1. Self-Selection

2. Self-Selection and Information Role of Online Product Reviews Xinxin Li, Lorin Hitt The Wharton School, University of Pennsylvania Workshop on Information Systems and Economics (WISE 2004) ，

3. Outline Introdution Data Collection Trend in Consumer reviews Impact of Consumer Reviews on Book Sales Theory Model and Implicatons Conclusion

4. Introduction Word of mouth has long been recognized as a major drivers of product sales. eBay-like online reputation systems : a large body of work product review websites : very little systematic research

5. Self-Selection Problem The efficacy of consumer-generated product reviews may be limited for at least two reasons. Firms may manipulate online rating services by paying individuals to provide high ratings. There are possibilities that the reported ratings are inconsistent with the preferences of the general population. Ratings of products may reflect both consumer taste as well as quality.

6. Major Research Questions Early adopters may have significantly different preferences than later adopters which will create trends in ratings as products diffuse. We consider whether consumers account for these biases in ratings when making product purchase decisions.

7. Data Collection A random sample of 2651 hardback books was collected from “ Books in Print ” covering books published from 2000-2004 that also have reviews on Amazon. Book characteristic information title author publisher publication date category publication date for corresponding paperback editions consumer reviews Sales-related data (every Friday from March to July in 2004) sales rank price the number of consumers reviews the average review shipping availability

8. Trend in Consumer Reviews The Box-Cox model : AvgRating it : the average review for book i at time t, T : the time difference between the date the average review was posted and the date the book was released u i : the idiosyncratic characteristics of each individual book that keep constant over time.

9. Trend in Consumer Reviews (contd.)

10. Impact of Consumer Reviews on Book Sales Sales rank is a log-linear function of book sales with a negative slope.

11. Impact of Consumer Reviews on Book Sales (contd.) All estimates are significant and have the right sign. With other demand-related factors controlled for, the time variant component R T has a significant impact on book sales when consumers compare different books at the same time period

12. Theory Model and Implicatons An individual consumer ’ s preferences over the product can be characterized by two components (x i, q i ). The element x i is known by each consumer before purchasing and represents the consumer ’ s preference over product characteristics that can be inspected before purchase The element q i measures the quality of the product for consumer I q e : expected quality

13. Conclusion Our findings suggest the significance of product design and early period product promotion

14. Self-Selection Bias in Reputation Systems Mark Kramer MITRE Corporation IFIPTM ’ 07

15. Outline Introduction Expectation and Self-Selection Avoiding Bias in Reputation Management Systems Conclusion

16. Motivation Can a reputation system based on user ratings accurately signal the quality of a resource?

17. Ratings Bias Reputation systems appear to be inherently biased towards better-than- average ratings Amazon: 3.9 out of 5 Netflix prize data set: 3.6 out of 5 stars

18. Ratings Bias (contd.) 87% of ratings are 3 or higher

19. Possible Reasons for Positive Bias People don ’ t like to be critical People don ’ t understand the rating system or cannot calibrate themselves Lake Wobegon Effect: Most movies are better than average Number of ratings for quality movies far exceeds number of ratings of poor movies

20. The SpongeBob Effect Oscar Winners 2000-2005 : Average Rating 3.7 Stars SpongeBob DVDs : Average Rating 4.1 Stars If SpongeBob effect is common, then ratings do not accurately signal the quality of the resource

21. What is Happening Here? People choose movies they think they will like, and often they are right Ratings only tell us that “ fans of SpongeBob like SpongeBob ” Self-selection Oscar winners draw a wider audience Rating is much more representative of the general population

22. What is Happening Here? (contd.) There might be a tendency to downplay the problem of biased ratings you already "know" whether or not you would like the SpongeBob movie you could look at written reviews one could get personalized guidance from a recommendation engine

23. Importance of Self-Selection Bias Bizrate 44% of consumers consult opinion sites before making online purchases High ratings are the norm, contain little information Written reviews also can be biased Discarding numerical (star) ratings would eliminate an important time-saver Consumers have no idea what “ discount ” to apply to ratings to get a true idea of quality No recommendation engine will ever totally replace browsing as a method of resource selection

24. Model of Self-Selection Bias Two groups: Evaluation group E Feedback group F where F  E Consider binary situation: E = Expect to be satisfied (T/F) S = Are satisfied R = Resource selected (and reviewed) P(S) = probability of satisfaction with resource in E P(S|R) = probability of satisfaction within F If P(R|E) > P(R|~E) Self-Selection And P(S|E) > P(S|~E) Realization of expectations Then P(S|R) > P(S) Biased Rating

25. Utility and Self-Selection Some distribution of expected utility in evaluation group E Resource will be selected only if expected utility is positive Very high reviews can shift the expected utility curve to the right and increase the number of people selecting the resource “ Swing ” group has a greater chance of disappointment Select # people Expected Utility (Evaluation Group)

26. Effect of Biased Rating: Example 10 people see SpongeBob ’ s 4-star ratings 3 are already SpongeBob fans, rent movie, award 5 stars 6 already know they don ’ t like SpongeBob, do not see movie Last person doesn ’ t know SpongeBob, impressed by high ratings, rents movie, rates it 1-star Result: Average rating remains unchanged: (5+5+5+1)/4 = 4 stars 9 of 10 consumers did not really need rating system Only consumer who actually used the rating system was misled

27. Paradox of Subjective Reputation “ Accurate ratings render ratings inaccurate ” The purpose of reputation systems is to increase consumer satisfaction Do better than random selection The mechanism is self-selection If self-selection works, ratings will become positively biased In the limit, all ratings will be 5-star ratings Self-Selection bias (SpongeBob Effect) distorts the information needed for accurate self-selection Rating system defeats itself

28. Dynamics of Ratings Paradox Inaccurate or biased prior information Accurate, complete prior information Happy consumers Positively biased ratings Good self- selection Mix of happy and unhappy consumers Unbiased ratings Poor self- selection

29. Example of Reputation Dynamics Resource with uniformly distributed satisfaction between 0 – 100 Successive groups decide whether to use the resource, based on rating # selecting resource is proportional to average rating

30. Example of Reputation Dynamics(contd.) Fans first Random people first

31. Ideas for Bias-Resistant Reputation Systems Use more demographics Kids like SpongeBob, most adults do not Self-selection is still at work within demographic subgroup Demographics might not create useful groups with different preferences Make personalized recommendations Yes, but people still like to browse Recommendations based on biased ratings might fail NetFlix recommendation engine has large error Use written reviews Self-selection bias is still present

32. Bias-Resistant Reputation System Want P(S) but we collect data on P(S|R) S = Are satisfied with resource R = Resource selected (and reviewed) However, P(S|E,R)  P(S|E) Likelihood of satisfaction depends primarily on expectation of satisfaction, not on the selection decision If we can collect prior expectation, the gap between evaluation group and feedback group disappears whether you select the resource or not doesn ’ t matter

33. Bias-Resistant Reputation System Before viewing: I think I will: Love this movie Like this movie It will be just OK Somewhat dislike this movie Hate this movie After viewing: I liked this movie: Much more than expected More than expected About the same as I expected Less than I expected Much less than I expected Big fans Everyone else Skeptics

34. Conclusions Self-selection bias exists in most cases of consumer choice Bias means that user ratings do not reflect the distribution of satisfaction in the evaluation group Consumers have no idea what “ discount ” to apply to ratings to get a true idea of quality Many current rating systems may be self-defeating Accurate ratings promote self-selection, which leads to inaccurate ratings Collecting prior expectations may help address this problem