(TIỂU LUẬN) TOPIC FACTORS INFLUENCING THE FREQUENCY OF USING ONLINE FOOD DELIVERY OF UNIVERSITY STUDENTS IN HO CHI MINH CITY

FOREIGN TRADE UNIVERSITY IN HO CHI MINH CITY ★★★ GROUP REPORT SUBJECT: ECONOMETRICS INSTRUTOR: MRS LÊ HẰNG MỸ HẠNH TOPIC FACTORS INFLUENCING THE FREQUENCY OF USING ONLINE FOOD DELIVERY OF UNIVERSITY STUDENTS IN HO CHI MINH CITY Group: Community Class: K58CLC5 Ho Chi Minh City – 06/2021 Class code: 203 Table of contents TABLE OF CONTENTS TABLE OF CONTENTS i LIST OF FIGURES ii LIST OF TABLES ii GROUP MEMBER LIST ii ABSTRACT CHAPTER 1: INTRODUCTION WHAT DOES “TOO-BIG-TO-FAIL” MEAN? 2 WHEN DO WE CALL A BANK “TOO-BIG-TO-FAIL”? CHAPTER 2: ANALYSIS OF “TOO-BIG-TO-FAIL” PROBLEMS HOW THESE BANKS AFFECT ON THE WHOLE ECONOMY PROS AND CONS OF “TOO-BIG-TO-FAIL” BANKS CHAPTER 3: BREAKING UP BIG BANKS PROS AND CONS OF BREAKING UP BIG BANKS OTHER SOLUTIONS TO “TOO-BIG-TO-FAIL” PROBLEM CHAPTER 4: CONCLUSION 11 REFERENCE 12 Danh sách thành viên – Danh mục hình ảnh LIST OF FIGURES Figure “Too-big-to-fail” problems Figure Too-big-to-fail definition Figure The first too-big-to-tail problem arose with Continential Illinois Figure Dodd-Frank Wall Street Reform and Consumer Protection Act Figure The bankruptcy of Lehman Brothers Figure Less fear of the big impacts of the failure of a group of banks Figure Conclusion 11 LIST OF TABLES Table out of 100 biggest banks ranked both by reported total assets and total assets when derivatives are on a gross, not net (U.S GAAP) in some countries in 2012 GROUP MEMBER LIST STT MSSV HỌ VÀ TÊN 1911155028 Nguyễn Hồng Gia Hân 1911155063 Lê Yến Nhi 1911155079 Chung Ngọc Nhã Thi 1911155080 Nguyễn Đan Thi 1911155087 Nguyễn Quý Thương GHI CHÚ Chapter 1: Introduction CHAPTER 1: INTRODUCTION Living in an era where technology is advancing at an unprecedented rate, consumers are becoming smarter and more demanding than ever before With the existence of many new products and services, consumers now have plenty of options in terms of price, quality and especially, convenience A busy lifestyle is a signature in this modern time, people barely have time for themselves when most of their time is spent on work Briefly speaking, they want to save as much time as possible That is the main reason why 75% of the total Vietnamese population have used Food Delivery Service because they find cooking meals, or even eating out, very time-consuming What is remarkable is that 25% of them are new users who have started to use food delivery services for the first time due to Covid-19, which means safety from the pandemic is the second most significant reason explaining the rise of Food Delivery Service Among some ordering methods, Online Food Delivery Service Application is the most popular one when greater than 90% use this to order food and drinks The Vietnam’s Online Food Delivery Service market is driven by increasing internet and smartphone penetration, along with an increase in the number of restaurants and other food chains Aided by the technical advancements in the Vietnam’s Online Food Delivery Service market, it is expected to witness a healthy growth in the forecast period of 2021-2026, growing at a CAGR of 24% At present, this market is facing fierce competition with the existence of major brands such as Grabfood, Now, Baemin, Gofood, and Loship However, recently, the entry of domestic enterprises such as VinID and Tiki is considered to re-divide the market share of this potential "piece of cake" Although the Online Food Delivery Service Application market is booming with the participation of many players sharing the pie, having an available strong customer base will retain the firm’s position and make it harder for other brands to enter the market Therefore, besides attempts to raise brand awareness and attract new customers, brands must invest in advancing the company and the application itself to keep its current customers and turn them into loyal ones The objective of this research is to study the factors that influence the frequency of ordering food and drinks from Online Food Delivery Service Applications To be more practical, the research helps Online Food Delivery Service firms to have a better insight of customers by providing information about which factors may limit the number of times a customer demands for Online Food Delivery Service Applications Thus, firms can create an effective strategy not only to meet customers’ needs but even exceed their expectations Chapter 2:Literature review CHAPTER 2: LITERATURE REVIEW OVERVIEW OF FOREIGN RESEARCH Today there are more than 10,000 studies, essays, large and small articles related to consumer behavior, purchasing behavior, and frequency of purchase in the world Among them must be the quality studies of Arian Oosthoek (2013); See Siew Sin, Khalil Md Nor, and Ameen M Al-Agaga (2012); Yogi Tri Prasetyo and his partners (2021); Haiyang Liu (2019) Here, the authors would like to present a summary of the results of the above works as a premise for the next steps of our research In the research “What is the impact of Facebook tie strength and behavior on purchase intention?” by Arian Oosthoek, the author used systematic empirical investigation and survey to collect data, then applying multiple regression model and correlation coefficient to analyze the data The results of the study reach four conclusions as follows ● Hypothesis 1: Strong ties on Facebook affect online purchase intention more than weak ties on Facebook ● Hypothesis 2: Social ties on Social Media affect the purchase intention more for alow involvement luxury good ● Hypothesis 3: The higher the Facebook activity, the higher the purchase intention and attitude towards the product with strong ties ● Hypothesis 4: People with higher income have a more positive attitude and intention toward the purchase of high involvement products In the study “Factors Affecting Customer Satisfaction and Loyalty in Online Food Delivery Service during the COVID-19 Pandemic: Its Relation with Open Innovation” of Yogi Tri Prasetyo and partners, the authors applied methods of the questionnaire, model fit, and structural equation modeling (SEM Model) Initially, there were 11 hypotheses stated on the causal relationships between variables but after the first time running the SEM Model it pointed out that several hypotheses were not important The final SEM model contained exogenous variables, the two hedonic motivations and information quality affecting “INTENTION TO USE” and the two prices and promotion affecting “ACTUAL USE” Haiyang Liu with his research “ Factors positively influencing customer satisfaction of online food delivery services of customers in Bangkok and its vicinity” is based on the instrument of Cronbach’s Alpha Coefficient, Multiple Regression Analysis, and Pearson’s Correlation Chapter 2:Literature review Coefficient to study customer satisfaction There were two accepted hypotheses, the most predictive independent variables were 38 hedonic motivations (β = 0.767), new experience (β =0.163) Hedonic motivations and new experience could positively influence customer satisfaction of OFD services of customers in Bangkok and its vicinity at 52%, the rest 48% were affected by any other variables, these variables were not used in this paper The result of VIF value was not more than and the tolerance value exceeded 0.2, indicating that there was not Multicollinearity in the independent variables Thus, the standard error equaled ±0.492, the calculation as: 𝑌 = 870 (customer satisfaction) + 767𝑋1 (hedonic motivations) + 163𝑋2 (new experience) This study can offer benefits for restaurants and other food industries in respect of 44 potential new markets of online purchase, online strategy improvement, or investment decision for online food business in the future regarding factors predicting the customer satisfaction towards online food delivery services In general, foreign research above quite specifically analyzes the factors affecting customers’ online purchase decisions and offers specific solutions for their countries Here, the authors would like to use the above quality studies for reference purposes and as a base to build a model for research in Vietnam, specifically Ho Chi Minh City OVERVIEW OF DOMESTIC RESEARCH In Vietnam, there have been many typical studies such as “Research on online food ordering behavior of customers aged 18 - 30 in Ho Chi Minh City”, “Research on factors affecting online shopping decisions of consumers of university students in Ho Chi Minh City” or “Factors affecting the behavior of using public transport that has technology applications – A case study with Grab Bike service” In the study “Research on factors affecting online shopping decisions of consumers of university students in Ho Chi Minh City”, the results show that the scales are reliable through the Cronbach Alpha test However, EFA analysis explained that the scale of CHAMSOCKHACHHANG components was excluded The remaining factors perceived usefulness, perceived convenience in payment, perceived ease of use, trust, influence from external factors and price expectations continued to go into the ANOVA analysis The results Chapter 2:Literature review show that the factor “Influence from external factors” was not eligible to participate in this model, so it should be removed The remaining factors are proportional to the decision on the purchase price of consumer products of university students in Ho Chi Minh City It proves that the proposed theoretical model is consistent with current reality as well as the hypotheses in the theoretical model are accepted Dr Nguyen Binh Minh and his partners (2017) with the research “Factors affecting the behavior of using public transport that have technology applications – A case study with Grab Bike service” applied Cronbach’s alpha and the multiple regression model to analyze the data They concluded that the analysis factors all have a positive impact on the usage behavior of customers The results provide suggestions for managers and marketers to pay attention to quality factors, safety, and ease of use In addition, factors such as "subjective standards' '; "perceived usefulness" also need attention As for the "usefulness" factor, especially taking advantage of technology and saving time to create a difference and advantage in competition In general, researchers in Vietnam mainly analyze online buying behavior, but they are somewhat incomplete Researches in Vietnam often pay little attention to providing solutions for enterprises or give them an unsatisfactory, unrealistic, or unfounded way Therefore, in our research, we will overcome the mistakes and shortcomings of previous studies to research and provide effective and appropriate solutions to develop the field of online food ordering in Vietnam Chapter 3: Methodology and data CHAPTER 3: METHODOLOGY AND DATA Chapter 4: Result CHAPTER 4: RESULTS SCALE RELIABILITY ANALYSIS Based on the respective internal consistency explained by the value of Cronbach’s Alpha on the Appendix on Page a, after conducting the scale reliability analysis by Cronbach’s Alpha on Stata, we receive the result of Cronbach’s Alpha as the tables below indicates: ● With the scale of the variable PROMO, there are smaller encoded variables including Promo1, Promo2 and Promo3 Table Cronbach’s Alpha of the Model – Group The overall Cronbach’s Alpha of Group is 0.8381 (0 8≤ α < 9) which means that the internal consistency in this variable group is good Moreover, the item-rest correlation of the variables Promo1, Promo2 and Promo3 are 0.7083, 0.7489 and 0.6945 respectively (> 4) ⇨ It is concluded that Promo1, Promo2 and Promo3 are well qualified for the PROMO variable and there would be no variable necessarily excluded from the variable group ● With the scale of the variable BARRIER, there are smaller encoded variables including BAR1, BAR2 and BAR3 Table Cronbach’s Alpha of the Model – Group Chapter 4: Result The overall Cronbach’s Alpha of Group is 0.8438 (0 8≤ α < 9) which means that the internal consistency in this variable group is good Moreover, the item-rest correlation of the variables BAR1, BAR2 and BAR3 are 0.7250, 0.6860 and 0.7208 respectively (> 4) ⇨ It is concluded that BAR1, BAR2 and BAR3 are well qualified for the BARRIER variable and there would be no variable necessarily excluded from the variable group ● With the scale of the variable CS, there are smaller encoded variables including CS1, CS2, CS3 and CS4 Table Cronbach’s Alpha of the Model – Group The overall Cronbach’s Alpha of Group is 0.6937 ( 8≤ α < 9) which means that the internal consistency in this variable group is good Moreover, the item-rest correlation of the variables CS1, CS2, CS3 and CS4 are 0.5420, 0.0937, 0.5660 and 0.7306 respectively ( > 4) ⇨ It is concluded that CS1, CS3 and CS4 are well qualified for the CS variable and there would be variable CS2 necessarily excluded from the variable group EXPLORATORY FACTOR ANALYSIS After conducting the exploratory factor analysis (EFA) on Stata with the minimum value of eigenvalue to be retained is (which means that we will exclude the factor with the value of eigenvalue smaller than 1), we receive the result of factor analysis as the figure in the Appendix on page a indicates: Result: Exclude the factor with the eigenvalue smaller than and retain the Factor1, Factor2 and Factor3, which can cumulatively altogether explain 74.66 percent of the deviation of the data Chapter 4: Result ● Rotation: orthogonal varimax displaying loading as blank when |loading| < 0.5 Result: There will be main variable groups including PROMO (Promo1, Promo2 and Promo3), BARRIER (BAR1, BAR2 and BAR 3) and CS (CS1, CS3 and CS4) ● Kaiser-Meyer-Olkin (KMO) Test Running on Stata: Result: KMO = 0.6277 (> 5) ⇨ The sampling adequacy for each variable in the model and for the complete model is moderate CORRELATION ANALYSIS Between the dependent variable and independent variables, the correlation analysis using Pearson’s Correlation Coefficient of Payment, Income, Promo, Barrier and CS that affects the frequency of using online food delivery *Correlation is significant at the 0.1 level From the analysis, it can be clearly seen that independent variables Payment (𝑟 = 2123), Income (𝑟 = 3452), PROMO ( 𝑟 = 1405) and BARRIER (𝑟 =− 1826) will have an effect on the frequency of using online food delivery at the significant level 0.1 Meanwhile, the Chapter 4: Result independent variable CS (𝑟 =− 0139 ) will have no effect due to its insignificance at the 0.1 level MULTICOLLINEARITY TEST In statistics, the definition of Multicollinearity was, among all the independent variables, a circumstance of a very positive relationship (StatisticSolutions, 2017) Higher multicollinearity proved the higher degree of correlation among independent variables which might cause deviation away from the true value Equally, multicollinearity should not appear as it could lead to incorrect interpreting of MRA results After having a regression model, we use the variance inflation factor (VIF) to test multicollinearity The value for VIF starts at and has no upper limit A general rule of thumb for interpreting VIFs is as follows: ● A value of indicates there is no correlation between a given explanatory variable and any other explanatory variables in the model ● A value between and indicates moderate correlation between a given explanatory variable and other explanatory variables in the model, but this is often not severe enough to require attention ● A value greater than indicates potentially severe correlation between a given explanatory variable and other explanatory variables in the model In this case, the coefficient estimates and p-values in the regression output are likely unreliable From the appendix, we can see that the VIF value of each independent variable value between and meaning Multicollinearity does not exist in the independent variables Chapter 4: Result 5.1 HETEROSKEDASTICITY TEST Detecting heteroskedasticity To test heteroskedasticity, our group uses the Park Test {𝐻0: ℎ𝑜𝑚𝑜𝑠𝑐𝑒𝑑𝑎𝑠𝑡𝑖𝑐𝑖𝑡𝑦 (𝑁𝑜 ℎ𝑒𝑡𝑒𝑟𝑜𝑠𝑘𝑒𝑑𝑎𝑠𝑡𝑖𝑐𝑖𝑡𝑦) 𝐻1: ℎ𝑒𝑡𝑒𝑟𝑜𝑠𝑘𝑒𝑑𝑎𝑠𝑡𝑖𝑐𝑖𝑡𝑦 𝑒𝑥𝑖𝑡𝑠 The figure illustrates the step-by-step process of performing a Park test in STATA In this case, the coefficient for the variable lin (using the natural log of income as specified by the Park test) is statistically significant with a p-value of 0.000 Therefore, we reject the hypothesis of homoscedasticity, it means heteroskedasticity in this model 5.2 Correcting for heteroskedasticity To correct this problem, we use heteroskedasticity – robust standard error because they are valid—at least in large samples—whether or not the errors have constant variance, and we not need to know which is the case (process illustrated in the appendix) 10 Chapter 4: Result With the significant level 0,05 there are statistically significant independent variables: Payment (sig=0,01), Income(sig=0,000), Promo(sig=0,047), Barrier(sig=0,008) In addition, CS (sig=0,759) is not a critical predictor of the frequency of using online food delivery Therefore, we have a regression model with heteroskedasticity – robust standard error: 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 = 0, 374 + 0, 689𝑃𝑎𝑦𝑚𝑒𝑛𝑡 + 0, 761𝐼𝑛𝑐𝑜𝑚𝑒 + 0, 357𝑃𝑟𝑜𝑚𝑜 – 0, 449𝐵𝑎𝑟𝑟𝑖𝑒𝑟 11 Chapter 5: Conclusion CHAPTER 5: CONCLUSION 12 Appendix APPENDIX CRONBACH’S ALPHA AND INTERNAL CONSISTENCY RELATION According to Cronbach (1951), Cronbach’s Alpha Rule of thumb indicates the fluctuation of internal consistency ranged from different value of Cronbach’s Alpha as below: Table Cronbach’s Alpha and internal consistency relation Cronbach’s Alpha Internal Consistency Cronbach’s Alpha Internal Consistency α≥0 Excellent 6≤α < Acceptable 8≤α < Good 5≤α < Poor 7≤α < Moderate α < Unacceptable APPENDIX FACTOR ANALYSIS The process of running factor analysis in Stata and the table of result mentioned in Chapter Appendix References REFERENCE ... on online food ordering behavior of customers aged 18 - 30 in Ho Chi Minh City? ??, “Research on factors affecting online shopping decisions of consumers of university students in Ho Chi Minh City? ??... markets of online purchase, online strategy improvement, or investment decision for online food business in the future regarding factors predicting the customer satisfaction towards online food delivery. .. decisions of consumers of university students in Ho Chi Minh City? ??, the results show that the scales are reliable through the Cronbach Alpha test However, EFA analysis explained that the scale of CHAMSOCKHACHHANG