Real Options in Small Technology-Based Companies
Bibliographic record
Abstract
This thesis reports on the results of a research study conducted by the Institute for Information Technology, National Research Council, over the summer of 2001. The study assessed the relevance of an emerging valuation approach known as real options to small technology-based firms. The approach addresses evaluation of investment decisions under uncertainty by viewing a firm's ability to respond to changing conditions as a bundle of options that can be exercised at the right time and under the right conditions. Interviews were conducted with the representatives of the six participating firms, who found the concept of real options appealing. Systematically scanning different functional areas for possible sources of uncertainty can help identify viable option scenarios in a firm. The functional areas include operations, procurement, R&D, IP management, distribution, sales, after-sales, finance, strategic planning, marketing, and IT infrastructure. Such a methodology can help to reveal opportunities that may otherwise be overlooked or remain implicit. The scenarios discovered in the firms under study involved staged investments, partnerships with lead customers, patents, arrangements for securing manufacturing capacity, flexible pricing strategies, make or buy decisions, design of a product to allow outsourcing, right to buy out licensed IP, IT infrastructure initiatives, and flexible core technology. Rudimentary quantitative analyses of selected option scenarios confirmed their potential value. Some classical option scenarios reported in the literature were rejected. For example, exit strategies were not deemed viable real options by start-up firms.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".