Critical success factors for the innovativeness of the electronic industry: An analysis in developed and developing countries
Bibliographic record
Abstract
The purpose of this study is to investigate the influence of critical success factors (CSFs) on the innovativeness of the electronic industry. We propose a novel conceptual model. To validate the model, we used structural equation modelling. Data were collected in a survey with 261 responses from companies in Brazil, Canada and the USA. A multigroup analysis was carried out. The results for the whole sample show that all the CSF investigated have a positive and significant influence at 1% on innovativeness, with the greatest influence obtained by the construct innovative culture and strategy, followed by research and development infrastructure, management knowledge and the innovative environment. The originality lies (1) in a proposition of a novel theoretical model that investigates both the individual effect and joint action of the constructs related to CSF, in a broader set of CSF; (2) in a deeper analysis of the relationship between innovativeness with the innovation performance of organizations in the electronic industry; (3) in a different dimension of analysis comparison considering three different innovation systems; and (4) in a managerial contribution by encouraging the construction of an organizational culture that has as its values the constant search for innovation and for encouraging the creation of innovative solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".