{"title":"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","authors":"Hyunho Kim, Eunmi Lee, S. Cha","doi":"10.4018/ijdwm.349226","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing Cadet Squad Organizational Satisfaction by Integrating Leadership Factor Data Mining and Integer Programming\",\"authors\":\"Hyunho Kim, Eunmi Lee, S. Cha\",\"doi\":\"10.4018/ijdwm.349226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":54963,\"journal\":{\"name\":\"International Journal of Data Warehousing and Mining\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2024-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Data Warehousing and Mining\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.4018/ijdwm.349226\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Warehousing and Mining","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.4018/ijdwm.349226","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
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.
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