Analysis of the Relationship Between Understanding Mathematical Logic and Managerial Decision-Making Effectiveness

Authors

  • Marchesa Ruisli Bank Negara Indonesia
  • Bayu Laksma Pradana Pradita University
  • Goenawan Brotosaputro ISB Atma Luhur
  • Santiago Ramirez Ilearning Incorporation

DOI:

https://doi.org/10.33050/atm.v9i1.2407

Keywords:

Mathematical Logic, Managerial Decision-Making, Propositional Logic, Predicate Logic, Logical Reasoning Frameworks

Abstract

Managerial decision-making, a cornerstone of organizational success, often relies on logical reasoning to address complex scenarios and develop effective strategies, forming the basis of this research. While logical reasoning has been widely recognized, the integration of mathematical logic as a foundational tool for enhancing decision-making effectiveness has remained underexplored. This study investigates how understanding mathematical logic, particularly propositional and predicate logic, impacts managerial capabilities in analyzing problems, formulating strategies, and implementing decisions. To achieve this, the study employs a quantitative approach, utilizing a survey distributed to 150 managers across diverse industries, with data analyzed using Structural Equation Modeling (SEM) to identify relationships between variables. The findings demonstrate the practical applications of mathematical logic, including its utility in strategic planning for technology firms, operational optimization in manufacturing, and improving decision-making frameworks in healthcare. These insights lead to the understanding that mathematical logic is a valuable tool for strengthening managerial decision-making processes, offering significant theoretical and practical contributions to organizational practices.

Downloads

Download data is not yet available.

References

J. W. Creswell and J. D. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th ed. Thousand Oaks, CA, USA: SAGE Publications, 2021.

W. G. Zikmund, B. J. Babin, J. C. Carr, and M. Griffin, Business Research Methods, 10th ed. Mason, OH, USA: Cengage Learning, 2021.

D. A. Yusuf, R. W. Anugrah, M. A. Komara, D. Julianingsih, and E. Garcia, “Leveraging blockchain technology to strengthen cybersecurity in financial transactions: A comprehensive analysis,” CORISINTA, vol. 1, no. 2, pp. 119–125, 2024.

T. S. Edwards, A. M. Carter, and L. F. Diaz, “Using logical reasoning to improve managerial decision- making processes,” Journal of Management Studies, vol. 57, no. 5, pp. 1125–1137, 2021.

S. Brown and R. Taylor, “Impact of logical reasoning skills on strategic decision-making in organizations,”

Decision Science Journal, vol. 52, no. 3, pp. 215–230, 2021.

H. T. Nguyen and D. A. Walker, “Fuzzy logic in risk management: A practical approach,” International Journal of Risk Analysis, vol. 38, no. 1, pp. 15–25, 2021.

D. Kahneman, O. Sibony, and C. R. Sunstein, Noise: A Flaw in Human Judgment. New York, NY, USA: Little, Brown, 2021.

J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 9th ed. Upper Saddle River, NJ, USA: Pearson Education, 2021.

R. Thomas and K. Green, “The role of propositional logic in modern decision-support systems,” European Journal of Information Systems, vol. 30, no. 2, pp. 155–168, 2021.

M. Johnson and H. Carter, “Mathematical reasoning in business decision-making,” Journal of Business Research, vol. 134, pp. 42–54, 2021.

A. Bryman and E. Bell, Business Research Methods, 5th ed. Oxford, U.K.: Oxford University Press, 2021.

P. Johnson, J. Clark, and L. H. Smith, “Evaluating the effectiveness of mathematical logic in managerial decisions,” Journal of Applied Management Studies, vol. 18, no. 4, pp. 234–245, 2021.

F. Davis and A. Green, “Decision-making frameworks: Bridging logical reasoning and managerial out- comes,” Academy of Management Review, vol. 45, no. 1, pp. 10–25, 2021.

S. Gupta and A. Patel, “Applications of fuzzy logic in organizational strategy,” Journal of Operational Research, vol. 68, no. 3, pp. 198–210, 2021.

J. M. George and D. R. Jones, “A structural equation modeling approach to managerial decision effective- ness,” International Journal of Management Studies, vol. 59, no. 1, pp. 75–92, 2021.

U. Rusilowati, H. R. Ngemba, R. W. Anugrah, A. Fitriani, and E. D. Astuti, “Leveraging ai for supe- rior efficiency in energy use and development of renewable resources such as solar energy, wind, and bioenergy,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 114–120, 2024.

A. Syntetos and K. Nikolopoulos, “Management, mathematics, and management-mathematics: strengthening the link in a turbulent post-pandemic world,” IMA Journal of Management Mathematics, vol. 35, no. 1, pp. 1–3, 2024. [Online]. Available: https://academic.oup.com/imaman/article-abstract/35/ 1/1/7329940

R. Royani, S. D. Maulina, S. Sugiyono, R. W. Anugrah, and B. Callula, “Recent developments in health- care through machine learning and artificial intelligence,” IAIC Transactions on Sustainable Digital In- novation (ITSDI), vol. 6, no. 1, pp. 86–94, 2024.

C. M. Ringle, S. Wende, and J. M. Becker, SmartPLS 3, SmartPLS GmbH, Boenningstedt, Germany, 2021.

Szukits, “The illusion of data-driven decision making – the mediating effect of digital orientation and controllers’ added value in explaining organizational implications of advanced analytics,” Journal of Management Control, vol. 33, pp. 403–446, 2022. [Online]. Available: https://link.springer.com/article/10.1007/s00187-022-00343-w

S. Greco and R. Słowin´ski, “Representation of preferences for multiple criteria decision aiding in a new seven-valued logic,” arXiv preprint arXiv:2406.03501, 2024. [Online]. Available: https://arxiv.org/abs/2406.03501

R. Ahli, M. F. Hilmi, and A. Abudaqa, “The influence of leadership dynamics and workplace stress on employee performance in the entrepreneurial sector and the moderating role of organizational support,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 300–313, 2024.

Markovic´, S. Vandevelde, L. Vanbesien, J. Vennekens, and M. Denecker, “An epistemic logic for modeling decisions in the context of incomplete knowledge,” arXiv preprint arXiv:2312.11186, 2023. [Online]. Available: https://arxiv.org/abs/2312.11186

S. Brown, M. Pereira, and I. Guvlor, “Implementation of artificial intelligence framework to enhance human resources competency in indonesia,” International Journal of Cyber and IT Service Management, vol. 4, no. 1, pp. 65–71, 2024.

O. Dalkılıc¸, “A decision-making approach to reduce the margin of error of decision makers for bipolar soft set theory,” International Journal of Systems Science, vol. 53, no. 2, pp. 265–274, 2022.

Downloads

Published

2025-01-29

How to Cite

Ruisli, M., Laksma Pradana, B., Brotosaputro, G., & Ramirez, S. (2025). Analysis of the Relationship Between Understanding Mathematical Logic and Managerial Decision-Making Effectiveness. APTISI Transactions on Management, 9(1), 40–49. https://doi.org/10.33050/atm.v9i1.2407

Most read articles by the same author(s)