2017
DOI: 10.1016/j.intmar.2016.07.003
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Evaluation Set Size and Purchase: Evidence from a Product Search Engine

Abstract: The last decade has seen a dramatic increase in the popularity of product search engines, yet the analysis of consumer behavior at such sites remains a challenging research problem despite its timeliness and importance. In this article, we develop and estimate a copula model of evaluation set size and purchase behavior employing data from 3,182 hotel searches by customers at a large travel search engine. The model allows us to jointly study purchase behavior, evaluation sets, and their antecedents. Our results… Show more

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Cited by 19 publications
(9 citation statements)
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References 63 publications
(85 reference statements)
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“…In this context, exclusion appears to be more effortful for large assortments, but some attribute‐based decision aid tools may also be efficient. For instance, online tools where one can, with the click of a mouse, exclude thousands of products in which one is not interested (Choudhary et al, 2017), are very helpful—but fundamentally different from the alternative‐based screening we study. Future research should also explore additional boundary conditions, such as when the quality of the items is low or the potential for downside risk is more salient (e.g., large investments or irreversible decisions).…”
Section: Discussionmentioning
confidence: 99%
“…In this context, exclusion appears to be more effortful for large assortments, but some attribute‐based decision aid tools may also be efficient. For instance, online tools where one can, with the click of a mouse, exclude thousands of products in which one is not interested (Choudhary et al, 2017), are very helpful—but fundamentally different from the alternative‐based screening we study. Future research should also explore additional boundary conditions, such as when the quality of the items is low or the potential for downside risk is more salient (e.g., large investments or irreversible decisions).…”
Section: Discussionmentioning
confidence: 99%
“…The marketing field typically focuses on the antecedents and consequences related to micro individual-level customer and firm actions, which use the customer or the firm as the unit of analysis. For example, most of the customer behavior and customer choice modeling research examines what affects customer preferences, choices and actions (Choudhary et al , 2017), whereas much of the marketing strategy and analytics research examines what affects firms’ marketing decisions and performance (Mintz and Currim, 2015). Further, much of this marketing research also includes an analysis of how meso industry-level drivers further impact customer or firm actions (Kirca et al , 2011).…”
Section: Current State Of Knowledgementioning
confidence: 99%
“…Choudhary et al. (2017) show that for model Ml$M_l$: f(y|Ml)=i=1nfyi|yjj<i,Ml\begin{eqnarray} f(y|M_l) &=& \prod _{i=1}^n f {\left(y_i | {\left\lbrace y_j \right\rbrace} _{j&lt;i},M_l \right)} \end{eqnarray} badbreak=i=1nfyi|yjj<i,βl,Mlπβl|yjj<i,Mldβl.\begin{eqnarray} &=& \prod _{i=1}^n \int f {\left(y_i | {\left\lbrace y_j \right\rbrace} _{j&lt;i}, \beta _l, M_l \right)} \pi {\left(\beta _l | {\left\lbrace y_j \right\rbrace} _{j&lt;i} , M_l \right)} d \beta _l. \end{eqnarray}Equation (11) represents the marginal likelihood as the product of n$n$ one‐step‐ahead sequential predictive densities, which follows from the law of total probability.…”
Section: Network Features and Bank Survivorshipmentioning
confidence: 99%