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Forecasting in Austrian companies

Author

Listed:
  • Peter Hofer
  • Christoph Eisl
  • Albert Mayr

Abstract

Purpose - – The purpose of this paper is a comparison of forecasting behaviour of small and large Austrian firms, analysing their forecast practices in a volatile business environment. Design/methodology/approach - – The empirical analysis of the paper, deductive by nature, was conducted by means of a quantitative online-survey (199 data sets). The relationship of perceived volatility and forecast predictability was evaluated by correlation analysis.t-Test and analysis of variances were used to examine significant differences in the forecast characteristics between small and large Austrian companies and different industries. Findings - – The study provides evidence that the surveyed companies have been hit by volatility, showing that Austrian SMEs are significantly more severely affected than large companies. The increasing volatility correlates with a reduced forecast predictability of sales quantities and commodity prices. Large Austrian companies primarily use a broad spectrum of qualitative forecasting methods. In contrast, Austrian SMEs utilize simple quantitative and qualitative forecast techniques, like the forward projection of historical data. Research limitations/implications - – Relevant for the forecasting of small and large companies. Practical implications - – Although management requests a broad spectrum of forecast qualities, the current usage of less sophisticated methods reveals a gap between intention and reality. Companies that supplement their qualitative techniques by sophisticated quantitative ones should expect less forecast bias. Originality/value - – This paper initially compares forecast methods in large and small Austrian firms and additionally provides the impact of volatility on the forecast predictability.

Suggested Citation

  • Peter Hofer & Christoph Eisl & Albert Mayr, 2015. "Forecasting in Austrian companies," Journal of Applied Accounting Research, Emerald Group Publishing Limited, vol. 16(3), pages 359-382, November.
  • Handle: RePEc:eme:jaarpp:v:16:y:2015:i:3:p:359-382
    DOI: 10.1108/JAAR-10-2014-0113
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    Citations

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    Cited by:

    1. Mareike Bergmann & Christian Brück & Thorsten Knauer & Anja Schwering, 2020. "Digitization of the budgeting process: determinants of the use of business analytics and its effect on satisfaction with the budgeting process," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(1), pages 25-54, April.
    2. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    3. Jiří Šindelář, 2019. "Sales forecasting in financial distribution: a comparison of quantitative forecasting methods," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 24(3), pages 69-80, December.

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