Skip to main content
SpringerLink
Log in

BI End-User Segments in the Public Health Sector

  • Conference paper
  • First Online:
Book cover Electronic Government and the Information Systems Perspective (EGOVIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11032))

  • 963 Accesses

Abstract

In public health services, business intelligence (BI) is used by medical secretaries, nurses, doctors and economists. Although some research has investigated the use of information systems in this regard, the results have been inconclusive. One explanation could be unobserved heterogeneity. This paper aims to apply use and task characteristics as well as perceptions of BI quality and user characteristics to characterize BI usage based on end-user segments. The finite mixture partial least squares (FINMIX-PLS), Kruskal-Wallis test and Bonferroni post-hoc test are used to provide a clear picture of the characteristics affecting the use of BI. The user segments are estimated using a sample of 746 BI users from 12 public hospitals and their administrations. The results highlight three segments that are primarily characterized by differences in task compatibility, BI experience and education. Only 16.5% of the respondents fit the definition of a BI user found in the extant literature. The remaining 83.5% represents new user types. This research in progress contributes to the extant BI literature by identifying new types of BI users and by showing how critical success factors differ by user types. As user types are identified using a quantitative method, qualitative studies could be applied to extend the understanding of the various types.

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. Gotz, D., Borland, D.: Data-driven healthcare: challenges and opportunities for interactive visualization. IEEE Comput. Graph. Appl. 36, 90–96 (2016)

    Article  Google Scholar 

  2. Parente, S.T., Dunbar, J.L.: Is health information technology investment related to the financial performance of US hospitals? An exploratory analysis. Int. J. Healthcare Technol. Manag. 3, 48 (2001)

    Article  Google Scholar 

  3. Mettler, T., Vimarlund, V.: Understanding business intelligence in the context of healthcare. Health Inform. J. 15, 254–264 (2009)

    Article  Google Scholar 

  4. Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2, 3 (2014)

    Article  Google Scholar 

  5. Raghupathi, W.: Data mining in health care. In: Kudyba, S. (ed.) Healthcare Informatics: Improving Efficiency and Productivity, pp. 211–223. Taylor & Francis, Abingdon (2010)

    Chapter  Google Scholar 

  6. Wixom, B., Watson, H.: The BI-based organization. Int. J. Bus. Intell. Res. 1, 13–28 (2010)

    Article  Google Scholar 

  7. Madsen, L.: Healthcare Business Intelligence: A Guide to Empowering Successful Data Reporting and Analytics. Wiley, Hoboken (2012)

    Book  Google Scholar 

  8. Petter, S., DeLone, W., McLean, E.R.: Information systems success: the quest for the independent variables. J. Manag. Inf. Syst. 29, 7–62 (2013)

    Article  Google Scholar 

  9. Schacht, S., Morana, S., Urbach, N., Maedche, A.: Are you a Maverick? Towards a Segmentation of Collaboration Technology Users (2015)

    Google Scholar 

  10. Agarwal, R., Prasad, J.: The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decis. Sci. 28, 557–582 (1997)

    Article  Google Scholar 

  11. Dishaw, M.T., Strong, D.M.: The effect of task and tool experience on maintenance CASE tool usage. Inf. Resour. Manag. J. 16, 1–16 (2003)

    Article  Google Scholar 

  12. Dishaw, M.T., Strong, D.M.: Extending the technology acceptance model with task–technology fit constructs. Inf. Manag. 36, 9–21 (1999)

    Article  Google Scholar 

  13. Lim, E.T.K., Pan, S.L., Tan, C.W.: Managing user acceptance towards enterprise resource planning (ERP) systems – understanding the dissonance between user expectations and managerial policies. Eur. J. Inf. Syst. 14, 135–149 (2005)

    Article  Google Scholar 

  14. Lawrence, M., Low, G.: Exploring individual user satisfaction within user-led development. MIS Q. 17, 195–208 (1993)

    Article  Google Scholar 

  15. Fitzgerald, G., Russo, N.L.: The turnaround of the London ambulance service computer-aided despatch system (LASCAD). Eur. J. Inf. Syst. 14, 244–257 (2005)

    Article  Google Scholar 

  16. Caldeira, M.M., Ward, J.M.: Understanding the successful adoption and use of IS/IT in SMEs: an explanation from Portuguese manufacturing industries. Inf. Syst. J. 12, 121–152 (2002)

    Article  Google Scholar 

  17. Burton-Jones, A., Hubona, G.S.: Individual differences and usage behavior. Data Base 36, 58–77 (2005)

    Article  Google Scholar 

  18. Oliver, L.W.: Research integration for psychologists: an overview of approaches. J. Appl. Soc. Psychol. 17, 860–874 (1987)

    Article  Google Scholar 

  19. Grublješič, T., Jaklič, J.: Business intelligence acceptance: the prominence of organizational factors. Inf. Syst. Manag. 32, 299–315 (2015)

    Article  Google Scholar 

  20. Gaardboe, R., Svarre, T.: Business intelligence success factors: a literature review. J. Inf. Technol. Manag. 29 (2018)

    Google Scholar 

  21. Sharifian, R., Askarian, F., Nematolahi, M., Farhadi, P.: Factors influencing nurses’ acceptance of hospital information systems in Iran: application of the Unified Theory of Acceptance and Use of Technology. Health Inf. Manag. J. 43, 23–28 (2014)

    Google Scholar 

  22. DeLone, W.H., McLean, E.R.: Information systems success: the quest for the dependent variable. Inf. Syst. Res. 3, 60–95 (1992)

    Article  Google Scholar 

  23. Leavitt, H.J.: Applied organizational change in industry: structural, technological and humanistic approaches. In: March, J. (ed.) Handbook of Organizations, pp. 1144–1170 (1965)

    Google Scholar 

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

    Article  Google Scholar 

  25. Herrmann, A., Hahn, C.H., Johnson, M.D., Huber, F.: Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Bus. Rev. 54(3), 243–269 (2002)

    Google Scholar 

  26. Hair, J., Sarstedt, M., Matthews, L.M., Ringle, C.M.: Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I – method. Eur. Bus. Rev. 28, 63–76 (2016)

    Article  Google Scholar 

  27. Ong, C.-S., Lai, J.-Y.: Gender differences in perceptions and relationships among dominants of e-learning acceptance. Comput. Hum. Behav. 22, 816–829 (2006)

    Article  Google Scholar 

  28. Aczel, A.D.: Complete Business Statistics. Wohl Publishing, Morristown (2012)

    Google Scholar 

  29. Batenburg, R., Van den Broek, E.: Pharmacy information systems: the experience and user satisfaction within a chain of Dutch pharmacies. Int. J. Electron. Healthcare 4, 119–131 (2008)

    Article  Google Scholar 

  30. Lee, Y.W., Strong, D.M., Kahn, B.K., Wang, R.Y.: AIMQ: a methodology for information quality assessment. Inf. Manag. 40, 133–146 (2002)

    Article  Google Scholar 

  31. Lewis, J.R.: IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. Int. J. Hum.-Comput. Interact. 7, 57–78 (1995)

    Article  Google Scholar 

  32. Wang, Y.-S., Liao, Y.-W.: Assessing eGovernment systems success: a validation of the DeLone and McLean model of information systems success. Gov. Inf. Q. 25, 717–733 (2008)

    Article  Google Scholar 

  33. Morgeson, F.P., Humphrey, S.E.: The Work Design Questionnaire (WDQ): developing and validating a comprehensive measure for assessing job design and the nature of work. J. Appl. Psychol. 91, 1321–1339 (2006)

    Article  Google Scholar 

  34. Daft, R.L., Macintosh, N.B.: A tentative exploration into the amount and equivocality of information processing in organizational work units. Adm. Sci. Q. 26, 207–224 (1981)

    Article  Google Scholar 

  35. Hair, J., Hult, T., Ringle, C., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications Inc., Thousand Oaks (2017)

    MATH  Google Scholar 

  36. Hair, J.: Advanced Issues in Partial Least Squares Structural Equation Modeling. SAGE, Los Angeles (2018)

    Google Scholar 

  37. Matthews, L.M., Sarstedt, M., Hair, J.F., Ringle, C.M.: Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part II – a case study. Eur. Bus. Rev. 28, 208–224 (2016)

    Article  Google Scholar 

  38. Sarstedt, M., Ringle, C.M.: Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies. J. Appl. Stat. 37, 1299–1318 (2010)

    Article  MathSciNet  Google Scholar 

  39. Negash, S., Gray, P.: Business intelligence. In: Handbook on Decision Support Systems, vol. 2. pp. 175–193. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-48716-6_9

    Chapter  Google Scholar 

  40. Gaardboe, R., Sandalgaard, N., Sudzina, F.: The importance of task compatibility for web-enabled business intelligence success in e-government. (2017). Presented at the

    Google Scholar 

  41. Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36, 1165–1188 (2012)

    Article  Google Scholar 

  42. Gaardboe, R.: Kritiske succesfaktorer for business intelligence - i kontekst af den offentlige sektor. Aalborg Universitetsforlag, Aalborg (2018)

    Google Scholar 

  43. Gaardboe, R., Sandalgaard, N., Nyvang, T.: An assessment of business intelligence in public hospitals. IJISPM – Int. J. Inf. Syst. Project Manag. 5–18 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Aalborg University, 9000, Aalborg, Denmark

    Rikke Gaardboe & Tanja Svarre

Authors
  1. Rikke Gaardboe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tanja Svarre
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tanja Svarre .

Editor information

Editors and Affiliations

  1. Corvinus University of Budapest, Budapest, Hungary

    Andrea Kő

  2. Institute of Legal Information Theory and Techniques, Florence, Italy

    Enrico Francesconi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gaardboe, R., Svarre, T. (2018). BI End-User Segments in the Public Health Sector. In: Kő, A., Francesconi, E. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2018. Lecture Notes in Computer Science(), vol 11032. Springer, Cham. https://doi.org/10.1007/978-3-319-98349-3_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98349-3_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98348-6

  • Online ISBN: 978-3-319-98349-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation