MétaCan
Menu
Back to cohort
Record W3022225427 · doi:10.1016/j.bushor.2006.05.005

When customers get clever: managerial approaches to dealing with creative consumers

2007· article· en· W3022225427 on OpenAlexafffund
Pierre Berthon, Leyland Pitt, Ian P. McCarthy, Steven Kates

Bibliographic record

VenueIRIS - Institutional Research Information System (Libera Università Internazionale degli Studi Sociali Guido Carli) · 2007
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Pittsburgh
KeywordsTypologyBusinessMarketingRevenueIntellectual propertyAction (physics)Value (mathematics)Business modelSociologyComputer science

Abstract

fetched live from OpenAlex

Creative consumers (defined as customers who adapt, modify, or transform a proprietary offering) represent an intriguing paradox for business. On one hand, they can signify a black hole for future revenue, with breach of copyright and intellectual property. On the other hand, they represent a gold mine of ideas and business opportunities. Central to business is the need to create and capture value, and creative consumers demand a shift in the mindsets and business models of how firms accomplish both. Based upon their attitude and action toward customer innovation, we develop a typology of firms' stances toward creative consumers. We then consider the implications of the stances model for corporate strategy and examine a three-step approach to dealing with creative consumers: awareness, analysis, and response. © 2006 Kelley School of Business, Indiana University.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.043
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0230.042
Scholarly communication0.0260.026
Open science0.0050.014
Research integrity0.0140.015
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.132
GPT teacher head0.315
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations342
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueIRIS - Institutional Research Information System (Libera Università Internazionale degli Studi Sociali Guido Carli)Same topicOpen Source Software InnovationsFrench-language works237,207