The digital transformation of organizations is boosting workplace networking and collaboration while making it 'observable' with unprecedented timeliness and detail. However, the informational and managerial potential of work datafication is still largely unutilized in Human Resource Management (HRM) and its social 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 social good. As part of a wider research program, this paper presents some data analysis we performed on a collection of Enterprise Collaboration Software (ECS) data, in search for promising correlations between behavioral and relational (digital) work patterns and employee attitudes. To this end, we transformed the digital actions performed by 106 employees during a one year period into a graph representation to analyze data under two different points of view: the individual (behavioral) perspective, according to the user who performed the action and the action undertaken, and the social (relational) perspective, making explicit the interactions between users and the objects of their actions. Different employees' rankings are thus derived and correlated with their attitudes. We discuss the obtained results and their benefits in terms of perspective social good for both the company and the employee

Work datafication and digital work behavior analysis as a source of social good / Bertolotti, F.; Fabbri, T.; Mandreoli, F.; Martoglia, R.; Scapolan, A. C.. - (2020). (Intervento presentato al convegno 17th IEEE Annual Consumer Communications and Networking Conference, CCNC 2020 tenutosi a Las Vegas; United States nel 10-13 January 2020) [10.1109/CCNC46108.2020.9045185].

Work datafication and digital work behavior analysis as a source of social good

Bertolotti F.;Fabbri T.;Mandreoli F.;Martoglia R.;Scapolan A. C.
2020

Abstract

The digital transformation of organizations is boosting workplace networking and collaboration while making it 'observable' with unprecedented timeliness and detail. However, the informational and managerial potential of work datafication is still largely unutilized in Human Resource Management (HRM) and its social 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 social good. As part of a wider research program, this paper presents some data analysis we performed on a collection of Enterprise Collaboration Software (ECS) data, in search for promising correlations between behavioral and relational (digital) work patterns and employee attitudes. To this end, we transformed the digital actions performed by 106 employees during a one year period into a graph representation to analyze data under two different points of view: the individual (behavioral) perspective, according to the user who performed the action and the action undertaken, and the social (relational) perspective, making explicit the interactions between users and the objects of their actions. Different employees' rankings are thus derived and correlated with their attitudes. We discuss the obtained results and their benefits in terms of perspective social good for both the company and the employee
2020
17th IEEE Annual Consumer Communications and Networking Conference, CCNC 2020
Las Vegas; United States
10-13 January 2020
Bertolotti, F.; Fabbri, T.; Mandreoli, F.; Martoglia, R.; Scapolan, A. C.
Work datafication and digital work behavior analysis as a source of social good / Bertolotti, F.; Fabbri, T.; Mandreoli, F.; Martoglia, R.; Scapolan, A. C.. - (2020). (Intervento presentato al convegno 17th IEEE Annual Consumer Communications and Networking Conference, CCNC 2020 tenutosi a Las Vegas; United States nel 10-13 January 2020) [10.1109/CCNC46108.2020.9045185].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1203677
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