Skip to main content
SpringerLink
Log in

Adoption Factors for Crowdsourcing Based Medical Information Platforms

  • Conference paper
  • First Online:
Book cover Knowledge Science, Engineering and Management (KSEM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9983))

Abstract

Platforms increasingly utilize crowdsourcing to offer knowledge intense services such as medical diagnostics while retaining low costs. The increased interest elevated the adoption of established theories for user acceptance, risk avoidance and motivational influencing factors. We propose a combination of elements from existing concepts, extended by newly identified factors, to postulate a novel theoretical model. Our analysis of a survey with 349 respondents reveals new constructs based on users’ perception of risks and features. We found that risks and features are significantly interrelated and both influence perceived usefulness, the technology acceptance model’s most important construct. Usefulness is diminished by perceived risks while it is increased by crowdsourcing features. Additionally, external motivation yields important influence factors. The revealed interrelations are discussed and should be accounted for in future research and implementations.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Estellés-Arolas, E., González-Ladrón-de-Guevara, F.: Towards an integrated crowdsourcing definition. J Inf. Sci. 38, 189–200 (2012)

    Article  Google Scholar 

  2. Kauffman, R.J., Techatassanasoontorn, A.A.: International diffusion of digital mobile technology: a coupled-hazard state-based approach. Inf. Technol. Manage. 6, 253–292 (2005)

    Article  Google Scholar 

  3. Mourelatos, E., Tzagarakis, M., Dimara, E., et al.: A review of online crowdsourcing platforms. South-East. Eur. J. Econ. 14, 59–73 (2016)

    Google Scholar 

  4. Ranard, B.L., Ha, Y.P., Meisel, Z.F., Asch, D.A., Hill, S.S., Becker, L.B., Seymour, A.K., Merchant, R.M.: Crowdsourcing - harnessing the masses to advance health and medicine, a systematic review. J. Gen. Intern. Med. 29, 187–203 (2014)

    Article  Google Scholar 

  5. Vogt, F., Seidl, F., Santarpino, G., van Griensven, M., Emmert, M., et al.: Healthcare IT utilization and penetration among physicians novel IT solutions in healthcare-use and acceptance in hospitals and private practice. J Health Med. Inform. 7, 2 (2016)

    Google Scholar 

  6. Allahbakhsh, M., Benatallah, B., Ignjatovic, A., Motahari-Nezhad, H.R., Bertino, E., Dustdar, S.: Quality control in crowdsourcing systems: issues and directions. IEEE Internet Comput. 17, 76–81 (2013)

    Article  Google Scholar 

  7. Brabham, D.C., Ribisl, K.M., Kirchner, T.R., Bernhardt, J.M.: Crowdsourcing applications for public health. Am. J. Prev. Med. 46, 179–187 (2014)

    Article  Google Scholar 

  8. Information, N.C. for B.: Crowdsourcing – MeSH. http://www.ncbi.nlm.nih.gov/mesh?term=%22crowdsourcing%22

  9. Swan, M.: Health 2050: the realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. J. Personalized Med. 2, 93–118 (2012)

    Article  Google Scholar 

  10. King, W.R., He, J.: A meta-analysis of the technology acceptance model. Inform. Manage. 43, 740–755 (2006)

    Article  Google Scholar 

  11. Yarbrough, A.K., Smith, T.B.: Technology acceptance among physicians: a new take on TAM. Med. Care Res. Rev. 64(6), 650–672 (2007)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Sussman, S.W., Siegal, W.S.: Informational influence in organizations: an integrated approach to knowledge adoption. Inf. Syst. Res. 14, 47–65 (2003)

    Article  Google Scholar 

  14. Baker, L., Wagner, T.H., Singer, S., Bundorf, M.K.: Use of the internet and e-mail for health care information: results from a national survey. JAMA 289, 2400–2406 (2003)

    Article  Google Scholar 

  15. Mun, Y.Y., Yoon, J.J., Davis, J.M., Lee, T.: Untangling the antecedents of initial trust in web-based health information: the roles of argument quality, source expertise, and user perceptions of information quality and risk. Decis. Support Syst. 55, 284–295 (2013)

    Article  Google Scholar 

  16. Sims, M.H., Bigham, J., Kautz, H., Halterman, M.W.: Crowdsourcing medical expertise in near real time. J Hosp. Med. 9, 451–456 (2014)

    Article  Google Scholar 

  17. Wang, J., Lam, R.W., Ho, K., Attridge, M., Lashewicz, B.M., Patten, S.B., Marchand, A., Aiken, A., Schmitz, N., Gundu, S., et al.: Preferred features of E-mental health programs for prevention of major depression in male workers: results from a Canadian National survey. J. Med. Internet Res. 18, e132 (2016)

    Article  Google Scholar 

  18. Norton, S., Lindborg, C., Delaplain, C.: Consent and privacy in telemedicine. Hawaii Med. J. 52, 340–341 (1993)

    Google Scholar 

  19. Menachemi, N., Collum, T.H.: Benefits and drawbacks of electronic health record systems. Risk Manage. Healthc. Policy 4, 47–55 (2011)

    Article  Google Scholar 

  20. Surowiecki, J.: The Wisdom of Crowds. Anchor Books, New York (2005)

    Google Scholar 

  21. Ozok, A.A., Wu, H., Garrido, M., Pronovost, P.J., Gurses, A.P.: Usability and perceived usefulness of personal health records for preventive health care: a case study focusing on patients’ and primary care providers’ perspectives. Appl. Ergon. 45, 613–628 (2014)

    Article  Google Scholar 

  22. Ruland, C.M., Maffei, R.M., Børøsund, E., Krahn, A., Andersen, T., Grimsbø, G.H.: Evaluation of different features of an eHealth application for personalized illness management support: cancer patients’ use and appraisal of usefulness. Int. J. Med. Inform. 82, 593–603 (2013)

    Article  Google Scholar 

  23. Fernández-Alemán, J.L., Señor, I.C., Lozoya, P.Á.O., Toval, A.: Security and privacy in electronic health records: a systematic literature review. J. Biomed. Inform. 46, 541–562 (2013)

    Article  Google Scholar 

  24. Hossain, M.: Users’ motivation to participate in online crowdsourcing platforms. In: International Conference on Innovation Management and Technology Research (ICIMTR), pp. 310–315 (2012)

    Google Scholar 

  25. Kietzmann, J.H., Silvestre, B.S., McCarthy, I.P., Pitt, L.F.: Unpacking the social media phenomenon: towards a research agenda. J. Public Affairs 12, 109–119 (2012)

    Article  Google Scholar 

  26. Yang, H.-L., Lai, C.-Y.: Motivations of Wikipedia content contributors. Comput. Hum. Behav. 26, 1377–1383 (2010)

    Article  Google Scholar 

  27. Gaskin, J.: Exploratory Factor Analysis. http://statwiki.kolobkreations.com/index.php?title=Exploratory_Factor_Analysis

  28. De Winter, J.C., Dodou, D.: Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. J. Appl. Stat. 39, 695–710 (2012)

    Article  MathSciNet  Google Scholar 

  29. Williams, B., Onsman, A., Brown, T.: Exploratory factor analysis: a five-step guide for novices. Australas. J. Paramed. 8, 1–13 (2010)

    Google Scholar 

  30. Cangelosi, R., Goriely, A.: Component retention in principal component analysis with application to cDNA microarray data. Biol. Dir. 2, 1 (2007)

    Article  Google Scholar 

  31. Frost, J., Massagli, M.: Social uses of personal health information within PatientsLikeMe, an online patient community: what can happen when patients have access to one another’s data. J. Med. Internet Res. 10, e15 (2008)

    Article  Google Scholar 

  32. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35, 982–1003 (1989)

    Article  Google Scholar 

  33. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd edn. Sage, Thousand Oaks (2017)

    MATH  Google Scholar 

  34. Liang, H., Xue, Y.: Avoidance of information technology threats: a theoretical perspective. MIS Q. 33(1), 71–90 (2009)

    Google Scholar 

  35. Gӧtz, O., Liehr-Gobbers, K., Krafft, M.: Evaluation of structural equation models using the partial least squares (PLS) approach. In: Vinzi, V.E., Chin, W.W., Henseler, J., Wang, H. (eds.) Handbook of Partial Least Squares, pp. 691–711. Springer, Heidelberg (2010)

    Google Scholar 

  36. Hatcliff, J., King, A., Lee, I., Macdonald, A., Fernando, A., Robkin, M., Vasserman, E., Weininger, S., Goldman, J.M.: Rationale and architecture principles for medical application platforms. In: Third International Conference on Cyber-Physical Systems (ICCPS), pp. 3–12 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ESCP Europe Business School, Business Information Systems, Berlin, Germany

    Till Blesik & Markus Bick

Authors
  1. Till Blesik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Markus Bick
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Till Blesik .

Editor information

Editors and Affiliations

  1. University of Passau, Passau, Germany

    Franz Lehner

  2. University of Passau , Passau, Germany

    Nora Fteimi

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Blesik, T., Bick, M. (2016). Adoption Factors for Crowdsourcing Based Medical Information Platforms. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47650-6_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47649-0

  • Online ISBN: 978-3-319-47650-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation