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COOPER Framework

Emrouznejad, A. and K. De Witte (2010). “COOPER-framework: A unified process for non-parametric projects.” European Journal of Operational Research207(3): 1573-1586. Download (Scopus) Download (DOI) Download

A Unified Process for Non-parametric Projects

Practitioners assess performance of entities in increasingly large and complicated datasets. If non-parametric models, such as Data Envelopment Analysis, were ever considered as simple push-button technologies, this is impossible when many variables are available or when data have to be compiled from several sources. This paper introduces by the ‘COOPER-framework’ a comprehensive model for carrying …

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Efficiency analysis has never been a simple push-bottom technology. Within a performance assessment, various interactions can intricate the analysis. Indeed, changing the modelling technique, or the input or output variables might result in significantly different efficiency scores. Therefore, a systematic check list with the several phases which are required to assess performance would make efficiency …

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What is COOPER-framework?

In large and complicated datasets, a standard process could facilitate performance assessment and help to (1) translate the aim of the performance measurement to a series of small tasks, (2) select homogeneous DMUs and suggest an appropriate input/output selection, (3) detect a suitable model, (4) provide means for evaluating the effectiveness of the results, and …

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Figure 2. Systematic presentation of the COOPER-framework

Systematic presentation of the COOPER-framework

The first two phases of the COOPER-framework, i.e., the ‘concepts and objectives’ and ‘On structuring data’, correspond to defining the problem and understanding how decision making units operate. The last two phases, i.e., the ‘evaluation’, and ‘results and deployment’ correspond to summarisation of the results and documentation of the project for non-DEA experts. In between, …

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Figure 3. Concepts and objectives phase

Concepts and objectives phase

A very large DEA project generally involves the expertise from numerous individuals. The concepts and objectives phase requires communication skills to work closely together with the evaluated entities. These are often (but not necessarily) the organisations which are interested in the DEA results. Naturally, the undertaking of a collaborative DEA project increases the complexity of …

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Figure 4: On structuring data phase

On structuring data

Having settled some preliminary questions in the first phase, in a second phase the researcher can start the analysis with the initial data collection. Especially in large datasets, it is worthwhile spending sufficient time with this phase (summarized in Figure 4). Various variables are potentially available and differences between them are sometimes subtle. In order …

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Figure 5: Operational models phase

Operational models

Dependant on (a) available data, (b) the quality of the data (e.g., noisy) and (c) the type of the data (e.g., negative values, discrete variables, desirable/undesirable values etc.), specific classes of models are available. Two main categories can be distinguished. As in Figure 5 the first class consists of parametric models (see, e.g., Greene, 2008). …

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Figure 6. Performance comparison phase

Performance comparison

Once a satisfactory dataset is collected, the analysis is performed in the performance comparison phase (for a summary, see Figure 6). These analyses allow researchers to obtain additional insights and to define a proper model and, finally, to run the model. The selection of the DMUs is an intrinsic and important step in a non-parametric …

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Figure 7. Evaluation Phase


Running a non-parametric model does not suffice for a meaningful analysis. In a fifth phase, the model and its results should be carefully reviewed according to the core objective of the study (systematically presented in Figure 7). The whole process (i.e., the preceding four phases) is reviewed and a list of possible actions is elaborated. …

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Figure 8. Result and deployment phase

Result and deployment

In the final phase, the result and deployment phase, the proposed models are put into action (Figure 8). The entire process is summarized in a report (which refers to all previous deliverables). The report should clearly interpret the results and compare the final results under different model specifications. Indeed, presenting different model specifications will allow …

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This paper provides a framework to deal with large data samples which are difficult to oversee. When different stakeholders have different objectives, when different data sources could differ in quality, when model techniques could result in different outcomes, a uniform approach to assess performance is advised. A standardized model will make non-parametric assessments more reliable, …

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Emrouznejad, A. and K. De Witte (2010). “COOPER-framework: A unified process for non-parametric projects.” European Journal of Operational Research207(3): 1573-1586. Download (Scopus) Download (DOI) Download  

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