JOURNAL ARTICLE

Dataveillance Duality: Navigating Employee Accountability and Privacy Concerns in the Digital Age.

  • Published In: Journal of Information Systems, 2025, v. 39, n. 3. P. 157 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Raddatz, Paul A.; Raddatz, Nirmalee I.; Ogunade, Kehinde M.; Kim, Sohee 3 of 3

Abstract

This study examines the impact of dataveillance on employee accountability, privacy invasion, and compliance intentions across various industries. By drawing on accountability theory and analyzing survey data from 149 employees, we explore how monitoring practices influence these perceptions. Our findings reveal that dataveillance significantly enhances accountability, which in turn promotes compliance with data security policies. However, heightened perceptions of privacy invasion due to monitoring can reduce compliance intentions, creating a tension between its positive and negative effects. These results emphasize the dual role of workplace monitoring, as it serves both to reinforce accountability and to provoke concerns about privacy. Organizations must navigate these tradeoffs carefully to maximize the benefits of dataveillance while minimizing its adverse impacts on employee behavior. This study contributes to the literature by demonstrating how dataveillance simultaneously enhances accountability and heightens privacy concerns, providing insights into the tradeoffs organizations face when implementing monitoring practices to ensure compliance. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Information Systems. 2025/09, Vol. 39, Issue 3, p157
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:0888-7985
  • DOI:10.2308/ISYS-2023-050
  • Accession Number:189036876
  • Copyright Statement:Copyright of Journal of Information Systems is the property of American Accounting Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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