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Breached But Not Broken: How Attributional Information Shapes Shareholder Reactions to Firms Following Data Breaches

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Abstract

Data breaches are increasingly consequential for many business organizations. How firms address data breaches has substantial ethical and societal implications. However, there is still uncertainty regarding why certain organizations experience more severe consequences than others following data breaches and what actions firms can take to minimize the negative outcomes. To answer these questions, we leverage insights from the literature on crisis communication strategies and attribution theory to predict stock market reactions. We argue that those reactions, as reflected in shareholders’ responses following a breach, depend on the nature of attributional information and are contingent upon firms’ responses and media sentiment. Analyzing a sample of 287 data breach events in 95 publicly traded US firms over a decade, we found that when shareholders attribute the data breach to internal and stable causes, the firm appears “broken,” resulting in a decline in stock market returns. However, our findings also indicate that firms’ ceremonial responses and media sentiment mitigate this negative relationship. Our discussion explores the benefits of understanding the role of crisis communication strategies and applying attribution theory within corporate-level studies. We also suggest potential avenues for future research.

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Notes

  1. RavenPack assigns relevance scores with the range between 0 and 100, which “indicates how strongly related the entity is to the underlying news story.” Higher values indicate greater relevance. A score of 100 means the firm described was prominent in the news story (RavenPack News Analytics 2023).

  2. In our post hoc analyses section, we report the findings involving alternative time windows; these analyses yielded very similar results.

  3. RavenPack contains new reports from the Dow Jones Financial Wires, Wall Street Journal, Baron’s, MarketWatch, and other newswires.

  4. Moderating relationships must pass a simple slope test for estimation accuracy (Preacher et al., 2006). A simple slopes test is a standard t test to determine whether individual slopes of the simple models are statistically different; thus, it exhibits the difference between the two lines of a moderating effect.

  5. Frank, K. A. 2000. Impact of a confounding variable on a regression coefficient. Sociological Methods and Research, 29: 147–194.

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Acknowledgements

We wish to thank Corporate Reputation Review Editor in Chief Dr. Guido Berens and anonymous reviewer for their constructive feedback.

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Appendix: ITCV Analysis and Results

Appendix: ITCV Analysis and Results

We employed the Impact Threshold of a Confounding Variable (ITCV) analysis (for recent applications and more discussion, see also Busenbark et al. 2022) to assess how strong the effect of a hypothetical confounding variable needs to be in order to overturn current findings. It calculates the correlations that must exist between a hypothetical endogenous independent variable and the dependent variable in question for endogeneity to be an issue. Notably, we followed recent developments of ITCV by Busenbark et al. (2022) to calculate the square root of the product of partial correlations among all covariates and used it as the benchmark to compare with the ITCV value. We used the “konfound” command in Stata 17.

The ITCV first calculated the threshold for the percent of bias to invalidate/sustain the inference. Regarding our key variable of interest (i.e., attributional information), the results showed that to invalidate its inference, 99% of the 284 cases would have to be replaced with cases for which there is a zero effect. The ITCV then calculated the impact of an omitted variable necessary to invalidate/sustain an inference for a regression coefficient. The results showed such an impact of attributional information: an omitted variable would have to be correlated at 0.459 with the outcome and at − 0.459 with the predictor of interest (conditioning on observed covariates. Signs are interchangeable) to invalidate an inference. Correspondingly, an omitted variable’s impact, as defined in Frank (2000),Footnote 5 must be 0.459 ×  − 0.459 =  − 0.2108 to invalidate an inference. Detailed ITCV results are shown n in Table 5.

Table 5 GLMs’ regression results for the stock market’s reaction (CAR) using an alternative measurement of media sentiment

To interpret the results of the ITCV analysis, we followed Busenbark et al.’s (2022) guidelines to compute the partial correlations among all covariates. The largest partial correlation between control variables and attributional information is 0.18, and the largest partial correlation between control variables and stock market performance is 0.14. These correlations are not outside the thresholds of − 0.459 and 0.459. Additionally, we calculated the absolute value of the square root of the product of these correlations, which equals 0.16. Notably, this value is smaller than the ITCV absolute value of 0.21. This comparison suggests that there are no control variables in the study exhibiting correlations stronger than what would be required for an omitted variable to invalidate causal inference. In sum, based on the ITCV analysis, we conclude that our results are relatively robust to potential endogeneity caused by confounding factors or omitted variables (Table 6).

Table 6 ITCV analysis correlation-based impact and replacement

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Wang, X., Yan, J., Munyon, T.P. et al. Breached But Not Broken: How Attributional Information Shapes Shareholder Reactions to Firms Following Data Breaches. Corp Reputation Rev (2024). https://doi.org/10.1057/s41299-024-00179-1

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