The influence of firm digitalization on sustainable innovation performance and the moderating role of corporate sustainability practices: An empirical investigation
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
Abstract This paper seeks to shed light on the relationship between firm digitalization and the likelihood of launching sustainable innovations (social and environmental, social only, and environmental only), for which the extant research has provided a paucity of evidence. In detail, the role of digitalization is considered in terms of (i) the specific effect of a given digital technology (DT)—among artificial intelligence, cloud computing, robotics, smart devices, big data analytics, high speed infrastructure, and blockchain—and (ii) the effect of the concurrent adoption of multiple DTs (degree of digitalization). Furthermore, the paper assesses if and how the effect of the degree of digitalization is moderated by the implementation of sustainability practices, as the two issues are often treated independently. Research questions are proposed instead of hypotheses. Econometric analysis to answer proposed questions is based on a sample of 14,125 firms, whose information is gathered from the survey Flash Eurobarometer 486. Results reveal that each DT differently affects the likelihood of launching sustainable innovations, while the degree of digitalization is always beneficial. Moreover, it appears that firm digitalization and the adoption of sustainability practices are not complementary. All in all, this paper helps to illuminate current representations of the interplay between digitalization, sustainability practices, and sustainable innovations at the firm level, with implications for research, managerial practice, and policymaking.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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