Research Article
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Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment

Year 2025, Volume: 8 Issue: 1, 112 - 122, 28.03.2025

Adel Alomairi , Abdullahi Abdu Ibrahim

https://doi.org/10.35377/saucis...1488149

Abstract

The present paper has been devoted to the study conducted with the purpose of examining the possibility of applying Machine Learning techniques in classifying leadership based on structured survey data. The objective was to create a predictive model that would allow classifying leadership into three groups – Low, Medium, and High – based on behavior scores. The model was expected to offer a reliable tool for improving leadership development programs and recruitment processes by providing a precise and scalable leadership classification, The study illustrates the potential of advanced ML techniques for rethinking the traditional approaches to the assessment of leadership. Due to the use of advanced ensemble modeling, it was possible to ensure the high accuracy of 93.3% in leadership predicting. Such outcomes can generate considerable advantages for organizational development strategies. The use of ensemble machine learning in the domain of organizational behavior studies can be considered as a valuable academic contribution as it has demonstrated the capacity of determining the application of ensemble techniques for enhancing leadership studies. at the same time, it offers a useful instrument to develop more sophisticated and data-driven practices for leadership development.

Keywords

Machine learning Leadership style classification Ensemble learning Predictive Analytics in HR Quantitative leadership evaluation AI in organizational development

References

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  • C. Beyan, F. Capozzi, C. Becchio, and V. Murino, “Prediction of the leadership style of an emergent leader using audio and visual nonverbal features,” IEEE Trans Multimedia, vol. 20, no. 2, pp. 441–456, Feb. 2018, doi: 10.1109/TMM.2017.2740062.
  • B. Chen, X. Chen, and H. Chen, “Construction of inclusive leadership knowledge graph based on Citespace and WOS core database,” Proceedings - 2022 International Conference on Machine Learning and Knowledge Engineering, MLKE 2022, pp. 337–340, 2022, doi: 10.1109/MLKE55170.2022.00071.
  • S. Pongpaichet, K. Nirunwiroj, and S. Tuarob, “Automatic Assessment and Identification of Leadership in College Students,” IEEE Access, vol. 10, pp. 79041–79060, 2022, doi: 10.1109/ACCESS.2022.3193935.

Year 2025, Volume: 8 Issue: 1, 112 - 122, 28.03.2025

Adel Alomairi , Abdullahi Abdu Ibrahim

https://doi.org/10.35377/saucis...1488149

Abstract

References

  • A. Siikaluoma, “LEADERSHIP PRACTICES SHAPED BY DIGITALIZATION”. 2020, http://www.theseus.fi/handle/10024/343550
  • A. Barthakur, V. Kovanovic, S. Joksimovic, Z. Zhang, M. Richey, and A. Pardo, “Measuring leadership development in workplace learning using automated assessments: Learning analytics and measurement theory approach,” British Journal of Educational Technology, vol. 53, no. 6, pp. 1842–1863, Nov. 2022, doi: 10.1111/BJET.13218.
  • L. Wei, “Genetic Algorithm Optimization of Concrete Frame Structure Based on Improved Random Forest,” in 2023 International Conference on Electronics and Devices, Computational Science (ICEDCS), IEEE, Sep. 2023, pp. 249–253. doi: 10.1109/ICEDCS60513.2023.00051.
  • K. Chen, H. Yao, and Z. Han, “Arithmetic optimization algorithm to optimize support vector machine for chip defect Identification,” in 2022 28th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), IEEE, Nov. 2022, pp. 1–5. doi: 10.1109/M2VIP55626.2022.10041106.
  • M. Kaur, C. Thacker, L. Goswami, T. TR, I. S. Abdulrahman, and A. S. Raj, “Alzheimer’s Disease Detection using Weighted KNN Classifier in Comparison with Medium KNN Classifier with Improved Accuracy,” in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE, May 2023, pp. 715–718. doi: 10.1109/ICACITE57410.2023.10183208.
  • Y. Fan and W. Lei, “Wind Speed Prediction Based on Gradient Boosting Decision Tree,” in 2022 International Conference on Big Data, Information and Computer Network (BDICN), IEEE, Jan. 2022, pp. 93–97. doi: 10.1109/BDICN55575.2022.00025.
  • J. Faria, S. M. Azmat Ullah, and Md. R. Hasan, “Stroke Detection Through Ensemble Learning: A Stacking Approach,” in 2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS), IEEE, Mar. 2024, pp. 01–06. doi: 10.1109/iCACCESS61735.2024.10499584.
  • M. Yang, N. Slam, and Z. Zheng, “A Classification Model of Urban Fire Level with Stacking Ensemble Learning,” in 2023 IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE), IEEE, Dec. 2023, pp. 22–26. doi: 10.1109/ICEACE60673.2023.10442652.
  • K. Kim and J. Jeong, “Multi-layer Stacking Ensemble for Fault Detection Classification in Hydraulic System,” in 2022 26th International Conference on Circuits, Systems, Communications and Computers (CSCC), IEEE, Jul. 2022, pp. 341–346. doi: 10.1109/CSCC55931.2022.00066.
  • V. N. Vasu, Surendran. R, Saravanan. M. S, and Madhusundar. N, “Prediction of Defective Products Using Logistic Regression Algorithm against Linear Regression Algorithm for Better Accuracy,” in 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), IEEE, Nov. 2022, pp. 161–166. doi: 10.1109/3ICT56508.2022.9990653.
  • “How AI Is Transforming the Organization | MIT Press eBooks | IEEE Xplore.” Accessed: Apr. 09, 2023. [Online]. Available: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/book/9072232
  • M. Sarstedt, “Revisiting Hair Et al.’s Multivariate Data Analysis: 40 Years Later,” The Great Facilitator, pp. 113–119, 2019, doi: 10.1007/978-3-030-06031-2_15.
  • “REGRESSION ANALYSIS: EFFECTS OF LEADERSHIP STYLES ON ORGANIZATIONAL... | Download Table.” Accessed: Apr. 09, 2023. [Online]. Available: https://www.researchgate.net/figure/REGRESSION-ANALYSIS-EFFECTS-OF-LEADERSHIP-STYLES-ON-ORGANIZATIONAL-CULTURE_tbl4_272011654
  • “Item Response Theory - Susan E. Embretson, Steven P. Reise - Google Books.” Accessed: Apr. 09, 2023. [Online]. Available: https://books.google.com.tr/books?hl=en&lr=&id=9Xm0AAAAQBAJ&oi=fnd&pg=PR1&dq=Embretson,+S.+E.,+%26+Reise,+S.+P.+(2013).+Item+response+theory+(2nd+ed.).+Psychology+Press&ots=Ec6UUtKXZi&sig=lX_g94yV0Phd1FMm1NO5mTusfok&redir_esc=y#v=onepage&q=Embretson%2C%20S.%20E.%2C%20%26%20Reise%2C%20S.%20P.%20(2013).%20Item%20response%20theory%20(2nd%20ed.).%20Psychology%20Press&f=false
  • S. Oltedal and T. Rundmo, “Using cluster analysis to test the cultural theory of risk perception,” Transp Res Part F Traffic Psychol Behav, vol. 10, no. 3, pp. 254–262, May 2007, doi: 10.1016/J.TRF.2006.10.003.
  • G. S. Insch, J. E. Moore, and L. D. Murphy, “Content analysis in leadership research: Examples, procedures, and suggestions for future use,” Leadersh Q, vol. 8, no. 1, pp. 1–25, Jan. 1997, doi: 10.1016/S1048-9843(97)90028-X.
  • B. M. Doornenbal, B. R. Spisak, and P. A. van der Laken, “Opening the black box: Uncovering the leader trait paradigm through machine learning,” Leadership Quarterly, vol. 33, no. 5, Oct. 2022, doi: 10.1016/j.leaqua.2021.101515.
  • E. Deopersaud and A. Capstone, “Natural Language Processing: Distinguishing Employee Views Toward Leadership,” 2022.
  • Y. Zhang, S. Xu, L. Zhang, and M. Yang, “Big data and human resource management research: An integrative review and new directions for future research,” J Bus Res, vol. 133, pp. 34–50, Sep. 2021, doi: 10.1016/J.JBUSRES.2021.04.019.
  • S. Bekesiene and S. Hoskova-Mayerova, “Decision tree-based classification model for identification of effective leadership indicators,” Journal of Mathematical and Fundamental Sciences, vol. 50, no. 2, pp. 121–141, 2018, doi: 10.5614/J.MATH.FUND.SCI.2018.50.2.2.
  • S. Bekesiene, Š. Hošková-Mayerová, and P. Diliunas, “Identification of effective leadership indicators in the Lithuania army forces,” Studies in Systems, Decision and Control, vol. 104, pp. 107–122, 2019, doi: 10.1007/978-3-319-54819-7_9.
  • C. Beyan, F. Capozzi, C. Becchio, and V. Murino, “Prediction of the leadership style of an emergent leader using audio and visual nonverbal features,” IEEE Trans Multimedia, vol. 20, no. 2, pp. 441–456, Feb. 2018, doi: 10.1109/TMM.2017.2740062.
  • B. Chen, X. Chen, and H. Chen, “Construction of inclusive leadership knowledge graph based on Citespace and WOS core database,” Proceedings - 2022 International Conference on Machine Learning and Knowledge Engineering, MLKE 2022, pp. 337–340, 2022, doi: 10.1109/MLKE55170.2022.00071.
  • S. Pongpaichet, K. Nirunwiroj, and S. Tuarob, “Automatic Assessment and Identification of Leadership in College Students,” IEEE Access, vol. 10, pp. 79041–79060, 2022, doi: 10.1109/ACCESS.2022.3193935.
There are 24 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Adel Alomairi 0000-0002-4125-8361

Abdullahi Abdu Ibrahim 0000-0001-9145-1939

Early Pub Date March 27, 2025
Publication Date March 28, 2025
Submission Date May 22, 2024
Acceptance Date March 13, 2025
Published in Issue Year 2025Volume: 8 Issue: 1

Cite

APA Alomairi, A., & Ibrahim, A. A. (2025). Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment. Sakarya University Journal of Computer and Information Sciences, 8(1), 112-122. https://doi.org/10.35377/saucis...1488149
AMA Alomairi A, Ibrahim AA. Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment. SAUCIS. March 2025;8(1):112-122. doi:10.35377/saucis.1488149
Chicago Alomairi, Adel, and Abdullahi Abdu Ibrahim. “Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment”. Sakarya University Journal of Computer and Information Sciences 8, no. 1 (March 2025): 112-22. https://doi.org/10.35377/saucis. 1488149.
EndNote Alomairi A, Ibrahim AA (March 1, 2025) Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment. Sakarya University Journal of Computer and Information Sciences 8 1 112–122.
IEEE A. Alomairi and A. A. Ibrahim, “Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment”, SAUCIS, vol. 8, no. 1, pp. 112–122, 2025, doi: 10.35377/saucis...1488149.
ISNAD Alomairi, Adel - Ibrahim, Abdullahi Abdu. “Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment”. Sakarya University Journal of Computer and Information Sciences 8/1 (March 2025), 112-122. https://doi.org/10.35377/saucis. 1488149.
JAMA Alomairi A, Ibrahim AA. Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment. SAUCIS. 2025;8:112–122.
MLA Alomairi, Adel and Abdullahi Abdu Ibrahim. “Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 1, 2025, pp. 112-2, doi:10.35377/saucis. 1488149.
Vancouver Alomairi A, Ibrahim AA. Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment. SAUCIS. 2025;8(1):112-2.
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