Social robots can benefit aging people, especially those with restricted social interactions and health care, but how do resource-poor older adults respond to them? In this study, 5 focus groups with 60 older participants in rural China revealed their perceptions of social robots, concerns about the technology, and the types of social robots they were likely to accept. The participants cited multiple technological, discomfort, privacy, safety, and financial fraud concerns. They struggled to define robots as machines, humans, or something else but preferred small-sized, animal-shaped, or young female-gendered human-like robots. Their interconnected perceptions, concerns, and preferences illuminate a resource-poor group’s struggles, imaginations, hopes, uncertainties, and vulnerabilities when a new social and technological actor is embedded in their social worlds, reflecting how people understand social robots in relation to themselves and themselves in relation to social robots. Our study findings contribute to understanding social robots’ subjectivities and ways to design culturally and socially acceptable robots.
AjzenI (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes50(2): 179–211.
2.
AlaiadAZhouL (2014) The determinants of home healthcare robots adoption: an empirical investigation. International Journal of Medical Informatics83(11): 825–840.
3.
BaddouraRVentureG (2013) Social vs. useful HRI: experiencing the familiar, perceiving the robot as a sociable partner and responding to its actions. International Journal of Social Robotics5(4): 529–547.
4.
BaddouraRVentureGMatsukataR (2012) The familiar as a key-concept in regulating the social and affective dimensions of HRI. In: 2012 12th IEEE-RAS international conference on humanoid robots (humanoids), Osaka, Japan, 29November–1December, pp. 234–241. New York: IEEE.
5.
BaumeisterRFSommerKL (1997) What do men want? Gender differences and two spheres of belongingness: comment on cross and Madson (1997). Psychological Bulletin122(1): 38–44.
6.
BemelmansRGelderblomGJJonkerP, et al. (2015) Effectiveness of robot Paro in intramural psychogeriatric care: a multicenter quasi-experimental study. Journal of the American Medical Directors Association16(11): 946–950.
7.
BerridgeCZhouYRobillardJM, et al. (2023) Companion robots to mitigate loneliness among older adults: perceptions of benefit and possible deception. Frontiers in Psychology14: 1106633.
8.
BeuscherLMFanJSarkarN, et al. (2017) Socially assistive robots: measuring older adults’ perceptions. Journal of Gerontological Nursing43(12): 35–43.
BroadbentE (2017) Interactions with robots: the truths we reveal about ourselves. Annual Review of Psychology68: 627–652.
11.
BroadbentEStaffordRMacDonaldB (2009) Acceptance of healthcare robots for the older population: review and future directions. International Journal of Social Robotics1(4): 319–330.
12.
BroekensJHeerinkMRosendalH (2009) Assistive social robots in elderly care: a review. Gerontechnology8(2): 94–103.
13.
CarpinellaCMWymanABPerezMA, et al. (2017) The robotic social attributes scale (Rosas) development and validation. In: Proceedings of the 2017 ACM/IEEE international conference on human-robot interaction, Vienna, 6–9March, pp. 254–262. New York: ACM.
Chinese State Council (2022) China’s average life expectancy rises to 78.2 years, 13 July. Available at: https://english.www.gov.cn/statecouncil/ministries/202207/13/content_WS62cdfd4dc6d02e533532da48.html
16.
CoghlanSWaycottJLazarA, et al. (2021) Dignity, autonomy, and style of company: dimensions older adults consider for robot companions. Proceedings of the ACM on Human-Computer Interaction5(CSCW1): 1–25.
17.
de GraafMMAllouchSBKlamerT (2015) Sharing a life with Harvey: exploring the acceptance of and relationship-building with a social robot. Computers in Human Behavior43: 1–14.
18.
de GraafMMAllouchSBLutfiS (2016) What are people’s associations of domestic robots? Comparing implicit and explicit measures. In: 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN), New York, 26–31August, pp. 1077–1083. New York: IEEE.
19.
de GraafMMAllouchSB (2013) Exploring influencing variables for the acceptance of social robots. Robotics and Autonomous Systems61(12): 1476–1486.
20.
DeutschIErelHPazM, et al. (2019) Home robotic devices for older adults: opportunities and concerns. Computers in Human Behavior98: 122–133.
21.
EdwardsA (2018) Animals, humans and machines: interactive implications of ontological classifications. In: GuzmanA (ed.) Human-Machine Communication: Rethinking Communication, Technology, and Ourselves. New York: Peter Lang, pp. 29–50.
22.
EdwardsAEdwardsCWestermanD, et al. (2019) Initial expectations, interactions, and beyond with social robots. Computers in Human Behavior90: 308–314.
FanJXYuZ (2022) Prevalence and risk factors of consumer financial fraud in China. Journal of Family and Economic Issues43(2): 384–396.
25.
FerrariFPaladinoMPJettenJ (2016) Blurring human–machine distinctions: anthropomorphic appearance in social robots as a threat to human distinctiveness. International Journal of Social Robotics8(2): 287–302.
26.
FiskeSTCuddyAJGlickP (2007) Universal dimensions of social cognition: warmth and competence. Trends in Cognitive Sciences11(2): 77–83.
27.
FlandorferP (2012) Population ageing and socially assistive robots for elderly persons: the importance of sociodemographic factors for user acceptance. International Journal of Population Research2012: 829835.
28.
FortunatiLEdwardsA (2021) Moving ahead with human-machine communication. Human-Machine Communication2: 7–28.
29.
FoxJGambinoA (2021) Relationship development with humanoid social robots: applying interpersonal theories to human–robot interaction. Cyberpsychology, Behavior, and Social Networking24(5): 294–299.
30.
GambinoAFoxJRatanRA (2020) Building a stronger CASA: extending the computers are social actors paradigm. Human-Machine Communication1: 71–85.
31.
GaudielloILefortSZibettiE (2015) The ontological and functional status of robots: how firm our representations are?Computers in Human Behavior50: 259–273.
32.
GibbsGR (2007) Thematic coding and categorizing. Analyzing Qualitative Data703: 38–56.
33.
GnambsTAppelM (2019) Are robots becoming unpopular? Changes in attitudes towards autonomous robotic systems in Europe. Computers in Human Behavior93: 53–61.
34.
GoetzJKieslerSPowersA (2003) Matching robot appearance and behavior to tasks to improve human-robot cooperation. In: Proceedings of the 12th IEEE international workshop on robot and human interactive communication, Millbrae, CA, 2November, pp. 55–60. New York: IEEE.
35.
GóngoraASHamriouiSde la TorreDíez I, et al. (2019) Social robots for people with aging and dementia: a systematic review of literature. Telemedicine and e-Health25(7): 533–540.
36.
GuzmanAL (2018) Human-Machine Communication: Rethinking Communication, Technology, and Ourselves. New York: Peter Lang.
37.
GuzmanAL (2020) Ontological boundaries between humans and computers and the implications for human-machine communication. Human-Machine Communication1: 37–54.
38.
GuzmanALLewisSC (2020) Artificial intelligence and communication: a human–machine communication research agenda. New Media & Society22(1): 70–86.
39.
HakkarainenP (2012) ‘No good for shovelling snow and carrying firewood’: social representations of computers and the Internet by elderly Finnish non-users. New Media & Society14(7): 1198–1215.
40.
HeerinkMKröseBEversV, et al. (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. International Journal of Social Robotics2(4): 361–375.
41.
HeppA (2020) Artificial companions, social bots and work bots: communicative robots as research objects of media and communication studies. Media, Culture & Society42(7–8): 1410–1426.
42.
HirschTForlizziJHyderE, et al. (2000) The ELDer project: social, emotional, and environmental factors in the design of eldercare technologies. In: Proceedings on the 2000 conference on universal usability, Arlington, VA, 16–17November, pp. 72–79. New York: ACM.
43.
HurrellA (2017) The retirement homes of the future are being planned today. The Bristol Post. Available at: http://www.bristolpost.co.uk/
44.
KonokVKorcsokBMiklósi GácsiÁM (2018) Should we love robots? The most liked qualities of companion dogs and how they can be implemented in social robots. Computers in Human Behavior80: 132–142.
45.
KüsterDSwiderskaAGunkelD (2021) I saw it on YouTube! How online videos shape perceptions of mind, morality, and fears about robots. New Media & Society23(11): 3312–3331.
46.
LatikkaRTurjaTOksanenA (2019) Self-efficacy and acceptance of robots. Computers in Human Behavior93: 157–163.
47.
LeeHRRiekLD (2018) Reframing assistive robots to promote successful aging. ACM Transactions on Human-Robot Interaction7(1): 1–23.
48.
LiCZhouRYaoN, et al. (2020) Health care utilization and unmet needs in Chinese older adults with multimorbidity and functional impairment. Journal of the American Medical Directors Association21: 806–810.
49.
LiDRauPPLiY (2010) A cross-cultural study: effect of robot appearance and task. International Journal of Social Robotics2(2): 175–186.
50.
LiuSXShenQHancockJ (2021) Can a social robot be too warm or too competent? Older Chinese adults’ perceptions of social robots and vulnerabilities. Computers in Human Behavior125: 106942.
51.
LombardMXuK (2021) Social responses to media technologies in the 21st century: the media are social actors paradigm. Human-Machine Communication2: 29–55.
52.
LutzCTamòAGuzmanA (2018) Communicating with robots: analyzing the interaction between healthcare robots and humans with regards to privacy. In: GuzmanA (ed.) Human-Machine Communication: Rethinking Communication, Technology, and Ourselves. New York: Peter Lang, pp. 145–165.
53.
MacDormanKFVasudevanSKHoCC (2009) Does Japan really have robot mania? Comparing attitudes by implicit and explicit measures. AI & Society23(4): 485–510.
54.
MieczkowskiHLiuSXHancockJ, et al. (2019) Helping not hurting: applying the stereotype content model and BIAS map to social robotics. In: 2019 14th ACM/IEEE international conference on human-robot interaction (HRI), Daegu, South Korea, 11–14March, pp. 222–229. New York: IEEE.
55.
MoQMaZWangG, et al. (2020) The pattern of stressful life events prior to suicide among the older adults in rural China: a national case-control psychological autopsy study. BMC Geriatrics20(1): 1–8.
56.
MoriMMacDormanKFKagekiN (2012) The uncanny valley [from the field]. IEEE Robotics & Automation Magazine19(2): 98–100.
57.
NassCSteuerJTauberER (1994) Computers are social actors. In: Proceedings of SIGCHI ’94 human factors in computing systems, Boston, MA, 24–28April, pp. 72–78. New York: ACM.
58.
NomuraTKandaTSuzukiT (2006) Experimental investigation into influence of negative attitudes toward robots on human–robot interaction. Ai & Society20: 138–150.
59.
PalDPapasratornBChutimaskulW, et al. (2019) Embracing the smart-home revolution in Asia by the elderly: an end-user negative perception modeling. IEEE Access7: 38535–38549.
60.
PapacharissiZ (2018) A Networked Self and Human Augmentics, Artificial Intelligence, Sentience. London: Routledge.
61.
PapadopoulosIKoulougliotiCLazzarinoR, et al. (2020) Enablers and barriers to the implementation of socially assistive humanoid robots in health and social care: a systematic review. BMJ Open10(1): e033096.
62.
PhillipsEUllmanDde GraafMM, et al. (2017) What does a robot look like? A multi-site examination of user expectations about robot appearance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting61: 1215–1219.
63.
PhillipsEZhaoXUllmanD, et al. (2018) What is human-like? Decomposing robots’ human-like appearance using the anthropomorphic roBOT (ABOT) database. In: Proceedings of the 2018 ACM/IEEE international conference on human-robot interaction, Chicago, IL, 5–8March, pp. 105–113. New York: ACM.
64.
PinoMBoulayMJouenF, et al. (2015) “Are we ready for robots that care for us?” Attitudes and opinions of older adults toward socially assistive robots. Frontiers in Aging Neuroscience7: 141.
65.
PolsJWillemsD (2011) Innovation and evaluation: taming and unleashing telecare technology. Sociology of Health and Illness33(3): 484–498.
66.
PrakashAKempCCRogersWA (2014) Older adults’ reactions to a robot’s appearance in the context of home use. In: 2014 9th ACM/IEEE international conference on human-robot interaction (HRI), Bielefeld, 3–6March, pp. 268–269. New York: IEEE.
67.
ReevesBNassCI (1996) The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge: Cambridge University Press.
68.
ReevesBHancockJLiuSX (2020) Social robots are like real people: first impressions, attributes, and stereotyping of social robots. Technology, Mind, and Behavior1(1): 1–14.
69.
RiekLD (2017) Healthcare robotics. Communications of the ACM60(11): 68–78.
70.
RobinsonHMacDonaldBBroadbentE (2014) The role of healthcare robots for older people at home: a review. International Journal of Social Robotics6(4): 575–591.
71.
RuebenMAroyoAMLutzC, et al. (2018) Themes and research directions in privacy-sensitive robotics. In: 2018 IEEE workshop on advanced robotics and its social impacts (ARSO), Genoa, 27–29September, pp. 77–84. New York: IEEE.
72.
RuipingF (2012) Confucian reflective equilibrium: why principlism is misleading for Chinese bioethical decision-making. Asian Bioethics Review4(1): 4–13.
73.
ŠabanovićSChangWLBennettCC, et al. (2015) A robot of my own: participatory design of socially assistive robots for independently living older adults diagnosed with depression. In: ZhouJSalvendyG (eds) International Conference on Human Aspects of IT for the Aged Population. Cham: Springer, pp. 104–114.
74.
ScoglioAAReillyEDGormanJA, et al. (2019) Use of social robots in mental health and well-being research: systematic review. Journal of Medical Internet Research21(7): e13322.
75.
SeabornKPennefatherP (2022) Neither “hear” nor “their”: interrogating gender neutrality in robots. In: HRI ’22: Proceedings of the 2022 ACM/IEEE international conference on human-robot interaction, Sapporo, Japan, 7–10March, pp. 1030–1034. New York: ACM.
76.
ShibataTWadaKIkedaY, et al. (2009) Cross-cultural studies on subjective evaluation of a seal robot. Advanced Robotics23(4): 443–458.
77.
SongYZhangJZhangX (2021) Cultural or institutional? Contextual effects on domestic violence against women in rural China. Journal of Family Violence36: 643–655.
78.
StraussACorbinJM (1997) Grounded Theory in Practice. Thousand Oaks, CA: Sage.
79.
SundarSSJungEHWaddellTF, et al. (2017) Cheery companions or serious assistants? Role and demeanor congruity as predictors of robot attraction and use intentions among senior citizens. International Journal of Human-Computer Studies97: 88–97.
80.
TaipaleSFortunatiL (2018) Communicating with machines. In: GuzmanA (ed.) Human-Machine Communication: Rethinking Communication, Technology, and Ourselves. New York: Peter Lang, pp. 201–220.
81.
The Lancet T (2022) Population ageing in China: crisis or opportunity?The Lancet400: 1821.
82.
TropeYLibermanN (2010) Construal-level theory of psychological distance. Psychological Review117(2): 440–463.
83.
VandemeulebrouckeTDziKGastmansC (2021) Older adults’ experiences with and perceptions of the use of socially assistive robots in aged care: a systematic review of quantitative evidence. Archives of Gerontology and Geriatrics95: 104399.
84.
VollmerALReadRTrippasD, et al. (2018) Children conform, adults resist: a robot group induced peer pressure on normative social conformity. Science Robotics3(21): eaat7111.
85.
Von der PüttenAMKrämerNC (2012) A survey on robot appearances. In: Proceedings of the seventh annual ACM/IEEE international conference on human-robot interaction, Boston, MA, 5–8March, pp. 267–268. New York: ACM.
86.
WangSBollingKMaoW, et al. (2019) Technology to support aging in place: older adults’ perspectives. Healthcare7: 60.
87.
WuL (2014) Psychological analysis of trust crisis. Journal of Suzhou University2(4): 50–57.
88.
WuY-HFassertCRigaudA-S (2012) Designing robots for the elderly: appearance issue and beyond. Archives of Gerontology and Geriatrics54(1): 121–126.
89.
XinZXinS (2017) Marketization process predicts trust decline in China. Journal of Economic Psychology62: 120–129.
90.
YogeeswaranKZlotowskiJLivingstoneM, et al. (2016) The interactive effects of robot anthropomorphism and robot ability on perceived threat and support for robotics research. Journal of Human-Robot Interaction5(2): 29–47.
91.
ZhaoS (2006) Humanoid social robots as a medium of communication. New Media & Society8(3): 401–419.
92.
ZhongBLRuanYFXuYM, et al. (2020) Prevalence and recognition of depressive disorders among Chinese older adults receiving primary care: a multi-center cross-sectional study. Journal of Affective Disorders260: 26–31.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
