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Are Individuals More Willing to Lie to a Computer or a Human? Evidence from a Tax Compliance Setting

Author

Listed:
  • Ethan LaMothe

    (Oklahoma State University)

  • Donna Bobek

    (University of South Carolina)

Abstract

Individuals are increasingly switching from hiring tax professionals to prepare their tax returns to self-filing with tax software, yet there is little research about how interacting with tax software influences compliance decisions. Using an experiment, we examine the effect of preparation method, tax software versus tax professional, on willingness to lie. Results from a structural equation model based on data collected from 211 actual taxpayers confirm the hypotheses and show individuals are more willing to lie to tax software than a human tax professional. Our results also suggest this effect is jointly mediated by perceptions of social presence and the perceived detectability of the lie. Beyond the practical implications for tax enforcement, our findings broadly contribute to accounting and other literatures by examining the theoretical mechanisms that explain why individuals interact differently with computers versus humans. We also extend prior research on interactions between humans and computers by examining economically motivated lies.

Suggested Citation

  • Ethan LaMothe & Donna Bobek, 2020. "Are Individuals More Willing to Lie to a Computer or a Human? Evidence from a Tax Compliance Setting," Journal of Business Ethics, Springer, vol. 167(2), pages 157-180, November.
    • Handle: RePEc:kap:jbuset:v:167:y:2020:i:2:d:10.1007_s10551-019-04408-0
    DOI: 10.1007/s10551-019-04408-0
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    References listed on IDEAS

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    Cited by:

    1. Marilyn Giroux & Jungkeun Kim & Jacob C. Lee & Jongwon Park, 2022. "Artificial Intelligence and Declined Guilt: Retailing Morality Comparison Between Human and AI," Journal of Business Ethics, Springer, vol. 178(4), pages 1027-1041, July.
    2. TaeWoo Kim & Hyejin Lee & Michelle Yoosun Kim & SunAh Kim & Adam Duhachek, 2023. "AI increases unethical consumer behavior due to reduced anticipatory guilt," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 785-801, July.
    3. Li, Tian-Ge & Zhang, Chu-Bing & Chang, Ying & Zheng, Wei, 2024. "The impact of AI identity disclosure on consumer unethical behavior: A social judgment perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    4. Yuanyuan Zhou & Zhuoying Fei & Yuanqiong He & Zhilin Yang, 2022. "How Human–Chatbot Interaction Impairs Charitable Giving: The Role of Moral Judgment," Journal of Business Ethics, Springer, vol. 178(3), pages 849-865, July.
    5. Navitha Singh Sewpersadh, 2023. "Disruptive business value models in the digital era," Journal of Innovation and Entrepreneurship, Springer, vol. 12(1), pages 1-27, December.

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