Renewed talent management: more productive development teams with digitalization supported HR tools

IF 0.4 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC EMITTER-International Journal of Engineering Technology Pub Date : 2021-09-28 DOI:10.14419/ijet.v10i2.31705
Antonis Vatousios, A. Happonen
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引用次数: 3

Abstract

In most companies and organizations, performance is related to talent management and skills to analyze what and why people are working on. However, many companies do fail to implement a long-term strategy for the performance enhancement activities, considering the talents they have recruited. In this article, we propose a tool for HR work, in context of talent management and how to utilize people skills and productivity analytics to improve team performance and related KPIs. A project data-based case study is illustrated, in which a set of devel-oper and content marketers were analyzed as core team members. In practice, the presented framework makes an important contribution to decision-making activities, where people analytics and proper software tools are used to build new novel knowledge into talent pool of the team. With the framework-based analysis, it is possible to analytically compare team members’ performance and enhance the team’s skill and structural development which means that we can employ analytics to find best performers and set their roles for more optimally working teams. Our research supports the concept of using the right framework can make a big positive difference in team analytics.  
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更新的人才管理:使用数字化支持的人力资源工具提高开发团队的生产力
在大多数公司和组织中,绩效与人才管理和分析人们工作的内容和原因的技能有关。然而,考虑到他们所招募的人才,许多公司确实没有实施绩效提升活动的长期战略。在本文中,我们提出了一个人力资源工作的工具,在人才管理的背景下,以及如何利用人际技能和生产力分析来提高团队绩效和相关的关键绩效指标。本文阐述了一个基于项目数据的案例研究,其中将一组开发人员和内容营销人员作为核心团队成员进行分析。在实践中,所提出的框架对决策活动做出了重要贡献,其中使用人员分析和适当的软件工具来构建新的新颖知识到团队的人才库中。使用基于框架的分析,可以分析比较团队成员的表现,并增强团队的技能和结构发展,这意味着我们可以使用分析来找到最佳表演者,并为更优化的工作团队设置他们的角色。我们的研究支持这样一个概念:使用正确的框架可以在团队分析中产生巨大的积极影响。
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来源期刊
EMITTER-International Journal of Engineering Technology
EMITTER-International Journal of Engineering Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
自引率
0.00%
发文量
7
审稿时长
12 weeks
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