Although naming-and-shaming (shaming) is a commonly used tax enforcement mechanism, little is known about the efficacy of shaming tax evaders. Through two experiments, this study examines the effects of shaming tax evaders on third-party observers’ perceptions of retributive justice and tax compliance intentions, and whether the salience of persuasion of observers moderates these relationships. Based on insights from defiance theory, the message learning model, and persuasive communications, this study predicts and finds that shaming evaders increases observers’ tax compliance intentions. Furthermore, (...) the results show that higher persuasion, which includes sanction and normative appeals, affects observers’ tax compliance intentions. This study also suggests that shaming has a positive effect on perceptions of retributive justice. Importantly, the results reveal that perceptions of retributive justice in shaming punishment mediate the effect of shaming on tax compliance intentions. The implications for theory and practice are discussed. (shrink)

Recent advances in machine learning methods have created opportunities to eliminate unfairness from algorithmic decision making. Multiple computational techniques (i.e., algorithmic fairness criteria) have arisen out of this work. Yet, urgent questions remain about the perceived fairness of these criteria and in which situations organizations should use them. In this paper, we seek to gain insight into these questions by exploring fairness perceptions of five algorithmic criteria. We focus on two key dimensions of fairness evaluations: distributive fairness and procedural fairness. (...) We shed light on variation in the potential for different algorithmic criteria to facilitate distributive fairness. Subsequently, we discuss procedural fairness and provide a framework for understanding how algorithmic criteria relate to essential aspects of this construct, which helps to identify when a specific criterion is suitable. From a practical standpoint, we encourage organizations to recognize that managing fairness in machine learning systems is complex, and that adopting a blind or one-size-fits-all mentality toward algorithmic criteria will surely damage people’s attitudes and trust in automated technology. Instead, firms should carefully consider the subtle yet significant differences between these technical solutions. (shrink)

Since taking office, the President of the United States has consistently refused to make his tax returns available for public scrutiny. In so doing, he has broken with presidential tradition and kept people guessing about what his tax returns would show if they were disclosed. Interestingly enough, in the absence of concrete knowledge about the President’s tax circumstances, some taxpayers perceive that he did not pay his fair share and others perceive that he did. This situation presents an opportunity for (...) us to consider how vertical equity perceptions about the President’s fair share of taxes, and identification with the President via voting behavior, are jointly associated with taxpayers’ compliance. Using insights from equity theory as reported in Adams Advances in experimental social psychology, Academic Press, New York, 1965) and social learning theory as reported in Bandura, we anticipate and find that those taxpayers who perceive that the President did not pay his fair share of taxes and who most closely socially identify with him have the lowest tax compliance. We use a survey of 600 American taxpayers to capture their perceptions of vertical equity, voting behavior, and tax compliance intentions. Our results may reflect that taxpayers who identify with the President will tend to model his tax-related actions, or may also indicate that some taxpayers vote for a president whose actions are similar to their own. Insights and implications for ethics and tax researchers and tax policy makers are discussed. (shrink)