The Financial Performance of Global Information and Communication Technology Companies
Bibliographic record
Abstract
ABSTRACT This study examines the cross-sectional financial performance among firms from the global information and communication technology (ICT) sector over the period 1998–2007. Using a pooled linear regression, the results show that U.S.-based ICT companies are on average underperforming the rest of the world after controlling for firm-specific variables known to affect firm financial performance. The results also show that characteristics of the firm's host country explain a statistically significant portion of the variation in firm performance, incremental to firm-level characteristics. More specifically, firms located in countries with attractive tax environments and high-government subsidies outperform their competitors in countries with less attractive tax environments and subsidies. Firms in financial markets that provide ICT firms with relatively favorable cost of capital underperform those in markets with a cost of capital less conducive to business development, which may suggest the cost of capital attracts new market competition that reduces overall profit. Countries with the best performing ICT firms are those with the highest industry focus, where a few industries dominate rather than an even distribution of firms across a broad range of industries. The findings have important implications for policymakers, business strategists, and investors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".