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  1. Research Outputs

Work Datafication and Digital Work Behavior Analysis as a Source of HRM Insights

Chapter
Publication Date:
2022
Short description:
Work Datafication and Digital Work Behavior Analysis as a Source of HRM Insights / Fabbri, T., Scapolan, A., Bertolotti, F., Mandreoli, F., Martoglia, R. (LECTURE NOTES IN INFORMATION SYSTEMS AND ORGANISATION). - In: Do Machines Dream of Electric Workers? / [a cura di] Solari L; Martinez M; Braccini A.M.; Lazazzara A.. - [s.l] : Springer Science and Business Media Deutschland GmbH, 2022. - ISBN 9783030833206. - pp. 53-65 [10.1007/978-3-030-83321-3_4]
abstract:
The digital transformation of organizations is boosting workplace networking and collaboration while making it “observable” with unprecedented timeliness and de-tail. However, the informational and managerial potential of work datafication is still largely unutilized in Human Resource Management (HRM) and its benefits, both at the individual and the organizational level, remain largely unexplored. Our research focuses on the relationship between digitally tracked work behaviors and employee attitudes and, in so doing, it explores work datafication as a source of data driven HRM policies and practices. As a chapter of a wider research pro-gram, this paper presents some data analysis we performed on a collection of En-terprise Collaboration Software (ECS) data, in search for promising correlations between behavioral and relational (digital) work patterns and employee attitudes. To this end, the digital actions performed by 106 employees in one year are trans-formed into a graph representation in order to analyze data under two different points of view: the individual (behavioral) perspective, according to the user who performed the action and the performed action, and the social (relational) perspec-tive, making explicit the interactions between users and the objects of their ac-tions. Different employees’ rankings are thus derived and correlated with their at-titudes. Finally, we discuss the obtained results and their implications in terms of People Analytics and data driven HRM.
Iris type:
Capitolo/Saggio
Keywords:
Work datafication - HRM data driven - People Analytics
List of contributors:
Fabbri, T.; Scapolan, A.; Bertolotti, F.; Mandreoli, F.; Martoglia, R
Authors of the University:
BERTOLOTTI Fabiola
FABBRI Tommaso
MANDREOLI Federica
SCAPOLAN Anna Chiara
Handle:
https://iris.unimore.it/handle/11380/1253635
Book title:
Do Machines Dream of Electric Workers?
Published in:
LECTURE NOTES IN INFORMATION SYSTEMS AND ORGANISATION
Series
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