Advertisement

An Approach for Using Data Mining to Support Theory Development

  • Kweku-Muata Osei-BrysonEmail author
  • Ojelanki Ngwenyama
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 34)

Abstract

The rapid and constant change in information technologies (IT), organizational forms, and social structures is challenging our existing theories of the impact IT on organizations and society. A basic problem for researchers is how to generate testable hypotheses about the given area of research. However, new IT offer opportunities for information processing and problem solving that could extend the capacity of researchers to generate hypotheses and systematically explore the limitations of any theory. The idea of using IT to support IS research is not new. In this chapter, we explore and illustrate how data mining techniques could be applied to assist researchers in systematic theory testing and development.

Keywords

Information System Data Mining Technique Mediator Variable Data Mining Software Local Hypothesis 
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.

Notes

Acknowledgments

Some of the material in this chapter previously appeared in the paper “Using Decision Tree Modelling to Support Peircian Abduction in IS Research: A Systematic Approach for Generating and Evaluating Hypotheses for Systematic Theory Development,” Information Systems Journal 21:5, 407–440 (2011).

References

  1. Benbasat I, Zmud R (1999) Empirical research in information systems: the practice of relevance. MIS Q 23(1):3–16CrossRefGoogle Scholar
  2. Brusic V, Zeleznikow J (1999) Knowledge discovery and data mining in biological databases. Knowl Eng Rev 14:257–277CrossRefGoogle Scholar
  3. Chalmers AF (1994) What is this thing called science? 3rd edn. Hackett PublishingGoogle Scholar
  4. Dipert R (1995) Peirce’s underestimated role in the history of logic. In: Ketner K (ed) Peirce and contemporary thought. Fordham University Press, New YorkGoogle Scholar
  5. Doll WJ, Torkzadeh G (1988) The measurement of end-user computing satisfaction. MIS Q 12(2):259–274CrossRefGoogle Scholar
  6. Etezadi-Amoli J, Farhoomand AF (1996) A structural model of end user computing satisfaction and user performance. Inf Manage 30(2):65–73CrossRefGoogle Scholar
  7. Fann KT (1970) Peirce’s theory of abduction. Martinus Nijhoff, AmsterdamCrossRefGoogle Scholar
  8. Grimes TR (1990) Truth, content, and the Hypothetico-Deductive method. Philosophy of Science, 57, 514–522Google Scholar
  9. Goodhue DL, Thompson RL (1995) Task-technology fit and individual performance. MIS Q 19(2):213–236CrossRefGoogle Scholar
  10. Hanson NR (1961) Is there a logic of discovery. In: Feigle H, Maxwell G (eds) Current issues in the philosophy of science. Holt, Rinehart and Winston, pp 20–35Google Scholar
  11. Harman G (1965) Inference to the best explanation. Philos Rev 74:88–95CrossRefGoogle Scholar
  12. Hintikka J (1968) The varieties of information and scientific explanation. In: van Rootselaar B, Staal JF (eds) Logic, methodology and philosophy of science III. North Holland, pp 151–171Google Scholar
  13. Hintikka J (1997) The place of CS Peirce in the history of logical theory. In: Lingua Universalis vs Calculus Ratiocinator, selected papers 2. Kluwer, pp 140–161Google Scholar
  14. Kim H, Koehler G (1995) Theory and practice of decision tree induction. Omega 23(6):637–652Google Scholar
  15. Ko M, Osei-Bryson K-M (2004a) Exploring the relationship between information technology investments and firm performance productivity using regression splines analysis. Inf Manage 42:1–13CrossRefGoogle Scholar
  16. Ko M, Osei-Bryson K-M (2004b) Using regression splines to assess the impact of information technology investments on productivity in the health care industry. Inf Syst J 14(1):43–63CrossRefGoogle Scholar
  17. Lee C, Irizarry K (2001) The GeneMine system for genome/proteome annotation and collaborative data mining. IBM Syst J 40(2):592–603CrossRefGoogle Scholar
  18. Niiniluoto I (1993) Peirce’s theory of statistical explanation. In: Moore EC (ed) Charles S Peirce and the philosophy of science. The University of Alabama Press, Tuscaloosa, pp 186–207Google Scholar
  19. Niiniluoto I (1999) Defending abduction. Proc Philos Sci 66:S436–S451CrossRefGoogle Scholar
  20. Palys TS (2003) Research decisions: quantitative and qualitative perspectives, 3rd edn. Nelson, ScarboroughGoogle Scholar
  21. Popper KR (1957) The aim of science. Ratio 1Google Scholar
  22. Popper K (1963) Conjectures and refutations: the growth of scientific knowledge. Routledge and Kegan Paul, London, UKGoogle Scholar
  23. Popper KR (1968) The logic of scientific discovery. Harper Torch Books, New YorkGoogle Scholar
  24. Putnam H (1982) Peirce the logician. Historia Math 9:290–301CrossRefGoogle Scholar
  25. Quine WV (1995) Peirce’s Logic. In: Ketner KL (ed) Peirce and contemporary thought. Fordham, New York, pp 23–31Google Scholar
  26. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106Google Scholar
  27. Tursman R (1987) Peirce’s theory of scientific discovery. Indiana University Press, BloomingtonGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Information SystemsVirginia Commonwealth UniversityRichmondUSA
  2. 2.Ted Rogers School of Management, Ryerson UniversityTorontoCanada

Personalised recommendations