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 • Hypotheses • Data and Model • Estimation Results • Implications • Future Research 14-Jul-14 2 / 29

Introduction • Social media platforms – Social network sites, micro-blogs, video sharing sites, product review sites, discussion forums, chat rooms • E.g., Facebook, Twitter, LinkedIn, Youtube, Yelp • User-Generated Content (UGC) – Reduce consumer uncertainty (Dellarocas 2003) – Help consumers explore new product (Anderson 2006; Chen and Xie 2008) Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 3 / 29

Research Questions • How does consumers’ online UGC search influence new product exploration behaviors? • How do consumers’ prior product consumption experiences affect their search or usage of online UGC to explore new products? • To what extent does competition across online UGC of competing alternatives influence individual consumers’ purchase decision, especially when they explore new products? Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 4 / 29

Related Work • Economic impact of online UGC – 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) • Why consumers seek variety? – Satiation: internal or personal desire for variety (Givon 1984; McAlister 1982) – External constraints • Such as multiple needs, multiple situations, multiple Uses (McAlister and 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). Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 5 / 29

Contributions • Consumers’ search and usage online UGC • Consumers’ new product exploration • UGC may play different roles at different stages of consumers’ decision process Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 6 / 29

Awareness Effect • Online UGC as new information source – 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) • H1: A consumer is more likely to choose a new product when he or she searches more new product alternatives from online UGC. Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 7 / 29

Experience Effect • Prior experience and new product exploration – Consumers can easily evaluate the relative attractiveness of products that they are unfamiliar with by comparing with the products they have purchased before. • H2: A consumer is more likely to choose a new product when the new products he or she searches from online UGC can potentially provide higher utility than that of prior choice alternatives. Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 8 / 29

Competition Effect • Consumers’ relative judgments (Laroche and Brisoux 1989; Laroche et al. 1994). – Consumers’ choice for one product is not only influenced by online UGC of the focal product but also by those of competing products in a choice set (Li et al. 2011). • H3: Information attributes from online UGC have a significant influence on a consumer’s choice decision among competing alternatives. Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 9 / 29

Competition Effect • The moderating role of experience (Punj and Staelin 1983; Urbany et al. 1989). – Consumers are likely to have more uncertainty on products which they are unfamiliar with or have not purchased before • H4: Online UGC has a more significant influence when a consumer is choosing from a set of new products, compared to when he or she is choosing from a set of products with prior purchase experiences. Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 10 / 29

Data: Overview • Restaurant patronage choices – Experience good – Frequently purchased product or service • Data sets – Restaurants • Reviews from Dianping.com – Taste, ambience, service (0=very bad, 4=very good) – Average price per person, recommended dishes, review texts and comments • Attributes information – Location, coupon promotion, search ads, cuisine type, price category – Consumers • Dining transaction records – Consumer ID, restaurant ID, expenditure, transaction date • Activities on Dianping.com – Log-in, posting, browsing Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 11 / 29

Data: Restaurant Reviews Restaurant name Overall rating Average price per person Average ratings for taste, ambience, service Number of ratings Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 12 / 29

Data: Restaurant Reviews Distribution of overall ratings Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 13 / 29

Data: Restaurant Reviews Ratings for taste, ambience and service, and average price per person Overall rating Review texts and comments Recommended dishes Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 14 / 29

Sampled Data • Sample periods: from Dec 2007 to March 2008 • 798 consumers – Whose browsing history is observable – At least 3 transactions in the sample period – At least 1 transaction before the sample periods • 3335 dining records in total • 215 restaurants Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 15 / 29

Data Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 16 / 29

Data Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 17 / 29

Econometric Model • Consumers’ two-stage choice decision process – 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. • Notations – i: consumer – j: restaurant – t: time, or purchase occasion – c: a group of restaurant Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 18 / 29

Whether to Patronize a New Restaurant? • First stage utility function: = α + γ + λ + ε 1 new U Z IV = , it c new i it it it = λ + ε 1 old U IV = , it c old it it α α σ 2 – α i : consumer-specific fixed effect, ~ N( , ) α i – Z it : a vector of control variables • InterPurchaseTime it , NumOfPerson it , NewRestSearchPercent it , NewRestSearchPercent_sq it , and OldRestNum it new old – and : “inclusive value”, which measures the expected IV IV it it value of the maximum utility from a set of new/old alternatives Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 19 / 29

Whether to Patronize a New Restaurant? • First stage choice – Assuming error term follows an extreme value distribution, consumer i ’s choice probability for the new products set and the old products set at time t will be: α + γ + λ new Z IV e i it it = P = , it c new α + γ + λ λ new + old Z IV IV e e i it it it λ old IV e it = P = , it c old α + γ + λ λ new + old Z IV IV e e i it it it Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 20 / 29

Which Restaurant to Patronize? • Second stage utility function: = ϖ + β + δ + ϕ + ε 2 * U D R R NewRest X | ijt c j i ijt ijt ijt ijt ijt – D j : the fixed effect of alternative j . • We use fixed effect of cuisine type in estimation – R ijt : a vector of variables that measure the influence of UGC. • Volume ijt , QualityRating ijt , VarianceOfQualityRating ijt , Price ijt , and VarianceOfPrice ijt . β β σ 2 ~ N( ) • We assume to capture consumer heterogeneity i , – NewRest ijt : dummy variable which equals 1 if consumer i hasn’t patronized restaurant j at time t . – X ijt : a vector of control variables • TagNum j , SearchNum ijt , TripNum ijt , Promotion ijt , and UserRestDistance ij . Future Research Introduction Hypotheses Data and Model Estimation Results Implications 14-Jul-14 21 / 29