A4 Conference proceedings

Transforming HR and Improve Talent Profiling with Qualitative Analysis Digitalization on Candidates for Career and Team Development Efforts


Publication Details

Authors: Vatousios Antonis, Happonen Ari

Publisher: Springer Verlag (Germany): Conference Proceedings

Publication year: 2021

Language: English

Related journal or series: Lecture Notes in Networks and Systems

Title of parent publication: Proceedings of the 2021 Computing Conference, Volume 1

Volume number: 283

ISBN: 978-3-030-80118-2

eISBN: 978-3-030-80119-9

ISSN: 2367-3370

eISSN: 2367-3389

JUFO level of this publication: 1

Digital Object Identifier (DOI): http://dx.doi.org/10.1007/978-3-030-80119-9_78

Permanent website address: https://link.springer.com/chapter/10.1007/978-3-030-80119-9_78

Social media address: https://www.researchgate.net/publication/346735642_Transforming_HR_and_Improve_Talent_Profiling_with_Qualitative_Analysis_Digitalization_on_Candidates_for_Career_and_Team_Development_Efforts

Open Access: Not an Open Access publication


Abstract

The
digital transformation of HR [1] with data analysis tools and processes is the
new normal in the enterprise business world. However, though, quantitative
analysis is the most typical main route for digitalization, HR decision making
will remain qualitative for the long unforeseen future. This study presents
digitally enhanced qualitative methods for talent profiling and team
development which is based on a brainstorming type questionnaire. This work
sets out itself the questions: Could talent profiling and team development be
improved by understanding each member mindset, visualizing his views with
brainstorming type questionnaire data? This research is based on two base
principles. Firstly, by touching several broad focus and narrow focus topics at
the same time with a properly set questionnaire, which unfolds respondent’s
mindset [1]. Secondly, data analysis and visualization toolsets are employed,
using wordclouds and heatmaps to present the talent ‘fingerprint’ for each
respondent. Additionally, a group analysis is visualized for the whole
respondent set. As a result, a stand-alone solution is being developed which
improves HR operations and talent profiling for individuals and organizations.
Furthermore, this abductive approach [1] shows that this open-ended conceptual
framework [3], [4] returns a promising qualitative analysis implying that
prediction and validation, although it is hard in human resources models, it is
feasible. In practice the demand for robust qualitative analysis tools on
individuals, organizations, and businesses, grows rapidly [5], [6]. This
research addresses the issues of subjective nature of human resources with an
open-ended approach which sharpens our decision-making without excluding it
[5], [8], [9]. Lastly, a simple, but novel means is provided to analyze HR data
e.g. job applicants mindset compared to current working team mindset.


Last updated on 2021-26-07 at 07:21