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Record W2140908990

Cannibalization, Innovation and Spin-outs

2012· preprint· en· W2140908990 on OpenAlexaff
April Franco, James D. Campbell

Bibliographic record

VenueRePEc: Research Papers in Economics · 2012
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCannibalizationProduct (mathematics)Competition (biology)Industrial organizationNew product developmentBusinessProduct proliferationFixed costEconomicsMicroeconomicsCommerceMarketingProduct management
DOInot available

Abstract

fetched live from OpenAlex

How does a firm decide which products to introduce when faced with the cannibalization of profits from its current offerings? In a simple spatial model of imperfectly substitutable products, we show that when the cost of product development is fixed, the firm must take into account the impact on both the lowered profits of its current offerings and the profits from the new product. As a result, there is a gap between the firm's offerings. The substitutability of the products, the cost of product development and the magnitude of demand all impact this distance. When the cost of product introduction increases with the distance between the old and new products, the firm will offer those products that are sufficiently far away from its current offerings, but will not offer a new product if it is \too far away from its current offerings. Hence, the range of its possible offerings is smaller. The fixed cost of product development, the substitutability of the products and the magnitude of demand help to determine the gap between the products and the increased cost of product development due to fit limits the range of product offerings. We investigate the impact of external competition and show that firms will introduce new products further from their current offerings when there is a threat of competition. When the firm cannot prevent a spin-out, it is more likely to introduce a new product, and to discard a new product even

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.040
GPT teacher head0.287
Teacher spread0.247 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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