Industry 4.0: the impact of realized absorptive capacity on environmental performance in the context of global distribution channels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it