15 2.3.2.4 Students' Engagement EN is a factor effect on Social media application use SMU, Student’s Satisfaction SS and Student’s Academic Performance SAP 16 2.3.2.5 A factor effect on
INTRODUCTION
Background
Nowadays, our world is continually changing Every passing day we can hear and know new things in the world through the Internet and social media applications People often said that "The Internet is always followed by social networks", this is a true judgment because if you compare the early days of social networking on the Internet with the present, it has changed very much Specifically, in the past, people could only send emails or search for information, today we can do everything through social networks such as entertainment, connecting with people around the world, and even use it as a tool for learning and research
From a macro perspective, according to statistics from Simon Kemp's
Engagement (EN) 2.104 0.035 0.027
Interactivity with Lecturers (IN-L) -> Engagement (EN) 2.268 0.023 0.023
Active Collaborative Learning (ACL) ->Engagement
Perceived ease of use (PEOU) -> Engagement (EN) 4.863 0.000 0.123
Perceived usefulness (PU) -> Engagement (EN) 2.626 0.009 0.044
Perceived ease of use (PEOU) -> Social media use
The use of perceived usefulness (PU) -> Social media use (SMU)
A massive relation between perceived ease of use
Engagement (EN) -> Social media use (SMU) 2.807 0.005 0.038
Engagement (EN) -> Student’s Satisfaction (SS) 6.875 0.000 0.221
Engagement (EN) -> Student’s academic performance
Social Media Use (SMU) -> Student’s Satisfaction (SS) 5.812 0.000 0.195
Social Media Use (SMU) -> Student’s Academic
Student’s Satisfaction (SS) > Student’s Academic -
Table 4 6 Numerical values of T-statistics, P-values and F square
In table 4.7 will analyze the relationship that exists in the model both directly and indirectly through path coefficients, standard deviation and T-statistics values
ACL -> EN -> SMU -> SAP 0.010 0.005 1.905 0.057 ACL -> EN -> SMU -> SS 0.017 0.009 1.703 0.089 ACL -> EN -> SMU -> SS ->
INL -> EN -> SMU -> SAP 0.005 0.003 1.766 0.078 INL -> EN -> SMU -> SS 0.008 0.005 1.709 0.088 INL -> EN -> SMU -> SS -> SAP 0.002 0.002 1.477 0.140
INP -> EN -> SMU -> SS -> SAP 0.003 0.002 1.364 0.173 INP -> EN -> SS -> SAP 0.014 0.007 1.783 0.075 INP -> EN -> SMU -> SS 0.008 0.005 1.593 0.111
PEOU -> SMU -> SS -> SAP 0.017 0.011 1.571 0.116 PEOU -> PU -> EN -> SMU ->
Table 4 7 T Statistics values, path coefficients, and standard deviation of variables
Based on the results in table 4.7, we can see that the direct and indirect relationships mostly have T-statistics values greater than or equal to 1.96 and P-values less than 0.05, only the relationship between PEOU affecting SMU is value does not meet the above criteria Therefore, this has proved that the hypotheses from H1 to H5 and from H7 to H14 are very reliable and have been tested through the above data specifically as follows:
Hypothesis H1 is Interactivity with Peers (INP) affected on Engagement (EN) with T-statistics of 2,104 and path coefficient of 0.111; similar in hypothesis H2 is The remarkable relationship between Interactivity with Lecturers (INL) and Engagement (EN) with T-statistics of 2.268 and path coefficient of 0.104; hypothesis H3 is An important relationship between Active Collaborative Learning (ACL) and Engagement (EN) with T-statistics of 3,269 and path coefficient of 0.210; hypothesis H4 is A meaningful connection between perceived ease of use (PEOU) and Engagement (EN) has a T-statistic of 4.863 and a path coefficient of 0.339 ; Hypothesis H5 is A strong relationship between Perceived usefulness (PU) and Engagement (EN) has T-statistics of 2.626 and path coefficient is 0.188 ; Hypothesis H6 is A considerable link between perceived ease of use (PEOU) and Social media use (SMU) has a T-statistics of 1,674 and a path factor of 0.153 ; hypothesis H7 is A positive impact was observed between the use of perceived usefulness (PU) and Social media use (SMU) has a T-statistics of 4,946 and a path coefficient of 0.373 ; hypothesis H8 is A massive relation between perceived ease of use (PEOU) and perceived usefulness (PU) has a T-statistics of 21,527 and a path coefficient of 0.724 ; Hypothesis H9 is Engagement (EN) impact on Social media use (SMU) with T-statistics of 2.807 and path coefficient of 0.205 ; hypothesis H10 is Engagement (EN) impact on Students Satisfaction (SS) has T-statistics of 6,875 and path coefficient of 0.404 ; Hypothesis H11 is Engagement (EN) impact on Student's academic performance (SAP) with T-statistics of 4,911 and path coefficient of 0.312; Hypothesis H12 is Social Media Use (SMU) impact on Students Satisfaction (SS) with T-statistics of 5,812 and path coefficient of 0.376 ; Hypothesis H13 is Student's Academic Performance (SAP) has engaged by Social Media Use (SMU) has a T-statistics of 3,996 and a path coefficient of 0.247 ; and finally the hypothesis H14 Student's Satisfaction (SS) has a great connection with Student's Academic Performance (SAP) has a T-statistics of 4,729 and a path coefficient of 0.301.
In conclusion, all hypotheses from H1-H5 and H7-H14 are reliable and accepted while hypothesis H6 is unreliable and rejected.
Variance analysis of an One Way-ANOVA factor
4.4.1 Method variance analysis of an One Way-ANOVA factor
4.4.1.1 Examining variances between value groups
This method is intended to examine and consider there are average differences of a quantitative variable with respect to different values of qualitative variables, including quantitative variables that are frequency of social network application and qualitative variables that are demographic related factors such as: sex, marital status, age, grade, profession, and median income The One Way-ANOVA was tasked with examining the differences between qualitative values for a research issue and in this research my team jog on analyzed the Oneway-ANOVA variance with SPSS software, and the analysis process will include the following two parts:
Check Sig Levene Test to determine the variance between homogeneous or heterogeneous value groups
If Sig Levene Test at this Test is greater than 0.05, then The variance between the groups of values is homogeneous to the qualitative variable above and not the same, then we shall use the ANOVA result table If the Sig Levene Test at this Test is smaller or equal to 0.05, then the variance between the groups is not uniform and we use the Robust Test table.
In the ANOVA table, if SIG F is less than 0.05, we conclude that there is an average or reverse difference if SIG F is greater than 0.05, we conclude that there is no average difference On the other hand in the Robust Test, if SIG Welch is less than 0.05 we conclude that there is an average or vice versa difference if Sig Welch is greater than 0.05, we conclude that there is no average difference.
After qualifying, practice ANOVA study with the average variational hypothesis between groups:
If SIG in the ANOVA table is less than 0.05, then the non-equal variational average makes the difference between the groups with respect to the dependent variable.
Conversely, if SIG in the ANOVA table is larger than or equal to 0.05, then the mean of equal variances means that there is no difference between groups for dependent variables.
In this study, the sig numbers in each variable exhibit less than 0.05, which is a statistically significant pair of variables.
4.4.2.1 Independent Sample T-test for Gender
Tests of Homogeneity of Variances
Levene Statistic df1 df2 Sig
Based on Median and with adjusted df 787 1 307.847 376
Table 4 8 Result of T-test for Gender
Sum of Squares df Mean Square F Sig
Table 4 9 Result of ANOVA for Gender
According to the table 4.8 the table 4.9, at the table 4.8 Levene’s test sig value is 0.385 > 0.05 demonstrates that the distinction between these two genders is identical At the table 4.9 Levene’s test sig 0.673 > 0.05 there is no statistically significant difference in using social media applications between the two genders
4.4.2.2 Independent Sample T-test for Status
Tests of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
Based on Median and with adjusted df
Table 4 10 Result of T-test for Status (Source: Authors Synthesized)
Sum of Squares df Mean Square F Sig.
Table 4 11 Result of ANOVA for Status
According to the table 4.10 and the table 4.11, at the table 4.12 Levene’s test sig value is 0.870 > 0.05 indicates that there is no different in the variance between the selection of the age group, we will use the ANOVA sig value: sig value of ANOVA is 0.183 > 0.05 there is no statistically significant difference in using social media applications between the selection of the status group.
Tests of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
Based on Median and with adjusted df
Table 4 12 Result of T-test for Status
Sum of Squares df Mean Square F Sig.
Table 4 13 Result of ANOVA test for Age
In terms of age according to the table 4.12 and the table 4.13, at the table 4.12 Levene’s test sig value is 0.771 > 0.05 indicates that there is no different in the variance between the selection of the age group, we will use the ANOVA sig value: sig value of ANOVA is 0.179 > 0.05 there is no statistically significant difference in using social media applications between the selection of the age group.
Tests of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
Based on Median and with adjusted df
Table 4 14 Result of T-test for Degree
Sum of Squares df Mean Square F Sig.
Table 4 15 Result of ANOVA test for Degree
In terms of degree according to the table 4.14 and the table 4.15, at the table 4.14 Levene’s test sig value is 0.637 > 0.05 indicates that there is no different in the variance between the selection of the age group, we will use the ANOVA sig value: sig value of ANOVA is 0.285 > 0.05 there is no statistically significant difference in using social media applications between the selection of the degree group.
4.4.2.5 Independent Sample T-test for Job
Tests of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
Based on Median and with adjusted df
Table 4 16 Result of T-test for Job
SAP Sum of Squares df Mean Square F Sig.
Table 4 17 Result of ANOVA test for Job
In terms of Job according to the table 4.16 and the table 4.17, at the table 4.16 Levene’s test sig value is 0.759 > 0.05 indicates that there is no different in the variance between the selection of the age group, we will use the ANOVA sig value: sig value of ANOVA is 0.511 > 0.05 there is no statistically significant difference in using social media applications between the selection of the job group.
Tests of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
Based on Median and with adjusted df
Table 4 18 Result of T-test for Income
Sum of Squares df Mean Square F Sig.
Table 4 19 Result of ANOVA for Income
Based on the results of the table 4.18 And the board 4.19 The sig number on the 4.18 a value of 0.968 > 0.05 can be concluded that the variance is equal and statistically significant, however, the sig number on the table Equals 0.214 > 0.05, which means that there is no difference between the four income groups under 5 million VND, from 5 to 20 million VND, from 21 to 35 million VND, Over 50 million VND there is no statistically significant difference in using social media applications between the selection of the income group and we will go deep analysis in One-way ANOVA test.
Tests of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
Based on Median and with adjusted df
Table 4 20 Result of T-test for SMU
SAP Sum of Squares df Mean Square F Sig.
Table 4 21 Result of ANOVA for SMU
In terms of SMU according to the table 4.20 and the table 4.21, at the table 4.20 Levene’s test sig value is 0.269 > 0.05 indicates that there is no different in the variance between the selection of the age group, we will use the ANOVA sig value: sig value of ANOVA is 0.859 > 0.05 there is no statistically significant difference in using social media applications between the selection of the SMU group.
CONCLUSION, AND RECOMMENDATIONS/ LIMITATIONS
Research summary
The purpose of this study was to identify the elements of using social networks that influence students' academic performance as well as their satisfaction with using them in the learning process In general, the data in chapter four has been extensively analyzed, and here is a summary of the research findings that my group has drawn, specifically as follows:
EN, SMU, and SS all have an impact on students' academic performance (SAP), with the engagement factor having the most impact (12.6 percent ) compared to student satisfaction (11 percent ) and social media use - SMU only has one (8.5 percent ) Furthermore, the engagement factor is influenced by the components that our group classifies as independent variables: Interactivity with Peers (INP), Interactivity with Lecturers (INL), Active Collaborative Learning (ACL), Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) The PEOU factor has the most impact on EN (12.3 percent), followed by the second influencing factor ACL (6 percent), PU (4.4 percent), and the other two variables have the lowest impact levels of INP (2.7 percent ) and INL (2.3 percent ) Furthermore, we cannot help but notice that the PEOU factor has a significant impact on the PU factor, accounting for 113.1 percent of all correlations (hypothesis) Furthermore, for students to feel happy when utilizing social networks for learning (SS), two criteria, engagement (EN) and social media use (SMU), appear to have an impact The impact (22.1 percent) is more than SMU's impact (19.5 percent ) Furthermore, three other characteristics influence social media use (SMU), including perceived ease of use (PEOU), perceived usefulness (PU), and engagement (EN), specifically, PU (11.3 percent) has the greatest impact on job performance via social networks, and EN has the second greatest impact (3.8 percent ) When the reliability level is less than the significant level, the PEOU factor has no effect on the SMU factor (2.2 percent) (90.6 percent is less than 95 percent ) In summary, the research results all show that the use of social networks in learning has a positive impact on learning outcomes as well as student satisfaction; however, for their learning results to improve, we must find ways to increase student engagement as well as allow them to use social media so that students have enough opportunities to make learning better, have more improvement, and be satisfied When we do this, we will be able to demonstrate and determine student satisfaction with social networks in an educational setting and to do so, we will need a lot of effort from five different factors, which are Interactivity with Peers, Interactivity with
Lecturers, Active Collaborative Learning, Perceived ease of use, and Perceived usefulness.
In this study, after accounting for all demographic factors such as gender, status, age, degree, and income, the results show that SMU has no impact on student academic performance (SAP) because of Sig index is greater than 0.05, indicating that there is no difference between groups in each of the above demographic factors, and it is not necessary to evaluate the demographic factor that affects SAP.
Recently, there are many research papers on the impact of social networks on young people in general and students in particular focused on student research at only one King Faisal university (Mahdi M Alamri and partners, 2020) and in Malaysia (Nasser Alalwan and partners, 2019), the rest they completed and presented the theory is based on a well-developed model and hypothesis Besides, based on the research results based on the research model proposed by Mahdi in 2020, it is generally quite good to apply in our country, it is still not exactly the same and exact, namely In the 6th hypothesis (H6), when applied and studied within Ho Chi Minh City, Vietnam was rejected because each country has different cultures as well as lifestyle and thinking of each person
In general, after having obtained the research results, the group also considers it as the basis for making recommendations to students, leaders in the field of education and social networking software development such as teachers and students schools, businesses and so on, so that they can improve the quality, specific features for learning on social networks as well as recognize and allow students to use it widely but still have discipline throughout the learning process, whether at home or in classrooms, to improve student outcomes in the future.
Recommendations
Based on the findings of this study, we can conclude that social networks, in general, have a favorable impact on both the learning process and the learning outcomes of students To begin, in the context of the present COVID-19 epidemic, social networks are the only instrument that may enable students and those involved in the area of education in general to preserve their teaching and learning progress If we are unable to attend class or take the tests, social media will serve as an alternative, as it may assist people through distance learning and online testing Second, students use social media in learning to improve their self-study ability and to participate more actively in academic activities, such as when lecturers ask students to lecture presenting in front of the class, they may be shy and unwilling to do it, but if you ask them to record a video of the presentation and post it on Youtube or even Tiktok, they will certainly do it The final reason to show that using social media in learning has a positive impact on student learning outcomes is creativity For example, when asked questions by the teacher and
53 asked students to discuss, if they do not use the Internet or social networks to investigate, they tend to be silent, have no ideas to answer, and gradually fall into a passive position in the classroom, sometimes even becoming disruptive for other students Hence, students require something to increase their ability to explore as well as their creativity because they still do not have enough knowledge or experience in life
However, in addition to the benefits, the usage of social networks in the learning process may have drawbacks The first disadvantage is that it might be a source of distraction when learning Assuming students are permitted to use them in the classroom, what percentage of students actually focus entirely on this? In Vietnam, it is very common to see students in class abusing the teacher's permission to do private things such as texting, listening to music, surfing Facebook/Instagram/Tiktok, etc The second argument is that social media can be an impediment to learning Many students in the following situation: they are working very hard on their homework, but suddenly a text message rings or they simply have a short break, they will often take advantage to check phones, laptops, specifically social networking sites to see if there is any new news, then they immerse themselves in it for a few more hours or even a whole day Finally, they will never be able to complete the assignment on time, according to the original aim set Every day, we have many negative causes from social media, and eventually, all of your work will be postponed, resulting in poor academic outcomes.
In sum, everything has advantages and disadvantages, and social media is no exception As a result, when selecting to explore this topic, my group would like to discover which aspects and causes of using social networks in learning have directly or indirectly influenced student learning outcomes All of the collected results, whether positive or negative, serve as the foundation for the team to make appropriate recommendations to the leaders, assisting them in finding ways to improve their performance useful things and minimize the consequences that social networks cause to improve the software to become truly useful and applicable in the educational environment Here are a few suggestions that the group would like to mention:
According to the research results, the ACL factor has an impact on the SAP factor, which means that students need to actively cooperate in learning in addition to using social networks to improve their own learning results is important and should be taken care of by the school The person who plays an important role in this case is none other than the students, they need to be self-aware of how they learn and cultivate that will directly affect their learning outcomes Actively cooperating actively in learning is not difficult, we can do this through group discussions, contact to exchange knowledge or share tasks to find documents together faster.
However, Vietnamese students nowaday are still too passive and timid and they often feel embarrassed when giving their opinions in class, that's why my group suggested that universities should implement implementing plans to improve this, such as by creating
54 contests on a social media platform about an academic issue that will not only help students improve their knowledge, may collide with many other people and most importantly, this is the best solution to support students while the COVID-19 pandemic shows no signs of stopping In addition, lecturers can also promote active cooperation between students in the learning process In addition to the current online learning sessions, lecturers should organize small exercises for students Participants work in groups and it can be a short essay or a presentation In this way, the students will have the opportunity to work together, come up with ideas to solve a problem, it is important that they are willing to work together because no one is perfect and good at everything in all fields, so they will complement each other's knowledge to help the final learning result achieve the score they expect and when this is successfully applied, the learning environment at schools university will be extremely vibrant, dynamic and creative, it will definitely be a place for people to develop themselves every day.
According to the research findings, the INL factor has an effect on SAP, hence enhancing communication with lecturers is critical Furthermore, there are three essential factors in this interaction, such as the use of social networks by students in learning, which has generated chances for students to talk, connect, and allow the sharing of information with lecturers to be done conveniently and quickly As a result, the point here is that teachers also play an important role in influencing student learning outcomes; actions that demonstrate cooperation, understanding, and respect will undoubtedly help students improve in their learning, and today's social networking tools serve as a bridge to make this more convenient and faster
To begin, in order for social media platforms to make the most of their advantages, lecturers could create closed groups on Facebook, Zalo, Instagram, and other platforms in order to support students more swiftly on all subject-related concerns Aside from particular groups for online teaching and learning, such as Google Classroom, closed groups on social media are simpler locations to communicate with students due to the familiar interface and the amount of time they spend on social media quite much Lecturers will be able to use social media to post information about the lesson, introduce students to academic materials or books, assign homework, and address difficulties directly in the content's comments section With that content, students and lecturers will be able to track more specifically the difficulties and obstacles that students are facing without having to waste time calling or checking emails too many times because people today are very concerned about environmental issues, each email sent will generate 19 grams of CO2, which has a negative impact on the environment, so in this case, social neologism will be used.
Second, conversing with students via social media is another method to assist students in improving their learning outcomes because time in class is limited yet the quantity of knowledge and experience that many lecturers have to provide is really helpful To discuss more effectively, lecturers can set aside time and organize short
55 sharing sessions or talk shows on social media platforms to discuss related issues in the subject, or simply give the two sides the opportunity to share their points of view with each other, so that teachers can better understand and assist students During these talks, lecturers can expose students to other social media that are also for learning purposes, such as Cambly, LinkedIn Learning, Academia, Skillshare, Quizlet, and so on.
The INP factor, like the INL factor, has an effect on the SAP factor, and while it is minor, it has a significant impact In this study, INP is characterized in three ways: the use of social networks in learning, the creation of conditions for students to communicate, discuss, and exchange information swiftly with peers In Vietnam, we have a proverb that says, "Better learn your friend than your teacher." with the meaning of encouraging the spirit of learning from our friends; after all, if we can't exchange learning issues, friends will be terrific companions as teachers, in addition to their own learning endeavors So, it is necessary to improve communication between classmates through social networks To improve interaction with peers, we need to improve these 3 aspects.
To begin with, in order to increase interaction with peers on social networking sites, businesses need to update with new features for a more learning environment It is possible for businesses that own social networking sites to develop additional learning- specific features Today, most social networking sites do quite well in terms of connecting with people, besides they should develop other features for teamwork such as managing and tracking activities group exercise, in this regard currently Google Calendar and Notion are the two best platforms, but to exchange, they still need intermediate social networking platforms to exchange information with each other, so this will be It is very useful and convenient if between businesses can cooperate and integrate maximum tools only on the same platform, this platform must both help students in learning and a place for them to easily Easily connect, exchange with friends to improve interaction with each other and ultimately improve their scores.
Next, some other recommendations to increase the rapid exchange between students is that businesses should create a large data storage space about academic documents to help students find content quickly and more efficient In addition, the school should also organize courses on soft skills on computers or provide students with websites to self-study these basic skills because through the process of working in groups with many Other students, my group admitted that some people have weak computer skills, they don't know how to use Microsoft's learning tools or even do not know how to perform operations on the Google Drive utility This inadvertently becomes an indirect cause of difficulties for teamwork In addition to direct exchange, currently on social networks, it is allowed to upload attachments, but it still exists a limitation that the upload capacity is only within the allowable limit, so businesses need to This limit must be extended so that students can exchange assignments and documents faster so that students will learn more easily with their friends and academic results will definitely improve.
It can be said that the PEOU factor affecting the SAP factor is one of the important relationships, it includes 5 different aspects but only aims to find out whether the use of social networks in learning whether it is easy for students or not, students can use it to quickly gain access to tutors and friends As mentioned in the previous chapter, although the hypothesis between PEOU and SMU is rejected on the grounds that Vietnamese students have no difficulty in using social networks while SMU has an impact on SAP (hypothesis 13) but that does not mean that the relationship between PEOU and SAP is meaningless In fact, PEOU - the degree to which students believe that using social media for effortless learning depends on their ability to use it, whether they know how to do it or not, it is certain that there will be obstacles for students who do not regularly use technology software in their study and study results will be affected badly For example, students don't know how to submit assignments on Google Classroom, they send their articles to the lecturer via email, but the lecturer has requested and clearly stated that they won't accept assignments through this form In this case, there is a high probability that the student will receive a score of 0 not because they don't know how to do the test, but because they have difficulty in using social networks to study.
Limitations and directions for future research
In comparison to the previous study, this study has addressed the limitation that the scope of survey and research conclusions in only one institution has now reached additional schools, but certain restrictions remain
Firstly, it is still geographically constrained, and despite efforts to survey additional colleges, this number is still insufficient, and it is still limited to Ho Chi Minh City As a result, the results are not generalizable to the entire country Due to limited time and resources, our team expects that these contributions can assist improve the next research publications, bringing statistical data that are more applicable over a number of larger surveys
Second, though our study only found five factors influencing the use of social networks and their impact on students' learning outcomes, we believe that there are other additional elements that we have not had enough time to study, therefore we hope that future research will produce a more extensive research model to address issues that we are still on a trip to discover a complete answer to.
With industry 4.0 rising, the use of social media for students is no longer entertaining but is fully capable of applying it in learning if they know how to use it properly Above all, because of the outbreak of COVID-19, people's lives are increasingly centered around high-tech utilities, especially social media - software with a wide variety of different forms and features that will make it easier for people to connect with each other not only in the country but also globally to build their own networks of learning with huge participants To gain a consistent and popular position as it is now so difficult and every business has to work hard and fight for their software so much that they want people who have been using a social media application to get a learning result worthy of what they have spent and a certain loyalty to it
Throughout the study, the group demonstrated the importance of using social media in learning to get the students' academic performance and we also made appropriate recommendations for leaders from schools to organizations, to be able to find new planning directions that would allow social media to be used widely in learning similar to today's student systems or special platforms like Google Meet and Zoom, it aims to support and improve student performance In addition to its main goal, the study focuses on assessing user satisfaction with social media applications for learning The group wanted to find differences in the experience between different students regarding gender, age, and background use more often so that further suggestions could be made in the future
While there are limitations to using social media for students such as overuse of time, not knowing how to exploit useful content to develop themselves and improve learning outcomes, the group hopes that not only university leaders, businesses behind the success of many social media platforms will change policies Changing features to help students that are most important is that students must be self-aware of the strengths and weaknesses of social media, know how to balance them both in learning and in life because any tool is the same, only when used properly, for the right purposes will it be like a wonderful companion of our own
Research model in Smart PLS software after data analysis
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