How to compare performances of firms operating in different sectors?

Abstract

This paper seeks to address the question ‘How to measure different SMEs’ performances comparatively?’ An initial review reveals that the literature does not provide objective and explicit debate on the subject. Consequently, an approach is developed, informed by the literature, which is used to compare the performances of 37 SMEs. The consistency and reliability of the approach is tested, resulting in a ranking of the 37 firms according to their performances. Using cluster and factor analysis the paper demonstrates that leading indicators are somewhat redundant, and that lagging indicators have greater significance for the purpose of comparative measurement of different SMEs performances. Whilst the approach adopted here withstood internal and external validity tests and can be seen as a robust way of comparing SMEs performances, these results may be limited to this study.

Keywords:

• benchmarking
• comparisons
• measurement
• performance
• SMEs

Acknowledgements

The authors would like to thank the Engineering and Physical Sciences Research Council (EPSRC) of the UK for funding and facilitating the research that underpinned the work reported on this paper.

Notes

Notes

1. The background to some of these measures and the academic debate on this area is further discussed in the background literature section of this paper.

2. For example, at a given time, one sector may be growing rapidly whilst other shrinking. Thus, comparing performances of firms operating in these two sectors would be meaningless unless we account for sectoral differences.

3. Independent companies employing less the 250 people and with turnover not exceeding €50 m or with a balance sheet total not exceeding €43 m.

4. According to Kirby (2005) and Richard et al. (2009) in performance studies, a 10-year timeframe is the minimum appropriate timeframe to overcome random variation.

5. FAME is a database that contains information for companies in the UK and Ireland. For FAME and similar databases covering other regions visit http://www.bvdinfo.com/Products/Company-Information/National.aspx

6. Varimax is the most commonly used of all the rotation techniques available and is applied to further differentiate the level of importance between principal components (Oktay-Firat and Demirhan 2001, Johnson and Wichern, 2002, p.505).

7. The analysis detailed in Appendix 1 identifies two groups (or components) of indicators that explain overall performance. The dominant group, Component 1, comprising mainly lagging indicators explain majority of the variation in performance (50.04%) when compared to component 2 (27.92%) comprising of mainly of leading indicators. This conclusion was arrived as a result of interpretation of this data using literature that was backed up with other analysis as explained and justified in full detail in Appendix 1.