Environmentally sustainable development initiatives in upstream strategic outsourcing relationships: Examining the role of innovative capabilities
Bibliographic record
Abstract
Abstract Research on the potential impact of environmental sustainable development initiatives such as environmental collaboration with the supplier (ECS) on environmental and manufacturing performance is inconclusive. Specifically, it has overlooked the intermediary role that dynamic capabilities play in the relationship between ECS and performance. This explains why previous research, while correct in theory, found conflicting statistical results between environmental collaboration upstream and various performance outcomes. This study examines the following questions: What is the impact of environmental collaboration on manufacturing and environmental performance in outsourcing relationships? Do capabilities mediate the relationship between environmental collaboration and performance? Further, we propose that one particular mediating factor—innovative capabilities (ICs)—can influence the strength of this relationship and thus explain why previous research found conflicting statistical results. This paper uses structural equation modeling to analyze survey data from 247 North American manufacturers that outsourced their manufacturing. IC fully mediates the relationship between ECS and manufacturing performance and partially mediates the relationship between environmental collaboration and environmental performance. These findings enrich existing knowledge as it views ECS through the lens of resource‐based theory. Further, we shed light on the crucial role of IC in firms that choose to outsource critical capabilities. From a managerial perspective, the empirical results will inform outsourcing managers making strategic and tactical decisions to achieve desired environmental and manufacturing outcomes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".