Facilitating University–Industry Interaction by Visually Showcasing Researcher Profiles Via Metadata

Marianne Lykke, Brigita Perchutkaite
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Abstract

: University–industry interaction (UII) has grown significantly over the past two decades. Yet, contacting uni- versities to establish collaboration continues to be perceived as challenging by the industry. This paper studies the challenges associated with UII for analysing how metadata, taxonomies and visualizations in research information management systems (RIMS) may support UII for large enterprises. The first step was a systematic literature review conducted to understand the challenges associated with UII. The second step was interviews with R&D managers from three large enterprises to extend our knowledge about this industry group. The results showed that the primary channel for establishing UII was through personal connections. The informants saw the RIMS as an important tool for browsing research literature and exploring research groups to gain insights into research topics and individual researchers. In terms of metadata, the findings showed that multi-disciplinarity and high-level granularity were important aspects. Furthermore, the visuali-sation of relationships and description of international collaborations was perceived a useful indicator of re- searchers’ overall quality and impact. Similarly, metadata describing job titles, departments, and citations was central for judging the credibility of experts. An interesting finding was that enterprise managers found it difficult to develop personal relationships with relevant academic experts. Future studies may benefit from interviews with HR management professionals exploring how to support recruitment by using metadata and taxonomies.
通过元数据可视化地展示研究人员简介,促进大学与行业的互动
在过去的二十年里,大学与产业的互动(UII)有了显著的增长。然而,与大学建立合作关系仍然被业界视为具有挑战性。本文研究了与ui相关的挑战,以分析研究信息管理系统(RIMS)中的元数据、分类法和可视化如何支持大型企业的ui。第一步是进行系统的文献综述,以了解与ii相关的挑战。第二步是采访来自三家大型企业的研发经理,以扩大我们对这个行业群体的了解。结果表明,建立用户满意度的主要渠道是人际关系。举报人认为RIMS是浏览研究文献和探索研究小组以深入了解研究主题和个人研究人员的重要工具。在元数据方面,研究结果表明多学科性和高粒度性是重要的方面。此外,可视化的关系和国际合作的描述被认为是研究人员的整体素质和影响的有用指标。同样,描述职称、部门和引文的元数据是判断专家可信度的核心。一个有趣的发现是,企业管理者发现很难与相关学术专家建立个人关系。未来的研究可能会受益于与人力资源管理专业人士的访谈,探讨如何通过使用元数据和分类法来支持招聘。
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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