Materials: sample characteristics

All SMEs in the sample shared four features: (1) size: SMEs, i.e. they employed fewer than 250 persons; (2) location: peripheral, i.e. located in Eastern Finland; (3) performance: evaluated as the most successful SMEs in the industry sectors in the region; and (4) ownership: independent firms, not subsidiaries of other companies. The original sample was 270 SMEs, of which 145 responded, giving an overall response rate of 53.7%. The representativeness of the sample could be assessed by three measures: industry sector, location, and being one of the firms in the group of “top firms” (Kera Ltd. 1996) in the region. These measures show that there were no differences between firms that responded and those that did not.

The response rate varied by industry sectors (see Table 4.1): in manufacturing, it was 54.5% (n=121), and in the service sector, i.e. business services and tourism, it was 50.0% (n=24). Some of those who did not respond reported that they had no time to reply and so returned an empty questionnaire form. The sample can be considered to represent successful SMEs in the selected industry sectors in their location.

Also, there were no differences in the distribution of SMEs that responded and those who did not by location. The response rates were between 56% and 58% in the regions of Inner Savo, Kuopio, and Upper Savo. The response rates were slightly lower in the Varkaus region (45%) and North-Eastern Savo (29%). In the latter region the relative impact of one response was emphasized due to the smaller total number of SMEs selected. Moreover, the lower response rates in North-Eastern Savo and the Varkaus region can be explained to some degree by the differences in sectoral activity in responding, i.e. SMEs in the sector of tourism (see Table 4.1) in those regions were somewhat reluctant to participate in the research.

Table 4.1 Number of respondents and response rates by industry sectors

Industry sector No. of respondents Response rate

Food industry 12 48%

Textile, clothing, leather, and shoe industry 12 52%

Mechanical woodworking industry 23 56%

Printing industry 11 50%

Chemical industry 3 30%

Building material industry 8 53%

Metal industry 26 63%

Machinery industry 7 50%

Electro-technical industry 19 61%

Business services 18 67%

Tourism 6 29%

Total/Average 145 54%

Moreover, there were no differences in the response activity of SMEs that were classified as “top firms” in the region (Kera Ltd. 1996) by their industry sectors. The response rate of the “top firms” in the region was 58.1%, which means that the material can be regarded as representative of the “top firms” as well.

The success of SMEs in the sample can also be evaluated by five performance measures: (1) firm age; (2) growth in terms of turnover; (3) the entrepreneur’s self-evaluation of firm success; (4) the entrepreneur’s satisfaction with firm success; and (5) the firm’s competitive power in the market of the main products. The average age of the SMEs was 20 years. Four out of five of the SMEs have grown during the last decade in terms of turnover. Here growth was interpreted as a linear trend of turnover between two points of time, ignoring any decline in turnover during, for example, the economic recession. The firm’s turnover at the beginning of the 1990s was compared with its turnover at the end of the 1990s. However, growth is obviously not an applicable performance measure for firms which do not have growth as an aim. On the other hand, it seems that firms aiming at growth have succeeded if measured by growth of their turnover, and especially if we take into consideration the economic cycles in the Finnish economy and the fact that economic recovery clearly took more time in Eastern Finland than in Southern Finland, for example.

Consequently, entrepreneurs’ subjective evaluations of the firm’s business success during recent years were elicited (cf. O’Neill et al. 1987; Jennings & Beaver 1995: 190). This made it possible to overcome the problem of incommensurability of goals and objectives. Almost two thirds of the respondents (61%) thought that their firm has succeeded better than their most important competitors. Only one out of ten thought that the firm’s success has been weaker than that of their most important competitors.

They were also asked how satisfied they were with their firm’s success, and more than four out of five (82%) reported that they were satisfied with this. Moreover, a firm’s market share can be seen to be related to firm success, since the bigger the market share, the more competitive power and influence a firm has in the market. Five out of six respondents (85%) considered that their firm had at least quite good competitive power in the market of the firm’s main products. Thus, the common problem of SMEs – the transfer of costs forward in the supply chain – which is based on the weaker power of SMEs in the market compared with large companies, seems not to be as significant a problem for the SMEs studied as it may be for other firms.

In the light of this evi dence it can be argued that the SMEs in this sample are more successful than SMEs in general, i.e. those chosen by random sampling. It seems that these performance measures measure partly different aspects of performance because of their moderate correlations (see Table 4.2) (cf. McMahon 2001). Moreover, it should be noted that an SME can be seen as successful when measured by one performance measure and unsuccessful when measured by another.

Table 4.2 Correlations of performance measures^a

Measures 1 2 3 4

1 Firm age

2 Growth in turnover -.16

3 Business success compared with competitors .08 .42**

4 Entrepreneur’s satisfaction with business success .16 .47** .46**

5 Competitive power in the market of the main products .19* -.02 .30** .21*

a. rs, **=p<.01, *=p<.05

Overall, it seems that the survey material represents successful SMEs in selected industry sectors in the area. The loss of firms in the study seems to be random.

Moreover, by using subjective performance measures it was possible to overcome the problems related to the use of absolute scores, which may be affected by industry-related factors. This was important because of the intersectoral nature of the data collected.