Proceedings of the Annual Vietnam Academic Research Conference on Global Business, Economics,
Finance & Social Sciences (AP16Vietnam Conference) ISBN: 978-1-943579-92-1
Hanoi-Vietnam. 7-9 August, 2016. Paper ID: V606
The Impact of Innovation Capacity on Firm Performance:
Evidence from the Turkish Industrial Clusters
Tugba GURCAYLILAR YENIDOGAN,
Faculty of Economics and Administrative Sciences,
Akdeniz University, Turkey.
E-mail: [email protected]
Safak AKSOY,
Faculty of Economics and Administrative Sciences,
Akdeniz University, Turkey.
E-mail: [email protected]
___________________________________________________________________________
Abstract
This study aims to explore the relationships between innovation capacity and firm
performance. On the one hand, we measure the capacity of the firm to innovate in the
expanded coverage of innovation from product and process (technological innovation) to
include marketing and organizational innovation (non-technological innovation). On the
other hand, we consider the impact of input (i.e. R&D intensity) and output (i.e. the ratio of
new products to total sales) indicators in enhancing innovative capacity of the firms. The
firm-level data obtained from an industrial district in Turkey show that technological
innovation has a positive effect on firm performance whereas there is no direct significant
relationship between non-technological innovation and firm performance. However, nontechnological innovation strengthens the positive impact of technological innovation on firm
performance. The findings of this study indicate the possibility of a non-linear relationship
between firm performance and input/output indicators of innovation.
___________________________________________________________________________
Key Words: technological innovation, non-technological innovation, R&D intensity, new
product sales, firm performance
JEL Classification: O 30, L 25, M 10
www.globalbizresearch.org
1. Introduction
Shaping the 20th century to a large extent, the New Economy has led major
transformations in the economic structure. Also known as the “Knowledge Economy”,
“Digital Economy”, and “Cyber Economy”, this new economic order has been the cursor for
upcoming threats and opportunities for businesses (Lee, 2002). Defined as “the ability of
creating and deriving economic value from an invention (Hagel & Seely Brown, 2005),
innovation has been a critical driver of nations’ economic welfare as well as shareholder
value maximization (Russell et al., 2011). In today’s world, innovation has become a
necessity for businesses in order to counteract against environmental and technological shifts.
Therefore, it may be foreseen that the 21st century, in which capital -and labour- based
growth dynamics are being transformed into innovation- and knowledge-based industrial
development, will be named as entrepreneurs’ century.
The New Economy has created many important opportunities for small- and mediumsized enterprises (SMEs) (Lee, 2002). Firstly, those transaction costs and fixed costs which
make scale disadvantages and retard SMEs’ operations in the competitive environment have
declined tremendously. Secondly, the nature of competition has been shifted. The New
Economy, which has emerged as a Schumpeterian influence arising from a “creative
destruction” process and taking place in a market atmosphere of continuous innovations,
differs from the traditional static competitiveness of the neo-classic economies. Based on the
superiority of innovation, the Schumpeterian competition enables the relatively smaller size
innovative firms to act in the competitive environment. However, the new economic order has
linked the competitive superiority to several factors: Cost (production cost per unit, cost of
goods sold), flexibility (meeting non-standard and crucial orders, new product offering speed,
response speed in product and volume adjustments), service (product accessibility, technical
support/after sale services, number of product lines) and accuracy in delivery (on time
delivery and response speed to correct failure) (Lei & Huang, 2014). Innovation activities
affect firm performance by way of increasing the demand or lowering the costs (OECD,
2005). Process innovations that improve productivity lead to relative cost advantages against
competitors and thus bear the potential of increasing financial performance. In product
innovations, on the other hand, new product introductions are aimed at gaining a competitive
advantage by shifting demand curve of the firm’s products. Thus, it may be hypothesized that
increasing innovative capacities of firms lead to better performance by influencing the
different sources of competitive advantage in the New Economy. Departing from this point,
the present research concentrates on the effect of innovative capacities of SMEs on firm
performance. The firm-level data obtained from an industrial district in Turkey shows that
technological innovation has a positive effect on firm performance whereas there is no direct
significant relationship between non-technological innovation and firm performance.
However, non-technological innovation strengthens the positive impact of technological
innovation on firm performance. The findings of this study indicate the possibility of a nonlinear relationship between firm and input/output indicators of innovation.
2. Literature Review
2.1 Evolution of Innovation Concept
In the past century, conceptual approaches were developed in order to comprehend and
stimulate innovation (Figure 1). At the beginning of the 20th century when the productionfocused and technology-driven perspective dominated, entrepreneurs’ role was oriented
towards making fundamental changes in production systems (Russell et al., 2011). In the
second half of the 20th century, the linear perspective that led innovation emphasized the role
of end users and environmental forces in the diffusion of innovations. During the 1970s, the
role of creativity in the coupling process which was required for innovation was underlined,
and innovation, which existed first in the creative individuals’ mind (sometimes a group of
people), was perceived in a continuously changing position among science, technology, and
the market. The coupling has a different position than an intuitive process. This requires a
creative dialogue along the lengthy process of research, experimental design, and
development. According to Drucker (1985), innovation is the differentiating tool of the
entrepreneur. As a leadership discipline in 1980s (Hamel & Prahalad, 1994), the strategic
importance of innovation was underlined, and innovation was seen as the origin of business
revolution.
Figure 1: Models of Innovation, Russell et al., 2011.
At the beginning of the 21st century, the concept of open innovation (Chesbrough, 2003)
was introduced. This concept assumes that those products which were transformed into
economic returns can be obtained from both the internal and external resources of the firm.
Thus, the focus of innovation shifts from businesses to individuals. The meaning of open
innovation has been further elaborated as the purposeful use of the internal and external flows
of knowledge for speeding up internal innovation. The consecutive and rapid growth of
individual and open collaborative innovation has led to the appreciation of collaborative
innovation networks in production, distribution, and marketing (Gloor, 2005), and to the
identification of human resources as the “fuel of the innovation system.”
With the term of the “innovation ecosystem” (Russell et al., 2011; Asheim et al., 2011) it
is referred to inter-organizational, political, economic, environmental, and technological
systems which will serve to the emergence of an atmosphere suitable to businesses’ growth.
The systems approach is used to define these transmission processes which reflect the multilevel (national, regional, technological, industrial) and multi directional nature of innovation
and serve to the establishment of research capabilities and the triple helix interactions among
businesses, government, and academia.
The ecosystem metaphor enriches the systems approach with the inclusion of value and
culture components. The transformation of an ecosystem for incremental and radical common
value creation is achieved through a continuous harmonization of those synergistic relations
between people, knowledge, and resources. In the dynamic innovation ecosystem, the
necessity for responding to changing internal (losses in capacity and productivity, shrinking
market size, decreasing sales etc.) and external (unfavourable economic conditions,
technological shifts, changes in social and cultural values, globalization of markets, rivalry
conditions etc.) turns “co-creation” into a fundamental power.
2.2 Historical Development of Innovation Metrics
There are two fundamental research directions on the measurement of innovation (Gamal,
2011). The first one measures innovation with input indicators such as R&D intensity and
output indicators such as patents/patent-related indexes (Figure 2). These measures constitute
a limited subset of innovation activities. Therefore, by using such measures it is not possible
to fully explain the relationship between organizational innovation and economic growth. The
global innovation research which was conducted on this topic (Jaruzelski et al., 2005)
depicted that there is not a significant relationship between the R&D expenditures and all
measures of business performance. Furthermore, number of patents as an indicator of
innovation has a very limited explanatory power.
Figure 2: Innovation Metrics, Gamal, 2011, p. 10.
The methodological approach of the Eurostat and the OECD in the “innovative capability
measurement” field at the beginning of the 1990s resulted in the emergence of a new
paradigm (Carney & Ryan, 2010). This new paradigm represents a directional shift from the
output indicators approach of 1970s to the conceptual structure of innovation activities
(OECD, 2005; European Innovation Scoreboard, 2015). The new paradigm does not assess
the development of innovative skills regarding purely to technological knowledge.
Emphasizing the importance of other knowledge types which are used in achieving product,
marketing, process, and organizational innovations, the new paradigm defends that
innovativeness is embedded in a joint social, political, and cultural ground which affects
knowledge movement. Thus, the “measurement issue” is relocated from a micro level (firmbased innovation theories) to a macro level which allows the evaluation of skills at system
level. The system approach to innovation considers the effects of interactive processes
between organizations during the phases of knowledge generation, diffusion, and application.
The Oslo Manual (OECD, 2005) is a fundamental source of reference for macro level studies
aiming to investigate innovation in a given industry/cluster. The conceptual framework put
forward by this manual integrates the perspectives of systems-based innovation approach and
firm-based theories of innovation.
According to Oslo Manual (OECD, 2005), a firm can make many improvements and
changes in business practices, utilization of the factors of production, and product/service
outputs all of which are effective on business performance and productivity. Thus,
innovations are defined in four distinct types in a way to encompass a wide range of changes
in firms’ activities; these are product innovations, process innovations, marketing innovations,
and organizational innovations. Product and process innovations are closely associated with
the concepts of technological product innovation and technological process innovation.
Marketing innovations and organizational innovations which are later added into the more
recent versions of the manual have expanded the content of the innovation concept compared
to the previous definitions. Marketing innovations and organizational innovations can also be
called non-technological innovations.
3. Methodology
3.1 Sample and Data Collection
The aim of this study is to analyze the impact of innovation capacity (including both
input/output indicators and innovation activities) on firm performance. To test the hypotheses,
a questionnaire-based survey was conducted in the year 2015. The empirical data of this study
were collected from the firms operating in the organized industrial district of Antalya/Turkey.
As an earlier-phase development instrument, a pilot study was performed to overcome the
validity problems. The results of the pilot survey allowed us to have a more refined version of
the questionnaire form. In the data collection process, the total of 269 executives of the cluster
firms were contacted via telephone and informed about the research. 183 firms accepted to
participate in the research. Then the surveys were conducted through face-to-face interviews
and 125 valid questionnaire forms were obtained. The effective response rate is
approximately 47%. The data from the Turkish industrial cluster were analyzed with the
SPSS (version 20), and the hypotheses were tested by applying ordinal regression analyses.
3.2 Measurement
Dependent variable. Firm performance covers a wide range of factors relating to the
objectives and effects of innovation (OECD, 2005). An index for firm performance as the
ordinal dependent variable was calculated in this study. At the first stage, this construct was
measured with twenty items (e.g. opening up new markets, product quality, production
capacity, volume flexibility of production, speed in supplying and delivering products, lead
time of new product development, increase in market share, product range, customer
satisfaction, total sales, return on investment, return on assets, gross operating profit) to
capture the extent of the average performance level of the firm compared to competitors over
the past three years. Each respondent assessed its relative performance level on a 7-point
Likert scale ranging from much lower than competitors (=1) and equal to competitors (=4) to
much higher than competitors (=7). The value of Cronbach’s alpha is 0.91 which is
compatible with the recommended threshold level of 0.70 (Hair et al., 1998). At the second
stage, a performance index was constructed by splitting the variable of firm performance into
three groups (tertile split). The higher the index, the higher is the degree of performance to the
firm.
Independent variables. Consistent with the studies of O'brien (2003) and Becker & Dietz
(2004), this study considers R&D intensity as input indicator of innovation. In the
questionnaire form, the cluster firms’ managers were asked to fill in the blank for the average
ratio of R&D expenditures to total sales over the past three years. The scores of all
participants were converted into a categorical variable with six levels. Similarly, sales of
product innovations by firms were measured as the average percentage of firm sales (Liu &
White, 1997; Faber & Hesen, 2004; Carvalho et al., 2015) realized from new and
substantially improved products to total sales over the past three year and then transformed to
a categorical variable in the data processing process.
Innovation capacity referred to as innovation activities of the firms in the Oslo Manual
(OECD, 2005) was measured with two categories of innovation including technological and
non-technological innovations. Technological innovation includes four items and the other
seven items belong to the measurement of non-technological dimension of innovation.
Respondents assessed each innovation sub-activity to reflect the changes (radical and/or
incremental improvements) in product and process as well as marketing and organizational
innovation activities with using a 5-point scale ranging from (1) no change, (2) minor
improvements, (3) minor but continuous improvements, (4) major/remarkable improvements,
(5) radical/transformational improvements. The Cronbach’s alpha value for technological
innovation is 0.77 and for non-technological innovation is 0.84. In addition to the reliability
test for the two main factors of the innovation activities, exploratory factor analysis with
varimax rotation was conducted to check the validity of the constructs. The Kaiser-MeyerOlkin (KMO) measure of sampling adequacy is 0.85 and the Bartlett test of sphericity is
highly significant (Bartlett's Test: 442.230; p = 0.000), indicating that the data are suitable for
factor analysis. The exploratory factor analysis yielded a two-factor solution accounting for
55.50 percent of the cumulative variance. Factor loadings of the 11 items are above 0.50 (Hair
et al., 1998), ranging from 0.60 to 0.85.
Control variables. We controlled for two variables that might affect the degree of firm
performance in industrial clusters. The first control variable reflects whether there is a R&D
department in the organization, and the second control variable covers the types of businesses
categorized as project-based, non-project-based and plant species.
4. Results
4.1 Descriptive Statistics
Table 1 presents the descriptive statistics and Pearson correlation coefficients for all
variables. As expected, firm performance is positively correlated with technological
innovation. However, firm performance is positively but not significantly correlated with nontechnological innovation. Unexpectedly, there is no statistically significant relationship
between firm performance and R&D intensity. Similarly, firm performance is negatively but
not significantly correlated with new product sales. The presence of R&D department is
significantly and positively correlated with technological innovation and firm performance
whereas a positive correlation exists between type of business and input/output indicators of
innovation.
Table1: Descriptive statistics and correlation coefficients
Variables
Mean
1. Firm performance
2. Technological innovation
3. Non-technological innovation
4. R&D intensity
5. New product sales
6. Type of business
7. R&D department
S.D.
2.00
2.90
2.45
2.09
3.17
1.11
1.30
0.81
0.92
0.79
1.62
2.19
0.33
0.46
1
2
1.000
0.275** 1.000
0.180 0.419**
-0.092 0.105
-0.046 0.267**
0.091 0.014
0.295** 0.195*
3
1.000
0.131
0.130
0.073
0.164
Correlations
4
5
6
1.000
0.440** 1.000
0.204* 0.225** 1.000
-0.004 -0.039
0.096
*p<0.05, **p<0.01 (two-tailed tests)
4.2 Regression Analysis
To test the hypotheses we ran ordinal regression analysis consistent with a stepwise
procedure to assess the explanatory power of each set of variables. Model 1 only consists of
control variables. The innovation indicators were sequentially included into Model 2. Then
interaction term (technological innovation*non-technological innovation) was added in
Model 3 that tests the complementary effect of technological innovation and nontechnological innovation on firm performance. The models are as follows:
Model 1: Firm performance=
α + β1 (type of business) + β2 (R&D department)
Model 2: Firm performance=
α + β1 (type of business) + β2 (R&D department) +
β3 (R&D intensity) + β4 (new product sales) + β5 (technological innovation) + β6 (nontechnological innovation)
α + β1 (type of business) + β2 (R&D department) +
Model 3: Firm performance=
β3 (R&D intensity) + β4 (new product sales) + β5 (technological innovation) + β6 (nontechnological
innovation)
+
β7
(technological
innovation*non-technological
innovation)
Table 2 reports the results of the regression models. The significant chi-square statistic
(p<0.05) indicates that the final model (see model 3) gives a significant improvement over the
baseline model. According to Pearson and Deviance statistic (p>0.05), each model fits the
data well. The explanatory power of the dependent variable increased from 0.268 to 0.314
(see the difference between the values of Nagelkerke R2) by including the interaction effect
between technological innovation and non-technological innovation in the model.
7
1.000
Table 2: Ordinal regression results
Independent variables
Threshold constants
Type of business
poject-based
non-project-based
plant species
R&D department
no
yes
Input indicators
R&D intensity
less than 1%
1-5 %
6-10 %
16-20 %
more than 20 %
Output indicators
new product sales
less than 1%
1-5 %
6-10 %
11-15 %
16-20 %
more than 20 %
Innovation indicators
technological innovation
non-technological innovation
technological innovation*non-technological innovation
Model statistics
Model chi-square
–2 Log likelihood
Nagelkerke R2
Coefficients
Model 1
Model 2
Model 3
–20.512*** (0.874)
–21.504*** (1.149)
–21.438*** (1.259)
–18.823*** (0.847)
–19.573*** (1.133)
–19.432*** (1.253)
–18.945*** (0.792)
–18.602
(0.000)
0a
–20.134*** (0.924)
–19.835
(0.000)
0a
–20.596*** (0.974)
–20.199
(0.000)
0a
–1.237*** (0.451)
0a
–1.194**
0a
–1.207**
0a
10.243**
20.188
0.125
(0.497)
(0.511)
+0.884
+0.329
–0.576
–1.250
0a
(1.000)
(0.990)
(1.288)
(1.259)
+1.259
+0.870
–0.235
–0.893
0a
(1.046)
(1.042)
(1.332)
(1.356)
–0.187
–0.505
–0.400
+0.072
–1.025
0a
(0.634)
(0.841)
(0.917)
(0.871)
(1.165)
–0.108
–0.757
–0.424
+0.397
–0.633
0a
(0.645)
(0.848)
(0.926)
(0.896)
(1.169)
+0.596** (0.280)
+0.252
(0.322)
+0.699** (0.291)
+0.273
(0.322)
+0.822** (0.358)
23.348*
164.968
0.268
28.142**
160.174
0.314
Values in parentheses are standard errors.
0 a = T his parameter is set to zero because it is redundant.
*p<0.06, **p<0.05, ***p<0.01
The results indicate that increasing capacity of the firm for technological product and
process innovations leads to higher degrees of firm performance. However, non-technological
marketing and organization innovations are positively but not significantly associated to
performance improvements in manufacturing firms. In addition to the direct effects, the
results show that interaction effect of innovation activities has a stronger effect on
performance. Accordingly, this study provides support for the complementary role of
technological and non-technological innovations in enhancing performance. Regarding the
input/output indicators of innovation, no significant differences exist between R&D intensity
and firm performance as well as between the new product sales and firm performance.
However, the findings of this study indicate the possibility of a non-linear relationship
between firm performance and input/output indicators of innovation. Depending on this
possibility, a great amount of investments in R&D does not necessarily result in increasingly
improved performance and there may be a saturation point above which an increase in R&D
intensity leads to negative returns (Molina-Morales & Expósito-Langa, 2012).
5. Conclusions
This study aims at analyzing the different types of innovation outputs from the
contemporary evolutionary-based approach to process innovations. According to the
proponents of a stream of thought called as “evolutionary tradition” in economics,
innovations involve not only the radical transformations of markets and/or society through
technological advances but also incremental improvements in existing products and
processes. Therefore, innovations should not simply be a matter of excessive R&D
expenditures. Technology-oriented innovation indicators (e.g. investments in resource and
development activities) are questionable for assessing the influence of innovativeness skills
on the growth of SMEs, which in turn creates a paradigmatic conflict. The emergence of new
paradigm on innovative capability measurement has gradually shifted to focus on the broader
systemic nature of innovation including reciprocal relationship between technological and
non-technological facets of innovation. Consistent with the recent research agenda, this study
presents a way that the interplay between innovation activities (technological and nontechnological innovations) in enhancing firm performance can be captured using indicators
derived from old and new paradigm-based concepts.
The analysis of data from the manufacturing firms operating in a Turkish industrial
cluster demonstrates some major findings. First, technological innovation leads to better
performance whereas non-technological innovation exerts positive but non-significant
influence over firm performance. Second, non-technological innovation positively moderates
technology-oriented innovations and hence strengthens the impact of technological innovation
in enhancing performance. This finding indicates that non-technological innovations
constitute complementary elements of innovation process. However, it should be noted that
the reinforcing effect of the specific type of innovation on one another may have an adverse
reflection according to the industry. In connection with this argument, productivity gains from
non-technological innovations would be higher in service industries. Third, the results of this
study show that no significant differences exist between old paradigm-based concepts of
innovation indicators (i.e. R&D intensity and new product sales) and firm performance. It
supports that traditional innovation indicators are not useful for measuring innovative
capacity of SMEs in developing countries. Overall, this study provides evidence for
comparative effect of traditional (focusing on science and technology) and knowledge-based
indicators of innovation on firm performance and hence contributes to the innovation research
literature at the organizational level of analysis. However, its implications for organizations
should be extended by investigating the firm- and network-level determinants of innovation
capacity and their effects to innovation outcomes.
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