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Performance Management: Analysis Approach

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Industrial Research Performance Management

Part of the book series: Contributions to Management Science ((MANAGEMENT SC.))

Abstract

This chapter provides an overview on the methodology and introduces the step-by-step development of a performance management system via a series of in-depth case studies. The analysis approach aims at developing a model for performance management systems built on the basic components found in practice such as organizational department goals and individual KPIs. The model is extended with additional components that allow us to compare the various performance management systems and to identify relationships between these components. Performance clusters (i.e. groups of KPIs), which represent one significant component of our developed model, are introduced. Furthermore, the goals are analyzed for the similarity of their sub-components. Altogether, Chap. 4describes a preliminary approach on how to deal with the individual elements of performance management, that is, with KPIs, KPI classes, performance clusters and finally organizational department goals.

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Notes

  1. 1.

    Eastery-Smith et al. (2002), Kasper (2006).

  2. 2.

    Collis and Hussey (2003) argue that the application of a mixture of approaches is quite usual when conducting business research. “The use of different research approaches, methods and techniques in the same study is known as triangulation and can overcome the potential bias and sterility of a single-method approach” (Collis and Hussey 2003, p. 78). The main advantage of triangulation is the greater validity and reliability of the research results (Denzin 1978).

  3. 3.

    Eastery-Smith et al. (2002).

  4. 4.

    “Simultaneous triangulation is the use of the qualitative and quantitative methods at the same time. In this case, there is limited interaction between the two datasets during the data collection, but the findings complement one another at the end of the study. Sequential triangulation is used of the results of one method are essential for planning the next method. The qualitative method is completed before the quantitative method is implemented or vice versa” (Morse 1991, p. 120).

  5. 5.

    Yin (2006).

  6. 6.

    Samsonowa et al. (2009), p. 162.

  7. 7.

    The EU industrial R&D investment SCOREBOARD groups companies into four sectors of R&D intensity: high (above 5%), medium (2%–5%), low (1%–2%) and very-low (less than 1%).

  8. 8.

    The detailed list of the first round interviews is in Appendix C.

  9. 9.

    Eisenhardt (1989), Yin (2006).

  10. 10.

    Information about all interviews, including names and positions of interviewees, interview dates and methods are summarized in Appendix C.

  11. 11.

    Internet source: http://www.sap.com/about/index.epx.

  12. 12.

    Internet source: http://www.sap.com/about/investor/stock/shareholders/index.epx.

  13. 13.

    Internet source: http://www.sap.com/germany/about/press/archive/press_show.epx?ID=4266.

  14. 14.

    Brown and Svenson (1998), pp. 30–35.

  15. 15.

    Samsonowa et al. (2009), p. 162.

  16. 16.

    A Social Bookmark and Publication Sharing System.

  17. 17.

    Chesbrough (2003), Chesbrough et al. (2007).

  18. 18.

    Here we consider intra-organizational technology transfer within a company – between departments, not between different organizations or institutions.

  19. 19.

    Edvinsson and Malone (1997), Pham (2008), p. 92.

  20. 20.

    Taschler and Chappelow (1997), pp. 29–34.

  21. 21.

    Sometimes also referred to as “idea push”.

  22. 22.

    Tipping et al. (1995), pp. 32–63, especially their fourth managerial factor IWB, which stands for “Integration with Business”; Schmoch et al. (2000), Sommerlatte (2006), Geschka (1988).

  23. 23.

    Smith and Alexander (1999).

  24. 24.

    Heuser (2006), pp. 271–290.

  25. 25.

    OECD (2005), Oslo Manual.

  26. 26.

    Scholl (2006), pp. 163–194, Heuser (2006), pp. 271–290, Geschka (2006), pp. 217–248.

  27. 27.

    OECD (2002), Frascati Manual, p. 19, clause 26.

  28. 28.

    OECD (2002), Frascati Manual, p. 91, clause 293.

  29. 29.

    OECD (2002), Frascati Manual, pp. 45–46, clause 132.

  30. 30.

    Altogether Five Managerial Factors have been identified by Tipping et al. (1995), pp. 32–63; compare Sect. 3.3.2, pp. 107–108.

  31. 31.

    Cooper et al. (2001), p. 3.

  32. 32.

    OECD (2009), Patent Statistics Manual.

  33. 33.

    For example: DPMA – Deutsches Patent- und Markenamt; INPI – Institut national de la propriété industrielle in France; USPTO – United States Patent and Trademark Office.

  34. 34.

    For example: WIPO – World International Property Organization; EPO – European Patent Office.

  35. 35.

    Amabile et al. (2003), p. 1157, Woodman et al. (1993), p. 295, Amabile and Conti (1994).

  36. 36.

    OECD (1995), Canberra Manual.

  37. 37.

    Klein (1999).

  38. 38.

    Fortune Magazine (2009).

  39. 39.

    Kotler (1996).

  40. 40.

    Meadows (1998), pp. 177–194.

  41. 41.

    Rowland (2002).

  42. 42.

    Chorafas (1963), p. 67.

  43. 43.

    OECD (2002), Frascati Manual, clause 149, p. 49.

  44. 44.

    Fine (2010).

  45. 45.

    Especially in the case of intellectual property, this might depend on the overall organizational setup of the individual company.

  46. 46.

    Semantic technologies would be an approach to go in this direction. However they fail due to the fact that for a general approach they need extensible ontologies and their pragmatic applicability is not (yet) given.

  47. 47.

    Other ways to define similarity of performance cluster spectra could be: the extent of deviation between clusters expressed in its threshold value, or to define the similarity relationship on a fine-granular level taking into account not just one, but two or more maximum weighting clusters, etc.

  48. 48.

    Similarity indicator is a function which compares two performance cluster compositions and provides a yes or no decision at the end.

  49. 49.

    Please see our explanations for the misleading title of this goal in our comments on the SI-1 indicator.

  50. 50.

    Regarding the analysis results, we would have expected that in each goal there is at least one dominant performance cluster present in the cluster spectrum.

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  1. SAP Research, SAP AG, Dietmar-Hopp-Allee 16, 69190, Walldorf, Germany

    Tatjana Samsonowa

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  1. Tatjana Samsonowa
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Samsonowa, T. (2012). Performance Management: Analysis Approach. In: Industrial Research Performance Management. Contributions to Management Science. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2762-0_4

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