<b>Research Note</b>—Using Real Options to Investigate the Market Value of Virtual World Businesses
Bibliographic record
Abstract
Virtual worlds are relatively nascent IT platforms with the potential to radically transform business processes and generate significant payoffs. However, in striving to achieve specific outcomes, firms may incur significant risks. Although many companies claim to have attained substantial benefits from their virtual world initiatives, many others have recently scaled down or even abandoned their experimental virtual world projects. This paper assesses the value proposition of virtual world initiatives from the real options perspective. Specifically, we argue that virtual worlds act as a firm's growth option, and we adopt the lens of real options to evaluate the value of this emerging and uncertain technological platform. We employ the event study method to assess the stock market's perception of the future revenue streams of 261 virtual world initiatives announced between 2006 and 2008. Our results indicate that, overall, the market reacts positively to virtual world initiatives. Our findings also show that investors' reactions to virtual world initiatives are contingent on four key characteristics of virtual world initiatives: interpretive flexibility (i.e., technologies that allow managers to experiment), divisibility (i.e., ability to incrementally implement the technology), strategic importance (i.e., an initiative that affects a process of strategic importance to the firm), and exploitable absorptive capacity (i.e., ability to exploit the knowledge acquired through the initiative). We discuss the key implications for real-world practitioners and suggest directions for future research.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".