Report business research methods the influence of the social media application on university students results in ho chi minh city

TON DUC THANG UNIVERSITY FACULTY OF BUSINESS ADMINISTRATION INTERNATIONAL BUSINESS REPORT BUSINESS RESEARCH METHODS THE INFLUENCE OF THE SOCIAL MEDIA APPLICATION ON UNIVERSITY STUDENTS RESULTS IN HO CHI MINH CITY Lecturer: Tran Thi Van Trang Group: Infinity IQ – Shift: (Monday) and (Wednesday) Ho Chi Minh City, 1st April 2021 LIST OF GROUP MEMBERS Full name Le Lien Long Student ID 719D0081 Duty Evaluation Signature Chapter (1.2) Chapter (2.1.4 – 2.1.5 – 2.3.2) Chapter (3.7) 100% Signed Huynh Muoi Luy 719D0082 Chapter (1.6 – 1.7) Chapter (2.1.1 – 2.1.2) Chapter (3.3) Design questionaire, design research model and references 100% Signed Nguyen Thi Hong Ngoc (leader) 719D0105 Chapter (1.3 – 1.5) Chapter (2.1.1 – 2.1.2 – 2.3.1 – 2.3.2) Chapter (3.1 – 3.5) Synthesis report 100% Signed Truong Minh Thi 719D0173 Chapter (1.4) Chapter (3.4) Design questionaire 100% Signed Tran Le Thinh (subleader) 719D0263 Chapter (1.1) Chapter (2.1.3 – 2.1.4 – 2.2.1 – 2.3.2) 100% Signed Chapter (3.2) Synthesis report Hinh Van Ty 719D0221 Chapter (1.2 – 1.2.2) Chapter (2.1.6 – 2.1.7 – 2.2.3 – 2.3.2) Chapter (3.6) 100% Signed COMMENTS FROM LECTURER ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ACKNOWLEDGEMENT First of all, our team would like to give their sincere thankfulness and deep gratitude to Ms Tran Thi Van Trang Ms Trang was the one who gave us endless support and conscientious guidance during the course of our research and finished our survey on the subject: “The influence of the social media application on university students results in Ho Chi Minh City” Base on precious expertise and research experience, she was a passionate help and provided specific guidance on the direction of development, the direction of the study of the subject, while also providing guidance on the appropriate approach, presentation of content, to go in and analysis of the research laid out That is the reason why we can get our survey as well as the research in the best possible way Secondly, our team would like to thank the Department of Business administration at Ton Duc Thang University for allowing us a chance and good condition to explore and study the subject Nevertheless, because of limited time conditions, along with the fact that lack of practical experience, he was inevitably flawed, eager to receive comments from teachers and friends Thank you so much Research Team ABSTRACT The main goal of this is to identify the factors that influence learning outcomes from social networking applications, to examine how it has affected student learning outcomes as well as other stakeholders Then, we will give some methods to research, analyze specific results and finally send suggestions and recommendations to schools, businesses, … Key words: social media applications (SMAs); students’ academic performance; sustainability for education; structural equation modelling (SEM); TAM Table of Contents CHAPTER 1: INTRODUCTION 1.1 Background 1.2 Problem statement 1.3 Object and scope of the study 1.3.1 Research object 1.3.2 Scope of the study 1.4 Objectives of the study 1.5 Research questions 1.6 Significant of research 1.7 Structure of the research paper CHAPTER 2: LITERATURE REVIEW 2.1 Concepts and theories 2.1.1 INP and INL 2.1.2 ACL 2.1.3 PEOU and PU 2.1.4 EN 2.1.5 SMU 2.1.6 SS 2.1.7 SAP 2.2 Previous studies 10 2.2.1 The research by Mahdi M Alamri, Mohammed Amin Almaiah, and Waleed Mugahed Al-Rahmi (2020) 10 2.2.2 The research conducted by Nasser Alalwan, Waleed Mugahed Al-Rahmi, Osama Alfarraj, Ahmed Alzahrani, Noraffandy Yahaya, and Ali Mugahed AlRahmi (2019) 11 2.2.3 The research articles performed by Jaffar Aman, Mohammad Nurunnabi, and Shaher Bano (2019) 12 2.3 Research Model and Hypothesis 14 2.3.1 Research Model 14 2.3.2 Hypothesis 15 2.3.2.1 Factors effect on Engagement (EN): Interactivity with Peers (INP) and Interactivity with Lecturers (INL) 15 2.3.2.2 A factor effect on Engagement (EN): Active Collaborative Learning (ACL) 15 2.3.2.3 The relationship between Perceived ease of use (PEOU) and Perceived usefulness (PU) as well as the effect of each factor on Engagement (EN) and Social media application use (SMU) 16 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) 17 2.3.2.5 A factor effect on Student’s Satisfaction (SS) and Student’s Academic Performance (SAP): Social media application use (SMU) 17 2.3.2.6 The relationship between Student’s Satisfaction (SS) and Student’s Academic Performance (SAP) 18 CHAPTER 3: RESEARCH METHODOLOGY 19 3.1 Research design 19 3.2 Pilot test 19 3.3 Questionaires design 20 3.4 Measurement scales 20 3.5 Sample 24 3.6 Collection method 25 3.7 Data analysis 25 REFERENCES 28 LIST OF ABBREVIATIONS AND ACRONYMS SPSS Statistical Product and Services Solutions ANOVA Analysis of Variance VIF Variance Inflation Factor H Hypothesis TAM Technology Acceptance Model SEM Structural Equation Modeling TRA Theory of Reasoned Action Abbreviations of variables INP Interactivity with Peers INL Interactivity with Lecturers ACL Active Collaborative Learning PEOU Perceived Ease of Use PU Perceived Usefulness EN Engagement SMU Social Media Applications Use SMAs Social Media Applications SS Student’s Satisfaction SAP Student’s Academic Performance LIST OF FIGURES Figure The World's most-used social platforms in 2021 by Authors Synthesized (Source: Datareportal.com) Figure Time per day spent using the internet in the world in 2020 by Authors Synthesized (Source: Datareportal.com) Figure Social media in Vietnam in 2021 by Authors Synthesized (Source: Datareportal.com) Figure Social media behaviors in Vietnam in 2021 by Authors Synthesized (Source: Datareportal.com) Figure Most-used social media platforms in Vietnam in 2021 by Authors Synthesized (Source: Datareportal.com) Figure Research model of the relationship between social media application and student’ academic performance (2020) by Authors Synthesized 10 Figure The research model illustrates the factors Affecting Students' Academic Performance in Higher Education by Authors Synthesized 12 Figure Research model about the relationship between positive media factors (PMF), negative media factors (NMF), and Students(2019) by Authors Synthesized 13 Figure Research Model by Authors Synthesized 14 LIST OF TABLE Table Measurement scales 20 Table Criteria for measuring SEM in Smart PLS 26 Table 3 Indicators of Cronbach's Alpha in Smart PLS 27 been perceived to be positively influenced by the extended conformation model (ECM) from online ACL, which shows Students' Satisfaction (SS) and Perceived Ease of Use (PU) (Junjie, 2017) [20] Also in the early research stages, the hypothesis about the important relationship between Active Collaborative Learning (ACL) and Engagement (EN) was carried (Al-Rahmi, 2019) [21], so this research recommended the hypothesis as follow:  Hypothesis (H3): An important relationship between Active Collaborative Learning (ACL) and Engagement (EN) 2.3.2.3 The relationship between Perceived ease of use (PEOU) and Perceived usefulness (PU) as well as the effect of each factor on Engagement (EN) and Social media application use (SMU) As mentioned in the definition above, PEOU and PU are the two basic elements of the Technology Application Model (TAM) This model has a significant impact on research assessing the acceptability of technological advances and the use of social media for learning According to previous studies showing that using social networking applications is very easy when we use them to improve the learning process of students (Zeithaml, 2000) [22] Besides the combination of two variables PEOU and PU in the TAM model, there are also other variables, so there exist TAM variations that combine in many different ways with the variable of Student’ Academic Performance (SAP) However, our research model involves only two fundamental variables in the TAM model as well as Engagement (EN), Student’ Academic Performance (SAP), and student satisfaction (SS) when they use social media for learning When a student becomes aware that using social networking applications will be beneficial for their studies, they will begin to think about it more positively (Lin, 2011) [23] In this case, PU is considered as the factor that has the greatest influence on the application of mobile technology platforms in learning (Almaiah, 2019) [24,25] A group of study authors has demonstrated that students have found it useful to use social networking applications and that they have high intentions of using it as an online learning resource (Hsu, 2012) [26] Besides, not only students but lecturers also feel useful when applying social networking applications to their lectures Through the above information, this study gives the following Hypothesis:    Hypothesis (H4): A meaningful connection between perceived ease of use (PEOU) and Engagement (EN) Hypothesis (H5): A strong relationship between Perceived usefulness (PU) and Engagement (EN) Hypothesis (H6): A considerable link between perceived ease of use (PEOU) and Social media use (SMU) 16   2.3.2.4 Hypothesis (H7): A positive impact was observed between the use of perceived usefulness (PU) and Social media use (SMU) Hypothesis (H8): A massive relation between perceived ease of use (PEOU) and perceived usefulness (PU) Students' Engagement (EN) is a factor effect on Social media application use (SMU), Student’s Satisfaction (SS) and Student’s Academic Performance (SAP) All technological innovations are meaningless without the participation of anyone (Rueda, 2017) [27] and social networking platforms are no exception (Junco, 2013) [28] The use of personalized social media platforms allows each student to design their learning and this will make it easier for them to build knowledge For instance, Facebook has provided many features and social benefits to students when they apply this platform for learning (Camus, 2016) [29] When students participate in social networking applications for learning purposes, they will have a greater sense of learning, know how to participate in tasks, allow me to have and thus their performance, learning outcomes will significantly improve and students will surely be satisfied with it (Moafa, 2018) [30,31,32,33] Therefore, this study has proposed the following hypotheses:    2.3.2.5 Hypothesis (H9): Engagement (EN) impact on Social media use (SMU) Hypothesis 10 (H10): Engagement (EN) impact on Student’s Satisfaction (SS) Hypothesis 11 (H11): Engagement (EN) impact on Student’s academic performance (SAP) A factor effect on Student’s Satisfaction (SS) and Student’s Academic Performance (SAP): Social media application use (SMU) The use of social media is believed to be a key factor contributing to improving student learning outcomes, thereby measuring sustainability in education as well as related to student satisfaction when using it (Al-Rahmi, 2019) (Al-Maatouk, 2020) (Cao, 2013) [34,35,36] This is why this study has attempted to find a relationship between them, but unlike previous studies, this paper has been consulted from the perspective of many different universities in Ho Chi Minh City Because the learner has increased the popularity of social media use among students and faculty, it is seen as playing a very useful role in improving, enhancing learning outcomes as well as improving for students to be satisfied when using Therefore, this study gives the following hypothesis:  Hypothesis 12 (H12): Social Media Use (SMU) impact on Student’s Satisfaction (SS) 17  2.3.2.6 Hypothesis 13 (H13): Student’s Academic Performance (SAP) has impacted by Social Media Use (SMU) The relationship between Student’s Satisfaction (SS) and Student’s Academic Performance (SAP) This research studied and pointed out that when students achieve learning outcomes as expected, they will get satisfaction, specifically in this case they were satisfied when using social networking has brought good benefits in learning Therefore, this study focuses on the relationship between SS and SAP, the hypothesis is given as follows:  Hypothesis 14 (H14): Student’s Satisfaction (SS) has a great connection with Student’s Academic Performance (SAP) 18 CHAPTER 3: RESEARCH METHODOLOGY 3.1 Research design The research process of the group goes through two main stages: experimental and formal research Because of the limited time and resources available at the experimental stage, the team does not use any of the methods but only the models and scales of previous studies so that it can adjust the survey subjects to be university students in Viet Nam Later, when moving to the formal phase of research, the group decided to adopt a quantitative approach by conducting a survey directly through questionnaires On issues related to the survey, after having examined the questionnaire, translated the questionnaire into Vietnamese in order to better suit Vietnamese students as well as submit it to their staff for review and review After the questionnaire was accepted, the group began calling for the correct study group that the students were studying at universities in Ho Chi Minh City, Vietnam The questionnaire was designed by Google Form utility, which included a number of simple questions on the personal information section and the main questions, and chose the Likert scale for the survey 3.2 Pilot test After the group has built the questionnaire itself and before the teacher evaluates it as appropriate, to be able to take it to the survey, testing whether the true level of respondents' questionnaire is essential because it will directly affect the statistical results, run the model or analyze the data In cases where, if the experimenter doesn't understand and they answer questions based on sentiment or random selection, the results will finalize, and so the pilot experiment will be the first step that will help the group test the quality of the words used The method of asking questions and the order of questions in the questionnaires before the start of the questionnaire's release - a formal survey form, also helped the group avoid errors that were too late to be reworked For the subjects of the pilot test, the group selected only the subjects of the survey in small numbers of about 20 However, since the group focused mainly on retrieval of quantitative data, participants in the pilot section conducted surveys using pre-existing questionnaires, after they answered the group would not ask further open questions regarding the research issue and the data collected would not enter the official data processing software However, instead the group will ask and take advice from the participants on questions, answers, and so on So that we can then synthesize and adapt the questionnaire to a more appropriate level 19 3.3 Questionaires design The sample for this research is collected using a structured questionnaire The basis on the results of the preliminary study, the framework was designed The questionnaire consisted of two parts portions: Part 1: Personal information Gender, age, industry, and personal income were assembled to characterize the study's participants and understand the major individuals who completed the questionnaires based on demographic standards  Part 2: Survey content Factor claims here include accompanying: Interactivity with Peers, Lecturers, Active Collaborative Learning, Engagement, Perceived Ease of Use, Usefulness, Social Media Use, Students’ Satisfaction, Students’ Academic Performance All of these statements are measured by the Likert scale of points (from – Strongly Disagree to – Strongly Agree)  3.4 Measurement scales The template is based on the construction scale theory and previous studies on how definitions using social media applications affect the learning of UNIVERSITY students in HCM City consists of factors, 39 items: (1) Interactivity with peers has items; (2) Interactivity with lectures has items; (3) Active collaborative learning has items; (4) Engagement there are items; (5) Perceived ease of use has items; (6) Perceived usefulness has items; (7) Social media use has items; (8) Students' satisfaction has items; (9) Students' academic performance has items The scale used in this study is the five-level Likert (1- Strongly disagree, 2- Disagree, - Uncertain, 4- Agree, 5- Strongly agree) These variables represent the following components that influence college student learning through social networking applications: Table 3.1 Measurement scales Factors Interactivity with Peers Encryption Questions Source INP1 SMAs facilitate interaction with peers Patera, 2008 INP2 SMAs give me the opportunity to discuss with peers 20 Interactivity with Lecturers Active Collaborative Learning Engagement INP3 SMAs give me the opportunity to discuss with peers INL1 SMAs facilitate interaction with lecturers INL2 SMAs give me the opportunity to discuss with lecturers INL3 SMAs allow the exchange of information with lecturers ACL1 By using SMAs I felt that I actively collaborated in my experience ACL2 By using SMAs I felt that I have co-created my own experience ACL3 By using SMAs I felt that I had free reign to co-create my own experience ACL4 By using SMAs I am satisfied with active collaboration in my research EN1 By using SMAs I engage in interactions with my peers Patera, 2008 Dillenbourg, 1999 Rueda, 2017 21 Perceived Ease of Use Perceived Usefulness EN2 By using SMAs I engage in interactions with my lecturers EN3 By using SMAs I learned how to work with others effectively EN4 By using SMAs I am satisfied with the EN in my studies PEOU1 I feel that using SMAs will be easy in my studies PEOU2 I feel that using SMAs will be easy to incorporate in my studies PEOU3 I feel that using SMAs makes it easy to reach peers PEOU4 I feel that using SMAs makes it easy to reach lecturers PEOU5 Using SMAs is clear and understandable PEOU6 SMAs not require a lot of my mental effort PU1 I believe that using SMAs is useful for learning Fishbein, 1980 Davis, 1989 22 Social Media Use Students' Satisfaction PU2 I feel that using SMAs will help me to learn more PU3 I believe that using SMAs enhances my effectiveness PU4 SMAs enable me to accomplish tasks more quickly PU5 SMAs enhance my learning performance PU6 SMAs enhance effectiveness in my studies SMU I use SMAs for interaction with my peers SMU I use SMAs for interaction with my lecturers SMU I use SMAs for active collaborative learning SMU I use SMAs for engagement SS SS I enjoy the experience of using SMAs with peers Al-Rahmi, 2019; Al-Maatouk, 2020; Cao, 2013 Rueda, 2017 I enjoy the experience of using SMAs with lecturers 23 Students' Academic Performance SS I am satisfied with using SMAs for learning SS I am satisfied with using SMAs to improve my studies SAP Has improved my comprehension of the concepts studied SAP Has led to a better learning experience in this module SAP SMAs have allowed me to better understand my studies SAP SMAs are helpful in my studies and make it easy to learn SAP SMAs improve my academic performance Rueda, 2017 3.5 Sample The number of samples chosen plays a crucial role because the study's results and reliability are guaranteed depending on it Theoretically, the study results are more accurate and significant in practice as the sample size increases However, as mentioned, the time and resources were so limited that the group was unable to survey large numbers of samples, so the group used the formulas of researchers to help facilitate the sampling process According to research by Hair, Anderson, and Black (1998), they found the minimum sample size to be five times the total observed variable The formula below is used by researchers to analyze elements (Comrey, 1973 and Roger, 2006): n = *m (m is numbers of question) 24 According to research by Tabachnick and Fidell (1996), the minimum sample size required is: N = 50 +8 *m (m is numbers of independent variables) Based on these two formulations, my team has the following results: N = 39*5 = 195 N = 50 + 8*5 = 90 Thus, the minimum sample size would be 195, but to increase the reliability and persuasion of the analysis, my team estimated that it would collect about 350 samples for data analysis purposes 3.6 Collection method The average study would take about five minutes for students who were both thinking and doing, and to make this study more widely known, the group sent links to this form through forums popular with large numbers of students involved: The student community group, Fanpage /blog about student problems or even calls for conducting personal Facebook surveys as well as sending messages to their friends through Messenger, Instagram and Zalo The group had begun to collect data in early April 2021, and within two weeks of the survey, between 12-25/04/2021, the number of polling notes was recorded to be 323 forms, but in a rescreening process, the full number of samples were filled, correct for use in the analysis to only 310 samples That is, the group eliminated 13 forms of the nonresearch object type or that one that was not suitable 3.7 Data analysis As mentioned in the design of the study article, the group chose a quantitative approach through an existing questionnaire The reason we use this method is that it is suitable for the study of the attitudes, opinions, and behavior of the people surveyed In addition, in 2004, Kothari argued that theories are often associated with this method to measure research factors, examining the relationship between variables in numerical and statistical form 25 Quantitative results from a sample group will be generalized to a larger sample overall and the software used by the em to run the data in this study is Smart PLS This is the software recommended by a lot of students and faculty because it is highly regarded as lightweight software Make visual results compared to software of the same type that still ensures performance in model estimation In addition, Smart PLS is suitable for the analysis of primary data, in particular for studies using SEM models in business, economics, sociology, and psychology So to sum up, this group of children is using Smart PLS software to run their data primarily, and there are some other parts that need support from SPSS However, for a more general and relatively accurate analysis, there are parameters of this two software that the team needs to keep in mind during the process, namely: SRMR (Standardized root mean square residual) = 0.7 Convergent validity AVE >= 0.5 Load factor >= Discriminant validity Sqrt (AVE) > Latent variable correlations VIF VIF > Bootstrapping P-value < 0.05 Table Criteria for measuring SEM in Smart PLS a/ Cronbach's Alpha reliability analysis: In 1951, Lee Cronbach developed the Alpha, which is a test designed to analyze and evaluate the reliability of the scale and has a variable value as a number in paragraph [0.1] Following are some of the standards in this scale of reliability savings:   If a measured variable has a Total Correlation coefficient, Corrected Item - Total Correlation ≥ 0.3 then the variable is required (source: Nunnally, J (1978), Psychometric Theory, New York, McGraw-Hill) For disused questions and Likert scale questions, we have a common rule for explaining Alpha: 26 Cronbach's alpha Internal consistency ɑ >= 0.9 Excellent 0.9 > ɑ >= 0.8 Good 0.8 > ɑ >= 0.7 Acceptable 0.7 > ɑ >= 0.6 Questionable 0.6 > ɑ >= 0.5 Poor 0.5 > ɑ Unacceptable Table 3 Indicators of Cronbach's Alpha in Smart PLS b/ Correlation Coefficient: The correlation coefficient (symbol: R), developed by Karl Pearson in the 1880s, is a test statistic that measures the statistical relationship or links between variables dependent on continuous variables and has movement values in the continuous range − to +1 Consider for example the relationship between the satisfaction of using social networks and the learning outcomes of students or the relationship between student participation in the classroom and their interaction with the faculty Some of the things to note when using this coefficient are:     This coefficient is only significant if and only if the observed significance level (SIG.) is less than the α = 5% significance level If they are between 0.5 and ± 1: strongly correlated If the range is between 0.3 and ± 0.49: median correlation If are below ± 0.29: weak correlation 27 REFERENCES Paolo M Pumilia-Gnarini; Elena Pacetti; Jonathan Bishop; Luigi Guerra, 2012, ICI Global, 10.4018/978-1-4666-2122-0 [CrossRef] Kerri-Lee Krause; Sandra Bochner; Sue Duchesne, 2006, MACQUARIE University, 9780170128520 [CrossRef] 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Base on precious expertise and research. .. intentions in using the media The use of media in learning and information exchange The use of TAM with constructivist theory and communication theory in testing students'' behavioral intentions in using... influence of the social media application on university students results in Ho Chi Minh City? ?? for the research When choosing this topic, our group had the desire to clarify the factors affecting student