Abstract
The willingness to use service robots plays a pivotal role in human–robot interaction. To establish a valid measure in the Chinese context, this study aimed to revisit the validity and reliability of the Service Robot Integration Willingness (SRIW) Scale among Chinese adults. A total of 955 participants were recruited to complete the Chinese version of the SRIW. Our findings revealed a four-factor model comprising 31 items, indicating a strong model fit. Furthermore, trust in automation correlated positively with the Chinese SRIW, while negative attitudes toward robots exhibited a significant inverse correlation, supportting the Chinese SRIW’s substantial criterion-related validity. In conclusion, this article introduces an updated Chinese SRIW, underscoring its efficacy in measuring the readiness to adopt service robots in China.
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References
Appel M, Izydorczyk D, Weber S, Mara M, Lischetzke T (2020) The uncanny of mind in a machine: humanoid robots as tools, agents, and experiencers. Comput Hum Behav 102:274–286. https://doi.org/10.1016/j.chb.2019.07.031
Babamiri M, Heidarimoghadam R, Ghasemi F, Tapak L, Mortezapour A (2022) Insights into the relationship between usability and willingness to use a robot in the future workplaces: studying the mediating role of trust and the moderating roles of age and STARA. PLoS ONE 17:1–12. https://doi.org/10.1371/journal.pone.0268942
Bartneck C, Kulić D, Croft E, Zoghbi S (2009) Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int J Soc Robot 1(1):71–81. https://doi.org/10.1007/s12369-008-0001-3
Belanche D, Casaló LV, Flavián C, Schepers J (2020) Service robot implementation: a theoretical framework and research agenda. Serv Ind J 40(3–4):203–225. https://doi.org/10.1080/02642069.2019.1672666
Borland J, Coelli M (2017) Are robots taking our jobs? Australian Econom Rev 50(4):377–397. https://doi.org/10.1111/1467-8462.12245
Brown SA, Venkatesh V (2005) Model of adoption of technology in households: a baseline model test and extension incorporating household life cycle. MIS Q: Manage Inf Syst 29(3):399–426. https://doi.org/10.2307/25148690
Cai J, Sun Q, Mu Z, Sun X (2022) Psychometric properties of the Chinese version of the trust between People and Automation Scale (TPAS) in Chinese adults. Reflexão e Crítica, Psicologia. https://doi.org/10.1186/s41155-022-00219-x
Chang C, Shao B, Li Y, Zhang Y (2022) Factors influencing consumers’ willingness to accept service robots: based on online reviews of Chinese hotels. Front Psychol 13:1–16. https://doi.org/10.3389/fpsyg.2022.1016579
Cheng H, Jia R, Li D, Li H (2019) The rise of robots in China. J Econom Perspect 33(2):71–88. https://doi.org/10.1257/jep.33.2.71
Chi OH, Denton G, Gursoy D (2020) Artificially intelligent device use in service delivery: a systematic review, synthesis, and research agenda. J Hosp Market Manag 29(7):757–786. https://doi.org/10.1080/19368623.2020.1721394
Childers TL, Carr CL, Peck J, Carson S (2001) Hedonic and utilitarian motivations for online retail shopping behavior. J Retail 77(4):511–535. https://doi.org/10.1016/S0022-4359(01)00056-2
Chuah SHW, Yu J (2021) The future of service: the power of emotion in human-robot interaction. J Retail Consum Serv 61:102551. https://doi.org/10.1016/j.jretconser.2021.102551
De Visser EJ, Monfort SS, Goodyear K, Lu L, O’Hara M, Lee MR, Krueger F (2017) A little anthropomorphism goes a long way: effects of oxytocin on trust, compliance, and team performance with automated agents. Hum Factors 59(1):116–133. https://doi.org/10.1177/0018720816687205
De Visser EJ, Peeters MMM, Jung MF, Kohn S, Shaw TH, Pak R, Neerincx MA (2020) Towards a theory of longitudinal trust calibration in human-robot teams. Int J Soc Robot 12(2):459–478. https://doi.org/10.1007/s12369-019-00596-x
Drnec K, Marathe AR, Lukos JR, Metcalfe JS (2016) From trust in automation to decision neuroscience: applying cognitive neuroscience methods to understand and improve interaction decisions involved in human automation interaction. Front Hum Neurosci 10:1–14. https://doi.org/10.3389/fnhum.2016.00290
Duffy RD, Allan BA, England JW, Blustein DL, Autin KL, Douglass RP, Santos EJR (2017) The development and initial validation of the decent work scale. J Couns Psychol 64(2):206
Dzindolet MT, Peterson SA, Pomranky RA, Pierce LG, Beck HP (2003) The role of trust in automation reliance. Int J Hum Comput Stud 58(6):697–718. https://doi.org/10.1016/S1071-5819(03)00038-7
Epley N, Waytz A, Cacioppo JT (2007) On seeing human: a three-factor theory of anthropomorphism. Psychol Rev 114(4):864–886. https://doi.org/10.1037/0033-295X.114.4.864
Frantz R (2003) Herbert Simon, Artificial intelligence as a framework for understanding intuition. J Econom Psychol 24(2):265–277. https://doi.org/10.1016/S0167-4870(02)00207-6
Gonzalez-Aguirre JA, Osorio-Oliveros R, Rodríguez-Hernández KL, Lizárraga-Iturralde J, Menendez RM, Ramírez-Mendoza RA, de Lozoya-Santos J, J. (2021) Service robots: trends and technology. Appl Sci (Switzerland) 11(22):1–22
Gursoy D, Chi OH, Lu L, Nunkoo R (2019) Consumers acceptance of artificially intelligent (AI) device use in service delivery. Int J Inf Manage 49:157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
Hancock PA, Billings DR, Schaefer KE, Florida C, Chen JYC, De Visser EJ, Parasuraman R (2011) A meta-analysis of factors affecting trust in human-robot interaction. Hum Factors 53(5):517–527. https://doi.org/10.1177/0018720811417254
Hayton JC, Allen DG, Scarpello V (2004) Factor retention decisions in exploratory factor analysis: a tutorial on parallel analysis. Organ Res Methods 7(2):191–205. https://doi.org/10.1177/1094428104263675
Hinton PR., McMurray I, Brownlow C (2014). SPSS explained. Routledge (Routledge). London,England: Routledge
Hlee S, Park J, Park H, Koo C, Chang Y (2022) Understanding customer’s meaningful engagement with AI-powered service robots. Inf Technol People. https://doi.org/10.1108/ITP-10-2020-0740
Hu Q, Pan X, Luo J, Yu Y (2022) The effect of service robot occupational gender stereotypes on customers’ willingness to use them. Front Psychol 13:1–15. https://doi.org/10.3389/fpsyg.2022.985501
Huang MH, Rust RT (2018) Artificial intelligence in service. J Serv Res 21(2):155–172. https://doi.org/10.1177/1094670517752459
Hussein A, Elsawah S, Abbass HA (2020) Trust mediating reliability-reliance relationship in supervisory control of human-swarm interactions. Hum Factors 62(8):1237–1248. https://doi.org/10.1177/0018720819879273
Ismatullaev UVU, Kim SH (2022). Review of the Factors Affecting Acceptance of AI-Infused Systems, Human Factors: J Human Factors Ergonom Soc, https://doi.org/10.1177/00187208211064707
Ivanov S, Webster C (2021) Willingness-to-pay for robot-delivered tourism and hospitality services – an exploratory study. Int J Contemp Hosp Manag 33(11):3926–3955. https://doi.org/10.1108/IJCHM-09-2020-1078
Javaid M, Haleem A (2020) Critical components of industry 5.0 towards a successful adoption in the field of manufacturing. J Indust Int Manage 5(3):327–348
Jian J-Y, Bisantz AM, Drury CG (2000) Foundations for an empirically determined scale of trust in automated systems. Int J Cogn Ergon 4(1):53–71. https://doi.org/10.1207/s15327566ijce0401_04
Karabegović I, Doleček V (2017) The role of service robots and robotic systems in the treatment of patients in medical institutions. Lect Notes Netw Syst 3:9–25. https://doi.org/10.1007/978-3-319-47295-9_2
Kelley TL (1939) The selection of upper and lower groups for the validation of test items. J Educ Psychol 30(1):17–24. https://doi.org/10.1037/h0057123
Kieslich K, Lünich M, Marcinkowski F (2021) The threats of artificial intelligence scale (TAI): development, measurement and test over three application domains. Int J Soc Robot 13(7):1563–1577. https://doi.org/10.1007/s12369-020-00734-w
Kim YW, Lim C, Ji YG (2022) Exploring the user acceptance of urban air mobility: extending the technology acceptance model with trust and service quality factors. Int J Human-Comput Int. https://doi.org/10.1080/10447318.2022.2087662
Kimmig R, Verheijen RHM, Rudnicki M (2020) Robot assisted surgery during the COVID-19 pandemic, especially for gynecological cancer: a statement of the society of european robotic gynaecological surgery (SERGS). J Gynecol Oncol 31(3):1–7. https://doi.org/10.3802/jgo.2020.31.e59
Körtner T (2016) Ethical challenges in the use of social service robots for elderly people. Z Gerontol Geriatr 49(4):303–307. https://doi.org/10.1007/s00391-016-1066-5
Lee I (2021) Service robots: a systematic literature review. Electronics 10:2658. https://doi.org/10.3390/electronics10212658
Lu L, Cai R, Gursoy D (2019) Developing and validating a service robot integration willingness scale. Int J Hosp Manag 80:36–51. https://doi.org/10.1016/j.ijhm.2019.01.005
Mende M, Scott ML, van Doorn J, Grewal D, Shanks I (2019) Service robots rising: how humanoid robots influence service experiences and elicit compensatory consumer responses. J Mark Res 56(4):535–556. https://doi.org/10.1177/0022243718822827
Morris MW, Peng K (1994) Culture and cause : american and chinese attributions for social and physical events psychological approaches to explaining causal attribution. J Pers Soc Psychol 67(6):949–971
Nomura T, Kanda T, Yamada S, Suzuki T (2011). Exploring influences of robot anxiety into HRI. In: HRI 2011 Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction, 213–214. https://doi.org/10.1145/1957656.1957737
Nomura T, Suzuki T, Kanda T, Kato K (2006) Measurement of negative attitudes toward robots interaction studies. Social Behav Commun Biol Artif Syst 7(3):437–454
NunnallyJC, Bernstein IH (1994). Psychometric Theory(3rd ed.). New York: McGraw-Hill.
Paliga M, Pollak A (2021) Development and validation of the fluency in human-robot interaction scale. a two-wave study on three perspectives of fluency. Int J Human Comput Stud 155:102698–102798. https://doi.org/10.1016/j.ijhcs.2021.102698
Peng K, Nisbett RE (1999) Culture, dialectics, and reasoning about contradiction. Am Psychol 54(9):741–754
Pillai R, Sivathanu B (2020) Adoption of AI-based chatbots for hospitality and tourism. Int J Contemp Hosp Manag 32(10):3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259
Rahwan I, Cebrian M, Obradovich N, Bongard J, Bonnefon JF, Breazeal C, Wellman M (2019) Machine behaviour. Nature 568(7753):477–486. https://doi.org/10.1038/s41586-019-1138-y
Regmi K, Naidoo J, Pilkington P (2010) Understanding the processes of translation and transliteration in qualitative research. Int J Qual Methods 9(1):16–26. https://doi.org/10.1177/160940691000900103
Robichaud-Ekstrand S, Haccoun RR, Millette D (1994) A method for validating a translated questionnaire. Canadian J Nurs Res 26(3):77–87
Roseman IJ (2008) Motivations and emotivations: Approach, avoidance, and other tendencies in motivated and emotional behavior. In: Elliot AJ (ed) Handbook of approach and avoidance motivation. Psychology Press, New York
Shank DB, Graves C, Gott A, Gamez P, Rodriguez S (2019) Feeling our way to machine minds: People’s emotions when perceiving mind in artificial intelligence. Comput Hum Behav 98:256–266. https://doi.org/10.1016/j.chb.2019.04.001
Smith ER, Sherrin S, Fraune MR, Šabanović S (2020) Positive emotions, more than anxiety or other negative emotions, predict willingness to interact with robots. Pers Soc Psychol Bull 46(8):1270–1283. https://doi.org/10.1177/0146167219900439
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q: Manage Inf Syst 27(3):425–478. https://doi.org/10.2307/30036540
Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q: Manage Inf Syst 36(1):157–178. https://doi.org/10.2307/41410412
Wirtz J, Patterson PG, Kunz WH, Gruber T, Lu VN, Paluch S, Martins A (2018) Brave new world: service robots in the frontline. J Serv Manag 29(5):907–931. https://doi.org/10.1108/JOSM-04-2018-0119
Wolbring G, Yumakulov S (2014) Social robots: views of staff of a disability service organization. Int J Soc Robot 6(3):457–468. https://doi.org/10.1007/s12369-014-0229-z
Yoon SN, Lee DH (2019) Artificial intelligence and robots in healthcare: What are the success factors for technology-based service encounters? Int J Healthcare Manage 12(3):218–225. https://doi.org/10.1080/20479700.2018.1498220
Yu CE, Ngan HFB (2019) The power of head tilts: gender and cultural differences of perceived human vs human-like robot smile in service. Tour Rev 74(3):428–442. https://doi.org/10.1108/TR-07-2018-0097
Zhang T, Tao D, Qu X, Zhang X, Lin R, Zhang W (2019) The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transport Res Part C: Emerg Technol 98:207–220. https://doi.org/10.1016/j.trc.2018.11.018
Złotowski J, Yogeeswaran K, Bartneck C (2017) Can we control it? Autonomous robots threaten human identity, uniqueness, safety, and resources. Int J Hum Comput Stud 100:48–54. https://doi.org/10.1016/j.ijhcs.2016.12.008
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Funding was provided by Science Foundation of Zhejiang Sci-Tech University (ZSTU), (21062112-Y), Jie Cai
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Cai, J., Tang, X., Lu, X. et al. Psychometric Properties of the Chinese Version of Service Robot Integration Willingness (SRIW) Scale in the Chinese Sample of Adults. Int J of Soc Robotics 16, 245–256 (2024). https://doi.org/10.1007/s12369-023-01075-0
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DOI: https://doi.org/10.1007/s12369-023-01075-0