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How to Compensate After a Data Breach?: Investigating Compensation Types and Role of Fairness in Customer Repatronage Intentions

Authors:

Zareef A. Mohammed,

Gurvirender P. TejayAuthors Info & Claims

ACM SIGMIS Database: the DATABASE for Advances in Information Systems, Volume 54, Issue 1

Pages 110 - 127

https://doi.org/10.1145/3583581.3583588

Published: 07 February 2023 Publication History

Abstract

Data breach recovery is often likened to service failure recovery. Like service failure recovery, compensation is a major factor toward data breach recovery. However, compensation involves different types, which may have varying effects on data breach recovery. This study investigates the effect of compensation types on customer's repatronage intentions to organizations that have suffered a data breach. Three compensation types were identified in this study: monetary, service, and psychological compensation. The findings of this study indicate that only service and psychological compensation have a significant effect on customers' perceptions of being treated fairly by the organization following a data breach. Furthermore, service compensation impacts the trust a customer regains in the company. Monetary compensation did not play a statistically significant role in the data breach recovery process. In turn, both perceived fairness and regained trust influences customer repatronage intentions. This study contributes to the information privacy field by identifying the types of compensation that are impactful to re-engage individuals who suffered PII related data breach.

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Cited By

Casaca JMiguel L(2024)The Influence of Personalization on Consumer SatisfactionData-Driven Marketing for Strategic Success10.4018/979-8-3693-3455-3.ch010(256-292)Online publication date: 12-Jul-2024
https://doi.org/10.4018/979-8-3693-3455-3.ch010
Zhang JLu XZheng WWang X(2024)It’s better than nothing: The influence of service failures on user reusage intention in AI chatbotElectronic Commerce Research and Applications10.1016/j.elerap.2024.10142167(101421)Online publication date: Sep-2024
https://doi.org/10.1016/j.elerap.2024.101421

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Information

Published In

cover image ACM SIGMIS Database: the DATABASE for Advances in Information Systems

ACM SIGMIS Database: the DATABASE for Advances in Information Systems Volume 54, Issue 1

February 2023

121 pages

ISSN:0095-0033

EISSN:1532-0936

DOI:10.1145/3583581

Editors:
K.D. Joshi
University of Nevada - Reno, Renvo, NV
,
Nancy Deng
California State University, Dominguez Hills, Carson, CA
,
Heidi Seward
Baylor University, Waco, TX
,
Mina Jafarijoo
Stockton University, Galloway, NJ

Issue’s Table of Contents

Copyright © 2023 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2023

Published in SIGMIS Volume 54, Issue 1

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Cited By

Casaca JMiguel L(2024)The Influence of Personalization on Consumer SatisfactionData-Driven Marketing for Strategic Success10.4018/979-8-3693-3455-3.ch010(256-292)Online publication date: 12-Jul-2024
https://doi.org/10.4018/979-8-3693-3455-3.ch010
Zhang JLu XZheng WWang X(2024)It’s better than nothing: The influence of service failures on user reusage intention in AI chatbotElectronic Commerce Research and Applications10.1016/j.elerap.2024.10142167(101421)Online publication date: Sep-2024
https://doi.org/10.1016/j.elerap.2024.101421

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