TY - JOUR
T1 - Forecasting in Austrian companies: Do small and large Austrian companies differ in their forecasting processes?
AU - Hofer, Peter
AU - Eisl, Christoph
AU - Mayr, Albert
PY - 2015/11/9
Y1 - 2015/11/9
N2 - 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.
AB - 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.
KW - Finance
KW - International accounting
KW - Management accounting
KW - Risk management
UR - http://www.scopus.com/inward/record.url?scp=84945579321&partnerID=8YFLogxK
U2 - 10.1108/JAAR-10-2014-0113
DO - 10.1108/JAAR-10-2014-0113
M3 - Article
SN - 0967-5426
VL - 16
SP - 359
EP - 382
JO - Journal of Applied Accounting Research
JF - Journal of Applied Accounting Research
IS - 3
ER -