How Does User Generated Content Influence Consumers New Product - - PowerPoint PPT Presentation
How Does User Generated Content Influence Consumers New Product Exploration? An Empirical Analysis of Consumer Search and Choice Behaviors Wang Qingliang Goh Khim Yong Lu Xianghua 14-Jul-14 1 / 29 Presentation Agenda Introduction
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– Social network sites, micro-blogs, video sharing sites, product review sites, discussion forums, chat rooms
– Reduce consumer uncertainty (Dellarocas 2003) – Help consumers explore new product (Anderson 2006; Chen and Xie 2008)
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– Impact of UGC on product sales (Chevalier and Mayzlin 2006; Chintagunta et al. 2010;
Goh, et al. 2013; Netzer et al. 2012) and stock returns (Luo 2009)
– Moderating factors on UGC impact (Forman et al. 2008; Zhu and Zhang 2010)
– Satiation: internal or personal desire for variety (Givon 1984; McAlister 1982) – External constraints
Pessemier 1982), price promotion (kahn and Louie 1990; Kahn and Raju 1991), retail
environment (Menon and Kahn 1995)
– Preference uncertainty: within purchase occasion (Harlam and Lodish 1995).
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– has become a pivotal source of product information to consumers
(ChannelAdvisor 2010; ComScore 2007)
– are helpful for consumers to identify the products that best match their idiosyncratic preferences (Anderson 2006; Chen and Xie 2008)
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– Consumers can easily evaluate the relative attractiveness of products that they are unfamiliar with by comparing with the products they have purchased before.
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– Consumers’ choice for one product is not only influenced by
products in a choice set (Li et al. 2011).
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– Consumers are likely to have more uncertainty on products which they are unfamiliar with or have not purchased before
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– Experience good – Frequently purchased product or service
– Restaurants
– Taste, ambience, service (0=very bad, 4=very good) – Average price per person, recommended dishes, review texts and comments
– Location, coupon promotion, search ads, cuisine type, price category
– Consumers
– Consumer ID, restaurant ID, expenditure, transaction date
– Log-in, posting, browsing
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Number of ratings Average ratings for taste, ambience, service 14-Jul-14 Overall rating Average price per person Restaurant name
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Distribution of
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Overall rating Ratings for taste, ambience and service, and average price per person Review texts and comments Recommended dishes 14-Jul-14
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– Whose browsing history is observable – At least 3 transactions in the sample period – At least 1 transaction before the sample periods
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– At the first stage, a consumer decides whether to choose from a set of new restaurants that he or she has no prior consumption experience or from a set of restaurants patronized before (whether to patronize a new restaurant). – At the second stage, the consumer decides which specific restaurant to patronize.
– i: consumer – j: restaurant – t: time, or purchase occasion – c: a group of restaurant
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– αi : consumer-specific fixed effect, – Zit : a vector of control variables
NewRestSearchPercent_sqit, and OldRestNumit
– : “inclusive value”, which measures the expected value of the maximum utility from a set of new/old alternatives
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it c old it it
= =
2
~ N( , )
i α
α α σ
and
new
it it
IV IV
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– Assuming error term follows an extreme value distribution, consumer i’s choice probability for the new products set and the
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, ,
new i it it new
i it it it
it new
i it it it
Z IV it c new Z IV IV IV it c old Z IV IV
α γ λ α γ λ λ λ α γ λ λ + + = + + = + +
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– Dj : the fixed effect of alternative j.
– Rijt : a vector of variables that measure the influence of UGC.
VarianceOfPriceijt.
– NewRestijt : dummy variable which equals 1 if consumer i hasn’t patronized restaurant j at time t. – Xijt : a vector of control variables
UserRestDistanceij.
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2 |
ijt c j i ijt ijt ijt ijt ijt
2
~ N( )
i
β β σ ,
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– Represents the expected maximum utility consumer i can get from category c (Ben-Akiva and Lerman 1985; Chintagunta 1999). – Defines how consumers’ first stage choice depends on the expected utility from the second stage choice.
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j i ijt ijt ijt ijt
D R R NewRest X c it c
ϖ β δ ϕ + + +
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– Iit, j=new is a dummy variable which equals 1 if alternative j is a new product for consumer i at time t.
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j i ijt ijt ijt ijt j i ijt ijt ijt ijt
D R R NewRest X ijt c D R R NewRest X c
ϖ β δ ϕ ϖ β δ ϕ + + + + + +
, | , |
ijt it j new ijt new new it j new ijt old
= =
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– Consumers who have the intention to seek variety are more likely to search for new products from online UGC – Control function approach (Petrin and Train 2010)
– Consumers’ prior contributions to the UGC site – Instrument for square term (Wooldridge 2002)
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3 it it it
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14-Jul-14 H1: Awareness Effect H2: Experience Effect
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14-Jul-14 Mean for H3 H4
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– First stage: whether to explore new product
product when they are exposed to online UGC
product exploration behaviors depends on consumers’ prior consumption experiences
– Second stage: which product to choose
product choice, especially for new products .
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– How researchers can make use of consumers’ search data to explicitly model consumers’ decision process in the light of online UGC.
– How individual consumers perceive and use UGC information to guide their new product exploration and purchase decisions.
– Online UGC as an external trigger of consumer variety seeking behaviors.
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14-Jul-14 instrument quality Experience Effect Awareness Effect
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– Consumers simultaneously evaluate all (both “new” and “old”) choice alternatives and choose the alternative which yields the maximum utility.
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,
ijt j i ijt i it ijt it j new ijt ijt
=
, ,
( )* ( )*
j i ijt i it ijt it j new ijt j i ijt i it ijt it j new ijt
ijt
D R Z R I X D R Z R I X
ϖ β α γ δ ϕ ϖ β α γ δ ϕ
= =
+ + + + + + + + + +
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14-Jul-14 Comparable to the first stage
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14-Jul-14 The influence
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– 10 Halton draws for random parameters – Tolerance for convergence: 1.e-4
– The interdependence of parameters, and – The interdependence of the two stage decisions,
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– First, it is beneficial to stimulate consumers to generate more word of mouth information for new products. – Second, it is necessary and important to take consumers’ prior consumption experiences into consideration when influencing consume choice via UGC – Third, positive online word of mouth not only increases a firm’s customer base but can also mitigate against customer defections.
– Take individual consumers’ consumption experience into consideration when designing recommendation systems. – Show how this product is relatively rated in the market
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– We have a separate paper to study this question
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