Green Product Innovation in Manufacturing Firms: A Sustainability‐Oriented Dynamic Capability Perspective
Bibliographic record
Abstract
Abstract Despite environmental sustainability being identified as one of the key drivers of innovation, extant literature lacks a theoretically sound and empirically testable framework that can provide specific insights into green product innovation from a capability perspective. This study develops a theoretical framework from a sustainability‐oriented dynamic capability (SODC) perspective. We conceive SODCs as consisting of three underlying processes (external resource integration, internal resource integration, and resource building and reconfiguration) that influence the change/renewal of sustainability‐oriented ordinary capabilities (SOOCs) (green innovation capability and eco‐design capability). This study answers two key questions: which SODCs are needed to develop green innovation and eco‐design capabilities? Which of these capabilities lead to better market performance of green products? We test a structural model linking SODCs to market performance in 189 Italian manufacturing firms. First, we find that the nature of the SODC–performance link (direct or indirect) depends on the SODC type. Specifically, resource building and reconfiguration is the only SODC with a direct effect on market performance. Second, all three types of SODC affect the eco‐design capability, which mediates the link between SODCs and market performance. Third, we find that external resource integration is the only SODC affecting the green innovation capability, which mediates the link between external resource integration and market performance. Resource building and reconfiguration is the SODC with the overall (direct and indirect) highest impact on market performance. This study, among the first to consider capabilities for green product innovation under a dynamic capability perspective, provides implications for scholars, managers and policy makers. Copyright © 2016 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".