Hungarian Quality Journal

Cognitive Biases in Controlling Decision-Making

Metadata

Author(s): Szabó Tamás, Tóth Edina, Dr. Gáspár Sándor, Dr. Hegedűs Szilárd

DOI: https://doi.org/10.65513/MaMi.2026.1.15

Publisher: Nemzetközi Oktatási és Kutatási Központ Alapítvány

Volume: 2026. január

Volume number: 35

Issue number: 1

Journal: Hungarian Quality Journal

ISSN (Print): ISSN 1416‑9576

ISSN (Online): ISSN 1789-5510

Pages: 15–31

Keywords: kontrolling, behavioral economics, prospect theory, cognitive bias, cluster analysis

Abstract

In the field of corporate decision support, controlling has traditionally been regarded as a tool for rational and optimal decision-making. However, recent research has demonstrated that behavioral biases may also emerge in management accounting and controlling practices This study is situated at the intersection of controlling decision-making processes and behavioral economics. Its primary objective is to identify and systematically map the cognitive biases that arise in the decision-making of professionals working in the field of controlling, as well as to uncover their characteristic patterns. Particular emphasis is placed on phenomena described by Prospect Theory such as reference dependence, loss aversion, and framing effects and on how these mechanisms may distort controllers’ judgments A mixed-methods research design was applied. First, an online, gamified questionnaire was used to assess the decision preferences of 148 controllers, followed by a cluster analysis to identify distinct decision-maker profiles. Subsequently, qualitative in-depth interviews were conducted within each cluster to support the interpretation and validation of the identified patterns. The questionnaire included decision scenarios specifically designed to capture cognitive biases (e.g., project continuation dilemmas framed in terms of gains versus losses). For the cluster analysis, the k-means algorithm and the silhouette coefficient were employed to determine the optimal number of clusters The quantitative results revealed three clearly distinguishable decision-making clusters within the sample. The first group (rational and cautious controllers) exhibited decision patterns largely consistent with the Homo oeconomicus model, with low levels of observed bias. The second group (bias-prone controllers) displayed a cumulative presence of behavioral distortions, including strong loss aversion and, depending on the framing of the decision context, tendencies toward excessive risk-taking. The third, smaller group (risk-seeking under loss conditions) demonstrated a paradoxical pattern: while generally avoiding uncertainty, its members tended to engage in risky continuation strategies when confronted with losses. Cluster membership showed a statistically significant association with demographic characteristics such as age, organizational position, and functional specialization (p < 0.01 indicating that experience and hierarchical role influence decision-making styles. The qualitative interviews largely confirmed these cluster profiles and provided a more nuanced understanding of the underlying mechanisms. For instance, older and more experienced controllers tended to avoid losses more consistently, whereas younger professionals showed a greater propensity for risk-taking in certain contexts. Overall, the findings confirm that controllers’ decisions are also subject to psychological biases, suggesting that the traditional assumption of full rationality has limited applicability in the field of controlling The three identified decision-making profiles highlight the potential benefits of implementing tailored decision-support tools and targeted training programs in controlling practice. By integrating principles of behavioral economics into controlling, this research contributes to both the theoretical and practical advancement of management accounting, enabling a more conscious and systematic treatment of human factors in organizational decision-making.

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References

  • Alm, J., & Torgler, B. (2011). Do Ethics Matter? Tax Compliance and Morality. Journal of Business Ethics, 101(4), 635-651. https://doi.org/10.1007/s10551-011-0761-9
  • Austin, C. R., Bobek, D. D., & Jackson, S. (2021). Does prospect theory explain ethical decision making? Evidence from tax compliance. Accounting, organizations and society, 94, 101251. https://doi.org/https://doi.org/10.1016/j.aos.2021.101251
  • Banerjee, D. (2023). Risk Preference, Gender, Responsibility: A Cross-Cultural Study from India. Journal of Cross-Cultural Psychology, 54(6-7), 722-741. https://doi.org/10.1177/00220221231193995
  • Bigus, J. (2015). Loss Aversion, Audit Risk Judgments, and Auditor Liability [Article]. European Accounting Review, 24(3), 581-606. https://doi.org/10.1080/09638180.2014.899920
  • Díez-Esteban, J. M., García-Gómez, C. D., López-Iturriaga, F. J., & Santamaría-Mariscal, M. (2017). Corporate risk-taking, returns and the nature of major shareholders: Evidence from prospect theory. Research in International Business and Finance, 42, 900-911. https://doi.org/https://doi.org/10.1016/j.ribaf.2017.07.025
  • do Nascimento Junior, A. J., Klotzle, M. C., Brandão, L. E. T., & Pinto, A. C. F. (2021). Prospect theory and narrow framing bias: Evidence from emerging markets. The Quarterly Review of Economics and Finance, 80, 90-101. https://doi.org/https://doi.org/10.1016/j.qref.2021.01.016
  • Egiyi, M., & Ogbodo, N. (2023). Behavioral Accounting: Analyzing How Cognitive Biases affect Financial Decisions and Reporting. 7, 20-30. https://doi.org/10.5281/zenodo.8278753
  • Enslin, Z., Hall, J., & du Toit, E. (2023). The susceptibility of management accountants to framing bias. Meditari Accountancy Research, 31(7), 133-155. https://doi.org/10.1108/medar-02-2021-1185
  • Fiegenbaum, A. (1990). Prospect theory and the risk-return association: An empirical examination in 85 industries. Journal of Economic Behavior & Organization, 14(2), 187-203. https://doi.org/https://doi.org/10.1016/0167-2681(90)90074-N
  • Horváth, P. (1978). Controlling–Entwicklung und Stand einer Konzeption zur Lösung der Adaptions-und Koordinationsprobleme der Führung. Zeitschrift für Betriebswirtschaft, 48(3), 194-208.
  • Lázár, E., & et al. (2023). Játékosított kérdőívek hatékonyságának mérése. Marketing & Menedzsment, 57(Különszám EMOK 1), 51-59.
  • March, J. G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall.
  • March, J. G., & Simon, H. A. (1993). Organizations. John wiley & sons.
  • Ruggeri, K., Alí, S., Berge, M. L., Bertoldo, G., Bjørndal, L. D., Cortijos-Bernabeu, A., Davison, C., Demić, E., Esteban-Serna, C., Friedemann, M., Gibson, S. P., Jarke, H., Karakasheva, R., Khorrami, P. R., Kveder, J., Andersen, T. L., Lofthus, I. S., McGill, L., Nieto, A. E.,…Folke, T. (2020). Replicating patterns of prospect theory for decision under risk. Nature Human Behaviour, 4(6), 622-633. https://doi.org/10.1038/s41562-020-0886-x
  • Simon, H. A. (1955). A behavioral model of rational choice. The quarterly journal of economics, 99-118.
  • Tóth, A., & Zéman, Z. (2018). Stratégiai pénzügyi controlling és menedzsment. https://doi.org/10.1556/9789634540311
  • Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323. https://doi.org/10.1007/BF00122574
  • Wen, Y.-F. (2010). Capital investment decision, corporate governance, and prospect theory. Procedia – Social and Behavioral Sciences, 5, 116-126. https://doi.org/https://doi.org/10.1016/j.sbspro.2010.07.060
  • Zéman, Z., Vajda, G., Thalmeiner, G., & Gáspár, S. (2022). „A controlling fejlődés új iránya, az adatvezérelt controlling”. Controller Info, 10(3), 49-56.
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