Michael Johnson
Papers
7
Total Citations
156
H-Index
5
About
Michael Johnson is a researcher specializing in human-robot interaction (HRI), with a particular focus on improving statistical methodologies and measurement practices within the field. His most influential contributions center on the rigorous analysis of Likert scale usage in HRI research, a body of work that has collectively attracted over 120 citations. His landmark studies, including "Four Years in Review: Statistical Practices of Likert Scales in Human-Robot Interaction Studies" (2020) and "Concerning Trends in Likert Scale Usage in Human-Robot Interaction" (2022), have become essential references for researchers seeking to strengthen empirical standards in the discipline. Beyond measurement methodology, Johnson has made meaningful contributions to robot learning and workplace automation, investigating how robot performance influences human teachers in Learning from Demonstration contexts and examining the social and team dynamics that shape worker adoption of collaborative robotic systems. His applied work extends into aerospace manufacturing, where he has explored human-robot collaborative assembly processes. With a research portfolio spanning foundational methodology, trust dynamics, and real-world industrial deployment, Johnson has established himself as a thoughtful and impactful voice in shaping how HRI research is both conducted and evaluated.
Research Focus
Key Achievements
Top Papers
- 1Four Years in Review65 citations · 2020
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- 7Development and validation of a low-cost mobile robotics testbed2 citations · 2011