Stuck in the middle?
Are medium‐sized firms performing differently in terms of innovation and
overall performance compared to small and large firms?
By
Rick Terpstra
University of Groningen
Faculty of Economics and Business
MSc BA Strategic Innovation Management
1 Introduction
Innovation is important for any firm as it plays an important role in shaping the survival of firms (Cefis & Marsili, 2006). A lot of research has been conducted to see what organizational factors influence a firm’s innovative activity. A frequently debated issue in the classic economic theory is the relationship between firm size and innovation. The traditional theory is economics‐oriented and examines both innovation patterns across countries and industries as well as the differences in the propensity of firms to innovate (Brown & Eisenhardt, 1995, 343; Dosi, 1988). This has been a topic of interest ever since Schumpeter (1934 & 1942) argued that innovation activity is promoted (1) by the presence of imperfect competition, and (2) by large rather than small firms. These fundamental tenets of the Schumpeterian hypothesis have been subjected to several empirical tests with inconclusive results. Some studies state that there is a positive relationship between size and innovation (Damanpour, 1992; Kimberley & Evanisko, 1981; Ettlie et al., 1984), few other studies actually found a negative relationship between size and innovation (Wade, 1996; Hage, 1980), while others claim that there is no relation between size and innovation (Aiken et al., 1980).
and a more bureaucratic environment (Hitt et al, 1990). While small and medium‐sized firms suffer from a lack of resources compared to larger firms, there are, however, inherent advantages to small and medium‐sized enterprises, including their flexibility as well as their ability to accept and implement changes faster and easier compared to large firms (Damanpour, 1996).
As innovation is important for the survival of firms, it also seems to be one of the key determinants of firm performance. The debate on innovation and firm performance is primarily focused on the relationship between innovative input (R&D intensity) and innovative output (the number of innovations), often controlling for firm size. While R&D intensity is said to increase proportionally with firm size (Scherer, 1992; Scherer and Ross, 1990), innovative output is said to be proportionally higher in small firms in relation to their size (Acs & Audretsch, 1991; Pavitt et al., 1987).
results concerning medium‐sized firms in recent research, more empirical research is necessary to understand the differences in performance between medium‐sized firms, small, and large firms. The objective of this research is, therefore, to examine the performance of medium‐sized firms in comparison to smaller and larger firms. Accordingly, the main research question of this study will be how medium‐sized firms perform differently from small and large firms both from an overall performance perspective as well as an innovation performance perspective.
This paper will process according to the following outline, section two will present a literature review on the size ‐ (innovation)performance relationship followed by the development of research hypotheses. Section three will outline the methodology used to test the different hypotheses, as well as a description of the variables used followed by a discussion of the results in section 4. The conclusion as well as managerial and theoretical implications will be discussed in section 5 followed by limitation and suggestions for further research in section 6.
2. Literature review & Hypotheses
2.1 Firm size and overall performance
2.2 Firm size and product innovation performance:
less efficient in converting innovation input into innovative output. Not all research however, is equally confident about this relationship. In some studies, the relationship between size and innovation output is either marginal (Mohnen & Kleinknecht, 2002), or insignificant (Lööf & Heshmati, 2002). Because of the lack of significant positive effects of firm size on innovation output, a negative relationship is expected between firm size and product innovation performance. This leads us to the following hypotheses: H2a: Compared to small firms, medium‐sized firms perform relatively worse in terms of product innovation performance. H2b: Compared to large firms, medium‐sized firms perform relatively better in terms of product innovation performance 2.3 Firm size and process innovation performance
3.2 Variables
3.2.1 Dependent variables
Performance can be measured in many different ways as mentioned in earlier research. In this thesis a distinction is made between overall performance measured by turnover per employee in 2004, and innovation performance measured by the percentage of turnover in new or improved products in the period 2002‐2004 in total turnover of the firm, as well as the percentage of cost reduction due to process innovation, and whether or not the firm has introduced a process innovation.
Overall firm performance; several measures of performance have been used in earlier
research. The most commonly employed performance measures are productivity, sales, export revenues and profits, although sometimes financial measures such as the returns on assets have also been employed (Lööf a&Heshmati, 2002; Bessler & Bittelmeyer, 2008). In this research one of these common measures is used in line with recent research (Ballot et al., 2015) to measure overall firm performance as the natural logarithm of turnover per employee in 2004 to measure overall firm performance.
Innovation performance; the database allows for the use of two different measures of a
firm’s innovation performance. We distinguish product innovation performance and process innovation performance.
do so to reduce costs and to create a more efficient organization, with the ultimate goal of lowering the firm’s average cost of production (Cohen & Klepper, 1996). We further use a second measure of process innovation performance, in the Fourth CIS, organizations indicated whether or not the firm introduced onto the market a new or significantly improved method of production, logistic, delivery or supporting activities as a measure of process innovation. This information was used to construct the dummy variable Processinnovation, which takes on the value of “0” if the firm did not introduce a process innovation and the value of “1” if the firm did introduce a process innovation. The reason for adding this second variable is because of concerns around the number of observations with regard to the percentage cost reduction due to process innovation variable. The response rate on the percentage of cost reduction due to process innovation is 42,4 % across the entire sample. So the number of observations in the different size classes may be quite small. The response rate on whether or not the firm introduced a process innovation is 100%, which means that it is representative across all size classes. The reason for using both measures is because of the fact that the process innovation dummy is not so much an actual performance measure compared to the percentage cost reduction due to process innovation. It does, however give an indication of the focus of firms towards process innovation which might have implications for their performance. 3.2.2 Independent variables
Firm size; recent research has found that company size is one of the most influential
measures of output; measured as the level of success of an organization over a given period of time (sales volume), and financial resources; measured as the wealth of the organization and net assets. We will use the most conventional measure of size in this research, which is the number of employees. This is not only the most conventional measure of organizational size but it also seems that counting the total number of employees is as good as many other measures of size. For instance, one study found the correlation between number of employees and the organization’s net assets to be .78 (Pugh et al., 1969). We distinguish three different size classes in line with the guidelines of the European Commission1 that define, small‐sized firms (<50 employees), medium‐ sized firms (50‐249 employees), and large‐sized firms (>249 employees). Based on these definitions, a dummy variable is created for medium‐sized firms. This dummy variable takes on the value of “0” if the firm is either large or small, and the value of “1” if the firm is of medium‐size. Another distinction is made within the medium‐sized class between, Small‐medium‐sized firms (50‐149 employees) and Large‐medium‐sized firms (150‐249 employees). To compare the medium‐sized firms to their smaller and larger counterparts, we created a dummy variable that gets assigned a value of “1” when the firm is a medium‐sized firm and a “0” when the firm is a small or a large firm. We did the same within the medium‐sized class, where the large‐medium‐sized firms are assigned a value of “1” and the small‐medium‐sized firms are assigned a value of “0”. In the sample, 26% of firms are of medium‐size.
3.2.3 Control variables
Manufacturing: In the Fourth CIS, companies were asked to provide their main
NACE code. Based on these codes the dummy variable Manufacturing was constructed. This variable takes on the value “0” if the firm is a non‐manufacturing firm (Based on its NACE 2 digit code), and the value of “1” when the firm is a manufacturing firm. (Based on its NACE 2 digit code). In the manufacturing sector, innovation generally focuses on process innovation. These innovations need formal structures and systems to squeeze
out costs. It has been shown that large manufacturing firms have generally succeeded with this strategy by focusing on process innovations (Wheelen & Hunger, 1999; Bessant & Tidd, 2007). As firms active in different industries seem to focus on different types of innovation, this might also influence the performance of these firms, which means we have to control for it. Enterprise part of a group: Respondents to the fourth CIS also reported whether
their firm was part of a group between 2002 and 2004. This information was used to construct the dummy variable enterprise part of a group which takes on the value of “0” when the firm was not part of group between 2002 and 2004, and the value of “1” when the firm was part of a group between 2002 and 2004. There are multiple reasons to control for the fact whether a firms is part of a group or not. Research has shown that firms belonging to a concern are more likely than individual firms to invest a larger amount in R&D, because it is assumed that these large concerns are more able to secure the necessary funding for R&D. This leads to a lower risk for firms that are part of a concern compared to individual firms (Frenkel et al., 2001). Furthermore, Dachs et al., (2008) found that being part of a group can influence firms’ propensity to cooperate, and to be engaged in successful innovation and R&D cooperation activities. Accordingly, the amount of possible partners increases when a firm is part of a formal network of firms. Whether or not the firm cooperates with others and how intense they cooperate can influence performance and therefore we control for the fact whether a firm is part of a group or not.
R&D intensity: Research shows that R&D expenditure is associated with
Cooperation arrangement on innovation activities: Respondents to the Fourth
CIS were further asked to indicate whether they had cooperation arrangements on innovation activities with other firms or institutions in the period 2002‐2004. This information was used to construct the dummy variable cooperation arrangements on
innovation activities, which takes the value of “0” when the firm did not have any
cooperation arrangements on innovation activities in the period 2002‐2004 and the value of “1” if the firm did have cooperation arrangements on innovation activities in the period 2002‐2004. The reasoning behind the influence of cooperation arrangements on both overall as well as innovation performance is that, according to transaction cost economics, firms can pursue a limited amount of technologies and research on their own. By collaborating with external partners, firms can achieve “economies of specialization” where each partner exploits their own core competencies that are complementary to the core competencies of the other partners (Koufteros et al., 2007). Cooperation arrangements have been shown to represent a substantial vehicle for knowledge spillovers. In addition, empirical evidence strongly suggests that this type of cooperation fosters firms’ innovation performance (Powell et al., 1996; Uzzi, 1996; Boschma & ter Wal, 2007). Furthermore, these arrangements have been shown to shape firms’ abilities to profit from innovation as such relationships may allow the firm to draw on the resources and capabilities of other organizations (Ballot et al, 2015).
Percentage of export in turnover; Respondents to the Fourth CIS were asked to
indicate the share of total export in turnover in 2004. The percentage of export in turnover is a firm‐level measure of export attitude that captures the intensity of the competition that a firm faces (Abramovsky et al., 2005). According to Hashi & Stojčić (2013), higher export intensity forces firms to be more innovative and efficient, thereby affecting firm performance. They further find that firms that are able to withstand foreign competition perform better as they are more efficient in the first place.
Number of competitors: Respondents were also asked to indicate how many
competitors), 1 (1‐3 competitors), 2(4‐6 competitors), 3(7‐15 competitors), and 4(>15 competitors). Research has shown that a higher number of competitors is a barrier to new product success and thus to firm performance (Cooper 1979; Song & Parry 1997). This is because, when a firm introduces a new product to the market, its competitors may react in a way that hinders the success of the launch, and the more competitors a firm has to face the higher the chance that this hinders the firms’ performance (Debruyne et al. 2002).
Age: Respondents to the Fourth CIS reported the founding year of their
enterprise. This information was used to create the Age variable, constructed as 2005‐ founding year. Age is an influencer of firm performance, with evidence from research showing that as firms get older their profitability lowers, due to organizational rigidities, rising costs, and slower growth (Loderer & Waelchli, 2010).
Innovation barriers: Finally, respondents were asked to indicate how far certain
factors proposed a barrier to innovation activities in 2002‐2004. This information was used to construct the dummy variable Innovation barriers, which takes on the value “0” if firms indicated that there were no factors that proposed a barrier to their innovation activities in 2002‐2004, and the value of “1” if firms indicated that one or more factors proposed a barrier to their innovation activities during 2002‐2004. Successful innovation has been associated with subsequent growth and therefore performance of the firm (Freeman, 1982). It is expected then that barriers to innovation will also affect negatively the economic performance of a firm. The reservation for their possible positive effect on the success of innovation in some cases makes, however, the direction of association between barriers and performance inconclusive (Hadjimanolis, 1999).
3.3 Model & Descriptive statistics
comes from new or improved products, for large firms this is 8% and for medium‐sized firms 7%. The small firms in the sample also have the highest process innovation performance, as they are able to attain an 11% cost reduction due t process innovation, medium‐sized firms attain 7% and large firms also 7%. This is especially interesting when we see that 81% of large firms has introduced a process innovation compared to 69% and 61% for respectively medium and small‐sized firms.
An inspection of the correlation matrix (table 3) does not reveal any multicollinearity issues, with a mean variance inflation factor (VIF) of only 1.174.
Variable N m s.d. Min Max
Turnover per employee (ln) 887 5.29 0.39 3.69 7.50 Percentage of turnover in new or improved products introduced during 2002‐2004 872 0.08 0.16 0.00 1.00 Percentage cost reduction due to process innovation 376 0.09 0.07 0.00 0.60 Processinnovation 887 0.67 0.47 0.00 1.00 Manufacturing 887 0.56 0.50 0.00 1.00 Enterprise part of a Group 887 0.57 0.50 0.00 1.00 R&D intensity 873 0.07 0.34 0.00 5.48 Cooperation arrangements on innovation activities 884 0.47 0.50 0.00 1.00 Percentage of export in turnover 794 0.41 0.38 0.00 1.00 Number of competitors 837 2.57 1.12 0.00 4.00 Age (Ln) 810 3.05 0.92 0.00 5.40 Innovation barriers 886 0.52 0.50 0.00 1.00 Small firm 887 0.56 0.50 0 1 Medium firm 887 0.26 0.44 0 1 Large firm 887 0.18 0.39 0 1 Turnover per employee(ln)
Firm size (2004) Mean N Std. Deviation
4. Results & Discussion
4.1 Small and Medium‐sized firms
Table 4 shows the regression results with regards to the sample of small and medium‐ sized firms. The table shows the four dependent variables of interest as well as the independent variables of interest. For each dependent variable two models are estimated. Model 1 can be considered the baseline model testing the general relationship between the control variables and the dependent variables. Model 2 adds the dummy variable of being a medium sized firm.
Sample of small and medium firms
Turnover per employee Percentage of turnover in new or improved products introduced during 2002-2004
Percentage cost reduction due to process innovation
Process innovation
I II I II I II I II
Medium sized firm -0.096
4.1 Medium and Large‐sized firms
Table 5 shows the regression results containing a sample of medium and large‐sized firms. Model 1 can again be considered the baseline model testing the general relationship between the control variables and the dependent variables within this specific sample. Model 2 adds the dummy variable of being a medium sized firm.
We see from table 5 that the coefficient of the medium‐sized firm on overall firm performance (turnover per employee) is negative although not statistically significant, this implies that we cannot conclude that medium‐sized firms perform better in terms of turnover per employee than large firms. This does not support hypothesis 1b. This again is in line with the research of Klomp & van Leeuwen (2001), which claims that size has no significant relationship with innovation output and therefore overall firm performance. The coefficient on product innovation performance (% of turnover in new or improved products) is also not significant, thereby not supporting hypothesis 2b. This is a surprising result, as we would expect medium‐sized firms to outperform large firms in terms of innovation output measured as the percentage of turnover in new or improved products. The coefficient on percentage cost reduction due to process innovation is not significant, not supporting hypothesis 3b. The fact that we do not find a significant relationship might be because of the limited number of observations pertaining to this variable. Further research is needed to investigate the relationship between firm size and this particular process innovation performance variable (Andries & Czarnitzki, 2014). The coefficient on process innovation is highly negative and significant. Implying that medium‐sized firms perform less process innovation than large firms do. This is in line with research confirming that as firms increase in size they focus more on process innovation instead of product innovation (Wheelen & Hunger, 1999; Bessant & Tidd, 2007), confirming hypothesis 3b. Because of the fact that the percentage cost reduction variable has a limited number of responses, we do confirm hypothesis 3B as, the coefficient on process innovation is that large and significant.
Sample of medium and large firms
Turnover per employee Percentage of turnover in new or improved products introduced during 2002-2004
Percentage cost reduction due to process innovation
Process innovation
I II I II I II I II
Medium sized firm -0.036
input effort (Lööf & Heshmati, 2002; Hashi & Stojčić, 2013). The coefficient on process innovation performance is insignificant. This is also surprising as some studies find a positive relationship between R&D intensity and process innovation (Klomp & van Leeuwen, 2001), while others find a negative relationship (Ballot et al., 2015). The coefficients on cooperation arrangement are insignificant for all performance measures, while the coefficient on process innovation is strongly positive and significant. This is not surprising as there is strong empirical evidence that this type of cooperation fosters firms’ innovation performance (Powell et al., 1996; Uzzi, 1996; Boschma & ter Wal, 2007). Export intensity has a positive and significant effect on both overall performance as well as product innovation performance. This relationship is as expected, higher export intensity encourages firms to innovate as foreign competition is more intense than domestic, which requires constant upgrading of the firm’s products and processes.
with Hadjimanolis (1999), who mentions that the direction of association between barriers and performance is inconclusive.
4.1 Medium‐sized firms
To complete our analysis regarding medium‐sized firms, an additional regression was performed to investigate whether there are significant performance differences on any of the dependent variables within the medium‐sized firm class.
Table 6 shows the regression results containing a sample of small‐medium and large‐ medium‐sized firms. The table shows the four dependent variables of interest as well as the independent variables of interest. For each dependent variable two models are estimated. Model I can be considered the baseline model testing the general relationship between the control variables and the dependent variables within the medium‐sized firm class. Model 2 adds the dummy variable of being a large‐medium sized firm.
Sample of small-medium and large-medium firms
Turnover per employee Percentage of turnover in new or improved products introduced during 2002-2004
important than updating the firm’s processes. The coefficient on number of competitors has a positive and significant effect on process innovation, which might be expected as firms that face more competitors, have a hard time outperforming them with new products and instead might be able to outperform them by focusing on improving their processes. In the sample of medium‐sized firms we also see a significant and negative effect of the number of competitors on the percentage cost reduction due to process innovation. Age has a significant negative effect on percentage cost reduction due to process innovation, this might be expected because as firms age, they already have improved their processes compared to younger firms, so the percentage cost reduction from process innovations lowers. Finally, innovation barriers show a positive and significant effect on the percentage cost reduction due to process innovation. The positive coefficient on cost reduction due to process innovation is not mentioned before in research. A reason might be that as firms face innovation barriers, this mostly influences product innovation, which forces firms to increase their process innovation performance. However, the most surprising result is that there is no significant negative relationship between innovation barriers and product innovation performance. It would be expected that as firms face innovation barriers, product innovation performance would be significantly harmed. This result is in line with Hadjimanolis (1999), who mentions that the direction of association between barriers and performance is inconclusive.
5. Conclusion:
innovation. Because we are especially interested in real performance measures, and other research calling for a true performance measure (Andries & Carnitzki, 2014), we also used the percentage cost reduction due to process innovation to measure process innovation performance. What we found is that medium‐sized firms are a special case when it comes to performance. We found that these firms do not outperform both small and large firms simultaneously on either of the performance variables. Medium‐sized firms do not outperform either small or large when it comes to overall firm performance. We do see, however, that medium‐sized firms are outperformed by small firms on both innovation performance measures (Percentage sales in new products and percentage cost reduction). We further see that medium‐sized firms are outperformed by large firms when it comes to process innovation. This is in line with previous research an confirms the hypothesis that as firms grow in size, their focus shifts from product to process innovation. This can have important implications for managers of medium‐sized firms, as increasing firm size, by itself, does not result in increasing performance on all measures. In fact, it seems that medium‐sized firms do not perform better than either small or large firms, regardless of the performance measure used. This might stimulate managers of these firms to actively participate in damage control. This study did find, however, that firms that are part of a group, and have high export intensity improve their overall firm performance regardless of the size of the firm, while a high R&D intensity harms overall firms performance regardless of the size of the firm. Although harming overall performance, increasing R&D performance does increase product innovation performance in every size class. Furthermore, it seems that although literature mentions that cooperation agreements foster a firm’s innovation performance this only holds for process innovation. Finally, we see that regardless of the size class, a higher number of competitors leads to more process innovation.
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