Internationalization and CSR reporting: evidence from US companies and their Polish subsidiaries
Bibliographic record
Abstract
Purpose This study examines the internationalization effects of corporate social responsibility (CSR) reporting, specifically aiming to identify and compare the CSR reporting practices of large US multi-national corporations (MNCs) and their Polish subsidiaries. Design/methodology/approach Based on content analysis and using a disclosure index, the authors examined the CSR information posted on, or linked to, the corporate websites of a sample of 60 US-based MNCs and their subsidiaries operating in Poland. Findings The findings indicate that US companies, despite operating in a less regulated environment, had more extensive disclosure than their Polish subsidiaries and covered more CSR-related topics. CSR disclosures within the US subsample were analogous in volume and detail. By contrast, only about half of Polish companies provided CSR disclosures, which were more diverse in volume and in the types of activities disclosed. The authors did not find a significant positive correlation between the CSR disclosures of the two subsamples. Originality/value The study contributes to the literature on internationalization processes and sustainability practices. It provides insights into the CSR reporting of companies located in Central and Eastern European countries. The findings also have implications for policymakers in incentivizing the enhancement of the reporting disclosure practices of companies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".