Advertisement

Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach

  • Debora BettigaEmail author
  • Lucio Lamberti
  • Emanuele Lettieri
Article
  • 58 Downloads

Abstract

Healthcare is moving towards new patterns and models, with an increasing attention paid to prevention. Smart technologies for mobile health care are emerging as new instruments to monitor the state of essential parameters in citizens. A very debated subject in literature is the critical role played by citizens’ acceptance and willingness to pay for mobile health technologies, especially whereas the services provided are preventive rather than curative. The adoption of such technologies is, indeed, a necessary condition for the success of mobile personalized health care. In this view, a conceptual framework, grounded on Technology Acceptance Model, is developed to explore the determinants of users’ willingness to adopt and pay for a mobile health care application for cardiovascular prevention. Empirical data are collected from a sample of 212 non-hypertensive Italian individuals and analyzed through Structural Equation Modeling. Results confirm that usefulness and ease of use determine both intention to accept and willingness to pay for mobile health smart technologies. Results show also the significant role played by social influence as well the role as antecedents played by technology promptness, innovativeness and prevention awareness. This study offers novel insights to design and promote smart application to improve mobile health care, with implications for researchers and practitioners in health care, research & development, and marketing.

Keywords

Preventive health Mobile health care Smart technologies Technology acceptance model Structural equation modeling 

Notes

References

  1. 1.
    Worrall P, Chaussalet TJ (2015) A structured review of long-term care demand modelling. Health care Manag Sci 18:173–194.  https://doi.org/10.1007/s10729-014-9299-6
  2. 2.
    Mallor F, Azcárate C, Barado J (2015) Optimal control of ICU patient discharge: from theory to implementation. Health Care Manag Sci 18:234–250.  https://doi.org/10.1007/s10729-015-9320-8 CrossRefGoogle Scholar
  3. 3.
    Demirbilek M, Branke J, Strauss A (2018) Dynamically accepting and scheduling patients for home healthcare. Health Care Manag Sci:1–16Google Scholar
  4. 4.
    Osei-Frimpong K, Wilson A, Lemke F (2016) Patient co-creation activities in healthcare service delivery at the micro level: the influence of online access to healthcare information. Technol forecast Soc change 126:14–27.  https://doi.org/10.1016/j.techfore.2016.04.009
  5. 5.
    Davari S, Kilic K, Ertek G (2015) Fuzzy bi-objective preventive health care network design. Health Care Manag Sci 18:303–317.  https://doi.org/10.1007/s10729-014-9293-z CrossRefGoogle Scholar
  6. 6.
    Cohen JT, Neumann PJ, Weinstein MC (2008) Does preventive care save money? Health economics and the presidential candidates. N Engl J Med 358:661–663.  https://doi.org/10.1056/NEJMp0708558 CrossRefGoogle Scholar
  7. 7.
    Or C, Karsh B (2009) A systematic review of patient acceptance of consumer health information technology. J Am Med Inform Assoc 16:550–560CrossRefGoogle Scholar
  8. 8.
    Kim D, Chang H (2007) Key functional characteristics in designing and operating health information websites for user satisfaction: an application of the extended technology acceptance. Int J Med Inform 76:790–800CrossRefGoogle Scholar
  9. 9.
    Muessig K, Pike E, LeGrand S, Hightow-Weidman LB (2013) Mobile phone applications for the care and prevention of HIV and other sexually transmitted diseases: a review J Med Internet Res 15:Google Scholar
  10. 10.
    Burke LE, Ma J, Azar KM et al (2015) Current science on consumer use of Mobile health for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation 132:1157–1213.  https://doi.org/10.1161/CIR.0000000000000232 CrossRefGoogle Scholar
  11. 11.
    Lobelo F, Kelli HM, Tejedor SC et al (2017) The wild wild west: a framework to integrate mHealth software applications and wearables to support physical activity assessment, counseling and interventions for cardiovascular disease risk reduction. Prog Cardiovasc Dis 58:584–594.  https://doi.org/10.1016/j.pcad.2016.02.007.The CrossRefGoogle Scholar
  12. 12.
    Yu P, Wu MX, Yu H, Xiao GQ (2006) The challenges for the adoption of m-health. In: 2006 IEEE international conference on service operations and logistics, and informatics. SOLI 2006:181–186Google Scholar
  13. 13.
    Gustafson DH, Hawkins RP, Boberg EW et al (2002) CHESS: 10 years of research and development in consumer health informatics for broad populations, including the underserved. Int J Med Inform 65:169–177CrossRefGoogle Scholar
  14. 14.
    Slack WV (1997) Cybermedicine: how computing empowers doctors and patients for better health care. Jossey-bass Inc. In: Publishers. USA, San Francisco, CAGoogle Scholar
  15. 15.
    Wilson E, Lankton N (2004) Modeling patients’ acceptance of provider-delivered e-health. J Am Med Inform Assoc 11:241–248CrossRefGoogle Scholar
  16. 16.
    Steinhubl SR, Muse ED, Topol EJ (2015) The emerging field of Mobile health. Sci Transl Med 7:1–6.  https://doi.org/10.1126/scitranslmed.aaa3487 CrossRefGoogle Scholar
  17. 17.
    Pai F, Huang K (2011) Applying the technology acceptance model to the introduction of healthcare information systems. Technol Forecast Soc Change 78:650–660.  https://doi.org/10.1016/j.techfore.2010.11.007 CrossRefGoogle Scholar
  18. 18.
    Lee E, Han S (2015) Determinants of adoption of mobile health services. Online Inf Rev 39:556–573.  https://doi.org/10.1108/OIR-01-2015-0007 CrossRefGoogle Scholar
  19. 19.
    Holden R, Karsh B (2010) The technology acceptance model: its past and its future in health care. J Biomed Inform 43:159–172CrossRefGoogle Scholar
  20. 20.
    Behkami NA, Daim TU (2012) Forecasting for health information technology ( HIT ), using technology intelligence. Technol Forecast Soc Chang 79:498–508.  https://doi.org/10.1016/j.techfore.2011.08.015 CrossRefGoogle Scholar
  21. 21.
    Davis F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340CrossRefGoogle Scholar
  22. 22.
    Ullman JB, Bentler PM (2012) Structural equation modeling. In: Handbook of Psychology, Second Edition. John Wiley & Sons, Inc., Hoboken, NJ, USAGoogle Scholar
  23. 23.
    Bagozzi RP (2007) The legacy of the technology acceptance model and a proposal for a paradigm shift. J Assoc Inf Syst 8:244–254Google Scholar
  24. 24.
    Gefen D, Straub D (1997) Gender differences in the perception and use of e-mail: an extension to the technology acceptance model. MIS Q 21:389–400CrossRefGoogle Scholar
  25. 25.
    Wang C, Lo S, Fang W (2008) Extending the technology acceptance model to mobile telecommunication innovation : the existence of network externalities. J Consum Behav 7:101–110. 10.1002/cbGoogle Scholar
  26. 26.
    Edmunds R, Thorpe M, Conole G (2012) Student attitudes towards and use of ICT in course study, work and social activity: a technology acceptance model approach. Br J Educ Technol 43:71–84.  https://doi.org/10.1111/j.1467-8535.2010.01142.x CrossRefGoogle Scholar
  27. 27.
    Schepers J, Wetzels M (2007) A meta-analysis of the technology acceptance model: investigating subjective norm and moderation effects. Inf Manag 44:90–103.  https://doi.org/10.1016/j.im.2006.10.007 CrossRefGoogle Scholar
  28. 28.
    Svendsen GB, Johnsen J-AK, Almås-Sørensen L, Vittersø J (2013) Personality and technology acceptance: the influence of personality factors on the core constructs of the technology acceptance model. Behav Inform Technol 32:323–334.  https://doi.org/10.1080/0144929X.2011.553740 CrossRefGoogle Scholar
  29. 29.
    Davis FD (1993) User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. Int J Man Mach Stud 38:475–487.  https://doi.org/10.1006/imms.1993.1022 CrossRefGoogle Scholar
  30. 30.
    Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46:186–204.  https://doi.org/10.1287/mnsc.46.2.186.11926 CrossRefGoogle Scholar
  31. 31.
    Schwarz N, Ernst A (2009) Agent-based modeling of the diffusion of environmental innovations—an empirical approach. Technol Forecast Soc Change 76:497–511.  https://doi.org/10.1016/j.techfore.2008.03.024 CrossRefGoogle Scholar
  32. 32.
    Claudy MC, Michelsen C, O’Driscoll A (2011) The diffusion of micro generation technologies – assessing the influence of perceived product characteristics on home owners’ willingness to pay. Energy Policy 39:1459–1469CrossRefGoogle Scholar
  33. 33.
    Sheth JN, Newman BI, Gross BL (1991) Why we buy what we buy: a theory of consumption values. J Bus Res 22:159–170.  https://doi.org/10.1016/0148-2963(91)90050-8 CrossRefGoogle Scholar
  34. 34.
    Venkatesh V, Brown S (2001) A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges. MIS Q 25:71–102CrossRefGoogle Scholar
  35. 35.
    Sheth JN, Newman BI, Gross BL (1991) Why we buy what we buy. J Bus Res 22:159–171CrossRefGoogle Scholar
  36. 36.
    Davis FD, Venkatesh V (2004) Toward Preprototype user acceptance testing of new information systems: implications for software Project Management. IEEE Trans Eng Manag 51:31–46.  https://doi.org/10.1109/TEM.2003.822468 CrossRefGoogle Scholar
  37. 37.
    Tornatzky L, Klein K (1982) Innovation characteristics and innovation adoption-implementation: a meta-analysis of findings. Eng Manag IEEE Trans 0n(29):28–45CrossRefGoogle Scholar
  38. 38.
    Collier J, Kimes S (2013) Only if it is convenient understanding how convenience influences self-service technology evaluation. J Serv Res 16:39–51CrossRefGoogle Scholar
  39. 39.
    Venkatesh V, Davis FD (1996) A model of the antecedents of perceived ease of use: development and test. Decis Sci 27:451–481.  https://doi.org/10.1111/j.1540-5915.1996.tb01822.x CrossRefGoogle Scholar
  40. 40.
    Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211.  https://doi.org/10.1016/0749-5978(91)90020-T CrossRefGoogle Scholar
  41. 41.
    Ajzen I, Albarracín D, Hornik R (2007) Prediction and change of health behavior: applying the reasoned action approach. Psychology press, Lawrence Erlbaum Associates, Inc, Mahwah, New YerseyGoogle Scholar
  42. 42.
    Venkatesh V, Morris M, Davis G, Davis F (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478CrossRefGoogle Scholar
  43. 43.
    Davis F, Bagozzi R, Warshaw P (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 46:186–204Google Scholar
  44. 44.
    Agarwal R, Prasad J (1999) Are individual differences germane to the acceptance of new information technologies? Decis Sci 3:361–391CrossRefGoogle Scholar
  45. 45.
    Sarker S, Wells J (2003) Understanding mobile handheld device use and adoption. Commun ACM 46:35–40CrossRefGoogle Scholar
  46. 46.
    Folsom AR, Sprafka JM, Luepker RV, Jacobs DRJ (1988) Beliefs among black and white adults about causes and prevention of cardiovascular disease: the Minnesota heart survey. Am J Prev Med 4:121–127CrossRefGoogle Scholar
  47. 47.
    Folsom AR, Iso H, Sprafka JM et al (1988) Use of aspirin for prevention of cardiovascular disease-1981-1982 to 1985-1986: the Minnesota heart survey. Am Heart J 116:827–830.  https://doi.org/10.1016/0002-8703(88)90344-4 CrossRefGoogle Scholar
  48. 48.
    Mosca L, Mochari-Greenberger H, Dolor RJ et al (2010) Twelve-year follow-up of American women’s awareness of cardiovascular disease risk and barriers to heart health. Circ Cardiovasc Qual Outcomes 3:120–127.  https://doi.org/10.1161/CIRCOUTCOMES.109.915538 CrossRefGoogle Scholar
  49. 49.
    Mallat N, Rossi M, Tuunainen VK, Öörni A (2009) The impact of use context on mobile services acceptance: the case of mobile ticketing. Inf Manag 46:190–195.  https://doi.org/10.1016/j.im.2008.11.008 CrossRefGoogle Scholar
  50. 50.
    Karahanna E, Straub DW (1999) The psychological origins of perceived usefulness and ease-of-use. Inf Manag 35:237–250CrossRefGoogle Scholar
  51. 51.
    Tsikriktsis N (2004) A technology readiness-based taxonomy of customers a replication and extension. J Serv Res 7:42–52CrossRefGoogle Scholar
  52. 52.
    Parasuraman A (2000) Technology readiness index (tri): a multiple-item scale to measure readiness to embrace new technologies. J Serv Res 2:307–320.  https://doi.org/10.1177/109467050024001 CrossRefGoogle Scholar
  53. 53.
    Liljander V, Gillberg F, Gummerus J, van Riel A (2006) Technology readiness and the evaluation and adoption of self-service technologies. J Retail Consum Serv 13:177–191.  https://doi.org/10.1016/j.jretconser.2005.08.004 CrossRefGoogle Scholar
  54. 54.
    Lin CH, Shih HY, Sher PJ (2007) Integrating technology readiness into technology acceptance: the TRAM model. Psychol Mark 24:641–657.  https://doi.org/10.1002/mar.20177 CrossRefGoogle Scholar
  55. 55.
    Wang Y-S, Wang Y-M, Lin H-H, Tang T-I (2003) Determinants of user acceptance of internet banking: an empirical study. Int J Serv Ind Manag 14:501–519.  https://doi.org/10.1108/09564230310500192 CrossRefGoogle Scholar
  56. 56.
    Davis FD, Venkatesh V (1996) A critical assessment of potential measurement biases in the technology acceptance model: three experiments. Int J Hum Comput Stud 45:19–45.  https://doi.org/10.1006/ijhc.1996.0040 CrossRefGoogle Scholar
  57. 57.
    Ajzen I (2002) Constructing a TPB questionnaire: Conceptual and methodological considerationsGoogle Scholar
  58. 58.
    Gall-Ely M Le (2009) Definition, measurement and determinants of the consumer’s willingness to pay: a critical synthesis and avenues for further research. Rech Appl en Mark English Ed 24:91–112Google Scholar
  59. 59.
    Avkiran NK (2018) An in-depth discussion and illustration of partial least squares structural equation modeling in health care. Health Care Manag Sci 21:401–408.  https://doi.org/10.1007/s10729-017-9393-7 CrossRefGoogle Scholar
  60. 60.
    Lowry PB, Gaskin J (2014) Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: when to choose it and how to use it. IEEE Trans Prof Commun 57:123–146CrossRefGoogle Scholar
  61. 61.
    Hwang H, Malhotra N, Kim Y et al (2010) A comparative study on parameter recovery of three approaches to structural equation modeling. J Mark Res 47:699–712CrossRefGoogle Scholar
  62. 62.
    Hair J, Ringle C, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. J Mark Theory Pract 19:139–152CrossRefGoogle Scholar
  63. 63.
    Gefen D, Straub D, Boudreau MC (2000) Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst 4:1–77Google Scholar
  64. 64.
    Schwarzer R (2008) Modeling health behavior change: how to predict and modify the adoption and maintenance of health behaviors. Appl Psychol 57:1–29Google Scholar
  65. 65.
    Buhi ER, Goodson P, Neilands TB (2007) Structural equation modeling: a primer for health behavior researchers. Am J Health Behav 31:74–85CrossRefGoogle Scholar
  66. 66.
    Barclay D, Higgins C, Thompson R (1995) The partial least squares (PLS) approach to causal modelling: personal computer adoption and use as an illustration. Technol Stud 2:285–309Google Scholar
  67. 67.
    Hair J, Sarstedt M, Ringle C, Mena J (2012) An assessment of the use of partial least squares structural equation modeling in marketing research. J Acad Mark Sci 40:413–433CrossRefGoogle Scholar
  68. 68.
    Haenlein M, Kaplan A (2004) A beginner’s guide to partial least squares analysis. Underst Stat 3:283–297CrossRefGoogle Scholar
  69. 69.
    Werts C, Linn R, Jöreskog K (1974) Intraclass reliability estimates: testing structural assumptions. Educ Psychol Meas 34:25–33CrossRefGoogle Scholar
  70. 70.
    Wong K (2013) Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark Bull 24:1–32Google Scholar
  71. 71.
    Bagozzi RP, Yi Y (1988) On the evaluation of structural equation models. J Acad Mark Sci 16:74–94CrossRefGoogle Scholar
  72. 72.
    Fornell C, Larcker D (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18:39–50CrossRefGoogle Scholar
  73. 73.
    Chin W (1998) The partial least squares approach to structural equation modeling. Mod Methods Bus Res 295:295–336Google Scholar
  74. 74.
    Podsakoff P, MacKenzie S, Lee J, Podsakoff N (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88:879–903CrossRefGoogle Scholar
  75. 75.
    Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Ser B 36:111–147Google Scholar
  76. 76.
    Geisser S (1974) A predictive approach to the random effect model. Biometrika 61:101–107CrossRefGoogle Scholar
  77. 77.
    Tenenhaus M, Vinzi V (2005) PLS path modeling. Comput Stat Anal 48:159–205CrossRefGoogle Scholar
  78. 78.
    Cohen J (1988) Statistical power analysis for the behavioral sciences. In: Lawrence Erlbaum associates. Hillsdale, New JerseyGoogle Scholar
  79. 79.
    Song M, Parry ME, Kawakami T (2009) Incorporating Network Externalities into the Technology Acceptance Model 23:291–307Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Management, Economics and Industrial EngineeringPolitecnico di MilanoMilanItaly

Personalised recommendations