A recent study published in the journal Frontiers in Robotics and AI has found that humans working alongside robots may exhibit a behavior psychologists call “social loafing.” This behavior refers to the tendency to offload responsibilities when working in a group setting. The study specifically looked at human workers checking manufacturing defects on circuit boards and found that if they were told a competent robot had already reviewed the board, they spotted fewer errors.
The participants in the experiment were divided into two groups. One group was told they were inspecting the circuit board for the first time, while the other group was told that a robot named “Panda” had already inspected it. The participants working with Panda even had a visual and auditory presence of the robot while inspecting the boards. Both groups spent a similar amount of time inspecting the boards and reported similar feelings of responsibility for the task.
However, the group that believed they were working with Panda actually spotted fewer errors than the group working independently. The researchers suggest that this phenomenon is similar to the “looking but not seeing” effect observed in traditional social loafing cases. It seems that humans in this study trusted the robot’s performance and overlooked their own work.
These findings could have implications for companies that are increasingly automating their warehouses and factories. There may be a risk of overconfidence in the robot’s performance, leading to a lack of quality oversight and potential safety concerns. The longer workers are exposed to this environment, the greater the loss of motivation and potentially negative impact on work outcomes.
Overall, teamwork can be a mixed blessing. While it can motivate people to perform well, it can also lead to a loss of motivation when individual contributions are not as visible. As automation continues to play a larger role in various industries, understanding and addressing social loafing behaviors will be crucial for optimizing productivity and ensuring safety.
Source: Frontiers in Robotics and AI (journal)