Measures of farm business efficiency.
ABSTRACT Purpose – The aim of this paper is to investigate technical, scale, allocative and economic efficiencies by data envelopment analysis (DEA) and stochastic frontier methods to provide a decision-making tool and managerial implications in the measurement of farm business performance and efficiency. Design/methodology/approach – Technical, scale, allocative and economic efficiencies are analyzed with the Farm Accountancy Data Network (FADN) sample for 13 farm business branches in Slovenia in the period 1994-2003. DEA models are used with an output-orientation, three outputs and four inputs. The non-parametric DEA estimations are compared with a parametric stochastic frontier approach. The cluster analysis is used to identify three different farm business groups according to their performance. Findings – The average technical, scale, allocative and economic efficiencies for the whole FADN sample over the analyzed period are relatively high (around or over 0.90), suggesting that, although the FADN sample contains very different farms, the latter have similar management practices, and are similarly able to make the best use of the existing technology. Five farm branches (crop, dairy, livestock using own feed, fruit, and forestry) are fully efficient with respect to all four analyzed efficiency measures, suggesting that these specializations have the best chance to compete on the European and world markets. Originality/value – Studies of technical, scale, allocative and economic efficiencies are rare for transitional farm businesses, especially in Slovenia. The research contributes to the crucial issue of whether small family farm businesses might be able to compete on international markets, as Slovenian agriculture is characterized by such structures.
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ABSTRACT: Management performance measurement is a complex task since multiple inputs and multiple outputs are involved in the process. This study attempted to develop an integrated framework to encompass the basic concepts of balance scorecards (BSC) and data envelope analysis (DEA) for measuring management performance. BSC and DEA are complementary to each other. On the one hand, BSC can provide appropriate outputs of performance for DEA. On the other hand, DEA can set benchmarking for companies based on their inputs and outputs, as well as transform performance measures into managerial information. Accordingly, the synergy of BSC and DEA can translate the appropriate performance indices into managerial implications. This study selected auto and commercial bank industries as the targets for empirical investigation. The results indicated that the interrelationships among four perspectives of BSC were empirically valid. However, the most crucial indicators in each perspective were distinct in different industries. About 46% of auto companies and 57% of commercial banks are located at efficiency frontiers. Managerial implications and research limitations are addressed as well.Total Quality Management and Business Excellence 01/2009; 20(11):1153-1172. · 0.59 Impact Factor
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ABSTRACT: Data Envelopment Analysis (DEA) is a powerful data analytic tool that is widely used by researchers and practitioners alike to assess relative performance of Decision Making Units (DMU). Commonly, the difference in the scores of relative performance of DMUs in the sample is considered to reflect their differences in the efficiency of conversion of inputs into outputs. In the presence of scale heterogeneity, however, the source of the difference in scores becomes less clear, for it is also possible that the difference in scores is caused by heterogeneity of the levels of inputs and outputs of DMUs in the sample. By augmenting DEA with Cluster Analysis (CA) and Neural Networks (NN), we propose a five-step methodology allowing an investigator to determine whether the difference in the scores of scale heterogeneous DMUs is due to the heterogeneity of the levels of inputs and outputs, or whether it is caused by their efficiency of conversion of inputs into outputs. An illustrative example demonstrates the application of the proposed methodology in action.European Journal of Operational Research 01/2010; 206(2):479-487. · 2.04 Impact Factor
- 12/2011, Degree: PhD Thesis, Supervisor: Bo Öhlmer, Helena Hansson and Dragi Dimitrievski