Distal and Proximal Influences on the Use of Business and Moral Frames in Responsible Decisions
Bibliographic record
Abstract
We empirically contribute to the debate on business education in building on a decision frame perspective of decision making in corporate responsibility settings. Business schools have been accused to teach amoral theories, leading their students to behave less morally and engendering corporate responsibility scandals. Research has also pointed toward self-selection: business students would differ from non-business students before entering business school. We examine the role of socioeconomic status, core self-evaluations in this regard. Further, we investigate the belief in a free market as a distal influence triggering a business frame, and moral intensity as a proximal influence triggering a moral frame on responsible decision making by business and non-business students. Cross-sectional data obtained from 566 students on two decision making scenarios mostly supported our hypotheses. Socioeconomic status but not core self-evaluations explain the belief in a free market, and had indirect effects on the likelihood to make a less responsible decision. Importantly, the relationship between business studies and the belief in a free market remained significant after accounting for these variables. Our study thus contributes to the socialization and self-selection arguments. We discuss theoretical and practical implications for research on decision frames and for business education, respectively.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".