Students’ key determinant structure towards educational technology acceptance at universities, during COVID 19 lockdown: Pakistani perspective

Abstract The coronavirus disease-2019 (COVID-19) situation has caused serious concerns to education systems worldwide, and educational institutions have transitioned to online classes. This article explores university students’ perceptions regarding the acceptance and usage of COVID-19 online learning. More specifically, this article aims to answer the question: “What are the key determinant structures that affect students’ attitude towards the acceptance and use of digital educational technology for online learning during the COVID-19 lockdown?” The primary information was gathered via an Online-Structured-Questionnaire from 477 students at universities in Pakistan. The results showed that the correlation between the explanatory variables and the response variables is mediated by the perceived ease of use. The research offers recommendations on how to enhance the acceptance of COVID-19 outbreak online classes and how to develop the acceptance of the COVID-19 situation online learning. The output may be used in policy-making and resolving learning challenges towards a new normal time. The students preferred physical learning over online learning. The structure of the research presents research gaps and promising topics for future studies. This is the first empirical study to investigate the elements that are mostly associated with students’ behavior and attitudes towards online learning in the pandemic.


PUBLIC INTEREST STATEMENT
The COVID-19 pandemic has forced all Pakistani universities to switch their physical education to online learning. Therefore, many students had to switch from physical learning to online education in the middle of the semester. The result can negatively impact the students' learning performance if they lack self-confidence in the educational technology, as the platform is learning management system (LMS), they are applying. Knowing the determinant structure of the substantial driver of students' acceptance and use is vital because it will offer powerful points to establish constructive perceptions and help promote students' acceptance and use. Similarly, the more effective transitions to online classes are affected by the students' acceptance level of the educational technology. Consequently, it is essential to study the factors relevant to the educational technology acceptance model in terms of the COVID-19 lockdown situation. Hence, this article responds to the need for a more crucial analysis of students' behavior or attitudes towards online classes using LMS.

Introduction
Coronavirus disease-2019  has had significant impacts on the world's workplaces, including education. To decrease the spread of COVID-19, countries worldwide instituted measures on disease prevention and control by reducing interactions between human beings. This has also affected education (Tejedor et al., 2020). Amidst widespread uncertainty, global fear, and unprecedented disruption, university administrations are struggling to slow the transmission of the virus to protect vulnerable students as well as staff and faculty and to provide a safe learning environment (Aguilera-Hermida, 2020; Cao et al., 2020;Huang et al., 2020). Hence, many universities transitioned physical classrooms to a virtual world of learning.
Online learning in higher education literature broadly conceptualizes the theme in multiple ways. A large number of studies (Aguilera-Hermida, 2020; Tejedor et al., 2020) in digital online literacy in the higher education literature have supported the significance of perceived ease of use (PEU) on preliminary students acceptance and constant use of the systems. Despite the significant number of studies on the PEU construct, little research has been done on the determinants of this vital driver of educational technology acceptance and usage, especially during the COVID-19 lockdown. Knowing the determinant structure of this significant driver of students' acceptance and use is crucial because it will offer influential points to establish constructive perceptions and help promote students' acceptance and use. In addition, the research made considerable progress in the scores of both models, online and physical, when students were restricted because of the COVID-19 lockdown. Although there are known and objective advances in performance, there is no sufficient data regarding how the COVID-19 lockdown and online classes have influenced the learning method from a students' perspective. In addition, there is some indication that online classes in the emergency had some benefits (Aguilera-Hermida, 2020).
People think that through educational technology, online learning is fast, cheaper, can be used easily and by everyone, and is suitable for all levels of learning. On the other hand, it has been reported (Wang et al., 2013;Wilde & Hsu, 2019) that with distance learning, students are physically far away from the teachers and require an appropriate delivery method. Furthermore, (Pérez-Escoda et al., 2019) indicated that implementing emerging educational technologies alone cannot enhance the teaching and learning results if they are not accompanied by proper training. Similarly, (Poore, 2011) stated that students would be unable to accomplish a liberating, collective intellect unless they can reach collective educational technology literacy. Therefore, this paper responds to the need for a more critical analysis of students' behavior or attitudes towards online learning during a time of pandemic lockdowns.
Online learning has attracted attention over the last several years, and effective online literacy is increasing in a cautious instructional system (Hodges et al., 2020). Nevertheless, owing to the COVID-19 situation, many students worldwide had to transfer from physical classes to online classes in the middle of the semester. The outcome may negatively influence the students' learning performance if they lack self-confidence in the educational technology they are applying or do not feel a sense of intellectual engagement and social links (Bower, 2019). Similarly, the more effective transitions to online classes are affected by the students' acceptance level of the educational technology (Aguilera-Hermida, 2020; Kemp et al., 2019;Yakubu & Dasuki, 2019). Consequently, it is essential to study the factors relevant to the educational technology acceptance model in terms of the COVID-19 lockdown situation. Therefore, the present article attempts to fill the above gap by presenting a full picture of the factors that lead to students' acceptance of educational technology, the platform is the LMS, in universities during the COVID-19 lockdown. More precisely, this research aims to pursue the following questions: What appropriate and timely measures should be taken to empower students to accept online classes to support their literacy? What factors affect students' attitudes towards the acceptance of educational technology for online literacy during the COVID-19 lockdown emergency? What are university students' perceptions about the acceptance and use of educational technology for online classes during the COVID-19 lockdown? To accomplish these research objectives, partial least-square SEM (PLS-SEM) was implemented through the SmartPLS package. The suggestions offered in this paper will assist universities in implementing suitable and timely procedures to empower students to improve and implement online education to sustain their learning. More specifically, the study findings show that students' intentions influence the effectiveness of online learning. Moreover, these results will contribute to the current literature of online learning in COVID-19 lockdowns allow for more advances in online learning. Research gaps are discovered, and recommendations are made for further research to develop more effective virtual learning at the university level.
The remainder of the article is structured as follows. Section 2 offer the theoretical framework and working hypotheses of this study. Section 3 outlines the study method. The article analysis and results are reported in Section 4. Finally, section 5 discusses the contribution to theory, implications, and directions for further research, followed by the conclusions of the article.

Theoretical model and hypotheses development
Several theoretical models have been applied to study students' acceptance and implantation behavior of developing information technologies (IT). These include the Technology Acceptance Model (TAM), the Theory of Diffusion of Innovations (Rogers, 1995), the Reasonable-Action-Theory (Fishbein & Ajzen, 1977), and Decomposed Theory of Planned Behavior (Taylor & Todd, 1995). On the other hand, many models include PEU as a cause of acceptance, but the TAM (Davis, 1989) is the most extensively employed model of student acceptance and use. In 1989, Davis (Davis, 1989) applied TAM to describe the computer usage attitude. TAM suggests that PEU affects a person's attitudes regarding technology usage that has been associated with successive behavior (Sheppard et al., 1988;Taylor & Todd, 1995). TAM has attracted a wide range of empirical support because it is robust across time, situations, populaces, and technologies. PEU is the degree to which a person considers that employing a technology will be without effort (Davis, 1989). Although the present study concentrates on PEU from the perspective of TAM, it must be emphasized that other theoretical frameworks examining user acceptance have also used related constructs (Lai, 2017;Luarn & Lin, 2005;Van Der Heijden, 2004;Venkatesh & Morris, 2000). The TAM is appropriate in this research perspective because it explains how information is employed successfully concerning the determinants that influence the acceptance and application of educational technologies, especially in a highly uncertain situation. In particular, research has considered the associations among motivation, self-efficacy (SE), PEU, usage attitude and compatibility based on TAM (Bhat & Beri, 2016;Lai, 2017Lai, , 2016Luarn & Lin, 2005;Van Der Heijden, 2004;Venkatesh & Morris, 2000).
Based on the justification, the theoretical framework of this study is grounded in the TAM perspective adopted from the theory of reasoned action (Fishbein & Ajzen, 1977) because TAM is specially designed for developing students' acceptance of IT (Davis, 1989). Consequently, this research's theoretical framework can be planned informed based on TAM.

Motivation and perceived ease of use
Motivation refers to a student's underlying motivation to learn (Aguilera-Hermida, 2020). It comprises the satisfaction intrinsic in the activity and the aim to accomplish a task. Motivation implies the perceived significance of an activity that influences behavior intention. Students who are encouraged will participate in self-governing activities that assist them in attaining their objectives (Kemp et al., 2019). Studies have indicated that a lack of motivation and SE in online classes can result in students devoting spare time accomplishing projects, turning in late assignments, or general poor-quality work (Albelbisi & Yusop, 2019). On the other hand, PEU implies a level to which the students expect the target educational digital technology to be accessible (Davis, 1989). The PEU is the body that presents the motivation and leads the students to apply the technology (Lai, 2016). Therefore, motivation influences the PEU, as proposed. Thus, the role of motivation is hypothesized as H1: Motivation is positively related to the perceived ease of use.

Self-efficacy and perceived ease of use
SE is the positive psychological situation of an individual for creativity and inner feelings of motivation that regulate their behavior in a specific way (Alotaibi, 2016;Bandura, 2007). Students with a greater SE have a greater capability to accomplish objectives, and it acts as an essential predictor of innovation (Hu & Zhao, 2016;Rabbani et al., 2020).
The hypothesis related to the effect of SE and PEU is greatly supported by the research. Students who had confidence in their ability to apply the educational technology had a greater expectation of the results of using the technology and perceived highness of use than those who lacked this confidence (Compeau & Higgins, 1995). SE comes into perform to incline a person's psyche towards innovative idea generation (Rabbani et al., 2020). Entrepreneurial passion is also significant in research & development (R&D) in IT, whereas inventions are developing rapidly, and a student's creativity is the critical input in tailored new product development. The significance of PEU in terms of SE is supported by a wide range of studies (Bandura, 1982), which are explained as judgments of how well a student can implement courses of action needed to deal with potential situations. SE is related to PEU as defined above. The SE viewpoints are hypothesized to function as proximal causes of behavior. Consequently, this study examined SE as an antecedent of PEU from the perspective of universities in Pakistan. As a result, the following hypothesis is tested: H2: selfefficacy is significantly related to perceived ease of use.

Perceived ease of use and usage attitude
The theory of reasonable action (Fishbein & Ajzen, 1977) is about one feature that governs the behavioral intention of the student's attitudes toward that behavior (Lai, 2017). Attitudes are influential and based on a set of feelings about the object of a behavior (Lai, 2017). Attitudes toward a behavior are related to the student's positive or negative assessment of that behavior (Aguilera-Hermida, 2020; Botero et al., 2018;Kemp et al., 2019). (Aguilera-Hermida, 2020) reported that a students' attitude substantially influences their intention to apply educational technology. In uncertain environments and other cases, students have identified the system to be too challenging to apply. They have not been capable of scaling that difficulty to students' acceptance and use of the emerging educational technology (Venkatesh, 2000). Because of the determination and significance of this problem, supporting students' acceptance has been a long-standing concern for researchers (Davis, 1989;Swanson, 1988).
The focus is to know the students' usage attitude toward digital educational technology through usableness testing and assessment approaches, which will show that students can use technology appropriately. Hence, identifying student acceptance and use of emerging educational technology is attracting similar attention from researchers and practitioners (Venkatesh, 2000). Concerning educational technology acceptance, the study framework shows that PEU influences the relationship between the students' motivation and SE. The theory of reasonable action supports the usage attitude. Thus, the following hypothesis was tested: H3: Student's perceived ease of use mediates motivation, self-efficacy, and students' usage attitude. The motivation and self-efficacy of students significantly affect the perceived ease of use. Consequently, the perceived ease of use affects the students' usage attitude.

Perceived ease of use (PEU) and compatibility
Digital educational compatibility can be defined as the level to which a digital system is perceived as being constant with a student's learning expectation (Kemp et al., 2019). Online learning compatibility is a sign of learning effectiveness that can be defined as the capacity of the learning resource to provide the desired literacy outcomes. Educational technology is valuable merely if it produces a learning resource and then assists the student to accomplish their learning objectives. Another way that educational technology will benefit a system is if it can provide quality to a student's learning life (Tarhini et al., 2015). This can be defined by determining if the educational technology will enhance the students' work-life quality in terms of timing and expenses. Moreover, the class of PEU is based on the level to which the student thinks the emerging educational technology to be simple to apply and applies previous experience or continued application. Students tend to apply or not apply the educational technology to the amount they consider to assist them in performing their task better (Davis, 1989).
Therefore, based on previous research, the present work proposed that students' PEU of the educational technology in the COVID-19 lockdown will significantly influence the compatibility of the students during online learning. Hence, the following can be proposed: H4. Student's perceived ease of use significantly mediates between motivation, self-efficacy, and students' compatibility. Motivation and self-efficacy of students significantly affect the perceived ease of use. Consequently, the perceived ease of use affects the students' compatibility.

Usage attitude and compatibility
Emerging educational technology provides a perspective for significantly enhancing the whitecollar performance of the student (Curley, 1984;Davis, 1989;Edelman, 1981;Khan et al., 2021). On the other hand, enhancing performance is hindered mainly by the students' reluctance to accept and utilize available technology (Davis, 1989). Therefore, students' attitudes regarding educational technology directly influence their literacy process (Aguilera-Hermida, 2020). The student acceptance of educational technology is critical to the diffusion of the technology (Holden & Rada, 2011). Technological implementation has changed the role of students from users to creators of information (Prensky, 2001). Hence, based on the general discussion, it can be proposed that the usage attitude of educational technology may help bring compatibility in students, which is postulated in the present research as follows: H5. Student's usage attitude significantly influences their compatibility.

Research design and methodology
This research was designed to investigate the students' acceptance and usage attitude regarding online learning in universities during the COVID 19 lockdown in Pakistan. Based on the previous literature, this study applied an online questionnaire generated in Google-drive to check the reliability, validity, and psychometry soundness of the research framework. The article hypothetical model of the article contained two explanatory, two mediate, and a response variable (see, Figure 1). Previous studies have applied a deductive approach to develop the indicators for scale development as per the suggestions by Hinkin (Hinkin, 1998). This study initially outlined 61 items from a broad review of the literature. A panel of 10 subject-specialist (Professors and Researchers) examined the preliminary pool of statements on the tierce classes as suggested by (Lin & Hsieh, 2011). In this session, the panel recommended the removal of 22 items because of overlap. The questionnaire was modified and again sent to the panel. It was again reviewed on several occasions by the professionals, and again six more statements were deleted. To indicate the context of the students' educational technology acceptance model in the pandemic, the ultimate pool of 33 indicators were once again evaluated by the professionals and they recommended applying common wording rather than phrases, but they did not remove any item at this time.
The construct of motivation was evaluated by six items reshaped from the literature (Adnan & Anwar, 2020;Bhat & Beri, 2016;Tejedor et al., 2020). SE was assessed by six indicators acquired from (Compeau & Higgins, 1995;Venkatesh, 2000). Seven items were used for PEU modified from (Davis, 1989;Venkatesh, 2000). The five items used for usage attitudes were modified from Adnan and Anwar (Adnan & Anwar, 2020). The nine items used for the compatibility were adapted from (Bhat & Beri, 2016;Compeau & Higgins, 1995). Overall, there are 33 items used for the study that were rated by the students on a five point-Likert-scale. After a pilot test, the questionnaire was revised slightly to make it ready for data gathering.

Sample and data collection
In Pakistan, the 1 st patient with Covid-19 was identified on 26 February 2020 (Dogar et al., 2020). Whereas for this study quantitative data were collected from January to April 2021. From 13 March 2020 Higher Education Commission (HEC) of Pakistan, closed all 213 universities and other degree awarding institutes for academic activities (Akram & Meo, 2020). Considering the ambiguous future and duration of closure of all academic activities, universities were directed to deliver online classes, that caused huge challenges not only for the universities but also for students. Even though, online learning at university level in Pakistan is offered since 2002 at Virtual University. Similarly, COMSATS University Islamabad, Pakistan also offers online classes in  (Dogar et al., 2020). To focus on related challenges, HEC of Pakistan directed universities that online classes should be implemented in two stages. (i) all universities with LMS were advised to deliver online classes. The rest of the universities that were not executing LMS were ordered to apply such techniques that can enable the universities to deliver online education properly. Hence, during Covid-19 pandemic, almost all the universities of Pakistan are using online learning platforms to maintain academic activities (Sarwar et al., 2021). However, online learning is still in the growth stages owning to which the students complain regarding ineffective learning. The selected universities have their own LMS. The teachers were uploading the study materials on the LMS and sharing the screen through either Zoom or Microsoft Team. Anyhow, it can be argued that majority of the respondents were in the developing stages in the online classes. Moreover, all the respondents were familiar of using LMS at the time they were responding.
The targeted population of the present research was students at universities in Pakistan. The universities were selected based on a learning management system (LMS) for online classes in the precent COVID-19 emergency. (i) All of the respondents were enrolled in theory programs where there was no requirement of laboratory practice and courses must be delivered totally online. (ii) The respondents were enrolled in different programs (PhD, MBA, MA, MSc, MCS, BBA, BS, BCS) but in all of these programs the structure of the class remained similar. This might assist well understand the issues confronted by the respondents in lecture delivery by reducing the effect of heterogeneity that might have an influence on class-room environment. (iii) The selection of a graduate students meant students will have well knowing of software used for the lecture delivery and finally at graduate level, students have a diverse background as some students are engaged in job, whereas some of them are married and have domestic responsibilities and some have continued university after a lengthy break from schooling. Consequently the sample offers similarity in terms of programs offered and a great combination of respondents who give variety in the sample in terms of their experience. In addition, these chosen universities represented all other universities because most of the universities selected had an excellent position in the 2020 rankings by the world Times Higher Education Ranking. The main similarity among the chosen universities were the use of online LMS by universities and belong to higher rank in the country. Whereas the classification of universities were based on geographical regions such as rural and urban areas, some universities were public sectors universities, whereas other were private universities.
A link to an online questionnaire was shared via Email, Facebook, & WhatsApp groups to reach the university students taking classes online. The students were requested to complete the questionnaire, of which 553 questionnaire responses were obtained. Of these, 477 complete responses were applied for data analysis.

Data analysis
In a statistical presentation, no procedure should be thought of as either good or bad, but it is necessary to understand the technique that can be applied. (Garson, 2016) indicated that the best method is covariance-based SEM (CB-SEM) rather than PLS-SEM in basic social sciences, whereas after 2010, the majority of the scholars have applied PLS-SEM (Hair et al., 2019). PLS-SEM focuses mainly on prediction instead of explanation, making PLS-SEM particularly beneficial for examinations of the foundations of competitive-advantage and success driver research (Hair et al., 2017). Every methodology has some strengths and weaknesses, but this was outside the scope of the study.
Multiple approaches were used to assist the constructs' reliability, validity, and psychometrical soundness. The reliability was measured through Cronbach's alpha and the composite reliability (CR). The average variance extracted (AVE) was applied to assess the convergent validity. The Fornell-Larcker criterion and Heterotrait-Monotrait Ratio (HTMT) were applied to evaluate the discriminant validity (DV). The r Stone-Geisser indicator was used to measure the predictive validity (Q2). The skewness and kurtosis were used for normality, whereas the variance inflation factor (VIF) was applied to measure multicollinearity. Multiple robustness tests were used for further validity assessments of the results. The PLS-SEM method through SmartPLS software was used for all five constructs and 33 indicators.

Descriptive statistics
For this study, the control variables were gender, age, degree perusing, locale, and type of university. The variables were examined afterward, testing for normality. Questionnaire practices, directives, instructions, and exercises were performed in Pakistan. Four hundred and seventy-seven respondents (62% male and 48% aged between 25 to 34 years) were obtained from university students selected through purposive sampling based on online learning. Approximately 37%, 54% and 56% were pursuing a master's degrees, belonged to rural areas, and were studying at public sector universities, respectively. The mean, standard deviation and variance of these variables were also evaluated (Table 1).

Results and analysis
Before applying the SmartPLS3 software, the skewness and kurtosis were assessed in SPSS to measure the normality of the study, as listed in Table 2. The data was normal, and the values were within the accepted range of ±2 (George, 2011). Next, a VIF test was conducted to check the   multicollinearity. The multicollinearity was not a problem for further concerns, as the values of VIF < 3 (Neter et al., 1983); (Cfroteod, 2020) recommended that the values of VIF must be less than 10.

Common method bias
The information was gathered via a sole source, which may lead to Common Method Bias (CMB) and possibly threaten the research validity (Podsakoff & Organ, 1986). If all (factor level) VIF values from a complete collinearity test are ≤3.3, the model should be used without CMB (Kock, 2015). In the present research, the inner VIF values were 1.332, which is <3.3. Hence, there was no risk of CMB, as listed in Table 3.

R square (R2) and adjusted R square (AR2)
The Pearson's coefficient R2 and AR2 were evaluated to identify variance values of the endogenous variables. As shown in Table 4, the R2 and AR2 values were similar: 0.22, 0.35, and 0.29 for compatibility, PEU, and usage attitude, respectively. Hence, the results showed a large and medium effect size and a good fit model (Cohen, 1988).

Reliability of the measurement model
Cronbach's alpha assesses the inner reliability of the variables, whereas the more lenient reliability of the variables is CR. By the rule of thumb, a reliability of 0.60 or greater is appropriate for exploratory purposes, whereas Cronbach's alpha <0.60 shows that the items do not fit well. For Cronbach's alpha, the value was higher than the recommended value. Therefore, the model has a good fit (Hair et al., 2009). Similarly, the CR values were greater than the cut-off value of 0.70.  Therefore, based on the CR, the model also showed a good fit and revealed high reliability (Chin, 1998;Hair et al., 2009;Henseler et al., 2015), as seen in Table 4.

Validity of the measurement model
A model may not be reliable without validity (Neuman, 1994). Thus, both convergence and discriminant validity tests were used in the research. The factor-loading values for some indicator variables were not greater than the threshold value of 0.70. Similarly, the AVE values for all constructs except the usage attitude were below 0.50 (Chin, 1998), showing an issue with convergent validity based on the AVE and factor loading data (see , Table 4, Figure 2). Nevertheless, no item was deleted to find the AVE and the factor loading result good. The measurement model was then evaluated for the DV to determine if an item accounts for greater variance in its associated manifest construct than it reveals with other constructs in the related model (Fornell & Larcker, 1981). On the basis of the Fornell Larcker criterion, no issue with the DV was identified (Hair et al., 2017), as listed in Table 5. On the other hand, for the DV, Voorhees et al. (Voorhees et al., 2016) reported that HTMT is the ideal method in SEM.
Similarly, the HTMT values were less than the threshold value of ≤ 0.85 (Henseler et al., 2015) for all variables that confirm DV, as shown in Figure 3.

Correlation
A Pearson-Correlation assessment was performed in SPSS because it provides early support to the study assumptions. The values indicated a significant positive correlation between all the variables (see , Table 5).

Hypothesis testing
In terms of hypothesis testing, the results showed that motivation had a significant positive effect on PEU (T 4.502, P 0.00). Consequently, H1 was supported. SE had a significant positive impact on PEU (T 3.16, P 0.00), which supported H2. PEU had a highly positive-significant impact on the usage attitude (T 13.74, P 0.00) and supported H3. PEU had a significant influence on compatibility (T 3.16, P 0.00), which supported H4. The usage attitude significantly influenced the compatibility (T 5.89, P 0.00), which validated H5.

Robustness tests
Many robustness tests have been performed to ensure the further validity of the measurement model. In addition, a lonely test was conducted for the control variables and to offer a good fit model.
In addition, ANOVA (See , Table 7) and linear regression (See , Table 8) examinations were performed using SPSS; the results showed a good fit to the model. Furthermore, the results of SEM and regression analysis were similar. Consequently, robustness examinations confirmed the validity of this research model.

Discussion
This study developed and an empirically emerging educational technology acceptance model for university students in Pakistan during the COVID-19 global lockdown situation. This attempt was successful in many aspects because the relevant variables had significant psychometric soundness, and substantial empirical correlations were observed between variables. Furthermore, numerous new insights were identified concerning the educational technology acceptance model. Studies that have concentrated on online literacy focused mostly on education from different perspectives. For example, (Aguilera-Hermida, 2020) examined the college students' emergency online learning with a focus on cognitive engagement and the academic performance of students. (Henaku, 2020) analyzed the college students' online learning perception and experiences from the perspective of Ghana and applied a descriptive methodology. (Tejedor et al., 2020) compared three countries (Spain, Italy, and Ecuador) focusing on online classes. (Hodges et al., 2020) discussed the difference between pandemic online teaching and learning. Researchers have ignored the importance of students' acceptance, usage attitude, and compatibility towards online literacy in universities during the COVID 19 lockdown in developing a country's perspectives. Thus, this research is different from the previous studies, even though it followed previous research (Aguilera-Hermida, 2020;Chaudhary et al., 2020) to assess the research hypothetical model empirically with the stated intention of students' acceptance of online learning from Pakistani students' perspectives.
Based on the study conclusions, students are informed that they need to increase their focus on online classes. In addition, they should accept digital technology because they can achieve satisfaction and certain other outcomes compared to those who are not taking classes online. As shown in the literature , educational digital model acceptance by students at the university level can bring sustainable development in their lives, avail R&D opportunities, and develop their digital skills, ultimately increasing the students' employability in the future. Furthermore, online learning skills enhance the compatibility of the students through helpful digital information because the others are locked in their stations. TAM (Davis, 1989) was employed in this article to investigate the students' acceptance, usage, and compatibility regarding online learning in the COVID-19 lockdown. This research provides some valuable contributions to theory. Regarding the motivation to pursue the ease of educational technology during the COVID-19 lockdown situation, empirical data showed that the respondents were more motivated before this situation. Consistent with the existing literature (Aguilera-Hermida, 2020; Albelbisi & Yusop, 2019;Kemp et al., 2019;Venkatesh et al., 2003), the present research affirms that if students are motivated, their degree of perception regarding educational technology will be higher, and vice versa. Furthermore, motivation affects the students' work and the level of dedication spent on specific tasks. In response to the H1, the results of this study have shown that motivation has a significant positive effect on a student's PEU. Consequently, H1 is accepted.

Contributions to theory
In response to H2, the student's SE has a large effect size on educational digital technology PEU. Hence, this hypothesis is accepted. These results are aligned with suggestions reported elsewhere (Alotaibi, 2016;Bandura, 2007;Bandura & Walters, 1977;Compeau & Higgins, 1995) in that the SE has a positive effect on the perception of easiness of use. The successful use and acceptance of online classes are SE. These results affirm the previous literature stating that students motivated in a range of self-regulated approaches are more likely to complete their educational responsibilities. SE attitudes affect task preference, effort, determination, flexibility, and success, which is clearly associated with PEU and ultimately compatibility (Aguilera-Hermida, 2020). When delivering online teaching in a pandemic, it is essential to foster students' influence by motivating them to understand their prior talents and experience and assist them to trust in their SE. Therefore, universities need to focus on the student's SE during this challenging time to develop a positive image and perceptions of their brand in the minds of their students. The present study results show that those students who have not applied educational technology have a lower perception of SE. Moreover, those with a lower perception of SE have a lower PEU, and the utilization of emerging technologies may be harmful if not perceived appropriately.
In response to the H3, this study showed that student's educational technology PEU has a significant positive mediating role between motivation, SE, and usage attitude. Therefore, this hypothesis is also accepted. These findings have theoretical support from previous studies (Aguilera-Hermida, 2020; Botero et al., 2018;Davis, 1989;Fishbein & Ajzen, 1977;Kemp et al., 2019;Lai, 2017;Swanson, 1988) regarding the role of PEU and usage attitude. Therefore, universities should adopt the latest LMS that must be easy and accessible to their students. This will help strengthen the university brand during the COVID-19 situation and the computability of the student, which support H4 and H5.
Thus, building the trust of students in the university can help universities to enhance the positive role of the student's satisfaction in increasing their performance and employability.

Practical implications
Grounded on the conclusions of this research, the following provides some suggestions for better students' acceptance, usage attitude and compatibility towards online literacy in universities during the COVID 19 lockdown.
Students' motivation is a complicated element, but it can be manipulated. Professors should ask students to demonstrate why the university is valuable to them. Professors should also apply slight encouragement to motivate them continuously. This does not need to involve more effort for the professor, but a short pronouncement indicating support may make a difference in the students' motivation. Similarly, the results affirm that during pandemics, such as the COVID 19 lockdown situation, students cannot decide on the delivery approach and need to engage in online learning. Nevertheless, instructors need to consider the students' motivation levels.
Students need to boost an optimistic attitude regarding a temporary situation. Students need to know that that their attitude can significantly impact the direction of their online learning skills. Hence, professors should continuously attempt to develop their attitudes towards online classes. Moreover, it is imperative to talk to students' concerning their worries and move them towards opportunities. The professors may ask questions like, What does he/she think regarding online classes? (to know the bias of the students). What are his/her concerns regarding online classes? How should he/ she control his/her fears? What capabilities do he/she have that can assist him/her to be productive? Asking these kinds of questions simply encourages students to reflect on their studying method and take a greater positive attitude. Bandura and Walters (Bandura & Walters, 1977) stated that the expectations are mostly concerned with the students' expectations for positive results.

Limitations
This article had some limitations. First, the study results are restricted to the feedback from a survey (Students' regarding the acceptance, usage attitude, and compatibility towards online education in universities during the COVID-19 Lockdown) conducted in Pakistan. On the other hand, in developed countries, such as Italy, the USA, UK, and China, this approach may reduce the consistency and limit the generalizability of the findings as they were also affected by this pandemic. Consequently, further study will be needed in developed regions using mixed methods to improve the research results. Second, the research was conducted in the South Asian region, where the education, social, and culture systems are diverse from other regions. Similarly, university students stated that their assessment might have been impacted by several aspects, such as the research was conducted in the middle of the emergency, where there were fears, uncertainty, and stress. Therefore, it can be beneficial to test this study model outside of the region for comparison. Third, the findings explored the educational technology acceptance model, and information was gathered from students at the universities of Pakistan. In contrast, information was not included from other stakeholders, including faculty. Therefore, the enclosure of teachers' views in further examinations can assist in understating the challenges confronted by students regarding online learning. Fourth, the study findings are based primarily on the students' viewpoints of high-ranked universities of Pakistan. Incorporating the data from lower-ranked educational institutions with a shorter reach to emerging educational technologies could produce more crucial findings. Finally, the research did not include the students' feedback, who have limited access to the internet as the data were collected online.

Conclusion
Learning is viewed as a journey, occurring through the mutual construction of meaning between students and teachers. COVID-19 jammed the physical learning system of educational institutions worldwide. Therefore, the universities' administration chose online classes as an alternative to continue learning. This study addressed the university students' acceptance, usage attitude and compatibility towards online education during the COVID 19 lockdown. In particular, the findings showed that the article explanatory variables (Figure 1) are mediated by educational technology PEU with student compatibility. Moreover, this article provides university administrations with measures to decide the essential components that enhance students' acceptance of educational technology during the COVID 19 lockdown because university administrations incessantly seek strategies to support students. Thus, this study provides universities with guidelines to govern the essential factors for increasing students' acceptance level in terms of educational technologies and, ultimately their compatibility. Except for some educational insights, the main conclusion from this study is that it highlights motivating creative online learning experience with some measures for examination that can transform learning. Although the research results have some limitations, they provide the groundwork for future research in multiple ways to investigate online learning systematically at the university level and lower levels. Overall, the information explored in this study was collected through an online structured questionnaire from students from higher-ranking universities in Pakistan.