Scholarly article on topic 'The Impact of External Collaborations on Firm Innovation Performance: Evidence from Turkey'

The Impact of External Collaborations on Firm Innovation Performance: Evidence from Turkey Academic research paper on "Economics and business"

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Abstract of research paper on Economics and business, author of scientific article — Derya Fındık, Berna Beyhan

Abstract This paper aims to understand the impact of collaborations on the firm innovation performance. Although some of the previous studies support that external collaborations of firms improves their innovation capabilities, our knowledge on the impact of collaborations on firm innovation performance is still limited. In this research, using innovation survey carried out by Turkish Statistical Institute in 2009 to measure the innovation activities of firms in Turkey between 2006 and 2008, we aim to understand how collaborations influence the innovation performance of firms. We introduce a new indicator to measure the innovation performance of firms which is simply based on the perception of firms regarding to the impacts of innovation. In order to create performance indicators we conducted a factor analysis to group the firms’ perceptions on the impacts of innovation. Factor analysis gives us two basic impact of innovation: product and process oriented impacts of innovation. We find out a positive relationship between external collaboration and product oriented impacts of innovation. In other words, firms engaged in external collaboration during the innovation process observe better improvements in their products and markets. External collaboration also brings improvements to the production process of firms.

Academic research paper on topic "The Impact of External Collaborations on Firm Innovation Performance: Evidence from Turkey"

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ScienceDirect

Procedía - Social and Behavioral Sciences 195 (2015) 1425 - 1434

World Conference on Technology, Innovation and Entrepreneurship

The Impact of External Collaborations on Firm Innovation Performance: Evidence from Turkey

Derya Findika*, Berna Beyhanb

aYildinm Bayazit University, Faculty of Business, Dept. of Management Information Systems Cankaya, Ankara, Turkey bBahcesehir University Faculty of Engineering, Dept. of Engineering Management Besiktas, 34353, Istanbul, Turkey

Abstract

This paper aims to understand the impact of collaborations on the firm innovation performance. Although some of the previous studies support that external collaborations of firms improves their innovation capabilities, our knowledge on the impact of collaborations on firm innovation performance is still limited. In this research, using innovation survey carried out by Turkish Statistical Institute in 2009 to measure the innovation activities of firms in Turkey between 2006 and 2008, we aim to understand how collaborations influence the innovation performance of firms. We introduce a new indicator to measure the innovation performance of firms which is simply based on the perception of firms regarding to the impacts of innovation. In order to create performance indicators we conducted a factor analysis to group the firms' perceptions on the impacts of innovation. Factor analysis gives us two basic impact of innovation: product and process oriented impacts of innovation. We find out a positive relationship between external collaboration and product oriented impacts of innovation. In other words, firms engaged in external collaboration during the innovation process observe better improvements in their products and markets. External collaboration also brings improvements to the production process of firms.

© 2015TheAuthors.PublishedbyElsevier Ltd.This is an open access article under the CC BY-NC-ND license

(http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of Istanbul Univeristy.

Keywords: External collaboration, product oriented impact, process oriented impact, Turkey.

Corresponding author. Tel.: +90 212 381 0861 E-mail address: dfindik@gmail.com

1877-0428 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of Istanbul Univeristy.

doi: 10.1016/j.sbspro.2015.06.439

1. Introduction

Today, it is highly acknowledged by innovation management scholars, firms and policy makers that external collaborations are important for organizations to generate new knowledge. External collaborations provide many advantages to firms including having access to new knowledge, decreasing costs of generating new knowledge and creating innovations, decreasing risks associated with R&D activities and innovation projects (Schilling, 2013). Many empirical studies provide evidence that external collaboration increases firms' innovation performance (e.g. Faems et al., 2005; Un et al., 2010; Powell et al., 1996).

In the studies focusing on the impact of external collaboration on firms' innovation activities, innovation performance is mostly measured by the firm's propensity to generate product or process innovations. However, in this paper, we measure innovation performance of firms with more qualified measures which use the perceptions of firms on the impact of innovations over firms' main activities. In the innovation survey carried out by Turkish Statistical Institute (TUIK) in 2009 firms are asked about how important are the effects of your innovation activities introduced during the three years on the improvements of product range and newness of products, market entry, market share, product quality, flexibility in production process, production capacity, efficiency or health and environmental impact of their products and processes (for details see Table 2). Innovation activities may influence these products and processes in different ways. Moreover, observing a significant improvement in products and processes for firms at the end of the innovation activities are very important. Not every innovation may create an observable improvement in products, processes or markets. Therefore measuring innovation performance of firms with the innovation itself may not be able to create a framework to understand the actual performance of firms.

The aim of this paper is to understand the role of external collaborations on innovation performance. As emphasized, the innovation performance of firms will not be measured by the innovation activity but the degree to which firms feel the effects of the innovation activity on the firm's product- and process-related activities. In Section 2 we will review the literature studying on the link between external collaboration and innovation performance. Section 3 will discuss the data and methodology used. In the following section we will discuss the main results of the research and section 5 concludes the paper.

2. Literature Review And Hypotheses

Experiential learning theory argues that learning is a cyclical process in which experience is translated into concepts which can be used while selecting new experiences (Kolb, 1976). Organizations also learn like individuals; experiences and trial-and-error processes are important for organizations. Routines of an organization can change by trial-and-error experimentation (Cyert and March, 1963). Organizations adopt routines, procedures and strategies that bring favorable outcomes (Levitt and March, 1988). Therefore, we can assume that firms which observe a significant improvement in product, process, market share, efficiency, etc. at the end of innovation activities appreciate the value of activities which have brought favorable outcomes. When firms, which have made external collaborations to generate innovations, observe apparent improvements in their products and processes, they are expected to learn from this experience and replicate the same activity in the future. Therefore it can be argued that firms which get improvements in products and processes at the end of innovation activities will tend to continue innovation activities. If the positive feedback and rewards are increased due to external collaboration made by firms for innovation projects, firms are expected to develop a positive attitude towards external collaborations and in the future they tend to collaborate more with the external organizations in the innovation process.

There is complex knowledge behind each innovation. In our time the complexity of knowledge needed to generate innovations increases rapidly. Furthermore, the increasing cost of creating new knowledge drives firms to have innovation strategies which are based on accessing and acquiring external resources. External collaborations are effective mechanisms to increase innovation capacity of firms (Faems et al., 2005; Hagedoorn 1993; 2002). Firms collaborate with other organizations for different reasons. Sometimes decreasing risks of creating new

technologies and products is the reason for collaboration, or sometimes increasing efficiency is the reason (Das and Teng, 2000; Belderbos et al., 2004)

In terms of innovation performance, external collaborations are important because they let the firms to access knowledge residing in other organizations, hence to improve organizational learning and finally innovation capabilities (Powell et al., 1996). Firms can establish linkages with the partners in the same industry (Katz and Ordover, 1990; Mowery, 1989; Vonortas, 1997) or universities and public research institutes (Faulkner and Senker, 1994; Lee, 1996; Leyden and Link, 1999). Freel (2003) have found that customer links and public sector links have positive effect on introducing new products in total sample.

Many studies show that external collaborations increase the innovation performance of firms. Faems et al (2005) based on the data collected from Belgian manufacturing firms suggest that there is a positive relationship between a firm's innovation performance and its inter-organizational collaborations. Huang and Yu (2011) find that, among Taiwanian firms in the information and communication technology industry, both competitive and non-competitive collaborations have positive effect on firm innovation performance. The impact of external collaboration differs according to the type of innovation. Un and Asakawa (2015) find out that R&D collaboration with suppliers and universities have a positive impact over process innovations but other types of collaborations do not have a significant or positive impact. External collaborations support firms in their innovation activities therefore we can hypothesize that

H1: Firms collaborated with other organizations observe a significant positive effect of innovation over their products.

H2: Firms collaborated with other organizations observe a significant positive effect of innovation over their production processes.

3. Methodology

3.1. Research Goal

In this study we aim to identify the effects of external collaboration on product oriented and process oriented activities of firms. We use Innovation Survey Statistics (2009) in order to construct the dependent and explanatory variables. In the survey, external collaboration of firms in the innovation process is measured by the question asking the type of co-operation partner by location. Firms are asked about whether they have collaborated with other enterprises within the enterprise group, suppliers, customers, competitors, consultants, universities, and government and public research institutes. The responses are collected on a binary scale (yes or no).

3.2. Sample and Data Collection

In this study, we use Innovation Survey Statistics (2009) applied to firms with more than 10 employees. Innovation surveys are carried out by Turkish Statistical Institute (TURKSTAT). The survey aims to understand innovation capabilities of firms as well as their innovative activities such as introducing new products/processes or improving the existing ones during the period of 2006-2008. The data set consists of 5863 observations. However, the questions on innovation impact is asked to firms which perform i) product or process innovation ii) ongoing innovation activities iii) abandoned innovation activities during the period of 2006-2008. The total number of those firms is 2080.

3.3. Dependent Variable and Econometric Model

Our dependent variables are product oriented impact and process oriented impact. These variables are constructed based on the factor analysis. When conducting factor analysis, we use the responses of each firm in the sample to the

following question "How important were the effects of your innovation activities introduced during the three years 2006-2008?"(TURKSTAT, 2009). In this question, for 9 different possible effects of innovations are asked and responses were collected on four-point likert scale from "no importance" to "very important". These 9 possible effects of innovations which were asked are shown in Table (2). As shown in Table (1), factor analysis produces relevant components whose eigenvalue is higher than 1 (Kaiser, 1960).

Factor rotation matrix produces two-factor model regarding the impact of innovation which are product oriented impact of innovation and process oriented impact of innovation. Table (2) demonstrates the respective factor loadings of each component. As for the first one, firm's innovation activities could increase the variation in products or in services or improve the outdated ones. In addition to these, new markets could emerge or the existing markets could expand with the introduction of new products or services. As far as process oriented impact is considered; improvement in product quality, flexibility in production processes and increase in production capacity as well as health and social security conditions or reduction in labor costs are indicated in the survey.

Table 1. Factor analysis table

Components

Factor 1 5.31180

Factor 2 1.17049

Factor 3 0.62231

Factor 4 0.48004

Factor 5 0.36130

Factor 6 0.32420

Factor 7 0.30694

Factor 8 0.26503 Factor 9

LR test: independent vs. saturated: chi2(36) = 1.2e+04 Prob>chi2 = 0.0000

Table 2. Factor loadings of two-factor model

Components Factors Factor loadings

Increased range of goods or services 0.8664

Improved outdated products/services PRODUCT 0.6836

Entered new markets ORIENTED 0.8525

IMPACT

Increased market share 0.8222

Improved quality of the products/services 0.6295

Improved flexibility in the production of 0.7145

goods and services PROCESS

Increased capacity of production or ORIENTED 0.7695

services IMPACT

Improvement in themes of health and 0.8182

security

Reduced labour costs per unit output 0.8513

LR test: independent vs. saturated: chi2(36) = 1.2e+04 Prob>chi2 = 0.0000

4.14131 0.5902 0.5902

0.54818 0.1301 0.7203

0.14227 0.0691 0.7894

0.11874 0.0533 0.8427

0.03710 0.0401 0.8829

0.01726 0.0360 0.9189

0.04191 0.0341 0.9530

0.10715 0.0294 0.9825

In the second stage of the study, we estimate the effect of firm specific variables (firm size and foreign share), innovation capabilities (internal and external R&D activities and radical innovation), financial sources of innovation (national funds), and external collaboration on the innovation impact which is measured at two levels as product oriented impact and process oriented impact (see Eq.1). Ordinary least square (OLS) procedure which is controlled for robust standard errors is applied in the estimation.

Innovation Impacti=a+p*Firm Sizei+y*Foreign Sharei+A*Internal R&D Activitiesi+d*Externa! R&D Activitiesi+^*Radical Innovationi+ v*National Fundi + p*External Collaborationi+ ei

(Eq. 1)

3.4. Explanatory Variables

In this study, we control for firm size and foreign capital share of the firm in the estimation of innovation impact. A positive link between firm size and innovation activities is established in a wide range of studies (Ettlie and Rubenstein, 1987; Acs and Audretsch, 1988; Santarelli and Sterlacchini, 1990). How size of the firm affects product and process innovation, on the other hand, necessitates further consideration. Cohen and Klepper (1996) examined the effect of firm size on allocation of R&D effort between product and process innovation and found out that incentive to conduct innovation activities of large firms is relatively greater for process innovations. Likewise, Fritsch and Meschede (2001) have observed that the size effect is stronger for process oriented R&D activities but the difference between process and product R&D with regard to the effect of size is small. Pianta and Vaona (2007) observed differences between large firms and small ones in terms their performance in the introduction of new products or processes. Behaviour of large firms is largely determined by market expansion strategies while small firms apply patenting strategy for new products and flexibility in production processes. Large firms perform better than small firms both in product and process innovations. However, recent evidence points out that process innovation performance does not depend on size of the firm (Hervas-Oliver et al. 2014). Firm size can be measured by various indicators such as number of employees, net sales or profits. In many of them, total number of employees is commonly used in the literature. So that, in this study, we use the logarithm of total number of employees. Table 3 demonstrates the definitions of the variables.

We also use foreign share to control for its impact on product and process oriented impact. Foreign-owned firms due to having strategic resources (Teece, 1986) such as technological knowledge and tacit knowledge are much prone to introduce new products or processes. Chen et al. (2014) have found that there are differences between foreign-owned firms and domestic firms in terms of their innovation performance. Girma et al. (2008) have also emphasized that private and collectively owned firms with foreign share are inclined to innovate than others do. The positive effect of FDI on introduction of new products and processes is observed through better access to bank credits. In addition to these, foreign owned firms perform better in introducing new products and processes than the domestic firms (Deeds and Hill, 1996; Belderbos et al. 2004). We use threshold point, 10 percent, in order to measure foreign share in this study. For firms having foreign share higher than 10 percent, the variable takes the value of 1 and 0 otherwise. As far as the link between internal/external R&D and innovation performance is considered, organizations acquire R&D activities if the external resources are cheaper or in some cases, external expertise in the field of R&D is necessary for the efficiency of the innovation processes. Cassiman and Veugelers (2006) have found that internal R&D and external R&D are complementary innovation activities. Internal R&D helps building up of absorptive capacity to ease the adoption of the latter. They found that firms engaged in internal R&D activities have positive effect on sales from new products. In addition to these, conducting both internal and external R&D activities have positive effect on firm performance both in total observations and in high basic R&D performers. Internal and external R&D activities are measured as continuous variable in this study. Internal R&D intensity indicates the share of R&D expenditure to total sales. R&D activities could also be acquired from external sources. We use external R&D intensity variable which is measured by share of purchased R&D expenditures to total sales in this study to determine the effect of R&D activities outside of the firm on innovation performance.

Firms could need financial support from other organizations when introducing new products and processes. Government or national funds is a type of financial source for innovation which is commonly used by small and medium sized firms. The history of supporting industry dates back war years. Most countries rely on public sources in order to build up their national industry regime both in large income countries and low income countries (Nelson and Rosenberg, 1993). Various types of funding mechanisms such as tax incentives, preferential financing or direct subsidies are used in the public funding system. Griffith et al. (2006) have examined the role of public support in R&D processes in France, Germany, Spain and UK and found that national funding positively affects the probability of performing continuous R&D activities in those countries. The intensity of R&D activities, on the other hand, is not so much affected by national funding. This result implies that national funding generates a push effect on industries. In the later stage of the production, firms survive with their own resources or could create different sourcing channels for innovation. We use the question on receiving any public financial support for innovation activities in the survey. It is a binary variable which takes the value of 1 if the firm has received any financial support during the period of 2006 and 2008.

We also include radical innovation in this study. Plessis (2007) defines radical innovation as a competence destroying activity which relies on different management techniques rather existing internal resources. Ettlie et al. (1984) found that radically new innovation strategy is necessary for process adoption while more conventional strategy and arrangements are required for the introduction of new products. Chandy and Tellis(1998) pointed out that producing radically new products eliminates the customer oriented production system and requires more independency for the firms. In much cases, firms are reluctant to introduce radically new products to protect their market share. We use the question on introducing a new or significantly improved good or services onto firm's market before the competitors in the survey. Again, it is a binary variable which takes the value of 1 if the firm has introduced a new product to the market during the period of 2006 and 2008.

External collaboration which we want to test is measured by the number of external organizations that a firm collaborates. In the questionnaire form, firms are asked about whether they collaborate with (i) suppliers of equipment, materials, components, or software ii) clients or customers iii) competitors iv) consultants, commercial labs, or private R&D institutes v) universities or other higher education institutes vi) government or public research institutes. The responses are collected on a binary scale as 0 or 1. To measure the external collaboration of firms the value of each indicator is summed. In other words, external collaboration indicates the total number of different partners that a firm collaborates during innovation process.

Table 3. Explanations of variables

Variable name

Definition

Firm size

Continuous

Logarithm of total number of employees

If foreign share of the firm is higher than 10, it takes the value of 1; otherwise 0

Share of R&D expenditure to total sales

Share of purchased R&D expenditures to total sales

If firm received any public financial support for innovation activities, it takes the value of 1 otherwise 0

If firm introduced a product that to the market, it takes the value of 1, otherwise 0

If firm cooperates with (i) suppliers of equipment, materials, components, or software ii)clients or customers iii)competitors iv)consultants, commercial labs, or private R&D institutes v)universities or other higher education institutes vi) government or public research institutes, the value of each indicator is summed and a new value is obtained.

Foreign capital

Categorical

Internal R&D Intensity External R&D Intensity National funds

Share Share Categorical

Radical innovation

Categorical

External collaboration

Continuous

Table 4 demonstrates the summary statistics of the data. Our dependent variables, product oriented impact and process oriented impact are factor variables having values in a range of -0.39 and 5.01.The mean values of each factors are 2.72 and 2.28 respectively indicating a high number of responses to the questions on innovation impact. The mean value of product oriented impact, on the other hand, is higher than that of process oriented impact has. The mean of foreign share of the firms in the data is about 12 percent indicating relatively small percentage of foreign-owned firms in the sample. In the dataset, firms engaged in internal R&D activities are 46 percent while the share of external R&D performers is about 21 percent. This indicates that firms rely more on internal sources for R&D activities. We use R&D intensity in order to measure R&D activities in the data. Differences between internal and external R&D activities are also observed in the pattern of intensity. Firms in the sample focus more on internal R&D activities. Regarding the last indicator, external collaboration is used by firms to a large extent (60 percent) in the sample. This indicates that firms, through suppliers, clients, competitors, universities, and government research institutes, widely use external linkages in the innovation process.

Table 4. Summary Statistics

Variables N Mean Std. Dev. Min Max

Product oriented impact 2080 2.72 1.07 -0.39 5.01

Process oriented impact 2080 2.28 1.09 -0.61 5.26

Size 2080 5.96 1.56 3.69 11.80

Foreign 2080 0.12 0.32 0 1

Internal R&D 2080 0.46 0.50 0 1

External R&D 2080 0.21 0.41 0 1

Internal R&D intensity 2080 0.04 0.38 -0.05 9.16

External R&D intensity 2080 0.005 0.09 -0.02 3.79

External Collaboration 2080 0.61 1.27 0 4

As far as the correlations among dependent and independent variables are considered, we observe negative and significant relation between process oriented impact and product oriented impact. As Table (5) shows, much of the explanatory variables are strongly correlated with dependent variables, product-oriented impact and process-oriented impact.

Table 5 Correlation Matrix

Product oriented impact Process oriented impact Size Foreign Internal R&D External R&D Internal R&D intensity

Product oriented impact

Process oriented impact -0.09*

Size 0.01 0.04

Foreign 0.01 0.00 0.29*

Internal R&D 0.23* 0.09* 0.22* 0.06*

External R&D 0.16* 0.09* 0.22* 0.12* 0.43*

Internal R&D intensity 0.05* 0.03 -0.05* -0.03 0.12* 0.01

External R&D intensity 0.03 -0.00 0.02 -0.00 0.05* 0.10* 0.28*

External Collaboration 0.16* 0.10* 0.21* 0.06* 0.18* 0.26* -0.00

*p<0.05

4. Discussion and conclusions

In this paper, we argue that external collaboration positively associates with the effects of innovation activities perceived by the firms. In other words, firms which made external collaboration find that innovations have greater effect on product-oriented and process-oriented firm activities. The estimation results to test our model (Table 6) support our hypotheses. External collaboration has positive and significant influence both perceptions associated with the product-oriented and process-oriented impacts of innovation.

As for the effect of firm size on dependent variables, it has only negative and significant effect on product oriented effect of innovation. In the literature, it is emphasized that large firms are much prone to innovate than small firms (Rogers, 2004). This contradictory result is supported by Ettlie and Rubenstein (1987). According to this, large firms focus more on process innovations that turn into product innovations instead of introducing new products. As far as the effect of foreign share on dependent variables, we do not observe any significant effect of this variable. Internal R&D intensity has a significant and positive effect both for product oriented impact and process oriented impact. Its effect is slightly higher for process oriented impact.

The variable national funds has only significant and positive effect on product oriented impact indicating that the introduction of new products requires more external funding than that of new processes. External collaboration has positive effects both for product oriented and process oriented impacts.

Table 6. Estimation results for innovator firms

Variables Product Process

oriented impact oriented impact

Size - .0327** 0.00609

(0.0157) (0.0168)

Foreign share 0.0365 -0.0113

(0.0723) (0.0747)

Internal R&D Intensity 0.0703** 0.0958**

(0.0281) (0.0379)

External R&D Intensity 0.145 -0.109

(0.104) (0.0840)

National funds 0.266*** 0.0912

(0.0520) (0.0573)

Radical innovator 0.538*** 0.0142

(0.0452) (0.0485)

External collaboration 0.105*** 0.0883***

(0.0168) (0.0183)

Hightech 0.0674 0.158**

(0.0624) (0.0690)

Mediumtech 0.0385 0.205***

(0.0641) (0.0676)

Lowtech -0.00712 0.169***

(0.0610) (0.0652)

R-squared 0.108 0.021

Observations 2,080 2,080

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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