通过整合领导力因素数据挖掘和整数编程优化学员队组织满意度

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING International Journal of Data Warehousing and Mining Pub Date : 2024-07-17 DOI:10.4018/ijdwm.349226
Hyunho Kim, Eunmi Lee, S. Cha
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引用次数: 0

摘要

军校学员居住在由学员组织的大队中。尽管班组很重要,但传统上班组的组织都是基于四年级班长的个人喜好,而不考虑班组成员的兼容性。本研究提出了一种更科学的方法,以提高学员对班组的满意度,促进他们的领导力发展。首先,进行了多元线性回归分析,以确定对班级组织满意度有显著影响的班长领导力因素。该模型最大化了班长之间的因子得分之和,以提高班级组织满意度;最大化了因子得分之差,以提高领导纪律的有效性。在韩国军事学院学员的数据中应用班级编组算法后发现,与现有的班级编组方法相比,班级组织满意度和领导纪律有效性都有显著提高。
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Optimizing Cadet Squad Organizational Satisfaction by Integrating Leadership Factor Data Mining and Integer Programming
Military academy cadets reside in a brigade organized by cadets. Despite its importance, squads have traditionally been organized based on the personal preferences of the fourth-year squad leader without considering the compatibility of the squad members. This study proposes a more scientific approach to increase cadet satisfaction with their squads and foster their leadership development. Initially, a multiple linear regression analysis was conducted to identify the leadership factors of squad leaders that significantly affect squad organizational satisfaction. The model maximized the sum of the factor scores among squad leaders to enhance squad organizational satisfaction and maximized the difference in factor scores to improve the effectiveness of leadership discipline. Applying the squad formation algorithm to data from cadets at the Korea Military Academy revealed that the squad organizational satisfaction and leadership discipline effectiveness were significantly increased compared to the existing squad formation methods.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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