Innovating Business Ethics Pedagogy for the Future
Bibliographic record
Abstract
With spectacular corporate scandals, business ethics issues have been well-documented and the Association to Advance Collegiate Schools in Business (AACSB) recognizes in their criteria Ethics education as a critical topic. Nevertheless, a review of business ethics decision-making theories, psychology, and neuroscience research, indicates ethics pedagogy relies mainly on rationality, ignoring the importance of emotions (DeTienne, et al., 2019). Frederick (2008) contends that business education has failed to engage students into considering the morality of their decisions, due to exclusive rationalist pedagogy. Robertson and colleagues (2017) agree that both reason and emotion should be addressed in business ethics education. This research provides a qualitative analysis of business students’ experiences in an innovative business ethics course based on virtue ethics. Virtue ethics calls for development of character through experiential learning and contemplative practices, leading to enhanced self-awareness and self-regulation. Innovation comes from taking an entirely novel approach to instilling ethical conduct, not through exclusive rational reasoning but by enabling students to better understand themselves, their values and become more aware of their emotions. Students engage in meditation, journaling, and other practices to develop awareness of self and of others and discernment in decision-making. Interview data of 29 students participating in a third-year business ethics class is analyzed to gain in-sights on their perceptions of these experiences. Students' reflections are interpreted through the lens of their Character and Emotional Intelligence development. Findings indicate that students obtain insights into themselves and others, suggesting enhanced self-awareness, regulation of emotions and improved relationships, as well as developing ethical judgment and behavior. This paper’s contribution is to understand the impact of such practices on business students’ experiences and on their ethical development.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".