When journalists at The Daily Telegraph in London turned up to their large open plan office on top of Victoria Station in London, they were horrified to find a new addition to their desks.
Confused looks and bemused chatter ensued as they found a small square box attached to the underside of their desk. A quick Google search of the name on the side of them – ‘OccupEye’ – revealed they were wireless motion detectors to monitor whether people were at their desks.
Four hours later following reports in the national press, the National Union of Journalists criticising the “Big Brother style surveillance” and feedback from staff, bosses decided to remove the devices.
The Daily Telegraph later told staff the devices were to make the office more energy efficient by monitoring energy use.
Technology is increasingly allowing companies to monitor their employees with Gartner finding in 2018 that 50 per cent of 239 large corporations surveyed were using some type of non-traditional monitoring techniques such as analysing emails and social media messages or gathering biometric data.
Studies have described the datafication of people’s work as standardised, reductionist and highly controlling. Indeed, this ‘management-by-results’ surveillance can lead to de-motivated staff, general cynicism, distrust of managers and even gaming of the system.
But our research has found that the data tracking of employees can lead to greater job satisfaction and workers being able to find more meaning in their career, not less. With sympathetic managers that listen to employees and work with them on the data tracking system it can help personal development and help justify the value of somebody’s job.
In fact, as professionals are increasingly familiar with apps and technology that tracks their movement, we found they are comfortable with similar devices in the workplace as long as it is done transparently and they know what it is for.
We studied the implementation of data tracking systems at two US universities, one a ‘state’ university and the other ‘private’. By conducting interviews with leaders and managers who implemented the system and staff working with it we were able to ascertain its progress.
The system saw academics have to fill in information on research papers they had published, with scores from student assessment of their teaching added into a standardised database. This allowed university bosses to track their performance and their different departments so they could compare them and spot patterns.
It might not have been automatically tracking them with algorithms, but it was turning their job into data and allowing their profession to be quantified for management purposes.
Interestingly there was a significant difference between the universities’ use of the system. The state university patched the ‘off-the-shelf’ package straight into its computers, while the private university customised it to its needs. It added a box to the drop-down menus so that faculty could add an explanation to any activity.
How should organisations use data to track workers?
Thus, if a paper was published in a low-ranking journal the academic could then explain the reasoning behind this submission.
The private institution also integrated the database into its website, so it would automatically populate academics’ bio page with research papers, a short biography, media coverage and teaching activities.
These customisations were added after listening to faculty and working with them on adapting it to their needs as much as possible. But at the state university it was ‘like it or lump it’, with no consultation, so staff had the sense their work was being reduced to numbers with no contextual richness added.
When the state university proposed using the people analytics (PA) system for a new way to grade faculty’s pay, the union responded angrily, saying: “Administration is now proposing to use the data generated through PA to initiate a gradated pay increase… This proposal is unacceptable… it fails to recognise meaningful standards of quality in academic work.
“PA only assesses the quantity of papers published, students taught, committees chaired; it can’t measure quality: whether one accomplishes those tasks poorly or well...”
Staff at the state university complained of a ‘one-size-fits-all system’ leaving them unable to fully explain themselves and being able to adapt the system to their situation, plus a lack of trust developed with how management would use the data. It created a sense of alienation and diminished the meaningfulness of their job.
As a department professor at the state university said: “This [entering data into PA] is one more meaningless thing we have to do, … and we are never going to understand what happens to the data, we’re never going to see the actual results. We just know that if we don’t do it, we’re going to lose. … [there’s] just no transparency in the process at all.”
Whereas the customisation had built trust at the private university, with the visibility on academics' bio pages giving an extra sense of worth and motivation to adapt the system to their needs.
A history academic at the private university said: “I know if someone is going to be looking for me on the web and invite me someplace, they’re going to want to find what I have done most recently; so even book reviews, I put them into PA almost immediately to make sure that they get represented. So, I can keep the outside world updated. … if I blog I make sure I include a link to it [web profile generated from PA data].”
Thus, the private university framed the system as a way for faculty to update their public-facing bio page as well as conveying achievements to their department heads. But at the state university it was advertised to staff as a reporting tool to provide evidence of their productivity.
At the private university individual academics were allowed to tailor the system by working with the implementers. For example, a professor was able to show what status each paper they were working on was at, by adding tabs for ‘submitted’, ‘in review’, ‘published’ and so on.
So datafication is not all bad, it depends very much on how the organisation handles its implementation. Being sympathetic, open and customising it to the needs and desires of staff can see it become a positive for the employee and the employer. Just sticking a device under worker’s desk one morning is not going to work.
Stein, M., Wagner, E. L., Tierney, P., Newell, S. and Galliers, R. D. (2019) "Datification and the pursuit of meaningfulness in work", Journal of Management Studies, 56, 3, 685-717.
Newell, S. and Marabelli, M. (2015) "Strategic opportunities (and challenges) of algorithmic decision-making : a call for action on the long-term societal effects of ‘datification’", The Journal of Strategic Information Systems, 24, 1, 3-14.
Sue Newell is Professor of Information Systems & Management. She teaches Knowledge, Work and Innovation on the MSc Management of Information Systems & Digital Innovation. She also lectures on Digital Ventures on the Undergraduate programme.
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