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A Bayesian Network-Based Management of Individual Creativity: Emphasis on Sensitivity Analysis with TAN

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6592))

Abstract

Creativity emerges as one of important resources for management. However, definitions of creativity have varied with researchers, and there is no universally agreed consensus about how to manage creativity in organizations. In this sense, managers who are interested in adopting specific type of creativity management strategy were confused. To avoid this problem, this study proposes a new method to creativity management by using a Bayesian Network (BN) that consists of nodes and arcs, and enables sensitivity analyses with various scenarios of interest. By focusing on individual creativity and its relationships with knowledge characteristic, intrinsic motivation, knowledge heterogeneity among team members, and organizational learning, we collected 222 valid questionnaires and performed what-if/goal seeking simulations based on TAN (Tree Augmented Naïve Bayesian Network) structure. Empirical results were promising and its practical meanings were well interpreted.

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References

  1. Amabile, T.M.: The social psychology of creativity: A componential conceptualization. Journal of Personality and Social Psychology 45(2), 357–376 (1983)

    Article  Google Scholar 

  2. Amabile, T.M.: A model of creativity and innovation in organizations. Research in Organizational Behavior 10, 123–167 (1988)

    Google Scholar 

  3. Barron, F.B., Harrington, D.M.: Creativity, intelligence, and personality. Annual Review of Psychology 32, 439–476 (1981)

    Article  Google Scholar 

  4. Charniak, E.: Bayesian networks withoug tears. AI Magazine, 50–63 (1991)

    Google Scholar 

  5. Deci, E.L., Ryan, R.M.: Intrinsic motivation and self determination. Social Networks 1, 215–239 (1979)

    Google Scholar 

  6. Ettlie, J.E., O’Keefe, R.D.: Innovative attitudes, values, and intentions in organizations. Journal of Management Studies 19(2) (1982)

    Google Scholar 

  7. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29(2), 131–163 (1997)

    Article  MATH  Google Scholar 

  8. Garvin, D.A.: Building a learning organization. Harvard Business Review 71(4), 78–91 (1993)

    Google Scholar 

  9. Cooper, G.F.: An overview of the representation and discovery of causal relationships using Bayesian networks. In: Glymour, C., Cooper, G.F. (eds.) Computation, Causation & Discovery, pp. 3–62. AAAI Press/MIT Press, Cambridge, MA (1999)

    Google Scholar 

  10. Hoegl, M., Parboteeah, K.P., Munson, C.L.: Team-level antecedents of individuals’ knowledge networks. Decision Sciences 34(4) (2003)

    Google Scholar 

  11. Jensen, F.V.: Bayesian networks. WIREs Computational Statistics 1(1), 307–315 (2009)

    Article  Google Scholar 

  12. Kurtzberg, T.R., Amabile, T.M.: From Guilford to creative synergy: opening the balck box of team-level creativity. Creativity Research Journal 13, 285–294 (2001)

    Article  Google Scholar 

  13. Munoz-Doyague, M.F., Gonzalez-Alvarez, N., Nieto, M.: An examination of individual factors and employees’ creativity: The case of Spain. Creativity Research Journal 20(1), 21–33 (2008)

    Article  Google Scholar 

  14. Nelson, K.M., Cooprider, J.G.: The contribution of shared knowledge to IS group performance. MIS Quarterly 20(4), 409–432 (1996)

    Article  Google Scholar 

  15. Pelled, L.H., Eisenhardt, K.M., Xin, K.R.: Exploring the black box: An analysis of work group diversity, conflict, and performance. Administrative Science Quarterly 44(1), 1–28 (1999)

    Article  Google Scholar 

  16. Senge, P.M.: The fifth discipline: The art and practice of the learning organization. Doubleday, New York (1990)

    Google Scholar 

  17. Spirtes, P., Glymour, C., Scheines, R.: Causation prediction and search. In: Berger, et al. (eds.) Lecture Notes in Statistics, 1st edn., vol. 81. Springer, Berlin (1993)

    Google Scholar 

  18. Tiwana, A., McLean, E.R.: Expertise integration and creativity in information systems development. Journal of Management Information Systems 22(1), 13–43 (2005)

    Google Scholar 

  19. Woodman, R.W., Sawyer, J.E., Griffin, R.W.: Toward a theory of organizational creativity. Academy of Management Review 18(2), 293–321 (1993)

    Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Lee, K.C., Choi, D.Y. (2011). A Bayesian Network-Based Management of Individual Creativity: Emphasis on Sensitivity Analysis with TAN. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_52

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  • DOI: https://doi.org/10.1007/978-3-642-20042-7_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20041-0

  • Online ISBN: 978-3-642-20042-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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