Information Technology and Intangible Output: The Impact of IT Investment on Innovation Productivity
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
Prior research concerning IT business value has established a link between firm-level IT investment and tangible returns such as output productivity. Research also suggests that IT is vital to intermediate processes such as those that produce intangible output. Among these, the use of IT in innovation and knowledge creation processes is perhaps the most critical to a firm's long-term success. However, little is known about the relationship between IT, knowledge creation, and innovation output. In this study, we contribute to the literature by comprehensively examining the contribution of IT to innovation production across multiple contexts using a quality-based measure of innovation output. Analyzing annual information from 1987 to 1997 for a panel of large U.S. manufacturing firms, we find that a 10% increase in IT input is associated with a 1.7% increase in innovation output for a given level of innovation-related spending. This relationship between IT, research and development (R&D), and innovation production is robust across multiple econometric methodologies and is found to be particularly strong in the mid to late 1990s, a period of rapid technological innovation. Our results also demonstrate the importance of IT in creating value at an intermediate stage of production, in this case, through improved innovation productivity. However, R&D and its related intangible factors (skill, knowledge, etc.) appear to play a more crucial role in the creation of breakthrough innovations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it