Unethical employee behavior (UEB), an important organizational phenomenon, is dynamic and multi-faceted. Recent renewed interest in the role of emotion in ethical decision-making (EDM) suggests that unethical behaviors are neither always rationally derived nor deliberately undertaken. This study explores how to integrate the conscious and nonconscious dimensions of unethical decision-making. By broadening the scope of inquiry, we explore how integral affect—the emotion tied to anticipated decision outcomes for the employee engaging in misconduct—can shed light on UEB. We review related literature (...) on affect and EDM and propose a model in which we assert that at a decision point, an employee experiences multiple integral affects that are either persuasive or dissuasive toward UEB. We further posit that among these integral affects, a dominant integral affect (DIA) emerges, determining the nature and direction of unethical behavior. In addition, our model considers the role of affective residue and the influence of other factors, such as incidental affect, disposition, context, and retrospection, to derive propositions. Our DIA model can help managers gain a comprehensive understanding of how affect, characterized by its locus (self or other orientation) and valence (enhancing or harming), determines the characteristics of UEB. (shrink)

Ethical dissonance arises from conflicts between beliefs or behaviors and affects ethical factors such as normality or conformity. This paper proposes a weak signal-oriented framework to investigate ethical dissonance from experiences linked to human–machine interactions. It is based on a systems engineering principle called human-systems inclusion, which considers any experience feedback of weak signals as beneficial to learn. The framework studies weak signal-based scenarios from testimonies of individual experiences and these scenarios are assessed by other people. For this purpose, the (...) framework proposes several databases as sources of weak signals, formalization tools of experience feedback of weak signals, models of references of conformity, ethical factors, and a list of examples of ethical dissonance. It also includes sequential steps to make the latter credible regarding the results from the experimental protocols. The framework was used to investigate ethical dissonance by analyzing experiences pertaining to achieving inclusive mobility. The first example focuses on ethical dissonance in terms of hindrance that goes against autonomous mobility due to a misunderstanding of how the system functions in terms of negative and positive emotions. Two other examples present possible ethical dissonance when the use of safety systems such as car driver-assistance systems, may create danger. Investigating ethical dissonance can then help system-inclusive design or evaluation processes by taking into account scenarios from weak signal-based experiences and making them credible. (shrink)

Abusive supervision negatively affects its direct victims. However, recent studies have begun to explore how abusive supervision affects third parties. We use the emotion-based process model of schadenfreude as a basis to suggest that third parties will experience schadenfreude and increase their work engagement as a response to peer abusive supervision. Furthermore, we suggest that the context of competitive goal interdependence facilitates the indirect relationship between PAS and third parties’ work engagement on schadenfreude. We use a mixed-method approach to test (...) our hypotheses. Data from an experimental study conducted by facial expression analysis technology and a multi‐wave field study generally support our hypotheses. Overall, our study extends PAS literature and meaningfully informs practitioners who aim to promote ethical workplace environments. (shrink)

Several technological developments, such as self-service technologies and artificial intelligence, are disrupting the retailing industry by changing consumption and purchase habits and the overall retail experience. Although AI represents extraordinary opportunities for businesses, companies must avoid the dangers and risks associated with the adoption of such systems. Integrating perspectives from emerging research on AI, morality of machines, and norm activation, we examine how individuals morally behave toward AI agents and self-service machines. Across three studies, we demonstrate that consumers’ moral concerns (...) and behaviors differ when interacting with technologies versus humans. We show that moral intention is less likely to emerge for AI checkout and self-checkout machines compared with human checkout. In addition, moral intention decreases as people consider the machine less humanlike. We further document that the decline in morality is caused by less guilt displayed toward new technologies. The non-human nature of the interaction evokes a decreased feeling of guilt and ultimately reduces moral behavior. These findings offer insights into how technological developments influence consumer behaviors and provide guidance for businesses and retailers in understanding moral intentions related to the different types of interactions in a shopping environment. (shrink)