Experiment Outcome &Literature Review Presented by Fang Liyu
Experiment outcome 1. Data from JD Sample size: 1) Data contains 3325 products in 8 days 2) There are 2000-3000 missing values in each data sheet on average. What s new? 1) We have added the new independent variable (search interest) in our model, here we use baidu_adv to replace the search interest. 2) We choose two different variables to represent rating : rating and rating with pic respectively.
First, we use the traditional rating to do the experiment
Experiment in Random Effets. xtreg rank rating num_review price baidu_adv releasedays Random-effects GLS regression Number of obs = 15808 Group variable: product Number of groups = 2800 R-sq: within = 0.9746 Obs per group: min = 1 between = 0.5582 avg = 5.6 overall = 0.7749 max = 8 Wald chi2(5) = 501764.89 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 rank Coef. Std. Err. z P> z [95% Conf. Interval] rating 2.691517.0139529 192.90 0.000 2.66417 2.718864 num_review -.234534.0036829-63.68 0.000 -.2417523 -.2273156 price -.7054546.0063715-110.72 0.000 -.7179426 -.6929667 baidu_adv -.0155185.0028503-5.44 0.000 -.0211049 -.0099321 releasedays.0052724.0091589 0.58 0.565 -.0126787.0232234 _cons.0199332.0241738 0.82 0.410 -.0274466.067313 sigma_u 1.1150936 sigma_e.33048475 rho.91925491 (fraction of variance due to u_i)
Experiment in Fixed Effets. xtreg rank rating num_review price baidu_adv releasedays, fe Fixed-effects (within) regression Number of obs = 15808 Group variable: product Number of groups = 2800 R-sq: within = 0.9746 Obs per group: min = 1 between = 0.5582 avg = 5.6 overall = 0.7749 max = 8 F(5,13003) = 99770.82 corr(u_i, Xb) = -0.0081 Prob > F = 0.0000 rank Coef. Std. Err. t P> t [95% Conf. Interval] rating 2.693823.0158887 169.54 0.000 2.662679 2.724967 num_review -.2369635.0041672-56.86 0.000 -.2451318 -.2287952 price -.7098161.0071496-99.28 0.000 -.7238304 -.6958018 baidu_adv -.0150629.0030092-5.01 0.000 -.0209613 -.0091645 releasedays.011167.010629 1.05 0.293 -.0096675.0320014 _cons -.0551022.0116471-4.73 0.000 -.0779322 -.0322721 sigma_u 1.1289159 sigma_e.33048475 rho.92106494 (fraction of variance due to u_i) F test that all u_i=0: F(2799, 13003) = 70.16 Prob > F = 0.0000
. hausman FE RE, constant sigmamore Hausman test Coefficients (b) (B) (b-b) sqrt(diag(v_b-v_b)) FE RE Difference S.E. rating 2.693823 2.691517.0023062.0076206 num_review -.2369635 -.234534 -.0024296.0019553 price -.7098161 -.7054546 -.0043615.0032532 baidu_adv -.0150629 -.0155185.0004556.0009707 releasedays.011167.0052724.0058946.0054064 _cons -.0551022.0199332 -.0750354. b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(6) = (b-b)'[(v_b-v_b)^(-1)](b-b) = 6.21 Prob>chi2 = 0.3998 (V_b-V_B is not positive definite) Insignificant P value, there s no difference in choosing random effects or fixed effects
Then, we use the new rating with pic as the dependent variable to do the experiment again.
Experiment in Random Effects. xtreg rank rating_pic num_review price baidu_adv releasedays Random-effects GLS regression Number of obs = 12832 Group variable: product Number of groups = 2278 R-sq: within = 0.9308 Obs per group: min = 1 between = 0.0286 avg = 5.6 overall = 0.3639 max = 8 Wald chi2(5) = 111962.14 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 rank Coef. Std. Err. z P> z [95% Conf. Interval] rating_pic -.9761163.0173674-56.20 0.000-1.010156 -.9420768 num_review.7109793.0143 49.72 0.000.6829518.7390068 price.1868354.009257 20.18 0.000.1686921.2049787 baidu_adv -.0416271.0053348-7.80 0.000 -.0520831 -.0311711 releasedays.7381732.014543 50.76 0.000.7096694.7666769 _cons.4897002.0382611 12.80 0.000.4147099.5646905 sigma_u 1.2860233 sigma_e.53907165 rho.85055022 (fraction of variance due to u_i)
Experiment in Fixed Effects. xtreg rank rating_pic num_review price baidu_adv releasedays, fe Fixed-effects (within) regression Number of obs = 12832 Group variable: product Number of groups = 2278 R-sq: within = 0.9309 Obs per group: min = 1 between = 0.0313 avg = 5.6 overall = 0.3643 max = 8 F(5,10549) = 28436.00 corr(u_i, Xb) = -0.3400 Prob > F = 0.0000 rank Coef. Std. Err. t P> t [95% Conf. Interval] rating_pic -.9662675.019379-49.86 0.000-1.004254 -.9282811 num_review.6925175.0159968 43.29 0.000.6611608.7238742 price.1495704.0102889 14.54 0.000.1294022.1697385 baidu_adv -.0286771.0052466-5.47 0.000 -.0389614 -.0183928 releasedays.8060181.0168168 47.93 0.000.7730541.8389821 _cons.2299069.0208886 11.01 0.000.1889612.2708525 sigma_u 2.0572424 sigma_e.53907165 rho.93574879 (fraction of variance due to u_i) F test that all u_i=0: F(2277, 10549) = 73.28 Prob > F = 0.0000
. hausman FE RE, constant sigmamore Hausman test Coefficients (b) (B) (b-b) sqrt(diag(v_b-v_b)) FE RE Difference S.E. rating_pic -.9662675 -.9761163.0098489.0130401 num_review.6925175.7109793 -.0184618.0108125 price.1495704.1868354 -.037265.0068752 baidu_adv -.0286771 -.0416271.01295.0024724 releasedays.8060181.7381732.0678449.0119873 _cons.2299069.4897002 -.2597933. b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(6) = (b-b)'[(v_b-v_b)^(-1)](b-b) = 2606.76 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) Significant P value, so we choose the fixed effects model.
Experiment outcome 2. Data from Amazon Sample size: 1) Data contains 2920 products in 21 days 2) There are about 7000 missing values in each data sheet on average.
Experiment in Random Effects. xtreg rank rating num_review price Random-effects GLS regression Number of obs = 39248 Group variable: product Number of groups = 2920 R-sq: within = 0.0240 Obs per group: min = 1 between = 0.2116 avg = 13.4 overall = 0.2176 max = 21 Wald chi2(3) = 1474.16 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 rank Coef. Std. Err. z P> z [95% Conf. Interval] rating -.5840032.0891183-6.55 0.000 -.7586719 -.4093344 num_review -.3605504.0112308-32.10 0.000 -.3825623 -.3385385 price -.2270351.0219164-10.36 0.000 -.2699905 -.1840798 _cons 15.35478.2487955 61.72 0.000 14.86715 15.84241 sigma_u 1.8243656 sigma_e 1.5349809 rho.58550864 (fraction of variance due to u_i)
Experiment in Fixed Effects. xtreg rank rating num_review price, fe Fixed-effects (within) regression Number of obs = 39248 Group variable: product Number of groups = 2920 R-sq: within = 0.0263 Obs per group: min = 1 between = 0.0542 avg = 13.4 overall = 0.0771 max = 21 F(3,36325) = 327.24 corr(u_i, Xb) = -0.6194 Prob > F = 0.0000 rank Coef. Std. Err. t P> t [95% Conf. Interval] rating -.9173834.2681059-3.42 0.001-1.442879 -.391888 num_review -.7462613.0246825-30.23 0.000 -.7946397 -.6978829 price.7695795.133854 5.75 0.000.5072218 1.031937 _cons 7.539041 1.420237 5.31 0.000 4.755334 10.32275 sigma_u 2.7758919 sigma_e 1.5349809 rho.7658292 (fraction of variance due to u_i) F test that all u_i=0: F(2919, 36325) = 17.54 Prob > F = 0.0000
. hausman FE RE, constant sigmamore Hausman test Coefficients (b) (B) (b-b) sqrt(diag(v_b-v_b)) FE RE Difference S.E. rating -.9173834 -.5840032 -.3333802.2539596 num_review -.7462613 -.3605504 -.3857109.0220865 price.7695795 -.2270351.9966147.1325721 _cons 7.539041 15.35478-7.815738 1.403852 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(4) = (b-b)'[(v_b-v_b)^(-1)](b-b) = 364.56 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) Significant P value, so we choose the fixed effects model.
Different types of ewom 1) discussion forums 2) UseNet groups 3) product reviews 4) blogs 5) social networking sites (SNS)
Characteristics of ewom 1) Enhanced volume 2) Dispersion 3) Persistence and observability 4) Anonymity and deception 5) Salience of valence 6) Community engagement
4 quadrants of ewom Quadrant 1: Antecedents of ewom Senders (Why Do People Talk Online?) Quadrant 2: Consequences for ewom Senders (What Happens to the Communicator?) Quadrant 3: The Antecedents of the Receiver (Why do people listen?) Quadrant 4: The Consequences to the Receiver (The Power of ewom)
The framework of ewom ewom Characteristics C1. Enhanced Volume C2. Dispersion C3. Persistence and Observability Quadrant 1: Antecedents of ewom Senders What We Know Self-Enhancement, Consumer Psychographics, Product/Retailer Performance, Altruism/Concern for Others, Need for Social Interaction Relevant ewom Characteristics: C1, C3, C6 What We Need to Know RQ1, RQ2 Quadrant 2: Consequences to the Senders What We Know Enhanced Product Learning, Impression Management Social Capital and Reputation Relevant ewom Characteristics: C3, C4, C6 What We Need to Know RQ3 Research Questions RQ1- How can firms foster higher-quality reviews and reviewers? RQ2- What is the potential of visual ewom? RQ3- How does ewom affect consumer engagement? RQ4- Are there latent or counterintuitive aspects of ewom seeking? C4. Anonymity and Deception C5. Salience of valence C6. Community Engagement Quadrant 3: Antecedents of the Receiver What We Know Search/Evaluation Efforts, Risk Reduction, Social Assurance Leisure Activity Relevant ewom Characteristics: C1, C2, C3, C4 What We Need to Know RQ4, RQ5, RQ6 Quadrant 4: Consequences to the Receiver What We Know Product ROI, Willingness-to-Pay, Trust and Loyalty Relevant ewom Characteristics: C1, C2, C3, C5 What We Need to Know RQ7, RQ8, RQ9, RQ10, RQ11 RQ5- How do consumers process the textual content in ewom messages? RQ6- How does ewom differ cross-culturally? RQ7- What are the disaggregate effects on receivers? RQ8- How does trust change the power of ewom? RQ9- How does ewom change the consumer decision journey? RQ10- How does ewom affect service delivery modes and costs? RQ11- How can firms utilize ewom s inherent endogeneity?
The track of WOM research 1. consumer-generated information V.S. sellercreated information 2. internal and external WOM 3. multiple WOM sources 4. analyze variations of WOM influence across different WOM sources