Does ESG Performance in the Construction Industry Have an Impact on Digital Technology Innovation? A Stakeholder Theory Perspective
Bibliographic record
Abstract
The sustainable growth of the construction industry has brought the ESG performance of construction firms into the spotlight, with its impact on business operations becoming increasingly significant. However, the question of whether ESG performance can foster digital technology innovation remains unexplored. Therefore, based on stakeholder theory, this study utilizes digital technology patent data from listed construction firms in China between 2009 and 2021 to examine the relationship between ESG performance and digital technology innovation output among construction firms. Additionally, the study investigates the impact of firm age, profitability, and size on the number of digital technology patents filed. The findings indicate a positive correlation between ESG performance and digital technology innovation output, suggesting that firms with superior ESG records prioritize sustainable development and social responsibility. This positive image may attract more investors and collaborators, thus providing more resources and support for their digital technology innovation efforts. Understanding the mechanisms behind ESG’s impact on digital technology innovation can help construction industry firms integrate sustainable development concepts, mitigate environmental impacts, enhance social responsibility, and achieve economic, social, and environmental harmony. This not only enhances the competitiveness of construction industry firms but also facilitates the transformation and upgrading of the industry.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".