Corporate ethical lapses: do markets and stakeholders care?
Bibliographic record
Abstract
Purpose The purpose of this paper is to assess if a firm’s ethical lapses, which result from unethical behavior or actions, influence its social disclosure (SD) practices as well as how ethical lapses affect both the firm’s legitimacy within society and its standing in financial markets. This study addresses two-related questions: do a firm’s ethical lapses undermine the credibility of its SD in financial markets, either directly or through a firm’s legitimacy? Do ethical lapses affect a firm’s market value and is this effect mediated by SD and legitimacy? Design/methodology/approach Three hypotheses are derived based on two theoretical approaches, information economics and institutional theory. The hypotheses lead ultimately to an examination of a firm’s legitimacy. Ethical lapses are inspired by the Global Reporting Initiative grid and by ISO 26000. Findings The results suggest that a firm’s ethical lapses underlie its SD practices and affect its legitimacy and standing in financial markets, the latter being proxied by financial analysts’ forecasts. Research limitations/implications The limitations of this study include that alternative ways exist to measure the constructs employed, the measurement of SD is subject to discretionary choices, and the North American sample results may not be generalizable to other countries. Originality/value The originality and contributions of this study are based on the use of information economics and institutional theory in a complementary way that recognizes information as serving various purposes and constituencies. Additionally, the paper extends prior research on the SD aspects of CSR by showing it matters to both financial markets and non-financial stakeholders.
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 | 0.022 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".