robot acceptance
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2022 ◽  
Vol 64 ◽  
pp. 102813
Author(s):  
Áurea Subero-Navarro ◽  
Jorge Pelegrín-Borondo ◽  
Eva Reinares-Lara ◽  
Cristina Olarte-Pascual

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Laura Fuentes-Moraleda ◽  
Carmen Lafuente-Ibañez ◽  
Natalia Fernandez Alvarez ◽  
Teresa Villace-Molinero

PurposeThe aim of this exploratory study is to identify the factors that influence the acceptance of social robots in museum environments and determine if this influence depends on the visitor's profile (age, gender, education and occupation).Design/methodology/approachData collected from an electronic questionnaire include 433 responses from Spanish visitors. The authors subjected the variables proposed by De Kervenoael et al. (2020) adapted to museums. The initial descriptive analysis only showed significant differences by age (under or over 30 years old). Based on these previous results, an exploratory factor analysis was carried out to test the applicability of the questionnaire to museums. After identifying the factors, the authors applied an ANOVA test to determine whether there are age-related differences between the factors related to robot acceptance in museums.FindingsThe authors developed a multidimensional instrument for measuring willingness to accept social robots in museum contexts. Willingness is determined by three factors: museum visitor experience (which is a factor specific to museums), empathy and personal engagement (which are both usually relevant in other tourist sectors as well). The younger individuals (under 30 years old) have a higher degree of acceptance than do visitors over 30.Originality/valueSocial robot use in museums is still very low, so the key factors for their acceptance have yet to be ascertained. The specific skills of social robots could prove to be a major draw for young people and contribute significantly to the future of museums.


2021 ◽  
Author(s):  
Connor Esterwood ◽  
Kyle Essenmacher ◽  
Han Yang ◽  
Fanpan Zeng ◽  
Lionel Robert

Author(s):  
Melanie Dudek ◽  
Stefanie Baisch ◽  
Monika Knopf ◽  
Thorsten Kolling

Abstract Although companion-type robots are already commercially available, little interest has been taken in identifying reasons for inter-individual differences in their acceptance. Elders’ age-related perceptions of both their own self (self-image) and of the general older robot user (user image) could play a relevant role in this context. Since little is known to date about elders’ companion-type robot user image, it is one aim of this study to investigate its age-related facets, concentrating on possibly stigmatizing perceptions of elder robot users. The study also addresses the association between elders’ age-related self-image and robot acceptance: Is the association independent of the user image or not? To investigate these research questions, N = 28 adults aged 63 years and older were introduced to the companion-type robot Pleo. Afterwards, several markers of robot acceptance were assessed. Actual and ideal self- and subjective robot user image were assessed by a study-specific semantic differential on the stereotype dimensions of warmth and competence. Results show that participants tended to stigmatize elder robot users. The self-images were not directly related to robot acceptance, but affected it in the context of the user image. A higher fit between self- and user image was associated with higher perceived usefulness, social acceptance, and intention to use the robot. To conclude, elders’ subjective interpretations of new technologies play a relevant role for their acceptance. Together with elders’ individual self-images, they need to be considered in both robot development and implementation. Future research should consider that associations between user characteristics and robot acceptance by elders can be complex and easily overlooked.


2020 ◽  
Vol 36 ◽  
pp. 100751 ◽  
Author(s):  
Laura Fuentes-Moraleda ◽  
Patricia Díaz-Pérez ◽  
Alicia Orea-Giner ◽  
Ana Muñoz- Mazón ◽  
Teresa Villacé-Molinero

Author(s):  
C. Mele ◽  
T. Russo Spena ◽  
M. Tregua ◽  
C. Laddaga ◽  
A. Ranieri ◽  
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2020 ◽  
Vol 57 (5) ◽  
pp. 103220 ◽  
Author(s):  
Tuuli Turja ◽  
Iina Aaltonen ◽  
Sakari Taipale ◽  
Atte Oksanen

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