Global judgements and ideas.

Why are people resisting quantified work?


Who/what is the conductor in the quantified workplace?

Simondon (1958/1980) discusses transindividuality as a link to emancipation through describing technical objects as having an infinite number of possible uses when they are individualised, but notes that their convergence is the point at which they are useful and become a system. He looks at the case of a ‘made to measure’ car, indicating that only non-essential parts are contingent and work ‘against the essence of technical being, like a dead weight imposed from without’ (18). Simondon defends the human as the organiser of the technical, stating that automation is never perfect nor complete and always contains a ‘certain margin of indetermination’ (4). He states that ‘far from being the supervisor of a squad of slaves, man is the permanent organiser of technical objects which need him as much as musicians in an orchestra need a conductor’ (4).  In a similar way, people can recognise their individual existence without becoming atomised or hostile, instead, realise that our interrelations are what strengthen us and prevent us from abdicating and delegating our humanity to a robot (2).

While Simondon’s observations highlight human agency, Marx observed during his own lifetime the ways in which early industrialisation turned ‘living labour into a mere living accessory of this machinery, as the means of its action, also posits the absorption of the labour process in its material character as a mere moment of the realisation process of capital’ (Marx, 1858/1993: 693) in the much-cited Fragment on Machines. It is worth recognising these early comments in the context of new uses of technology for quantification where data is used for decisionmaking in ways beyond his and Engels’ wildest dreams, where ‘[machinic] knowledge appears as alien’ and ‘external’ to the worker where the worker is ‘superfluous’ (brackets added) (1858/1993: 605). While artificial intelligence and its role in people analytics and algorithmic decisionmaking processes were not featured in early industrialisation, nonetheless, Marx explicitly identifies the role of the machine in the labour process and describes its capacity for quantification and division and of abstracting labour, commenting that ‘the worker’s activity, reduced to a mere abstraction of activity, is determined and regulated on all sides by the movements of the machinery, and not the opposite’ (1858/1993: 693). In this way, Marx identifies agency, and even authority, to machinery, where ‘objectified labour confronts living labour within the labour process itself as the power which rules it; a power which, as the appropriation of living labour, is the form of capital’ (Ibid). The means of labour, Marx wrote, is transformed, controlled and absorbed by machinery.

The Quantified Workplace

So it may come as no surprise that workers have begun to resist the integration of machines into workplaces and increasingly into decisionmaking by quantified management strategies involving big data accumulated by wearable and computer embedded technology. I worked with a group of employees in a professional office context from 2015-16, where 30 workers tracked their activities at work every day for up to a year, using RescueTime software, FitBit Charge HR armbands and daily lifelog emails asking them to rank their subjective projectivity and stress levels. I conducted extensive semi-structured interviews with participants in month 3 and 8 to assess how people respond to such levels of tracking and monitoring in the professional workplace.

Importantly, the project occurred during a period of change management as one multinational company absorbed a smaller company that had been a tight knit group of real estate and work design consultants. The activity was part of a move toward a more agile workplace, the manager running the project informed me. The project manager indicated that his intentions were to help workers adapt to what he saw as a more agile working environment, where change was to be expected and red tape reduced, and to see to what extent employees’ self-awareness, stress, wellbeing and ‘wellbilling’ (the amount of revenue an employee generates for the company), would be impacted during the period of transition.

In this context, workers were expected to both transform affective and physical aspects of themselves, through becoming healthier, happier and more productive with the use of intensely investigatory devices; and to affectively and emotionally manage the processes of change that they were asked to accommodate. The techologies then could both aid workers in coping with change and track their responses to the merger (which I note involved extensive affective labour, see forthcoming article in Body & Society, Moore and Piwek 2018).

But in discussions with the team organising the project, the company was interested in comparing subjectively and objectively measured productivity, as linked to health and activity tracking and ‘billability’. I was not given access to the data gathered by the company on whether improved activity led to higher productivity and billability. However, the project fit with the company’s moves toward agility,  in a gig-like scenario, which was encouraged at the time that the project merger was put in place which included increases in teamwork, more client facing services, horizontal decisionmaking, ‘working anywhere’ philosophies as well as efficiency and the reduction of red tape (as ‘agility’ requires) and the all-of-life ethos. Suffice to say that workplace changes are likely to have impacts on workers’ wellbeing, and the timing of this self-tracking initiative, as framed in corporate wellness terms, is significant. In any case, the findings reveal that participants were very keen at first to participate in the project, but that interest waned relatively quickly. Next, I focus on people’s responses to involvement and look at possible reasons for exit, including resistance to it on the basis of sensitivity toward privacy and use of data.


One comment in the first interviews, which I conducted in month 3, indicated that employees originally thought there would be more ‘complaints about privacy’. Three comments indicate concern about what personal data management were viewing, which increased to 21 in the interviews in month 8.

There was a high rate of exit from the project and there a high rate of increase in people indicating they had stopped using the technologies continuously, at 73%. One comment in the first interviews indicated employees originally thought there would be more ‘complaints about privacy’ than they perceived through discussions with colleagues. In the first interviews, three comments indicate concern about what personal data management had access to, increasing to 21 in the final interviews.

Responses to the question ‘How/have your thoughts about the Quantified Workplace project changed?’ indicate: 

I still [have] and even [have] more doubt the project. And I don’t wear the Fitbit very often. And when I will wear it, it is for myself and to see how active I am.

After monitoring my workplace behaviour over a couple of months I found out that it didn’t change a lot.

It confirmed my thoughts, which I had in the beginning. It is better to change your behaviour based on your feelings rather than a device.

I learned not very much from it.

Nine interview responses indicated FitBit abandonment either for a period, or altogether in the first two months. Some used the FitBit for almost the entire project, while others engaged with it for less than one month/ occasionally. FitBit use decreased significantly throughout the project, reflected by the monthly total average step count recorded from all employees. There was a 30% drop in average steps recorded within the first three months, 50% drop within six with a 75% drop by the end.

Interviews show that exit from the project may have been a response to increasing concerns about the reliability of the devices; privacy and concerns surrounding the use of data; and general concerns about corporate surveillance that a project of this type engenders. Responses in the first interviews demonstrate scepticism about the validity of the FitBit’s readings and desire for more device intelligence:

A big question for me and for a few others as well, is uh, how reliable the FitBit is.

…this thing [FitBit] might be more intelligent than just recording my data.

One respondent in the second interviews indicated frustration:

I don’t get any answers, I just fill in my things, but I don’t get an answer if it’s good or not, I just want to know if I were good and just start working.

There was significant evidence of the lack of trust in the devices being used informed people’s decisions, as well as uncertainty about their impacts on privacy and data accumulation. While people felt there were some positive aspects to participating in the experiment, the high rate of exit demonstrates everyday forms of resistance, leading me to ask, why are people resisting quantified work?

Next steps for research: Psychosocial hazards

Perhaps people feel uncomfortable ascribing too much agency and authority to machines. While this sample cannot represent all workplaces, it may support the claim that digitally managed work, in the contemporary context of agility and unstable working conditions and contexts, demonstrate that machines are now more than ever before, being seen as the symbols for ‘the ordering of life itself’ (Merchant 1990, 227): accelerating the labour process and overlooking workers’ basic rights. Workers’ reluctance to join worker wellness programmes or to be monitored and tracked at work may demonstrate increasing sensitivity to the tensions surrounding new control mechanisms; ongoing struggles in the contemporary labour process where agile is a key meme; and bring about an urgency of review of digitalised work in an all-of-life context. Research should look at the increasing psychosocial hazards that digitalised work poses for workers and at forms of resistance that are beginning to emerge against modulation and control methods in the quantified workplace.


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This entry was posted on November 30, 2017 by .
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