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Judging Decisions : Essays on Moral Judgement and Ethical Decisions in the Context of Tax Morale

Authors: Grym Jori;

Judging Decisions : Essays on Moral Judgement and Ethical Decisions in the Context of Tax Morale

Abstract

This doctoral dissertation, "Judging Decisions: Essays on Moral Judgment and Ethical Decisions in the Context of Tax Morale," presents an academic endeavor to probe the depths of moral psychology and ethical decision-making concerning tax morale. This comprehensive work elucidates the intricate dynamics at play in the realm of tax compliance, particularly under the influence of burgeoning digital economies and cryptocurrencies. The dissertation is a researched and thoughtfully argued piece that advances our understanding of the moral considerations underlying tax behavior. The first of the three presented studies within this dissertation reveals tax morale as a nuanced, multi-faceted construct, challenging the prevailing singular perspectives on tax compliance motivation. By empirically demonstrating the distinct pathways through which communal and rule-following motivations influence different dimensions of tax morale, the dissertation underscores the necessity of a holistic approach in conceptualizing tax morale. This nuanced understanding beckons for more sophisticated policy measures that recognize the complex interplay between different motivational factors driving tax compliance and evasion. The second study ventures into the realm of gender differences in moral reasoning related to tax compliance, shedding light on the underlying reasons for observed variances in tax morale between men and women. The findings suggest that women tend to employ different moral foundations, particularly those emphasizing harm, care, and fairness, when evaluating the ethicality of tax evasion. This critical insight into gender-specific moral reasoning patterns offers a valuable perspective for tailoring tax policy and education to address and harmonize these differences effectively. The third and final study explores the impact of the perpetrator's perceived character and the stereotypes associated with their actions on the moral judgment of tax evasion. The dissertation innovatively demonstrates that the medium through which tax evasion is conducted—be it through traditional financial instruments or cryptocurrencies—significantly influences the moral evaluation of the act. This study reveals a tendency to judge the individual committing the act alongside the act itself, suggesting that stereotypes and the innovative allure of cryptocurrencies can mitigate the perceived wrongness of tax evasion. This dissertation offers insights into the moral and ethical considerations that shape tax compliance behaviors. The work not only enriches the academic discourse on moral psychology and ethics but also provides practical implications for policymakers, educators, and society at large, urging a reevaluation of how tax compliance is fostered in an increasingly digitalized global economy.

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Country
Finland
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Keywords

moralpsykologi, kringgående av skatt, moral psychology, tax evasion, digital transformation, moral judgement, etik, ethics, tax morale, cryptocurrency, Supply Chain Management and Social Responsibility, virtuell valuta

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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