AI as Technostress

Introduction

Fads and trends for the most part are to be avoided, particularly if you’re interested in finding a long term solution to a middle aged problem - I mean, a problem that isn’t ancient, but has been around long enough to have a mortgage and some crows feet.

It becomes a particular challenge when the general conversation about such problems comes wrapped in fad-ish language and reactions rather than responses. The deluge of opinion pieces, podcast episodes, very expensive VIP talks, and screechy headlines have made a lot of noise - and it’s difficult to think skillfully when there is too much noise.

We have a middle aged problem - stress from tech. We seek a long term solution to reducing it in order to function better in our work. How can we manage new digital solutions like those coming out of our current AI boom? How can we make better use of them so that it’s not just another hindrance stressor, negatively impacting our ability to work well?

Technostress

Technostress is defined as stress that’s “caused by an individual’s attempts to deal with constantly evolving ICTs and the changing physical, social, and cognitive responses demanded by their use,” (Ragu-Nathan et al., 2008).

The term almost sounds quaint now, doesn’t it? In the 80s and 90s, as personal computers became more commonplace in offices, research began reflecting the specific impact of technologies use on workers. Back then, researchers were interested in how the use of information and communication technologies like PCs and emails was impacting the users. If you ignore the dates on the published papers, you’d easily think the discussions were a more recent reflection of AI use at work. For example, employees reported feelings of stress and frustration as a result of having to learn new skills, devote more time, and adapt different work patterns from a study nearly 40 years ago (Hudiburg, 1989). My, my - doesn’t that sound contemporary?

More recent research has continued unpacking the pros and cons that come along with technologies at work, with some papers reflecting on the ‘dark side’ of digitization (Atanasoff & Venable, 2017; Bondanini et al., 2020; Scholze & Hecker, 2024). As I’ve referenced job demands and resources (JDR theory) here before, and have a post on work stressors (click on the button below) - let’s consider digital solutions, including AI tools, through those frameworks of job demands and hindrance stressors.

Welcome to the Dark Side

In a recently published paper, Scholze & Hecker (2024) investigated digital job demands and resources. The ‘demands’ referred to the “physical, psychological, social, and organizational aspects that generally necessitate prolonged physical or psychological exertion,” (ibid., p. 3) due to using digital information communication technologies (DICT). The study outcomes highlighted the dark side of digitization via an association between digital job demands and psychological strain. This relationship underscores that digitization has consequences for employee psychological well-being as they report experiencing more technology dependence, work intensification, and availability (i.e., not being able to ‘switch off’). Other recent studies have found evidence that employees feeling they were ‘on’ anytime, anywhere - without a clear line between work and personal life - was more likely to cause work exhaustion (Bauwens et al., 2021; Zhang et al., 2022).

As digital technologies continue to evolve and take up more space in our everyday working tasks, there is opportunity to learn from past iterations and do a better job of managing it this time with the current AI wave. These technologies continue to influence working conditions, our communication, and teamwork approaches, and organisational culture.

So, how can we manage new technologies so that we might enjoy the bright side of digitization - the efficiency, the autonomy, and the collaboration potential - without suffering the strain that comes from the disequilibrium of digital demands?

Actions

As with every solutions-seeking section of discussion in Organisational Psychology, it is useful to think on individual, team/group, and organisational levels. Given that you, dear reader, may have little say in your organisation’s design and policy-making, let’s focus on individual and team/group actions.

Digital Detox

Non-use Timeouts - create periods of time in your daily/weekly work where you are unavailable, not online, and have silenced notifications. This can be time to do individual thinking, reading, writing but is also an option for certain types of meetings and group discussions. Make those Timeout spaces, communicate them (so others leave you alone), protect the spaces.

Usage self-assessment - ask yourself if you are adding digitisation where it either hinders or harms. For example, can you call rather than email? Can you not check social media until your work day is done? Probably. Can you have lunch one day with your phone on airplane mode? Likely.

Collaboration, Efficiency, Autonomy

The addition of new technologies ought to facilitate collaboration, efficiency, and autonomy. And must do so from the user’s perspective. If your team feels they don’t know how to use the digital ‘solution’, the benefits will not come. Engage users in participation and feedback through training, clear communication on application of the new technology, and modifying the use as employee feedback informs on how collaboration, efficiency, or autonomy is being stymied.

The addition of digital resources must not result in an increase of hindrance stressors. The challenge stressor of learning how to use and usefully apply a new AI tool is a good way to enjoy outcomes like skill mastery and competence demonstration. The hindrance stressor of learning the new tool only to discover it’s inefficient and a double-up of something that already exists is a good way to stitch frustration into daily work activity. Sometimes being ‘data-driven’ is not the answer for every single issue.

Organisational Priorities

If you are in a strategy-setting (and hopefully, decision-making) role with your organisation, prioritise simplicity and user-friendliness when considering digital resources. Monitor employee workload and manage it effectively. Regularly review and adapt, with user feedback informing your choices. Be aware of the potential flaws in chosen digital solutions (e.g., if the training data used for the AI tool/system lacked accurate representation, it will produce skewed results when applied to a population), and chose person-solutions where digital doesn’t work.

Final Thoughts

With the allure of something new and shiny, it’s easy to forget the foundational components which allow humans to work well. Organisational cultures that value learning and knowledge also drive experimentation, encourage adaptive skill set development, and open employees to new realities (Zahra et al., 1999). When adding new technologies and digital resources to work activity, prioritise employee use and experience. This perspective will help to keep new additions like AI systems in the job ‘resource’ category - that is, “aspects of the job that have motivating potential, that are functional in achieving work goals, that regulate the impact of job demands, and that stimulate learning and personal growth,” (Bakker et al., 2023, p. 33).

Works Cited and Further Reading

Atanasoff, L., & Venable, M. A. (2017). Technostress: Implications for adults in the workforce. The Career Development Quarterly, 65(4), 326–338. https://doi.org/10.1002/cdq.12111

Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. (2022). Job Demands–Resources Theory: Ten years later. Annual Review of Organizational Psychology and Organizational Behavior, 10(1), 25–53. https://doi.org/10.1146/annurev-orgpsych-120920-053933

Bauwens, R., & Meyfroodt, K. (2021). Debate: Towards a more comprehensive understanding of ritualized bureaucracy in digitalized public organizations. Public Money & Management, 41(4), 281–282. https://doi.org/10.1080/09540962.2021.1884349

Hudiburg, R. A. (1989). Psychology of Computer Use: VII. Measuring Technostress: Computer-Related Stress. Psychological Reports, 64(3), 767–772. https://doi.org/10.2466/pr0.1989.64.3.767

Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The Consequences of technostress for end users in Organizations: Conceptual development and empirical validation. Information Systems Research, 19(4), 417–433. https://doi.org/10.1287/isre.1070.0165

Scholze, A., & Hecker, A. (2024). The job demands-resources model as a theoretical lens for the bright and dark side of digitization. Computers in Human Behavior, 155, 108177. https://doi.org/10.1016/j.chb.2024.108177

Zahra, S. A., Nielsen, A. P., & Bogner, W. C. (1999). Corporate entrepreneurship, knowledge, and competence development. Entrepreneurship Theory and Practice, 23(3), 169–189. https://doi.org/10.1177/104225879902300310

Zhang, Z., Ye, B., Qiu, Z., Zhang, H., & Yu, C. (2022). Does Technostress Increase R&D Employees’ Knowledge Hiding in the Digital Era? Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.873846