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Record W2023725113 · doi:10.1108/10878570310483942

Demand innovation: GM’s OnStar case

2003· article· en· W2023725113 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueStrategy and Leadership · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategies and Innovation
Canadian institutionsnot available
FundersAssociation of Canadian Universities for Research in Astronomy
KeywordsBusinessAsset (computer security)Product (mathematics)MarketingPerspective (graphical)Value (mathematics)Product innovationNew product developmentBusiness modelIndustrial organizationComputer science

Abstract

fetched live from OpenAlex

A new form of business innovation, a response to the current challenges to growth initiatives, is being pioneered by a handful of farsighted companies. These companies have shifted their approach from product innovation to demand innovation. Such new‐growth businesses focus on growing new value by discovering new forms of demand. For example, in the mid‐1990s, engineers within several GM business units realized that technological advances might enable the creation of a new business focused on the needs of drivers. The crucial factors in GM’s success have been its ability to look at customers’ driving needs from a fresh perspective and its decision to serve these needs through a business design that leverages GM’s unique hidden asset – its unequaled installed base of vehicles.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.162
GPT teacher head0.255
Teacher spread0.094 · 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