This paper is in the following e-collection/theme issue:

Design and Usability of Medical Devices (141) Consumer & Patient Education and Shared-Decision Making (865) Focus Groups and Qualitative Research for Human Factors Research (1221) Usability Evaluation Case Studies (259) Methods in Human Factors Research (61) Design and Usability of Consumer Health Tech and Home Monitoring Devices (326) Robots in Healthcare (123)

Published on in Vol 12 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64224, first published .
Factors Shaping Public Perceptions of a Range of Robotic Technologies in Surgery: Cross-Sectional Web-Based Survey

Factors Shaping Public Perceptions of a Range of Robotic Technologies in Surgery: Cross-Sectional Web-Based Survey

Factors Shaping Public Perceptions of a Range of Robotic Technologies in Surgery: Cross-Sectional Web-Based Survey

Authors of this article:

Sarek Shen1 Author Orcid Image ;   Deborah Xie1 Author Orcid Image ;   Andy Ding1 Author Orcid Image ;   Lisa Zhang2 Author Orcid Image ;   Francis Creighton1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins School of Medicine, 601 N Caroline Dr, Baltimore, MD, United States

    2Department of Otolaryngology-Head and Neck Surgery, Ohio State University School of Medicine, Columbus, OH, United States

    Corresponding Author:

    Sarek Shen, MD, MS


    Background: Within the surgical field, there has been an evolution in the application of robotic technology. Fully automatic robotic systems and augmented visualization tools are being introduced and may eventually replace existing surgical extenders such as the da Vinci surgical system. The literature on public perception of robotic surgery is growing, though specific drivers of these attitudes remain under investigation.

    Objective: The aim of this study is to investigate the underlying motivators of public perceptions toward robotic surgeries with varying levels of autonomy through a formal technology acceptance model.

    Methods: An online survey was distributed via the Amazon Mechanical Turk platform. Survey participants were provided definitions of a continuum of robotic technologies: robotic surgical extenders (technology without independent actions), semiautonomous robotic surgery (technology that provides guidance to the surgeon and requires surgeon input), and fully autonomous robotic surgery (technology that performs tasks autonomously without direct human interaction). The survey assessed overall attitudes toward each application of robotic technology in surgery and included questions delineating specific receptivity based on (1) perceived usefulness, (2) social risk, (3) time risk, (4) personal risk, and (5) reliability. A technology acceptance model was built to identify associations between these factors and overall attitudes toward robotic and semiautonomous surgeries.

    Results: A total of 1221 survey responses were recorded (mean age 38, SD 12 y; females: n=635, 52%). Individuals were more willing to accept robotic surgical extenders and semiautonomous robotic surgery compared to autonomous robotic surgery. Higher levels of education and better self-reported health were correlated with more positive attitudes toward autonomous robotic surgery. Perceptions of these technologies were not associated with age, gender, or income. Overall, attitudes toward robotic technologies in surgery were driven by views on the reliability, safety, and efficiency of the procedures. There was less concern regarding time risk and social risk associated with robotic and semirobotic surgeries.

    Conclusions: The public is more accepting of semiautonomous surgery and surgical extenders than fully autonomous surgery. General perceptions of the reliability, safety, and efficiency of these technologies drive variations in attitude. Time and social risk do not appear to have a significant impact on receptivity. Understanding these perspectives can help guide education within an advancing surgical field.

    JMIR Hum Factors 2025;12:e64224

    doi:10.2196/64224

    Keywords

    perception; robotic surgery; semiautonomous robotic surgery; surgical extenders; safety; reliability; structural equation model; technology; acceptance; model; public; perceptions; cross-sectional; web-based; assessment; robot-assisted; surgery; robot; robotic technology; medical care 


    Applications of robotic and artificial intelligence technologies in surgery are rapidly developing [1-6]. The predominant robotic surgical technology currently available is surgical extenders such as the da Vinci (Intuitive)—these robotic technologies move only in response to surgeon input and provide nonaugmented visualization to the surgeon. Following surgical extenders, several semiautonomous systems have emerged in various surgical fields. At present, these technologies generally involve the incorporation of image guidance or augmented reality to identify anatomic structures and navigate an operative field [7,8]. Within orthopedics, semiautonomous systems are being used for improved hardware placement during spine surgery and hip surgery [9-13]. Looking further, researchers have developed fully autonomous robotic systems capable of performing tasks such as cochlear implantation [14-16] and laparoscopic bowel anastomosis [17,18]. Although not yet commercially available, these systems may emerge as standard complements to the surgical workflow.

    Despite the promising developments within robotic surgery technologies, there remain social, financial, and regulatory barriers to these systems becoming commonplace. One obstacle is a nascent understanding of the public’s perception and tolerance of these technologies. Currently available robotic systems have been shown to improve operating room efficiency and surgical planning [19-23], and the public’s attitudes toward these technologies continue to evolve. There is a growing body of research investigating the relationship between sociodemographic factors and views on autonomous technologies within medicine [24,25]. However, the underlying drivers of these perceptions are not fully explored. Given the growing prevalence of autonomous and semiautonomous technologies in our day-to-day lives, we sought to investigate the public’s perspectives of the spectrum of robotic systems in surgery. Using a technology acceptance model, we investigate the public’s attitudes, and drivers of those attitudes, toward a range of robotic surgery technologies.


    Ethical Considerations

    This study was reviewed and approved by the Johns Hopkins institutional review board (IRB00267594). A survey was designed on Qualtrics (Qualtrics LLC) and distributed through Amazon Mechanical Turk (MTurk) in October 2020 (Multimedia Appendix 1). MTurk is a crowdsourcing platform that allows individuals to complete assignments online in an anonymous fashion. All study data were deidentified after collection was complete. A US $0.25 compensation was provided for those who completed the survey and correctly answered all attention check questions.

    Participant Recruitment

    Respondents were included if they were over 18 years of age and a US resident. The survey included multiple attention check questions to ensure active engagement (eg, “Please select the number 3”). Respondents were excluded if their primary language was not English or if they failed to answer attention check questions correctly. We do not have data on the number of people who viewed but elected not to partake in the survey; therefore, we cannot calculate traditional participation rates. The completion rate of those who started the survey was 97.2%.

    Survey Instrument

    The survey instrument was designed to assess perceptions and attitudes toward applications of robotic technology in surgery. Each survey started by providing the participant with definitions of 3 levels of robotic technology in surgery:

    • Robotic surgical extenders: robot controlled via surgeon, does not perform actions independently without the input of the surgeon.
    • Semiautonomous robotic surgery: provides guidance to surgeon based on a patient’s information. The robot does not perform actions independently without surgeon input.
    • Autonomous robotic surgery: operates and performs tasks on its own, without direct interaction with the surgeon.

    Questions in the survey were adopted from previously validated surveys assessing acceptance of technology [26-28]. Respondents rated statements on a 7-point Likert scale from “strongly disagree” to “strongly agree.” Higher scores indicated a more positive or favorable opinion toward the technology being assessed. A respondent’s overall attitude (A) was interrogated directly via questions in the survey. Further questions were designed to assess one of the following latent variables:

    • Perceived usefulness (PU): The ability for robotic technology to improve the care that one receives in surgery.
    • Social risk (SR): The possibility that using robotic technology in surgery would lead to disapproval from one’s friends, family, or social group.
    • Time risk (TR): The loss of time and inconvenience that would result from the use of robotic technology in surgery.
    • Personal risk (PR): The physical safety of robotic technology in surgery.
    • Reliability (R): The ability of robotic technology in surgery to work reliably over time.

    Each latent variable was evaluated by multiple questions, abbreviated by the question number and the latent variable assessed (eg, PU1 and PU2 were the two questions assessing perceived usefulness). All questions were completed for each application of robotic technology in surgery (ie, each latent variable was assessed for robotic surgical extenders, semiautonomous robotic surgery, and autonomous robotic surgery). The relationship between the latent variables and the overall attitude was determined with structural equation models (SEMs) based on the technology acceptance model [29] and theory of planned behavior [30,31]. Participants were also asked to rate their personal comfort and trust with technology, as well as their own health on a 5-point Likert scale. Demographic data of each respondent were also collected. The survey instrument is included in the Multimedia Appendix 1.

    Data Analysis

    Descriptive analyses, including frequency distribution and central tendency, were performed to illustrate the demographic characteristics and participants’ questionnaire responses. Participant education and self-perceived health levels were further stratified into quartiles. ANOVA, t tests, and Pearson correlation coefficient were used to compare participants’ attitudes and intentions among the surgical extender types, as well as differences in viewpoints among demographic variables.

    Confirmatory factor analysis was used to measure the relationship between the measured variables (question responses) and the latent variables (key factors) through factor loading. Multiple SEMs were built to identify associations between the latent variables and overall attitude. Standard regression coefficients calculated by the model represent the ability of our survey questions to capture data on its latent variable. The assumption of multivariate normality was assessed and confirmed with skewness and kurtosis tests. Cronbach α was computed for each key factor to determine the internal consistency of the research construct. Values of >0.7 indicate high reliability, between 0.5 and 0.7 indicate moderate reliability, and <0.5 indicate low reliability [32].

    Root mean square error of approximation and Χ2 tests were used to measure the discrepancy between the model-based and observed correlation matrices. The final structural model was built and standard regression coefficients were calculated. Data analyses were performed with R (version 4.1.2; R Foundation for Statistical Computing), with SEM calculations performed using the lavaan package. All α levels and error probabilities were set at the standard .05 level.


    Background Characteristics

    A total of 1221 surveys were included for analysis. The mean age of participants was 37.7 (SD 12.1) years. Females made up 51.7% (n=631) of respondents, and the majority (n=940, 77%) were White. Full demographics are listed in Table 1.

    Table 1. Participant sociodemographics and personal health ratings.
    CharacteristicsValues
    Age (years), mean (SD)37.7 (12.1)
    Gender, n (%)
     Male580 (47.5)
     Female631 (51.7)
     Nonbinary10 (0.8)
    Race/ethnicity, n (%)
     White940 (77)
     African/African American124 (10.2)
     American Indian/Alaska Native37 (3)
     Asian91 (7.5)
     Other29 (2.4)
    Highest level of education, n (%)
     High school graduate or less78 (6.4)
     Some college226 (18.5)
     College degree671 (55)
     Advanced degree (Master’s, PhD, MD, etc)246 (20.1)
    Personal rating of health, n (%)
     Excellent266 (21.8)
     Very good533 (43.7)
     Good334 (27.4)
     Fair73 (6)
     Poor15 (1.2)

    Sociodemographic Associations

    The surveyed cohort expressed more positive attitudes toward robotic surgical extenders and semiautonomous robotic surgery compared to autonomous robotic surgery (Figure 1). There was no significant correlation between age and attitude toward robotic surgical extenders (r=0.024, 95% CI −0.032 to 0.080), semiautonomous robotic surgery (r=0.017, 95% CI −0.039 to 0.073), or autonomous robotic surgery (r=−0.0124, 95% CI −0.069 to 0.044). Similarly, there was no significant correlation between gender and race relative to attitude toward all applications of robotic technologies in surgery. There was a positive correlation between comfort with technology and overall attitudes for robotic surgical extenders (r=0.090, 95% CI 0.034 to 0.145), semiautonomous robotic surgery (r=0.088, 95% CI 0.032 to 0.143), and autonomous robotic surgery (r=0.118, 95% CI 0.062 to 0.173).

    ‎
    Figure 1. Comparison of overall attitudes toward different applications of robotic technology in surgery. ***P<.001.

    Attitudes toward robotic surgical extenders (P=.24) and semiautonomous robotic surgery (P=.57) did not vary based on level of education. However, respondents with higher levels of education did have more positive attitudes toward autonomous robotic surgery (P<.001, Figure 2). Similarly, there was no significant difference in attitudes toward robotic surgical extenders (P=.60) and semiautonomous robotic surgery (P=.29) across all levels of personal ratings of health. Those with higher ratings of health held more favorable attitudes toward autonomous robotic surgery (P=.003, Figure 3). There was no significant difference in attitudes toward all applications of robotic technology between individuals with or without a surgical history (all P>.05).

    ‎
    Figure 2. Relationships between education and overall attitudes toward different applications of robotic technology in surgery.
    ‎
    Figure 3. Relationships between personal ratings of health and overall attitudes toward different applications of robotic technology in surgery.

    Technology Acceptance Model  

    An SEM was built to explore the relationships between the latent variables (PU, SR, TR, PR, and R) and overall attitude (Figure 4). The correlation between survey questions for each latent variable was calculated using Cronbach α (Table 2). The low coefficient magnitude (SR1: 0.06, SR2: 0.22) combined with the lack of statistical significance suggest that the concept of “social risk” was poorly characterized by our survey questions. Attitudes toward robotic technologies in surgery were predicted by TR (β=0.71, P<.05), PR (β=0.61, P<.05), and R (β=0.53, P<.05). Together, these variables explain 62% of the variance of attitudes (R2=0.62, coefficient of determination). SR (β=−0.01, P>.05) and PU (β=−0.33, P>.05) did not significantly affect attitude.

    ‎
    Figure 4. Results of structural equation modeling analysis. A: attitude; PR: personal risk; PU: perceived usefulness; R: reliability; SR: social risk; TR: time risk. *P<.05.
    Table 2. Cronbach α of the survey questions assessing the included latent variables (perceived usefulness, social risk, time risk, personal risk, reliability) and overall attitude.
    Robotic surgical extendersSemiautonomousAutonomousTotal
    Perceived usefulness0.810.460.260.59
    Social risk0.010.090.150.02
    Time risk0.760.80.680.75
    Personal risk0.010.160.340.22
    Reliability0.240.280.380.26
    Attitude0.460.520.450.63

    Principal Findings

    Implementation of robotic technology in the operating room setting has the potential to improve the delivery of health care. From a surgeon’s perspective, the technical benefits of robotic surgery include improved visualization, better maneuverability, and favorable ergonomics [33,34]. As automated technologies become increasingly integrated into medicine, there has been a growing body of literature on how the public perceives the continuum of robotic procedures [35]. Palmisciano et al [19] found that patients are more favorable toward the use of autonomous technology in noninvasive applications, such as operative planning and preoperative imaging, compared to surgical procedures. In our study, we similarly found that the public is more open to surgical extenders and semiautonomous technologies than fully autonomous systems. Based on our technology acceptance model, the differences in these attitudes appear to be driven primarily by concerns involving safety and reliability, more so than social pressures or apprehension about the usefulness of the technologies. These findings may help guide efforts in patient education as these robotic systems become more commonplace in surgery.

    Sociodemographic Correlations

    Our study found that individuals with higher levels of education, as well as those with higher personal ratings of health, tend to be more receptive toward fully autonomous surgeries. Education has been well-studied within the technology acceptance model; numerous investigations have demonstrated increased acceptance and utilization of technology among those with secondary or tertiary degrees [36-38]. Werner et al [39] have postulated that this is associated with a relative increase in access to and familiarity with new technologies, while Chen et al [40] have noted that these cohorts are more self-efficacious and may have less technology-related anxiety. From a health perspective, we found that participants with poorer self-ratings of health were associated with a more negative attitude toward fully robotic surgeries. This finding is congruent with studies investigating patterns of use of mobile health devices; for example, Chandrasekaran et al [41] showed that healthier patients are more likely to adopt new health care–associated technology [42]. However, these associations are complex and likely depend on the specific technology in question. Further study is necessary to elucidate the complicated relationship between the perceived health and technology acceptance of surgical patients.

    Interestingly, we did not find a correlation between age and attitude toward fully autonomous surgeries. Recent studies investigating technology acceptance within aging populations, such as in Japan and South Korea, have similarly demonstrated a positive perception of robotic technology in an older cohort [43,44]. This is in part driven by the growing ubiquity of semiautonomous technology within day-to-day life, which increases familiarity, builds trust, and changes beliefs and attitudes toward new technology [45].

    Drivers of Public Perception

    In investigating the drivers of overall patient attitude toward robotic surgeries, we found that risk to patient, reliability of technology, and perceived time risk were the primary concerns. There was less consideration for usefulness or social risk. These results are similar to those found in the autonomous automobile industry [46,47] and for assistive technology in patients undergoing physical rehabilitation [48]. Other studies have also found that apprehension toward adoption of robotic surgeries is driven by fears of equipment error or malfunction [34,49]. The evolution of attitudes toward autonomous driving may be particularly informative. Studies in this space suggest that increased system transparency, which refers to improved user understanding of the operational predictability, is positively associated with acceptance of the technology [46]. Within surgery, an increase in system transparency may begin with addressing misconceptions about these robotic surgical technologies. The colloquial definition and usage of the term “robot” tends to imply a programmable machine that can replicate the actions of a person. For those not familiar with robotic technology in health care, the nuances of its uses may not be readily obvious. In 2016, Boys et al [34] published a survey-based study specifically investigating the perceptions of robotic surgical extenders; interestingly, 21% of their 747 respondents believed that robotic surgical extenders had autonomous function during surgery. To address these misconceptions, the creation of accessible patient education materials, specifically focusing on reliability, safety, and efficiency, may be the best way to impact overall attitudes toward robotic technologies in surgery.

    Limitations

    There are several limitations to this study. First, while questions in our instrument had been previously validated [26-28], the consistency of responses related to the latent variables, specifically the social risk, was lower than expected. This may reflect inherent difficulty in summarizing a broad variable within 2‐3 questions, particularly within health care. However, the drivers of patient attitude that our group identified are similar to those implicated in other areas with a growing autonomous technological presence, including online banking and self-driving cars [26,27,50]. This concordance is reassuring and suggestive of a similar pattern of technological acceptance between fields. Second, we understand that there may be a slight selection bias in the population sampled, as we used an online survey platform, which may have a younger and more technologically savvy population compared to the prototypical patient. However, it is notable that the mean age of patients undergoing the most common robotic procedure in the United States is around 41.5 years, consistent with our survey population. The racial and ethnic breakdown of patients undergoing robotic surgeries is similar to our survey cohort [51]. Additionally, prior studies have also shown that the Amazon MTurk survey results are comparable to those collected by more traditional methods [52-54]. Third, our survey was not able to capture additional socioeconomic variables, such as income or employment status, which may be hidden confounders of our findings. To address some of these limitations, future studies will include an in-person investigation into treatment decision-making and patient preference within robotic surgeries.

    Conclusion 

    Current public perception toward surgical extenders and semiautonomous robotic surgery is more positive than that toward autonomous robotic surgery. Overall attitudes appear to be driven by concerns about the reliability, safety, and efficiency of these technologies. As robotic technologies emerge and continue to evolve within surgery, these findings can inform efforts in directed education. Comprehensive preoperative discussions targeted at the reliability and efficacy—rather than the usefulness or social risk—of these technologies may be more impactful. However, further work is needed in exploring potential obstacles to implementation of robotic systems within surgical practices.

    Funding

    This work was funded in part by the National Institute on Deafness and Other Communication Disorders (NIDCD) grant number 5T32DC000027-33.

    Data Availability

    The datasets generated or analyzed during this study are available from the corresponding author on reasonable request.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Survey instrument

    PDF File, 125 KB
    1. Bankar GR, Keoliya A. Robot-assisted surgery in gynecology. Cureus. Sep 2022;14(9):e29190. [CrossRef] [Medline]
    2. Lira RB, Kowalski LP. Robotic head and neck surgery: beyond TORS. Curr Oncol Rep. Jul 9, 2020;22(9):88. [CrossRef] [Medline]
    3. Rassweiler JJ, Autorino R, Klein J, et al. Future of robotic surgery in urology. BJU Int. Dec 2017;120(6):822-841. [CrossRef] [Medline]
    4. Shlobin NA, Huang J, Wu C. Learning curves in robotic neurosurgery: a systematic review. Neurosurg Rev. Dec 12, 2022;46(1):14. [CrossRef] [Medline]
    5. Walshaw J, Huo B, McClean A, et al. Innovation in gastrointestinal surgery: the evolution of minimally invasive surgery-a narrative review. Front Surg. 2023;10:1193486. [CrossRef] [Medline]
    6. Yamout T, Orosz LD, Good CR, Jazini E, Allen B, Gum JL. Technological advances in spine surgery: navigation, robotics, and augmented reality. Orthop Clin North Am. Apr 2023;54(2):237-246. [CrossRef] [Medline]
    7. Tamaki A, Rocco JW, Ozer E. The future of robotic surgery in otolaryngology - head and neck surgery. Oral Oncol. Feb 2020;101:104510. [CrossRef] [Medline]
    8. Chan JYK, Holsinger FC, Liu S, Sorger JM, Azizian M, Tsang RKY. Augmented reality for image guidance in transoral robotic surgery. J Robot Surg. Aug 2020;14(4):579-583. [CrossRef] [Medline]
    9. Lieberman IH, Kisinde S, Hesselbacher S. Robotic-assisted pedicle screw placement during spine surgery. JBJS Essent Surg Tech. 2020;10(2):e0020. [CrossRef] [Medline]
    10. Hu X, Scharschmidt TJ, Ohnmeiss DD, Lieberman IH. Robotic assisted surgeries for the treatment of spine tumors. Int J Spine Surg. 2015;9:1. [CrossRef] [Medline]
    11. Kochanski RB, Lombardi JM, Laratta JL, Lehman RA, O’Toole JE. Image-guided navigation and robotics in spine surgery. Neurosurgery. Jun 1, 2019;84(6):1179-1189. [CrossRef] [Medline]
    12. Loomans L, Leirs G, Vandenneucker H. Operating room efficiency after the implementation of MAKO robotic-assisted total knee arthroplasty. Arch Orthop Trauma Surg. Sep 2023;143(9):5501-5506. [CrossRef] [Medline]
    13. Perazzini P, Trevisan M, Sembenini P, et al. The Mako robotic arm-assisted total hip arthroplasty using direct anterior approach: surgical technique, skills and pitfals. Acta Biomed. May 30, 2020;91(4-S):21-30. [CrossRef] [Medline]
    14. Caversaccio M, Wimmer W, Anso J, et al. Robotic middle ear access for cochlear implantation: first in man. PLoS One. 2019;14(8):e0220543. [CrossRef] [Medline]
    15. Labadie RF, Noble JH, Dawant BM, Balachandran R, Majdani O, Fitzpatrick JM. Clinical validation of percutaneous cochlear implant surgery: initial report. Laryngoscope. Jun 2008;118(6):1031-1039. [CrossRef] [Medline]
    16. Warren FM, Balachandran R, Fitzpatrick JM, Labadie RF. Percutaneous cochlear access using bone-mounted, customized drill guides: demonstration of concept in vitro. Otol Neurotol. Apr 2007;28(3):325-329. [CrossRef] [Medline]
    17. Saeidi H, Le HND, Opfermann JD, et al. Autonomous laparoscopic robotic suturing with a novel actuated suturing tool and 3D endoscope. IEEE Int Conf Robot Autom. May 2019;2019:1541-1547. [CrossRef] [Medline]
    18. Saeidi H, Opfermann JD, Kam M, et al. Autonomous robotic laparoscopic surgery for intestinal anastomosis. Sci Robot. Jan 26, 2022;7(62):eabj2908. [CrossRef] [Medline]
    19. Palmisciano P, Jamjoom AAB, Taylor D, Stoyanov D, Marcus HJ. Attitudes of patients and their relatives toward artificial intelligence in neurosurgery. World Neurosurg. Jun 2020;138:e627-e633. [CrossRef] [Medline]
    20. Titano JJ, Badgeley M, Schefflein J, et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med. Sep 2018;24(9):1337-1341. [CrossRef] [Medline]
    21. Bennett CC, Hauser K. Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach. Artif Intell Med. Jan 2013;57(1):9-19. [CrossRef] [Medline]
    22. Kim JS, Merrill RK, Arvind V, et al. Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine (Phila Pa 1986). 2018;43(12):853-860. [CrossRef]
    23. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. Jul 2018;268(1):70-76. [CrossRef] [Medline]
    24. Torrent-Sellens J, Jiménez-Zarco AI, Saigí-Rubió F. Do people trust in robot-assisted surgery? Evidence from Europe. Int J Environ Res Public Health. Nov 28, 2021;18(23):12519. [CrossRef] [Medline]
    25. McDonnell C, Devine M, Kavanagh D. The general public’s perception of robotic surgery - a scoping review. Surgeon. Apr 2025;23(2):e49-e62. [CrossRef] [Medline]
    26. Lee MC. Factors influencing the adoption of internet banking: an integration of TAM and TPB with perceived risk and perceived benefit. Electron Commer Res Appl. May 2009;8(3):130-141. [CrossRef]
    27. Lee C, Ward C, Raue M, D’Ambrosio L, Coughlin JF. Age differences in acceptance of self-driving cars: a survey of perceptions and attitudes. In: Zhou J, Salvendy G, editors. Human Aspects of IT for the Aged Population Aging, Design and User Experience. ITAP 2017. Lecture Notes in Computer Science. [CrossRef]
    28. Kim J, Park HA. Development of a health information technology acceptance model using consumers’ health behavior intention. J Med Internet Res. Oct 1, 2012;14(5):e133. [CrossRef] [Medline]
    29. Fishbein M, Ajzen I. Belief, Intention and Behavior: An Introduction to Theory and Research. Longman Higher Education; 1975. ISBN: 978-0201020892
    30. Ajzen I. Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior. J Applied Social Pyschol. Apr 2002;32(4):665-683. [CrossRef]
    31. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. Dec 1991;50(2):179-211. [CrossRef]
    32. Tavakol M, Dennick R. Making sense of Cronbach’s alpha. Int J Med Educ. Jun 27, 2011;2:53-55. [CrossRef] [Medline]
    33. Lee N. Robotic surgery: where are we now? Lancet. Oct 18, 2014;384(9952):1417. [CrossRef] [Medline]
    34. Boys JA, Alicuben ET, DeMeester MJ, et al. Public perceptions on robotic surgery, hospitals with robots, and surgeons that use them. Surg Endosc. Apr 2016;30(4):1310-1316. [CrossRef] [Medline]
    35. Muaddi H, Zhao X, Leonardelli GJ, et al. Fear of innovation: public’s perception of robotic surgery. Surg Endosc. Aug 2022;36(8):6076-6083. [CrossRef] [Medline]
    36. McMurray J, Strudwick G, Forchuk C, et al. The importance of trust in the adoption and use of intelligent assistive technology by older adults to support aging in place: scoping review protocol. JMIR Res Protoc. Nov 2, 2017;6(11):e218. [CrossRef] [Medline]
    37. Jarvis MA, Sartorius B, Chipps J. Technology acceptance of older persons living in residential care. Information Development. Sep 2020;36(3):339-353. [CrossRef]
    38. Pan S, Jordan-Marsh M. Internet use intention and adoption among Chinese older adults: from the expanded technology acceptance model perspective. Comput Human Behav. Sep 2010;26(5):1111-1119. [CrossRef]
    39. Werner JM, Carlson M, Jordan-Marsh M, Clark F. Predictors of computer use in community-dwelling, ethnically diverse older adults. Hum Factors. Oct 2011;53(5):431-447. [CrossRef] [Medline]
    40. Chen K, Chan AHS. Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics. 2014;57(5):635-652. [CrossRef] [Medline]
    41. Chandrasekaran R, Katthula V, Moustakas E. Patterns of use and key predictors for the use of wearable health care devices by US adults: insights from a national survey. J Med Internet Res. Oct 16, 2020;22(10):e22443. [CrossRef] [Medline]
    42. Fowe IE, Boot WR. Understanding older adults’ attitudes toward mobile and wearable technologies to support health and cognition. Front Psychol. 2022;13:1036092. [CrossRef] [Medline]
    43. Cavallo F, Esposito R, Limosani R, et al. Robotic services acceptance in smart environments with older adults: user satisfaction and acceptability study. J Med Internet Res. Sep 21, 2018;20(9):e264. [CrossRef] [Medline]
    44. D’Onofrio G, Fiorini L, Hoshino H, et al. Assistive robots for socialization in elderly people: results pertaining to the needs of the users. Aging Clin Exp Res. Sep 2019;31(9):1313-1329. [CrossRef] [Medline]
    45. McCreadie C, Tinker A. The acceptability of assistive technology to older people. Ageing Soc. May 2005;25(1):91-110. [CrossRef]
    46. Koul S, Eydgahi A. Utilizing Technology Acceptance Model (TAM) for driverless car technology adoption. Journal of Technology Management & Innovation. 2018;13(4):37-46. [CrossRef]
    47. Nastjuk I, Herrenkind B, Marrone M, Brendel AB, Kolbe LM. What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user’s perspective. Technol Forecast Soc Change. Dec 2020;161:120319. [CrossRef]
    48. Debeuf R, Fobelets M, Vaneyghen J, et al. Healthcare professionals’ perspectives on development of assistive technology using the comprehensive assistive technology model. Assist Technol. Jan 2, 2024;36(1):51-59. [CrossRef] [Medline]
    49. Szabó B, Őrsi B, Csukonyi C. Robots for surgeons? Surgeons for robots? Exploring the acceptance of robotic surgery in the light of attitudes and trust in robots. BMC Psychol. Jan 24, 2024;12(1):45. [CrossRef] [Medline]
    50. Hewitt C, Politis I, Amanatidis T, Sarkar A. Assessing public perception of self-driving cars: the autonomous vehicle acceptance model. Presented at: IUI ’19: Proceedings of the 24th International Conference on Intelligent User Interfaces; Mar 17-20, 2019:518-527; Marina del Ray, CA. [CrossRef]
    51. Sheetz KH, Claflin J, Dimick JB. Trends in the adoption of robotic surgery for common surgical procedures. JAMA Netw Open. Jan 3, 2020;3(1):e1918911. [CrossRef] [Medline]
    52. Mortensen K, Hughes TL. Comparing Amazon’s Mechanical Turk platform to conventional data collection methods in the health and medical research literature. J Gen Intern Med. Apr 2018;33(4):533-538. [CrossRef] [Medline]
    53. Chandler J, Shapiro D. Conducting clinical research using crowdsourced convenience samples. Annu Rev Clin Psychol. 2016;12:53-81. [CrossRef] [Medline]
    54. Buhrmester MD, Talaifar S, Gosling SD. An evaluation of Amazon’s Mechanical Turk, its rapid rise, and its effective use. Perspect Psychol Sci. Mar 2018;13(2):149-154. [CrossRef] [Medline]

    ‎
    A: attitude
    MTurk: Mechanical Turk
    PR: personal risk
    PU: perceived usefulness
    R: reliability
    SEM: structural equation model
    SR: social risk
    TR: time risk

    Edited by Andre Kushniruk; submitted 11.Jul.2024; peer-reviewed by Cheng Kian Tan, Jonathan Odumegwu, Lindsey Philpot, Randa Salah Gomaa Mahmoud; final revised version received 12.Jul.2025; accepted 15.Jul.2025; published 20.Nov.2025.

    Copyright

    © Sarek Shen, Deborah Xie, Andy Ding, Lisa Zhang, Francis Creighton. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 20.Nov.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license information must be included.