Moral Identity in Business Situations: A Social-Cognitive Framework for Understanding Moral Functioning
Bibliographic record
Abstract
The concept of moral identity has gained considerable theoretical and empirical traction since Augusto Blasi (1983) used the term in his Self Model of Moral Functioning over 20 years ago. Since then, a number of scholars (Aquino & Reed, 2002; Colby & Damon, 1992; Hoffman, 2000; Lapsley & Narvaez, 2004) have expanded on Blasi's ideas, and the collected papers in this volume testify to the variety and richness of these perspectives. In this chapter, we contribute to the ongoing conversation about the role of moral identity in guiding moral action by presenting a social-cognitive model that we apply to the domain of business. As researchers whose areas of study are organizational behavior and marketing, we are convinced that the concept of moral identity holds enormous promise for broadening our understanding of how moral constructs and concerns influence business activities, ranging from negotiations, leadership, and teamwork, to strategic decision making, advertising, and consumer behavior. Our aim is to take moral identity from its roots in developmental psychology and apply it to a new arena where moral decisions – questions about right and wrong – are unavoidable, and where people often have to make difficult tradeoffs among competing and equally compelling moral values. The outline of our chapter is as follows. First, we briefly review the social-cognitive perspective on moral identity, highlighting a conception proposed by Aquino and Reed (2002) that defines moral identity in terms of its self-importance . Second, we present a model that situates moral identity within a network of other constructs that have been shown by prior theory and research to be related to moral behavior.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".