Advertisement

An Acceptance Model for the Adoption of Smart Glasses Technology by Healthcare Professionals

  • Dilek Özdemir-Güngör
  • Müge Göken
  • Nuri Basoglu
  • Amir Shaygan
  • Marina DabićEmail author
  • Tugrul U. Daim
Chapter
Part of the Palgrave Studies of Internationalization in Emerging Markets book series (PSIEM)

Abstract

In recent years, there has been an increase in the interest from different industries in the adoption of smart wearable devices in the light of their inevitable ubiquity. One type of these devices is the Augmented Reality Smart Glasses (ARSGs), which can have great effect in different areas through providing timely information to users. One of the industries that can significantly reap the benefits of this technology is health care. However, as healthcare is a very multidimensional industry, there is a need for a multifaceted look into the adoption and acceptance of smart glasses by health professionals. This study tends to examine the acceptance of smart glasses by healthcare professionals based on Technology Acceptance Model (TAM) as there is an imperative for empirical studies on user perceptions, attitudes, and intentions. For this purpose, five external factors are extracted from the literature and field study, being integration with information systems, external effects, hands-free feature, technological compatibility, and documentation. The model is examined by using PLS-SEM methodology. This study found documentation to have the strongest impact on intention due to the substitution of paperwork by mobile devices and facilitation of continuous documentation.

References

  1. Albrecht, U.-V., Folta-Schoofs, K., Behrends, M., & von Jan, U. (2013). Effects of mobile augmented reality learning compared to textbook learning on medical students: Randomized controlled pilot study. Journal of Medical Internet Research, 15(8), e182.  https://doi.org/10.2196/jmir.2497.CrossRefGoogle Scholar
  2. Albrecht, U. V., Von Jan, U., Kuebler, J., Zoeller, C., Lacher, M., Muensterer, O. J., et al. (2014). Google glass for documentation of medical findings: Evaluation in forensic medicine. Journal of Medical Internet Research, 16(2).  https://doi.org/10.2196/jmir.3225.
  3. Aldaz, G., Shluzas, L. A., Pickham, D., Eris, O., & Sadler, J. (2015). Hands-free image capture, data tagging and transfer using Google Glass: A pilot study for improved wound care management. PLoS ONE, 10(4), 1–21.  https://doi.org/10.1371/journal.pone.0121179.CrossRefGoogle Scholar
  4. Amft, O., Wahl, F., Ishimaru, S., & Kunze, K. (2015). Making regular eyeglasses smart. IEEE Pervasive Computing, 14(3), 32–43.  https://doi.org/10.1109/MPRV.2015.60.CrossRefGoogle Scholar
  5. Armstrong, D. G., Rankin, T. M., Giovinco, N. A., Mills, J. L., & Matsuoka, Y. (2014). A heads-up display for diabetic limb salvage surgery: A view through the Google looking glass. Journal of Diabetes Science and Technology, 8(5), 951–956.  https://doi.org/10.1177/1932296814535561.CrossRefGoogle Scholar
  6. Başoğlu, N., Göken, M., Dabic, M., et al. (2018). Exploring adoption of augmented reality smart glasses: Applications in the medical industry. Frontiers of Engineering Management , 5(2), 167–181.Google Scholar
  7. Başoğlu, N., Ok, E. A., & Daim, T. U. (2017). What will it take to adopt smart glasses: A consumer choice based review? Technology in Society, 50, 50–56.  https://doi.org/10.1016/j.techsoc.2017.04.005.CrossRefGoogle Scholar
  8. Bhattacherjee, A., & Hikmet, N. (2007). Physicians’ resistance toward healthcare information technology: A theoretical model and empirical test. European Journal of Information Systems, 16(6), 725–737.  https://doi.org/10.1057/palgrave.ejis.3000717.CrossRefGoogle Scholar
  9. Borchers, C. (2014). Beth Israel to use Google Glass throughout emergency room. The Boston Globe.Google Scholar
  10. Brusie, T., Fijal, T., Keller, A., Lauff, C., Barker, K., Schwinck, J., et al. (2015). Usability evaluation of two smart glass systems. 2015 Systems and Information Engineering Design Symposium, 0(c), 336–341.  https://doi.org/10.1109/SIEDS.2015.7117000.
  11. Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689–707.CrossRefGoogle Scholar
  12. Chau, P. Y. K., & Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of Management Information Systems, 18(4), 191–230.  https://doi.org/10.2307/40398548.CrossRefGoogle Scholar
  13. Cheng, S.-T., Hsu, C.-W., & Li, J.-P. (2013). Combined hand gesture—Speech model for human action recognition. Sensors, 13, 17098–17129.  https://doi.org/10.3390/s131217098.CrossRefGoogle Scholar
  14. Chin, W. W. (2000). Partial least squares for IS researchers: An overview and presentation of recent advances using the PLS approach. In International Conference on Information Systems (pp. 741–742).Google Scholar
  15. Chin, W. W. (2010). How to write up and report PLS analyses. In E. V. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbooks of computational statistics series (pp. 655–690). Cham: Springer.Google Scholar
  16. Czuszynski, K., Ruminski, J., Kocejko, T., & Wtorek, J. (2015, November). Septic safe interactions with smart glasses in health care. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Vol. 2015, pp. 1604–1607). Milan, Italy.  https://doi.org/10.1109/EMBC.2015.7318681.
  17. Daim, T. U., Basoglu, N., & Topacan, U. (2013). Adoption of health information technologies: The case of a wireless monitor for diabetes and obesity patients. Technology Analysis & Strategic Management, 25(8), 923–938.  https://doi.org/10.1080/09537325.2013.823150.CrossRefGoogle Scholar
  18. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Information Technology MIS Quarterly, 13(3), 319–340.Google Scholar
  19. Davis, F., & Bagozzi, R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.CrossRefGoogle Scholar
  20. Davis, C. R., & Rosenfield, L. K. (2015). Looking at plastic surgery through Google evidence and the first plastic surgical procedures. Plastic and Reconstructive Surgery, 135(3), 918–928.  https://doi.org/10.1097/PRS.0000000000001056.CrossRefGoogle Scholar
  21. Ducey, A. J., & Coovert, M. D. (2016). Predicting tablet computer use: An extended technology acceptance model for physicians. Health Policy and Technology, 5(3), 268–284.  https://doi.org/10.1016/j.hlpt.2016.03.010.CrossRefGoogle Scholar
  22. Feng, S., Caire, R., Cortazar, B., Turan, M., & Wong, A. (2014). Immunochromatographic diagnostic test analysis using Google Glass. ACS Nano, 8(3), 3069–3079.CrossRefGoogle Scholar
  23. Fornell, C., & Larcker, D. F. (1981). Evaluation structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  24. Friedman, E. (2016). Top features of smart glasses: Hands-free documentation. Retrieved September 12, 2017, from https://brainxchange.io/4-features-smart-glasses-hands-free-documentation/.
  25. Göken, M., Başoğlu, N. A., & Dabic, M. (2016). Exploring adoption of smart glasses: Application in medical industry. PICMET. Hawaii.Google Scholar
  26. Gregg, H. (2014a). 5 hospitals using, piloting Google Glass.Google Scholar
  27. Gregg, H. (2014b). Why hospitals are hesitant to use Google Glass. Health IT & CIO Review, 1–7.Google Scholar
  28. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152.  https://doi.org/10.2753/MTP1069-6679190202.CrossRefGoogle Scholar
  29. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012). Partial least squares: The better approach to structural equation modeling? Long Range Planning, 45(5–6), 312–319.  https://doi.org/10.1016/j.lrp.2012.09.011.
  30. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12.  https://doi.org/10.1016/j.lrp.2013.01.001.CrossRefGoogle Scholar
  31. Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The Use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning, 45(5–6), 320–340.  https://doi.org/10.1016/j.lrp.2012.09.008.
  32. Hein, D. W. E., & Rauschnabel, P. A. (2016). Augmented reality smart glasses and knowledge management: A conceptual framework for enterprise social networks. In Enterprise social networks (pp. 83–109). Wiesbaden: Springer Gabler.Google Scholar
  33. Hofmann, B., Haustein, D., & Landeweerd, L. (2017). Smart-glasses: Exposing and elucidating the ethical issues. Science and Engineering Ethics, 23(3), 701–721.  https://doi.org/10.1007/s11948-016-9792-z.CrossRefGoogle Scholar
  34. Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172.  https://doi.org/10.1016/j.jbi.2009.07.002.CrossRefGoogle Scholar
  35. Hong, J. (2013). Considering privacy issues in the context of Google glass. Communications of the ACM, 56(11), 10–11.Google Scholar
  36. Hsiao, J., & Chen, R. (2016). Critical factors influencing physicians’ intention to use computerized clinical practice guidelines: An integrative model of activity theory and the technology acceptance model. BMC Medical Informatics and Decision Making, 16(3), 1–15.  https://doi.org/10.1186/s12911-016-0241-3.CrossRefGoogle Scholar
  37. Hung, S., Tsai, J. C., & Chuang, C. (2014). Investigating primary health care nurses â€TM intention to use information technology: An empirical study in Taiwan. Decision Support Systems, 57, 331–342.CrossRefGoogle Scholar
  38. Kalantari, M., & Rauschnabel, P. A. (2018). Exploring the early adopters of augmented reality smart glasses: The case of Microsoft HoloLens. In T. Jung & M. C. T. Dieck (Eds.), Augmented reality and virtual reality (pp. 1–17).  https://doi.org/10.1007/978-3-319-64027-3.
  39. Karahanna, E., Agarwal, R., & Angst, C. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(4), 781–804.  https://doi.org/10.2307/25148754.CrossRefGoogle Scholar
  40. Kassirer, J. P. (2000). Patients, physicians, and the Internet. Health Affairs, 19(6), 115.CrossRefGoogle Scholar
  41. Kim, K. J., & Dong-Hee, S. (2015). An acceptance model for smart watches implications for the adoption of future wearable technology. Internet Research, 25(4), 527–541.  https://doi.org/10.1108/MBE-09-2016-0047.CrossRefGoogle Scholar
  42. Kolodzey, L., Grantcharov, P. D., Rivas, H., Schijven, M. P., & Grantcharov, T. P. (2017). Wearable technology in the operating room: A systematic review. BMJ Innovations, 3, 55–63.  https://doi.org/10.1136/bmjinnov-2016-000133.CrossRefGoogle Scholar
  43. Kuo, K. M., Liu, C. F., & Ma, C. C. (2013). An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC Medical Informatics and Decision Making, 13(8), 1–14.  https://doi.org/10.1186/1472-6947-13-88.CrossRefGoogle Scholar
  44. Kwong, K., & Wong, K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, Technical Note.Google Scholar
  45. Lapointe, L., & Rivard, S. (2005). A multilevel model of resistance to information technology implementation. MIS Quarterly, 29(3), 461–491.  https://doi.org/10.2307/25148692.CrossRefGoogle Scholar
  46. Lazuras, L., & Dokou, A. (2016). Mental health professionals’ acceptance of online counseling. Technology in Society, 44, 10–14.  https://doi.org/10.1016/j.techsoc.2015.11.002.CrossRefGoogle Scholar
  47. Lee, F., Teich, J. M., Spurr, C. D., & Bates, D. W. (1996). Implementation of physician order entry: User satisfaction and self- reported usage patterns. Journal of the American Medical Informatics Association, 3(November), 42–55.  https://doi.org/10.1136/jamia.1996.96342648.CrossRefGoogle Scholar
  48. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204.  https://doi.org/10.1016/S0378-7206(01)00143-4.CrossRefGoogle Scholar
  49. Lukowicz, P., Kirstein, T., & Tröster, G. (2004). Wearable systems for health care applications. Methods of Information in Medicine, 43(3), 232–238.  https://doi.org/10.1267/METH04030232.CrossRefGoogle Scholar
  50. May, C., Gask, L., Atkinson, T., Ellis, N., Mair, F., & Esmail, A. (2001). Resisting and promoting new technologies in clinical practice: The case of telepsychiatry. Social Science and Medicine, 52(12), 1889–1901.  https://doi.org/10.1016/S0277-9536(00)00305-1.CrossRefGoogle Scholar
  51. Mechanic, D. (2003). Physician discomfort challenges and opportunity. Journal of the American Medical Association, 290(7), 941–946.  https://doi.org/10.1001/jama.290.7.941.CrossRefGoogle Scholar
  52. Meyers, S. (2003). ‘Concierge’ medicine. Who really pays for gold standard access to doctors? Trustee: The Journal for Hospital Governing Boards, 56(1), 12–14.Google Scholar
  53. Mitrasinovic, S., Camacho, E., Trivedi, N., Logan, J., Campbell, C., Zilinyi, R., et al. (2015). Clinical and surgical applications of smart glasses. Technology and Health Care, 23(4), 381–401.  https://doi.org/10.3233/THC-150910.
  54. Monroy, G. L., Shemonski, N. D., Shelton, R. L., Nolan, R. M., & Boppart, S. A. (2014). Implementation and evaluation of Google Glass for visualizing real-time image and patient data in the primary care office. Proceedings of SPIE, 8935, 893514–893519.  https://doi.org/10.1117/12.2040221.CrossRefGoogle Scholar
  55. Moon, S., & Seo, J. (2015). Integration of smart glass technology for information exchange at construction sites. In Proceedings of the International Symposium on Automation and Robotics in Construction (pp. 1–2).Google Scholar
  56. Moshtaghi, O., Kelley, K. S., Armstrong, W. B., Ghavami, Y., Gu, J., & Djalilian, H. R. (2015). Using Google Glass to solve communication and surgical education challenges in the operating room. The Laryngoscope, 125(10), 2295–2297.  https://doi.org/10.1002/lary.25249.CrossRefGoogle Scholar
  57. Muensterer, O. J., Lacher, M., Zoeller, C., & Bronstein, M. (2014). Google Glass in pediatric surgery: An exploratory study. International Journal of Surgery, 12(4), 281–289.CrossRefGoogle Scholar
  58. Ni, T., & Baudisch, P. (2009). Disappearing mobile devices. In Proceedings of UIST 2009 (pp. 101–110).  https://doi.org/10.1145/1622176.1622197.
  59. Oremus, W. (2015). Google Glass: The future’s not very bright. Charlotte Observer.Google Scholar
  60. Overhage, J. M., Perkins, S., Tierney, W. M., & McDonald, C. J. (2001). Controlled trial of direct physician order entry. Journal of the American Medical Informatics Association, 8(4), 361–371.  https://doi.org/10.1136/jamia.2001.0080361.CrossRefGoogle Scholar
  61. Procurement Guide for Hospital Information Management Systems. (2010). Ankara: TC Ministry of Health Administrative and Financial Affairs Department.Google Scholar
  62. Quint, F., & Loch, F. (2015). Using smart glasses to document maintenance processes. Mensch und Computer 2015–Workshopband.Google Scholar
  63. Ratanawongsa, N., Barton, J. L., Lyles, C. R., Wu, M., Yelin, E. H., Matinez, D., et al. (2015). Association between clinician computer use and communication with patients in safety-net clinics. JAMA, 176(1), 125–127.  https://doi.org/10.1001/jamainternmed.2015.6102.4.CrossRefGoogle Scholar
  64. Rauschnabel, P. A., Brem, A., & Ivens, B. S. (2015). Who will buy smart glasses? Empirical results of two pre-market-entry studies on the role of personality in individual awareness and intended adoption of Google Glass wearables. Computers in Human Behavior, 49, 635–647.  https://doi.org/10.1016/j.chb.2015.03.003.CrossRefGoogle Scholar
  65. Richardson, L., Keefe, K., Huber, C., Racevskis, L., Reynolds, G., Thourot, S., et al. (2014). Assessing the value of the Central Everglades Planning Project (CEPP) in Everglades restoration: An ecosystem service approach. Ecological Economics, 107, 366–377.  https://doi.org/10.1016/j.ecolecon.2014.09.011.CrossRefGoogle Scholar
  66. Ro, Y. K., Brem, A., & Rauschnabel, P. A. (2018). Augmented reality smart glasses: Definition, concepts and impact on firm value creation (pp. 169–181). Cham: Springer.  https://doi.org/10.1007/978-3-319-64027-3_12.
  67. Rodríguez, H. B. M., Salem, S. J., & Gomez, I. T. J. (2017). Feasibility and safety of augmented reality—Assisted urological surgery using smartglass. World Journal of Urology, 35, 967–972.  https://doi.org/10.1007/s00345-016-1956-6.CrossRefGoogle Scholar
  68. Rogers, E. M., & Everett, M. (1983). Diffusion of innovation (3rd ed.). New York: The Free Press. https://doi.org/82-70998.
  69. Rowe, M., Bozalek, V., & Frantz, J. (2013). Using Google Drive to facilitate a blended approach to authentic learning. British Journal of Educational Technology, 44(4), 594–606.  https://doi.org/10.1111/bjet.12063.CrossRefGoogle Scholar
  70. Rubin, R. (2017). With Enterprise Edition, Google Glass finds its ROI calling. ZDNet.Google Scholar
  71. Ruminski, J., Bujnowski, A., Andrushevich, A., Biallas, M., & Kistler, R. (2016). The data exchange between smart glasses and healthcare information systems using the HL7 FHIR standard. In 2016 9th International Conference on Human System Interactions (HSI) (pp. 525–531).Google Scholar
  72. Sanchez, G. (2013). PLS path modeling with R. R package notes. https://doi.org/citeulike-article-id:13341888.
  73. Shaoa, P., Ding, H., Wang, J., Liu, P., Ling, Q., Chen, J., et al. (2014). Designing a wearable navigation system for image-guided cancer resection surgery. Annals of Biomedical Engineering, 42(11), 2228–2237.  https://doi.org/10.1007/s10439-014-1062-0.Designing.
  74. Shaygan, A., Ozdemir-Gungor, D., Kutgun, H., & Daneshi, A. (2017). Adoption criteria evaluation of activity tracking Wristbands for university students. PICMET.Google Scholar
  75. Sijie, X., Sujie, Z., Yisheng, J., Binyao, J., Xiaohua, T., Xuesheng, Z., et al. (2017). iBlink: Smart glasses for facial paralysis patients. In MobiSys ’17 Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (pp. 359–370). New York, USA.  https://doi.org/10.1145/3081333.3081343.
  76. TOBB. (2017). Turkey healthcare landscape. Retrieved from https://www.tobb.org.tr/saglik/20171229-tss-genel-bakis-en.pdf.
  77. Township, D., & District, L. (2017). The staffs’ adoption intention of knowledge management system in green hospital—The theory of technology acceptance model applied. Internation Journal of Organizational Innovation, 9(3), 27–36.Google Scholar
  78. Turan, A. H., & Palvia, P. C. (2014). Critical information technology issues in Turkish healthcare. Information & Management, 51(1), 57–68.  https://doi.org/10.1016/J.IM.2013.09.007.CrossRefGoogle Scholar
  79. Vallurupalli, S., Paydak, H., Agarwal, S. K., Agrawal, M., & Assad-Kottner, C. (2013). Wearable technology to improve education and patient outcomes in a cardiology fellowship program—A feasibility study. Health and Technology, 3(4), 267–270. https://doi.org/10.1007/s12553-013-0065-4.CrossRefGoogle Scholar
  80. Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/WOS:000086130700002.
  81. Venkatesh, V., & Zhang, X. (2014). Unified theory of acceptance and use of technology: U.S. vs. China. Journal of Global Information Technology Management, 13(1), 5–27.  https://doi.org/10.1080/1097198X.2010.10856507.
  82. Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 47–83). Berlin, Heidelberg: Springer.  https://doi.org/10.1007/978-3-540-32827-8.
  83. Wang, C.-H. (2015). A market-oriented approach to accomplish product positioning and product recommendation for smart phones and wearable devices. International Journal of Production Research, 53(8), 2542–2553.  https://doi.org/10.1080/00207543.2014.991046.CrossRefGoogle Scholar
  84. Wu, J. H., Wang, S. C., & Lin, L. M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International Journal of Medical Informatics, 76(1), 66–77.  https://doi.org/10.1016/j.ijmedinf.2006.06.006.CrossRefGoogle Scholar
  85. Yarbrough, A. K., & Smith, T. B. (2007). Technology acceptance among physicians: A new take on TAM. Medical Care Research and Review: MCRR, 64(6), 650–672.  https://doi.org/10.1177/1077558707305942.CrossRefGoogle Scholar
  86. Yu, P., Li, H., & Gagnon, M.-P. (2009). Health IT acceptance factors in long-term care facilities: A cross-sectional survey. International Journal of Medical Informatics, 78(4), 219–229.  https://doi.org/10.1016/j.ijmedinf.2008.07.006.CrossRefGoogle Scholar
  87. Zak, C., D’Aprix, T., & Billitier, A. (2002). Electronic data gathering for emergency medical services. United States.Google Scholar
  88. Zheng, X. S., Foucault, C., Silva, P. M. da, Dasari, S., Yang, T., & Goose, S. (2015). Eye-wearable technology for machine maintenance: Effects of display position and hands-free operation. Proceedings of the ACM CHI’15 Conference on Human Factors in Computing Systems, 1, 2125–2134.  https://doi.org/10.1145/2702123.2702305.

Copyright information

© The Author(s) 2020

Authors and Affiliations

  • Dilek Özdemir-Güngör
    • 1
  • Müge Göken
    • 2
  • Nuri Basoglu
    • 3
  • Amir Shaygan
    • 4
  • Marina Dabić
    • 5
    • 6
    Email author
  • Tugrul U. Daim
    • 4
  1. 1.Izmir Katip Celebi UniversityIzmirTurkey
  2. 2.Istanbul Technical UniversityIstanbulTurkey
  3. 3.Izmir Institute of TechnologyIzmirTurkey
  4. 4.Portland State UniversityPortlandUSA
  5. 5.Faculty of Economics and BusinessUniversity of ZagrebZagrebCroatia
  6. 6.Nottingham Trent UniversityNottinghamUK

Personalised recommendations