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Record W178116008

The Bass Model of Diffusion: Recommendations for Use in Information Systems Research and Practice

2014· article· en· W178116008 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

VenueJournal of the Association for Information Systems · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsnot available
FundersMcGill UniversityGeorgia State UniversityUniversity of LouisvilleUniversity of Missouri
KeywordsImitationDiffusion of innovationsPopulationInformation systemMarketingSociologyManagement scienceKnowledge managementPsychologyComputer scienceEconomicsBusinessSocial psychologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

The Bass Model (TBM), first introduced in 1969, has been used in several fields – including sociology, economics, marketing, and communication studies – to understand diffusion of products and innovations, but has received limited attention in information systems (IS) research and practice. TBM views diffusion as occurring through a combination of innovation (p) and imitation (q). Innovation and imitation describe the extents to which influences external to the population and influences internal to the population respectively affect diffusion. To encourage and enable greater use of TBM in IS research and practice, we describe an application process for using TBM and illustrate potential applications of TBM.

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)0.053
metaresearch head score (Gemma)0.081
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0530.081
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.006
Open science0.0010.000
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 head0.250
GPT teacher head0.423
Teacher spread0.173 · 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