Human resources mining for examination of R&D progress and requirements

Sercan Ozcan, C. Okan Sakar, Metin Suloglu

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    The amount of job advertisement data is rapidly growing, and this rich dataset is expected to have implications for the employment market, sector trajectories, and the education sector. Most significantly, human resources (HR) data has never previously been examined with the lens of tech mining for science and technology analyses. This article is the first to examine job advertisement data considering research and development (R&D) progress and requirements, and hereafter, we refer to this as HR mining. The aim of this article is to use HR mining with the purpose of R&D and human capital intelligence using the job advertisement data of Turkey for the 2015–2017 period. The method of this study follows classification as part of the preprocessing step to determine R&D, engineering, and high-tech industry-related job advertisements. Afterward, we use clustering methods to identify areas where key human capital is required, and investments are made by R&D-oriented companies. The results show that it is possible to identify sector-oriented skill requirements and that the significance of the R&D skills varies. For the case of Turkey, we can clearly show the national human capital and R&D by identifying nine key clusters that indicate R&D progress and directions.
    Original languageEnglish
    Article number0
    Pages (from-to)0
    Number of pages16
    JournalIEEE Transactions on Engineering Management
    Early online date2 Jun 2020
    Publication statusEarly online - 2 Jun 2020


    • Research and development
    • Clustering algorithms
    • Patents
    • Text mining
    • Bibliometrics
    • Classification algorithms


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