Driving IS Value Creation by Knowledge Capturing: Theoretical Aspects and Empirical Evidences

  • R. P. Dameri
  • C. R. Sabroux
  • Ines Saad


Business process change and information systems development are usually associated in best business practices. However, it is not ever clear if the quality of business process change really impacts on quality and value of information systems. To realize value from business process change through information systems quality, it is necessary to clearly define an improvement strategy regarding both business activities and operations and the IT applications embedding them.

Davenport et al. [8] identified three most important key factors driving IS value, deriving from business process change: integrate, optimize and informate.

We suggest to add a key factor driving IS value deriving from business process change: Identify Knowledge. Identify Knowledge, means to identify knowledge, when and how users need it, improving services and process decision. Information Technologies bear the potential of new uses. These uses provoke a new organisation which induces a new vision of IS strategy. Under the influence of globalization, and the impact of Information and Communication Technologies (ICT) that radically modifies our relationship with space and time, the hierarchical company locked up on its local borders becomes an Extended Company, without borders, opened and adaptable. In this context, this paper proposes a shift in the way the design of Information Systems is viewed based on business process. The adopted approach is a global philosophy based on Business Process Management (BPM) within the framework of all the methodological principles.

Empirical evidences are available, by an Italian large company, using business process management and knowledge capturing as an improvement strategy for IS value.


Business Process Business Process Management Crucial Knowledge Business Process Analysis Business Process Change 
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.


  1. 1.
    Greco, S., B. Matarazzo and R. Slowinski, Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129, 1–47, 2001.CrossRefGoogle Scholar
  2. 2.
    Pawlak, Z. Rough sets, International Journal of Information & Computer Sciences 11, pp.341-356, 1982.CrossRefGoogle Scholar
  3. 3.
    Grundstein, M. Rosenthal-Sabroux C and Pachulski A. Reinforcing Decision Aid By Capitalizing On Company’s Knowledge. European Journal of Operational Research 14(5), 256–272, 2006.Google Scholar
  4. 4.
    Saad, I., C. Rosenthal-Sabroux and M. Grundstein. Improving the Decision Making Process in The Design project by Capitalizing on Company’s Crucial Knowledge. Group Decision and Negotiation, 14, 131–145, 2005.CrossRefGoogle Scholar
  5. 5.
    Rosenthal-Sabroux C. and V. Thion-Goasdoué. A first step towards evaluating business process quality using a structural analysis of a social network, INFORSID, p.177-192, 2010.Google Scholar
  6. 6.
    Dameri R.P., Privitera S., IT Governance. Franco Angeli, Milano 2009.Google Scholar
  7. 7.
    Belton, V. and J. Pictet, ‘A framework for group decision using a MCDA model: sharing, aggregation or comparing individual information’, Journal of Decision Systems, 6(3), 283–303, 1997.Google Scholar
  8. 8.
    Davenport T., J. Harris and S. Cantrell. Enterprise systems and ongoing process change Business Process Management Journal 10(1), 16–26, 2004.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Business AdministrationUniversity of GenovaGenovaItaly
  2. 2.Paris-Dauphine University, LAMSADEParisFrance
  3. 3.Amiens Business SchoolUniversity de Picardie Jules VerneAminesFrance

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