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Record W3204812829 · doi:10.1177/10946705211036944

The Effects of Service Crises and Recovery Resources on Market Reactions: An Event Study Analysis on Data Breach Announcements

2021· article· en· W3204812829 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Service Research · 2021
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsSeriousnessData breachProfitability indexHackerBusinessEvent studyMarket liquidityContext (archaeology)Actuarial scienceFinanceComputer securityComputer scienceLaw

Abstract

fetched live from OpenAlex

Building on the literatures on service failure and crisis seriousness, we develop a framework to understand the effects of a specific type of service crisis (i.e., data breaches) and organizational recovery resources on the reactions of the stock market. To do so, we conduct an event study analysis with a sample of 217 data breach announcements, as our empirical context. Our analyses reveal that a firm suffers from negative abnormal stock returns when either the outcome of the breach (e.g., the breach of financial data) or its causal process (e.g., hacker attack) indicates a high level of seriousness. Moreover, considering organizational recovery resources, we find that in the case of financial data breaches, age, size, profitability, liquidity, and brand familiarity are the primary resources that can help a firm’s recovery. For hacker attacks, these organizational recovery resources include size, profitability, and liquidity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.074
GPT teacher head0.390
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it