IT Service Disruptions and Provider Choice
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.
Bibliographic record
Abstract
Digital supply chains are increasingly interconnected and vulnerable to disruption, causing service interruptions impacting many firms and their customers. Combating threats to the digital supply chain is the top challenge for leaders in most supply chain industries, demonstrated by the tacit approval of nation-states for cyber-attacks on corporate supply chains to disrupt downstream firms. Disruptions to digital supply chains are not new. In April 2019, hundreds of flights in the United States were delayed when a critical service provider, AeroData, had a computer systems failure. AeroData delivers flight planning services to many airlines, including Southwest, United, American, and Delta. All flight operations for AeroData’s more than 100 clients simultaneously ceased, and thousands of customers were stranded at airports across the country. In an increasingly connected business environment, competitors may be simultaneously disrupted due to a common service provider, impacting all affected firms’ demand. The synchronization of disruptions for firms that use a common service provider has implications for service provider choice and investment. We use a two-stage game to model how a firm’s customer demand is impacted by disruptions at a service provider, and how this subsequently affects the firms’ choices in managing service provider risk. Considering downstream demand effects from upstream service disruptions, the contribution of this article is the examination of how risk synchronization impacts provider choice decisions and profits. In addition, we illustrate how these choices impact upstream industry concentration.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it