Text mining of industry 4.0 job advertisements
Text mining of industry 4.0 job advertisements
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DOI：
10.1016/j.ijinfomgt.2019.07.014
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发表时间：
2020-02-01
影响因子：
21
通讯作者：
Krstic, Zivko
中科院分区：
文献类型：
--
作者：
Pejic-Bach, Mirjana;Bertoncel, Tine;Krstic, Zivko
由于工业4.0等领域的工作特征变化迅速，因此需要快速分析招聘广告的工具。目前对工业4.0所需能力的了解还很缺乏。本文的目标是开发一个工业4.0招聘广告的配置文件，使用文本挖掘公开的招聘广告，这通常是用来作为一个渠道，收集有关快速变化的行业所需的知识和技能的相关信息。我们搜索发布与工业4.0相关的招聘广告的网站，并对从这些招聘广告中收集的数据进行文本挖掘分析。对招聘广告的分析显示，大部分招聘职位为全职、助理和中高级管理职位，主要来自美国和德国。文本挖掘分析产生了两组职位简介。第一组职位描述仅关注与工业4.0相关的知识：用于自动化生产的网络物理系统和物联网;以及智能生产设计和生产控制。第二组职位描述侧重于更通用的知识领域，这些领域适用于工业4.0：供应变更管理、客户满意度和企业软件。对提取的短语进行主题挖掘，生成各种多学科的职位简介。高等教育机构、人力资源专业人士以及已经受雇于或希望受雇于工业4.0组织的专家将从我们的分析结果中受益。
Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements, related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements. Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior level management positions and mainly came from the United States and Germany. Text mining analysis resulted in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production design and production control. The second group of job profiles was focused on more general knowledge areas, which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software. Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in Industry 4.0 organizations, would benefit from the results of our analysis.