Understanding the effect of role gender and personality stereotypes on user acceptance of social robots
The combined impact of women joining the workforce, working couples, and elderly population has made social robots a prospective solution in household. In 2012, the market for consumer robots, mainly focused on personal services at home, reached US$1.6 billion. Therefore, like other deployments of n...
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Format: | Theses and Dissertations |
Language: | English |
Published: |
2014
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Online Access: | http://hdl.handle.net/10356/55440 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | The combined impact of women joining the workforce, working couples, and elderly population has made social robots a prospective solution in household. In 2012, the market for consumer robots, mainly focused on personal services at home, reached US$1.6 billion. Therefore, like other deployments of new technologies, understanding user acceptance of social robots has become a key element to this sizeable economy. The frontier of this emerging interest applied humanly inspired traits, such as gender and personality, into social robot, as a key to provide users with important social cues and affordances. However, existing literature has given little attention to study how the role relationship of robots could influence user acceptance. With the predominant influence of social stereotypes in people’s perceptions and attitudes of their interaction partners in everyday life, this study reconnoitered a social relationship highlighting the role gender and personality stereotypes and their influence on user acceptance of social robots. Three studies were therefore conducted. The foremost study (N=50) examined people’s general perceptions of social robots through a paper card-sorting experiment. Results of the exploratory study highlighted two important dimensions of perceived personality and thereby facilitating the conduct of subsequent studies. The second study (N=198) surveyed whether people hold a set personality as well as gender stereotype of social robots with different roles of robots including companion, domestic helper, healthcare, and security. Based on peoples’ consensus of such stereotypes, the third study (N=164) tested the effect of violating robot role stereotypes on users’ evaluations and acceptance.
Across the studies, we found that people employ a set of clear, distinctive gender and personality stereotypes of different roles of social robots. Further, the violation of these role-stereotypes hindered people’s acceptance of the robots. The effect was mediated through various antecedents, including attitudes and subjective norms, in the Theory of Planned Behaviors. The outcomes of this research largely correspond to the phenomenon of social stereotypes in human society and hence supported our hypothesized human-robot social relationship that constitutes role stereotypes. One notable difference is that though earlier research in sociology often treats the effect of personality stereotypes secondarily in addition to gender stereotypes, this research had uncovered a stronger effect of the role-personality violations on user responses toward social robots. Another important finding of this study is that people’s affective attitudes are as important as their cognitive attitudes in evaluating a social robot.
The contribution of this study is three-fold. Firstly, it distinguished stereotypes as a notable element that enhances the quality of human-robot interaction. Secondly, the study embellished the Theory of Planned Behaviors with several additional antecedents, including cognitive, affective attitudes and perceived trust to predict and explain user acceptance of social robots. Thirdly, the findings of this research corroborate robot designers’ decisions to enhance the design of current social robots, by taking role gender and personality stereotypes into considerations. The findings of this research are also expected to enhance human interactions with social robots from other sectors and even other forms of human-computer/machine interactions. Hence, future research can focus on empirical work to expand the current findings in a long run. |
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