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Descriptors of Modular Formation of Accounting and Analytical Cluster in Innovation Development of Agricultural Holdings

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  • Zhanna Degaltseva

    (Kuban State Agrarian University, Russia)

Abstract

In the context of the division of accounting into financial accounting, taxation accounting, management accounting and statistical accounting a problem of improving their relationship arises. The accounting and analytical cluster plays the role of the correlating factor of the relationship between the subdivisions of the agricultural holding property. To improve its work a modular principle of its building based on information technology was introduced. Practical implementation of modular accounting and analytical cluster revealed its shortcomings. They were as follows: each type of account and each production unit used its own natural and cost parameters. The number and nature of these parameters were different. To eliminate the shortcomings in the information security of managers and specialists of the agricultural holding, we attempt to develop a methodology for establishing a rational number of descriptors (binding parameters) for each module. The proposed descriptors are designed on the basis of validity of the methodological approach to their calculations and legal support. The proposed method allowed to limit the asymmetric information in all kinds of records, to improve its quality and to bring a synergistic effect from the scale and structure of the use of the agricultural holdings property complex.

Suggested Citation

  • Zhanna Degaltseva, 2015. "Descriptors of Modular Formation of Accounting and Analytical Cluster in Innovation Development of Agricultural Holdings," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 3(4), pages 56-64.
  • Handle: RePEc:ejn:ejefjr:v:3:y:2015:i:4:p:56-64
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    References listed on IDEAS

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    1. David Epshtein & Konstantin Hahlbrock & Jürgen Wandel, 2013. "Why are agroholdings so pervasive in Russia's Belgorod oblast '? Evidence from case studies and farm-level data," Post-Communist Economies, Taylor & Francis Journals, vol. 25(1), pages 59-81, March.
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