A pair of scholars from the University of Kansas recently proposed a refreshing take on how people engage with workplace technology. In a paper titled Socio-Technical Exchange with Machines: Worker Experiences with Complex Work Technologies, they refine the classic Social Exchange Theory, born in the 1960s, which says we interact with others based on rewards and costs, to better capture how people build relationships with machines, tells Tech Xplore.
To craft their argument, Cameron Piercy and Reaia Turner-Leatherman interviewed nearly two dozen workers from diverse fields. They found that people develop what they call “machine heuristics,” that is, stable intuitions about what each technology does well and what it doesn’t. It’s not just that we generally trust machines to be consistent and objective and mistrust them for subjective judgment. Our heuristics become much more nuanced over time: we come to believe that certain tools are reliable for routine or embarrassing tasks but not for complex decisions.
The researchers discovered that people tend to prefer other humans for expertise-driven work, where nuance and judgment matter, but they turn to machines for straightforward or possibly awkward tasks, where objectivity and anonymity are welcome.
That dynamic doesn’t fit neatly into Social Exchange Theory, because machines don’t weigh the “costs” or “benefits” like people do. Hence, the proposal of Socio-Technical Exchange theory, which frames these human–machine interactions as a distinct form of relationship building.
It’s worth noting that this study was conducted in 2022, before AI like ChatGPT became widespread, yet even then, people felt a meaningful interdependence with machines and formed lasting beliefs about their role at work.