The influence of firm digitalization on sustainable innovation performance and the moderating role of corporate sustainability practices: An empirical investigation
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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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".