Identifying Gender Differences in Information Processing Style, Self-efficacy, and Tinkering for Robot Tele-operation

As robots become more ubiquitous it is important to understand how different groups of people respond to possible ways of interacting with the robot. In this study, we focused on gender differences while users were tele-operating a humanoid robot that was physically co-located with them. We investigated three factors during the human-robot interaction (1) information processing strategy (2) self-efficacy and (3) tinkering or exploratory behavior. Experimental result show that the information on how to use the robot was processed comprehensively by the female participants whereas males processed them selectively $(\pmb{p} < \mathbf{0.001})$. Males were more confident when using the robot than females $(\pmb{p}=\mathbf{0.0002})$. Males tinkered more with the robot than females $(\pmb{p} = \mathbf{0.0021})$. Tinkering might have resulted in greater task success and lower task completion time for males. Similar to existing work on software interface usability, our results show the importance of accounting for gender differences when developing interfaces for interacting with robots.

[1]  Alice M. Tybout,et al.  Cognitive and Affective Responses to Advertising , 1988 .

[2]  A. Bandura Human agency in social cognitive theory. , 1989, The American psychologist.

[3]  Frank M. Pajares,et al.  Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. , 1994 .

[4]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..

[5]  M. G. Jones,et al.  Gender Differences in Motivation and Strategy Use in Science: Are Girls Rote Learners?. , 1996 .

[6]  Margaret M. Burnett,et al.  Effectiveness of end-user debugging software features: are there gender issues? , 2005, CHI.

[7]  Sara B. Kiesler,et al.  Eliciting information from people with a gendered humanoid robot , 2005, ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005..

[8]  Tatsuya Nomura,et al.  Experimental investigation into influence of negative attitudes toward robots on human–robot interaction , 2006, AI & SOCIETY.

[9]  Ronan G. Reilly,et al.  The Influence of Motivation and Comfort-Level on Learning to Program , 2005, PPIG.

[10]  Mary Beth Rosson,et al.  Supporting end-user debugging: what do users want to know? , 2006, AVI '06.

[11]  N. Leech,et al.  An Array of Qualitative Data Analysis Tools: A Call for Data Analysis Triangulation. , 2007 .

[12]  Charles R. Crowell,et al.  Robot social presence and gender: Do females view robots differently than males? , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[13]  John Baer,et al.  Gender Differences in Creativity , 2008 .

[14]  Cynthia Breazeal,et al.  Persuasive Robotics: The influence of robot gender on human behavior , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Bruce A. MacDonald,et al.  Age and gender factors in user acceptance of healthcare robots , 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.

[16]  Riitta Jääskeläinen Think-aloud protocol , 2010 .

[17]  Ilse Mesters,et al.  Risk Perception and Information Processing: The Development and Validation of a Questionnaire to Assess Self‐Reported Information Processing , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[18]  Austen Rainer,et al.  Case Study Research in Software Engineering - Guidelines and Examples , 2012 .

[19]  Margaret M. Burnett,et al.  End-user debugging strategies: A sensemaking perspective , 2012, TCHI.

[20]  Fons J. Verbeek,et al.  Tinkering in Scientific Education , 2013, Advances in Computer Entertainment.

[21]  Joan Meyers-Levy,et al.  Revisiting gender differences: What we know and what lies ahead☆ , 2015 .

[22]  Margaret M. Burnett,et al.  GenderMag experiences in the field: The whole, the parts, and the workload , 2016, 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).

[23]  Margaret M. Burnett,et al.  Finding Gender-Inclusiveness Software Issues with GenderMag: A Field Investigation , 2016, CHI.

[24]  윤재량 2004 , 2019, The Winning Cars of the Indianapolis 500.