A Novel Metaphor Concerning the Terminology of Open Innovation
Bibliographic record
Abstract
The prime objective of the research was to determine how the apparel manufacturing sector is embracing digitization and its leaders are preparing for the digital age? so we wanted to find out what type of leadership style is needed for digital leadership. The present study used a sample of 50 RMG companies. We investigated relationships between three variables, Internet od Things, use of digitization-automation, use of smart phones and apps. Further, the variables’ influence on digitization has been assessed through multiple factors leading to digitization by allotment of weightage for each factor. The findings in this paper supports two variables; use of automized digital machines and internet of things being significant whereas, use of smartphones and apps is insignificant. It implies that preparation for leading in the digital age remains limited which require change oriented leadership behavior at all levels. Limitations of the paper include the data which is specific to Bangladesh RMG industry, therefore it cannot be generalized, further the economic meltdown due to COVID-19 pandemic might have influenced the results. The paper’s prime contribution is based on the assessment of predictor variables and their influence that it makes in providing leadership in the digital age which demand change oriented behavior of leaders.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".