Author List: Wattal, Sunil; Telang, Rahul; Mukhopadhyay, Tridas; Boatwright, Peter;
Information Systems Research, 2012, Volume 23, Issue 3, Page 679-697.
In this study, we examine how consumers respond to firms' use of two types of information for personalization: product preferences and name. We collect a unique data set of over 10 million e-mail advertisements sent by a website to over 600,000 customers who could buy the advertised products from the online merchant. We estimate a two-stage hierarchical model using Bayesian analysis to account for observable and unobservable consumer heterogeneity. Our analysis suggests several interesting results regarding consumers' responses to firms' use of information. When firms use product-based personalization (where the use of information is not explicitly mentioned), consumers respond positively. On the other hand, consumers respond negatively when firms are explicit in their use of personally identifiable information (i.e., a personalized greeting). We also find that negative responses to personalized greetings are moderated by consumers' familiarity with firms. The main contribution of this study is that it not only indicates the economic benefits of personalization in e-mails but also highlights consumers' concerns over the use of information in personalization.
Keywords: hierarchical Bayesian model; information use; personalization; privacy
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#13 0.149 personalization content personalized willingness web pay online likelihood information consumers cues customers consumer services elaboration preference experiment framing customized timing
#262 0.147 impact data effect set propensity potential unique increase matching use selection score results self-selection heterogeneity evidence measure associated estimate leads
#118 0.137 online consumers consumer product purchase shopping e-commerce products commerce website electronic results study behavior experience b2c impact internet purchases websites
#174 0.125 use support information effective behaviors work usage examine extent users expertise uses longitudinal focus routine revealed volume constructs contributes operations
#168 0.101 firms firm financial services firm's size examine new based result level including results industry important account does suggests characterize limited
#219 0.073 response responses different survey questions results research activities respond benefits certain leads two-stage interactions study address respondents question directly categories
#173 0.065 effect impact affect results positive effects direct findings influence important positively model data suggest test factors negative affects significant relationship