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Revisiting Rogers: the diffusion of his innovation development process as a normative framework for innovation managers, students and scholars

2019· article· en· W2920874388 on OpenAlexaff
Angèle M. Beausoleil

Bibliographic record

VenueJournal of Innovation Management · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNormativeCompetence (human resources)TypologyKnowledge managementProcess (computing)Innovation managementMultidisciplinary approachFunction (biology)Innovation processCore competencyBusinessSociologyPsychologyComputer scienceWork in processMarketingPolitical science

Abstract

fetched live from OpenAlex

Innovation has evolved into a core management function for most organizations. Business managers, regardless of sector or firm size, now require an understanding of and practice with the innovation process in order to develop a competence with navigating its winding path. To effectively engage in innovation processes, individuals require a distinct set of knowledge, aptitude and skills, or key innovative competencies. This paper examines a broad multidisciplinary literature focused on how innovation happens and the normative elements of its process, to inform key innovative competencies across its many phases. Through document analysis, empirically-based innovation process theories and models are examined with the intent to discover and propose a normative framework. The literature review provides a broad classification of innovation process descriptions and phases reflecting Everett Rogers’ original innovation-development process (IDP). Rogers’ IDP is proposed as a normative framework from which individual innovative competencies are identified and classified. Both the framework and typology are proposed as guides for innovation process understanding, participation and management. For innovation researchers and educators, this article suggests an innovation process normative framework may act as a recipe for further research on innovative competencies and innovation management pedagogical models.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.631
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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.

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

Citations25
Published2019
Admission routes1
Has abstractyes

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