This paper aims to investigate types, drivers and outcomes of e-HRM configurations to get a deeper understanding of the reasons, kinds and success of adoption of digital HRM. The paper draws on an online survey administered to HR directors of 176 companies operating in Italy. Hierarchical cluster analysis were employed and three digital HRM configurations emerged - non-users, relational-users, and power-users which distinguish different purposes for using web 2.0 in HRM. The three e-HRM configurations relate to some contextual variables economic sector, size, and HR and business strategies - while they do not present differences in terms of organizational performances. Results highlights that universal type of e-HRM does not exist and that the degree of digital support in HRM depends on organizational characteristics.

Martini, M., Cavenago, D. (2018). e-HRM configurations: an explorative analysis of types, drivers and outcomes of digital HRM. Intervento presentato a: Academy of Management Specialized Conference – Big Data and Managing in a Digital Economy (2018), University of Surrey (UK).

e-HRM configurations: an explorative analysis of types, drivers and outcomes of digital HRM

Martini M;Cavenago D
2018

Abstract

This paper aims to investigate types, drivers and outcomes of e-HRM configurations to get a deeper understanding of the reasons, kinds and success of adoption of digital HRM. The paper draws on an online survey administered to HR directors of 176 companies operating in Italy. Hierarchical cluster analysis were employed and three digital HRM configurations emerged - non-users, relational-users, and power-users which distinguish different purposes for using web 2.0 in HRM. The three e-HRM configurations relate to some contextual variables economic sector, size, and HR and business strategies - while they do not present differences in terms of organizational performances. Results highlights that universal type of e-HRM does not exist and that the degree of digital support in HRM depends on organizational characteristics.
No
paper
e-HRM; HR configuration; cluster analysis
English
Academy of Management Specialized Conference – Big Data and Managing in a Digital Economy (2018)
https://journals.aom.org/doi/10.5465/amgblproc.surrey.2018.0001.abs
Martini, M., Cavenago, D. (2018). e-HRM configurations: an explorative analysis of types, drivers and outcomes of digital HRM. Intervento presentato a: Academy of Management Specialized Conference – Big Data and Managing in a Digital Economy (2018), University of Surrey (UK).
Martini, M; Cavenago, D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/197512
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