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Standardising job descriptions in the humanitarian supply chain: A text mining approach for recruitment process.
Spada, Irene; Fabbroni, Valeria; Chiarello, Filippo; Fantoni, Gualtiero.
Afiliação
  • Spada I; Department of Energy, Systems, Land and Construction Engineering, School of Engineering, University of Pisa, Pisa, Italy.
  • Fabbroni V; Business Engineering for Data Science (B4DS) Research Lab, School of Engineering, University of Pisa, Pisa, Italy.
  • Chiarello F; Department of Civilisations and Forms of Knowledge, University of Pisa, Pisa, Italy.
  • Fantoni G; SDCC Department, Asian Development Bank, Fragile Countries, Manila, Philippines.
PLoS One ; 19(7): e0305961, 2024.
Article em En | MEDLINE | ID: mdl-38985717
ABSTRACT

PURPOSE:

Uncertainty and complexity have increased in recent decades, posing new challenges to humanitarian organisations. This study investigates whether using standard terminology in Human Resource Management processes can support the Humanitarian supply chain in attracting and maintaining highly skilled operators.

METHODOLOGY:

We exploit text mining to compare job vacancies on ReliefWeb, the reference platform for humanitarian job seekers, and ESCO, the European Classification of Skills, Competencies, and Occupations. We measure the level of alignment in these two resources, providing quantitative evidence about terminology standardisation in job descriptions for supporting HR operators in the Humanitarian field.

FINDINGS:

The most in-demand skills, besides languages, relate to resource management and economics and finance for capital management. Our results show that job vacancies for managerial and financial profiles are relatively more in line with the European database than those for technical profiles. However, the peculiarities of the humanitarian sector and the lack of standardisation are still a barrier to achieving the desired level of coherence with humanitarian policies.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mineração de Dados Limite: Humans Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Itália País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mineração de Dados Limite: Humans Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Itália País de publicação: Estados Unidos