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

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

Notes

  1. 1.

    Cohen [78] describes f2 values of 0.02, 0.15, and 0.35 as small, medium and large effects, respectively.

  2. 2.

    Chin [73] describes R2 values of 0.67, 0.33 and 0.19 in PLS path models as substantial, moderate, and small, respectively, with a moderate value that is acceptable for explorative models or if the endogenous construct has only a few predictors.

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

    Article  Google Scholar 

  3. 3.

    Demirbilek M, Branke J, Strauss A (2018) Dynamically accepting and scheduling patients for home healthcare. Health Care Manag Sci:1–16

  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

    Article  Google 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

    Article  Google Scholar 

  7. 7.

    Or C, Karsh B (2009) A systematic review of patient acceptance of consumer health information technology. J Am Med Inform Assoc 16:550–560

    Article  Google 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 76:790–800

    Article  Google 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:

  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

    Article  Google 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

    Article  Google 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 2006:181–186

  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 65:169–177

    Article  Google 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, CA

    Google Scholar 

  15. 15.

    Wilson E, Lankton N (2004) Modeling patients’ acceptance of provider-delivered e-health. J Am Med Inform Assoc 11:241–248

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google Scholar 

  19. 19.

    Holden R, Karsh B (2010) The technology acceptance model: its past and its future in health care. J Biomed Inform 43:159–172

    Article  Google 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

    Article  Google Scholar 

  21. 21.

    Davis F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340

    Article  Google Scholar 

  22. 22.

    Ullman JB, Bentler PM (2012) Structural equation modeling. In: Handbook of Psychology, Second Edition. John Wiley & Sons, Inc., Hoboken, NJ, USA

  23. 23.

    Bagozzi RP (2007) The legacy of the technology acceptance model and a proposal for a paradigm shift. J Assoc Inf Syst 8:244–254

    Google 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 21:389–400

    Article  Google 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/cb

  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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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 39:1459–1469

    Article  Google 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

    Article  Google Scholar 

  34. 34.

    Venkatesh V, Brown S (2001) A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges. MIS Q 25:71–102

    Article  Google Scholar 

  35. 35.

    Sheth JN, Newman BI, Gross BL (1991) Why we buy what we buy. J Bus Res 22:159–171

    Article  Google 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

    Article  Google 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):28–45

    Article  Google Scholar 

  38. 38.

    Collier J, Kimes S (2013) Only if it is convenient understanding how convenience influences self-service technology evaluation. J Serv Res 16:39–51

    Article  Google 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

    Article  Google 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

    Article  Google 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 Yersey

    Google Scholar 

  42. 42.

    Venkatesh V, Morris M, Davis G, Davis F (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478

    Article  Google Scholar 

  43. 43.

    Davis F, Bagozzi R, Warshaw P (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 46:186–204

    Google Scholar 

  44. 44.

    Agarwal R, Prasad J (1999) Are individual differences germane to the acceptance of new information technologies? Decis Sci 3:361–391

    Article  Google Scholar 

  45. 45.

    Sarker S, Wells J (2003) Understanding mobile handheld device use and adoption. Commun ACM 46:35–40

    Article  Google 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 4:121–127

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google Scholar 

  50. 50.

    Karahanna E, Straub DW (1999) The psychological origins of perceived usefulness and ease-of-use. Inf Manag 35:237–250

    Article  Google Scholar 

  51. 51.

    Tsikriktsis N (2004) A technology readiness-based taxonomy of customers a replication and extension. J Serv Res 7:42–52

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google Scholar 

  57. 57.

    Ajzen I (2002) Constructing a TPB questionnaire: Conceptual and methodological considerations

  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 24:91–112

  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

    Article  Google 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 57:123–146

    Article  Google 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 47:699–712

    Article  Google Scholar 

  62. 62.

    Hair J, Ringle C, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. J Mark Theory Pract 19:139–152

    Article  Google Scholar 

  63. 63.

    Gefen D, Straub D, Boudreau MC (2000) Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst 4:1–77

    Google Scholar 

  64. 64.

    Schwarzer R (2008) Modeling health behavior change: how to predict and modify the adoption and maintenance of health behaviors. Appl Psychol 57:1–29

    Google Scholar 

  65. 65.

    Buhi ER, Goodson P, Neilands TB (2007) Structural equation modeling: a primer for health behavior researchers. Am J Health Behav 31:74–85

    Article  Google 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 2:285–309

    Google 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 40:413–433

    Article  Google Scholar 

  68. 68.

    Haenlein M, Kaplan A (2004) A beginner’s guide to partial least squares analysis. Underst Stat 3:283–297

    Article  Google Scholar 

  69. 69.

    Werts C, Linn R, Jöreskog K (1974) Intraclass reliability estimates: testing structural assumptions. Educ Psychol Meas 34:25–33

    Article  Google Scholar 

  70. 70.

    Wong K (2013) Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark Bull 24:1–32

    Google Scholar 

  71. 71.

    Bagozzi RP, Yi Y (1988) On the evaluation of structural equation models. J Acad Mark Sci 16:74–94

    Article  Google Scholar 

  72. 72.

    Fornell C, Larcker D (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18:39–50

    Article  Google Scholar 

  73. 73.

    Chin W (1998) The partial least squares approach to structural equation modeling. Mod Methods Bus Res 295:295–336

    Google 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 88:879–903

    Article  Google Scholar 

  75. 75.

    Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Ser B 36:111–147

    Google Scholar 

  76. 76.

    Geisser S (1974) A predictive approach to the random effect model. Biometrika 61:101–107

    Article  Google Scholar 

  77. 77.

    Tenenhaus M, Vinzi V (2005) PLS path modeling. Comput Stat Anal 48:159–205

    Article  Google Scholar 

  78. 78.

    Cohen J (1988) Statistical power analysis for the behavioral sciences. In: Lawrence Erlbaum associates. Hillsdale, New Jersey

    Google Scholar 

  79. 79.

    Song M, Parry ME, Kawakami T (2009) Incorporating Network Externalities into the Technology Acceptance Model 23:291–307

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Debora Bettiga.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

Appendix 2

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bettiga, D., Lamberti, L. & Lettieri, E. Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach. Health Care Manag Sci 23, 203–214 (2020). https://doi.org/10.1007/s10729-019-09468-2

Download citation

Keywords

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