THE STATE AND TRENDS OF TECHNOLOGY
Overview of Enterprise Development in Slovenia
These days technology entrepreneurship represents a small yet crucial market niche of the small business sector. Slovenia has emerged as one of the most developed countries of Central Europe, partly as a result of its previous relatively liberal economy under self-management. The import- ance of entrepreneurship for growth, new job creation, innovation and general prosperity was then widely accepted, particularly in intellectual circles.
Small- and medium-sized companies (SMEs) now outnumber large enterprises to a great extent (99.7 per cent of SMEs compared to 0.3 per cent of large enterprises) (see Table 3.1). This is similar to the size structure of all 19 European member states (Europe-19) enterprises (99.8 per cent of SMEs). However, differences exist when comparing job shares according to the size of enterprises. Whereas in Slovenia the biggest employers are large enterprises, in Europe most employees (39.4 per cent) work in micro enterprises. This is mainly due to the greater share of large enterprises among all enterprises in Slovenia in comparison to Europe.
Nevertheless, SMEs together can still be described as more important employers compared to large enterprises regarding shares of employ- ment. Next, according to average sales per enterprise SMEs in Slovenia achieved far better results compared to Europe-19 than large Slovenian enterprises. The same is obvious when looking at average value added per employee, where Slovenian large enterprises lag far behind the 19 European countries included in our dataset (for more details, see Table 3.1).
Innovation and Technology Entrepreneurship in Slovenia
We begin our analysis of the role and development of technology entre- preneurship by examining data on the innovative activities of Slovenian firms in the sample compiled by the Statistical Office of the Republic of Slovenia described above. The data on inputs and results of innovation activities are shown in Table 3.2.2
Compared to the EU, the share of innovative firms is relatively low (21.1 per cent in 2002; see Table 3.2); during 1998–2000 the propensity to innov- ate in the EU-15 was 44 per cent.3In addition, the share of innovative firms in Slovenian manufacturing has not improved since 1994–96, the period for which the first round of the innovation activities survey was carried out (up until 2000, only manufacturing firms were included in these surveys).
The average innovation intensity of manufacturing enterprises, measured by innovation expenditures as a percentage of total sales, was 3.1 per cent
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Table 3.1Employment and added-value potential ofthe Slovenian business sector Size offirm (by number ofemployees) MicroSmallMediumSME LargeTotal Variables0–910–4950–249total250 Number offirmsSlovenia85,2404,7081,10791,05529791,352 Europe-19 (in ’000)17,8241,26118519,2704019,310 Share offirms (%)Slovenia93.35.21.299.70.3 Europe-1992.36.51.099.80.2 Number ofemployeesSlovenia142,92593,705116,309352,939203,136556,075 Europe-19 (in ’000)55,03824,27518,10597,41842,297139,715 Share ofemployees Slovenia25.716.920.963.536.5 in individual size classEurope-19 39.417.413.069.730.3 Average sales per Slovenia1252,09310,33335169,603 firm in 1000 €Europe-19 4403,61025,680890319,0201,550 Average value added perSlovenia17,18624,35123,65321,21928,67323,942 employee in 1000 €Europe-19 40,00060,00090,00055,000120,00075,000 Labour costs (as % Slovenia5462666161 ofadded value)Europe-19 5757555647 Source:Zˇakelj (2004,p.42).
in 2002 which reveals a negative trend compared to previous surveys.
A comparison with average innovation intensities for EU countries’ manu- facturing sectors shows that Slovenian firms cannot be said to be falling behind when it comes to innovation inputs.4
The results of product innovation (measured as a share of sales;
Table 3.2) are less favourable. Firms that reported introducing innovative products new to the firm but not new to the market in the period from 1999 to 2000 on average generated 4.9 per cent of their total sales with those products. The share of sales taking into account innovations which were also new to the market reached 5.3 per cent on average. Benchmarking against the average EU results shows that differences in the share of sales due to products new to the market are not that large (with the EU average being 5.9 per cent) but we have to take into account that only 11 per cent of Slovenian firms introduce ‘new to the market’ innovative products, (Kotnik, 2004) and that firms’ interpretations of ‘new to market’ might differ according to the markets they sell in (Thuriaux and Couchot, 2000).
When it comes to sales of ‘new to the firm’ innovative products, the share for Slovenian enterprises is much lower than for EU enterprises (4.9 per cent as compared to 17.1 per cent of sales). Results of consecutive innova- tive activities surveys also show that the share of innovative sales in manu- facturing has dropped since 1996 and that the number offirms with a larger share of innovative sales is decreasing (Kotnik, 2004). We can conclude that a relatively small share of firms introduce innovations. Of those manufac- turing firms that innovate, the intensity of their innovation inputs is Table 3.2 Inputs and outputs of innovation activities of Slovenian firms
Slovenia, Slovenia, EU-15,
2000 2002 2000
Share offirms with innovation 21.7 21.1 44.0
activities* (%)
Innovation expenditures (as % of 3.4 3.1 3.5
all turnover), manufacturing (%)
Share of sales of ‘new to the firm 4.9 N/A 17.1
but not new to the market’
products (%)
Share of sales of ‘new to market’ 5.3 N/A 5.9
products (%)
Note: * The data on the share offirms with innovation activity refer to 1998–2000.
Sources: European Innovation Scoreboard (2003 and 2004); Innovation in Europe (2004);
Rapid Reports (Research & Development, Science & Technology) (2004).
comparable to that of the average EU firm. However, the data indicate that they are falling behind in creating innovative output on the basis of these expenditures.
Firms’ Cooperation in the Innovation Process
Regarding knowledge transfer issues, two factors that influence the innova- tive behaviour of enterprises are of special interest: cooperation in innov- ation and the exploitation of technological opportunities. As we have shown above this is undertaken through activities such as licensing, publi- cations, meetings and cooperative research and development agreements (see Kirwan et al., ch. 12 in this book).
Cooperation might entail innovating with other firms or research insti- tutions, whereas technological opportunities refer to the knowledge stock outside of the boundaries of the firm which increases with scientific dis- coveries and can thus contribute to the knowledge stock of the firm itself.
Their effect on innovative inputs and outputs was estimated with a regres- sion model of the determinants of innovation intensity using a group of explanatory variables X1, whereas X2 was used as a group of variables explaining innovation output (measured as the share of sales due to innov- ative products):
X1{SIZE,DFINANCE,DCOOP,SCIENCE,DD–PULL, EX,DGROUP,DI
1, ...,DI
11},
X2{INN_INT, SIZE,|DPERMANENT, DCOOP,SCIENCE, DD–PULL, EX,DGROUP,H,DI
1,...,DI
11}.
SIZE represents the size of a firm; DFINANCE is a dummy variable for financial constraints (referring to the lack offinancial resources for innov- ation activities), DCOOP is a dummy for cooperation in innovation, SCIENCE stands for technological opportunities, DD–PULL for the demand–pull effect (referring to the inducement to innovate by market demand), INN_INT for innovation intensity and H for human capital (referring to the skills and qualifications of the employees). Other variables control for additional characteristics of the firm that might affect innova- tion activities:EXrepresents export intensity, DGROUP is a dummy for a firm being part of a group offirms,DI
1,. . ., DI
11are dummies for indus- try, and DPERMANENT is a dummy for firms with R&D activities being organized as permanent activities. Cooperation in innovation was mea- sured as a dummy variable with a value of 1 if a firm innovated in cooper- ation with other firms or research institutions.
To create a variable for technological opportunities principal compon- ents analysis was used on data showing the importance of various sources of information for innovation. One of the factors combined universities and research institutes as important sources of information so this was used as a proxy for technological opportunities. The equations were esti- mated with ordinary least squares (OLS), using a robust variance estimate in the case of the innovation input equation. Cross-section data for 2000 were used, with the sample including 344 firms for the first equation and 235 for the second. The results of this econometric model are reported in Table 3.3.
Our results do not support the premise that cooperation in innovation encourages a firm’s own innovation expenditure. Additional analysis with Table 3.3 Innovation input and output equation estimates, 2000
Innovation Innovation intensity output
Innovation intensity 0.1277
(1.99)*
No. of employees 0.1435 0.1568
(2.32)* (1.96)*
Human capital † 2.5863
(2.05)*
Technological opportunities 0.1428 0.07930
(1.98)* (1.04)
Dummy for demand–pull 0.2542 0.3887
(1.69) (2.38)*
Dummy for cooperation in product innovations 0.2072 0.3172 (1.23) (1.69) Dummy for cooperation in process innovations 0.2574 0.0651
(1.66) (0.35)
Export intensity 1.28e–07 6.11e–07
(0.47) (1.54) Dummy for financial constraints 0.2463
(1.69)
Other controlling variables ‡
Controls for industry
Notes: * Significant at 0.05. † Since the values of the variable are expressed as (1-share of employees with a higher education), a negative coefficient indicates a positive relationship between explanatory and dependent variables.‡Other controlling variables: a firm being part of a group offirms; a firm having its own R&D department.
Sources: Calculations based on Statistical Office data; Kotnik (2004).
panel data found a statistically significant impact of cooperation on innov- ation intensity but it turned out to be negative, which implies a substitu- tion effect. This corresponds to the fact that, within this kind of cooperation, the one with customers is most common for Slovenian firms.
It might also indicate that the absorptive capacity of firms is weak.
Veugelers (1997) concluded that cooperation in R&D increases a firm’s own R&D expenditure only when the firm’s absorptive capacity is sufficient. The effect of cooperation in innovation on innovation output also could not be confirmed by the results, which raises the question of the effectiveness of this kind of cooperation. Technological opportunities were confirmed as a statistically significant determinant of innovation intensity. Whereas larger technological opportunities encourage the innov- ation expenditures of the firms, the same cannot be confirmed for their effect on innovation output. Evidently, a larger stock of knowledge outside the firm affects the innovation activities of the firm indirectly, through larger innovation expenditures, but without these the firm’s knowledge stock does not increase. This might also be a sign of insufficient absorptive capacity.
The innovation behaviour of SMEs reflects their size.5As evident from Figure 3.1, larger firms are more innovative (55 per cent of all large firms were innovative in 2001–02, compared to 28 per cent of medium ones and only 13 per cent of small ones). The empirical analysis of the innovative activities of manufacturing firms confirms the role of size in determining the innovative status of a firm, even when controlling for other firm char- acteristics. The probability that a firm will invest in innovative activities is greater the larger the number of employees, the larger the export propen- sity of the firm and the smaller the financial constraints the firm faces.
These results are consistent with conclusions from the literature that uncer- tainties surrounding innovation activities are smaller for larger firms (Symeonidis, 1996); together with the greater availability of financial resources, this increases the propensity to innovate with the size of a firm.
However, in the EU these differences are smaller, while in Slovenia the data show a lack of R&D activities in the small business sector (Vidrih, 2002:
58). One can find an explanation in the (non-)existent infrastructure aimed at supporting the development of new technology enterprises and the scarce financial resources available to smaller enterprises since smaller com- panies still largely rely on bank financing whereas risk capital and business angels represent only a negligible share (Zˇakelj, 2004: 18–19). Further, political and social pressures have slowed down new venture creations and the government has also discontinued tax facilities and other advantages for small entrepreneurs that resulted in the stalling of knowledge-based small firm development (Glas and Drnovsˇek, 2003).
However, once a firm decides to invest in innovative activities the effect of its size varies. As shown in Figure 3.2, innovation and R&D intensity fall with the size of a firm. SMEs are less likely to invest in innovative activities but, once they do decide to invest, the innovation (and R&D) expenditures represent a larger share of their sales. This agrees with part of the empirical literature on the impact of size on innovation intensity, a possible explanation being that the sales of smaller firms only starting to develop or market innovations are relatively low compared to the cost of innovating (Freeman and Soete, 1997). Other explanations include the arguments that larger firms are less flexible, more bureaucratic and have less effective internal communication within departments which all decreases the incentives to innovate (Symeonidis, 1996). Compared to differences in innovation intensity, the differences in R&D intensities between firms of different sizes are not that large. A reason for this is the structure of innovation expenditures.
Figure 3.2 shows that the share of R&D in total innovation expenditures increases with size. SMEs devote a larger part of these expenditures to purchases of the machinery and equipment needed for innovations. Yet the relationships change when it comes to the effectiveness of innovation
Source: Rapid Reports (Research & Development, Science & Technology) (2004).
Figure 3.1 Innovating firms as a share of all firms by size group, Slovenia, 2001–2002
21.1%
Share of all firms (in %)
All enterprises
Small enterprises
Large enterprises Medium-sized
enterprises 12.7%
28.3%
55.4%
0%
10%
20%
30%
40%
50%
60%
expenditures in creating the output of innovation activities. Empirical analysis of innovation output controlling for other determinants (Figure 3.3) confirmed that the productivity of innovation activities increases with firm size. This result is consistent with a study that took into account not only the number of innovations but also their economic value (measured by the value of sales) and showed that their value is increasing with a firm’s size (Tether, 1998). Other possible explanations for the advan- tages of larger firms in creating innovation output are economies of scale in the production of innovations and the greater diversification of larger firms that offers a better position to exploit unforeseen innovations (Symeonidis, 1996).
The knowledge created by innovative activities should increase the eco- nomic performance of firms. The comparison of innovative and non- innovative firms based on descriptive statistics of the data on the innovative activities of manufacturing firms shows that they differ in the level of labour productivity and in the share of sales created by exports (Table 3.4). The sample data show that in 2000 the average labour productivity (measured by value added per employee) was €154,000 for innovative firms and €137,000 for non-innovative ones, with the difference being statistically significant.
Something similar holds for export propensity where innovative firms created around half of their sales through exports while non-innovative
Source: Calculations based on Statistical Office data; Kotnik (2004).
Figure 3.2 R&D and innovation intensity by firm size, 2000 0
1 2 3 4 5 6 7 8
Less than 49 50–249 250–499 No. of employees
As proportion of sales (%)
Innovation intensity R&D intensity
500–999 1000
and above
firms only had a third. But the same cannot be said for the growth of labour productivity, value added, employment and sales in this period, which raises questions about the effects of innovation efforts. The same question can be brought up when studying the country’s ability to commercialize the results of research and innovation in international markets, as reflected by exports of high-tech products (Figure 3.4). The comparison of countries studied in this book shows that Slovenia has the lowest concentration of high-tech products in its exports.
The question of the effective use of inputs in the innovation process is also raised by the results of an analysis of the impact of introduced inno- vations on firm performance (Kotnik, 2004). The analysis used information on firms’ current accounts and balance sheets, together with data on innov- ation activities to estimate the production function of manufacturing firms.
The production function was augmented by knowledge capital approxi- mated as the share of a firm’s innovative sales (that is, innovation output).
The results show that the positive effects of knowledge capital on produc- tivity can be confirmed only for medium- and high-tech industries. The esti- mated elasticity of value added with respect to knowledge capital for these industries was relatively low when compared to the results of similar studies for other countries. A positive effect could not be proved for
Source: Calculations based on Statistical Office data; Kotnik (2004).
Figure 3.3 Structure of innovation expenditures by firm size, 2000 0%
Large Medium
sized
Small 20%
40%
60%
80%
100% Preparation of
production Market introduc- tion of innovations Training
Intangible fixed assets
Machinery and equipment Extramural R&D expenditure Intramural R&D expenditure
low-tech industries. Slovenian firms thus seem to be falling behind in their ability to increase their productivity through innovation efforts.
Cross-border Cooperation in Research and Innovation
For Slovenia as a small open economy with an internal market of only 2 million people, the geographical dimension of the national innovation system holds special relevance. Firms and industries have strong ties with Table 3.4 Comparison of the economic performance of innovative and
non-innovative firms, 2000
Variables Mean t-value
Innovators Non-innovators
Level of productivity 3683 3296 2.78**
(in ’000 SIT, 1996100)
Average growth rate of productivity 6.23 5.81 0.19 Average growth rate of value added 13.01 11.50 0.62
Average growth rate of employment 6.80 7.10 0.19
Average growth rate of sales 12.47 12.44 0.01
Exports as a share of total sales (in %) 51.35 32.86 8.53**
Note: ** Significant at the 5 per cent level.
Sources: Calculations based on Statistical Office data; Kotnik (2004).
Source: Key Figures 2003–2004 (2003).
Figure 3.4 High-tech exports as a % of total exports, 2001 4.8
9.0 14.6
15.8 22.3
25.6
40.8
0 5 10 15 20 25 30 35 40 45
Slovenia Belgium Estonia Germany Netherlands France Ireland
foreign markets – exports represent more than 60 per cent of Slovenia’s GDP. The EU is the most important of these markets and this was the case even before Slovenia became a full EU member in May 2004. This mile- stone did not change the situation for most firms significantly since the Slovenian economy, especially its tradable sector, had already largely adapted to the demands of the common market in the pre-accession process with trade being largely liberalized in 1996 when the Europe Agreement was signed. On average, between 1996 and 2004 EU countries represented 69 per cent of Slovenia’s exports of goods and 76 per cent of its imports of goods (Bednasˇ, 2005). But there are some signs of a change in the regional structure of external trade following full membership. A comparison of foreign trade data for the first two months of 2005 with that for the first two months of 2004 shows that exports increased by 15.6 per cent to EU-15 countries and by 25.2 per cent to EFTA (European Free Trade Association) countries, whereas they only increased by 4.9 per cent to former Yugoslav countries and by 2 per cent to other countries (SURS, 2005). These changes can be accounted for by the lifting of customs for- malities with the EU, the improved recognition of Slovenia in some markets that were not traditional external trading partners before membership, and – to a smaller degree – the expiry of free-trade agreements with the countries of former Yugoslavia (Bednasˇ, 2005).
When it comes to the cross-border cooperation of firms in innovation, most of it is already focused on the EU. Figure 3.5 shows that around one- third of innovative manufacturing firms are involved in innovation coop- eration with partners from the EU (and EFTA) countries, whereas only 7 per cent of them cooperate with candidate countries and only 3 per cent with the last category of countries (the category ‘Other’ in the figure) that also includes ex-Yugoslav countries. Cross-border cooperation in innova- tion is on the whole less strong for firms in services industries, but the loca- tion of partners shows similar patterns with EU partners prevailing.
Given the established ties with EU markets and the trends of an even stronger reorientation of foreign trade towards these markets, strengthen- ing the ties with the European Innovation System should become a stronger priority of public policy.
Development of Support Infrastructure for Technology Entrepreneurship Because of the great innovation potential that SMEs demonstrate and the fact that they need to purchase expensive machinery and equipment if they want to innovate, most governments provide some kind of publicly financed support environment to technology development and innovation commer- cialization. The early beginnings of the development of entrepreneurial