Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics

Transcription

1 Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics Feng Zhu University of Southern California Xiaoquan (Michael) Zhang HKUST Business School and MIT Center for Digital Business LIYING PIAO March 13th

2 Purpose of Study Examine contextual factors that moderate the relation between online reviews and product sales. 2 aspects: Online consumer reviews can significantly influence consumers' purchasing decisions. With the proliferation of online review systems, online consumer reviews are a good proxy for overall word of mouth (WOM). Some firms even strategically manipulate online reviews in an effort to influence consumers' purchasedecisions The efficacy of online reviews could be limited. Online reviews may just represent consumers' preferences. Reviewers are not a random sample of the user population. Can be easily manipulated Contributions: Empirically demonstrate the differential impact of consumer reviews across products in the same product category. 2

3 Conceptual Framewor sufficient high influencer response Product characteristic - Product popularity : measured by the products sales Consumer characteristic - Consumer Internet experience :measured by the length of time consumers have been using the Internet 3

4 Conceptual Framewor---Product Popularity H1a: The relations between online reviews (e.g., online ratings and the number of online reviews) and product sales are stronger for popular products than for less popular products. Reasons: popular products tend to receive more reviews A large number of reviews maes such online reviews seem more trustworthy. Consumers may be more confident that they can fnd reviews for a popular product online. Exposure is suffcient to create a favorable feeling and can be interpreted as a preference later. 4

5 Conceptual Framewor---Product Popularity H1b: The relations between online reviews (e.g., online ratings and the number of online reviews) and product sales are stronger for less popular products than for popular products. Reasons: The use of ris-handling activities is positively correlated with the amount of ris. (popular products means less ris) Consumers feel more regret if they choose a lesser-nown brand that turns out to be inferior than if they choose a well-nown brand.(in order to avoid this regret) WOM effectiveness depends on the closeness of the relationship between the recommendation source.(less popular products are barely discussed in offline) 5

6 Conceptual Framewor---Consumer Internet Experience H2a: The relations between online reviews (e.g., online ratings and the number of online reviews) and product sales are stronger for products targeting consumers with greater Internet experience. Reasons: For experienced consumers, cost of collecting information online is lower than the offine channel. For less-experience consumers, using online information may evoe perceptions of uncertainty and complexity. (quit) 6

7 Conceptual Framewor---Consumer Internet Experience H2b: The relations between online reviews (e.g., online ratings and the number of online reviews) and product sales are stronger for products targeting consumers with less Internet experience. Reasons: For experienced consumers may find online information to be less credible, while less-experience consumer may easily trust online opinions. For experienced consumers can easily find many reviews about a product from multiple sources. This higher coginitive cost maes the relation between online reviews and their purchase decisions weaer. 7

8 Data Console sales and game sales data: come from the NPD Fun Group monthly data for PlayStation 2 and Xbox and their associated games from October 2000 to October Review data: comes from GameSpot.com 3 inds of reviews editors' reviews reviews from other sources rarely update, eliminated by DID average rating players' reviews the coefcient of variation of ratings total number of reviews *rating: For each of five aspects reviewers use a scale ranging from 1 to 10 for their reviews (10-best, 1- worst) 8

9 Methodology nested logit demand model for games console g maret i player t time u p r j 1, J ijt ijt game player ' s utility price j, t 1 lagged review var iable popular offline s s s jt 0t jt g s dummy other v jt jgt ijt j share maret jt jt player player dummy of (1 s game 0t utility i' s idiosyncra 0,1 measures the share var iable, offline potential ) the j within 2 measures of popular: If the game s aggregate sales across the two consoles are greater than the mean performance of all games in the month, popular=1, otherwise =0 Captures the inter-temporal pattern of games life-cycles: The first 4 months after release could be considered the popular time period. of var iable, wether characteri common the the tic correlatio outside share stics to all taste maret of for n of captured option game a only games game games is in maret unobserved by game in period popular in maret t for maret g utility j of console g g among console games in the in period t maret g 9

10 Methodology DID, solve endogenous problem jt contains both observed and unobserved game - specifc characteristics console - specifc effect j i.i.d. normal error term jt 10

11 Regression Results 11

12 Regression Results 12

13 Conclusions H1b: The relations between online reviews (e.g., online ratings and the number of online reviews) and product sales are stronger for less popular products than for popular products. H2a: The relations between online reviews (e.g., online ratings and the number of online reviews) and product sales are stronger for products targeting consumers with greater Internet experience. Additionally, the influence of online reviews becomes greater after the early months of introduction. 13