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Record W2007195154 · doi:10.1142/s1363919613400148

INNOVATION PROCESS, DECISION-MAKING, PERCEIVED RISKS AND METRICS: A DYNAMICS TEST

2013· article· en· W2007195154 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

VenueInternational Journal of Innovation Management · 2013
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsNipissing University
Fundersnot available
KeywordsMarketingMetric (unit)Risk perceptionDecision makerTest (biology)BusinessRisk managementProcess (computing)Knowledge managementPsychologyComputer scienceManagement scienceEconomicsPerceptionFinance

Abstract

fetched live from OpenAlex

Innovation processes result from a series of decisions and these are influenced by the perceived risks and success metrics faced by the decision-maker. Aiming to understand whether innovation risks and success metrics change during and between innovations, four hypotheses were developed and a questionnaire-based survey was adopted targeting managers of mechanically based manufacturers. Respondents were asked to indicate the importance of perceived risks throughout specific innovations for four domains of risk: marketing, technical, organizational and financial. Respondents were also asked to identify changes in type and magnitude of innovation risk and success metric. Descriptive and statistical tests were conducted to analyse the data. The results suggest that innovation risk changes in type and magnitude during and between innovations and success metrics change in type and magnitude during innovation. This study calls for situation specific research to provide helpful advice to practitioners.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
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 head0.013
GPT teacher head0.300
Teacher spread0.287 · 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