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International Journal of Social Robotics

Erschienen in:

05.03.2021

Factors Predicting Patients’ Willingness to Use Robotic Dental Services

verfasst von: Mattie Milner, Rian Mehta, Scott R. Winter, Stephen Rice, Emily Anania, Nadine Ragbir, Cynthia Smith

Erschienen in: International Journal of Social Robotics | Ausgabe 7/2021

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Abstract

Prior research into automated dental procedures has largely focused on design and engineering issues; however, there is very little research that investigates the public’s perceptions of, or their willingness to undergo, robotic dentistry. Therefore, the purpose of the current study is to better understand the characteristics of a person who would be willing to undergo robotic dental cleanings by building a predictive model. Using a correlational research design with regression, two stages were utilized to complete the study. Through two stages of research, 475 participants were recruited via an online database. Stage one evaluated several statistical factors on user willingness to create a predictive regression model. Stage two employed a secondary sample to validate the predictive model created in stage one by testing the regression equation for model fit. This predictive model accounted for approximately 82% of the variance in willingness to use dental robots for cleanings. The significant predictors were feelings of fear, feelings of happiness, complexity, useful factor, fun factor, fear of dental visits, and age respectively. These results are useful for the design and marketing of dental robots.

Vorheriger Artikel Use of a Social Robot in the Implementation of a Narrative Intervention for Young People with Cystic Fibrosis: A Feasibility Study

Nächster Artikel Adaptive Control of Robot Series Elastic Drive Joint Based on Optimized Radial Basis Function Neural Network

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Anania E, Milner MN, Ragbir N, Pierce M, Walters NW, Rice S (2018) Factors affecting consumers’ acceptance of robotic dentists. In: Poster presented at the 2018 international symposium on human factors and ergonomics in health care, Boston, Massachusetts

Anania EC, Rice S, Winter SR (2020) Building a predictive model of US patient willingness to undergo robotic surgery. J Robot Surg. https://doi.org/10.1007/s11701-020-01092-5CrossRef

Aron R, Dutta S, Janakiraman R, Pathak P (2011) The impact of automation of systems on medical errors: evidence from field research. Inf Syst Res 22(3):429–446. https://doi.org/10.1287/isre.1110.0350CrossRef

Babin BJ, Attaway JS (2000) Atmospheric affect as a tool for creating value and gaining share of customer. J Bus Res 49(2):91–99. https://doi.org/10.1016/S0148-2963(99)00011-9CrossRef

Baker J, Cameron M (1996) The effects of the service environment on affect and consumer perception of waiting time: an integrative review and research propositions. J Acad Mark Sci 24(4):338–349. https://doi.org/10.1177/0092070396244005CrossRef

Beasley RA (2012) Medical robots: current systems and research directions. J Robot 2012:1–14. https://doi.org/10.1155/2012/401613CrossRef

Bisler A, Bockholt U, Voss G (2002) The virtual articulator—Applying VR technologies to dentistry. In: Proceedings of sixth international conference on information visualization, London, UK. DOI: 10.1109/IV.2002.1028835

Buhrmester M, Kwang T, Gosling SD (2011) Amazon’s Mechanical Turk: a new source of inexpensive, yet high-quality, data? Perspect Psychol Sci 6(3):3–5. https://doi.org/10.1177/1745691610393980CrossRef

Byrne A, Utkus SP (2013) Behavioural finance. Understanding how the mind can help or hinder investment success. Vanguard. Retrieved from https://www.vanguard.co.uk/documents/portal/literature/behavourial-finance-guide.pdf

10.

Byrnes JP, Miller DC, Schafer WD (1999) Gender differences in risk taking: a meta-analysis. Psychol Bull 125(3):367–383. https://doi.org/10.1037/0033-2909.125.3.367CrossRef

11.

Charness G, Gneezy U (2012) Strong evidence for gender differences in risk taking. J Econ Behav Organ 83(1):50–58. https://doi.org/10.1016/j.jebo.2011.06.007CrossRef

12.

Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 35(8):982–1003. https://doi.org/10.1287/mnsc.35.8.982CrossRef

13.

Dwyer PD, Gilkeson JH, List JA (2002) Gender differences in revealed risk taking: evidence from mutual fund investors. Econ Lett 76(2):151–158. https://doi.org/10.1016/S0165-1765(02)00045-9CrossRefMATH

14.

Eckel C, Grossman P (2008) Chapter 113 men, women, and risk aversion: experimental evidence. Handb Exp Econ Res 1:1061–1073. https://doi.org/10.1016/s1574-0722(07)00113-8CrossRef

15.

Ekman P, Friesen W (1971) Constants across cultures in the face and emotion. J Pers Soc Psychol 17(2):124–129. https://doi.org/10.1037/h0030377CrossRef

16.

Falan S, Han B (2011) Moving towards efficient, safe, and meaningful healthcare: issues for automation. Int J Electron Healthc 6(1):76–93. https://doi.org/10.1504/IJEH.2011.03906CrossRef

17.

Ferrer R, Klein W, Lerner J, Reyna V, Keltner D (2015) Emotions and health decision making: extending the appraisal tendency framework to improve health and healthcare. In: Roberto C, Kawachi I (eds) Behavioral Economics and Public Health, pp 1–25. https://doi.org/10.1093/med/9780199398331.001.0001

18.

Field A (2013) Discovering statistics using IBM SPSS Statistics. Sage, London

19.

Germine L, Nakayama K, Duchaine BC, Chabris CF, Chatterjee G, Wilmer JB (2012) Is the web as good as the lab? Comparable performance from web and lab in cognitive/perceptual experiments. Psychon Bull Rev 19(5):847–857. https://doi.org/10.3758/s13423-012-0296-9CrossRef

20.

Griffiths PG, Gillespie RB (2005) Sharing control between humans and automation using haptic interface: primary and secondary task performance benefits. Hum Factors 47(3):574–590. https://doi.org/10.1518/001872005774859944CrossRef

21.

Groeger M, Arbter K, Hirzinger G (2008) Motion tracking for minimally invasive robotic surgery. Med Robot. https://doi.org/10.5772/5244CrossRef

22.

Gulati M, Anand V, Salaria SK, Jain N, Gupta S (2015) Computerized implant-dentistry: advances toward automation. J Indian Soc Periodontol 19(1):5–10. https://doi.org/10.4103/0972-124X.145781CrossRef

23.

Gupta AK, Arora SK (2013) Industrial automation and robotics [Google Books version]. Retrieved from https://books.google.com/books?id=Y7rgCP7iC18C&printsec=copyright#v=onepage&q&f=false

24.

Gustafsod PE (1998) Gender Differences in risk perception: theoretical and methodological perspectives. Risk Anal 18(6):805–811. https://doi.org/10.1023/B:RIAN.0000005926.03250.c0CrossRef

25.

Hair JF Jr, Anderson RE, Tatham RL, Black WC (1995) Multivariate data analysis, 3rd edn. Macmillan Publishing Company, New York

26.

Hillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, Taylor R (2005) Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff 24(5):1103–1117. https://doi.org/10.1377/hlthaff.24.5.1103CrossRef

27.

Hoff KA, Bashir M (2014) Trust in automation: integrating empirical evidence on factors that influence trust. Hum Fact 57(3):407–434. https://doi.org/10.1177/0018720814547570CrossRef

28.

Hofstede G (1980) Motivation, leadership, and organization: do American theories apply abroad? Org Dyn 9(1):42–63. https://doi.org/10.1016/0090-2616(80)90013-3CrossRef

29.

Hofstede G (2001) Culture’s recent consequences: using dimension scores in theory and research. Int J Cross Cult Manag 1(1):11–30. https://doi.org/10.1177/147059580111002CrossRef

30.

Howard S (2012) Risk-aversion: understanding teachers’ resistance to technology integration. Technol Pedag Educ 22(3):357–372. https://doi.org/10.1080/1475939X.2013.802995CrossRef

31.

Im I, Kim Y, Han H (2008) The effects of perceived risk and technology type on users’ acceptance of technologies. Inf Manag 45(1):1–9. https://doi.org/10.1016/j.im.2007.03.005CrossRef

32.

Kim G, Seo H, Im S, Kang D, Jeong S (2009). A study on simulator of human-robot cooperative manipulator for dental implant surgery. In: Proceedings of the 2009 IEEE international symposium on industrial electronics, Seoul, South Korea, pp 2159–2164. https://doi.org/10.1109/isie.2009.5222561

33.

Kohn LT, Corrigan JM, Donaldson MS (2000) To err is human: Building a safer health system. The National Academies Press, Washington, DC

34.

Kumar PY, Dixit P, Kalaivani V, Rajapandian K (2017) Future advances in robotic dentistry. J Dental Health Oral Disorders Ther. https://doi.org/10.15406/jdhodt.2017.07.00241CrossRef

35.

Lamb TL, Winter SR, Rice S, Ruskin KJ, Vaughn A (2020) Factors that predict passengers willingness to fly during and after the COVID-19 pandemic. J Air Transp Manag 89:101897CrossRef

36.

Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Hum Fact 46(1):50–80. https://doi.org/10.1518/hfes.46.1.50_30392MathSciNetCrossRef

37.

Lerner JS, Keltner D (2000) Beyond valence: toward a model of emotion-specific influences on judgment and choice. Cogn Emot 14(4):473–493. https://doi.org/10.1080/026999300402763CrossRef

38.

Lerner AGStoianovici D, Whitcomb LL, Kavoussi LR (1999) A passive positioning and supporting device for surgical robots and instrumentation. In: Proceedings in the second international conference on medical image computing and computer-assisted intervention, Cambridge, UK, pp 1052–1061. https://doi.org/10.1007/10704282_114

39.

Mahoney RM, Van der Loos HF, Lum P, Burgar C (2003) Robotic stroke therapy assistant. Robotica 21(1):33–44. https://doi.org/10.1017/S0263574702004617CrossRef

40.

Makary MA, Daniel M (2016) Medical error—the third leading cause of death in the US. BMJ. https://doi.org/10.1136/bmj.i2139CrossRef

41.

Matthews ML, Moran AR (1986) Age differences in male drivers’ perception of accident risk: the role of perceived driving ability. Accid Anal Prev 18(4):299–313. https://doi.org/10.1016/0001-4575(86)90044-8CrossRef

42.

Markus HR, Kitayama S (1991) Culture and the self: implications for cognition, emotion, and motivation. Psychol Rev 98(2):224–253. https://doi.org/10.1037/0033-295X.98.2.224CrossRef

43.

McVeigh FL, Tarbett AK, Betts AM, Boal TR (2008) Efficiency of automation and electronic health records in optometric practice. Optom J Am Optom Assoc 79(1):43–49. https://doi.org/10.1016/j.optm.2007.06.013CrossRef

44.

Mettler L, Ibrahim M, Jonat W (1998) One year of experience working with the aid of a robotic assistant (the voice-controlled optic holder AESOP) in gynecological endoscopic surgery. Hum Reprod 13(10):2748–2750. https://doi.org/10.1093/humrep/13.10.2748CrossRef

45.

Moran ME (2009) Stationary and automated laparoscopically assisted technologies. J Laparoendosc Surg 3(3):221–227. https://doi.org/10.1089/lps.1993.3.221CrossRef

46.

Morris MG, Venkatesh V (2006) Age differences in technology adoption decisions: implications for a changing workforce. Pers Psychol 53(2):375–403. https://doi.org/10.1111/j.1744-6570.2000.tb00206.xCrossRef

47.

Morrow D, North R, Wickens CD (2005) Reducing and mitigating human error in medicine. Rev Hum Fact Ergon 1(1):254–296. https://doi.org/10.1518/155723405783703019CrossRef

48.

Najarian S, Afshari E (2012) Evolutions and future directions of surgical robots: a review. Int J Clin Med 3(2):74–82. https://doi.org/10.4236/ijcm.2012.32017CrossRef

49.

Naqvi N, Shiv B, Bechara A (2006) The role of emotion in decision making: a cognitive neuroscience perspective. Curr Dir Psychol Sci 15(5):260–264. https://doi.org/10.1111/j.1467-8721.2006.00448.xCrossRef

50.

Nof S (2009) Spring handbook of automation. Retrieved from https://books.google.com/books?hl=en&lr=&id=2v_91vSCIK0C&oi=fnd&pg=PR1&dq=automation+in+healthcare&ots=vOcK5gZlXr&sig=REm0BqPyZUEgROnGHBb8sBT1gUM#v=onepage&q=automation%20in%20healthcare&f=false

51.

Parasuraman R, Riley V (1997) Humans and automation: use, misuse, and abuse. Hum Factors 39(2):230–253. https://doi.org/10.1518/001872097778543886CrossRef

52.

Parasuraman R, Sheridan TB, Wickens CD (2000) A model for types and levels of human interaction with automation. IEEE Trans Syst Man Cybern Part A Syst Hum 30(3):286–297. https://doi.org/10.1109/3468.844354CrossRef

53.

Patel MN, Hemal AK (2018) Does advancing technology improve outcomes? Comparison of the Da Vinci Standard/S/Si to the Xi Robotic Platforms during robotic nephroureterectomy. J Endourol 32(2):133–138. https://doi.org/10.1089/end.2017.0477CrossRef

54.

Pfister H-R, Böhm G (2008) The multiplicity of emotions: a framework of emotional functions in decision making. Judgm Decis Mak 3(1):5–17

55.

Pradham AK, Hammel KR, DeRamus R, Pollatsek A, Noyce DA, Fisher DL (2005) Using eye movements to evaluate effects of driver age on risk perception in a driving simulator. Hum Factors 47(4):840–852. https://doi.org/10.1518/001872005775570961CrossRef

56.

Rice S, Winter SR (2019) Do gender and age affect willingness to ride in driverless vehicles: if so, then why? Technol Soc 58:1–7CrossRef

57.

Rice S, Winter SR (2015) Which passenger emotions mediate the relationship between type of pilot configuration and willingness to fly? Aviat Psychol Appl Hum Factors 5(2):83–92. https://doi.org/10.1027/2192-0923/a000081CrossRef

58.

Rice S, Winter SR, Doherty S, Milner MN (2017) Advantages and disadvantages of using internet-based survey methods in aviation-related research. J Aviat Technol Eng 7(1):58–65. https://doi.org/10.7771/2159-6670.1160CrossRef

59.

Rice S, Winter SR, Mehta R, Ragbir NK (2019) What factors predict the type of person who is willing to fly in an autonomous commercial airplane? J Air Transp Manag 75:131–138CrossRef

60.

Rice S, Winter SR, Capps J, Trombley J, Robbins J, Milner M, Lamb TL (2020) Creation of two valid scales: willingness to fly in an aircraft and willingness to pilot an aircraft. Int J Aviat Aeron Aerosp https://doi.org/10.15394/ijaaa.2020.1440CrossRef

61.

Rhodes N, Pivik K (2011) Age and gender differences in risky driving: the roles of positive affect and risk perception. Accid Anal Prev 43(3):923–931. https://doi.org/10.1016/j.aap.2010.11.015CrossRef

62.

Robbins S, Judge T (2009) Power and Politics, Organizational Behavior. Pearson Education Inc, Upper Saddle River

63.

Sayegh L, Anthony WP, Perrewé PL (2004) Managerial decision-making under crisis: the role of emotion in an intuitive decision process. Hum Resour Manag Rev 14(2):179–199. https://doi.org/10.1016/j.hrmr.2004.05.002CrossRef

64.

Schwarz N (2000) Emotion, cognition, and decision making. Cogn Emot 14(4):433–440. https://doi.org/10.1080/026999300402745CrossRef

65.

Schwarz N, Clore GI (1983) Mood, misattribution, and judgment of well-being: informative and directive functions of affective states. J Pers Soc Psychol 45(3):513–523. https://doi.org/10.1037/0022-3514.45.3.513CrossRef

66.

Speich JE, Rosen J (2004) Medical robotics. In: Wnek GE, Bowlin GL (eds) Encyclopedia of Biomaterials and Biomedical Engineering, pp 1804–1815. https://doi.org/10.1081/e-ebbe120024154

67.

Srite M, Karahanna E (2006) The role of espoused national cultural values in technology acceptance. Manag Inf Syst Q 30(3):679–704. https://doi.org/10.2307/25148745CrossRef

68.

Summers B, Duxbury D (2012) Decision-dependent emotions and behavioral anomalies. Organ Behav Hum Decis Process 118(2):226–238. https://doi.org/10.1016/j.obhdp.2012.03.004CrossRef

69.

Tan WS, Chua SL, Yong KW, Wu TS (2009) Impact of pharmacy automation on patient waiting time: an application of computer simulation. Ann Acad Med Sing 38(6):501–507

70.

Taylor RH (2006) A perspective on medical robots. Proc IEEE 94(9):1652–1664. https://doi.org/10.1109/JPROC.2006.880669CrossRef

71.

Taylor RH, Menciassi A, Fichtinger G, Dario P (2008). Medical robotics and computer-integrated surgery. In: Siciliano B, Khatib O (eds) Springer Handbook of Robotics, pp 1657–1684. https://doi.org/10.1007/978-3-319-32552-1

72.

Turner C, McClure R (2003) Age and gender differences in risk-taking behaviour as an explanation for high incidence of motor vehicle crashes as a driver in young males. Injury Control Saf Prom 10(3):123–130. https://doi.org/10.1076/icsp.10.3.123.14560CrossRef

73.

Varkarakis IM, Rais-Bahrami S, Kavoussi LR, Stoianovici D (2005) Robotic surgery and telesurgery in urology. Urology 65(5):840–846. https://doi.org/10.1016/j.urology.2004.10.040CrossRef

74.

Venkatesh V (1999) Creation of favorable user perceptions: exploring the role of intrinsic motivation. Manag Inf Syst Q 23(2):239–260. https://doi.org/10.2307/249753CrossRef

75.

Venkatesh V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf Syst Res 11(4):342–365. https://doi.org/10.1287/isre.11.4.342.11872CrossRef

76.

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. Manag Inf Syst Q 27(3):425–478CrossRef

77.

Weingart NS, Wilson M, Gibberd RW, Harrison B (2000) Epidemiology of medical error. BMJ. https://doi.org/10.1136/bmj.320.7237.774CrossRef

78.

Winter SR, Rice S, Lamb TL (2020) A prediction model of consumer’s willingness to fly in autonomous air taxis. J Air Transp Manag 89:101926CrossRef

79.

Zuckerman M, Kuhlman DM (2000) Personality and risk-taking: common biosocial factors. J Pers 68(6):999–1029. https://doi.org/10.1111/1467-6494.00124CrossRef

Titel: Factors Predicting Patients’ Willingness to Use Robotic Dental Services
verfasst von: Mattie Milner
Rian Mehta
Scott R. Winter
Stephen Rice
Emily Anania
Nadine Ragbir
Cynthia Smith
Publikationsdatum: 05.03.2021
Verlag: Springer Netherlands
Erschienen in: International Journal of Social Robotics / Ausgabe 7/2021
Print ISSN: 1875-4791
Elektronische ISSN: 1875-4805
DOI: https://doi.org/10.1007/s12369-020-00737-7

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