Industry 4.0: the impact of realized absorptive capacity on environmental performance in the context of global distribution channels
Bibliographic record
Abstract
Purpose This paper aims to examine the impact of realized absorptive capacity, focusing on transformation and exploitation aspects, on the firm’s environmental performance, which includes emissions, innovation and resource efficiency. Design/methodology/approach A questionnaire was developed using established scales. In total, 255 respondents from the USA and Canada were collected using the Qualtrics marketing panel. The respondents worked in companies at least in the limited deployment stage of Industry 4.0 technologies. They were employed in marketing, business development or sales/distribution roles and worked for an international, multinational or global company. PLS-SEM was used for statistical analysis. Findings The results indicate that realized absorptive capacity positively impacts all aspects of environmental performance examined in this research. Consequently, it could reduce emissions, enhance innovative capabilities and improve the efficiency of organizational resource use. Originality/value This paper’s originality lies in examining realized absorptive capacity as a dynamic capability driving environmental performance, measured through emissions, innovation outcomes and resource efficiency. Distinctively, the findings challenge conventional assumptions by showing that both absorptive capacity and environmental performance are best captured through formative, not reflective, measurement models, highlighting that measurement approaches must adapt to contextual factors rather than assuming universal validity.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".