How Jungian Cognitive Functions Explain MBTI Type Prevalence in Computer Industry Careers
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Abstract (arXiv)
This study investigates the relationship between Carl Jung's cognitive functions and success in computer industry careers by analyzing the distribution of Myers-Briggs Type Indicator (MBTI) types among professionals in the field. Building on Carl Jung's theory of psychological types, which categorizes human cognition into four primary functions, Sensing, Intuition, Thinking, and Feeling, this study investigates how these functions, when combined with the attitudes of Extraversion and Introversion, influence personality types and career choices in the tech sector. Through a comprehensive analysis of data from 30 studies spanning multiple countries and decades, encompassing 18,264 individuals in computer-related professions, we identified the most prevalent cognitive functions and their combinations. After normalizing the data against general population distributions, our findings showed that individual Jungian functions (Te, Ni, Ti, Ne), dual function combinations (Ni-Te, Ti-Ne, Si-Te, Ni-Fe), and MBTI types (INTJ, ENTJ, INTP, ENTP, ISTJ, INFJ, ESTJ, ESTP) had significantly higher representation compared to general population norms. The paper addresses gaps in the existing literature by providing a more nuanced understanding of how cognitive functions impact job performance and team dynamics, offering insights for career guidance, team composition, and professional development in the computer industry, and a deeper understanding of how cognitive preferences influence career success in technology-related fields.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Apr 2025.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulationcited by 2
- 2026Structured Personality Control and Adaptation for LLM Agentscited by 1
- 2026MentalGame: Predicting Personality-Job Fitness for Software Developers Using Multi-Genre Games and Machine Learning Approachescited by 0
- 2026Structured Personality Control and Adaptation for LLM Agents15 Jan 2026
- 2026MentalGame: Predicting Personality-Job Fitness for Software Developers Using Multi-Genre Games and Machine Learning Approaches3 Jan 2026
- 2025PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation22 Dec 2025