Online shopping vs in store shopping an analysis of choice behavior

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 ONLINE SHOPPING VS IN-STORE SHOPPING: AN ANALYSIS OF CHOICE BEHAVIOR BY PHAM NHU MAN MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2017 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 ONLINE SHOPPING VS IN-STORE SHOPPING: AN ANALYIS OF CHOICE BEHAVIOR A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By PHAM NHU MAN Academic Supervisor: Dr TRUONG DANG THUY HO CHI MINH CITY, DECEMBER 2017 ACKNOWLEDGEMENT I would like to express my deep gratitude to my supervisor Dr Truong Dang Thuy, for his guidance, support and encouragement, whereby I could complete the program I would like to thank Dr Pham Khanh Nam for his valuable advices for my thesis I would like to thank Mr Do Huu Luat for his support and guidance during the process of data collection I also would to express my thanks to VNP officers who support me during my thesis process I would like to thank my family for their encouragement and support during my master program i ABSTRACT This study examines the consumer’s choice between online and in-store shopping for a specific product of books For this purpose, a survey of 352 book buyers was implemented to collect revealed and stated preference data Conditional logit and mixed logit models are used to analyze the choice We found that 40 minute travelling to store and come back is worth VND 22,303 to VND 33,849, and that willingness to pay for one day earlier delivery is from VND 15,617 to VND 19,028 We also found that shoppers are more sensitive to online price than to price in store Those who are office workers and those who spend more time to access the internet are more likely to shop online Moreover, in-store shoppers with higher income are less sensitive to price than those who have lower income Especially, shoppers with high income are more concerned about travel time than those with low income The female shoppers are more patient waiting for the delivery In addition, respondents evaluated that the convenience of payment method, the quality of books, ability to read books before purchasing and large variety offered products, are important and effect partially on their choice ii TABLE OF CONTENTS LIST OF TABLES v LIST OF FIGURES vi CHAPTER 1: INTRODUCTION 1.1 Overview of Vietnam’s retail sector 1.2 Trend in shopping behavior in Viet Nam 1.3 Research questions and objectives 1.2 The scope of study 1.3 Organization of the thesis CHAPTER 2: LITERATURE REVIEW 2.1 Binary Choice models 2.2 Frequency of online shopping 11 2.3 Random Utility Models (RUM) 13 2.4 Structural Equation Models (SEM) 15 CHAPTER 3: RESEARCH METHODOLOGY 17 3.1 The choice of online shopping versus traditional store shopping 17 3.2 Revealed preference data: methods of collection 26 3.3 The stated preference data: methods of collection including choice experimental design 29 3.3.1 The stated preference method 29 3.3.2 Choice of experimental design 31 3.4 Sample size and sampling 35 3.5 Survey 36 3.6 The questionnaire 37 3.7 Estimation methods 38 CHAPTER 4: RESEARCH RESULTS 41 4.1 Summary statistics 41 4.2 Estimation results 47 4.2.1 The Basic model 47 4.2.2 The Full model 52 4.2.3 Valuation indicators of time and cost attributes 56 4.2.4 Store attributes and the choice probability 61 CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATIONS 69 REFERENCE 72 APPENDIX 76 THE QUESTIONNAIRE 76 LIST OF TABLES Table 1: The store choice attributes description 22 Table 2: Individual characteristics variable description .24 Table 3.3: Attributes and levels in stated choice experimental design 30 Table 3.4: The attribute’s levels of online and physical store 32 Table 3.5: Designed choice sets .34 Table 3.6: The presentation of a sample choice set in block .35 Table 4.1: Survey location and number of respondents 42 Table 4.2: Descriptive statistics of the sample 43 Table 4.3: Descriptive statistics analysis of consumer attitudes 45 Table 4.4: Revealed data between online shoppers and in-store shoppers 46 Table 4.5: Summary statistics of alternative-specific attributes 48 Table 4.6: Estimation results of the basic model .50 Table 4.7: Estimation results of the full model 53 Table 4.8: Willingness to pay of time and cost attributes 57 LIST OF FIGURES Figure 1.1: Vietnam’s retail sales and online retail sales from 2013 to 2015 Figure 1.2: Percentage online shoppers of the online accessing in Vietnam Figure 3.1: Choice model between online and in-store shopping 26 Figure 4.1: Frequency of Internet access and Internet access devices 44 Figure 2: The percentage change in online shopping probability given the change in purchase price, shipping fee and delivery time 62 Figure 3: The percentage change in online shopping probability given the change in purchase price, shipping fee and delivery time 64 Figure 4: The percentage change in online shopping probability given the change in purchase price, shipping fee and delivery time 67 CHAPTER 1: INTRODUCTION 1.1 Overview of Vietnam’s retail sector Vietnam has recently emerged as an attractive retail market thanks to the young population, with 70 percent in the age between 15 and 64 It is foreseen that the market size in Vietnam increases in terms of population as well as purchasing power The urban population, which is the most potential for the retail market, is expected to increase most rapidly at 2.6 percent annually from 2015 to 2020, the highest in the region (Worldbank, 2015 and United Nation, 2014) The urban population is further increased by the process of urbanization and ruralurban migration In addition, Vietnam appears to be the most energetic emerging economy with a rising living standards and intensifying disposable incomes The Boston Consulting Group (2013) has remarked that Vietnam has the highest growth speed of the wealthy and middle classes in the region, which is predicted to increase by 21 million compared to 12 million from 2012 to 2020 These consumers, whose monthly income is higher than VND 15 million (USD 714), will be a key group of potential customers for retailers In addition, the improving infrastructure is also a factor that makes Vietnam an alluring market for retailers Many international retail groups as Metro Cash & Carry, BigC, Emart, Aeon, Takashimaya, AuchanSuper and many others have entered the market These international retailers, together with domestic players such as Co-op Mart, Vincom Mall have created intense competition in the retail sector of Vietnam CBRE (2014) remarked that among the cities in the Asian for retail market enlargement, Hanoi and Ho Chi Minh City were rated in the top 10 municipalities Hanoi was ranked third after Shanghai and Beijing as the metropolis with the vivid retail market in the area In recent years, Vietnam’s retail industry has manifested effective growing rates, with retail revenues rising by 60 percent in the period of 2009 – 2013, and is estimated to approach US$109 billion in 2017 Therefore, the retail sector has an enormous potential for new investors The two main retail channels are traditional retail and electronic tailing (e-tailing) Traditional retail in the form that exchanges of goods or services directly to customers in the general stores, convenient stores, supermarkets, hypermarkets (a superstore incorporate of a department store into a supermarket) or shopping malls Thanks to the advancement of information technology and internet network facilities, e-tailing has been established Etailing requires the customer’s access the Internet and visiting the stores’ websites in order to purchase goods or services In online shopping, the purchasers can see the images of the products, explore information about the product specifications, the manufacturer, ingredients, texture, features, prices, and finally can choose the suitable payment methods All of these are completed by those mouse clicks By that way, the shoppers save their time, efforts and vehicles to transport products Figure 1.1 shows the online retail sales in comparison with total retail sales from 2013 to 2015 in Vietnam The sales of e-tailing, although being low, is increasing rapidly by more than 35 percent annually 160 144,2 140 110 120 100 80 66,5 60 40 20 2,2 4,07 2,97 2013 Online retail sales (billion USD) 2014 2015 Total retail sales (billion USD) Figure 1.1: Vietnam’s retail sales and online retail sales from 2013 to 2015 Source: General Statistics Office of Vietnam (2013, 2014, 2015) 1.2 Trend in shopping behavior in Viet Nam The rapid growth of Information and Communication Technologies (ICT) resulted in the remarkable change in consumer’s behavior and their preferences Consumers tend to prefer products or services that best serve the convenience of their busy lives Online shopping meets some requirements of convenience Nielsen (2014) stated that Vietnam is ranked number three in ASEAN region after Singapore and Philippines regarding the uses of mobiles for online shopping, accounted for 58 percentage of people using mobile phones to access replace limitcontact = if limitcontactpeople3 == (144 real changes made) replace limitcontact = if limitcontactpeople4 == (47 real changes made) replace limitcontact = if limitcontactpeople5 == (14 real changes made) tab limitcontact limitcontac | t | Freq Percent Cum + | 32 9.97 9.97 | 84 26.17 36.14 | 144 44.86 81.00 | 47 14.64 95.64 | 14 4.36 100.00 + Total | 321 100.00 tab limitcontact if useinternetforol==1 limitcontac | t | Freq Percent Cum + | 27 11.54 11.54 | 57 24.36 35.90 | 109 46.58 82.48 | 33 14.10 96.58 | 3.42 100.00 + Total | 234 100.00 gen avoidtrafficjams = replace avoidtrafficjams = if avoidtrafficjams1 == (15 real changes made) replace avoidtrafficjams = if avoidtrafficjams2 == (37 real changes made) replace avoidtrafficjams = if avoidtrafficjams3 == (70 real changes made) replace avoidtrafficjams = if avoidtrafficjams4 == (153 real changes made) replace avoidtrafficjams = if avoidtrafficjams5 == (46 real changes made) tab avoidtrafficjams avoidtraffi | cjams | Freq Percent Cum + | 15 4.67 4.67 | 37 11.53 16.20 | 70 21.81 38.01 | 153 47.66 85.67 | 46 14.33 100.00 + Total | 321 100.00 tab avoidtrafficjams if useinternetforol==1 avoidtraffi | cjams | Freq Percent Cum + | 14 5.98 5.98 | 24 10.26 16.24 | 49 20.94 37.18 | 112 47.86 85.04 | 35 14.96 100.00 + Total | 234 100.00 sum im_havingbook havingspace shopassistant readbeforebuy easinessreplace discountprogram varietyproduct multiplepurposes che > ckquality girfwrapping findoldbook cheapprice savingtime compareprice convenientpayment notmovefar limitcontact avoidtrafficj > ams Variable | Obs Mean Std Dev Min Max -+ -im_havingb~k | 321 3.548287 1.023935 havingspace | 321 3.302181 1.015025 shopassist~t | 321 3.062305 1.138466 readbefore~y | 321 4.056075 827252 easinessre~e | 321 3.46729 1.057572 -+ -discountpr~m | 321 3.70405 9982559 varietypro~t | 321 3.11838 9992209 multiplepu~s | 321 2.766355 9738401 checkquality | 321 4.165109 8144193 girfwrapping | 321 2.996885 1.020106 -+ -findoldbook | 321 3.82243 9792831 cheapprice | 321 3.676012 9687384 savingtime | 321 3.682243 9643469 compareprice | 321 3.464174 1.009081 convenient~t | 321 3.975078 8058393 -+ -notmovefar | 321 3.775701 921701 limitcontact | 321 2.772586 9656584 avoidtraff~s | 321 3.554517 1.023621 mean im_havingbook havingspace shopassistant readbeforebuy easinessreplace discountprogram varietyproduct multiplepurposes ch > eckquality girfwrapping findoldbook cheapprice savingtime compareprice convenientpayment notmovefar limitcontact avoidtraffic > jams Mean estimation Number of obs = 321 | Mean Std Err [95% Conf Interval] + -im_havingbook | 3.548287 0571505 3.435848 3.660725 havingspace | 3.302181 0566532 3.190721 3.41364 shopassistant | 3.062305 063543 2.93729 3.18732 readbeforebuy | 4.056075 0461727 3.965234 4.146915 easinessreplace | 3.46729 0590279 3.351158 3.583421 discountprogram | 3.70405 0557172 3.594432 3.813668 varietyproduct | 3.11838 0557711 3.008656 3.228104 multiplepurposes | 2.766355 0543545 2.659418 2.873292 checkquality | 4.165109 0454565 4.075678 4.25454 girfwrapping | 2.996885 0569367 2.884867 3.108902 findoldbook | 3.82243 0546583 3.714895 3.929965 cheapprice | 3.676012 0540697 3.569635 3.782389 savingtime | 3.682243 0538246 3.576348 3.788138 compareprice | 3.464174 0563214 3.353367 3.574981 convenientpayment | 3.975078 0449776 3.886589 4.063567 notmovefar | 3.775701 0514443 3.674489 3.876913 limitcontact | 2.772586 0538978 2.666547 2.878624 avoidtrafficjams | 3.554517 0571329 3.442113 3.666921 sum price_ol otime dcost dtime Variable | Obs Mean Std Dev Min Max -+ -price_ol | 321 213574.8 196092 15750 1800000 otime | 321 27.42991 37.78941 360 dcost | 321 9538.941 17584.42 250000 dtime | 321 3.009346 2.117614 20 sum price_is stime tcost ttime Variable | Obs Mean Std Dev Min Max -+ -price_is | 321 237409.5 228462.7 15000 2250000 stime | 321 50.43614 39.84858 240 tcost | 321 11339.56 13014.89 150000 ttime | 321 20.41121 13.07762 120 tabstat price_ol price_is otime stime dcost tcost dtime ttime, stat(mean variance sd max) stats | price_ol price_is otime stime dcost tcost dtime ttime -+ -mean | 213574.8 237409.5 27.42991 50.43614 9538.941 11339.56 3.009346 20.41121 variance | 3.85e+10 5.22e+10 1428.04 1587.909 3.09e+08 1.69e+08 4.484287 171.0241 sd | 196092 228462.7 37.78941 39.84858 17584.42 13014.89 2.117614 13.07762 | 15750 15000 0 max | 1800000 2250000 360 240 250000 150000 20 120 - pwcorr price_ol price_is otime stime dcost tcost dtime ttime, sig | price_ol price_is otime stime dcost tcost dtime -+ price_ol | 1.0000 | | price_is | 0.9754 1.0000 | 0.0000 | otime | 0.1182 0.1182 1.0000 | 0.0342 0.0342 | stime | 0.1923 0.2384 0.1976 1.0000 | 0.0005 0.0000 0.0004 | dcost | -0.0904 -0.1026 0.1813 0.0027 1.0000 | 0.1060 0.0665 0.0011 0.9610 | tcost | -0.0348 -0.0455 -0.0228 -0.0949 0.0321 1.0000 | 0.5339 0.4167 0.6836 0.0895 0.5672 | dtime | 0.0502 0.0186 0.1396 -0.0102 0.4734 -0.0499 1.0000 | 0.3699 0.7402 0.0123 0.8561 0.0000 0.3729 | ttime | -0.0931 -0.0921 0.0747 -0.0188 0.1184 0.1558 0.0528 | | 0.0958 0.0994 0.1820 0.7374 0.0339 0.0051 0.3458 | ttime -+ ttime | 1.0000 | | end of do-file "C:\Users\Man_man\AppData\Local\Temp\STD02000000.tmp" *** Convert data from wide form to long form reshape long percentprice1_ percentprice2_ percenttime1_ percenttime2_ percentcost1_ percentcost2_ percentdeliver1_ percentde > liver2_ percentchoice, i(id) j(task) (note: j = 6) Data wide -> long Number of obs 321 -> 1926 Number of variables 299 -> 255 j variable (6 values) -> task xij variables: percentprice1_1 percentprice1_2 percentprice1_6->percentprice1_ percentprice2_1 percentprice2_2 percentprice2_6->percentprice2_ percenttime1_1 percenttime1_2 percenttime1_6->percenttime1_ percenttime2_1 percenttime2_2 percenttime2_6->percenttime2_ percentcost1_1 percentcost1_2 percentcost1_6->percentcost1_ percentcost2_1 percentcost2_2 percentcost2_6->percentcost2_ percentdeliver1_1 percentdeliver1_2 percentdeliver1_6->percentdeliver1_ percentdeliver2_1 percentdeliver2_2 percentdeliver2_6->percentdeliver2_ percentchoice1 percentchoice2 percentchoice6->percentchoice rename percentprice1_ percentprice1 rename percentprice2_ percentprice2 rename percenttime1_ percenttime1 rename percenttime2_ percenttime2 rename percentcost1_ percentcost1 rename percentcost2_ percentcost2 rename percentdeliver1_ percentdeliver1 rename percentdeliver2_ percentdeliver2 reshape long percentprice percenttime percentcost percentdeliver, i(id task) j(alt) (note: j = 2) Data wide -> long - -Number of obs 1926 -> 3852 Number of variables 255 -> 252 j variable (2 values) -> alt xij variables: percentprice1 percentprice2 -> percentprice percenttime1 percenttime2 -> percenttime percentcost1 percentcost2 -> percentcost 142 percentdeliver1 percentdeliver2 -> percentdeliver rename percentchoice temp gen percentchoice = replace percentchoice = if alt == & temp ==1 (1099 real changes made) replace percentchoice = if alt == & temp ==0 (827 real changes made) drop temp egen percentchoiceid = group(id task) gen online = replace online = if alt == (1926 real changes made) * Separate each of these variables are percentcost, percentprice, percenttime, percentdeliver into two variables gen priceol = replace priceol = percentprice if alt == (1448 real changes made) gen priceis = replace priceis = percentprice if alt == (1367 real changes made) gen otimenew = replace otimenew = percenttime if alt == (1926 real changes made) gen stimenew = replace stimenew = percenttime if alt == (1288 real changes made) gen dcostnew = replace dcostnew = percentcost if alt == (1278 real changes made) gen tcostnew = replace tcostnew = percentcost if alt == (1926 real changes made) gen dtimenew = replace dtimenew = percentdeliver if alt == (1926 real changes made) gen ttimenew = replace ttimenew = percentdeliver if alt == (1287 real changes made) ** The interaction between alternative-specific attributes and individual characteristics replace income = 2500 if income == (1872 real changes made) replace income = 7500 if income == (1176 real changes made) replace income = 12500 if income == (480 real changes made) replace income = 17500 if income == (120 real changes made) replace income = 22500 if income == (60 real changes made) replace income = 27500 if income == (60 real changes made) replace income = 32500 if income == (12 real changes made) replace income = 37500 if income == (12 real changes made) replace income = 42500 if income == (0 real changes made) replace income = 47500 if income == 10 (12 real changes made) replace income = 50000 if income == 11 (48 real changes made) * Interaction bw those who are officers and those who love online shopping gen occup = replace occup = if career == (1200 real changes made) gen online_occup = online*occup * Interaction bw frequency of accessing internet and those who love online shopping replace freq_internet = if freq_internet == (1500 real changes made) replace freq_internet = if freq_internet == (972 real changes made) replace freq_internet = if freq_internet == (1956 real changes made) replace freq_internet = if freq_internet == (444 real changes made) replace freq_internet = 11 if freq_internet == (1452 real changes made) gen online_freq_internet = online*freq_internet *** The two models * Converting price, time, cost and delivery to 1000 VND and minutes gen vndpriceol = [(1+priceol)*price_ol]/1000 gen vndpriceis = [(1+priceis)*price_is]/1000 gen minotimenew = (1+otimenew)*otime gen minstimenew = (1+stimenew)*stime gen vndtcostnew = [(1+tcostnew)*tcost]/1000 gen minttimenew = (1+ttimenew)*ttime gen vndpriceolnew = replace vndpriceolnew = vndpriceol if alt == (1926 real changes made) gen vndpriceisnew = replace vndpriceisnew = vndpriceis if alt == (1926 real changes made) gen minotime = replace minotime = minotimenew if alt == (1926 real changes made) gen minstime = replace minstime = minstimenew if alt == (1926 real changes made) gen dcostol = replace dcostol = dcostnew/1000 if alt == (1278 real changes made) gen vndtcost = replace vndtcost = vndtcostnew if alt == (1752 real changes made) gen dtimeol = replace dtimeol = dtimenew if alt == (1926 real changes made) gen minttime = replace minttime = minttimenew if alt == (1926 real changes made) * Summary statistic sum vndpriceolnew if alt == Variable | Obs Mean Std Dev Min Max -+ -vndpriceol~w | 1926 171.1431 172.3482 9.45 1800 sum vndpriceisnew if alt == Variable | Obs Mean Std Dev Min Max -+ -vndpriceis~w | 1926 222.0931 216.6071 12 2362.5 sum minotime if alt == Variable | Obs Mean Std Dev Min Max -+ -minotime | 1926 22.21781 32.22376 450 sum minstime if alt == Variable | Obs Mean Std Dev Min Max -+ -minstime | 1926 48.74886 39.19061 264 sum dcostol if alt == Variable | Obs Mean Std Dev Min Max -+ -dcostol | 1926 12.28297 9.201659 22 sum vndtcost if alt == Variable | Obs Mean Std Dev Min Max -+ -vndtcost | 1926 12.26438 14.31559 195 sum dtimeol if alt == Variable | Obs Mean Std Dev Min Max -+ -dtimeol | 1926 1.961578 8427404 sum minttime if alt == Variable | Obs Mean Std Dev Min Max -+ -minttime | 1926 22.34237 15.91486 1.4 180 * Basic model clogit percentchoice online vndpriceolnew minotime dtimeol dcostol vndpriceisnew minstime minttime vndtcost, group(percentcho > iceid)robust Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood = = = = = -1297.8167 -1206.1502 -1203.6017 -1203.5945 -1203.5945 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -1203.5945 Number of obs Wald chi2(9) Prob > chi2 Pseudo R2 = = = = 3852 142.71 0.0000 0.0984 (Std Err adjusted for clustering on percentchoiceid) | Robust percentchoice | Std Err Coef z P>|z| [95% Conf Interval] + -online | 1.213165 189405 6.41 0.000 8419385 1.584392 vndpriceolnew | -.0118309 0013806 -8.57 0.000 -.0145369 -.0091249 minotime | -.0013593 0013902 -0.98 0.328 -.004084 0013654 dtimeol | -.3194379 058458 -5.46 0.000 -.4340134 -.2048623 dcostol | -.0223389 0053755 -4.16 0.000 -.0328747 -.0118032 vndpriceisnew | -.0088572 0011939 -7.42 0.000 -.0111972 -.0065172 minstime | -.0003602 001296 -0.28 0.781 -.0029003 0021799 minttime | -.0036684 0034726 -1.06 0.291 -.0104746 0031378 vndtcost | -.00026 0034632 -0.08 0.940 -.0070477 0065277 est store basic outreg2 basic using E:\\cbasic.doc E:\\cbasic.doc dir : seeout * WTP nlcom 1000*_b[minotime]/_b[vndpriceisnew] _nl_1: 1000*_b[minotime]/_b[vndpriceisnew] -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 153.4674 158.3306 0.97 0.332 -156.8548 463.7896 - nlcom 1000*_b[minstime]/_b[vndpriceisnew] _nl_1: 1000*_b[minstime]/_b[vndpriceisnew] -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 40.66627 146.4356 0.28 0.781 -246.3423 327.6748 - nlcom 1000*_b[dtimeol]/_b[vndpriceisnew] _nl_1: 1000*_b[dtimeol]/_b[vndpriceisnew] -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 36065.41 7895.458 4.57 0.000 20590.6 51540.23 - nlcom 1000*_b[minttime]/_b[vndpriceisnew] _nl_1: 1000*_b[minttime]/_b[vndpriceisnew] -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 414.1745 391.0546 1.06 0.290 -352.2784 1180.627 - * Interaction gen vndpriceol_income = vndpriceolnew*income gen vndpriceis_income = vndpriceisnew*income gen dtimeol_gender = dtimeol*gender gen minttime_income = minttime*income * Full model clogit percentchoice online vndpriceolnew minotime dtimeol dcostol vndpriceisnew minstime minttime vndtcost vndpriceol_income > vndpriceis_income dtimeol_gender minttime_income online_occup online_freq_internet, group(percentchoiceid)robust Iteration 0: Iteration 1: log pseudolikelihood = -1281.3903 log pseudolikelihood = -1182.6646 Iteration 2: Iteration 3: Iteration 4: log pseudolikelihood = -1178.9624 log pseudolikelihood = -1178.9488 log pseudolikelihood = -1178.9488 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -1178.9488 Number of obs Wald chi2(15) Prob > chi2 Pseudo R2 = = = = 3852 167.97 0.0000 0.1169 (Std Err adjusted for clustering on percentchoiceid) -| Robust percentchoice | Std Err Coef z P>|z| [95% Conf Interval] -+ -online | 3965213 2959999 1.34 0.180 -.183628 9766705 vndpriceolnew | -.0132798 0018089 -7.34 0.000 -.0168251 -.0097344 minotime | -.0017115 0014467 -1.18 0.237 -.0045469 0011239 dtimeol | -.3953342 066698 -5.93 0.000 -.5260598 -.2646086 dcostol | -.0216763 0054348 -3.99 0.000 -.0323283 -.0110243 vndpriceisnew | -.011088 0016564 -6.69 0.000 -.0143344 -.0078415 minstime | 0006317 0013385 0.47 0.637 -.0019917 0032551 minttime | 0005195 0045841 0.11 0.910 -.0084652 0095043 vndtcost | -.0003213 0037327 -0.09 0.931 -.0076371 0069946 vndpriceol_income | 1.07e-07 8.84e-08 1.21 0.228 -6.66e-08 2.80e-07 vndpriceis_income | 1.92e-07 7.43e-08 2.58 0.010 4.62e-08 3.37e-07 dtimeol_gender | 1132118 0484016 2.34 0.019 0183464 2080772 minttime_income | -6.35e-07 4.78e-07 -1.33 0.184 -1.57e-06 3.02e-07 online_occup | 3966483 1155248 3.43 0.001 1702238 6230727 online_freq_internet | 0832816 0274364 3.04 0.002 0295072 137056 - est store full outreg2 full using E:\\cfull.doc E:\\cfull.doc dir : seeout * Average WTP of sample gen WTPdelivery = 1000*(_b[dtimeol]+_b[dtimeol_gender]*gender)/(_b[vndpriceisnew] +_b[vndpriceis_income]*income) sum WTP Variable | Obs Mean Std Dev Min Max -+ -WTPdelivery | 3852 37485.24 27623.18 26594.16 263990.5 gen WTPtraveltime = 1000*(_b[minttime]+_b[minttime_income]*income)/(_b[vndpriceisnew] +_b[vndpriceis_income]*income ) sum WTPtraveltime Variable | Obs Mean Std Dev Min Max -+ -WTPtravelt~e | 3852 747.437 2477.54 100.6784 20855.56 gen WTPordertime = 1000*(_b[minotime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*income) sum WTPordertime Variable | Obs Mean Std Dev Min Max -+ -WTPordertime | 3852 191.9156 117.1655 161.3297 1142.85 gen WTPshoppingtime = 1000*(_b[minstime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*income) sum WTPshoppingtime Variable | Obs Mean Std Dev Min Max -+ -WTPshoppin~e | 3852 -70.84053 43.24853 -421.8528 -59.55055 * WTP of average individual sum gender income Variable | Obs Mean Std Dev Min Max -+ -gender | 3852 5825545 4932017 income | 3852 7375.389 7769.067 2500 50000 nlcom 1000*(_b[dtimeol]+_b[dtimeol_gender]* 5825545)/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) _nl_1: 1000*(_b[dtimeol]+_b[dtimeol_gender]* 5825545)/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 29710.1 6597.944 4.50 0.000 16778.37 42641.83 - nlcom 1000*(_b[minttime]+_b[minttime_income]*7.375389)/(_b[vndpriceisnew] +_b[vndpriceis_income]*7.37 5389) _nl_1: 1000*(_b[minttime]+_b[minttime_income]*7.375389)/(_b[vndpriceisnew] +_b[vndpriceis_income]*7.37 5389) -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | -46.43881 413.1316 -0.11 0.911 -856.1618 763.2842 - nlcom 1000*(_b[minotime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) _nl_1: 1000*(_b[minotime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 154.3723 130.9869 1.18 0.239 -102.3573 411.1019 - nlcom 1000*(_b[minstime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) _nl_1: 1000*(_b[minstime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | -56.98244 120.8654 -0.47 0.637 -293.8742 179.9094 - * Mixed logit regression * Mixed basic mixlogit percentchoice minotime dtimeol dcostol minstime minttime vndtcost, group(percentchoiceid) id(id) rand(online vndpri > ceolnew vndpriceisnew) nrep(50) Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: log likelihood = -1521.4088 log likelihood = -1500.3277 log likelihood = -1427.8099 log likelihood = -1360.7624 log likelihood = -1348.096 log likelihood = -1317.4877 log likelihood = -1298.6796 log likelihood = -1293.8187 log likelihood = -1281.724 log likelihood = -1275.0048 log likelihood = -1242.7281 log likelihood = -1194.0395 log likelihood = -1178.679 log likelihood = -1140.8565 log likelihood = -1085.9292 log likelihood = -1050.7137 log likelihood = -1023.6684 log likelihood = -1021.883 log likelihood = -1021.3606 log likelihood = -1020.4056 log likelihood = -1020.3633 log likelihood = -1020.3631 log likelihood = -1020.3631 (not (not (not (not (not (not (not (not (not (not (not (not (not (not (not concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) Mixed logit model Log likelihood = -1020.3631 Number of obs LR chi2(3) Prob > chi2 = = = 3852 366.46 0.0000 percentchoice | Coef Std Err z P>|z| [95% Conf Interval] + -Mean | minotime | -.0048268 0036887 -1.31 0.191 -.0120565 0024028 dtimeol | -.5527006 0817945 -6.76 0.000 -.7130148 -.3923864 dcostol | -.0461558 0080157 -5.76 0.000 -.0618662 -.0304454 minstime | -.0023207 0032656 -0.71 0.477 -.0087212 0040798 minttime | -.0086048 0057998 -1.48 0.138 -.0199723 0027626 vndtcost | -.0091883 0084158 -1.09 0.275 -.0256829 0073063 online | 1.448403 3725891 3.89 0.000 7181416 2.178664 vndpriceolnew | -.0339901 0028581 -11.89 0.000 -.0395918 -.0283884 vndpriceisnew | -.0290463 0027368 -10.61 0.000 -.0344103 -.0236824 + -SD | online | 1.1095 2275061 4.88 0.000 6635962 1.555404 vndpriceolnew | 0080857 0016348 4.95 0.000 0048815 0112899 vndpriceisnew | 0081344 0011441 7.11 0.000 005892 0103767 The sign of the estimated standard deviations is irrelevant: interpret them as being positive est store mixbasic outreg2 mixbasic using E:\\mix1.doc E:\\mix1.doc dir : seeout nlcom 1000*_b[minotime]/_b[vndpriceisnew] _nl_1: 1000*_b[minotime]/_b[vndpriceisnew] -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 166.1774 125.7493 1.32 0.186 -80.28675 412.6416 - nlcom 1000*_b[minstime]/_b[vndpriceisnew] _nl_1: 1000*_b[minstime]/_b[vndpriceisnew] -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 79.89628 113.3772 0.70 0.481 -142.319 302.1116 - nlcom 1000*_b[dtimeol]/_b[vndpriceisnew] _nl_1: 1000*_b[dtimeol]/_b[vndpriceisnew] -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 19028.25 3050.434 6.24 0.000 13049.51 25006.99 - nlcom 1000*_b[minttime]/_b[vndpriceisnew] _nl_1: 1000*_b[minttime]/_b[vndpriceisnew] -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 296.2457 200.1094 1.48 0.139 -95.96157 688.4529 - * Mixed full mixlogit percentchoice minotime dtimeol dcostol minstime minttime vndtcost vndpriceol_income vndpriceis_income dtimeol_gender > minttime_income online_occup online_freq_internet, group(percentchoiceid) id(id) rand(online vndpriceolnew vndpriceisnew) nr > ep(50) Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood log likelihood Mixed logit model = = = = = = = = = = = = = = = = = = = = = = = -1522.6948 (not -1491.4662 (not -1464.1066 (not -1420.0899 (not -1330.0379 (not -1288.9576 (not -1225.9754 (not -1214.3494 (not -1180.5229 (not -1165.9414 (not -1118.8065 (not -1115.521 (not -1102.0463 (not -1088.9613 (not -1079.4986 (not -1058.2762 (not -1042.2441 -1014.6448 (not -1011.1788 -1005.2056 -1004.567 -1004.5639 -1004.5639 concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) concave) Number of obs = 3852 LR chi2(3) Prob > chi2 Log likelihood = -1004.5639 = = 348.77 0.0000 -percentchoice | Coef Std Err z P>|z| [95% Conf Interval] -+ -Mean | minotime | -.0045823 0036854 -1.24 0.214 -.0118056 002641 dtimeol | -.6987081 103449 -6.75 0.000 -.9014645 -.4959518 dcostol | -.0458078 0080696 -5.68 0.000 -.0616239 -.0299917 minstime | -.0003081 0031134 -0.10 0.921 -.0064102 005794 minttime | -.0002584 007687 -0.03 0.973 -.0153246 0148078 vndtcost | -.0120721 0095772 -1.26 0.207 -.0308431 0066989 vndpriceol_income | 4.53e-07 2.54e-07 1.78 0.074 -4.48e-08 9.51e-07 vndpriceis_income | 7.15e-07 2.24e-07 3.19 0.001 2.77e-07 1.15e-06 dtimeol_gender | 2425638 1040393 2.33 0.020 0386505 4464771 minttime_income | -1.67e-06 9.75e-07 -1.72 0.086 -3.59e-06 2.37e-07 online_occup | 6158666 2799549 2.20 0.028 067165 1.164568 online_freq_internet | 1452158 0677172 2.14 0.032 0124926 277939 online | -.092157 7050294 -0.13 0.896 -1.473989 1.289675 vndpriceolnew | -.0378899 004016 -9.43 0.000 -.0457611 -.0300187 vndpriceisnew | -.0356967 0034588 -10.32 0.000 -.0424759 -.0289175 -+ -SD | online | -.7799397 3156464 -2.47 0.013 -1.398595 -.161284 vndpriceolnew | 0097426 0019404 5.02 0.000 0059394 0135458 vndpriceisnew | 0089974 0011474 7.84 0.000 0067485 0112463 -The sign of the estimated standard deviations is irrelevant: interpret them as being positive est store mixfull outreg2 mixfull using E:\\mix2.doc E:\\mix2.doc dir : seeout nlcom 1000*(_b[dtimeol]+_b[dtimeol_gender]* 5825545)/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) _nl_1: 1000*(_b[dtimeol]+_b[dtimeol_gender]* 5825545)/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 15617.24 2527.811 6.18 0.000 10662.82 20571.66 - nlcom 1000*(_b[minttime]+_b[minttime_income]*7.375389)/(_b[vndpriceisnew] +_b[vndpriceis_income]*7.37 5389) _nl_1: 1000*(_b[minttime]+_b[minttime_income]*7.375389)/(_b[vndpriceisnew] +_b[vndpriceis_income]*7.37 5389) -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 7.585362 215.2588 0.04 0.972 -414.3142 429.4849 - nlcom 1000*(_b[minotime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) 152 _nl_1: 1000*(_b[minotime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 128.3868 102.4091 1.25 0.210 -72.33143 329.105 - nlcom 1000*(_b[minstime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) _nl_1: 1000*(_b[minstime])/(_b[vndpriceisnew]+_b[vndpriceis_income]*7.375389) -percentcho~e | Coef Std Err z P>|z| [95% Conf Interval] -+ -_nl_1 | 8.631691 87.2661 0.10 0.921 -162.4067 179.6701 - end of do-file exit, clear ... ����� in the context of analyzing the choice in- store and is store? ??s utility � attribute from online shopping and in- store alternatives, ? ?online utility? ?of � marginal �, � level � shopping of between... impacts on in- store shopping and online searching frequency has also positive effects on online shopping and instore shopping frequencies These interactions shown that online shopping has a complementary... effect of purchase price of online shopping on online �� is the alternative specific constant of online shopping shopping utility • �����������: The effect of ordering time on online shopping utility