Returns to Cloud Computing Investments: The Role of Environmental Uncertainty.
Bibliographic record
Abstract
Although a substantial portion of IT spending has shifted to the cloud, empirical evidence on economic value of cloud computing is lacking. This study examines the effect of cloud computing on productivity and scrutinizes how its effect differs depending on environmental uncertainty. Using publicly available data on the product sales and the inter-industry purchase flows, we measure purchased cloud services in U.S. industries during 1997-2018 and distinguish between software-as-a-service (SaaS) and infrastructure-as-a-service (IaaS). Employing a production function approach, our findings suggest that cloud computing investments do not always lead to productivity gains, but its effect varies by the level of environmental uncertainty. Specifically, while cloud computing contributes to productivity under high environmental uncertainty, it may have an adverse effect under stable environments. Further, this positive impact under uncertain environments is found to be driven mainly by IaaS, rather than SaaS. This study provides important implications on cloud computing investment strategies.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".