Antecedent configurations toward supply chain resilience: The joint impact of supply chain integration and big data analytics capability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Many antecedents identified as essential to supply chain resilience (SCR) are often studied independently, without considering their synergistic effects. Based on a case study and resource orchestration theory, this article focuses on configurations of different antecedents regarding supply chain integration and big data analytics capability to develop proactive and reactive SCR. Using survey data from 277 Chinese manufacturing firms, we consider three dimensions of supply chain integration, information integration, operational integration and relational integration, and three dimensions of big data analytics capability, technical skills, managerial skills and data driven‐decision culture, and conduct fuzzy‐set qualitative comparative analysis (fsQCA) to explore antecedent configurations generating high proactive and reactive SCR. We find that multiple antecedent configurations can achieve high SCR and configurations for high proactive and reactive SCR are not identical, which may involve alternative effects across different antecedents. We further implement propensity score matching analysis and reveal that firms following these configurations for high SCR also have better economic and operational performance. Moreover, we check the robustness of findings by using secondary data and attributes analysis with machine learning. This article complements and extends existing SCR literature from the configurational perspective and provides practical insights for managers to build SCR.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it