Factors of consumers choices a reaveled preferences analysis for 3 in 1 coffee

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS FACTORS OF CONSUMER’S CHOICES: A REVEALED PREFERENCE ANALYSIS FOR 3IN1 COFFEE BY NGUYEN VAN VIEN MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, November 2016 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS FACTORS OF CONSUMER’S CHOICES: A REVEALED PREFERENCE ANALYSIS FOR 3IN1 COFFEE A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN VAN VIEN Academic Supervisor: TRUONG DANG THUY HO CHI MINH CITY, November 2016 ACKNOWLEDGEMENT I would first like to thank my thesis supervisor Dr Truong Dang Thuy of the Vietnam – The Netherlands Programme (VNP) at Ho Chi Minh City University of Economics He consistently allowed this paper to be my own work, but steered me in the right the direction whenever he thought I needed it I acknowledge the contribution of Dr Nguyen Ba Thanh (IUH) as the second reader of this thesis, and I am gratefully indebted to him for his very valuable advices on building idea for this thesis I would like to express my gratitude to the VNP officers who were involved in my thesis process by updating thesis schedule and providing good condition for my research process Without their passionate participation, the thesis process could not have been successfully conducted Finally, thanks are also due to my classmates for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis This accomplishment would not have been possible without them Thank you Nguyen Van Vien Ho Chi Minh City, November 2016 Page i ABSTRACT 3in1 coffee is known as an important product of instant coffee market in Vietnam, especially in Ho Chi Minh City The reason of that comes from the benefits which 3in1 coffee brings to consumers in term of convenience, product quality, and appropriate price In the above context of 3in1 coffee market, the main objective of this study is to identify the determinants of consumer’s choices in 3in1 coffee market such as price, main ingredients, packaging, manufacturer, discount, and weight promotion This study is a practical research with the basis of random utility theory Specifically, empirical result is produced from the estimation of conditional logit model for the dataset which is collected from consumers in Ho Chi Minh City in 2016 The survey process relies on the revealed preference method with several additional hypothetical scenarios The main finding of this study emphasizes the importance of main ingredients, packaging, and manufacturers of 3in1 coffee in consumer’s choices It is recognized that price may not matter consumer’s choices However, consumers love discount and weight promotion In addition, several manufacturers enjoy positive marginal utility of price for consumers while the others enjoy the negative one On the other hand, price changes may give small effects on choice probability of 3in1 coffee products According to those empirical findings, implications have been employed for manufacturers in order to understand more about 3in1 coffee market, widen their market share, and increase their profits Page ii TABLE OF CONTENT Chapter Page Acknowledgement i Abstract ii Table of content iii List of tables v List of figures vi Introduction 1.1 Research problem 1.2 Research objective 1.3 Scope of study 1.4 Thesis structure Literature review 2.1 Random utility theory 2.2 Random utility model forms 2.3 Random utility model for beverage or food 11 2.4 The investigation of coffee’s attribute 12 2.5 Consumer’s social-demographic characteristics 16 Research methodology .18 3.1 Revealed preference method 18 3.2 Attributes of coffee 20 3.3 Choice set 21 3.4 Questionnaire 23 3.5 Survey process 24 3.6 Model specification 25 Data and empirical result 30 4.1 Data 30 4.2 Empirical result 39 4.2.1 Determinants of consumer’s choices for 3in1 coffee 39 Page iii 4.2.2 Price and consumer’s utility of 3in1 coffee by manufacturers 46 4.2.3 Price change and choice probability 49 4.2.4 Marginal utility of price for respondents 52 4.2.5 Discount and weight promotion and consumer’s utility for 3in1 coffee .54 4.2.6 Manufacturers, social-demographic characteristics and consumer’s choices for 3in1 coffee .55 Conclusion 58 Reference vii Appendix xi Page iv LIST OF TABLES Table 2.1 Importance of factor on consumer’s coffee preferences 14 Table 3.1 List of suggested attributes 21 Table 3.2 Volume share and value share of main manufacturers .22 Table 3.3 List of all available 3in1 coffee products 23 Table 3.4 Variable description 26 Table 4.1 District, super-market, and the number of respondents 31 Table 4.2 Descriptive statistics of the sample 32 Table 4.3 Frequency of social-demographic characteristics 34 Table 4.4 Factors of consumer’s choices for 3in1 coffee 42 Table 4.5 Marginal utility of price for respondents by alternative 53 Table A.1 All 19 alternatives and their attributes xii Table A.2 Price fluctuation among super-markets xxi Table A.3 Consumer’s choices change among various choice scenarios .xxii Table A.4 Regression result of equation (3.1) and (3.3) (specific choice set) xxiii Table A.5 Regression result of equation (3.1) and (3.3) (single variable) xxiv Page v LIST OF FIGURES Figure 4.1 Frequency of choice of each alternative by gender 35 Figure 4.2 Alternative and gender, occupation, income, and frequency of 3in1 coffee consumption of respondents 37 Figure 4.3 Change of consumer’s choices in various choice scenarios 38 Figure 4.4 Price and consumer’s utility by manufacturer 48 Figure 4.5 Price changes and choice probabilities of alternative 2, 8, 15, 18 in case of all choice scenarios 51 Figure A.1 All 19 alternatives for survey process xi Figure A.2 Consumer’s utility along current price values .xxv Page vi CHAPTER 1: INTRODUCTION 1.1 Research problem In recent years, coffee is an important product of industry sector, agriculture sector, and service sector in Vietnam According to historical data of International Coffee Organization (ICO), beside Brazil, Vietnam is one of the key countries in coffee production and consumption in the world with 19.4 percent of total coffee production, and 4.5 percent of domestic consumption Annual yield of Vietnam coffee production increases by 21 times in the period 1990 – 2014 (ICO, 2015) The volume of coffee export in Vietnam contributes more than 2.4 percent of GDP in 2012 (Vietnam Ministry of Industry and Trade, 2012) Based on the report of AC Nielsen (2015), 3in1 coffee market in Vietnam are contributed by many manufacturers of which five main manufacturers are Vinacafe, Nestlé, Trung Nguyen, Fes Vietnam, and Tran Quang In 2015, total volume share of five main manufacturers is 88 percent (Vinacafe: 38%, Nestlé: 19%, Trung Nguyen: 14.6%, Fes Vietnam: 4.2%, Tran Quang: 12.2%) compared to about 99 percent in 2014 Moreover, in term of value share, 3in1 coffee products comprise 83 percent of total value of instant coffee market In term of package, bag, box, and sachet are three main kinds of package with 99.9 percent of volume share Therefore, it is concluded that total demand of 3in1 coffee is relatively high compared to other instant coffee products and the competition among manufacturers is also intense in order to capture more market share Due to the high demand of consumers, especially young consumers, many manufacturers have diversified their 3in1 coffee products in term of brands, prices, segments, packages, pack size, promotion, main ingredients For example, five main manufacturers including Vinacafe, Nestlé, Trung Nguyen, Fes Vietnam, and Tran Quang provide 27 different kinds of 3in1 coffee in term of main ingredients, packaging, and brands Moreover, the competition among these manufacturers is also reflected in the aspect of prices and promotions Price increase could help manufacturers enjoy the benefits from the increase of profit; however, they may also suffer the decrease of quantity sold Beside price change, manufacturers could conduct promotion activities in order to increase the number of consumers who know about their products or their brand names Promotion activities are conducted through many forms such Page as weight promotion, additional sachets, or a gift of related product, for example, spoon, plastic cup, or glass cup Thus, two important questions are raised that: (1) What are important factors which affect consumer’s choices?, and (2) Do price changes and promotions possibly help one manufacturer to gain consumers from the others? 1.2 Research objective According to Batsell and Louviere (1991), experimental methods relied on the framework of both econometric analysis and psychometric analysis and it is the most popular method to researches about consumer’s preferences Experimental methods explain consumer’s preferences through the process of identifying the range of significant factors, generating hypothetical profiles, collecting consumer’s choices, and analyzing choice data The datasets of experimental methods are collected from two main survey methods: revealed preference and stated preference methods Two main survey methods provide a wide application in understanding consumer’s preferences For example, Durevall (2007) investigated that decreasing price of coffee has less impact on coffee demand in the long term due to the combination of consumer’s preferences and population structure in Sweden In addition, Wolf et al (2011) suggested that the interaction of product attributes also have significant impact on consumer’s preferences, beside prices Therefore, the aim of this study is to achieve three research objectives: (1) Identifying the key determinants of consumer’s choices for 3in1 coffee, (2) Determining relationship between price and consumer’s utility for 3in1 coffee, (3) Evaluating the impact of discount, promotion, and price changes on consumer’s choices First, beside price, several factors are claimed to be reliably important and influence the consumer’s choices Thus, identifying these factors provides deeply understanding about consumer’s preference in order to suggest both implication for instant coffee market and development strategy for manufacturers Second, it is said that determining the relationship between price and consumer’s utility plays an important role in finding out the effect of price change on consumer’s utility or choice probability of products for each manufacturer This finding gives manufacturers an evaluation about their advantages or disadvantages to get higher profits in the 3in1 coffee market compared to the competitors Finally, this study also Page // Conditional logit model: squared variables / is not a valid command name r(199); clogit choice price price2 bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group(id) robust Iteration 0: log pseudolikelihood = -538.10755 Iteration 1: log pseudolikelihood = -537.96638 Iteration 2: log pseudolikelihood = -537.96628 Iteration 3: log pseudolikelihood = -537.96628 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -537.96628 Number of obs = Wald chi2(12) = 3,448 44.68 Prob > chi2 = 0.0000 Pseudo R2 = 0.0439 (Std Err adjusted for clustering on id) -| Robust choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -price | 0748233 0376358 1.99 0.047 price2 | -.0003747 0002315 -1.62 0.106 -.0008285 0000791 bitter | -4.825643 7.318675 -0.66 0.510 -19.16998 9.518696 bitter2 | -1.665778 7.985068 -0.21 0.835 -17.31622 13.98467 sweet | 1.389632 3048544 4.56 0.000 7921283 fat | 2.709244 6112958 4.43 0.000 1.511126 -.0252337 0110873 -2.28 0.023 | 3630109 1746734 2.08 0.038 pap_pack | -.2761876 2477704 -1.11 0.265 | 8.559927 1.963215 4.36 0.000 4.712097 12.40776 trungnguyen | 3.183092 1.179897 2.70 0.007 870536 5.495649 3.387774 1.425301 2.38 0.017 5942357 6.181311 weight_alt no_sachet vinacafe nestle | | 0010586 -.0469644 0206573 -.7618087 1485881 1.987136 3.907362 -.003503 7053644 2094334 - test price price2 bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle ( 1) [choice]price = ( 2) [choice]price2 = Page xxxii ( 3) [choice]bitter = ( 4) [choice]bitter2 = ( 5) [choice]sweet = ( 6) [choice]fat = ( 7) [choice]weight_alt = ( 8) [choice]no_sachet = ( 9) [choice]pap_pack = (10) [choice]vinacafe = (11) [choice]trungnguyen = (12) [choice]nestle = chi2( 12) = Prob > chi2 = 44.68 0.0000 // Conditional logit model: all kinds of variables / is not a valid command name r(199); clogit choice price price2 bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `interaction', group(id > ) robust Iteration 0: log pseudolikelihood = -530.78387 Iteration 1: log pseudolikelihood = -524.03165 Iteration 2: log pseudolikelihood = -523.07611 Iteration 3: log pseudolikelihood = -523.00657 Iteration 4: log pseudolikelihood = -523.00589 Iteration 5: log pseudolikelihood = -523.00589 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -523.00589 Number of obs = Wald chi2(28) = 3,448 Prob > chi2 = Pseudo R2 = 0.0705 (Std Err adjusted for clustering on id) -| choice | Robust Coef Std Err z P>|z| [95% Conf Interval] -+ -price | 0105693 price2 | -.0003766 056875 0.19 0.853 -.1009037 1220424 0003131 -1.20 0.229 -.0009904 0002371 Page xxxiii bitter | 24.94274 15.21993 1.64 0.101 -4.887769 bitter2 | -40.74544 18.68836 -2.18 0.029 -77.37397 sweet | 0735659 6308328 0.12 0.907 -1.162844 1.309976 fat | 1177642 1.234397 0.10 0.924 -2.30161 2.537138 | 0541594 0365326 1.48 0.138 -.0174432 125762 | -1.103827 6433185 -1.72 0.086 -2.364708 1570541 pap_pack | weight_alt no_sachet 54.77326 -4.116921 -.4327863 2763634 -1.57 0.117 -.9744487 1088761 | 1.883888 3.378918 0.56 0.577 -4.73867 8.506446 trungnguyen | -1.696056 2.298462 -0.74 0.461 -6.200959 2.808848 vinacafe nestle | 3831005 1.734024 0.22 0.825 -3.015524 3.781725 privin | 0279594 0273143 1.02 0.306 -.0255757 0814945 pritru | 1449513 0593004 2.44 0.015 0287247 261178 prines | 1213597 0444217 2.73 0.006 0342949 2084246 priin2 | 0079212 0108327 0.73 0.465 -.0133106 029153 priin3 | 0050583 0140365 0.36 0.719 -.0224527 0325692 priin4 | 0261014 0165183 1.58 0.114 -.0062739 0584767 priin5 | -.0404617 0329138 -1.23 0.219 -.1049715 0240482 priin6 | 0191906 0169313 1.13 0.257 -.0139942 0523753 priin7 | -.0343395 0263455 -1.30 0.192 -.0859757 0172967 priin10 | 003507 0117998 0.30 0.766 -.0196202 0266342 prioc2 | -.0122084 0152003 -0.80 0.422 -.0420005 0175837 prioc3 | 0215448 0143125 1.51 0.132 -.0065071 0495968 prioc4 | -.0002668 0166422 -0.02 0.987 -.0328849 0323513 prioc5 | -.0026192 0154504 -0.17 0.865 -.0329015 027663 prioc6 | -.1965365 0244599 -8.04 0.000 -.2444771 -.1485959 prioc7 | -.0212758 0112908 -1.88 0.060 -.0434053 0008538 prioc8 | 0038037 0177165 0.21 0.830 -.03092 0385274 primal | 0186226 0090569 2.06 0.040 0008714 0363738 priday | -.0159314 014777 -1.08 0.281 -.0448939 013031 priwee | -.0138459 0152013 -0.91 0.362 -.0436399 015948 - test price price2 bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `interaction' ( 1) [choice]price = ( 2) [choice]price2 = ( 3) [choice]bitter = ( 4) [choice]bitter2 = ( 5) [choice]sweet = ( 6) [choice]fat = ( 7) [choice]weight_alt = ( 8) [choice]no_sachet = ( 9) [choice]pap_pack = (10) [choice]vinacafe = (11) [choice]trungnguyen = Page xxxiv (12) [choice]nestle = (13) [choice]privin = (14) [choice]pritru = (15) [choice]prines = (16) [choice]priin2 = (17) [choice]priin3 = (18) [choice]priin4 = (19) [choice]priin5 = (20) [choice]priin6 = (21) [choice]priin7 = (22) [choice]priin10 = (23) [choice]prioc2 = (24) [choice]prioc3 = (25) [choice]prioc4 = (26) [choice]prioc5 = (27) [choice]prioc6 = (28) [choice]prioc7 = (29) [choice]prioc8 = (30) [choice]primal = (31) [choice]priday = (32) [choice]priwee = Constraint dropped Constraint dropped Constraint 25 dropped Constraint 30 dropped chi2( 28) = 9122.09 Prob > chi2 = 0.0000 clear // Second case: hypothetical choice scenarios / is not a valid command name r(199); use temp_data.dta drop if time == (3,743 observations deleted) Page xxxv // Conditional logit model: single variables / is not a valid command name r(199); clogit choice price weight discount bitter sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group(id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -1122.5249 Iteration 1: log pseudolikelihood = -1120.2138 Iteration 2: log pseudolikelihood = -1120.2131 Iteration 3: log pseudolikelihood = -1120.2131 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -1120.2131 Number of obs = Wald chi2(12) = 2,094 70.24 Prob > chi2 = 0.0000 Pseudo R2 = 0.0411 (Std Err adjusted for clustering on id) -| choice | Robust Coef Std Err z P>|z| [95% Conf Interval] -+ -| -.0172831 027035 -0.64 0.523 -.0702708 weight | price -.0182327 0088621 -2.06 0.040 -.035602 | 0224552 0039187 5.73 0.000 | 5.535878 3.263817 1.70 0.090 -.8610858 11.93284 sweet | -.7926451 4965164 -1.60 0.110 -1.765799 1805092 fat | -1.358692 1.015161 -1.34 0.181 -3.348372 6309871 | 0017038 0148487 0.11 0.909 -.0273991 0308067 | -.0040357 2442787 -0.02 0.987 -.482813 4747417 pap_pack | -.2883857 5083474 -0.57 0.571 -1.284728 7079569 vinacafe | -3.628694 3.146857 -1.15 0.249 -9.796421 2.539033 trungnguyen | -1.96607 1.568099 -1.25 0.210 -5.039487 1.107347 -1.522152 1.734 -0.88 0.380 -4.920729 1.876425 discount bitter weight_alt no_sachet nestle | 0147748 0357046 -.0008633 0301356 - test price weight discount bitter sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle Page xxxvi ( 1) [choice]price = ( 2) [choice]weight = ( 3) [choice]discount = ( 4) [choice]bitter = ( 5) [choice]sweet = ( 6) [choice]fat = ( 7) [choice]weight_alt = ( 8) [choice]no_sachet = ( 9) [choice]pap_pack = (10) [choice]vinacafe = (11) [choice]trungnguyen = (12) [choice]nestle = chi2( 12) = Prob > chi2 = 70.24 0.0000 // Conditional logit model: squared variables / is not a valid command name r(199); clogit choice price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group( > id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -1120.4226 Iteration 1: log pseudolikelihood = -1117.6378 Iteration 2: log pseudolikelihood = -1117.6373 Iteration 3: log pseudolikelihood = -1117.6373 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -1117.6373 Number of obs = Wald chi2(14) = 2,094 70.27 Prob > chi2 = 0.0000 Pseudo R2 = 0.0433 (Std Err adjusted for clustering on id) -| choice | Robust Coef Std Err z P>|z| [95% Conf Interval] -+ Page xxxvii price | -.0598778 0925234 -0.65 0.518 -.2412203 price2 | 0002984 0005589 0.53 0.593 -.000797 weight | -.0185582 0088612 -2.09 0.036 -.0359258 discount 0147181 1214647 0013939 -.0011906 | 0223737 003906 5.73 0.000 bitter | 1.258177 13.22888 0.10 0.924 -24.66995 27.18631 0300293 bitter2 | 4.48238 15.12241 0.30 0.767 -25.15699 34.12175 sweet | -.778222 5177499 -1.50 0.133 -1.792993 2365491 fat | -1.480806 1.040222 -1.42 0.155 -3.519604 5579916 | 0036457 0232498 0.16 0.875 -.0419231 0492146 | -.0382134 3757582 -0.10 0.919 -.7746859 698259 pap_pack | -.2828311 5225289 -0.54 0.588 -1.306969 7413066 vinacafe | -4.544782 3.338794 -1.36 0.173 -11.0887 1.999134 trungnguyen | -2.230604 1.998066 -1.12 0.264 -6.146741 1.685532 -2.265243 2.424922 -0.93 0.350 -7.018004 2.487518 weight_alt no_sachet nestle | - test price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle ( 1) [choice]price = ( 2) [choice]price2 = ( 3) [choice]weight = ( 4) [choice]discount = ( 5) [choice]bitter = ( 6) [choice]bitter2 = ( 7) [choice]sweet = ( 8) [choice]fat = ( 9) [choice]weight_alt = (10) [choice]no_sachet = (11) [choice]pap_pack = (12) [choice]vinacafe = (13) [choice]trungnguyen = (14) [choice]nestle = chi2( 14) = Prob > chi2 = 70.27 0.0000 // Conditional logit model: all kinds of variables / is not a valid command name r(199); clogit choice price price2 weight discount bitter bitter2 sweet fat weight_alt Page xxxviii no_sachet pap_pack vinacafe trungnguyen nestle `intera > ction', group(id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -1105.6076 Iteration 1: log pseudolikelihood = -1035.2261 Iteration 2: log pseudolikelihood = -1033.1658 Iteration 3: log pseudolikelihood = -1032.9919 Iteration 4: log pseudolikelihood = -1032.9576 Iteration 5: log pseudolikelihood = -1032.9501 Iteration 6: log pseudolikelihood = -1032.9483 Iteration 7: log pseudolikelihood = -1032.948 Iteration 8: log pseudolikelihood = -1032.9479 Iteration 9: log pseudolikelihood = -1032.9479 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -1032.9479 Number of obs Wald chi2(33) = = 2,094 Prob > chi2 = Pseudo R2 = 0.1158 (Std Err adjusted for clustering on id) -| choice | Robust Coef Std Err z P>|z| [95% Conf Interval] -+ -price | -.1820351 1141533 -1.59 0.111 -.4057714 0417012 price2 | 000095 0006539 0.15 0.884 -.0011866 0013766 weight | -.0166574 0093158 -1.79 0.074 -.0349159 0016012 | 0264571 0038075 6.95 0.000 0189944 0339197 bitter | 11.60804 22.34722 0.52 0.603 -32.1917 55.40779 bitter2 discount | -14.33617 27.61913 -0.52 0.604 -68.46866 39.79632 sweet | -1.751805 9461934 -1.85 0.064 -3.60631 1027002 fat | -3.498232 1.892311 -1.85 0.065 -7.207092 2106288 | 0536203 0512912 1.05 0.296 -.0469086 1541492 | -1.063678 9210173 -1.15 0.248 -2.868839 7414826 pap_pack | -.4006129 5616754 -0.71 0.476 -1.501476 7002506 vinacafe | -8.889098 6.074351 -1.46 0.143 -20.79461 3.016412 trungnguyen | -6.272952 4.093952 -1.53 0.125 -14.29695 1.751047 weight_alt no_sachet nestle | -5.136031 3.50599 -1.46 0.143 -12.00764 1.735583 privin | 01992 073974 0.27 0.788 -.1250664 1649064 pritru | 100401 0953109 1.05 0.292 -.0864049 2872068 prines | 0997277 0761846 1.31 0.191 -.0495914 priin2 | -.0854952 0434129 -1.97 0.049 -.170583 priin3 | -.0227783 0413039 -0.55 0.581 -.1037324 0581758 priin4 | 0448272 0787783 0.57 0.569 -.1095753 1992298 2490468 -.0004075 Page xxxix priin5 | -.1771435 1502122 -1.18 0.238 -.471554 117267 priin6 | -.0047118 1207281 -0.04 0.969 -.2413344 2319109 -.5093288 4867821 priin7 | -.0112734 2541146 -0.04 0.965 priin10 | 1.158004 0871971 13.28 0.000 prioc2 | -.0047272 0899093 -0.05 0.958 prioc3 | 1349585 0587326 2.30 0.022 prioc4 | 0593048 0470941 1.26 0.208 -.0329979 prioc5 | 0929088 0562369 1.65 0.099 -.0173135 prioc6 | -2.174753 5324643 -4.08 0.000 -3.218363 -1.131142 prioc7 | -1.581307 1365646 -11.58 0.000 -1.848969 -1.313645 prioc8 | -.0588697 0456308 -1.29 0.197 -.1483045 030565 primal | 0296589 024396 1.22 0.224 -.0181564 0774743 priday | 1109244 0612533 1.81 0.070 -.0091298 2309786 priwee | 1034773 0693351 1.49 0.136 -.032417 2393716 987101 -.1809462 0198447 1.328907 1714917 2500724 1516074 2031311 - test price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `interaction' ( 1) [choice]price = ( 2) [choice]price2 = ( 3) [choice]weight = ( 4) [choice]discount = ( 5) [choice]bitter = ( 6) [choice]bitter2 = ( 7) [choice]sweet = ( 8) [choice]fat = ( 9) [choice]weight_alt = (10) [choice]no_sachet = (11) [choice]pap_pack = (12) [choice]vinacafe = (13) [choice]trungnguyen = (14) [choice]nestle = (15) [choice]privin = (16) [choice]pritru = (17) [choice]prines = (18) [choice]priin2 = (19) [choice]priin3 = (20) [choice]priin4 = (21) [choice]priin5 = (22) [choice]priin6 = (23) [choice]priin7 = (24) [choice]priin10 = (25) [choice]prioc2 = (26) [choice]prioc3 = (27) [choice]prioc4 = Page xl (28) [choice]prioc5 = (29) [choice]prioc6 = (30) [choice]prioc7 = (31) [choice]prioc8 = (32) [choice]primal = (33) [choice]priday = (34) [choice]priwee = Constraint dropped chi2( 33) = 2.1e+09 Prob > chi2 = 0.0000 clear // Third case: all choice scenarios / is not a valid command name r(199); use temp_data.dta // Conditional logit model: single variables / is not a valid command name r(199); clogit choice price weight discount bitter sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group(id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -2501.6695 Iteration 1: log pseudolikelihood = -2501.4993 Iteration 2: log pseudolikelihood = -2501.4993 Conditional (fixed-effects) logistic regression Page xli Log pseudolikelihood = -2501.4993 Number of obs = Wald chi2(12) = 5,542 75.28 Prob > chi2 = 0.0000 Pseudo R2 = 0.0391 (Std Err adjusted for clustering on id) -| Robust choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -| 0066835 0112857 0.59 0.554 weight | price 0593704 009092 6.53 0.000 discount -.0154361 0415504 | 0282543 007887 3.58 0.000 | -2.822397 1.337308 -2.11 0.035 sweet | 5363297 2195279 2.44 0.015 106063 fat | 9210026 4605888 2.00 0.046 0182652 | -.0129203 006082 -2.12 0.034 | bitter weight_alt no_sachet 1841745 0937489 1.96 0.049 pap_pack | -.187218 2263209 -0.83 0.408 vinacafe | 2.623756 1.489892 1.76 trungnguyen | 651586 7966701 0.82 4707784 9282148 0.51 nestle | 0127959 -5.443472 -.0248407 0004299 028803 0771904 0437126 -.2013225 9665965 1.82374 -.0009998 367919 -.6307987 2563627 0.078 -.296378 5.543889 0.413 -.9098588 2.213031 0.612 -1.348489 2.290046 - test price weight discount bitter sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle ( 1) [choice]price = ( 2) [choice]weight = ( 3) [choice]discount = ( 4) [choice]bitter = ( 5) [choice]sweet = ( 6) [choice]fat = ( 7) [choice]weight_alt = ( 8) [choice]no_sachet = ( 9) [choice]pap_pack = (10) [choice]vinacafe = (11) [choice]trungnguyen = (12) [choice]nestle = chi2( 12) = Prob > chi2 = 75.28 0.0000 Page xlii // Conditional logit model: squared variables / is not a valid command name r(199); clogit choice price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group( > id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -2500.9698 Iteration 1: log pseudolikelihood = -2500.7156 Iteration 2: log pseudolikelihood = -2500.7156 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -2500.7156 Number of obs = Wald chi2(14) = 5,542 75.01 Prob > chi2 = 0.0000 Pseudo R2 = 0.0394 (Std Err adjusted for clustering on id) -| Robust choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -price | 0253697 0304712 0.83 0.405 -.0343529 0850922 price2 | -.0001242 0001902 -0.65 0.514 -.0004969 0002486 weight | 0592947 009103 6.51 0.000 0414531 | 0282588 0078728 3.59 0.000 0128285 bitter | -4.721002 5.254564 -0.90 0.369 -15.01976 bitter2 | 2.285835 5.945856 0.38 0.701 -9.367829 sweet | 5842252 2395073 2.44 0.015 discount fat | 1147996 0436891 5.577754 13.9395 1.053651 1.011793 506466 2.00 0.046 -.0163849 0089098 -1.84 0.066 -.0338477 0010779 | 2411902 1393015 1.73 0.083 -.0318358 5142162 pap_pack | -.1997144 2272208 -0.88 0.379 -.6450591 2456302 | 2.796552 1.673858 1.67 0.095 -.4841496 6.077254 trungnguyen | 5015338 9565249 0.52 0.600 -1.373221 2.376288 337785 1.191139 0.28 0.777 -1.996804 2.672374 weight_alt no_sachet vinacafe nestle | | 019138 0771363 2.004448 - test price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle ( 1) [choice]price = Page xliii ( 2) [choice]price2 = ( 3) [choice]weight = ( 4) [choice]discount = ( 5) [choice]bitter = ( 6) [choice]bitter2 = ( 7) [choice]sweet = ( 8) [choice]fat = ( 9) [choice]weight_alt = (10) [choice]no_sachet = (11) [choice]pap_pack = (12) [choice]vinacafe = (13) [choice]trungnguyen = (14) [choice]nestle = chi2( 14) = Prob > chi2 = 75.01 0.0000 // Conditional logit model: all kinds of variables / is not a valid command name r(199); clogit choice price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `intera > ction', group(id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -2466.0592 Iteration 1: log pseudolikelihood = -2433.778 Iteration 2: log pseudolikelihood = -2430.5099 Iteration 3: log pseudolikelihood = -2429.7732 Iteration 4: log pseudolikelihood = -2429.7334 Iteration 5: log pseudolikelihood = -2429.7332 Iteration 6: log pseudolikelihood = -2429.7332 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -2429.7332 Number of obs = Wald chi2(30) = 5,542 Prob > chi2 = Pseudo R2 = 0.0667 (Std Err adjusted for clustering on id) Page xliv | Robust choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -price | -.0546263 0432129 -1.26 0.206 -.139322 0300694 price2 | -.0000673 0002372 -0.28 0.777 -.0005322 0003977 weight | 0594902 009122 6.52 0.000 0416114 077369 0294846 0075459 3.91 0.000 0146949 0442743 discount | bitter | 26.89982 10.82788 2.48 0.013 bitter2 | -39.12604 13.44546 -2.91 0.004 -65.47866 sweet | -.7765076 4583982 -1.69 0.090 -1.674952 1219364 fat | -1.679608 9151699 -1.84 0.066 -3.473308 1140918 | 0657071 0268564 2.45 0.014 | -1.274387 4686745 -2.72 0.007 -2.192972 -.3558018 pap_pack | -.3550718 2298852 -1.54 0.122 -.8056385 0954949 vinacafe | -3.684817 2.688282 -1.37 0.170 -8.953752 trungnguyen | -4.195729 1.629328 -2.58 0.010 -7.389154 -1.002305 -.0051102 weight_alt no_sachet 5.677562 0130695 48.12207 -12.77342 1183447 1.584119 nestle | -2.759318 1.405234 -1.96 0.050 -5.513526 privin | 0195945 0230591 0.85 0.395 -.0256004 pritru | 1435674 0410234 3.50 0.000 063163 prines | 1239666 0314023 3.95 0.000 0624193 priin2 | -.0003728 0091668 -0.04 0.968 -.0183394 0175938 priin3 | 0077191 0112433 0.69 0.492 -.0143173 0297555 priin4 | 0216312 0164455 1.32 0.188 -.0106013 0538637 priin5 | -.0633063 0440288 -1.44 0.150 -.1496011 0229886 priin6 | 0029581 0116245 0.25 0.799 -.0198256 0257417 priin7 | -.0312546 0399962 -0.78 0.435 -.1096457 0471364 priin10 | 0161567 010547 1.53 0.126 -.0045149 0368284 prioc2 | -.0201671 0170412 -1.18 0.237 -.0535673 0132331 prioc3 | 0292238 0155413 1.88 0.060 -.0012366 0596842 prioc4 | 0075925 0139319 0.54 0.586 -.0197135 0348984 prioc5 | 0038381 0142507 0.27 0.788 -.0240927 prioc6 | -.3216017 0325991 -9.87 0.000 -.3854947 -.2577086 prioc7 | -.1084955 0169672 -6.39 0.000 -.1417507 -.0752403 prioc8 | -.0025634 017934 -0.14 0.886 -.0377135 0325866 primal | 0116561 0066991 1.74 0.082 -.0014739 0247861 priday | -.0046483 0140565 -0.33 0.741 -.0321985 0229019 priwee | -.0010328 0146459 -0.07 0.944 -.0297382 0276727 0647894 2239718 1855139 0317689 - test price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `interaction' ( 1) [choice]price = ( 2) [choice]price2 = ( 3) [choice]weight = ( 4) [choice]discount = Page xlv ( 5) [choice]bitter = ( 6) [choice]bitter2 = ( 7) [choice]sweet = ( 8) [choice]fat = ( 9) [choice]weight_alt = (10) [choice]no_sachet = (11) [choice]pap_pack = (12) [choice]vinacafe = (13) [choice]trungnguyen = (14) [choice]nestle = (15) [choice]privin = (16) [choice]pritru = (17) [choice]prines = (18) [choice]priin2 = (19) [choice]priin3 = (20) [choice]priin4 = (21) [choice]priin5 = (22) [choice]priin6 = (23) [choice]priin7 = (24) [choice]priin10 = (25) [choice]prioc2 = (26) [choice]prioc3 = (27) [choice]prioc4 = (28) [choice]prioc5 = (29) [choice]prioc6 = (30) [choice]prioc7 = (31) [choice]prioc8 = (32) [choice]primal = (33) [choice]priday = (34) [choice]priwee = Constraint dropped Constraint dropped Constraint 16 dropped Constraint 33 dropped chi2( 30) = 4015.81 Prob > chi2 = 0.0000 clear ******************************************************************************** Page xlvi ... 2 015 33 .3 19 .1 17.6 6.7 Value share, % 2 014 38 .7 29 .1 30 .0 2.2 2 015 24 .1 19 .3 18 .3 8.6 Table 3. 3 presents the list of 19 kinds of 3in1 coffee They are not all kinds of available 3in1 coffee in. .. various packaging Moreover, Table A. 1 presents a summary of all 19 alternatives and their attributes such main ingredients, packaging, and manufacturer Table 3. 3 List of all available 3in1 coffee. .. specific for each brand or manufacturer, and each kind of 3in1 coffee It means that attributes are different among brands, manufacturer, and kind of 3in1 coffee Table 3. 1 List of suggested attributes