Valuing Information Technology Related Intangible Assets1
Bibliographic record
Abstract
In this article, we assess the value of information technology related intangible assets and then use data on business practices and management capabilities to understand how this value is distributed across firms. Using a panel of 127 firms over the period 2003–2006, we replicate and extend the finding from Brynjolfsson, Hitt, and Yang (2002) that $1 of computer hardware is correlated with more than $10 of market value. We account for the “missing $9” by broadening the definition of IT to include capitalized software, and then include all purchased and internally developed software, other internal IT services, IT consulting, and IT-related training (whether or not it is capitalized by the firm). In addition, we use data on IT-related business practices in order to analyze the distribution of IT-related intangibles within the sample. Our results suggest that the “invisible” IT not accounted for on balance sheets is being priced into the market value of firms. We also estimate that there is a 45% to 76% premium in market value for the firms with the highest organizational IT capabilities (based on separate measures of human resource practices, management practices, internal IT use, external IT use, and Internet capabilities), as compared to those with the lowest organizational IT capabilities. Our results thus suggest that contributions of IT to value depend heavily on other factors, and are not a rising tide that lifts all boats.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".