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An empirical examination of voluntary profiling: Privacy and quid pro quo

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dc.contributor.authorKoh, Byungwan-
dc.contributor.authorRaghunathan, Srinivasan-
dc.contributor.authorNault, Barrie R.-
dc.date.accessioned2021-08-31T01:10:50Z-
dc.date.available2021-08-31T01:10:50Z-
dc.date.created2021-06-19-
dc.date.issued2020-05-
dc.identifier.issn0167-9236-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/56118-
dc.description.abstractEvidence suggests that firms which use customer data analytics perform better than those that do not. However, the current policy of voluntary profiling allows firms to collect and use customer information only if customers voluntarily disclose information with them. Further, surveys and literature show that many customers are not comfortable with firms collecting their information due to privacy concerns. A vast literature has examined customer information disclosing behavior using the privacy calculus. The primary premise of the privacy calculus is that despite strong privacy concerns, customers disclose information if the benefits they can get from disclosure justify the costs of losing privacy, or privacy costs. Based on the privacy calculus, firms and marketers believe that customers voluntarily disclose information in exchange for monetary benefits such as discount coupons or cash rewards. Using transaction data that we collected from a firm that sells skin care cosmetic products on its website, we investigate if there is statistical evidence that shows customers disclose information in exchange for monetary benefits. In line with the privacy calculus, we find that customers with low privacy costs and that expect high benefits from personalized services such as product recommendations are more likely to disclose information. Monetary incentives only work as an effective means to elicit information from customers in the age range of 12-21 years old. Customers, on average, and especially customers in the age range of 22-54 years old are not likely to disclose information simply as a tradeoff for monetary benefits. Personalized services outweigh monetary benefits in enticing these customers to disclose information.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER-
dc.subjectINFORMATION PRIVACY-
dc.subjectE-COMMERCE-
dc.subjectCALCULUS MODEL-
dc.subjectPERSONAL INFORMATION-
dc.subjectCONSUMER PRIVACY-
dc.subjectCOUPON PRONENESS-
dc.subjectDISCLOSURE-
dc.subjectONLINE-
dc.subjectWILLINGNESS-
dc.subjectSERVICES-
dc.titleAn empirical examination of voluntary profiling: Privacy and quid pro quo-
dc.typeArticle-
dc.contributor.affiliatedAuthorKoh, Byungwan-
dc.identifier.doi10.1016/j.dss.2020.113285-
dc.identifier.scopusid2-s2.0-85082536772-
dc.identifier.wosid000528011000009-
dc.identifier.bibliographicCitationDECISION SUPPORT SYSTEMS, v.132-
dc.relation.isPartOfDECISION SUPPORT SYSTEMS-
dc.citation.titleDECISION SUPPORT SYSTEMS-
dc.citation.volume132-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaOperations Research & Management Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryOperations Research & Management Science-
dc.subject.keywordPlusINFORMATION PRIVACY-
dc.subject.keywordPlusE-COMMERCE-
dc.subject.keywordPlusCALCULUS MODEL-
dc.subject.keywordPlusPERSONAL INFORMATION-
dc.subject.keywordPlusCONSUMER PRIVACY-
dc.subject.keywordPlusCOUPON PRONENESS-
dc.subject.keywordPlusDISCLOSURE-
dc.subject.keywordPlusONLINE-
dc.subject.keywordPlusWILLINGNESS-
dc.subject.keywordPlusSERVICES-
dc.subject.keywordAuthorInformation disclosing-
dc.subject.keywordAuthorCoupon redemption-
dc.subject.keywordAuthorPrivacy calculus-
dc.subject.keywordAuthorRx. cosmetics-
dc.subject.keywordAuthorVoluntary profiling-
dc.subject.keywordAuthorCustomer data analytics-
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