The effectiveness of functional and affective recovery strategies in restoring user trust after privacy violations: the moderating role of violation type

Purpose This study investigates the impact of organizational recovery strategies on user trust repair following an information privacy violation incident as well as the mediating mechanism and boundary condition. Design/methodology/approach We conducted an online scenario-based experiment employing a 2 (recovery strategies: functional vs affective) × 2 (privacy violation type: intentional vs accidental) between-subjects design. The experiment was conducted in China using 290 randomly selected platform users. Findings The functional recovery strategy is more effective in restoring user trust than the affective recovery strategy and recovery satisfaction mediates the relationship between recovery strategies and post-trust. The findings also confirmed that functional repair is more effective than affective repair for accidental privacy violations, though the effectiveness of these two strategies for intentional privacy violations did not differ significantly. Research limitations/implications Companies should prioritize functional repair following privacy violations and strive to improve user satisfaction with recovery measures. Furthermore, they should choose recovery strategies based on the type of privacy violation to restore user trust. Originality/value This study is among the first to examine the relative effectiveness of functional and affective recovery strategies as privacy violation recovery actions. Moreover, this research applies attribution theory to the context of privacy violations and investigates the moderating role of privacy violation type.

Authors

Publication Details

Journal
Information Technology and People
Published
2025-06-27
DOI
https://doi.org/10.1108/itp-11-2024-1397
Citations
1
Primary Topic
Privacy, Security, and Data Protection
Type
article
Field-Weighted Citation Impact
4.20
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article

The effectiveness of functional and affective recovery strategies in restoring user trust after privacy violations: the moderating role of violation type

Bingjia Shao, Shulin Chen, Yinghui Zhu
1 citations
Information Technology and People
Privacy, Security, and Data Protection
4.20
article

The effectiveness of functional and affective recovery strategies in restoring user trust after privacy violations: the moderating role of violation type

Bingjia Shao, Shulin Chen, Yinghui Zhu
article en
1 citations

Abstract

Purpose This study investigates the impact of organizational recovery strategies on user trust repair following an information privacy violation incident as well as the mediating mechanism and boundary condition. Design/methodology/approach We conducted an online scenario-based experiment employing a 2 (recovery strategies: functional vs affective) × 2 (privacy violation type: intentional vs accidental) between-subjects design. The experiment was conducted in China using 290 randomly selected platform users. Findings The functional recovery strategy is more effective in restoring user trust than the affective recovery strategy and recovery satisfaction mediates the relationship between recovery strategies and post-trust. The findings also confirmed that functional repair is more effective than affective repair for accidental privacy violations, though the effectiveness of these two strategies for intentional privacy violations did not differ significantly. Research limitations/implications Companies should prioritize functional repair following privacy violations and strive to improve user satisfaction with recovery measures. Furthermore, they should choose recovery strategies based on the type of privacy violation to restore user trust. Originality/value This study is among the first to examine the relative effectiveness of functional and affective recovery strategies as privacy violation recovery actions. Moreover, this research applies attribution theory to the context of privacy violations and investigates the moderating role of privacy violation type.

Information Technology and People
Openalex Percentile: Top 7%
Privacy, Security, and Data Protection
4.20
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