IT Capability and a Firm's Ability to Recover from Losses: Evidence from the Economic Downturn of the Early 2000s
Bibliographic record
Abstract
ABSTRACT Prior literature shows that during an economic downturn firms have difficulty sustaining superior performance, and a larger percentage of firms report losses. Motivated by this literature, we explore the role of sustainability of organizational IT capability (ITC) on a firm's performance during an economic downturn. Specifically, we examine how ITC sustainability contributes to a firm's ability to recover from losses. ITC sustainability reflects a firm's ability to resist competitors' attempts to imitate or improve on its ITC. We use ITC sustainability to classify firms as sustainable (Systematic ITC), as non-sustainable (Occasional ITC), and as having no ITC (Non-ITC). Using a sample of large U.S. firms during the economic downturn of the early 2000s, we show that Systematic ITC firms achieve higher levels of firm-specific abnormal earnings and are capable of faster recovery when compared to all competitors (Occasional ITC and Non-ITC firms) and competitors with only Occasional ITC.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".