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Business Sample Survey Measurement on Statistical Thinking and Methods Adoption: The Case of Croatian Small Enterprises

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  • Berislav Zmuk

    (Department of Statistics, Faculty of Economics and Business - Zagreb, University of Zagreb)

Abstract

The objective of this research is to investigate attitudes of management in Croatian small enterprises that use statistical methods towards statistical thinking in order to gain an insight into related issues. The research was conducted in 2013 using a web survey with a random sample of 631 Croatian small enterprises, but this paper focuses only on those enterprises that use statistical methods. In order to get detailed information, a complex stratified sample survey design was used. In the analysis, chi-square tests of independence were used. In the statistical tests of proportion, the nonresponse adjustment factors as weights and weighted proportions were used. It has been shown that the vast majority of Croatian small enterprises (65,93 %) do not even use statistical methods in their business. On the other hand, the enterprises which use statistical methods have recognized the value and capabilities of statistical methods use. The research has shown that the vast majority of enterprises do not use statistical methods due to administrative reasons. In spite of using statistical methods as a supporting tool in the decision-making process in very important and key business cases, Croatian small enterprises admitted the lack of statistical methods use in their business. Also, investments into the statistical methods use are very scarce. This has led to employees' low statistical methods use knowledge level. The statistical methods use led to better business results in more than 90 % of small enterprises. It has been shown that statistical methods use effects on business results have on average a 6-12 months lag. This research leads to the conclusion that more efforts should be put into development of statistical thinking in these enterprises and familiarizing them with statistical methods use, with the aim of increasing their use and improving business results.

Suggested Citation

  • Berislav Zmuk, 2015. "Business Sample Survey Measurement on Statistical Thinking and Methods Adoption: The Case of Croatian Small Enterprises," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 13(1), pages 154-166.
    • Handle: RePEc:zna:indecs:v:13:y:2015:i:1:p:154-166
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    References listed on IDEAS

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    1. Nada R. Sanders & Karl B. Manrodt, 1994. "Forecasting Practices in US Corporations: Survey Results," Interfaces, INFORMS, vol. 24(2), pages 92-100, April.
    2. Ronald D. Snee, 1999. "Discussion: Development and Use of Statistical Thinking: A New Era," International Statistical Review, International Statistical Institute, vol. 67(3), pages 255-258, December.
    3. S. B. Dransfield & N. I. Fisher & N. J. Vogel, 1999. "Using Statistics and Statistical Thinking to Improve Organisational Performance," International Statistical Review, International Statistical Institute, vol. 67(2), pages 99-122, August.
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    More about this item

    Keywords

    statistical thinking; business survey; complex sample survey design; weighted stratified proportion estimator; chi-square tests of independence;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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