Shareholder value implications of service failures in triads: The case of customer information security breaches
Bibliographic record
Abstract
Abstract The rise in front‐end service outsourcing in recent years, despite its advantages, has also exposed buyer firms to unique challenges. One of the most salient risks for buyer firms in service triads is service failure due to the service provider. Indeed such service failures may be more costly for firms due to the greater relational and operational costs that may arise from the presence of the third‐party provider. Yet, neither the services literature nor extant operations literature on service triads has paid much attention to the financial consequences to the buyer firm – i.e., service risks – of such service failures in triads. To fill this gap, we investigate the financial penalty of service failures due to the service provider using the event study methodology and a sample of 146 customer information security breaches as our empirical context. Analysis of the abnormal returns reveals that service failures due to the front‐end service provider lead to greater shareholder losses than such failures due to the buyer firm. This provides important new insight into the financial risks arising from outsourcing front‐end services. Further, we investigate the ability of the buyer firm's employee and financial resources to temper these shareholder losses. We find that buyer firm employee productivity can moderate the greater financial penalty associated with such triadic service failures but that buyer firm leverage tends to not have such a mitigating effect. This provides new guidance for theory and practice regarding how buyer firms can position themselves to buffer the financial risks arising from service failures due to front‐end service providers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".