Who or what is to blame? The role of attribution of responsibility in layoffs
Publication Date
1-1-2023
Document Type
Article
Publication Title
International Journal of Ethics and Systems
Issue
3
DOI
10.1108/IJOES-12-2022-0295
First Page
613
Last Page
627
Abstract
Purpose: This study aims to investigate the effects of attribution of responsibility (AOR) for layoffs on the components of ethical decision-making. Internal, external and no-fault AOR were examined using the model of moral intensity to determine if placement of blame for the layoff influences ethical awareness, judgment and intent. Design/methodology/approach: Surveys were collected from 397 students. The survey provided a scenario about a layoff situation involving an African-American woman and a Caucasian woman. Respondents then answered questions about moral intensity, moral judgment and moral intent concerning the layoff and identified the reasons they believed the layoff occurred. We tested our hypotheses using multiple regression analysis. Findings: Subjects were more likely to make a moral judgment about the situation when layoffs were blamed on the company’s actions (external AOR) and less likely to make a moral judgment when the layoff decision was blamed on employee performance (internal AOR) or on economic factors beyond anyone’s control (no-fault AOR). Results also indicate that layoffs blamed on employee performance negatively moderate the relationship between moral judgment and moral intent. Originality/value: Previous studies of layoff ethics have not examined the influence of AOR for layoffs using the model of moral intensity. Thus, this paper extends the current understanding of these concepts in ethical decision-making.
Keywords
Attribution of responsibility, Ethical decision-making, Layoffs, Moral intensity
Department
Management
Recommended Citation
Juliana Lilly, Kamphol Wipawayangkool, Meghna Virick, and Ronald Roman. "Who or what is to blame? The role of attribution of responsibility in layoffs" International Journal of Ethics and Systems (2023): 613-627. https://doi.org/10.1108/IJOES-12-2022-0295