Dr. Christoph Hienerth (Vienna University of Economics and BA)
Alexander Kessler (Vienna University of Economics and BA)

Vienna University of Economics and BA

Measurement problems of success in small businesses are primarily caused by a lack of data and by subjective bias. Common financial ratios are neither standard nor stipulated legally. As a consequence, the measurement process is dominated by estimations, partly by business owners or founders, partly by employees or also business partners or stakeholders.

The main purpose of this study is to compare different models of success estimation to explore, whether a combined and aggregated estimate (configurational fit of entrepreneur, resources, management and environment) can be used to overcome subjective bias.

graphical and numerical methods

In a recent survey of 97 small and primarily family owned businesses in the eastern Austrian border region, different indicators for success were evaluated. Indicators comprise rather broad and general aspects in success estimation as well as detailed and precise items concerning individual imperatives (parts) of the businesses. Additionally five growth indicators for past and estimated future development are employed.

Analyzing the data, graphical and numerical methods are used to show characteristics of the distribution. While some of the singular indicators show an asymmetric distribution (positive bias) the aggregated indicator using the concept of configurational fit seems to follow a normal curve and can be traced by a linear percentile function.
As a result, outcomes will show strengths and weaknesses of the indicators used and emphasize the concept of configurational fit due to the fact that it is able to overcome difficulties involved in the success measurement process of small businesses. Exemplary strengths are the feasible consideration of small size, the inclusion of different industries, the ability to consider the individual configurations of businesses, the possible extension of the model for customization and reusability for longitudinal studies.