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Evaluation of Healthcare IT Applications: The User Acceptance Perspective

  • Kai Zheng
  • Rema Padman
  • Michael P. Johnson
  • Herbert S. Diamond
Part of the Studies in Computational Intelligence book series (SCI, volume 65)

As healthcare costs continue to spiral upward, healthcare institutions are under enormous pressure to create cost efficient systems without risking quality of care. Healthcare IT applications provide considerable promises for achieving this multifaceted goal through managing inofrmation, reducing costs, and facilitating total quality management and continuous quality improvement programs. However, the desired outcome can not be achieved if these applications are not being used.

Keywords

Behavioral Intention Plan Behavior Technology Acceptance Model Perceive Behavioral Control User Acceptance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    D. Adams, R. Nelson, and P. Todd, Perceived usefulness, ease of use, and usage of information technology: a replication, MIS Quarterly, 16 (1992), pp. 227-247CrossRefGoogle Scholar
  2. 2.
    R. Agarwal and E. Karahanna, Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage, MIS Quarterly, 24 (2000), pp. 665-694CrossRefGoogle Scholar
  3. 3.
    R. Agarwal and J. Prasad, The role of innovation characteristics and per-ceived voluntariness in the acceptation of information technologies, Decision Sci, 28(1997), pp. 557-582CrossRefGoogle Scholar
  4. 4.
    Are individual differences germane to the acceptance of new information technologies?, Decision Sci, 30 (1999), pp. 361-391Google Scholar
  5. 5.
    I. Ajzen, From intentions to actions: a theory of planned behavior, in Springer series in social psychology, J. Kuhl and J. Beckmann, eds., Springer, Berlin, 1985, pp. 11-39Google Scholar
  6. 6.
    I. Ajzen, The theory of planned behavior, Organizational Behavior and Human Decision Processes, 50 (1991), pp. 179-211CrossRefGoogle Scholar
  7. 7.
    I. Ajzen and M. Fishbein, Understanding Attitudes and Predicting Social Behavior, Prentice-Hall, Englewood Cliffs, NJ, 1980Google Scholar
  8. 8.
    D. Albarracin, B. T. Johnson, M. Fishbein, and P. A. Muellerleile, Theories of reasoned action and planned behavior as models of condom use: a meta-analysis, Psychol Bull, 127 (2001), pp. 142-161CrossRefGoogle Scholar
  9. 9.
    J. Anderson, S. Jay, H. Schweer, M. Anderson, and D. Kassing, Physi-cian communication networks and the adoption and utilization of computer applications in medicine, in Use and Impact of Computers in Clinical Medicine, J. G. Anderson and S. J. Jay, eds., Springer-Verlag, New York, 1987, pp. 185-199Google Scholar
  10. 10.
    J. G. Anderson, C. E. Aydin, and S. J. Jay, Evaluating Health Care Infor-mation Systems, SAGE Publications, Thousand Oaks, CA, 1994Google Scholar
  11. 11.
    C. J. Armitage and M. Conner, Efficacy of the theory of planned behaviour: a meta-analytic review, Br J Soc Psychol, 40 (2001), pp. 471-499CrossRefGoogle Scholar
  12. 12.
    C. E. Aydin, Occupational adaptation to computerized medical information sys-tems, J Health Soc Behav, 30 (1989), pp. 163-179CrossRefMathSciNetGoogle Scholar
  13. 13.
    A. Bandura, Social Foundations of Thought and Action: A Social Cognitive Theory, Prentice Hall, Englewood Cliffs, New Jersey, 1986Google Scholar
  14. 14.
    Social cognitive theory, in Annals of Child Development Vol. 6, R. Vasta, ed., Jai Press Ltd, Greenwich, CT, 1989, pp. 1-60Google Scholar
  15. 15.
    H. Barki and J. Hartwick, Measuring user participation, use involvement, and user attitude, MIS Quarterly, 18 (1994), pp. 59-82CrossRefGoogle Scholar
  16. 16.
    L. Beck and I. Ajzen, Predicting dishonest actions using the theory of planned behavior, J of Research and Personality, 25 (1991), pp. 285-301CrossRefGoogle Scholar
  17. 17.
    E. Blair and S. Burton, Cognitive process used by survey respondents to answer to behavioral frequency questions, J of Consumer Research, 14 (1987), pp. 280-288CrossRefGoogle Scholar
  18. 18.
    N. M. Bradburn, J. Huttenlocher, and L. Hedges, Telescoping and tem-poral memory, in Autobiographical memory and the validity of retrospective reports, N. Schwarz and S. Sudman, eds., Springer Verlag, New York, 1994, pp. 203-216Google Scholar
  19. 19.
    N. M. Bradburn, L. J. Rips, and S. K. Shevell, Answering autobiographical questions: the impact of memory and inference on surveys, Science, 236 (1987), pp. 157-161CrossRefGoogle Scholar
  20. 20.
    R. S. Burt, Social contagion and innovation: Cohesion versus structural equiv-alence, The American Journal of Sociology, 92 (1987), pp. 1287-1335CrossRefGoogle Scholar
  21. 21.
    P. Chang, Y. S. Hsu, Y. M. Tzeng, Y. Y. Sang, I. C. Hou, and W. F. Kao, The development of intelligent, triage-based, mass-gathering emergency medical service PDA support systems, J Nurs Res, 12 (2004), pp. 227-236Google Scholar
  22. 22.
    P. Y. K. Chau and P. J. H. Hu, Information technology acceptance by profes-sionals: A model comparison approach, Decision Sciences, 32 (2001), pp. 699-719CrossRefGoogle Scholar
  23. 23.
    ——, Examining a model of information technology acceptance by individual professionals: An exploratory study, J of Management Information Systems, 18 (2002), pp. 191-229Google Scholar
  24. 24.
    ——, Investigating healthcare professionals’ decisions on telemedicine technol-ogy acceptance: An empirical test of competing theories, Information and Man-agement, 39 (2002), pp. 297-311Google Scholar
  25. 25.
    C. Chen, M. Czerwinski, and R. Macredie, Individual differences in vir-tual environments - introduction and overview, J of the American Society for Information Science, 51 (2000), pp. 499-507CrossRefGoogle Scholar
  26. 26.
    W. W. Chin, The measurement and meaning of IT usage: reconciling recent dis-crepancies between self reported and computer recorded usage, in Proceedings of the Administrative Sciences Association of Canada, Information Systems Divi-sion, Montreal, Quebec, Canada, 1996, pp. 65-74Google Scholar
  27. 27.
    W. G. Chismar and S. Wiley-Patton, Test of the technology acceptance model for the internet in pediatrics, Proc AMIA Symp, (2002), pp. 155-159Google Scholar
  28. 28.
    ——, Does the extended technology acceptance model apply to physicians, in HICSS ’03: Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS-36), Washington, DC, USA, 2003, IEEE Computer Society, p. 160Google Scholar
  29. 29.
    K. E. Clarke and A. Aish, An exploration of health beliefs and attitudes of smokers with vascular disease who participate in or decline a smoking cessation program, J Vasc Nurs, 20 (2002), pp. 96-105CrossRefGoogle Scholar
  30. 30.
    J. Coleman, E. Katz, and H. Menzel, Medical Innovation: A Diffusion Study, Bobbs-Merrill, New York: NY, 1966. 2ndGoogle Scholar
  31. 31.
    R. B. Cooper and R. W. Zmud, Information technology implementation research: a technological diffusion approach, Manage. Sci., 36 (1990), pp. 123-139CrossRefGoogle Scholar
  32. 32.
    F. D. Davis, A technology acceptance model for empirically testing new end-user information systems: theory and results, PhD thesis, Sloan School of Manage-ment, Massachusetts Institute of Technology, 1986Google Scholar
  33. 33.
    ——, Perceived usefulness, perceived ease of use, and user acceptance of infor-mation technology, MIS Quarterly, 13 (1989), pp. 319-342Google Scholar
  34. 34.
    F. D. Davis, User acceptance of information technology: system characteristics, user perceptions and behavioral impacts, Int J Human-Computer Studies, 38 (1993), pp. 475-487Google Scholar
  35. 35.
    F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, User acceptance of com-puter technology: a comparison of two theoretical models, Management Sci, 35 (1989), pp. 982-1003CrossRefGoogle Scholar
  36. 36.
    T. J. DeMaio, Social desirability and survey measurement: a review, in Sur-veying subjective phenomena, C. F. Turner and E. Martin, eds., Russell Sage, New York, 1984, pp. 257-281Google Scholar
  37. 37.
    D.R. Dixon and M. Stewart, Exploring information technology adoption by family physicians: survey instrument valuation, Proc AMIA Symp, (2000), pp. 185-189Google Scholar
  38. 38.
    W. J. Doll and M. U. Ahmed, Managing user expectations, J of Systems Management, 34 (1983), pp. 6-11Google Scholar
  39. 39.
    J. M. Eisenberg, D. S. Kitz, and R. A. Webber, Development of attitudes about sharing decision-making: a comparison of medical and surgical residents, J Health Soc Behav, 24 (1983), pp. 85-90CrossRefGoogle Scholar
  40. 40.
    M. Fishbein and I. Ajzen, Beliefs, Attitude, Intention and Behavior: An Intro-duction to Theory and Research, Addison-Wesley, Reading, MA, 1975Google Scholar
  41. 41.
    M. P. Gagnon, G. Godin, C. Gagne, J. P. Fortin, L. Lamothe, D. Reinharz, and A. Cloutier, An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians, Int J Med Inform, 71 (2003), pp. 103-115CrossRefGoogle Scholar
  42. 42.
    C. A. Gaither, R. P. Bagozzi, F. J. Ascione, and D. M. Kirking, A reasoned action approach to physicians’ utilization of drug information sources, Pharm Res, 13 (1996), pp. 1291-1298CrossRefGoogle Scholar
  43. 43.
    C. L. Gatch and K. D, Predicting exercise intentions: the theory of planned behavior, Research Quarterly For Exercise and Sport, 61 (1990), pp. 100-102Google Scholar
  44. 44.
    D. Gefen and D. Straub, Gender difference in the perception and use of E-Mail: an extension to the technology acceptance model, MIS Quarterly, 21 (1997), pp. 389-400CrossRefGoogle Scholar
  45. 45.
    G. Godin, P. Valois, L. Lepage, and R. Desharnais, Predictors of smoking behavior: an application of Ajzen’s theory of planned behavior, British Journal of Addition, 87 (1992), pp. 1335-1343CrossRefGoogle Scholar
  46. 46.
    M. S. Hagger, N. L. D. Chatzisarantis, and S. J. H. Biddle, Meta-analysis of the theories of reasoned action and planned behavior in physical activity: an examination of predictive validity and the contri-bution of additional variables, J of Sport and Exercise Psychol, 24 (2002), pp. 3-32Google Scholar
  47. 47.
    C. Hartley, M. Brecht, P. Pagerly, G. Weeks, A. Chapanis, and D. Hoecker, Subjective time estimates of work tasks by office workers, J of Occupational Psychology, 50 (1977), pp. 23-36Google Scholar
  48. 48.
    P. Hu, P. Y. K. Chau, O. R. L. Sheng, and K. Y. Tam, Examining the tech-nology acceptance model using physician acceptance of telemedicine technology, J of Management Information Systems, 16 (1999), pp. 91-112Google Scholar
  49. 49.
    G. S. Hubona and P. H. Cheney, System effectiveness of knowledge-based technology: the relationship of user performance and attitudinal measures, in HICSS ’94: Proceedings of the 27th Annual Hawaii International Conference on System Sciences (HICSS-27) Volume 4, Washington, DC, USA, 1994, IEEE Computer Society, pp. 532-541Google Scholar
  50. 50.
    M. Igbaria, T. Guimaraes, and G. B. Davis, Testing the determinants of microcomputer usage via a structural equation model, J of MIS, 11 (1995), pp. 87-114Google Scholar
  51. 51.
    M. Igbaria, J. Iivari, and H. Maragahh, Why do individuals use com-puter technology? a Finnish case study, Information & Management, 29 (1995), pp. 227-238CrossRefGoogle Scholar
  52. 52.
    B. Kaplan, Addressing organizational issues into the evaluation of medical sys-tems, Academy of Management Journal, 4 (1997), pp. 94-101Google Scholar
  53. 53.
    Evaluating informatics applications - some alternative approaches: the-ory, social interactionism, and call for methodological pluralism, Int J Med Inform, 64 (2001), pp. 39-56Google Scholar
  54. 54.
    E. Karahanna, D. W. Straub, and N. L. Chervany, Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs, MIS Quarterly, 23 (1999), pp. 183-213CrossRefGoogle Scholar
  55. 55.
    Y. Kashima, C. Gallois, and M. McCamish, Theory of reasoned action and cooperative behavior: it takes two to use a condom, British Journal of Social Psychology, 32 (1993), pp. 227-239Google Scholar
  56. 56.
    H. C. Kelman, Compliance, identification, and internalization: three processes of attitude change?, J of Conflict Resolution, 2 (1958), pp. 51-60CrossRefGoogle Scholar
  57. 57.
    T. H. Kwon and R. W. Zmud, Unifying the fragmented models of informa-tion systems implementation, in Critical issues in information systems research, R. J. Boland and R. A. Hirschheim, eds., John Wiley & Sons, Inc., Chichester, England, 1987, pp. 227-251Google Scholar
  58. 58.
    Y. Lee, K. A. Kozar, and K. R. T. Larsen, The technology acceptance model: past, present, and future, Communications of the AIS, 12 (2003), pp. 752-780Google Scholar
  59. 59.
    F. Legare, G. Godin, V. Ringa, S. Dodin, L. Turcot, and J. Norton, Variation in the psychosocial determinants of the intention to prescribe hormone therapy prior to the release of the Women’s Health Initiative trial: a survey of general practitioners and gynaecologists in France and Quebec, BMC Med Inform Decis Mak, 5 (2005), p. 31CrossRefGoogle Scholar
  60. 60.
    H. P. Lundsgaarde, P. A. Fischer, and D. J. Steele, Human problems in computerized medicine, in Publications in Anthropology, 13, The University of Kansas, Lawrence, KS, 1981Google Scholar
  61. 61.
    Y. Malhotra and D. F. Galletta, Extending the technology acceptance model to account for social influence: theoritical bases and empirical validation, in HICSS ’99: Proceedings of the 32nd Annual Hawaii International Conference on System Sciences (32), vol. 1, Washington, DC, USA, 1999, IEEE Computer Society, p. 1006Google Scholar
  62. 62.
    T. A. Massaro, Introducing physician order entry at a major academic medical center: I. impact on organizational couture and behavior, Acad Med, 68 (1993), pp. 20-25CrossRefGoogle Scholar
  63. 63.
    K. Mathieson, Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior, Information Systems Research, 2 (1991), pp. 173-191CrossRefGoogle Scholar
  64. 64.
    K. Mathieson, E. Peacock, and W. W. Chin, Extending the technology acceptance model: the influence of perceived user resources, ACM SIGMIS Data-base, 32 (2001), pp. 86-112CrossRefGoogle Scholar
  65. 65.
    S. K. Maue, R. Segal, C. L. Kimberlin, and E. E. Lipowski, Predict-ing physician guideline compliance: an assessment of motivators and perceived barriers, The American J of Managed Care, 10 (2004), pp. 382-391Google Scholar
  66. 66.
    E. Mayo, The Human Problems of an Industrial Civilization, MacMillan, New York, NY, USA, 1933Google Scholar
  67. 67.
    G. C. Moore and I. Benbasat, Development of an instrument to measure the perceptions of adopting an information technology innovation, Information Systems Research, 2 (1991), pp. 173-191CrossRefGoogle Scholar
  68. 68.
    D. M. Morrison, M. S. Spencer, and M. R. Gillmore, Beliefs about sub-stance use among pregnant and parenting adolescents, J Res Adolesc, 8 (1998), pp. 69-95CrossRefGoogle Scholar
  69. 69.
    R. S. Nickerson, Why interactive computer systems are sometimes not used by people who might benefit from them, Int J Human-Computer Studies, 51 (1999), pp. 307-321CrossRefGoogle Scholar
  70. 70.
    M. H. Olson and H. C. Lucas, The impact of office automation on the organi-zation: some implications for research and practice, Commun ACM, 25 (1982), pp. 838-847CrossRefGoogle Scholar
  71. 71.
    G. Paré, C. Sicotte, and H. Jacques, The effects of creating psychological ownership on physicians’ acceptance of clinical information systems, J Am Med Inform Assoc, 13 (2006), pp. 197-205CrossRefGoogle Scholar
  72. 72.
    D. Parker, A. S. R. Manstead, S. G. Stradling, and J. Reason, Intention to commit driving violations: An application of the theory of planned behavior, J of Applied Psychology, 77 (1992), pp. 94-101CrossRefGoogle Scholar
  73. 73.
    M. B. Prescott and S. A. Conger, Information technology innovations: a classification by it locus of impact and research approach, SIGMIS Database, 26 (1995), pp. 20-41CrossRefGoogle Scholar
  74. 74.
    E. M. Rogers, Diffusion of Innovations, The Free Press, New York, 1983. 3rd edGoogle Scholar
  75. 75.
    ——, Diffusion of Innovations, The Free Press, New York, 1995. 4th edGoogle Scholar
  76. 76.
    V. Sambamurthy and C. W.W., The effects of group attitudes toward alter-native gdss designs on the decision-making performance of computer-supported groups, Decision Sciences, 25 (1994), pp. 215-241CrossRefGoogle Scholar
  77. 77.
    N. Schwarz and D. Oyserman, Asking questions about behavior: cognition, communication, and questionnaire construction, American J of Evaluation, 22 (2001), pp. 127-160Google Scholar
  78. 78.
    A. Sharma, Professionals as agent: knowledge asymmetry in agengy exchanges, Academy of Management Review, 22 (1997), pp. 758-798CrossRefGoogle Scholar
  79. 79.
    B. H. Sheppard, J. Hartwick, and P. R. Warshaw, The theory of reasoned action: a meta-analysis of past research with recommendations for modifications and future research, J of Consumer Behavior, 15 (1988), pp. 325-343CrossRefGoogle Scholar
  80. 80.
    D. E. Sichel, The Computer Revolution: An Economic Perspective, Brookings, Washington, DC, 1997Google Scholar
  81. 81.
    D. Straub, M. Keil, and W. Brenner, Testing the technology acceptance model across cultures: a three country study, Information & Management, 33 (1997), pp. 1-11CrossRefGoogle Scholar
  82. 82.
    J. Stross and G. Bole, Evaluation of a continuing education program in rheumatoid arthritis, arthritis and rheumatism, Arthritis Rheum, 23 (1980), pp. 846-849CrossRefGoogle Scholar
  83. 83.
    G. Subramanian, A replication of perceived usefulness and perceived ease of use measurement, Decision Sciences, 25 (1994), pp. 863-874CrossRefGoogle Scholar
  84. 84.
    M. J. Succi and Z. D. Walter, Theory of user acceptance of information technologies: an examination of health care professionals, in HICSS ’99: Proceed-ings of the 32nd Annual Hawaii International Conference on System Sciences (HICSS-32), Los Alamitos, CA, 1999, IEEE Computer SocietyGoogle Scholar
  85. 85.
    H. Sun and P. Zhang, The role of moderating factors in user technology accep-tance, Int J Human-Computer Studies, 64 (2006), pp. 53-78CrossRefGoogle Scholar
  86. 86.
    E. B. Swanson, Information System Implementation: Bridging the Gap between Design and Utilization, Irwin, Homewood, IL, 1988Google Scholar
  87. 87.
    B. Szajna, Empirical evaluation of the revised technology acceptance model, Management Sci, 42 (1996), pp. 85-92CrossRefGoogle Scholar
  88. 88.
    S. Taylor and P. Todd, Understanding information technology usage: a test of competing model, Information Systems Research, 6 (1995), pp. 145-176CrossRefGoogle Scholar
  89. 89.
    R. L. Thompson, C. A. Higgins, and J. M. Howell, Personal computing: toward a conceptual model of utilization, MIS Quarterly, 15 (1991), pp. 124-143CrossRefGoogle Scholar
  90. 90.
    ——, Influence of experience on personal computer utilization: testing a concep-tual model, Journal of Management Information Systems, 11 (1994), pp. 167-187Google Scholar
  91. 91.
    L. Tornatzky and K. Klein, Innovation characteristics and innovation adop-tion implementation: a meta-analysis of findings, IEEE Transactions on Engi-neering Management, 29 (1982), pp. 28-45Google Scholar
  92. 92.
    M. L. Tushman and T. J. Scanlan, Boundary spanning individuals: their role in information-transfer and their antecedents, Academy of Management Journal, 24 (1981), pp. 289-305CrossRefGoogle Scholar
  93. 93.
    V. Venkatesh, Determinants of perceived ease of use integrating control, intrin-sic motivation, and emotion into the technology acceptance model, Information Systems Research, 11 (2000), pp. 342-365CrossRefGoogle Scholar
  94. 94.
    V. Venkatesh and F. D. Davis, A model of the antecedents of perceived ease of use development and test, Decision Sciences, 27 (1996), pp. 451-481CrossRefGoogle Scholar
  95. 95.
    A theoretical extension of the technology acceptance model: four longitu-dinal field studies, Management Sci, 46 (2000), pp. 186-204Google Scholar
  96. 96.
    V. Venkatesh and M. G. Morris, Why don’t men ever stop to ask for direc-tions? gender, social influence, and their role in technology acceptance and usage behavior, MIS Quarterly, 24 (2000), pp. 115-139CrossRefGoogle Scholar
  97. 97.
    V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, User accep-tance of information technology: toward a unified view, MIS Quarterly, 27 (2003), pp. 425-478Google Scholar

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Authors and Affiliations

  • Kai Zheng
  • Rema Padman
  • Michael P. Johnson
  • Herbert S. Diamond

There are no affiliations available

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