2018
DOI: 10.1509/jm.16.0124
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The Effect of a Data Breach Announcement on Customer Behavior: Evidence from a Multichannel Retailer

Abstract: In this study, the authors assess the effects of a data breach announcement (DBA) by a multichannel retailer on customer behavior. They exploit a natural experiment and use individual customer transaction data from the retailer to conduct a detailed and systematic empirical examination of the effects of a DBA on customer spending and channel migration behavior. To identify the effects, the authors compare the change in customer behavior before and after the DBA between a treatment group (customers whose inform… Show more

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Cited by 158 publications
(92 citation statements)
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References 66 publications
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“…We also found that, under the parameter-based policy, the DI mode outperforms the FI mode under ISB with increased frequency and low remediation length. Nevertheless, frequency of disruption occurrence has other impacts such as customer churn (Janakiraman, Lim, & Rishika, 2018), which have not been considered in this study but should not be taken lightly. Therefore, technology providers should invest more in effective remediation strategies, as these are crucial to the inventory performance of SME supply chains that depend on them for such services.…”
Section: Resultsmentioning
confidence: 99%
“…We also found that, under the parameter-based policy, the DI mode outperforms the FI mode under ISB with increased frequency and low remediation length. Nevertheless, frequency of disruption occurrence has other impacts such as customer churn (Janakiraman, Lim, & Rishika, 2018), which have not been considered in this study but should not be taken lightly. Therefore, technology providers should invest more in effective remediation strategies, as these are crucial to the inventory performance of SME supply chains that depend on them for such services.…”
Section: Resultsmentioning
confidence: 99%
“…10 We focused on brands that exist in both the calibration and estimation periods and used data from the calibration period (1996 to 2002) to compute a set of brand-specific mean prices of the products. This helps ensure that the brand classification does not confound with the estimation time period and allows for easy interpretation of the moderating effects of brands (Janakiraman, Lim, and Rishika 2018; Rishika et al 2013). To empirically examine the effect of premium brand (H 2a ), following recent studies (Goldfarb and Tucker 2011), we extend our DD model to the difference-in-difference-in-differences (DDD) modeling framework by interacting FOP pbfct (presented in Equation 1) with the focal moderating variable, an indicator variable associated with premium brands.…”
Section: Methodsmentioning
confidence: 99%
“…Observational field data provide insights with high external validity, but to use them to assess causal claims, we also must address endogeneity (Germann, Ebbes, and Grewal 2015). In line with recent studies (Boichuk et al 2019; Bommaraju and Hohenberg 2018; Janakiraman, Lim, and Rishika 2018), we apply a quasi-experimental approach that combines propensity score matching (PSM) with a difference-in-differences estimation to test the causality of a gift purchase on future purchase behavior. With PSM, we address self-selection effects (Kumar et al 2016) before we analyze the matched sample using a difference-in-differences estimation that controls for cross-sectional and time-series effects (Murray 2006).…”
Section: Study 1: Testing the Catalytic Effect Of Gift Purchases In Amentioning
confidence: 99%
“…With PSM, we address self-selection effects (Kumar et al 2016) before we analyze the matched sample using a difference-in-differences estimation that controls for cross-sectional and time-series effects (Murray 2006). By combining PSM and difference-in-differences modeling, we can control for observed and unobserved confounds of the effect of purchasing a gift (Janakiraman, Lim, and Rishika 2018).…”
Section: Study 1: Testing the Catalytic Effect Of Gift Purchases In Amentioning
confidence: 99%
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