Phân tích mức độ tiêu thụ Pizza của học sinh, sinh viên ở Hà Nội

ASSIGNMENT PIZZA CONSUMPTION OF STUDENTS IN HANOI September 25, 2015 TABLE OF CONTENT I ABSTRACT Bumble-bee team is a group of second-year students at Foreign Trade University, apart from worrying about the high degree, has considerable concerns about health and money Especially, food has been a matter of great controversies perplexing our team whether how we can pay a reasonable price for their goods and ensure the food safety Started from the actual needs of the students, the assignment is carried out through a detailed survey of the behavior of consuming pizza of the students in Hanoi, in September 2015 We hope that after this survey we will find what factors mainly affecting preferences to type of pizza of the young generation at the moment to explain the habit of eat fast food of the young in Hanoi at present The survey in forms of multiple-choice questions presented and filling information As well as this goal, our result may hopefully be a reference to the restaurant’s managers and chef in Hanoi to implement some policies and make their products become one of the most attractive dishes After collecting the data, we generalize and classify data on base of the knowledge that we have learned Softwares used are Typeform, Excel, Stata and Word to accomplish the assignment II INTRODUCTION Rationale Until French colonization on the mid-19th century, Vietnam’s economy was mainly agrarian and village-oriented In 1986 Vietnam launched a political and economic renewal campaign (Doi Moi) that introduced reforms intended to facilitate the transition from a centrally planned economy to form of market socialism officially termed "Socialist-oriented market economy” Doi Moi combined economic planning with freemarket incentives and encouraged the establishment of private businesses in the production of consumer goods and foreign investment, including foreign-owned enterprises The Gross Domestic Product (GDP) in Vietnam expanded 6.44 percent in the second quarter of 2015 over the previous quarter GDP Growth Rate in Vietnam averaged 6.15 percent from 2000 until 2015, reaching a high of 8.46 percent in the fourth quarter of 2007 and a record low of 3.14 percent in the first quarter of 2009 Moreover, Vietnamese people’s living standard has been increasing in the past few years A proof for this is that CPI of 2009 is 6.88% while in 2011, the CPI jumped to 18.12% and exceeded the target Therefore, they are able to afford their demand for food and services In this report, we carried out to analyse an aspect of that field that is consumer behaviour about eating pizza We mainly focused on analysing influences of many factors such as student’s income, quantity of pizza consumption, the ability of compete with spaghetti,… Objectives - Surveying the satisfaction level of students about pizza - Pinpointing different crucial elements influencing young generation uses in the opting of pizza - Investigating precise relationship in vital components reckoned by consumers for the choice between pizza and spaghetti Subject and Scope - Subject: students aged 18-24 in universities and colleges in Hanoi - Scope: data are collected from all students studying in universities and colleges, in September 2015 Method of Research - Data source: + Primary source: information collected from 60 students through online surveys + Second source: information collected from the Internet and textbook - Form: surveys in form of multiple-choice questions and filling information - Support tools: Excel, Word, Stata - Quantity: 59, in which 60 are valid After collecting the data from some different sources above, we summarized and analyse information through statistical techniques to show the final result then write a report as an overview of our team’s working process III LITERATURE REVIEW History Pizza is a baked pie of Italian origin consisting of a shallow bread-like crust covered with seasoned tomato sauce, cheese, and often other toppings such as sausage or olive The pizza could have been invented by the Phoenicians, the Greeks, Romans, or anyone who learned the secret of mixing flour with water and heating it on a hot stone In one of its many forms, pizza has been a basic part of the Italian diet since the Stone Age This earliest form of pizza was a crude bread that was baked beneath the stones of the fire After cooking, it was seasoned with a variety of different toppings and used instead of plates and utensils to sop up broth or gravies The first major innovation that led to flat bread pizza was the use of tomato as a topping It was common for the poor of the area around Naples to add tomato to their yeast-based flat bread, and so the pizza began While it is difficult to say for sure who invented the pizza, it is however believed that modern pizza was first made by baker Raffaele Esposito of Naples In fact, a popular urban legend holds that the archetypal pizza, Pizza Margherita, was invented in 1889, when the Royal Palace of Capodimonte commissioned the Neapolitan pizzaiolo Raffaele Esposito to create a pizza in honor of the visiting Queen Margherita Of the three different pizzas he created, the Queen strongly preferred a pie swathed in the colors of the Italian flag: red (tomato), green (basil), and white (mozzarella) Supposedly, this kind of pizza was then named after the Queen as Pizza Margherita Later, the dish has become popular in many parts of the world: • • The first pizzeria, Antica Pizzeria Port'Alba, was opened in 1830 in Naples In North America, The first pizzeria was opened in 1905 by Gennaro Lombardi at 53 1/3 Spring Street in New York City • The first Pizza Hut, the chain of pizza restaurants appeared in the United States during the 1930s We take an obvious example about a group of professors researching customer’s behaviors of Pizza Hut The research they conducted is descriptive as well as theoretical in nature They use the easiest method to reach a lot of pizza customers such as data collection, scaling technique, questionnaire development, pre – testing, sample techniques and field work Customer satisfaction It’s official The American Customer Satisfaction Index (ACSI) Report on Airlines, Hotels, Fast Food, Restaurants, and Express Delivery Services was released this week and shows that Papa John's posted the top customer satisfaction score for both the pizza business and the fast-food restaurant sector overall The ACSI uses a 100-point scale to measure customer expectations, perceived quality, value, complaints, and loyalty and Papa John's posted the top score of 80 points, 7% up from last year Little Caesar and Pizza Hut tied just behind the leader at 78 points each followed by Domino’s with 77 points The largest mainstream fast food brands all scored below their pizza chain competitors with Wendy’s being the burger-chain leader at 77 points Burger King showed the largest improvement in score from the year prior by gaining 7.2% to score 74 points this year Taco Bell tied the King at 74 points while McDonald’s lost 4.3% to rank at the bottom of this year’s ACSI study with just 64 points overall The ACSI report acknowledged that 2009 was a difficult year for restaurants of all types and fast food sales were down 2.9% overall for 2009 after falling 1.2% in 2008 The tough economy meant that consumers ate out less often and spent less when they did The pizza and fast food segment fared better than general restaurants due to their lower menu prices, but both categories (fast food and restaurant) lost some business in 2009 Price: Selection of a Pizza outlet depends upon price value The genre of restaurant is judged by customer through Pizza selling price; with the view that a costly restaurant will provide a better quality of both service and quality The kind of restaurant, type of occasion, profession and age group The relative vitality of the restaurant decision vary extensively by restaurant sort, eating event, age and occupation Rich clients select feel and ambience level as their determinant determination variables Different researchers have demonstrated price as client's first choice (Kara, 1995), (Park, 2004), (Andaleeb, 2006), (Tse, 2001) (Palazon, 2009) Introduction of novelty items and limited time deals reap fruitful and recurrence sales (Consuegra et al, 2007) demonstrated that recognized price impartiality impacts buyer's contentment and loyalty Nonetheless, client fulfillment and loyalty are two paramount predecessors of value acknowledgement In the meantime, (Iglesias and Guillen, 2004), agreed that cost can influence consumer satisfaction Furthermore (Cater and Cater, 2009) proposed that customer satisfaction is contrarily influenced by cost It could be characterized as "the procedure by which buyers translate cost and ascribe quality to a product or service" It has intrigued specialists for some years It is a well-known reality that cost and quality are two significant elements of value They both accelerate client gratification and additionally client upkeep, which help increment the benefits of any business So for a chief of fast food restaurant it is significant to know customers discernment of value and price Past studies analyzing the effect of cost on perceived value have inferred a negative connection: the higher the value, the lower the product value is discerned This is a general phenomenon that when clients go out for shopping they have a tendency to purchase items which have lower costs so they get a better value This is upheld by (Hutton, 1995) asserting that now more purchasers are attempting to boost quality for cash used, requesting better quality at lower level costs Even though this may not be fully right for all the consumers on the grounds, since a few customers are ready to pay more if they truly like a product Higher recognized quality brings about a more amazing eagerness by the purchaser to receive another item (Mcgowan & Sternquist, 1998) Clients who are eager to pay higher costs for an item or service have a tendency to be brand cognizant and renown touchy They likewise accept cost is an indicator of quality or status When clients are persuaded that they are getting the best quality product or service, they will have a tendency to improve reliability to it in the long run Research led by (Kandampully & Suhartanto, 2003) on hospitality industry revealed a positive relationship between cost and client loyalty Past studies looking at the effect of cost on observed worth have proposed a negative connection: the higher the value, the bring down the item quality is discerned (Dodds et al 1991; Grewal et al 1998) This is a general wonder that when clients go out for shopping they have a tendency to purchase items which have more level costs so they improve worth This is underpinned by (Hutton, 1995) guaranteeing that now more purchasers are attempting to boost worth for cash used, requesting better quality at easier costs In spite of the fact that this may not be completely accurate for all the clients since a few clients are eager to pay more assuming that they truly like an item Higher discerned worth brings about a more amazing eagerness by the customer to embrace another item (Mcgowan & Sternquist, 1998) IV THEORETICAL FRAMEWORK This entire research rests based on the theoretical framework Since the theoretical framework offers the conceptual foundation to proceed with the research, and since a theoretical framework is none other than identifying the network of relationships among the variables considered important to the study of any given problem situation, it is essential to understand what a variable means in this study Based on the literature review, this research concentrates on conceptual framework of pizza consumption and its impact on consumers‟ mind This framework emphasizes variables such as price of pizza (Pp), price of spaghetti (Ps), quantity of pizza consumers are willing to buy (W_t_b_p), quantity of spaghetti consumers are willing to buy (W_t_b_s) and income Comparing pizza to spaghetti, we can assess the relationship between two kinds in fast food market Outline of the model structure: The situation in which economic correlations involves only two variables are very rare Rather we have a situation where a dependent variable, Y, can depend on a whole series of factorial variables or regressions For example, the demand for pizza depends not only on price but also on the prices of substitutes goods (spaghetti), the general level of consumer prices and resources (income, satisfaction level) Thus, in practice, there are normally correlations as: Y = β1X1+β2X2 + β3X3 + + βkXk + ε where values Xj (j = 2, 3, , n) represents the variable factor or regressors, the values βj (j = 1, 2, 3, ,k) represents the parameters of the regression and ε is theresidual factor factor Residual factor reflects the random nature of human response and any other factors other than Xj, which might influence the variable Y Note that we have adopted the usual notation, we assigned to the first factor, notation X2, the second, notation X3 etc In fact, as we shall see, it is sometimes convenient that a parameter β to be considered a coefficient to a variable X1 whose value is always equal to unity Then it becomes possible to rewrite the equation in the form: Y = β1X1+β2X2 + β3X3 + + βkXk + ε In the case of pizza consumption of students in Hanoi, Conventional Linear Demand Model: Qx = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε QX – Index of pizza quantities (base year = 2015) X1 – Own price of a given type of pizza X2 – Price of related good (spaghetti) X3 – Disposable Income X4 – Trend Ei – Error term Without Trend 10 Source SS df MS Model Residual 15.8871138 38.8247506 53 3.17742277 732542464 Total 54.7118644 58 943308007 Qp Coef pp Ps W_t_b_p W_t_b_s income _cons 6.00e-06 -.0000112 3312113 -.0255937 2.47e-09 4066745 Std Err t 2.73e-06 4.80e-06 1016999 0804771 3.97e-09 5175505 2.20 -2.34 3.26 -0.32 0.62 0.79 Number of obs F( 5, 53) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.032 0.023 0.002 0.752 0.537 0.436 = = = = = = 59 4.34 0.0022 0.2904 0.2234 85589 [95% Conf Interval] 5.25e-07 -.0000209 1272272 -.1870104 -5.50e-09 -.6314003 0000115 -1.61e-06 5351954 1358229 1.04e-08 1.444749 (Marshallian) Demand Function: Maximize U(X,Y) you have optimal x*=x*(px,py,I) CES utility function: U(x, y) = where = δ ≤ Setting up the Lagrangian: L = First-order conditions: First-order conditions imply: Substituting into the budget constraint: Solving: where where 11 V EMPIRICAL FRAMEWORK Based on theoretical framework analysed above (multiple regression), we can use the method of least squares (OLS) Ordinary Least Squares (OLS) The parameters in econometric models are unknown constants There are many methods for estimating these parameters Here, we will use the most common method that is Ordinary Least Squares The purpose of testing hypothesis is to determine the appropriate level of models, the form of the model and sent out signals which violate the classical assumptions of econometric models We will use the appropriate model to evaluate the relationship between the dependent variables and the explanatory variables, whereby, we can assess, predict and make decisions on related issues If we assume, as in the case of two-variable regression that E(ε) = 0, then, by substitution results: Y = β1X1+β2X2 + β3X3 + + βkXk + ε Estimate the parameters of the model: The real value = + + The estimated value = The deviation = - = - Finding , so that the sum of the squares of deviation is Solving extreme function of two variables, we get: With 12 is the average value of X and is the average value of Y and The assumptions of OLS: Assumption 1: The relationship between Y and X is linear The value Xi is given and is not random Assumption 2: The deviation Ui is random variable with the average value of Assumption 3: The deviation Ui which was random variable has variance unchanged Assumption 4: There is no correlation between the U1 Assumption 5: There is no correlation between Ui and Xi Theorem Gauss - Markov: When this assumption is ensured, the estimates calculated by the method of OLS is the linear unbiased estimation, the most effective of the overall regression Assumption 6: The deviation Ui has the normal distribution The coefficient of determination of the model: Total Sum of Squares (TSS): 13 Explained Sum of Squares (ESS): Residual Sum of Squares (RSS): We have: The coefficient of determination: Note: R2 = 1: The model is perfectly suitable with the research sample R2 = 0: The model is not perfectly suitable with the research sample 14 VI DATA Online survey software is a powerful survey tool for designing and administering online surveys, collecting and managing accurate data, and facilitating advanced analysis and reporting Therefore, we used an online survey to examine the students’ behavior In an online survey, the respondent does not have the benefit of asking someone for clarification All they have to go on is the following information: • The Question Text – Words and Formation of the Sentence That Asks the Question of the Respondent • The Answer Set – The Range of Offered Answers for a Closed Question • The Context – Prior Questions, Instructions, Guidance within the Questionnaire  That survey questions are clearly understood and have the best chance of a truthful, accurate response We dug in and analysed the data by starting to export the data in form of an excel table Then, we ran Stata, crunched the numbers to get the final results The reason for changing topic: The previous topic was cake consumption behaviour of the students of FTU, but later we changed the topic because we felt that we should focus more on a specific product Moreover, the topic that we are doing is no longer restricted to FTU students but to all students That also makes it easier for us to gather data The process of making the survey form: determine what information we need • • • • • Income (subsidy from parents) Pizza and spaghetti consumption per month Pizza and spaghetti prices To what extend they love pizza? (from to 5) Time to complete the online survey: days There are 60 people answer the questions The result we collected: Price Pizza Spaghetti 35000 5/60 (8%) 27/60 (45%) 50000 70000 Other 13/60 (22%) 22/60 (37%) 20/60 (33%) 16/60 (27%) 11/60 (18% 6/60 (10%) 15 Frequency 25/60 (42%) 29/60 (48%) Eat Pizza Eat spaghetti Preference Score Pizza Spaghetti 4/58 (7%) Place to eat 9/60 (15%) 6/60 (10%) 12/60 (20%) 13/60 (22%) Other 14/23 (23%) 12/60 (20%) 3/59 (5%) 3/58 (5%) 6/59 (10%) 4/58 (7%) 16/59 (17%) 16/58 (29%) 17/59 (29%) 17/58 (24%) 17/59 (29%) 14/58 (28%) Pizza Hut Pepperonis Pizza Box 20/60 (33%) 17/60 (28%) 7/60 (12%) Spaghetti Box 7/60 (12%) The purpose of the survey: - Find information about students’ consumption Students’ satisfaction about products VII RESULTS AND DISCUSSION The output is shown below, followed by explanation of the output 16 Other 9/60 (15%) Source SS df MS Model Residual 15.8871138 38.8247506 53 3.17742277 732542464 Total 54.7118644 58 943308007 Qp Coef pp Ps W_t_b_p W_t_b_s income _cons 6.00e-06 -.0000112 3312113 -.0255937 2.47e-09 4066745 Std Err 2.73e-06 4.80e-06 1016999 0804771 3.97e-09 5175505 t 2.20 -2.34 3.26 -0.32 0.62 0.79 Number of obs F( 5, 53) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.032 0.023 0.002 0.752 0.537 0.436 = = = = = = 59 4.34 0.0022 0.2904 0.2234 85589 [95% Conf Interval] 5.25e-07 -.0000209 1272272 -.1870104 -5.50e-09 -.6314003 0000115 -1.61e-06 5351954 1358229 1.04e-08 1.444749 a) Source - Looking at the breakdown of variance in the outcome variable, these are the categories we will examine: Model, Residual, and Total The Total variance is partitioned into the variance which can be explained by the independent variables (Model) and the variance which is not explained by the independent variables (Residual, sometimes called Error) b) SS - These are the Sum of Squares associated with the three sources of variance, Total, Model and Residual c) df - These are the degrees of freedom associated with the sources of variance The total variance has N-1 degrees of freedom The model degrees of freedom corresponds to the number of coefficients estimated minus Including the intercept, there are coefficients, so the model has 6-1=5 degrees of freedom The Residual degrees of freedom is the DF total minus the DF model, 58-5=53 d) MS - These are the Mean Squares, the Sum of Squares divided by their respective DF e) Number of obs - This is the number of observations used in the regression f) analysis Our number of observations is 59 F( 5, 53) - This is the F-statistic is the Mean Square Model (3.17742277) divided by the Mean Square Residual (0.732542464), yielding F=4.34 17 g) Prob > F - This is the p-value associated with the above F-statistic It is used in testing the null hypothesis that all of the model coefficients are 0.0022 h) R-squared - R-Squared is the proportion of variance in the dependent variable (Qp) which can be explained by the independent variables (pp, Ps, W_t_b_p, W_t_b_s and income) This is an overall measure of the strength of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable You can see from our value of 0.2904 that our independent variables explain 29.04% of the variability of our dependent variable i) Adj R-squared - This is an adjustment of the R-squared that penalizes the addition of extraneous predictors to the model Adjusted R-squared is computed using the formula - ((1 - Rsq)((N - 1) /( N - k - 1)) where k is the number of predictors j) Root MSE - Root MSE is the standard deviation of the error term, and is the square root of the Mean Square Residual (or Error) k) Qp - This column shows the dependent variable at the top (Qp) with the predictor variables below it (pp, Ps, W_t_b_p, W_t_b_s , income and _cons) The last l) variable (_cons) represents the constant or intercept Coef - These are the values for the regression equation for predicting the dependent variable from the independent variable The regression equation is presented in many different ways, for example: Ypredicted = β0 + β1X1+β2X2 + β3X3 + β4X4 + ε The column of estimates provides the values for β0, β1, β2, β3 and β4 for this equation • pp (price of pizza) - The coefficient is 6.00e-06 So for every unit increase in pp, a 6.00e-06 unit increase in Qp is predicted, holding all other variables constant • Ps (price of spaghetti) - For every unit increase in Ps, we expect a 0.0000112 unit • decrease in the Qp , holding all other variables constant W_t_b_p (willingness to buy pizza) - The coefficient for W_t_b_p is 0.3312113 So for every unit increase in W_t_b_p, we expect an approximately 0.33 unit increase in the Qp holding all other variables constant 18 • W_t_b_s (willingness to buy spaghetti) - The coefficient for W_t_b_s is -0.0255937 So for every unit increase in W_t_b_s, we expect a 0.025 point • decrease in Qp Income – The coefficient for Income is 2.47e- 09 So for every unit increase in Income, we expect a 2.47e-09 unit increase in Qp • Std Err - These are the standard errors associated with the coefficients • t - These are the t-statistics used in testing whether a given coefficient is significantly different from zero • P>|t| - This column shows the 2-tailed p-values used in testing the null hypothesis • that the coefficient (parameter) is Using an alpha of 0.05: The coefficient for pp is statistically significantly because its p-value is 0.032, which is smaller than 0.05 • The coefficient for Ps (-0.0000112) is statistically significant at the since the pvalue (0.023) is smaller than 0.05 • The coefficient for W_t_b_p (0.3312113) is statistically significantly different • from because its p-value (0.002) is definitely less than 0.05 The coefficient for W_t_b_s (-0.0255937) is not statistically significant because its p-value (0.752) is larger than 0.05 • The coefficient for Income is not statistically significant because its p-value (0.537) is larger than 0.05 • The constant (_cons) is not statistically significantly at the 0.05 alpha level • because p-value (0.436) is larger than 0.05 p [95% Conf Interval] - These are the 95% confidence intervals for the coefficients The confidence intervals are related to the p-values such that the coefficient will not be statistically significant at alpha = 0.05 if the 95% confidence interval includes zero These confidence intervals can help to put the estimate from the coefficient into perspective by seeing how much the value could - vary  Conclusion: The coefficient of price of pizza is 6.00e-06 (>0), the coefficient is 0.3312113 (>0) so when Pp increases Qp increases => Pizza does not follow the demand rule => Customers researched (students in Hanoi) consider pizza as a Giffen Good 19 - P-value of variables (Ps, W_t_b_s, _cons) is larger than 0.05 so we cannot analyse and show any results Own Elasticity: Cross – price elasticity:  Pizza and spaghetti are complement goods VIII IMPLICATION AND LIMITATION Implication: - This can help customers to increase their likeability towards products This survey can give opportunities to marketers to that understand students’ needs - and wants by producing more tasty food Analyze students’ preference to food at the moment Limitations: In this study, there are several limitations - No interviewer: A lack of a trained interviewer to clarify and probe can possibly - lead to less reliable data Survey Fraud: Respondents may not feel encouraged to provide accurate, honest - answers Inability to reach challenging population: This method is not applicable for - surveys that require respondents who not have an access to the Internet Do not analyze students’ income IX ASSESSMENT OF OUR GROUP No Member Tasks Leader Nguyễn Thị Phương Ly Theoretical Framework Edit assignment Introduction Nguyễn Thị Duyên Data Implication and Limitation 20 Comment Complete the task well and responsibly Complete the task well and responsibly Lê Thùy Giang Vũ Cao Quỳnh Chi Đặng Ngọc Phương Thảo Results and Discussion Data Analysis Abstract Empirical Framework Literature Review Data Analysis 21 Complete the task well and responsibly Complete the task well and responsibly Complete the task well and responsibly X REFERENCES https://lyphuongnt.typeform.com/to/cDs4yt https://lyphuongnt.typeform.com/report/cDs4yt/LW7T http://www.grin.com/de/e-book/281199/factors-affecting-customer-satisfaction-in-fastfood-sector http://www.revistadestatistica.ro/suplimente/2013/2_2013/srrs2_2013a11.pdf https://en.wikipedia.org/wiki/Economy_of_Vietnam http://www.ats.ucla.edu/stat/stata/output/reg_output.htm http://www.snapsurveys.com/blog/advantages-disadvantages-surveys/ https://www.google.com/url? sa=t&rct=j&q=&esrc=s&source=web&cd=7&cad=rja&uact=8&ved=0CF4QFjAGahUK EwjEkpeNnpLIAhUh26YKHQeeA7o&url=https%3A%2F%2Fideas.repec.org%2Fp %2Fags%2Faaea13%2F150777.html&usg=AFQjCNGkWVePqP2_rqHAhrtOyaKgvnWjg&sig2=FdP2Lm1kgqqrzEWRG7FPMA http://123doc.org/document/244488-phuong-phap-uoc-luong-binh-phuong-nho-nhat.htm http://tailieu.vn/tag/phuong-phap-ols.html http://writingcenter.unc.edu/handouts/abstracts/ http://www.monash.edu.au/lls/llonline/writing/information-technology/report/1.3.1.xml http://giannini.ucop.edu/ResearchReports/331-TomatoProcessing.pdf 22