Losing to Win: Reputation Management of Online Sellers

Transcription

1 Fan, Ju and Xiao Reputation 1 Losing to Win: Reputation Management of Online Sellers Ying Fan (University of Michigan) Jiandong Ju (Tsinghua University) Mo Xiao (University of Arizona)

2 Introduction Fan, Ju and Xiao Reputation 2 Reputation is typically considered an asset Especially for entrepreneurs in e-commerce ebay, Amazon marketplace This paper addresses two research questions: How large is the return to reputation? How do sellers manage their reputation? Large literature on the first question, little on the second These two questions are intrinsically linked Only if there are significant returns to reputation will sellers spend resources to pursue higher reputation To study returns to reputation, researchers may in turn have to take into account sellers strategic reputation management

3 Why Manage Reputation? Fan, Ju and Xiao Reputation 3 Klein and Leffler (1981) and Shapiro (1983) In a repeated game, players may realize lower or even negative profits initially, while the community learns their type Need sufficiently high profit margins for high quality sellers so that the promise of future gains from sustaining a reputation offsets the short-term temptation to cheat Holmstrom (1999) on an agent s career concerns Employees work hardest early on in their career to build a reputation for competence

4 Heterogeneous Effect of Reputation Fan, Ju and Xiao Reputation 4 New Sellers: A seller may realize lower or even negative profits initially so as to build a good reputation A higher reputation may even motivates more aggressive reputation management i.e., a higher reputation may appear to have a negative impact on new sellers Established Sellers: have passed the initial phase of reputation accumulation; may enjoy the returns of reputation

5 What We Do Fan, Ju and Xiao Reputation 5 We collect a panel data from the largest online platform in China We study the heterogeneous effect of reputation on revenue and transaction volume seller and month fixed effect, and IV methods to deal with the enodgeneity of reputation We further investigate how sellers manage their reputation and whether sellers close to the next higher rating grade behave differently We finally examine the long-run effect of reputation (on survival)

6 Taobao.com Fan, Ju and Xiao Reputation 6 China s largest e-commerce platform 500 million users and 800 million product listings per day by 2012 In Jan 2010, about 2m C2C sellers and 10k B2C sellers (C2C seller = a seller without a brick-and-mortar store) Taobao reputation is similar to ebay s reputation system Rating is accumulative Positive feedback: +1; negative: -1; neutral: +0 Reports grade, score, % positive feedback, and more Difference: Taobao separates buyer reputation from seller reputation in the general rating Rating grade is highly visible to all parties of transactions

7 Taobao Seller Rating Fan, Ju and Xiao Reputation 7 Distribution of Seller Ratings

8 Data Fan, Ju and Xiao Reputation 8 An unbalanced panel data 580,428 unique sellers ( regular C2C sellers from a 25% random sample) between 03/2010 and 04/2011 We observe at the seller/month level: revenue, # transactions, main category of business, # categories of business, # months since registration seller reputation in several dimensions: grade, score, % positive ratings cumulative transaction volume as a buyer at the seller level: age, gender, birth province, residing city

9 Defining New and Established Sellers Fan, Ju and Xiao Reputation 9 Established Sellers: first appear on month 1 of our sample have made at least 251 transactions so far New Sellers: first appear during or after month 7 have been selling for no more than 6 months in the data have not reached 251 total transactions We try to be conservative (some sellers are of neither type) and conduct robustness checks

10 Summary Statistics Fan, Ju and Xiao Reputation 10 New Sellers Established sellers Mean Std. Dev. Mean Std. Dev. Monthly Revenue in $ 450 6,269 5,871 43,750 Monthly # Transactions ,331 Price in $ # Categories If Switch Main Category Seller Rating Score ,763 23,995 Seller Rating Grade Seller Rating Cat I Seller Rating Cat II n.a. n.a Seller Rating Cat III n.a. n.a % Seller Pos. Ratings # Months since Registration # seller/months 1,031,403 1,311,452 # sellers 473, ,276

11 Empirical Framework Fan, Ju and Xiao Reputation 11 y it RatingGrade 0 1 i, t 1 Dummy 2 it, 1 3 i, t 1 4 i, t 1 5 _ RatingScore X it i t it RatingCategory % PositiveRating i: seller i; t: month t X it : months since seller i registered on Taobao μ i : seller fixed effect ω t : month fixed effect

12 Instrumental Variables Fan, Ju and Xiao Reputation 12 IV: a seller s (lagged) cumulative transaction volume as a buyer Exclusion: A seller s transaction volume as a buyer is not observed by any buyer directly Relevance: It is positively correlated with the seller reputation even after controlling for the seller fixed effect and the months since registration Exogeneity: After controlling for the seller and month fixed effects, we argue that the IV can be reasonably assumed to be uncorrelated with the unobservable seller/monthspecific heterogeneity that affects her performance as a seller First-stage Results Underlying Assumptions & When the IV is Invalid

13 Heterogeneous Effect of Reputation Fan, Ju and Xiao Reputation 13 Log(Revenue) New Sellers Established Sellers L. Rating Grade *** (0.100) (0.076) L. Rating Category II (11.350) L. Rating Category III (11.926) L. Rating Score in 10k *** (27.500) (0.132) L. % Pos. Ratings 2.205*** *** (0.256) (3.323) Months since Regis *** *** (0.876) (1.390) R - Squared # seller/months 558,251 1,234,176 # sellers 229, ,138

14 Heterogeneous Effect of Reputation Fan, Ju and Xiao Reputation 14 Log(Revenue) Log(Transactions) New Sellers Established Sellers New Sellers Established Sellers L. Rating Grade *** 0.542*** 0.197*** (0.100) (0.076) (0.042) (0.046) L. Rating Category II (11.350) (6.679) L. Rating Category III (11.926) (7.079) L. Rating Score in 10k *** *** (27.500) (0.132) (11.713) (0.091) L. % Pos. Ratings 2.205*** *** 0.589*** *** (0.256) (3.323) (0.101) (2.406) Months since Regis *** *** *** *** (0.876) (1.390) (0.172) (0.317) R - Squared # seller/months 558,251 1,234, ,251 1,234,176 # sellers 229, , , ,138 Alternative Explanations of the Results

15 Fan, Ju and Xiao Reputation 15 Reputation Management Other reputation management activities? 1(switching main business category) Log(Business Categories) New Sellers Established Sellers New Sellers Established Sellers L. Rating Grade 0.028** *** 0.137*** 0.044*** (0.013) (0.009) (0.011) (0.012) L. Rating Category II (1.291) (1.660) L. Rating Category III (1.362) (1.744) L. Rating Score in 10k *** *** (3.743) (0.397) (2.938) (0.017) L. % Pos. Ratings *** (0.032) (0.285) (0.025) (0.529) Months since Regis *** 2.149*** 0.592*** 1.401*** (0.048) (0.045) (0.030) (0.043) # seller/months 1,234, ,251 1,234, ,251 # sellers 104, , , ,445

16 Reputation Management (cont.) Fan, Ju and Xiao Reputation 16 Another evidence of reputation management: marginal sellers behavior marginal seller = seller whose rating score is 10% within the lower bound of rating scores for the next higher grade Consumers mostly observe rating grades -> consumers do not have much reason to care whether a seller is a marginal seller

17 Reputation Management: Marginal Sellers Fan, Ju and Xiao Reputation 17 This table reports the estimated effect of the marginal seller dummy on various depend variables New Sellers Established Sellers Dep. Var.: log(revenue) *** 0.308*** (0.228) (0.043) Dep. Var.: log(transactions) 0.365*** 0.211*** (0.094) (0.026) Dep. Var.: 1(Switch main category) 0.099*** ** (0.031) (0.005) Dep. Var.: log(business Categories) 0.217*** 0.040*** (0.025) (0.006) # seller/months 558,251 1,234,176 # sellers 229, ,138

18 Long-run Effect of Reputation Fan, Ju and Xiao Reputation 18 Survival Analysis Take one snapshot of data: does a firm in this snapshot survive 6 months later? Survival RatingGrade Dummy _ RatingCategory i 0 1 i 2 i RatingScore 3 i 4 X 5 i 6 location _ trade i % PositiveRating i X i : seller characteristics (age, gender, if an immigrant) η location_trade : location/trade dummy (location = seller s residing city); trade = main business category)

19 Long-run Effect of Reputation Month 7 Snapshot Month 8 Snapshot New Sellers Established Sellers New Sellers Established Sellers Rating Grade *** *** (0.049) (0.019) (0.034) (0.018) Rating Category II (6.730) (10.501) Rating Category III (6.797) (10.542) Rating Score in 10k * (15.473) (0.023) (9.986) (0.012) % Pos. Ratings 0.192*** 1.298*** 0.166*** 1.155*** (0.038) (0.297) (0.034) (0.256) Months since Regis *** *** (0.0001) (0.0001) (0.0001) (0.0001) Seller Age 0.004*** *** (0.0003) (0.0001) (0.0002) (0.0001) Seller Gender (0.005) (0.003) (0.004) (0.003) If Province Immigrant ** (0.007) (0.003) (0.005) (0.003) # sellers 49,578 97,165 84,820 95,745 Alternative Explanation of the Results Fan, Ju and Xiao Reputation 19

20 Conclusion Fan, Ju and Xiao Reputation 20 We study returns to reputation as well as reputation management using panel data from the largest online trade platform in China We find that Returns to Reputation Seller reputation has a substantial value for established sellers One grade jump increases monthly revenue by 32% and survival likelihood after six months by 14-15% Reputation has a differential effect in a seller s lifecycle New sellers: active reputation management at the cost of short-run revenue and long-run survival Results support career concerns and reputation dynamics theory

21 Thank you! Fan, Ju and Xiao Reputation 21

22 Appendix: Time Trend Mar- 10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec- 10 Jan-11 Feb-11 Fan, Ju and Xiao Reputation 23 Mar- 11 Apr # Taobao Sellers (100,000), 25% Avg. Seller Rating Score (1,000) Avg. Monthly Revenue ($ 1,000)

23 Appendix: Distribution of Seller Ratings Fan, Ju and Xiao Reputation 24 Seller Rating Score Seller Rating Grade Below 4 points 0 Seller Rating Category Frequency Percent Cumulative 393, points 1 441, , I (hearts) , , , , , , ,001 2,000 8 II (diamonds) 404, ,001 5, , ,001 10, , ,001 20, , ,001 50, , , , , , , , III (crowns) 200, , , ,001 1,000, e ,000,001 2,000, e ,000,001 5,000, e Total 5,359, return

24 Appendix: Business Categories Fan, Ju and Xiao Reputation 25 ID Main Business Category ID Main Business Category ID Main Business Category ID Main Business Category 1 unclassified 23 skin care/body care/essential oil 45 miscellaneous 67 musical instruments 2 3 computer hardware/desktops/network digital cameras/camcorders/cameras 24 decor/curtains/rugs 46 jewelry/diamonds/jade/gold 68 office supplies 25 adults/anti-contraceptives/family planning 47 Internet games accessories 69 assembled computer 4 women's apparel 26 snacks/nuts/tea/local specialty 48 men's active wear 70 group discount (like groupon) 5 video games/accessories 27 personal care/health/massage 49 men's shoes 71 routers 6 home/storage and organization/gifts 28 calling card recharging 50 vacation/discount airfares/discount 72 hotels 7 antique/collectibles 29 watches/fashion watches 51 TV & entertainment electronics 73 creative design 8 toys/dolls/mannequin 30 early education 52 cell phones made in China 74 nutrition/drugs 9 automobile accessories/motorcycles/bicycles 31 luggage/handbags/bags 53 kitchen appliances 75 nutrition/food Yitao (search engine service developed by Taobao) 10 lamps/lights/bath 32 women's shoes 54 small appliances 76 interior decoration 11 men's accessories 33 flowers/cakes/gardening 55 flash drives/removable disks 77 home hardware 12 pet/pet food 34 office supplies 56 nutrition/food 78 home fixtures (such as light switch) 13 men's apparel 35 live shows/coupons 57 test 79 office furniture 14 books/magazines/newspaper 36 digital accessories 58 fashion accessories jewelry/women's 80 arts/crafts/sewing 15 music/movies/movie stars 37 bed/pillows/towels 59 outdoors/hiking/camping/travel 81 wall decoration 16 baby formula/baby nutrition 38 furniture/custom made furniture 60 Shanghai Expo 2010 merchandise 82 home misc. Internet services/custom-made 17 Tengxun text messaging service 39 children's clothes and shoes pregnancy/maternity software IP card/internet phones/calling card 18 Internet games diapers/feeding 84 home appliances number 19 laptops 41 athletic shoes 63 cleaning supplies 85 wig 20 MP3/MP4/iPod 42 accessories/belts/hats/scarves 64 kitchen & dining 86 purchase through agent 21 cell phones 43 sports/yoga/fitness 65 groceries/frozen food/meal delivery 87 virtual world 22 intimates/underwear/lounge wear 44 cosmetics/fragrance/hair care/tools 66 Internet game

25 Appendix: First-stage results Log(revenue) equation, new sellers Log(revenue) equation, established seller Instrument: L. Rating Grade L. Rating Score (10k) L. Rating Grade L. Rating Cat. II L. Rating Cat. III L. Rating Score (10k) L. Buyer Transaction Volume (10k) 2.867*** 0.014*** 4.455*** *** 1.021*** 4.946*** Grade (0-13) *** *** *** 0.558** ** ** Grade *** *** ** 4.925* * * Grade *** *** ** 4.373* * Grade *** *** ** 3.826* * Grade *** *** ** 3.269* * Grade *** *** ** Grade *** *** * Grade *** *** Grade *** *** Grade *** *** Grade *** *** Grade *** ** R - Squared F statistic # seller/months 558,251 1,234,176 # sellers 229, ,138 Fan, Ju and Xiao Reputation 26

26 Appendix: First-stage results Survival equation, new sellers, month 7 Survival equation, established sellers, month 7 Instrument: L. Rating Grade L. Rating Score (10k) L. Rating Grade L. Rating Cat. II L. Rating Cat. III L. Rating Score (10k) L. Buyer Transaction Volume (10k) 9.044*** 0.034*** 3.470*** *** 0.482*** (2.071) (0.007) (0.811) (0.184) (0.184) (2.718) Grade (0-13) *** (0.710) (0.002) (1.183) (0.245) (0.245) (2.687) Grade *** (6.139) (0.021) (11.826) (2.442) (2.442) (26.862) Grade *** (5.435) (0.018) (10.647) (2.198) (2.198) (24.195) Grade *** (4.732) (0.016) (9.467) (1.954) (1.954) (21.529) Grade *** (4.034) (0.014) (8.290) (1.711) (1.711) (18.872) Grade *** (3.343) (0.011) (7.114) (1.467) (1.467) (16.223) Grade * 0.044*** (2.658) (0.009) (5.940) (1.225) (1.225) (13.588) Grade * 0.035*** (1.994) (0.007) (4.770) (0.984) (0.984) (10.981) Grade ** 0.026*** (1.370) (0.004) (3.612) (0.745) (0.745) (8.424) Grade ** 0.014*** (0.821) (0.003) (2.472) (0.511) (0.511) (5.967) Grade Fan, Ju and Xiao Reputation 27

27 Appendix: Discussion on IV Fan, Ju and Xiao Reputation 28 What are the unobservable factors? Unobservable quality such as web design and average time spent on Taobao perfect persistent, captured by the user fixed effect Hiring a competent shopkeeper or inventory serially correlated Deviation from the average time spent on Taobao assumed to be serially uncorrelated. This is the underlying exogeneity assumption When the IV is invalid? When the deviation from the average time spent on Taobao is serially correlated For example, when the seller moves and the Internet speed of the her new residence is slow return

28 Appendix: Alternative Explanations of the Fan, Ju and Xiao Reputation 29 Short-run Results Established sellers enter the market earlier than new sellers They are intrinsically better than new sellers i.e. selection So, the regression for new sellers and that for established sellers yield different results But it is difficult to explain the change of the sign using this selection story Without reputation management, a higher reputation should be associated with a higher revenue even though the effect of reputation may be smaller for new sellers due to the selection return

29 Appendix: Alternative Explanations of the Fan, Ju and Xiao Reputation 30 Survival Results Established sellers have been in the market longer Low-quality sellers have been selected out already Such a selection may affect the average survival likelihood as well as the effect of reputation on survival (even though it is more likely to affect the former) But it is difficult to explain the short-run results using this selection story Without reputation management, a higher reputation should be associated with a higher revenue even though the effect of reputation may be smaller for new sellers due to the selection return