Evaluation of the state of the Business Intelligence among small Czech farms

https://doi.org/10.17221/108/2014-AGRICECONCitation:Tyrychtr J., Ulman M., Vostrovský V. (2015): Evaluation of the state of the Business Intelligence among small Czech farms. Agric. Econ. – Czech, 61: 63-71.
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Business Intelligence (BI) can assist in agricultural enterprises to strengthen their production potential and technical efficiency due to its effective support to the managerial, analytical, planning, and decision-making activities of managers and specialists. However, the state of the BI in the Czech Republic is not still completely understood. In this context, this paper aims at the evaluation of the current state of the art of the BI among small Czech farms. The focus of the research was put on the evaluation of both the state of the BI, and the relevant business information systems and software for agriculture. There was a survey among 135 agricultural entrepreneurs from various regions in the Czech Republic. The survey results are presented by the descriptive statistics and frequency tables. There is an examination of the relationship between the agricultural enterprise structure and the use of the BI. Dependencies among the examined characteristics were sought by the means of the analysis of qualitative variables. With 95% probability, it could be claimed that the type of production, the size of farmed land, the number of employees and the level of financial subsidies have no significant impact on using the BI, the expert and analytical systems in agricultural enterprises. .
References:
Abdelhédi F., Zurfluh G. (2013): User Support System for Designing Decisional Database. In: Sixth International Conference on Advances in Computer-Human Interactions, Nice: 377–382.
 
Abelló A., Romero O. (2009): On-Line Analytical Processing (OLAP). In: Ozsu T., Ling Liu (eds): Encyclopedia of Database Systems: 1949–1954. Springer.
 
Ballard C. et al. (1998): Data modeling techniques for data warehousing. IBM Redbooks.
 
Boehnlein M., Ulbrich-vom Ende A. (1999): Deriving initial Data Warehouse Structures from the Conceptual Data Models of the Underlying Operational Information Systems. ACM, New York.
 
Bolboacă Sorana D., Jäntschi Lorentz, Sestraş Adriana F., Sestraş Radu E., Pamfil Doru C. (2011): Pearson-Fisher Chi-Square Statistic Revisited. Information, 2, 528-545  https://doi.org/10.3390/info2030528
 
Cabrera García S., Imbert Tamayo J.E., Carbonell-Olivares J., Pacheco Cabrera Y. (2013): Application of the Game Theory with Perfect Information to an agricultural company. Agricultural Economics – Czech, 59: 1–7.
 
Canal L., Micciolo R. (2012): The Chi-square controversy: what if Pearson had R? Journal of Statistical Computation and Simulation, 84: 1015–1021.
 
Čechura L. (2010): Estimation of Technical Efficiency in Czech agriculture with respect to firm heterogeneity. Agricultural Economics – Czech, 56: 183–191.
 
Čechura L., Taussigová T. (2013): Avian influenza and structural change in the Czech poultry industry. Agricultural Economics – Czech, 59: 38–47.
 
Chaudhuri Surajit, Dayal Umeshwar (1997): An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26, 65-74  https://doi.org/10.1145/248603.248616
 
Choudhary K., Pandey U., Nayak M.K., Mishra D.K. (2011): Electronic data interchange: A review. IEEE Conference Publication: 323–327; doi: 10.1109/CICSyN.2011.74
 
Datta Anindya, Thomas Helen (1999): The cube data model: a conceptual model and algebra for on-line analytical processing in data warehouses. Decision Support Systems, 27, 289-301  https://doi.org/10.1016/S0167-9236(99)00052-4
 
Gupta A.K., Mazumdar B.D. (2013): Computational model for agricultural decision support system. International Journal of Computer Applications, 66, (15).
 
Heinrich Lutz J, Riedl René (): Understanding the dominance and advocacy of the design-oriented research approach in the business informatics community: a history-based examination. Journal of Information Technology, 28, 34-49  https://doi.org/10.1057/jit.2013.1
 
Janová J. (2014): Crop plan optimization under risk on a farm level in the Czech Republic. Agricultural Economics – Czech, 60: 123–132.
 
Jarke M., Jeusfeld M.A., Quix C.J., Vassiliadis P., Vassiliou Y. (2013): Data warehouse architecture and quality: impact and open challenges. In: Seminal Contributions to Information Systems Engineering. Springer: 183–189.
 
Khan A. (2005): SAP and BW Data Warehousing: How to Plan and Implement. iUniverse, Inc.
 
Kroupová Z. (2010): Technická efektivnost ekologického zemědělství České republiky. (Technical efficiency of organic farming in the Czech Republic.) Ekonomická Revue, 2: 61–73.
 
Kubata K., Tyrychtr J., Ulman M., Vostrovský V. (2014): Business informatics and its role in agriculture in the Czech Republic. AGRIS on-Line Papers in Economics and Informatics, 6: 59–66.
 
Kumar V. (2010): Customer Relationship Management. Published Online, Wiley Online Library.
 
Levene M., Loizou G. (2003): Why is the Snowflake Schema a Good Data Warehouse Design? Information Systems, 28: 225–240.
 
Lips M., Schmid D., Jan P. (2013): Labour-use pattern on Swiss dairy farms. Agricultural Economics – Czech, 2013, 59, 149–159.
 
Lososová J., Zdeněk R. (2013): Development of farms according to the LFA classification. Agricultural Economics – Czech, 2013, 59: 563–577.
 
Maryska M., Novotny O. (2013): The reference model for managing business informatics economics based on the corporate performance management – proposal and implementation. Technology Analysis & Strategic Management, 25: 129–146.
 
McGuff F. (1998): Designing the Perfect Data Warehouse. Available at http://members.aol.com/fmcguff/dwmodel/index.htm (accessed 30.09.1999).
 
MZe (2013): Zpráva o stavu zemědělství ČR za rok 2012. (Report on the Czech Agriculture for 2012.) Available at http://eagri.cz/public/web/file/291876/Zprava_o_stavu_zemedelstvi_CR_za_rok_2012.pdf
 
Novotný O., Pour J., Slánský D. (2005): Business Intelligence: Jak využít bohatství ve vašich datech. Grada Publishing, Praha.
 
Pedersen T.B. (2009a): Multidimensional Modeling. In: Lui L., Ôzsu M.T. (eds): Encyclopedia of Database Systems: 1777–1784. Springer, Berlin/Heidelberg.
 
Pedersen T.B. (2009b): Cube. In: Lui L., Ôzsu M.T. (eds): Encyclopedia of Database Systems: 538–539. Springer, Berlin/Heidelberg.
 
Pedersen T.B. (2009c): Dimension. In: Lui L., Ôzsu M.T. (eds): Encyclopedia of Database Systems: 836–836. Springer, Berlin/Heidelberg.
 
Pour J., Maryška M., Novotný O. (2012): Business Intelligence v podnikové praxi. Professional Publishing, Praha.
 
Rai Anil, Dubey Vipin, Chaturvedi K.K., Malhotra P.K. (2008): Design and development of data mart for animal resources. Computers and Electronics in Agriculture, 64, 111-119  https://doi.org/10.1016/j.compag.2008.04.009
 
Schulze Christian, Spilke Joachim, Lehner Wolfgang (2007): Data modeling for Precision Dairy Farming within the competitive field of operational and analytical tasks. Computers and Electronics in Agriculture, 59, 39-55  https://doi.org/10.1016/j.compag.2007.05.001
 
Sheskin D.J. (2003): Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press.
 
Sørensen C.G., Fountas S., Nash E., Pesonen L., Bochtis D., Pedersen S.M., Basso B., Blackmore S.B. (2010): Conceptual model of a future farm management information system. Computers and Electronics in Agriculture, 72, 37-47  https://doi.org/10.1016/j.compag.2010.02.003
 
Vassiliadis Panos, Sellis Timos (1999): A survey of logical models for OLAP databases. ACM SIGMOD Record, 28, 64-69  https://doi.org/10.1145/344816.344869
 
Wu M.-C., Buchmann A.P. (1997): Research issues in data warehousing. In: Dittrich K.R., Geppert A. (eds): Datenbanksysteme in Büro, Technik und Wissenschaft: 61–82. Springer.
 
Zádová V. (2009): Multidimenzionální modelování v rámci analýzy a návrhu IS/ICT. (Multidimensional modelling in analysis and design of IS/ICT.) Systémová integrace, 4: 66–76.
 
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