The best task allocation process is to decide on one's own: effects of the allocation agent in human-robot interaction on perceived work characteristics and satisfaction
New technologies are ever evolving and have the power to change human work for the better or the worse depending on the implementation. For human-robot interaction (HRI), it is decisive how humans and robots will share tasks and who will be in charge for decisions on task allocation. The aim of this online experiment was to examine the influence of different decision agents on the perception of a task allocation process in HRI. We assume that inclusion of the worker in the allocation will create more perceived work resources and will lead to more satisfaction with the allocation and the work results than a decision made by another agent. To test these hypotheses, we used a fictional production scenario where tasks were allocated to the participant and a robot. The allocation decision was either made by the robot, by an organizational unit, or by the participants themselves. We then looked for differences between those conditions. Our sample consisted of 151 people. In multiple ANOVAs, we could show that satisfaction with the allocation process, the solution, and with the result of the work process was higher in the condition where participants themselves were given agency in the allocation process compared to the other two. Those participants also experienced more task identity and autonomy. This has implications for the design of allocation processes: The inclusion of workers in task allocation can play a crucial role in leveraging the acceptance of HRI and in designing humane work systems in Industry 4.0.
This article is published in the Journal "Cognition, Technology & Work", Volume 24, Issue 1, pp. 39-55.
First Online: 21 December 2020
Bibliographic information
Title: The best task allocation process is to decide on one's own: effects of the allocation agent in human-robot interaction on perceived work characteristics and satisfaction.
in: Cognition, Technology & Work, Volume 24, Issue 1, 2022. pages: 39-55, Project number: F 2418, DOI: 10.1007/s10111-020-00656-7