Development of a Digital Innovation Framework that is Renowned Globally
Bibliographic record
Abstract
The evolvement of the digital era/Industry 4.0 forces us to think differently about our life, new product development, new manufacturing environment, new communication procedures and even new ways of managing innovation in today's digital era. Industry 4.0 shifts the manufacturing lines’ dynamics and improves organisations’ profit. Innovative management substantially changes the world's smart transformation perspective in the manufacturing and services industries. Very little research was found on the digital era implication on innovation management. Therefore, this paper aims to develop a digital innovation framework that considers almost the globe's involvement during the development and validation stages. This includes seven prestigious countries from the major parts of the world, namely; the UK, UAE, USA, Germany, Japan, China, and Canada. The proposed innovation framework was developed based on the practitioner's contributions from these seven countries, considering the impact of digitalisation-push and the demand-pull as main criteria, with many sub-criteria associated with each main criterion. The framework is then validated through a comprehensive questionnaire administrated by the practitioners from each of the mentioned seven countries using the Analytical Hierarchy Process (AHP), which has the flexibility to combine quantitative and qualitative mixed-methods and is used to collect data and carry out a pairwise-comparison between main criteria and sub-criteria. Moreover, the proposed framework provides the innovation processes required to handle the demand-pull and consider the digitalisation push.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".