An empirical examination of the relationship between globalization, integration and sustainable innovation within manufacturing networks
Bibliographic record
Abstract
Purpose While controlling for supply chain effects, the purpose of this paper is to investigate if globalization and collaborative integration within a firm-wide manufacturing network have significant implications for the adoption of sustainable production (SP) and sustainable sourcing (SS) practices at the plant level. Design/methodology/approach The authors conceptualize SP and SS as process innovations with moderate degrees of innovativeness and apply “Organizational integration and process innovation” theory to build our conceptual model. Then, the authors use primary survey data from 471 assembly manufacturing plants operating in the US, Europe and Asia to test our hypotheses rigorously. Findings This research finds that the adoption of SP practices at the plant level is significantly and positively associated with globalization and integration of the firm-wide manufacturing network. On the contrary, the adoption of SS practices is more strongly affected by integration in the external supply chain and benefits from the manufacturing network only indirectly, through the association with SP practices. Originality/value Operations management literature devoted to sustainability has studied sustainable practices mostly from a risk management angle. Also, there exists contrasting evidence in the operations strategy literature about the positive and negative effects that globalization of a manufacturing network may have on the adoption of sustainable practices at the plant level. Moreover, several studies show how integration with supply chain partners helps manufacturing plants transition into more SP and SS practices; however, related literatures have neglected that collaborative integration within a firm-wide manufacturing network may also help to develop, or adapt to, new sustainable practices. This research represents a first attempt to resolve discordance and unveil the positive effects that manufacturing networks may have on sustainable innovations at the plant level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".