Abstract
This chapter investigates the state-of-the-art in the performance management literature. In this context, the performance management systems, organizational goals and key performance indicators are examined at three levels of granularity: generic (literature reporting about generic approaches covering either one or more functions of a company), R&D (focusing on the specifics of the R&D function), and research function only. The insights in this chapter enable us to formulate the research gaps and refine the primary question with four research sub-questions.
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Notes
- 1.
See our discussion about definitions in Sects. 2.2.4 as well as 2.2.5, where we concluded that performance measurement builds a part of performance management.
- 2.
- 3.
Klingebiel (2000), p. 298.
- 4.
- 5.
Jetter (2004), Cokins (2009).
- 6.
Four constitutive elements of a PMgS have been defined in Sect. 2.2.5: planning, measurement, analysis and review.
- 7.
The R&D function justifies its existence within organizations due to the very dynamic markets with constant newcomers and, thus, potential competitors (increased levels of competition), continually-improving technologies (rapidly-increasing production processes and methods) and the growing transparency and fast-changing preferences of customers, Hauber (2002), pp. 26–34.
- 8.
BCG (2003), p. 5.
- 9.
Hauber (2002), p. 41. Note that author acknowledges that his work applies predominantly to development (D); nevertheless, he uses the term “R&D” because, according to Hauber, on the one hand, the basic philosophy applies also to research, and on the other hand, the strict distinction between research and development is not appropriate. Hauber 2002, p. 24.
- 10.
This problem is addressed in the literature under the term “time lag“. Schainblatt also observes that the time lag between fundamental discoveries and new industries can be from 15 to 50 years.
- 11.
Schainblatt (1982), p. 10.
- 12.
Brown and Svenson (1998), pp. 31–32: following five reasons of failure have been identified: too much emphasis on internal measurement; too much focus on behavior; measuring outputs of questionable value to the organization; measurement system is too complicated, and measurement system is too subjective.
- 13.
Kerssens-van Drongelen (2001), pp. 9–11.
- 14.
Hauber (2002), p. 119, Gaiser and Servatius (1990), p. 13, Zeidler (1986), p. 321.
- 15.
Hauber (2002), p. 119, Hronec (1993).
- 16.
Ojanen and Vuola (2006), pp. 279–290.
- 17.
E.g., externally: academia, industrial partners, political bodies and media, and internally: development department, as well as the communication and marketing departments and top management. Beck and Völker (2009), p. 34.
- 18.
We presented an overview of purposes of performance management in Sect. 2.2.5, pp. 37–43.
- 19.
Detailed differentiation between types of R&D activities is provided in the definition Sect. 2.1.3, pp. 8–15.
- 20.
Note that even though Hauber calls his work "R&D performance measurement", he acknowledges the fact that his work primarily focuses on the development part. Hauber (2002), p. 24. As such, the second layer of his proposed PMsS, which focuses on product projects is typical for the development function but might not necessarily find an equivalent type of projects in research only.
- 21.
Seiler, (1965), Mansfield et al. (1971), Leifer and Triskari (1987), pp. 71–78, Brown and Svenson (1998), pp. 30–35.
- 22.
Samsonowa et al. (2009), p. 158.
- 23.
There is no one set of measures that will remain definitive over time. Performance measures, as with the organization itself, should be flexible to change (cf. Driva et al. 2000).
- 24.
Laplante (2009), p. 13.
- 25.
Requirement – something essential to the existence or occurrence of something else, Merriam-Webster’s 11th Collegiate Dictionary (2004) [requirement].
A requirement is a singular documented need of what a particular product or service should be or perform. It is a statement that identifies a necessary attribute, capability, characteristic, or quality of a system in order for it to have value and utility to a user, Wikipedia, 17.02.1020 [http://en.wikipedia.org/wiki/Requirement]
Requirements try to describe the result, in our research the result is a performance management system. In this chapter we describe what the PMgS should do.
- 26.
Hence each and every organization has its unique ecosystem, and its own definition of stakeholder groups. For a listing of possible stakeholders in a typical organization see Hahn (1994), p. 62.
- 27.
Since the interpretation of performance management varies greatly, see Sects. 2.2.4 and 2.2.5, it especially differs in the definition given by different stakeholder groups, which are to be provided with information. As such, the design of interfaces also differs.
- 28.
We are aware of the fact that the four studies we have chosen for the analysis of the PMS requirements focus on different units of analysis. This is not a disadvantage for our examination as they study essentially the same phenomenon. Moreover, the examination of the same phenomenon in different units of analysis and on different abstraction levels, helps to produce a comprehensive view.
- 29.
Klingebiel (2000), p. 298.
- 30.
Klingebiel (2000), pp. 34–35.
- 31.
Translated from Wettstein (2002), p. 105.
- 32.
Please note that we have considered in our classifications the requirements which are business-oriented (WET01 – WET 17); remaining requirements. WET18 – WET45 describe the IT-system requirements and were omitted, since the software integration is out of the scope of this work.
- 33.
Hauber (2002), p. 109.
- 34.
Krause (2005), p. 109.
- 35.
Brown and Svenson (1998), pp. 30–35.
- 36.
The following six recommendation have been made on how to design a PMgS: (1) focus on external vs. internal measurement; (2) focus on measuring outcomes and outputs, not behavior; (3) measure only valuable accomplishments/outputs; (4) make the measurement system simple; (5) make the measurement system objective; and (6) separate R&D evaluation.
- 37.
The term “Contrasting” refers to the different functions for which the measures are relevant.
- 38.
Leifer and Triskari (1987), p. 76.
- 39.
Karlsson et al. (2004), pp. 179–186.
- 40.
Karlsson et al. (2004), p. 180.
- 41.
Refer to Schainblatt (1982), p. 10, Brown and Svenson (1998), pp. 30–35.
- 42.
The examination of KPIs within industrial research (only) will be addressed in Sect. 3.3.3.
- 43.
Leifer and Triskari (1987), pp. 71–78.
- 44.
We are aware of the military setting of this research, nevertheless the hypotheses examined in this study are perceived as valid for our considerations.
- 45.
Leifer and Triskari (1987), p. 71.
- 46.
The authors refer to four factors: time, originality, organization and knowledge depth.
- 47.
Karlsson et al. (2004), p. 180.
- 48.
Loch and Tapper (2002), p. 185.
- 49.
Brown and Svenson (1998), p. 31.
- 50.
Hauber (2002), p. 207.
- 51.
Note that not all R&D output types are also applicable to research. Instead of type 1 and 2 research, outputs could be concepts or feasibility studies.
- 52.
Prechelt (1997).
- 53.
Feltham and Xie (1994), pp. 429–453.s
- 54.
Loch and Tapper (2002), p. 185.
- 55.
Leifer and Triskari (1987), p. 73, note especially their second hypothesis. On both these measures as well as an overall measure of environmental uncertainty there was no statistical difference between the Research and Development units, meaning that both research and development groups perceived the environment similarly. The authors, however, did not examine the uncertainty of the output artifacts of each unit.
- 56.
Beck and Völker (2009), pp. 28–35. Amelingmeyer (2005), p. 353.
- 57.
For example public funded projects, depending on a form, can include different disciplines like academia, industrial companies, standard bodies, etc., and can be comprised from more than 20 different organizations.
- 58.
Beck and Völker (2009), p. 35.
- 59.
Note that the sixth hypothesis was not commented here as it was not relevant to our research.
- 60.
Karlsson et al. (2004), pp. 179–186.
- 61.
Stahl and Steger (1977), cited from Karlsson et al. (2004), p. 181.
- 62.
Karlsson et al. (2004), p. 181.
- 63.
Karlsson et al. (2004), p. 182, Roussel et al. (1991), cited from Karlsson et al. (2004), p. 182.
- 64.
Inter-unit dependence was operationalized with questions pertaining to the extent that work units depend on others to accomplish work objectives and the extent to which others depend on them.
- 65.
Karlsson et al. (2004), p. 180.
- 66.
Schwantag (1951) cited from Hamel (1992), p. 2635, Bidlingmaier (1964) cited from Hamel (1992), p. 2635, Hamel (1992), p. 2635, Nagel (1992), p. 2626, Specht and Beckmann (1996), p. 18, Hauschildt (1997).
- 67.
Specht and Beckmann (1996), p. 18 and p. 125.
- 68.
Hamel (1992), p. 2635.
- 69.
Hamel (1992), p. 2638.
- 70.
“Unternehmensziele“, Hamel (1992), p. 2638.
- 71.
“Entscheidungsziele“, Hauschildt (1970), p. 551, Hamel (1992), p. 2638, Hauschildt (1997), pp. 269–272.
- 72.
“Sachziel“, Kosiol (1966), or “Leistungsziel“ Schmidt (1969).
- 73.
Hauschildt (1997), p. 269.
- 74.
Hamel (1992), p. 2638.
- 75.
Hauschildt (1997), p. 271.
- 76.
Thommen and Achleitner (2003), p. 112. See also details for value benefit analysis Weber (1992), pp. 1435-1448, Hauschildt (1997), p. 271.
- 77.
As mentioned already in section 2.2 other disciplines such as Organizational and Occupational Psychology or Organizational Behavior and Human Performance examined individuals’ performance in an organizational context and had to therefore deal particularly with goal setting. Goal setting theory has been approached by Edwin Locke in mid-1960s who had developed this theory upon Aristotle’s speculation that purpose can cause action. Locke researched the impact that goals have on the individual activity of its time performance; he continued researching goal setting for 30 years.
- 78.
Locke (1968), p. 161.
- 79.
This hypothesis was also proven by other authors: Champion and Lord (1982), p. 267, Yukl and Latham (1978), Locke and Latham (1990).
- 80.
Locke et al. (1981), p.125.
- 81.
Tankoonsombut (1998), p. 12.
- 82.
Locke (1968), p. 162.
- 83.
Griffin (1990), p. 170.
- 84.
Locke (1968), p. 162.
- 85.
Tankoonsombut (1998), p. 13.
- 86.
- 87.
Greller (1980), cited from Tankoonsombut (1998), p. 11.
- 88.
Tankoonsombut (1998), p. 31.
- 89.
Hahn (1994), p. 62.
- 90.
Cesaroni et al. (2004), pp. 45–56.
- 91.
Cesaroni et al. (2004), p. 47.
- 92.
Cesaroni et al. (2004), p. 51.
- 93.
Karlsson et al. (2004), p. 182.
- 94.
These were alternative products because Volvo Aero saw a future decrease in military projects.
- 95.
RAND – the name is derived from a contraction of the term research and development.
- 96.
NASA stands for National Aeronautics and Space Administration, USA.
- 97.
AEC, abbreviation for Atomic Energy Commission, USA.
- 98.
NIH is the abbreviation of National Institute of Health, USA.
- 99.
As an example authors use the production of a refrigerator or a bomber, noticing that the objective for which research is supported by an army or a firm is a production of a useful item.
- 100.
Mesthene and Clintock (1962), p. 2.
- 101.
In our opinion, it is also possible to apply this term in the software industry with a slight modification. "Hardware" implies a degree product maturity. The authors give examples of hardware: a bomber, a submarine, an engine, a drug, etc. In the case of software providers, we think executable software could also be valid as an end item.
- 102.
Mesthene and Clintock (1962), p. 2.
- 103.
The authors admit that the term chosen is awkward, but must suffice for want of a better one.
- 104.
Note that this definition and distinction is made from the manager’s perspective. The authors note that a different distinction from that which can be made within intellectual goals on the basis of their apparent usefulness is from scientists.
- 105.
The authors claim that all research, whether academic or mission-oriented aims at least at intellectual goals. “This is necessary so long as it is to remain respectable research, because it is the intellectual ends of an inquiry that provides its rationale” (Mesthene and Clintock 1962, p. 13).
- 106.
The respective definitions are provided in Sect. 2.2.3.
- 107.
Parmenter (2007).
- 108.
Parmenter (2007), pp. 203–231, the database also contains metrics for R&D which will be examined in detail in the following section. On account of minor relevance the table with 342 measures will not be placed in this section, but can be found in Appendix B.
- 109.
“Key result indicators (KRIs) give a clear picture whether you are traveling in the right direction. They do not, however, tell one what is needed to do to improve these results; Key performance indicators (KPIs) represent a set of measures focusing on those aspects of organizational performance that are the most critical for the current and future success of the organization.” Parmenter does not really define performance indicators (PIs) as he only mentions: “In between KRIs and the true KPIs are numerous performance indicators. These complement the KPIs and are shown with them on the scorecard for the organization and the scorecard for each division, department, and team.” Parmenter (2007), pp. 3–7.
- 110.
Parmenter (2007), p. 8.
- 111.
- 112.
Geisler (2000), pp. 80–86. Please note that the author emphasizes: each category contains illustrative measures. The lists are not exhaustive and are not a complete listing of all measures found in the relevant literatures. Measures are not listed in any particular order. See Appendix B.
- 113.
The following characteristics are attached to this study: more than 2,300 interviews with top R&D managers, interviews in 59 enterprises of the investment and consumer goods industry, interviews with 28 consulting companies and various seminars and large-scale literature search.
- 114.
An extract of the indicators suggested by Ranftl can be found in Appendix B.
- 115.
Schainblatt (1982), pp. 10–18. Note that we summarized only relevant measures for R&D, and left measures reported for the engineering units out, we also tried to avoid double entries.
- 116.
Brown and Svenson (1998), pp. 30–35. The detailed list of measures suggested by Brown and Svenson is provided in Appendix B.
- 117.
Brown and Gobeli (1992), p. 330. The detailed list of measures suggested by Brown and Gobeli is provided in Appendix B.
- 118.
Tipping and Zeffren (1995), pp. 32–63. The detailed list of 33 measures suggested by Tipping and Zeffren is provided in Appendix B.
- 119.
Chiesa and Frattini (2007), pp. 294. The full list of performance indicators collected by Chiesa and Frattini is provided in Appendix B.
- 120.
Kerssens-van Drongelen (2001), p. 80.
- 121.
Tipping et al. (1995), pp. 32–63.
- 122.
Mertins (1998), cited from Krause, notes that Krause had adapted the structure in the course of his work and therefore the naming has been changed.
- 123.
Note that a critical success factor (CSF) is not a key performance indicator (KPI). Critical success factors are elements that are vital for a strategy to be successful. KPIs are a set of performance indicators that quantify management objectives and enable the measurement of strategic performance. A critical success factor, following Rockart, is what drives the company forward.
- 124.
Daniel (1961), Rockart (1979).
- 125.
Rockart (1979), p. 85.
- 126.
Mettänen (2005), pp. 178–188.
- 127.
Loch and Tapper (2002), pp. 185–198.
- 128.
Kerssens-van Drongelen has in particular shown in her work the need for performance measurement; see Kerssens-van Drongelen (2001), pp. 1–10.
- 129.
Philip Francis (1992), Vice President and Chief Technical Officer of Square D company, cited from Kerssens-van Drongelen (2001).
- 130.
Walter Robb (1991), Senior Vice President for Corporate Research and Development at General Electric, cited from Kerssens-van Drongelen (2001).
- 131.
One of our analyzed companies argues against the measurement of research.
- 132.
For example, Loch and Tapper (2002), p. 185.
- 133.
As summarized in Table 3.3.
- 134.
- 135.
- 136.
In Sect. 3.1.5.
- 137.
- 138.
Feltham and Xie (1994), Brown and Svenson (1998), Loch and Tapper (2002), Chiesa et al. (2007).
- 139.
Prechelt (1997).
- 140.
Feltham and Xie (1994), Leifer and Triskari (1987) Loch and Tapper (2002).
- 141.
Leifer and Triskari (1987), Amelingmeyer (2005), Beck and Völker (2009).
- 142.
- 143.
- 144.
- 145.
- 146.
E.g. balanced scorecard, ABC, etc.
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Samsonowa, T. (2012). State-of-the-Art in Performance Management. In: Industrial Research Performance Management. Contributions to Management Science. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2762-0_3
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