<b>Research Note</b>—Returns to Information Technology Outsourcing
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
This study extends existing information technology (IT) productivity research by evaluating the contributions of spending in IT outsourcing using a production function framework and an economywide panel data set from 60 industries in the United States over the period from 1998 to 2006. Our results demonstrate that IT outsourcing has made a positive and economically meaningful contribution to industry output and labor productivity. It has not only helped industries produce more output, but it has also made their labor more productive. Moreover, our analysis of split data samples reveals systematic differences between high and low IT intensity industries in terms of the degree and impact of IT outsourcing. Our results indicate that high IT intensity industries use more IT outsourcing as a percentage of their output, but less as a percentage of their own IT capital, and they achieve higher returns from IT outsourcing. This finding suggests that to gain greater value from IT outsourcing, firms need to develop IT capabilities by intensively investing in IT themselves. By comparing the results from subperiods and analyzing a separate data set for the earlier period of 1987–1999, we conclude that the value of IT outsourcing has been stable from 1998 to 2006 and consistent over the past two decades. The high returns we find for IT outsourcing also suggest that firms may be underinvesting in IT outsourcing.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.025 |
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