Relationship between integration, readiness and innovative product performance in a Brazilian supply chain
Bibliographic record
Abstract
There are few studies addressing the mediating effect of readiness in supply chains in developing countries. We use the theory of dynamic capabilities to investigate the impact of integration (INT) on readiness (REA) and on innovative product performance (IPP) and to examine the mediating effect of REA on INT and IPP. We conducted a survey with a sample of 213 supply chain (SC) managers of machinery and equipment for transport and lifting heavy loads in Brazil. To this end, we used structural equation modeling to test the hypotheses and PROCESS macro to confirm the indirect effect. The empirical results indicate a significant effect between INT and REA and REA and IPP. However, the indirect impact of REA was compromised. The literature review demonstrates that the microfoundations of dynamic capabilities (Sensing, Seizing and Reconfiguration) strengthen supply chain links, particularly between INT, REA and IPP. This article helps managers understand the functioning of SC routines and operations. To this end, they should develop strategies that strengthen SC ties with INT, REA and IPP to face future crises. This paper advances the findings on integration, readiness, and innovative product performance in an SC. The findings provide insightful implications for managers to improve their strategies. In doing so, we theorize how the microfoundations of dynamic capabilities support the efficiency of supply chain operations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".