Ask Me No Questions: Increasing Empirical Evidence for a Qualitative Approach to Technology Acceptance

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12181)


The Technology Acceptance Model and its derivatives position Perceived Ease of Use, sometimes mediated by Perceived Usefulness, as the primary indicator of an intention to adopt. However, an initial study cast doubt on such a causal relationship: poor ease-of-use scores using a standard instrument did not necessarily correspond to poor usefulness comments from users. We follow up in this paper to explore reproducibility and generalizability. Using secondary review of results from testing and validation activities, we find confirmation that the post hoc measurement of Perceived Ease of Use is less important to participants than their concern for task-oriented usefulness. An ambivalent relationship obtains, therefore, between quantitative measures of Perceived Ease of Use and qualitative review of comments on Perceived Usefulness across three sites in Italy, Spain and the UK. Participants seem to prioritize their professional responsibilities and focus on how the technology under test might support them in their role. We therefore offer an explanation based on psychological theories of work and suggest a controlled follow-on study exploring the narrative content of technology acceptance.


Technology acceptance User adoption Mixed methods System usability Technology affordance Job Characteristics Model Job demand-control model 



This work was conducting with support of the OPERANDO (EU H2020 research grant No 653704) and of the SHiELD project (EU H2020 research grant No 727301).


  1. 1.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35, 982–1003 (1989). Scholar
  2. 2.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989)CrossRefGoogle Scholar
  3. 3.
    Taherdoost, H.: A review of technology acceptance and adoption models and theories. Procedia Manuf. 22, 960–967 (2018)CrossRefGoogle Scholar
  4. 4.
    Chuttur, M.Y.: Overview of the technology acceptance model: origins, developments and future directions. Sprouts: Working Pap. Inf. Syst. 9, 1–21 (2009)Google Scholar
  5. 5.
    Bagozzi, R.P.: The legacy of the technology acceptance model and a proposal for a paradigm shift. J. Assoc. Inf. Syst. 8, 244–254 (2007)Google Scholar
  6. 6.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478 (2003)CrossRefGoogle Scholar
  7. 7.
    Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39, 273–315 (2008)CrossRefGoogle Scholar
  8. 8.
    King, W.R., He, J.: A meta-analysis of the technology acceptance model. Inf. Manag. 43, 740–755 (2006)CrossRefGoogle Scholar
  9. 9.
    Holden, R.J., Karsh, B.-T.: The technology acceptance model: its past and its future in health care. J. Biomed. Inform. 43, 159–172 (2010)CrossRefGoogle Scholar
  10. 10.
    Rogers, E.: The Diffusion of Innovations. The Free Press, New York (2003)Google Scholar
  11. 11.
    Benbasat, I., Barki, H.: Quo vadis TAM? J. Assoc. Inf. Syst. 8, 7 (2007)Google Scholar
  12. 12.
    Legris, P., Ingham, J., Collerette, P.: Why do people use information technology? A critical review of the technology acceptance model. Inf. Manag. 40, 191–204 (2003)CrossRefGoogle Scholar
  13. 13.
    Perlusz, S.: Emotions and technology acceptance: development and validation of a technology affect scale. In: 2004 IEEE International Engineering Management Conference (IEEE Cat. No. 04CH37574), pp. 845–847. IEEE (2004)Google Scholar
  14. 14.
    Latour, B.: Reassembling the Social-an Introduction to Actor-Network-Theory. Oxford University Press, Oxford (2005)Google Scholar
  15. 15.
    Pickering, B., Janian, M.N., López Moreno, B., Micheletti, A., Sanno, A., Surridge, M.: Seeing potential is more important than usability: revisiting technology acceptance. In: Marcus, A., Wang, W. (eds.) HCII 2019. LNCS, vol. 11586, pp. 238–249. Springer, Cham (2019). Scholar
  16. 16.
    Brooke, J.: SUS-A quick and dirty usability scale. In: Usability Evaluation in Industry, p. 189, 4–7 (1996)Google Scholar
  17. 17.
    Chakravarthy, A., Chen, X., Nasser, B., Surridge, M.: Trustworthy systems design using semantic risk modelling. In: 1st International Conference on Cyber Security for Sustainable Society, United Kingdom (2015)Google Scholar
  18. 18.
    Surridge, M., et al.: Modelling Compliance Threats and Security Analysis of Cross Border Health Data Exchange. In: Attiogbé, C., Ferrarotti, F., Maabout, S. (eds.) MEDI 2019. CCIS, vol. 1085, pp. 180–189. Springer, Cham (2019). Scholar
  19. 19.
    Pfleeger, S.L., Caputo, D.D.: Leveraging behavioral science to mitigate cyber security. Comput. Secur. 31, 597–611 (2012)CrossRefGoogle Scholar
  20. 20.
    European Commission: Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (2016)Google Scholar
  21. 21.
    Boyd, K.M.: Medical ethics: principles, persons, and perspectives: from controversy to conversation. J. Med. Ethics 31, 481–486 (2005)CrossRefGoogle Scholar
  22. 22.
    Lilford, R.J., Foster, J., Pringle, M.: Evaluating eHealth: how to make evaluation more methodologically robust. PLoS Med. 6, e1000186 (2009)CrossRefGoogle Scholar
  23. 23.
    Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101 (2006)CrossRefGoogle Scholar
  24. 24.
    Lee, J.D., See, K.A.: Trust in automation: Designing for appropriate reliance. Hum. Factors: J. Hum. Factors Ergon. Soc. 46, 50–80 (2004)CrossRefGoogle Scholar
  25. 25.
    Turkle, S.: Alone Together: Why We Expect More From Technology and Less From Each Other. Basic Books, New York (2017)Google Scholar
  26. 26.
    Bellman, S., Johnson, E.J., Kobrin, S.J., Lohse, G.L.: International differences in information privacy concerns: a global survey of consumers. Inf. Soc. 20, 313–324 (2004)CrossRefGoogle Scholar
  27. 27.
    Smith, H.J., Milberg, S.J., Burke, S.J.: Information privacy: measuring individuals’ concerns about organizational practices. MIS Q. 20, 167–196 (1996)CrossRefGoogle Scholar
  28. 28.
    Acquisti, A., Brandimarte, L., Loewenstein, G.: Privacy and human behavior in the age of information. Science 347, 509–514 (2015)CrossRefGoogle Scholar
  29. 29.
    Yarbrough, A.K., Smith, T.B.: Technology acceptance among physicians: a new take on TAM. Med. Care Res. Rev. 64, 650–672 (2007)CrossRefGoogle Scholar
  30. 30.
    Turner, M., Kitchenham, B., Brereton, P., Charters, S., Budgen, D.: Does the technology acceptance model predict actual use? A systematic literature review. Inf. Softw. Technol. 52, 463–479 (2010)CrossRefGoogle Scholar
  31. 31.
    Thatcher, J.B., McKnight, D.H., Baker, E.W., Arsal, R.E., Roberts, N.H.: the role of trust in postadoption IT exploration: an empirical examination of knowledge management systems. IEEE Trans. Eng. Manag. 58, 56–70 (2011)CrossRefGoogle Scholar
  32. 32.
    Dearing, J.W.: Applying diffusion of innovation theory to intervention development. Res. Soc. Work Pract. 19, 503–518 (2009)CrossRefGoogle Scholar
  33. 33.
    Hackman, J.R., Oldham, G.R.: Motivation through the design of work: test of a theory. Organ. Behav. Hum. Perform. 16, 250–279 (1976)CrossRefGoogle Scholar
  34. 34.
    Van der Doef, M., Maes, S.: The job demand-control (-support) model and psychological well-being: a review of 20 years of empirical research. Work Stress 13, 87–114 (1999)CrossRefGoogle Scholar
  35. 35.
    Lewicki, R.J., Wiethoff, C.: Trust, trust development, and trust repair. In: The Handbook of Conflict Resolution: Theory and Practice, vol. 1, pp. 86–107 (2000)Google Scholar
  36. 36.
    Ryan, R.M., Deci, E.L.: Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp. Educ. Psychol. 25, 54–67 (2000)CrossRefGoogle Scholar
  37. 37.
    Miranda, S.M., Saunders, C.S.: The social construction of meaning: an alternative perspective on information sharing. Inf. Syst. Res. 14, 87–106 (2003)CrossRefGoogle Scholar
  38. 38.
    Murray, M.: Narrative psychology and narrative analysis. In: Camic, P.M., Rhodes, J.E., Yardley, L. (eds.) Qualitative Research in Psychology: Expanding perspectives in methodology and design, pp. 95–112. American Psychological Association, Washington, DC (2003)CrossRefGoogle Scholar
  39. 39.
    Gergen, K.J., Gergen, M.M.: Narrative form and the construction of psychological science. In: Sarbin, T. (ed.) Narrative Psychology: The Storied Nature of Human Conduct, pp. 22–44. Praeger, New York (1986)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  2. 2.Oxford Computer ConsultantsOxfordUK
  3. 3.Fondazione Centro San RaffaeleMilanItaly
  4. 4.Biocruces Bizkaia InstituteBarakaldoSpain

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