A research on the mechanism of building total innovation management system via information and communication technology: Case studies in the context of China
Bibliographic record
Abstract
Total innovation management(TIM) is a new management paradigm adapting to Chinese context. It derives from innovation management theories and a large number of enterprises' business management practice in and out of China. Previous studies mainly focus on the role of TIM on enterprise's development and the internal relationship in TIM system, but lack of the concern for the antecedents of TIM. As a crucial factor promoting the social development, information and communication technology(ICT) is also considered as one of the major drivers of TIM. By the implementation of ICT, enterprises leverage their information capability (including information acquiring capability, information sharing capability, information processing capability and information materializing capability), which pushs forward the construction of TIM system. This paper first proposed a concept framework of building TIM System via ICT . With the case studies of Haier Group, Honyar and FPI, the mechanism that informationization facilitated the establishment of TIM system under different industries and business models was discussed, and the research framework for future empirical research was confirmed. Finally, the paper illustrated the limitation of theory and practice.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".