2. Interés
2.3 Tipos de interés
2.3.2 Interés Compuesto
Performance indicators are rarely reported in a manner that gives people sufficient information about the indicator. Any ambiguity leads to doubts which in turn hamper decision-making and learning. It is therefore important to provide a comprehensive picture of an indica-tor. The design template outlined in Figure 5.6, provides much of the information needed to improve the way performance is reported. In order to report performance comprehensively, the following informa-tion should be included:
䊏 Name of the indicator (see section above)
䊏 Strategic element being assessed – what strategic element is being assessed (see section above). Often the best way to do this is to provide a picture of the value creation map and highlight the elem-ent that is being assessed.
䊏 Purpose – why is the indicator being used (see section above)?
䊏 Confidence level – how confident are we that this is a ‘good’ or valid indicator (see section above)?
118 Managing performance in an enabled learning environment
䊏 Data collection method – it is important to clarify where the data comes from and how performance was assessed (see section above).
䊏 Narrative assessment of performance – any report of a perform-ance indicator should have a short written assessment of the per-formance that highlights what the data is telling us. This allows organizations to capture the performance in natural language, which makes it easier for people to interpret.
䊏 Traffic light assessment – provides at-a-glance assessment of the performance. Colour coding and traffic lighting is very intuitive and useful for most people. However, beware that there are a lot of people who have difficulties distinguishing colours (especially the difference between red and green), which is better known as colour blindness. It is estimated that about 8% of males and 1% of females have difficulties with colour vision impairments and, there-fore, it may be appropriate to complement or even replace colour-coding with symbols or icons (like thumb up or down, smiley face, etc.) in order to indicate performance.42 Some organizations prefer speedometer style displays that indicate current performance in comparison to the targets or expectations (see Figure 5.7).
䊏 Numerical presentation(if applicable) – this provides a number of the indicator status. However, in order to be meaningful, this num-ber has to be put into context of expectations, targets, or bench-marks. A number on its own is completely meaningless unless we understand the scale and the relative performance. This can be pro-vided in tabular format or in a graph (see next point).
䊏 Graphical representation– performance representations should be made easily understandable. One way to do this is in graphical rep-resentations. Generally speaking, line graphs or bar charts seem to work well. They allow organizations to show past performance levels and allow inclusion of target lines and benchmark informa-tion (see Figure 5.8).
Bad
Neutral
Good
Figure 5.7 Speedometer display
䊏 Comment by owner – the person(s) or function(s) responsible for the management of the strategic element that is being assessed should provide a comment on what this performance level means and whether there are any actions or initiatives being taken. This engages people in the active review of indicators and provides a starting point for a discussion or dialogue about improvement.
There are other elements and information that could also be included.
To identify the necessary components, it is best to think about the audience and their requirements. In Strategic Performance Management, data is primarily reported to facilitate learning and strategic decision-making. And so the more useful the information that is provided the better, since it consequently ensures people understand the indicator and what it means better.
Air force pilots are trained to trust the measures that they get from their instruments. They learn not to look out of the window but just to concentrate on the instruments. In a fighter jet, it is possible to reli-ably measure all critical dimensions of performance and, therefore, enable the pilot to base his or her decisions and actions on the meas-ures available. In the socio-cultural environment of modern day organ-izations, it is impossible to reliably ‘measure’ all critical dimensions of performance. Measures become indicators, with all their limitations, and have to be treated as such. Indicators become the decision-support instruments in a learning organization. However, for this to work, organizations need to align their processes and routines with the principles of a learning organization. They have to create what I call an enabled learning environment; how to create such an environ-ment will the subject of the next chapters.
Past Today
Benchmark
Bad Good
Performance
Actual performance Target
Period
Figure 5.8 Line graph display
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References and endnotes
1 Quoted in Boyle, D. (2001). The Sum of Our Discontent: Why Numbers Make Us Irrational.Texere: New York.
2 Neely, A. (1998). Measuring Business Performance: Why, What and How. Economist Books: London.
3 Stein, R. E. (1997). The Theory of Constraints. Marcel Dekker Inc.:
New York.
4 Meyer, M. W. (2002). Rethinking Performance Measurement – Beyond the Balanced Scorecard. Cambridge University Press:
Cambridge, p. 31.
5 This definition is based on the definition of motivational measure-ment by: Austin, R. D. (1996). Measuring and Managing Perform-ance in Organizations. Dorset House Publishing: New York, p. 193.
6 Campbell, N. R. (1928). An Account of the Principles of Measure-ment and Calculation. Longmans: London, p. 1.
7 Caws, P. (1959). Definition and Measurement in Physics. In Measure-ment: Definition and Theories (C. W. Churchman and P. Ratoosh, eds), pp. 3–17, John Wiley & Sons: New York.
8 Adams, C., Kennerley, M. and Neely, A. (2002). The Performance Prism: The Scorecard for Measuring and Managing Business Success. FT Prentice Hall: London.
9 Mason, R. O. and Swanson, E. B. (1981). Measurement for Manage-ment Decision: A Perspective. In MeasureManage-ment for ManageManage-ment Decision (R. O. Mason, Swanson, E. B.), Addison-Wesley: Reading, MA, pp. 10–25.
10 Ibid, Boyle, D. (2001), p. 30. (See note 1 above.)
11 Blair, M. M. and Wallman, S. M. H. (2001). Unseen Wealth. Brookings Institution Press: Boston, p. 15.
12 From an interview with Daniel Yankelovich quoted in Adam Smith [pseudonym of George J. W. Goodman] (1973). Supermoney.
Michael Joseph: London, p. 286.
13 Porter, T. M. (1995). Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton University Press:
Princeton.
14 Ibid, Boyle, D. (2001), p. 38. (See note 1 above.)
15 See for example: Gooday, G. J. N. (2004). The Morals of Measure-ment: Accuracy, Irony, and Trust in Late Victorian Electrical Practice. Cambridge University Press: Cambridge; and Porter, T. M.
(1995). Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton University Press: Princeton.
16 See: Austin, R. D. (1996). Measuring and Managing Performance in Organizations. Dorset House Publishing: New York, p. 13.
17 Sugarman, C. (1990). US Produce Standards Focus More on Appear-ance Than Quality, The Pittsburgh Press, August 5, p. E1.
18 See Torres, R. T., Preskill, H. S. and Piontek, M. E. (1996). Evaluation Strategies for Communicating and Reporting: Enhancing Learning in Organizations.Sage: Thousand Oaks, p. 2.
19 Johnson, T. H. and Kaplan, R. S. (1987). Relevance Lost: The Rise And The Fall Of Management Accounting. Harvard Business School Press: Boston, MA.
20 UBS Annual Review 2002, www.ubs.com (p. 25).
21 For more information on the Agency Model see for example:
Ross, S. A. (1973). The Economic Theory of Agency: The Principal’s Problem. The American Economic Review, Vol. 63, No. 2, pp. 134–9; and Holström, B. (1977). On Incentives and Control in Organizations (unpublished Ph.D. thesis), Stanford University, Stanford.
22 Meyer, M. W. (2002). Rethinking Performance Measurement – Beyond the Balanced Scorecard. Cambridge University Press:
Cambridge (page xxi).
23 The classic example is referral interview in a government agency, here the number of interviews is measured whereas the quality of referrals is not. See: Blau, P. M. (1963). The Dynamics of Bureau-cracy: A Study of Interpersonal Relations in Two Government Agencies. University of Chicago Press: Chicago.
24 This figure was inspired by the cases and diagrams used by Austin, R. D. (1996), Ibid (see note 5 above).
25 Ridgway, V. F. (1956). Dysfunctional Consequences of Performance Measurements. Administrative Science Quarterly, Vol. 1, No. 2, pp. 240–7.
26 Meyer, M. W. (2002). Rethinking Performance Measurement – Beyond the Balanced Scorecard. Cambridge University Press:
Cambridge, p. 8.
27 Ehin, C. (2000). Unleashing Intellectual Capital. Butterworth Heinemann: Boston, p. 138.
28 Ibid, Austin, R. D. (1996). (See note 5 above).
29 Meyer, M. W. (2002). Rethinking Performance Measurement – Beyond the Balanced Scorecard. Cambridge University Press:
Cambridge, p. 2.
30 Quoted in Boyle, D. (2001), Ibid, p. 29. (See note 1 above.)
31 See for example: Ketokivi, M. A. and Schroeder, R. G. (2004).
Perceptional Measures of Performance: Fact or Fiction? Journal of
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Operations Management, Vol. 22, No. 3, pp. 247–64; or Boyd, B. K., Dess, G. G. and Rasheed, A. M. A. (1993). Divergence Between Archival and Perceptional Measures of the Environment: Causes and Consequences. Academy of Management Review, Vol. 18, No. 2, pp. 204–26; or Ramanujam, V. and Venkatraman, N. (1987).
Measurement of Business Economic Performance: An Examination of Method Convergence. Journal of Management, Vol. 13, No. 1, pp. 109–12.
32 For more information see for example: Dillman, D. A. (1999). Mail and Internet Surveys: The Tailored Design Method. Wiley:
New York.
33 Preskill, H. and Russ-Eft, D. (2001). Evaluation in Organization – A Systematic Approach to Enhancing Learning, Performance, and Change. Perseus: Cambridge, MA, p. 200.
34 See for example: Yin, K. (2003). Case Study Research. Design and Methods (Applied Social Research Methods Series, Vol. 5). Sage:
Newbury Park, CA.
35 Preskill, H. and Russ-Eft, D. (2001). Evaluation in Organization – A Systematic Approach to Enhancing Learning, Performance, and Change. Perseus: Cambridge, MA.
36 Denzin, N. K. and Lincoln, Y. S. (eds) (2005). The Sage Handbook of Qualitative Research, 3rd edition. Sage: Thousand Oaks.
37 See for example: Mangel, M. and Samaniego, F. J. (1984). Abraham Wald’s Work on Aircraft Survivability. Journal of American Statistical Association, June, pp. 259–67.
38 The majority of the thinking behind this section is credited to the work conducted by my colleagues at Cranfield School of Manage-ment and Cambridge University. For more details see: Bourne, M., Neely, A., Mills, J., Platts, K. and Richards, H. (2002). Getting the Measures of Your Business. Cambridge University Press: Cambridge, p. 69; or Adams, C., Kennerley, M. and Neely, A. (2002). The Performance Prism: The Scorecard for Measuring and Managing Business Success. FT Prentice Hall: London, p. 34; or Bourne, M. C.
S., Mills, J. F., Neely, A.D., Platts, K. W. W. and Richards, H. (1997).
Designing Performance Measures: a Structured Approach. Inter-national Journal of Operations & Production Management, Vol.
17, No. 11–12, p. 1131.
39 Preskill, H. and Russ-Eft, D. (2001). Evaluation in Organization – A Systematic Approach to Enhancing Learning, Performance, and Change. Perseus: Cambridge, MA, p. 178.
40 Ibid, Austin, R. D. (1996), p. 66. (See note 5 above.)
41 Gray, D. J. (2005). A Multi-Method Investigation into the Costs and into the Benefits of Measuring Intellectual Capital Assets (unpub-lished Ph.D. thesis). Cranfield School of Management: Cranfield.
42 For more information see Prevent Blindness America: http//www.
preventblindness.org