Producción de capital según disposiciones

Implicit in assessing the validity of OR models is the conclusion of where models are not suitable. The last section demonstrated choice of model, i.e. others against the chosen DEA linear programming model for this thesis. This section describes the validation for the DEA approach used in this thesis.

Validation criteria for all models can include descriptive, analytical and experimental validation (Gass and Harris 2001). Descriptive validation addresses the attainment of the model’s objectives and the plausibility of the results, as well as the suitability of the model structure. Analytical validation again tests the plausibility of results but also their robustness and characteristics. Experimental validation assesses accuracy and efficiency of implementation, costs, data transfer and storage and methodological tests of model documentation.

These validations are done for DEA: in Chapter 4 for structural robustness, Chapter 5 for experimental application, and Chapter 6 for plausibility of results against indicators obtained elsewhere. Such a validation is in agreement with the Golany and Roll (1989) refinement which stipulates judgmental screening, non-DEA quantitative analysis, and a DEA based analysis. The judgmental analysis may be exercised inter alia along the following lines,

a) Is the factor related to, or contributing to, one or more of the objectives set for the application?

b) Is the factor conveying pertinent information not included in other factors? c) Does the factor contain elements (e.g. price) which interfere with the notion of

technical efficiency?

d) Are data on the factor readily available and generally reliable?

The non-DEA quantitative methods can include simple regression analysis to indicate the strength of factors in the relationship being investigated, as was done when comparing the DEA results against the regressions of Black (2004).

The DEA based analyses can test the discriminating power of different factors by grouping them in a series of combinations which identify the candidates eligible for elimination. This was done, and is explained in Chapter 5, where the factors were subjected to trials which would elicit the antecedent and consequence factors of corporate social responsiveness management capacity as defined by Black.

All the validations that have been conducted on the DEA model are described in its procedural application in Chapter 5.

3.9 Conclusion

This chapter has approached the task of applying a methodology to the research in this thesis. The methodology is traditionally supported, contemporarily based, but academically rare. It has shown a new approach to measuring performance in a novel conceptual setting. By using a model of the business environment in an optimization setting, it has expanded the virtues of adopting an operations research methodology which has substantive support of theory quantification through mathematical dogma. A number of operations research techniques were investigated to arrive at the mathematical programming model most suited to the task of this thesis. Linear programming through its particular specialization of DEA was chosen. DEA was judged as the most suitable means of measuring performance, as defined by the productivity paradigm, because of its strength of mathematical sophistication and the maturity demonstrated by its commercial availability, DEA was seen as the most adept at achieving the measurement requirements stipulated by this study. It is not incidental that its commercial availability, through ‘user-friendly’ spreadsheet applications software, has led to its choice for this particular investigation, and others of the same ilk. The development of DEA as the diagnostic tool, through its theoretical foundations to its technical robustness, is discussed in Chapter 4, and its validation testing and practical application for this thesis, as conducted for the CSR subset of CG, is reported in Chapter 5.

Chapter 4

DEA: Theory, Methods and Applications

Whenever you can, count.

Sir Francis Galton 1822-1911

4.1 Introduction

This chapter describes the mathematical foundations for performance expressed as an efficiency computation. The latter is based on the underlying relationship between inputs and outputs, and is often described as productivity. The chapter also shows an increasing use of the mathematical application known as DEA, that was developed initially as a measurement tool for application in organisations which do not have a profit-driven mission yet need to operate according to principles of business efficiency. The distinction of these organisations from the more conventional commercial ventures is not only the absence of profit-driven strategies but the explicit enunciation of other less tangible objectives, often conveyed in the corporate vision statement. The tenet of this thesis is that if performance can be sucessfully measured under the conditions of not-for- profit, how much better would such a measure be for those organisations that have the added luxury of financial and other tangible metrics.

The Total factor productivity considers all inputs and output’s and presents a ratio allows an investigation of a multitude of combinations of these factors, as viewed from vantages which are often bounded by external constraints. To understand these different perspectives it is necessary to know how the factors interact and what are the key determinants of particular outcomes. This can be done by extrapolating from a basic model and incrementally changing the parameters. For example, there is much value in simplifying the decision variables to two dimensions so that the various outcomes can be

While there are a number of strengths in mathematically based models such as DEA, there are also some weaknesses. It is necessary to address these and other issues which may compromise the model, before it can be applied in a non-tangible arena such as CG (in particular the aspect of CSR), which is significant in its tangible outcome of economic performance. This is done later in the chapter after a preliminary explanation of how DEA works.