Analyzing Criteria Affecting Decision-Making Processes of Human Resource Management in the Aviation Sector - A Fuzzy Logic Approach

Abstract

In today's fast-paced and ever-changing business landscape, effective decision-making is paramount to achieving success and maintaining a competitive edge. This holds particularly true in the aviation sector, where Human Resource Management (HRM) plays a pivotal role in optimizing workforce performance and ensuring operational efficiency. However, HRM decision-making processes are often confronted with multifaceted challenges that encompass various criteria and encompass both objective and subjective factors. To tackle this complexity, a novel and adaptive approach is needed. In this study, we employ a Fuzzy Logic Approach to analyze the criteria influencing decision-making processes in HRM within the aviation sector, aiming to provide a comprehensive and flexible decision-support system for HRM practitioners and contribute to the sector's overall performance and success. The contribution of this study lies in its innovative application of Fuzzy Logic to HRM decision-making in the aviation sector. By capturing the inherent uncertainties and vagueness that HRM practitioners encounter, the proposed Fuzzy Logic-based model offers a more robust and context-sensitive decision-support system. Based on the Fuzzy Logic application and sensitivity analysis, the findings reveal the significance of employee satisfaction as the most influential criterion in HRM decision-making within the aviation sector. The Fuzzy Logic model demonstrated a strong positive correlation between high employee satisfaction levels and favorable HRM Decision Outcomes. This finding emphasizes the pivotal role of employee satisfaction in shaping HRM strategies and outcomes within aviation organizations.

Keywords

• Human Resource Management
• Aviation
• Fuzzy Logic

References

1. Armstrong, M., & Taylor, S. (2023). Armstrong's Handbook of Human Resource Management Practice: A Guide to the Theory and Practice of People Management. Kogan Page Publishers.
2. Benkarim, A., & Imbeau, D. (2022). Exploring Lean HRM Practices in the Aerospace Industry. Sustainability, 14(9), 5208
3. Bhagyalakshmi, R., & Maria, E. F. (2021). Artificial intelligence and HRM: an empirical study on decision-making skills of HR through AI in HRM Practices. Annals of the Romanian Society for Cell Biology, 25(6), 11568-11578.
4. Çakır, E., & Ulukan, Z. (2021). Digitalization on Aviation 4.0: Designing a Scikit-Fuzzy control system for in-flight catering customer satisfaction. In Intelligent and Fuzzy Techniques in Aviation 4.0: Theory and Applications (pp. 123-146). Cham: Springer International Publishing.
5. de Andreis, F., Comite, U., Sottoriva, F. M., & Cova, I. (2022, October). Human resources management and training in aviation. In International Conference on Reliability and Statistics in Transportation and Communication (pp. 510-522). Cham: Springer International Publishing.
6. Demirel, Z., & Çubukçu, C. (2021). Measurement of employees on human resources with fuzzy logic. EMAJ: Emerging Markets Journal, 11(2), 1-7.
7. Dožić, S. (2019). Multi-criteria decision making methods: Application in the aviation industry. Journal of Air Transport Management, 79, 101683.
8. Gelard, P., Naghavi, S., & Mohebbi, S. (2022). Designing the Performance Management Model for Employees in the National Aviation Industry. Journal of Research in Human Resources Management, 14(2), 155-181.

9. Harvey, G., & Turnbull, P. (2020). Ricardo flies Ryanair: Strategic human resource management and competitive advantage in a Single European Aviation Market. Human Resource Management Journal, 30(4), 553-565.
10. Hendiani, S., & Bagherpour, M. (2019). Developing an integrated index to assess social sustainability in construction industry using fuzzy logic. Journal of cleaner production, 230, 647-662.
11. Kimseng, T., Javed, A., Jeenanunta, C., & Kohda, Y. (2020). Applications of fuzzy logic to reconfigure human resource management practices for promoting product innovation in formal and non-formal R&D firms. Journal of Open Innovation: Technology, Market, and Complexity, 6(2), 38.
12. Kizilcan, S., & Mizrak, K. C. (2022). An Airline Application on Burnout Syndrome in Pilots. Fiscaoeconomia, 6(2), 895-908.
13. Lasisi, T. T., Ozturen, A., Eluwole, K. K., & Avci, T. (2020). Explicating innovation-based human resource management's influence on employee satisfaction and performance. Employee Relations: The International Journal, 42(6), 1181-1203.
14. Mardani, A., Jusoh, A., Nor, K., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications–a review of the literature from 2000 to 2014. Economic research-Ekonomska istraživanja, 28(1), 516-571.
15. Mizrak, K. C. (2021). A Research on Effect of Performance Evaluation and Efficiency on Work Life. In Management Strategies to Survive in a Competitive Environment: How to Improve Company Performance (pp. 387-400). Cham: Springer International Publishing.
16. Nghiem, T. L., Dinh, T. H., & Nguyen, T. L. (2022). A Fuzzy Logic Approach to Career Orientation for Students: A Case Study in Human Resource Management. Global Changes and Sustainable Development in Asian Emerging Market Economies Vol. 1: Proceedings of EDESUS 2019, 225-239.
17. Nedosekin, A., Abdoulaeva, Z., Konnikov, E., & Zhuk, A. (2020). Fuzzy set models for economic resilience estimation. Mathematics, 8(9), 1516.
18. Nghiem, T. L., Dinh, T. H., & Nguyen, T. L. (2022). A Fuzzy Logic Approach to Career Orientation for Students: A Case Study in Human Resource Management. Global Changes and Sustainable Development in Asian Emerging Market Economies Vol. 1: Proceedings of EDESUS 2019, 225-239.
19. Papis, M., & Matyjewski, M. (2019). The use of fuzzy logic elements for the risk analysis in aviation. Journal of KONBiN, 49(2), 31-53.
20. Philip, K., & Arrowsmith, J. (2021). The limits to employee involvement? Employee participation without HRM in a small not-for-profit organisation. Personnel Review, 50(2), 401-419.
21. Pislaru, M., Herghiligiu, I. V., & Robu, I. B. (2019). Corporate sustainable performance assessment based on fuzzy logic. Journal of cleaner production, 223, 998-1013.
22. Qi, J., Ding, L., & Lim, S. (2023). Application of a decision-making framework for multi-objective optimisation of urban heat mitigation strategies. Urban Climate, 47, 101372.
23. Santhosh, R., & Mohanapriya, M. (2021). Generalized fuzzy logic based performance prediction in data mining. Materials Today: Proceedings, 45, 1770-1774.
24. Singh, K. V., Bansal, H. O., & Singh, D. (2020). Feed-forward modeling and real-time implementation of an intelligent fuzzy logic-based energy management strategy in a series–parallel hybrid electric vehicle to improve fuel economy. Electrical Engineering, 102, 967-987.
25. Spolaor, S., Fuchs, C., Cazzaniga, P., Kaymak, U., Besozzi, D., & Nobile, M. S. (2020). Simpful: a user-friendly python library for fuzzy logic. International Journal of Computational Intelligence Systems, 13(1), 1687-1698.
26. Şimşek, H., Güvendiren, İ., & Sarı, Ş. (2022). Determining the customer satisfaction index for civil aviation organizations based on fuzzy logic and servqual method. International Capital Conference on Multidisciplinary Scientific Research.
27. Papis, M., & Matyjewski, M. (2019). The use of fuzzy logic elements for the risk analysis in aviation. Journal of KONBiN, 49(2), 31-53.
28. Philip, K., & Arrowsmith, J. (2021). The limits to employee involvement? Employee participation without HRM in a small not-for-profit organisation. Personnel Review, 50(2), 401-419.
29. Pislaru, M., Herghiligiu, I. V., & Robu, I. B. (2019). Corporate sustainable performance assessment based on fuzzy logic. Journal of cleaner production, 223, 998-1013.
30. Talukdar, S., Naikoo, M. W., Mallick, J., Praveen, B., Sharma, P., Islam, A. R. M. T., ... & Rahman, A. (2022). Coupling geographic information system integrated fuzzy logic-analytical hierarchy process with global and machine learning based sensitivity analysis for agricultural suitability mapping. Agricultural Systems, 196, 103343.
31. Türk.A & Kavraz, Z. M. (2021) The Role of Talent Management in Human Resources Management: A Qualitative Research in Aviation Industry.
32. Turk, A., Cevher, M. F., & Mizrak, K. C. (2021). The effect of informal relations and executive support on organizational commitment in the aviation cector. International Journal of Innovative Science and Research Technology, 6(2), 243-253.
33. Vaishnavi, V., & Suresh, M. (2021). Assessment of readiness level for implementing lean six sigma in healthcare organization using fuzzy logic approach. International Journal of Lean Six Sigma, 12(2), 175-209.
34. Yalim, F., & Mizrak, K. C. (2021). Organizational Culture As A Pioneer Of Organizational Identification: A Research On Aviation Sector. Nevşehir Hacı Bektaş Veli Üniversitesi Sbe Dergisi, 11(2), 737-759.
35. Ziyadin, S., Borodin, A., Streltsova, E., Suieubayeva, S., & Pshembayeva, D. (2019). Fuzzy logic approach in the modeling of sustainable tourism development management. Polish Journal of management studies, 19(1), 492-504.