Incorporating Knowledge Building in Real Options Analysis of Technology Project Investment.
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
Real options theory has provided a useful framework for technology investment decision making. Researchers in this area have emphasized the importance of considering the option-like characteristics of IT investment projects. However, unlike financial options, investment in IT projects is typically irreversible: such investment cannot be recovered for other purposes without very costly rework. The objective of our work is to study the effects of knowledge building on the valuation of real options by using a continuous-time stochastic model. To our knowledge, this is the first model that formally builds a linkage between proactive learning and investment cost and examines the consequences of this linkage on the management of real options. Our main finding is that knowledge building can expedite the adoption of new technology and significantly enhance the value of technology options.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it