Improving Social Robot Recommendation Acceptance Through Geo-Gender Affinity Construction

As artificial intelligence and robotics advance, social robots are increasingly integrated into service domains, necessitating strategies to enhance user acceptance of their recommendations. Prior work has explored how gendered appearance influences user acceptance, yet the role of linguistic featur...

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Published in:IEEE robotics and automation letters Vol. 10; no. 9; pp. 9064 - 9071
Main Authors: Fu, Changzeng, Li, Zihan, Wang, Songyang, Ishiguro, Hiroshi, Yoshikawa, Yuichiro
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.09.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Affinity
Anthropomorphism
Artificial intelligence
Attraction
Cultural differences
Dialects
Gender
genderization
geo-gender affinity
Linguistics
recommendation acceptance
Recommender systems
Robotics
Robots
Social robots
Systematics
Training
ISSN:2377-3766, 2377-3766
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Abstract As artificial intelligence and robotics advance, social robots are increasingly integrated into service domains, necessitating strategies to enhance user acceptance of their recommendations. Prior work has explored how gendered appearance influences user acceptance, yet the role of linguistic features, such as regional dialects and gendered text, remains underexplored. Drawing on Social Influence Theory, we propose that linguistic similarity (e.g., dialect familiarity) and gendered communication styles may synergistically enhance attraction and identification, in turn boosting recommendation acceptance. To address this gap, we investigate three research questions: (1) How do regional dialects and gendered linguistic styles independently affect robot's attraction and identification (e.g., perceived robot likability, anthropomorphism, and intelligence)? (2) Does gendered text in robot speech enhance acceptance of recommendations? (3) To what extent do regional dialects and gendered styles interact to jointly shape outcomes? We introduce geo-gender affinity, a design principle combining regional dialects ("geo") and gendered linguistic features ("gender"), and test its effects via a 2 × 2 between-subjects experiment with 62 Chinese-speaking participants. Results demonstrate that regional dialects significantly heightened likability, anthropomorphism, and perceived intelligence (addressing RQ1), while gendered text improved recommendation acceptance (RQ2). Notably, the combination of dialects and gendered text revealed that geo-gender affinity amplified acceptance for perceiving linguistic elements as aligned with their identity (RQ3). These findings establish geo-gender affinity as a socially grounded framework for optimizing robot communication, offering practical implications for AI-driven services to enhance user engagement and trust.
AbstractList As artificial intelligence and robotics advance, social robots are increasingly integrated into service domains, necessitating strategies to enhance user acceptance of their recommendations. Prior work has explored how gendered appearance influences user acceptance, yet the role of linguistic features, such as regional dialects and gendered text, remains underexplored. Drawing on Social Influence Theory, we propose that linguistic similarity (e.g., dialect familiarity) and gendered communication styles may synergistically enhance attraction and identification, in turn boosting recommendation acceptance. To address this gap, we investigate three research questions: (1) How do regional dialects and gendered linguistic styles independently affect robot's attraction and identification (e.g., perceived robot likability, anthropomorphism, and intelligence)? (2) Does gendered text in robot speech enhance acceptance of recommendations? (3) To what extent do regional dialects and gendered styles interact to jointly shape outcomes? We introduce geo-gender affinity, a design principle combining regional dialects ("geo") and gendered linguistic features ("gender"), and test its effects via a 2 × 2 between-subjects experiment with 62 Chinese-speaking participants. Results demonstrate that regional dialects significantly heightened likability, anthropomorphism, and perceived intelligence (addressing RQ1), while gendered text improved recommendation acceptance (RQ2). Notably, the combination of dialects and gendered text revealed that geo-gender affinity amplified acceptance for perceiving linguistic elements as aligned with their identity (RQ3). These findings establish geo-gender affinity as a socially grounded framework for optimizing robot communication, offering practical implications for AI-driven services to enhance user engagement and trust.
Author Li, Zihan
Wang, Songyang
Fu, Changzeng
Ishiguro, Hiroshi
Yoshikawa, Yuichiro
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10.1109/HRI.2019.8673222
10.1089/gg.2016.29002.nom
10.1108/JHTT-12-2023-0428
10.1007/s12369-023-01035-8
10.1007/s12369-018-0502-7
10.1177/1094670511414551
10.1016/j.ijhm.2022.103166
10.3200/SOCP.148.2.253-256
10.1007/978-3-319-47437-3_20
10.1080/0144929X.2020.1723701
10.1007/s12369-008-0001-3
10.1109/IROS.2009.5354116
10.1016/j.jbusres.2022.03.087
10.3390/su13179655
10.1109/RO-MAN47096.2020.9223599
10.4324/9781315703855
10.1108/JPBM-05-2016-1199
10.1037/0033-295X.98.2.224
10.3390/robotics10010047
10.1007/s12369-020-00643-y
10.1109/LRA.2021.3094779
10.1109/RO-MAN47096.2020.9223432
10.1109/HRI.2019.8673232
10.1145/1463160.1463235
10.14807/ijmp.v13i1.1555
10.1111/ijcs.12755
10.3390/s22030991
10.1007/978-981-99-8718-4_7
10.1007/978-1-4419-1465-1_19
10.1002/mar.21498
10.1093/joc/50.1.46
10.1016/s0065-2601(07)00002-0
10.3389/frobt.2022.983955
10.20965/jrm.2023.p0844
10.1016/j.chb.2014.05.014
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References ref13
ref35
ref12
ref34
ref15
ref37
ref14
ref36
ref31
ref11
ref10
ref32
ref2
ref1
ref17
ref39
ref16
ref38
ref18
Zhang (ref25) 2007
Kraus (ref33) 2018
ref24
ref23
ref26
ref20
ref22
ref21
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
Jincheng (ref30) 2017; 39
ref40
Reeves (ref19) 1996; 10
References_xml – ident: ref14
  doi: 10.1108/IJCHM-12-2022-1523
– ident: ref12
  doi: 10.1109/HRI.2019.8673222
– ident: ref31
  doi: 10.1089/gg.2016.29002.nom
– ident: ref1
  doi: 10.1108/JHTT-12-2023-0428
– ident: ref8
  doi: 10.1007/s12369-023-01035-8
– ident: ref9
  doi: 10.1007/s12369-018-0502-7
– ident: ref24
  doi: 10.1177/1094670511414551
– ident: ref35
  doi: 10.1016/j.ijhm.2022.103166
– ident: ref20
  doi: 10.3200/SOCP.148.2.253-256
– ident: ref7
  doi: 10.1007/978-3-319-47437-3_20
– start-page: 116
  issue: 4
  year: 2007
  ident: ref25
  article-title: Dialectal metaphors and cultural identity in verbal communication
  publication-title: J. Dalian Maritime Univ. (Social Sci. Edition)
– ident: ref26
  doi: 10.1080/0144929X.2020.1723701
– volume: 10
  start-page: 19
  issue: 10
  year: 1996
  ident: ref19
  article-title: The media equation: How people treat computers, television, and new media like real people
  publication-title: Cambridge, U.K.
– ident: ref37
  doi: 10.1007/s12369-008-0001-3
– ident: ref11
  doi: 10.1109/IROS.2009.5354116
– ident: ref5
  doi: 10.1016/j.jbusres.2022.03.087
– ident: ref13
  doi: 10.3390/su13179655
– ident: ref18
  doi: 10.1109/RO-MAN47096.2020.9223599
– ident: ref23
  doi: 10.4324/9781315703855
– ident: ref22
  doi: 10.1108/JPBM-05-2016-1199
– volume: 39
  start-page: 41
  issue: 1
  volume-title: J. Univ. Shanghai Sci. Technol.
  year: 2017
  ident: ref30
  article-title: A study of gender differences in code-switching between the standard language and its dialects
– ident: ref38
  doi: 10.1037/0033-295X.98.2.224
– ident: ref4
  doi: 10.3390/robotics10010047
– ident: ref2
  doi: 10.1007/s12369-020-00643-y
– ident: ref3
  doi: 10.1109/LRA.2021.3094779
– ident: ref10
  doi: 10.1109/RO-MAN47096.2020.9223432
– ident: ref27
  doi: 10.1109/HRI.2019.8673232
– ident: ref28
  doi: 10.1145/1463160.1463235
– ident: ref21
  doi: 10.14807/ijmp.v13i1.1555
– ident: ref15
  doi: 10.1111/ijcs.12755
– ident: ref6
  doi: 10.3390/s22030991
– ident: ref17
  doi: 10.1007/978-981-99-8718-4_7
– volume-title: Proc. Eleventh Int. Conf. Lang. Resour. Eval.
  year: 2018
  ident: ref33
  article-title: Effects of gender stereotypes on trust and likability in soken human-robot interaction
– ident: ref34
  doi: 10.1007/978-1-4419-1465-1_19
– ident: ref36
  doi: 10.1002/mar.21498
– ident: ref40
  doi: 10.1093/joc/50.1.46
– ident: ref39
  doi: 10.1016/s0065-2601(07)00002-0
– ident: ref29
  doi: 10.3389/frobt.2022.983955
– ident: ref16
  doi: 10.20965/jrm.2023.p0844
– ident: ref32
  doi: 10.1016/j.chb.2014.05.014
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SubjectTerms Affinity
Anthropomorphism
Artificial intelligence
Attraction
Cultural differences
Dialects
Gender
genderization
geo-gender affinity
Linguistics
recommendation acceptance
Recommender systems
Robotics
Robots
Social robots
Systematics
Training
Title Improving Social Robot Recommendation Acceptance Through Geo-Gender Affinity Construction
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