crisis. PloS One, 17(10), e0274816. https:// doi.org/10.1371/journal.pone.0274816 Alhamdawee, N. (2023). online learning in Iraq challang- es and opportunities. 4, 1...7. Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and fac- tors influencing the E-learning system usage during COVID-19 pandemic. Education and Informa- tion Technologies, 25(6), 5261–5280. https://doi. org/10.1007/s10639-020-10219-y Alzubaidi, A. (2021). Measuring the level of cyber-securi- ty awareness for cybercrime in Saudi Arabia. Heliyon, 7(1), e06016. https://doi.org/https://doi.org/10.1016/j. heliyon.2021.e06016 Ameen, N., Willis, R., & Abdullah, M. (2017). The use of e-learning by students in Iraqi universities:Potential and challenges. https://doi.org/10.23918/vesal2017. a27 Arul, E., & Punidha, A. (2022). Artificial Intelligence to Protect Cyber Security Attack on Cloud E-Learning Tools (AIPCE). International Conference on Comput- ing, Communication, Electrical and Biomedical Sys- tems, 29–37. Barakat, M., Farha, R. A., Muflih, S., Al-Tammemi, A. B., Othman, B., Allozi, Y ., & Fino, L. (2022). The era of E-learning from the perspectives of Jordanian medi- cal students: A cross-sectional study. Heliyon, 8(7), e09928. https://doi.org/https://doi.org/10.1016/j.he- liyon.2022.e09928 Bottyan, L. (2023). Cybersecurity awareness among uni- versity students. Journal of Applied Technical and Educational Sciences, 13(3 SE-Articles and Studies), ArtNo: 363. https://doi.org/10.24368/jates363 Byrne, B. (2016). Structural equation modeling with AMOS. Routledge. Darawsheh, S. R., Alshurideh, M., Al-Shaar, A. S., Bar- som, R. M. M., Elsayed, A. M., & Ghanem, R. A. A. A. (2023). Obstacles to Applying the E-Learning Man- agement System (Blackboard) Among Saudi University Students (In the College of Applied Sciences and the College of Sciences and Human Studies) BT - The Effect of Information Technology on Business and Marketing Intelligence Systems (M. Alshurideh, B. H. Al Kurdi, R. Masa’deh, H. M. Alzoubi, & S. Salloum (eds.); pp. 389–414). Springer International Publish- ing. https://doi.org/10.1007/978-3-031-12382-5_21 Ewoh, P., & Vartiainen, T. (2024). Vulnerability to Cyber- attacks and Sociotechnical Solutions for Health Care Systems: Systematic Review. J Med Internet Res, 26, e46904. https://doi.org/10.2196/46904 Faez Hasan, M., & Al-Ramadan, N. S. (2021). Cyber-at- tacks and Cyber Security Readiness: Iraqi Private Banks Case. Social Science and Humanities Journal, 5(8), 2312–2323. Fauzi, M. A. (2022). E-learning in higher education in- stitutions during COVID-19 pandemic: current and future trends through bibliometric analysis. Heliyon, 8(5), e09433. https://doi.org/https://doi.org/10.1016/j. heliyon.2022.e09433 Geogiana Buja, A., Deraman, N. A., Wahid, S. D. M., & Mohd Isa, M. A. (2021). Cyber Security Featuresfor National E-Learning Policy. Turkish Journal of Com- puter and Mathematics Education, 12(5), 1729–1735. https://turcomat.org/index.php/turkbilmat/article/ download/2169/1889/4085 Gourisetti, S. N. G., Mylrea, M., & Patangia, H. (2020). Cybersecurity vulnerability mitigation framework through empirical paradigm: Enhanced prioritized gap analysis. Future Generation Computer Systems, 105, 410–431. https://doi.org/https://doi.org/10.1016/j.fu- ture.2019.12.018 Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate Data Analysis (7th ed.). Pearson. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks. Hameed, L. M. (2023). E-learning in Iraq from Defensive to supportive Strategies: Facts and Obstacles. Proceed- ings of the Iraqi Academics Syndicate 3rd Internation- al Conference on Arts and Humanities Sciences. Ibrahim, A. F., Attia, A. S., Bataineh, A. M., & Ali, H. H. (2021). Evaluation of the online teaching of architec- tural design and basic design courses case study: Col- lege of Architecture at JUST, Jordan. Ain Shams En- 102 Organizacija, V olume 58 Issue 1, February 2025 Research Papers gineering Journal, 12(2), 2345–2353. https://doi.org/ https://doi.org/10.1016/j.asej.2020.10.006 Ibrahim, A. H., Madhush, qadir eabd alhusayn, & Farhan, B. I. (2019). Obstacles to the application of e - learning in the Faculty of Information University of Dhi Qar. Lark Journal for Philosophy, Linguistics and Social Sciences, 2(33), 306–315. K. Elberkawi, E., Maatuk, A., F. Elharish, S., & M. Elta- joury, W. (2022). A Comparative Study of the Chal- lenges and Obstacles Facing E-Learning During the COVID-19 Pandemic from the Perspectives of Uni- versity Instructors and Students. Proceedings of the 2022 Australasian Computer Science Week, 186–192. https://doi.org/10.1145/3511616.3513114 Khadija Hassan, S., & Mustafa Jawad, R. (2022). Inter- nal and External Factors to Adopt a Cyber Securi- ty Strategy in Iraqi Organisations. Webology, 19(1), 5181–5198. https://www.webology.org/data-cms/arti- cles/20220123025726pmWEB19349.pdf Khan, M., Nabi, M. K., Khojah, M., & Tahir, M. (2020). Students’ perception towards e-learning during COVID-19 pandemic in India: An empirical study. Sustainability, 13(1), 57. Khan, N. F., Ikram, N., Saleem, S., & Zafar, S. (2022). Cyber-security and risky behaviors in a developing country context: a Pakistani perspective. In Security Journal (pp. 1–33). https://doi.org/10.1057/s41284- 022-00343-4 Khan, N. T., & Ahmed, S. (2018). Impact of Facebook ad- diction on studentsacademic performance. Research Medical and Engineering Sciences, 5(2), 424–426. Khlifi, Y . (2020). An Advanced Authentication Scheme for E-evaluation Using Students Behaviors Over E-learn- ing Platform. International Journal of Emerging Tech- nologies in Learning (IJET), 15(04 SE-Papers), 90– 111. https://doi.org/10.3991/ijet.v15i04.11571 Klaib, A. A., Talooh, M. A. M., & Arbi, A. (2022). E-Learn- ing, Challenges and Opportunities of Instructors in Libyan Higher Institutes. International Conference on Engineering & MIS (ICEMIS), 1–6. Kumar, A., Pandit, A., & Singh, S. (2022). Reliable Cy- ber Security And Improvement In E-Learning System. Journal of Positive School Psychology, 6(11), 1743– 1752. https://journalppw.com/index.php/jpsp/article/ view/14307/9274 Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). Interna- tional Journal of Research & Method in Education, 38(2), 220–221. https://doi.org/10.1080/174372 7X.2015.1005806 Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S., Rashaid- eh, H., & Alharbi, H. (2022). The COVID-19 pandem- ic and E-learning: challenges and opportunities from the perspective of students and instructors. Journal of Computing in Higher Education, 34(1), 21–38. MacKenzie, S. B., & Podsakoff, P. M. (2012). Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies. Journal of Retailing, 88(4), 542–555. https://doi.org/https://doi.org/10.1016/j.jre- tai.2012.08.001 Mohamed, O., & Kim, J. (2023). Adult E-Learning Is- sues in Middle East Educational Organizations Due to Covid-19 Pandemic: Challenges and Recommenda- tions. Https://Newprairiepress.Org/Aerc. https://new- prairiepress.org/cgi/viewcontent.cgi?article=4318&- context=aerc Mojarad, F. A., Hesamzadeh, A., & Yaghoubi, T. (2023). Exploring challenges and facilitators to E-learning based Education of nursing students during Covid-19 pandemic: a qualitative study. BMC Nursing, 22(1), 278. https://doi.org/10.1186/s12912-023-01430-6 Morrow, E. (2024). Scamming higher ed: An analysis of phishing content and trends. Computers in Human Behavior, 158, 108274. https://doi.org/https://doi. org/10.1016/j.chb.2024.108274 Muhammad, F. J. (2022). E-learning and The Obstacles to its Application From The Point of View of Secondary School Teachers. Basic Education College Magazine For Educational and Humanities Sciences, 14(57). Nagham Oudeh Alhamdawee. (2023). Online Learning In Iraq: Challenges And Opportunities. European Jour- nal of Humanities and Educational Advancements, 4(1 SE-), 1–7. https://scholarzest.com/index.php/ejhea/ar- ticle/view/3089 Pandian, T. (2023). Information And Multimedia Security In An Online Learning Environment. INTED2023 Pro- ceedings, 1166–1171. Peng, B. (2023). Analysis on the Relationships of Smart- phone Addiction, Learning Engagement, Depression, and Anxiety: Evidence from China. Iranian Jour- nal of Public Health, 52(11), 2333–2342. https://doi. org/10.18502/ijph.v52i11.14033 Salman, A. M., & Shahadab, F. H. (2022). Obstacles of Teaching Distance Universities Courses in Light of E-Learning Quality Standards. Cypriot Journal of Ed- ucational Sciences, 17(4), 1244–1257. Sefriani, R., Yunus, Y ., Ambiyar, Syah, N., & Fadhilah. (2023). Correlation of Social Media Addiction to Ac- ademic Achievement in E-Learning. Indonesian Jour- nal of Computer Science, 12. https://doi.org/10.33022/ ijcs.v12i6.3581 Sobaih, A. E. E., Palla, I. A., & Baquee, A. (2022). Social Media Use in E-Learning amid COVID 19 Pandemic: Indian Students’ Perspective. International Journal of Environmental Research and Public Health, 19(9). https://doi.org/10.3390/ijerph19095380 Sunday, O. J., Adesope, O. O., & Maarhuis, P. L. (2021). The effects of smartphone addiction on learning: A me- 103 Organizacija, V olume 58 Issue 1, February 2025 Research Papers ta-analysis. Computers in Human Behavior Reports, 4, 100114. https://doi.org/https://doi.org/10.1016/j. chbr.2021.100114 Syed, R. (2020). Cybersecurity vulnerability manage- ment: A conceptual ontology and cyber intelligence alert system. Information & Management, 57(6), 103334. https://doi.org/https://doi.org/10.1016/j. im.2020.103334 Tarrad, K. M., Al-Hareeri, H., Alghazali, T., Ahmed, M., Al-Maeeni, M. K. A., Kalaf, G. A., E. Alsaddon, R., & S. Mezaal, Y . (2022). Cybercrime Challenges in Iraqi Academia: Creating Digital Awareness for Preventing Cybercrimes. International Journal of Cyber Crimi- nology, 16(2), 15–31. Tenenhaus, M., Vinzi, V . E., Chatelin, Y .-M., & Lauro, C. (2005). PLS path modeling. Computational Statis- tics & Data Analysis, 48(1), 159–205. https://doi.org/ https://doi.org/10.1016/j.csda.2004.03.005 Vishal Verma, & Janardan Pawar. (2024). Assessment Of Students Cybersecurity Awareness And Strategies To Safeguard Against Cyber Threats. Journal of Ad- vanced Zoology, 45(S4 SE-Articles), 82–89. https:// doi.org/10.53555/jaz.v45iS4.4156 Wetzels, M., Odekerken-Schroder, G., & van Oppen, C. (2009). Using PLS Path Modeling for Assessing Hier- archical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly, 33(1), 177–195. https:// aisel.aisnet.org/misq/vol33/iss1/11/ Wijayanto, H., & Prabowo, I. A. (2020). Cybersecuri- ty Vulnerability Behavior Scale in College During the Covid-19 Pandemic. Jurnal Sisfokom (Sistem In- formasi Dan Komputer), 9(3), 395–399. https://doi. org/10.32736/sisfokom.v9i3.1021 Yahiaoui, F., Aichouche, R., Chergui, K., Brika, S. K. M., Almezher, M., Musa, A. A., & Lamari, I. A. (2022). The Impact of e-Learning Systems on Motivating Students and Enhancing Their Outcomes During COVID-19: A Mixed-Method Approach. Frontiers in Psychology, 13. https://www.frontiersin.org/journals/psychology/ articles/10.3389/fpsyg.2022.874181 Yeh, C.-Y ., & Tsai, C.-C. (2022). Massive Distance Educa- tion: Barriers and Challenges in Shifting to a Complete Online Learning Environment. Frontiers in Psycholo- gy, 13. https://www.frontiersin.org/journals/psycholo- gy/articles/10.3389/fpsyg.2022.928717 Yusif, S., & Hafeez-Baig, A. (2023). Cybersecurity Pol- icy Compliance in Higher Education: A Theoretical Framework. Journal of Applied Security Research, 18(2), 267–288. https://doi.org/10.1080/19361610.20 21.1989271 Zahid, I., Hussein, S., & Mahdi, S. (2023). Measuring Individuals Cybersecurity Awareness Based on De- mographic Features. Iraqi Journal for Electrical and Electronic Engineering, 20, 58–67. https://doi. org/10.37917/ijeee.20.1.6 Zhang, C., Hao, J., Liu, Y ., Cui, J., & Yu, H. (2022). Asso- ciations Between Online Learning, Smartphone Addic- tion Problems, and Psychological Symptoms in Chi- nese College Students After the COVID-19 Pandemic. Frontiers in Public Health, 10, 881074. https://doi. org/10.3389/fpubh.2022.881074 Zhang, J., & Zeng, Y . (2024). Effect of College Students’ Smartphone Addiction on Academic Achievement: The Mediating Role of Academic Anxiety and Moder- ating Role of Sense of Academic Control. Psychology Research and Behavior Management, 17, 933–944. https://doi.org/10.2147/PRBM.S442924 Zou, C., Li, P., & Jin, L. (2022). Integrating smartphones in EFL classrooms: students’ satisfaction and per- ceived learning performance. Education and Informa- tion Technologies, 27(9), 12667–12688. https://doi. org/10.1007/s10639-022-11103-7 Ibrahim Mohamed Taha: I hold a master’s degree in statistics. I have experience working on many advanced statistical programs. I currently work at Sadat University. I have a number of research papers published in Arab journals. Rajaa Hussein Abd Ali: I hold a Master’s and PhD from the University of Babylon/College of Science/ Department of Physics. I have experience in the field of English editing. I have experience in the field of statistical programs. Ali Abdulhassan Abbas: I have a master’s degreefrom the University of Karbala College of Administrationand Economics in the field of Production Managementand Operations in the year 2005. I have also obtained aPh.D. from Karbala University, College of Administrationand Economics in Organizational Behavior and HumanResources Management in 2014. I currently work atKarbala University, College of Administration andEconomics in the Accounting Department. I havetaught many subjects in my specific area, as well as inother areas. I have also taught Financial Managementfor postgraduate studies. I have translated 4 books onbusiness administration into Arabic. I have numerousresearch papers published in local and internationaljournals. 104 Organizacija, V olume 58 Issue 1, February 2025 Research Papers