The impact of corporate culture of dignity on cognitive biases, strategic decision-making and technical debt management in IT engineering

Authors

DOI:

https://doi.org/10.15587/2706-5448.2025.329635

Keywords:

dignity, cognitive biases, technical debt, decision-making, IT engineering, behavioral economics

Abstract

The object of research is the corporate culture of dignity as an interdisciplinary determinant of organizational behavior that operates at the intersection of IT engineering, cognitive science, behavioral economics and knowledge management. The analytical focus is on the impact of cultural variables on cognitive distortions in strategic decision-making, as well as on the dynamics of technical and social debt in IT companies.

The problem to be solved is the absence of a holistic cognitive-behavioral model that would describe the mechanisms of the transformative impact of a culture of dignity on organizational biases and structural inefficiencies in engineering systems. Existing approaches largely ignore the relationship between managerial ethics, team interaction architecture, and the cognitive ecology of decision-making.

The research methodology included a critical analysis of theoretical sources, the development of the author's analytical model, and a content analysis of cases of three global technology companies (Spotify, Google, Airbnb). A qualitative analysis of corporate practices and the content of open reports revealed a strong correlation between a high level of transparency, autonomy, psychological safety and feedback in organizations with a strong culture of dignity and a reduction in the frequency of cognitive distortions and the pace of technical debt elimination. The data are the result of analytical generalization rather than empirical quantitative research. Estimates show that such organizations demonstrate an acceleration in the pace of technical debt reduction by 1520% compared to those without established feedback practices.

The practical significance of research lies in the possibility of using the results to develop organizational development policies, training programmes for IT team leaders, strategic management systems and technical debt audits.

The findings contribute to the expansion of theoretical understanding of the role of humanistic factors in high-tech management and have the potential to implement the UN Sustainable Development Goals, in particular in terms of decent work, inclusive governance and innovation sustainability.

Author Biographies

Tetiana Korobkina, Kharkiv National University of Radio Electronics

Doctor of Philosophical Science, Professor

Department of Philosophy

Natalia Dashenkova, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Philosophy

Iryna Danchenko, State Biotechnological University

Doctor of Pedagogic Sciences, Professor

Department of the UNESCO “Philosophy of Human Communication” and Social and Humanitarian Disciplines

Halyna Omelchenko, State Biotechnological University

PhD, Associate Professor

Department of Tourism

References

  1. Mohanani, R., Salman, I., Turhan, B., Rodriguez, P., Ralph, P. (2020). Cognitive Biases in Software Engineering: A Systematic Mapping Study. IEEE Transactions on Software Engineering, 46 (12), 1318–1339. https://doi.org/10.1109/tse.2018.2877759
  2. Chattopadhyay, S., Nelson, N., Au, A., Morales, N., Sanchez, C., Pandita, R., Sarma, A. (2022). Cognitive biases in software development. Communications of the ACM, 65 (4), 115–122. https://doi.org/10.1145/3517217
  3. Máté, D., Kiss, J. T., Csernoch, M. (2025). Cognitive biases in user experience and spreadsheet programming. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13392-0
  4. Borowa, K., Kamoda, S., Ogrodnik, P., Zalewski, A. (2023). Fixations in Agile Software Development Teams. Foundations of Computing and Decision Sciences, 48 (1), 3–18. https://doi.org/10.2478/fcds-2023-0001
  5. Paulus, D., de Vries, G., Janssen, M., Van de Walle, B. (2022). The influence of cognitive bias on crisis decision-making: Experimental evidence on the comparison of bias effects between crisis decision-maker groups. International Journal of Disaster Risk Reduction, 82, 103379. https://doi.org/10.1016/j.ijdrr.2022.103379
  6. Drury-Grogan, M. L., O’Dwyer, O. (2013). An investigation of the decision-making process in agile teams. International Journal of Information Technology & Decision Making, 12 (6), 1097–1120. https://doi.org/10.1142/s0219622013400105
  7. Berthet, V. (2022). The Impact of Cognitive Biases on Professionals’ Decision-Making: A Review of Four Occupational Areas. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.802439
  8. Paulus, D., Fathi, R., Fiedrich, F., de Walle, B. V., Comes, T. (2022). On the Interplay of Data and Cognitive Bias in Crisis Information Management. Information Systems Frontiers, 26 (2), 391–415. https://doi.org/10.1007/s10796-022-10241-0
  9. Edmondson, A. C., Lei, Z. (2014). Psychological Safety: The History, Renaissance, and Future of an Interpersonal Construct. Annual Review of Organizational Psychology and Organizational Behavior, 1 (1), 23–43. https://doi.org/10.1146/annurev-orgpsych-031413-091305
  10. Alkalha, Z., Jum’a, L., Zighan, S., Abualqumboz, M. (2025). A multi-faceted approach for leveraging AI and intellectual capital for enhanced supply chain decision-making. Journal of Intellectual Capital, 26 (2), 491–525. https://doi.org/10.1108/jic-07-2024-0201
  11. Tamburri, D. A., Kruchten, P., Lago, P., Vliet, H. van. (2015). Social debt in software engineering: insights from industry. Journal of Internet Services and Applications, 6 (1). https://doi.org/10.1186/s13174-015-0024-6
  12. Martini, A., Stray, V., Moe, N. B.; Hoda, R. (Ed.) (2019). Technical-, Social- and Process Debt in Large-Scale Agile: An Exploratory Case-Study. Agile Processes in Software Engineering and Extreme Programming – Workshops. XP 2019. Lecture Notes in Business Information Processing, vol 364. Cham: Springer, 112–119. https://doi.org/10.1007/978-3-030-30126-2_14
  13. Saeeda, H., Ovais Ahmad, M., Gustavsson, T. (2024). Navigating social debt and its link with technical debt in large-scale agile software development projects. Software Quality Journal, 32 (4), 1581–1613. https://doi.org/10.1007/s11219-024-09688-y
  14. Besker, T., Ghanbari, H., Martini, A., Bosch, J. (2020). The influence of Technical Debt on software developer morale. Journal of Systems and Software, 167, 110586. https://doi.org/10.1016/j.jss.2020.110586
  15. Kniberg, H. (2014). Spotify engineering culture (Part 1). Try Spotify portal. Available at: https://engineering.atspotify.com/2014/03/spotify-engineering-culture-part-1/
  16. Pretty, N. (2024). Project Aristotle: Google’s Data-Driven Insights on High-Performing Teams. Avaialble at: https://www.aristotleperformance.com/post/project-aristotle-google-s-data-driven-insights-on-high-performing-teams
  17. Martines, K. S., O’Donnell, S., Yuan, L.-H., Zhu, Y. (2025). How Airbnb Measures Listing Lifetime Value. The Airbnb Tech Blog. Avaialble at: https://medium.com/airbnb-engineering/how-airbnb-measures-listing-lifetime-value-a603bf05142c
  18. Tamburri, D. A., Kruchten, P., Lago, P., van Vliet, H. (2013). What is social debt in software engineering? 2013 6th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), 93–96. https://doi.org/10.1109/chase.2013.6614739
The impact of corporate culture of dignity on cognitive biases, strategic decision-making and technical debt management in IT engineering

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Published

2025-05-16

How to Cite

Korobkina, T., Dashenkova, N., Danchenko, I., & Omelchenko, H. (2025). The impact of corporate culture of dignity on cognitive biases, strategic decision-making and technical debt management in IT engineering. Technology Audit and Production Reserves, 3(4(83), 6–13. https://doi.org/10.15587/2706-5448.2025.329635

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Economics and Enterprise Management