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Assistive Technologies for People with Cognitive Impairments – Which Factors Influence Technology Acceptance?

  • Susanne Dirks
  • Christian Bühler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10907)

Abstract

While in the general field of acceptance research convincing and empirically well-tested models for the acceptance of technical systems exist, only a few studies have been carried out in the area of acceptance of assistive software systems. Appropriate acceptance models play an important role, especially for user-centered and participative software development and quality assurance. In this article, the most important models from general acceptance research are briefly introduced. Based on the results of an acceptance study of an app for independent media access for users with cognitive impairments, a proposal for an acceptance model of assistive technology was developed, in which personal and environmental factors are considered more strongly than in the classical acceptance models.

Keywords

Assistive technology Cognitive accessibility Technology acceptance Acquired brain injury Participation Independent media use 

Notes

Acknowledgements

The authors would like to thank all participants of the study for their helpful feedback and good spirits during the interviews. Special thanks go to the v. Bodelschwingh Foundation Bethel, Bethel.regional office Dortmund, and gGmbH In der Gemeinde Leben, Düsseldorf, for their financial and practical support of the Mediata project.

References

  1. 1.
    Adolfsson, P., Lindsedt, H., Janeslätt, G.: How persons with cognitive disabilities experience electronic planning devices. NeuroRehabilitation 37(3), 379–392 (2015)CrossRefGoogle Scholar
  2. 2.
    Squires, L.A., Williams, N., Morrison, V.L.: Matching and accepting assistive technology in multiple sclerosis: a focus group study with people with multiple sclerosis, carers and occupational therapists. J. Health Psychol. (2016).  https://doi.org/10.1177/1359105316677293. Accessed 14 Aug 2017
  3. 3.
    Poudel, B.P.: Acceptance and use of assistive technology: perspective of high school and college students with high incidence disabilities. University of Deleware, School of Education (2014). http://udspace.udel.edu/handle-/19716/16818. Accessed 14 Aug 2017
  4. 4.
    Wang, J., Ding, D., Teodorski, E., Mahajan, H.P., Cooper, R.: Use of assistive technology for cognition among people with traumatic brain injury: a survey study. Mil. Med. 181, 560–566 (2016)CrossRefGoogle Scholar
  5. 5.
    Schäfer, M., Keppler, D.: Modelle der technikorientierten Akzeptanzforschung Überblick und Reflexion am Beispiel eines Forschungsprojekts zur Implementierung innovativer technischer Energieeffizienz-Maßnahmen. Technische Universität Berlin (2013)Google Scholar
  6. 6.
    Dethloff, C.: Akzeptanz und Nicht-Akzeptanz von technischen Produktinnovationen. Pabst, Lengerich (2004)Google Scholar
  7. 7.
    Davis, F.: Perceived usefulness, perceived ease of use, and user acceptance. MIS Q. 13(3), 319 (1989)CrossRefGoogle Scholar
  8. 8.
    Venkatesh, V., Davis, F.D.: A model of the antecedents of perceived ease of use: development and test. Decis. Sci. 27, 451–481 (1996)CrossRefGoogle Scholar
  9. 9.
    Arning, K., Ziefle, M.: Understanding age differences in PDA acceptance and performance. Comput. Hum. Behav. 23(6), 2904–2927 (2007)CrossRefGoogle Scholar
  10. 10.
    Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal filed studies. Manag. Sci. 46, 186–204 (2000)CrossRefGoogle Scholar
  11. 11.
    Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–312 (2008)CrossRefGoogle Scholar
  12. 12.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)CrossRefGoogle Scholar
  13. 13.
    DeLone, W.H., McLean, E.R.: Information systems success: the quest for the dependent variable. Inf. Syst. Res. 3(1), 60–95 (1992)CrossRefGoogle Scholar
  14. 14.
    Goodhue, D.L.: Development and measurement validity of a task-technology fit instrument for user evaluations of information systems. Decis. Sci. 29(1), 105–138 (1998)CrossRefGoogle Scholar
  15. 15.
    Gefen, D., Straub, D.W.: The relative importance of perceived ease of use in IS adoption: a study of ecommerce adoption. MIS Q. 21(4), 389–400 (1997)CrossRefGoogle Scholar
  16. 16.
    Kaleshtari, M.H., Ciobanu, I., Seiciu, P.L., Marin, A.G., Berteanu, M.: Towards a model of rehabilitation technology acceptance and usability. Int. J. Soc. Sci. Hum. 6(8), 612–616 (2016)Google Scholar
  17. 17.
    Scherer, M.J.: From people-centered to person-centered services, and back again. Disabil. Rehabil.: Assist. Technol. 9(1), 1–2 (2014)MathSciNetGoogle Scholar
  18. 18.
    Bühler, C.: Universal design – computer. In: Stone, J., Blouin, M. (eds.) Center for International Rehabilitation Research Information and Exchange (CIRRIE) Internation Encyclopedia of Rehabilitation (2010)Google Scholar
  19. 19.
    Bühler, C.: Management of design for all. In: Stephanidis, C. (ed.) The Universal Access handbook, Human Factors and Ergonomics. CCRC Press Taylor & Francis Group, Boca Raton, 56-1-12 (2009)Google Scholar
  20. 20.
    Bühler, C., Pelka, B.: Empowerment by digital media of people with disabilities. In: Miesenberger, K., Fels, D., Archambault, D., Peňáz, P., Zagler, W. (eds.) ICCHP 2014. LNCS, vol. 8547, pp. 17–24. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08596-8_4CrossRefGoogle Scholar
  21. 21.
    Bühler, C.: Empowered participation of users with disabilities in R&D projects. Int. J. Hum.-Comput. Stud. 55(4), 645–659 (2001)CrossRefGoogle Scholar
  22. 22.
    Scherer, M.J.: Assessing the benefits of using assistive technologies and other supports for thinking, remembering and learning. Disabil. Rehabil. 27(13), 731–739 (2005)CrossRefGoogle Scholar
  23. 23.
    Scherer, M.J., Craddock, G.: Matching person and technology assessment process. Technol. Disabil. 14, 125–131 (2016)Google Scholar
  24. 24.
    Scherer, M.J., Federici, S.: Why people use and don’t use technologies. NeuroRehabilitation 37, 315–319 (2015)CrossRefGoogle Scholar
  25. 25.
    ISO/IEC Guide 71:2014 (E): Guide for addressing accessibility in standards, 2014. http://www.iec.ch/webstore/freepubs/isoiecguide71%7Bed2.0%7Den.pdf. Accessed 27 Jan 2018
  26. 26.
    Bühler, C., Dirks, S., Nietzio, A.: Easy access to social media: introducing the mediata-app. In: Miesenberger, K., Bühler, C., Penaz, P. (eds.) ICCHP 2016. LNCS, vol. 9759, pp. 227–233. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-41267-2_31CrossRefGoogle Scholar
  27. 27.
    Dirks, S., Bühler, C.: Partizipation and autonomy for users with ABI through easy social media access. In: Cudd, P., de Witte, L. (eds.) Harnessing the Power of Technology to Improve Lives. Studies in Health Technologies and Informatics, vol. 242, pp. 813–819 (2017)Google Scholar
  28. 28.
    Dirks, S., Bühler, C.: Akzeptanz von assistiven softwaresystemen für Menschen mit kognitiven Beeinträchtigungen. In: Eibl, M., Gaedke, M. (eds.) Informatik 2017, pp. 345–359. Gesellschaft für Informatik, Bonn (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Rehabilitation Technology, School of Rehabilitation SciencesTU Dortmund UniversityDortmundGermany

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