Bibliographic record
Abstract
Purpose The purpose of this study is to examine the usage of non-financial information related to corporate social responsibility (CSR) issues from the perspective of sell-side analysts (SSAs) and buy-side analysts (BSAs) employed in Poland-based financial institutions. Design/methodology/approach The authors conducted a survey among financial analysts with the use of the computer-assisted telephone interview (CATI) method and an online questionnaire. The adopted methods included purposeful, quota sampling and snowball sampling. Findings Results indicate that financial analysts make use of CSR disclosures very rarely and attribute little importance to such information. Despite the limited use of CSR information and negative assessments of its quality, respondents are in favor of making a more frequent use of CSR disclosures. Finally, except for an analyst’s attitude toward the “comparability in time” information characteristic, results do not indicate any significant differences between SSAs’ and BSAs’ responses. Research limitations/implications The limited number of questionnaires prevented the use of more sophisticated statistical methods and the formulation of conclusions that could apply to the entire population. In addition, although the adopted CATI method provides a number of advantages, it also has its limitations – interviews had limited time and the questions along with the answers had to take into account the respondents’ limited perception ability. Practical implications The results of this study suggest that CSR disclosures have limited usage for financial analysts, at least in the Polish context. Further, not only do respondents rarely make use of CSR disclosures but they also give low assessments to their quality. This implies that the concept of CSR remains relatively far from becoming a priority; hence, some measures and incentives may be necessary. Originality/value The paper adds to a relatively small number of studies that have dealt with the issue of non-financial information and its usefulness for SSAs and BSAs in Central and Eastern Europe.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".