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Cheat, cheat, repeat: On the consistency of dishonest behavior in structurally comparable situations.

心理学 作弊 社会心理学 一致性(知识库) 计算机科学 人工智能
作者
Isabel Thielmann,Benjamin E. Hilbig,Christoph Schild,Daniel W. Heck
出处
期刊:Journal of Personality and Social Psychology [American Psychological Association]
被引量:1
标识
DOI:10.1037/pspp0000540
摘要

A fundamental assumption about human behavior forming the backbone of trait theories is that, to some extent, individuals behave consistently across structurally comparable situations. However, especially for unethical behavior, the consistency assumption has been severely questioned, at least from the early 19th century onward. We provide a strict test of the consistency assumption for a prominent instance of unethical behavior-dishonesty-in a large (N = 1,916) and demographically diverse sample. Dishonest behavior was measured three times-up to 3 years apart-using different variants of well-established, incentivized cheating paradigms. A key advantage of these paradigms is that lying is individually profitable but not self-incriminating. Besides varying the specific task at hand, we experimentally varied the nature of incentives (i.e., money vs. avoiding tedious work) as well as their magnitude across measurement occasions. The consistency of dishonest behavior was estimated using a newly developed statistical model. Results showed strong consistency of dishonest behavior across contexts in most cases. Furthermore, theoretically relevant personality traits (i.e., Honesty-Humility and the Dark Factor) yielded meaningful relations both with dishonesty and indeed its consistency. Thus, contrary to long-standing assumptions, there is notable consistency in dishonest behavior that can be attributed to underlying dispositional factors. Overall, the current findings have important implications for the theoretical understanding of dishonest behavior by providing strong evidence for (dis)honesty as a trait as well as for practice (e.g., honesty interventions). Moreover, the newly developed statistical approach can serve future research across scientific fields. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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