Briefing Paper: What impact does exposure to workplace technologies have on the quality of people's jobs?
Creators: name: Soffia, Magdalena
name: Skordis, Jolene
Publisher: name: Zenodo
Year: 2024

description: How people feel about the jobs that they do is important. Our work is more than the single most important determinant of our living standards. Work is the thread that connects individual lives with their communities and the economy. Work promotes social relationships, forges connections of mutual support and fosters collaboration, binding together our capabilities with the environmental conditions that can either promote – or diminish – individual and collective flourishing.

Work and wellbeing are inextricably connected - and as we shape the future of our work, we shape the future of our wellbeing too.

In the Briefing that we published in March, we outlined research that shows that the growing use of technologies in the workplace is having significant, measurable and varied impacts on employees’ wellbeing.

Exposure to digital ICTs such as laptops, tablets and smartphones was associated with higher quality of life, while exposure to other technologies such as wearables, AI software and robotics was negatively correlated with wellbeing (Soffia et al. 2024).

However, the mechanisms behind these associations have not been well explored. If we know that exposure to new technologies is impacting workers’ wellbeing, we need to understand how this is happening to inform what we do about it. In particular, we need to know how these technologies are interacting with job quality changes, and therefore impacting good work.

This Briefing adds to the work on wellbeing by providing a more nuanced understanding of the mechanisms by which exposures to workplace technologies may be connected to quality of life – of which job quality is a main contributor.

We know that the concept of ‘job quality’ has a significant association with the wellbeing of the person performing that job. Measurement of job quality has extrinsic aspects such as salary and future job prospects, but it also has intrinsic dimensions such as work intensity, autonomy, work environment and working time arrangements. At IFOW we have developed a ‘Good Work Charter’ to capture this multi-dimensional construct, including the extrinsic and intrinsic dimensions of job quality, offering clear pointers as to what good quality work should look like and a framework against which impacts can be considered. 

While it is both valid and necessary to monitor variations in workers’ wellbeing, from a practical policy perspective it is easier to design policy mixes and target actions towards improving job quality – with an understanding that improvements here are likely to convert into improved quality of life.

What we find here is that the feelings of persistent job insecurity that new technologies bring cancel out many of the potential job quality gains, unless such downsides are consciously managed.

In addition, the promise that AI and automation technologies would relieve people of routine work is found to be lacking substance, diminishing hopes that these new technologies inherently improve job quality in itself. This adds weight to calls for more attention to be made to choices that shape job quality impacts when new technologies are adopted, right from design through development and deployment.

Research in other parts of the Pissarides Review into the Future of Work and Wellbeing has shown that these approaches to how AI and automation technologies are designed, developed and deployed do shape job quality; what this work adds is understanding the extent to which exposure to these different technologies increases or reduces job quality – and how these impacts might be different for different social groups or different roles within a firm.

The basis of the study was primary survey data analysed from a sample of nearly 5,000 employees. An online survey was conducted between 22 May and 30 June 2023. A valid sample of 4802 employees was taken, representative of the UK working population in terms of age, gender, education and employment status.