Cluster Analysis Based on McKinsey 7s Framework in Improving University Services

Deny Jollyta, Dwi Oktarina, Gusrianty, R. Astri, Lina Arliana, Nurettin Kadi̇m, Ni Gusti, Ayu Dasriani, Dharma Andalas
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引用次数: 1

Abstract

The epidemic of Covid-19 has impacted all aspects of human life, including education. Academic and administrative services for academic community are suffering, as a result of the fact that not all universities are able to provide online services to help break the chain of Covid-19 distribution. This is due to a lack of human competencies to use technology and a lack of information technology resources, necessitating the development of new strategies by universities to address these flaws. The goal of this study is to develop a university service strategy based on McKinsey 7s cluster results on the part that is having issues based on questionnaire data. The questionnaire is organized on seven McKinsey elements. The Manhattan distance calculation and the K-Medoids algorithm results demonstrated that the structure, system, skill and staff are all part of elements that clustered in k=2 and has to be addressed in aiding services during the Covid-19 pandemic. The McKinsey 7s showed that universities service enhancements may be achieved by combining clustering techniques and McKinsey framework.
基于麦肯锡7s框架的大学服务改进聚类分析
新冠肺炎疫情影响了人类生活的方方面面,包括教育。由于并非所有大学都能提供在线服务,以帮助打破新冠病毒的传播链,学术界的学术和行政服务正在受到影响。这是由于缺乏人类使用技术的能力和缺乏信息技术资源,这就需要大学制定新的战略来解决这些缺陷。本研究的目的是根据麦肯锡7s对基于问卷数据的问题部分的集群结果,制定大学服务战略。调查问卷是根据麦肯锡的七个要素组织的。曼哈顿距离计算和k - medoids算法结果表明,结构、系统、技能和人员都是聚集在k=2中的要素的一部分,必须在Covid-19大流行期间的援助服务中得到解决。麦肯锡7s表明,通过将集群技术与麦肯锡框架相结合,可以实现大学服务的增强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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