Linking IT to Business Metrics
Bibliographic record
Abstract
Early efforts to link measures of IT investment with measures of business performance have often been challenged to show consistent organization-level relationships. Managers and researchers alike have often concluded in the past that the relationship between what is done in IT and what happens in business is considerably more complex than originally thought. It has long been argued that technology is not the major stumbling block to achieving business performance, but rather it is the business itself – the processes, the managers, the culture and the skills – that makes the difference. Therefore, a good business metrics program that considers not only IT investments but also how the business uses IT is important. If a business measurement program is carefully designed, properly linked to an incentive program, widely implemented and effectively monitored by management, it is highly likely that business performance will become an integral part of the mindset of all IT staff and ultimately pay off in a wide variety of ways.
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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 | 0.015 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".