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FIRST‐MOVER (DIS) ADVANTAGE AND REAL OPTIONS

2001· article· en· W2094776947 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of applied corporate finance · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsUniversity of CalgaryInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsFirst-mover advantageFlexibility (engineering)Value (mathematics)Profitability indexEconomicsRisk analysis (engineering)Computer scienceBusinessIndustrial organizationFinanceManagement

Abstract

fetched live from OpenAlex

Fear of losing first‐mover advantages has caused many corporate strategists to ignore real options analysis and simply go ahead with any project that they think is expected to have a positive net present value. But first‐mover advantages are not nearly as valuable as most strategists tend to assume. This article weighs the expected value of first‐mover advantages against the benefits of the real option arising from delay and flexibility. The real options model recognizes the value of delaying projects until important sources of uncertainty and risk can be resolved. After reviewing two well‐known cases of successful second movers—the triumph of VHS over Betamax in VCRs and Microsoft's remarkable late software entries—the authors go on to present more broadly based historical evidence for their view that first‐mover advantages often fail to confer lasting value. The article closes with an assessment of first‐mover advantages in the new economy, including a brief look at recent developments in the Internet and telecom sectors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.207
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it