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.

Work Datafication and Digital Work Behavior Analysis as a Source of HRM Insights / Fabbri, T.; Scapolan, A.; Bertolotti, F.; Mandreoli, F.; Martoglia, R. - 49:(2022), pp. 53-65. [10.1007/978-3-030-83321-3_4]

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

Fabbri T.;Scapolan A.;Bertolotti F.;Mandreoli F.;Martoglia R
2022

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.
2022
17-nov-2021
Do Machines Dream of Electric Workers?
Solari L; Martinez M; Braccini A.M.; Lazazzara A.
9783030833206
Springer Science and Business Media Deutschland GmbH
Work Datafication and Digital Work Behavior Analysis as a Source of HRM Insights / Fabbri, T.; Scapolan, A.; Bertolotti, F.; Mandreoli, F.; Martoglia, R. - 49:(2022), pp. 53-65. [10.1007/978-3-030-83321-3_4]
Fabbri, T.; Scapolan, A.; Bertolotti, F.; Mandreoli, F.; Martoglia, R
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1253635
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? ND
social impact