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Record W2000389016 · doi:10.1108/eum0000000005660

Toward a multi‐dimensional measure of individual innovative behavior

2001· article· en· W2000389016 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Intellectual Capital · 2001
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGenerativityConceptualizationStructural equation modelingConstruct (python library)Formative assessmentMeasure (data warehouse)Dimension (graph theory)PsychologySample (material)Reliability (semiconductor)Knowledge managementComputer scienceSocial psychologyMathematicsData mining

Abstract

fetched live from OpenAlex

Individual level innovation studies often assess only one dimension of innovative behavior. As such, they do not sufficiently capture the richness of the construct of individual innovation. Develops and tests a multi‐dimensional measure of individual innovative behavior. Identifies descriptions of 289 innovation related behaviors and codes these into a hypothesized factor structure consisting of the following five dimensions: opportunity exploration, generativity, formative investigation, championing, and application. Structural equation modeling used on a sample of 225 employees from nine different organizations delivered a relatively poor fit between the hypothesized factor structure and respondents’ job behaviors. However, a single factor measure based on items representing all five factors resulted in an alpha reliability of 0.95 thus supporting a multi‐dimensional conceptualization of innovative behavior in general. Discusses implications for future research.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.117
GPT teacher head0.371
Teacher spread0.254 · 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