The effect of an ambidextrous supply chain strategy on combinative competitive capabilities and business performance
Bibliographic record
Abstract
Abstract This study investigates the influence of an ambidextrous supply chain strategy on manufacturers’ combinative competitive capabilities – the ability to excel simultaneously on competitive capabilities of quality, delivery, flexibility, and cost – and, in turn, on business performance. Drawing upon March's (1991) notions of exploration and exploitation, an ambidextrous supply chain strategy is conceptualized as a simultaneous pursuit of both explorative and exploitative supply chain practices. We operationalize this concept as a second‐order latent construct that captures the co‐variation between exploration and exploitation within the context of a manufacturer's supply chain management strategy. Using survey‐based data gathered from 174 U.S. manufacturers, we find that an ambidextrous supply chain strategy coincides with combinative competitive capabilities and business performance. Our empirical finding contradicts conventional wisdom that argues for tradeoffs between exploration and exploitation. Instead, our empirical results are in line with an emerging complementarity view advocating that supply chain managers build practices to gain operational efficiency while simultaneously searching for opportunities to gain operational advantages. In addition, we provide insights regarding the role of combinative capabilities in mediating the relationship between an ambidextrous supply chain strategy and business performance.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".