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Everything we do, everything we press: Data-driven remote performance management in a mobile workplace

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conference contribution
posted on 2018-06-18, 15:59 authored by Lyndsey Bakewell, Konstantina Vasileiou, Kiel Long, Mark Atkinson, Helen Rice, Manuela Barreto, Julie Barnett, Michael WilsonMichael Wilson, Shaun Lawson, John Vines
This paper examines how data-driven performance monitoring technologies affect the work of telecommunications field engineers. As a mobile workforce, this occupational group rely on an array of smartphone applications to plan, manage and report on their jobs, and to liaise remotely with managers and colleagues. These technologies intend to help field engineers be more productive and have greater control over their work; however they also gather data related to the quantity and effectiveness of their labor. We conducted a qualitative study examining engineers' experiences of these systems. Our findings suggest they simultaneously enhance worker autonomy, support co-ordination with and monitoring of colleagues, but promote anxieties around productivity and the interpretation of data by management. We discuss the implications of datadriven performance management technologies on worker agency, and examine the consequences of such systems in an era of quantified workplaces.


This work was supported by RCUK grant ES/M003558/1, funded through the Empathy and Trust in Online Communicating (EMoTICON) funding call administered by the Economic and Social Research Council in conjunction with the RCUK Connected Communities, Digital Economy and Partnership for Conflict, Crime and Security themes, and supported by the Defence Science and Technology Laboratory (Dstl) and Centre for the Protection of National Infrastructure (CPNI).



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Conference on Human Factors in Computing Systems - Proceedings




BAKEWELL, L. ... et al, 2018. Everything we do, everything we press: Data-driven remote performance management in a mobile workplace. IN: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada, 21-26 April 2018, Paper No. 371.


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This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

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The published version of this conference paper is also available at https://doi.org/10.1145/3173574.3173945.




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