national innovation policies, innovation actors and firm
TRANSCRIPT
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National innovation policies, innovation actors and firm innovation
performance in India
Srinivas Kolluru
Department of Economics
Macquarie University, Sydney
Australia
E-mail: [email protected]
Abstract: Innovation output by a firm is essential for gaining competitive advantage over its
competitors in any sector or industry, particularly in the present era of open innovation.
Based on the data of 707 Indian enterprises during the period 2010-2012, the present study
explores the effect of government innovation support policies and external collaborative
efforts on the innovation outcome in the form of patent applications of firms based on the
National Innovation System (NIS) and Resource-based-View (RBV) approaches using the
technique of count data model. The findings show that there is significant positive
relationship between inter-firm collaborations, cooperation with universities and research
organisations and innovation performance of enterprises, of which the international
partnerships are more stronger than the domestic collaborations in the Indian context. Firm’s
internal capabilities in the form of training and up-gradation of the skill of human resources
have a positive influence on external innovative collaborations and innovation outputs. The
results also indicate the significance of government innovation support in fostering the firm’s
collaboration with international research universities and organisations as well as
international firms in demonstrating significant impact on the patenting activities of the
enterprises.
Keywords: Innovation performance, government innovation policies, contextual determinants
JEL codes: O31, O38, L14
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1. Introduction
The increasingly competitive global markets, where widely accessible knowledge-
base plays a dominant role, firms across the industries and sectors cannot afford to depend
entirely on their own R&D capabilities to stay ahead of all relevant technological
developments (Kafouros, Buckley, & Clegg, 2012). In the era of “open innovation” along
with the internal ideas, firms increasingly rely on external knowledge sources, resources and
networks to accelerate internal innovativeness, which are becoming inevitable for the creation
of successful innovations in the firms (Chesbrough H. , 2003)1. Chesbrough et al., (2006)
averred that these collaborations and external cooperation enable the firms to access
knowledge, technical know-how which fortify the innovative capacity of firms leading to
knowledge creation and innovation. The literature on innovation economics has highlighted
the growing significance of external knowledge sources, i.e., external collaborations with
domestic and foreign universities, R&D institutions, laboratories, and with other companies
or entities on firm’s innovation activities and performance (Trigo & Vence, 2012; Wu, 2012;
Vega-Jurado, Gutiérrez-Gracia, & Fernández-de-Lucio, 2009). According to Tomlinson
(2010) and Trigo & Vence (2012) firms occasionally innovate in isolation and the probability
of innovating and intensity of innovation escalates when external collaborators are involved
in the process. Of late, several researchers (Kang & Park, 2012; Wu, 2012; Trigo & Vence,
2012; Tomlinson, 2010; Zeng, Xie, & Tam, 2010; Tether & Tajar, 2008; Tether, Who co-
operates for innovation, and why: an empirical analysis, 2002) emphasised the importance of
external collaborations and knowledge for firm innovation performance, which lead to the
development of new products and processes. When firms enter into collaborations and
partnerships, acquire knowledge and resources they lack that are highly associated with
innovation activities (Lee & Wong, 2009; Tether, 2002).
In recent times, several National and State government’s huge investments in
innovation support programmes in developed as well as in emerging countries in the form of
R&D subsidies, grants, incentives, and framing innovation support policies also resulted in
1 Industries including computers, semiconductors, telecommunication equipment, pharmaceuticals,
biotechnology, military weapons and communication systems – transitioned from closed to open innovation.
Research shows, the number of critically important innovations in these sectors have increased drastically.
These industries focus has shifted from depending on internal R&D laboratories to universities, research
institutions, and external organisations for innovations. Following the trend, currently, other industries are such
as automotive, healthcare, banking services, and consumer packaged goods also adopting on open innovation
models (Chesbrough H. , 2003).
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enhancing the effectiveness and innovative competitiveness of firms and justify the
continuation of those programmes (Lee & Wong, 2009; Sakakibara, 1997). The studies by
Godin & Gingras (2000), Hewitt-Dundas (2006), and Smallbone et al., (2003) stressed the
importance of government initiatives in stimulating and nurturing the innovation processes of
firms. Smallbone et al., (2003) found that the UK government policy measures directly and
indirectly encourages the enterprises to undertake product and process innovations. Similarly,
Hewitt-Dundas (2006) explained that the government support in the form of innovation
policy initiatives, promote innovation of small firms. While studying in the Canadian context,
Godin & Gingras (2000) found that the policy initiatives focuses on developing a stronger
relationship between universities and enterprises through strategic programmes to promote
innovation in firms. However, according to Lee & Wong (2009), there is little evidence in the
literature about the interaction effects between government innovation support programmes
and inter-firm collaborations and their influence on firm innovation. Interestingly, the
empirical results by Wong & He (2003) highlighted an integrated view of firm level
innovation and argued that the effect of governemnt innovation support policies on firms is
directed towards establishing a strong relationship between firm and the organisational
contextual indicators. If the effect of these contextual indicators are ommitted from the
analysis, the effect of policy measures on firm innovativeness may be unclear.
The study of interactive effects between government R&D support programmes and
the firm’s endogenous and exogenous factors are crucial because, the government innovation
policies may provide stimulus to firms to understand the external processes, internal
capabilities, and competence to engage in external collaborations for innovation success.
Based on this idea, the present analysis used the theories of National Innovation System
(NIS) approach and Resource-Based-View (RBV) approach of firm in defining the
framework and to offer an integrative analysis of the influence of government innovation
support schemes and contextual indicators i.e., external collaborations and partnerships on the
innovation performance of firms.
1.1 The research gaps and context
In economic literature, it is argued that those firms achieve higher growth rate and
favourable terms of trade which specialise in knowledge intensive or innovative products.
This is the reason why, the policy makers worldwide trying to formulate policies which
stimulate expenditure on R&D and increase the innovation efficiency of the enterprises.
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While highlighting the potential sources of competitive advantages for enterprises, Porter
placed innovation at the top of the list and argued that innovation is more sustainable than
those sources which are simply based on price (Porter M. , 1990; 1985). The existing large
body of innovation economics literature dedicated to the study of internal factors such as firm
size, age, ownership, R&D investments, R&D intensity, in-house R&D, number of R&D
personnel, diversification, etc., external factors like foreign direct investment, market
concentration, export activity and export intensity, royalty payments, and contextual
indicators like external collaborations, mode of technology transfers, etc, to illustrate the
innovation and firm performance relationship (Bogliacino & Pianta, 2013).
Especially, in the innovation literature, the last two decades have witnessed
publication of large number of studies on the subject, the impact of contextual factors on firm
innovativeness i.e., firm’s external collaboration with R&D institutions & firms (national and
international) and their influence on innovation performance. The previous studies provided
meaningful insights showing that collaborations provide technological knowledge support for
new product development and enhance firm’s patenting success (Mindruta, 2013; Perkmann,
King, & Pavelin, 2011; Ponds, Van Oort, & Frenken, 2010; George, Zahra, & Wood, 2002).
The previous assumptions and findings on this subject are largely investigated in the context
of developed or Western countries (Kafouros, Wang, Piperopoulos, & Zhang, 2015). Eom &
Lee (2010) and Eun et al., (2006) investigated the reasons for the focus of Western world in
these studies is, the developed counties are characterised by well-established institutions and
national innovation systems, and world-class universities and R&D institutions. The
emerging economies differs from developed counties on these grounds, which is acting as a
strong barrier in understanding the role of external collaborations in enhancing the innovation
performance of enterprises in emerging economies. In the context of an emerging country
like India, an important gap where empirical researchers paid little attention to study the
influence of some external factors like government innovation policies (Seker, 2011; Ghosh,
2009) and contextual factors (external collaborations) which has profound implications on
innovation outcome of enterprises especially with the firm-level data (Tripathy, Sahu, & Ray,
2013; Sharma, 2012).
The empirical setting of the study encompasses enterprises in India. India spends
about 0.90% of GDP on R&D and has demonstrated a very high compound annual growth
rate during 2008 to 2012 (Westmore, 2013; WDI, 2012). In India, the R&D spending of the
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public sectors constitutes 80% of the total spending and 20% by the private sector. Data
shows that the country’s R&D spending has been stagnant over the past few years, but the
country has witnessed notable growth in patent filing by the enterprises (on an average of
15% increase) during 2005–2011. During this period, changes have been taken place in the
Indian patent policy in accordance with the agreement on Trade-Related Intellectual Property
Rights under the WTO (Ambrammal & Sharma, 2014). Indian enterprises is an ideal subject
of study because of few reasons: (i) the National Innovation Act 2008 of India set the goals
for the country becoming one of the most competitive knowledge based economies of the
world. (ii) like other emerging economies such as Indonesia, Malaysia, Brazil, South Africa,
Thailand, Argentina, Turkey, the Government of India (GoI) has also introduced a wide range
of innovation support schemes such as National Innovation Policy 2008, Decade of
Innovation 2010-2020 to provide innovative stimulus to firms, industry and sectors (GoI,
2011). Realising the importance of firm-level innovation, the Government of India
formulated a roadmap which includes the creation of State Innovation Councils and Sectoral
Innovation Councils with a focus on inclusive growth, foster inclusive innovation,
encouraging central and state governments, universities and R&D institutions, laboratories,
etc, to facilitate enterprises in the country to become globally competitive on one hand and to
respond to the needs of enterprises in the respective states and sectors on the other (GoI,
2011). (iii) In addition, it is believed that technological capabilities and competitiveness will
be enhanced by linking the knowledge entities with firms in the national innovation system.
Therefore, the GoI developed Global Innovation Roundtable (GIR) enabling national and
international collaborations on innovation, (iv) fast economic growth since the economic
reform period in general and since the early 2000’s (average growth of 6-8%) and it has been
playing an increasingly important role in the world economy. (v) On R&D front, India’s total
R&D expenditure is 0.90% of GDP in 2010 (WDI, 2012) and the country ranks 23 globally in
high-technology exports (aerospace, computers, pharmaceuticals, scientific instruments, and
electrical machinery).
Bowonder et al., (2006) highlighted that India is emerging as a global hub for low
cost R&D and high value innovative products and services. Also, Bowonder et al., (2006)
referred a workshop at Harvard Business School, which states that ‘India is ranked as the
most preferred destination for locating Innovation centres’. Govindarajan (2007, p. 86) stated
that “acquisitions by Indian enterprises in the global market signify an increasing trend by
leveraging the various possibilities of Innovation that the global market offers”. To ensure the
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survival of Indian enterprises in the global competition, the enterprises have to increase their
technological knowledge-base and improve their innovation capabilities. At the same time, as
Indian enterprises moving up the value chain to produce more innovative products, empirical
examination of the internal innovation capabilities and influence of government innovation
support policies and external knowledge sources on innovation performance are increasingly
important.
1.2 Objectives and research questions
The main objective to undertake in this study is to understand the important external
and contextual factors affecting the innovation performance of Indian enterprises particularly
in the era of “open innovation” and in the light of development of new innovation policies by
the government of India to enable the firms in different states and sectors to enhance the
innovative advantage. In this paper, we examine the effects of government innovation support
measures and external collaborative efforts of firms on their innovation performance in the
Indian context. We examined the direct effect of external collaborations on firm
innovativeness as well as indirect effects of government innovation support measures
interacted by firm’s internal innovation capabilities and external collaborations on the
innovation performance. The research questions that motivated this study are as follows: (i)
which identified types of external collaborations are conducive to the innovativeness of
enterprises, (ii) what are the effects of external collaborations distinguished by partner’s
location (national and international) on the innovation performance, and (iii) what is the role
of government innovation support measures in enhancing the firm innovativeness?
Particularly, from an emerging country like India’s point of view, these types of questions
were never asked or examined in the previous studies.
This paper has got some policy relevance. It aims to contribute to the ongoing policy
debate on the impact of government innovation support policies on firm innovation
performance as well as on the role of firm’s external networks (local and non-localised
learning) in facilitating innovation. This research differs from previous empirical work in at
least two main ways. First, to the best of our knowledge, our work is the first to analyse the
effect of government innovation support programmes (State and Sectoral Innovation
Councils by the Government of India) and firm’s external collaborations (national upstream
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& downstream and international upstream & downstream)2 on innovation performance
among Indian enterprises using a panel data estimation methods. Secondly, our approach is
comprehensive in the sense that we take into account different data samples and explanatory
variables along with the internal, external and contextual factors that have been argued to
affect the firm’s innovation performance in the literature.
The rest of the chapter is set out as follows. Section 2 presents the theoretical
background and review of literature along with the hypotheses for analysis. It also presents a
conceptual framework that integrates the theories of National Innovation System (NIS)
approach and Resource-Based-View (RBV) approach. Data sources, variables description
and methodology are discussed in Section 3. We present the empirical results, analysis and
discussion in Section 4. Section 5 discusses the conclusions, implications and limitations of
the study.
2. Theoretical background and literature review
Over the last two decades, tremendous changes have been indicated in the innovation
literature with reference to the firm’s innovative activities as a result of interactive learning
characterised by external collaborations and networks by the firms irrespective of size and
age (Doloreux, 2004; Hagedoorn, 2002). Dewick & Miozzo (2004) witnessed that inter-firm
and cross-sectional collaborations have emerged as a key strategy which accelerates the flow
of technical-knowhow and resources among the firms that are necessary for innovation
success. According to Hewitt-Dundas (2006) external innovation networks provide the firms
stimulus and capacity to innovate, whereas lack of innovative collaborations had a negative
impact on firm innovativeness. External networking is an important dimension of open
innovation and they are complimentary (Chesbrough, Vanhaverbeke, & West, 2006;
Chesbrough H. , 2003). Based on a study on Japanese small firms, Fukugawa (2006)
observed that external collaboration speed up the innovation process and provides access to
resources and expertise require for innovation. Additionally, innovation success requires
access to resources which are complimentary to the firm innovativeness. In addition,
researchers (Zeng, Xie, & Tam, 2010; Doloreux, 2004) indicate that generally government
establishes and support universities, R&D labs and other public institutions to enhance the
2 Park (2006) proposed the value-chain based innovation system (VIS) approach and in this approach the firms
are categorised into upstream and downstream enterprises. The upstream alliances include collaboration with
universities, R&D institutions and laboratories, whereas, downstream includes firm’s collaboration with
domestic and international firms.
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knowledge and innovation-base of the economy and local enterprises. Zeng et al., (2010) and
Doloreux (2004) also recognised that the government policy measures in the form of
innovation support programmes, strategic programmes, or R&D support schemes have strong
impact on the effectiveness of collaborations with external organisations and enable firms to
make their innovative activities more active and vibrant. Mohnen & Röller (2005) argue that
innovation success is influenced by many inter-related factors and the inter-relatedness is
described as complementary. According to Mohnen & Röller (2005, p. 1431)
“complementarity between a set of variables means that the marginal returns to one variable
increases in the level of any other variable”. The complementarities and integrative
dimension paved the way for examining the influence of various inter-firm, contextual and
inter-related factors on the firm’s level of innovativeness.
Following this view, the research integrated the concepts from National Innovation
System (NIS) approach and Resource-Based-View (RBV) approach. To get a fair
understanding of the usage of a specific concept such as NIS approach (using the term
“national system of innovation” “national innovation system”) in Google Scholar, the search
engine finds about 17,800 hits for the period 1980-2014. The obtained references shows that
the concept has been widely used and adopted by researchers and policy makes in many
countries including developed as well as emerging countries, and experts in international
organisations (for more literature on NIS approach see Lundvall (2007)). According to Kang
& Park (2012, p. 69) “NIS actors include private enterprises, universities, public research
institutions, and government agencies. The NIS approach allows government intervention in
the form of industry policy such that resources are effectively allocated to foster innovation”.
The NIS or national innovation policy is the interactive coordination of institutions,
universities, and public and private organisations to produce, diffuse and exploit the
knowledge. The interaction can be achieved through collaboration and network agreements
(Lundvall, 2010; Carlsson, 2006). It is a dynamic tool for formulating and planning the socio-
economic development with the help of technology and innovation as the main determinants
(Wonglimpiyarat, 2011) which also provided insights on the role of universities, government
agencies, public and private firms as the important actors in shaping the national innovation
systems where collaboration among them is important (Lundvall, 2010).
The RBV as emerged as one of most influential and cited theories in innovation and
management literature, which emphasises that a firm must acquire and control valuable, rare,
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inimitable, and non-substitutable (VRIN) resources and capabilities to achieve sustained
competitive advantage (for more detailed explanation see (Kraaijenbrink, Spender, & Groen,
2010)). The RBV approach was pioneered by Penrose (1959) who argued that a firm is an
economic unit, which is a combination of internal productive resources and strategic
management issues. A recent study by Lee & Wong (2009) highlights RBV is based on two
key concepts such as resources and capabilities. Resources are linked to the firm in the form
of tangible and intangible assets, whereas capabilities are achieving the innovation targets
based on those available resources. In RBV approach, the firm’s internal resources explains
the differences in the performance of firms in the same industry, and also focuses on firm’s
external environment which acts as the main determinant of firm performance (Kraaijenbrink,
Spender, & Groen, 2010). Galende & de la Fuente (2003) observed that in innovation studies,
RBV approach is mainly used to analyse the effect of firm’s internal resources on innovation
performance, however, the approach is extended to include the effect of external resources on
firm’s competitive advantage, because, firms require strong in-house capabilities to maintain
successful external technological collaborations.
Building on the existing recent NIS and RBV studies (Ebersberger & Herstad, 2013;
Wu, 2012; Trigo & Vence, 2012; Kang & Park, 2012; Tomlinson, 2010; Zeng, Xie, & Tam,
2010; Falk, 2007; Hadjimanolis, 1999) the present research examined the influence of
external factors in the form of government innovation policy measures and contextual factors
like external collaborations on firm innovativeness. Against this backdrop, the study employs
a panel of 707 innovative3 Indian enterprises during the period of 2010-2012 using Count
Data models. Relevant literature on the relationship of constructs and comparison of the
research findings with the previous findings are discussed in Appendix 1.
2.1 Hypothesis development
2.1.1 Past research on the determinants of innovativeness
Successful innovation outcome involves the interaction and dissemination of
knowledge. The previous empirical studies used various streams such as firm-level, external
and contextual characteristics to explain the innovative behaviour of the firms. The present
study also included the variables as possible factors determining the firm innovativeness.
However, in the context of an emerging economy like India, the interactive relationships 3 We used the word ‘innovative’ because the firms’ selection has been done based on the availability of R&D
expenditures data for those firms in the CMIE Prowess database. Only the firms that reported R&D
expenditures in at least three years have been included in the analysis.
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between different streams and their impact on the innovation outcome has not been integrated
in the previous studies. Hence, the present study made an attempt to understand the role
played by the various factors in determining the innovation performance, with particular
reference to the effect of government innovation support policies and external collaborative
efforts on firm innovativeness. Based on the above literature support and discussion, the
research hypothesised that:
2.1.2 Government innovation support
The NIS approach provides foundation for government intervention in the form of
policies to support the firms for effective allocation of resources to foster innovation. The
government innovation support to promote firm innovativeness is justified by strong
economic justification. The complications such as system and network problems which
cannot be solved by the market forces are justified by the government policy interventions
(Chaminade & Edquist, 2006). The support in the forms of subsidies, tax incentives, loans,
fostering certain sectors, etc., stimulates innovation and R&D-allied or patenting activities
(Souitaris, 2002; Hadjimanolis, 1999; Freel & De Jong, 2009; Tether, 2002; Falk, 2007;
Tang, 2006; Kang & Park, 2012; Ganter & Hecker, 2013). In recent years, governments in
developed as well as in emerging countries world-wide encouraging innovation and
economic growth by framing conducive R&D policies and supporting R&D activities to
create high social rate of return (Feldman & Kelley, 2006). Furthermore, the government
plays an investor role through innovation policies to support firms’ financially in R&D
activities and encourages networking activities among firms involved in the innovation
development (Kang & Park, 2012). Therefore, we tested the following hypothesis:
Hypothesis 1a (H1A): The government innovation support in the form of SINC is positively
related and enhances firm’s innovation outcome.
Hypothesis 1b (H1B): The government innovation support in the form of SEINC enhances the
firm innovation performance.
2.2.3 Inter-firm collaborations
The importance of inter-firm collaborations in enhancing the internal innovative
activities of the firms has been recognised in the past literature (see (Freeman, 1991) for a
detailed review). Previous research revealed the positive impact of collaborations with other
firms on innovation performance i.e., innovative products (Faems, Van Looy, & Debackere,
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2005; Loof & Heshmati, 2002; Klomp & Van Leeuwen, 2001), patent performance
(Vanhaverbeke, Duysters, & Beerkens, 2002; Ahuja, 2000) and also new knowledge is
persistently developed by external agents, business entities, and competitors in a fast pace
and size (Belussi, Sammarra, & Sedita, 2010; Vanhaverbeke, Van de Vrande, & Cloodt,
Connecting absorptive capacity and open innovation, 2008; Romijn & Albaladejo, 2002;
Ahuja, 2000). Further, Kang & Lee (2008) and Romijn & Albaladejo (2002) confirmed that
inter-firm collaborations help firms in overcoming the scientific knowledge and resources
deficiencies and improve the internal competencies. The inter-firm collaborations contribute
to the effectiveness of innovation outcome in numerous ways (Faems, Van Looy, &
Debackere, 2005). First, the inter-firm collaboration provides access to the complementary
resources required for commercialisation of innovations (Hagedoorn, 2002; 1993). Second,
collaborating with other organisations improves the internal competencies of the firms
through transfer of codified and scientific knowledge which result in the generation and
development of necessary resources (Kang & Lee, 2008; Ahuja, 2000). Lastly, inter-
organisational partnerships reduce the transaction costs, risks and uncertainties associated
with innovation-intensive activities, leading to increased productivity (Mention, 2011; Zeng,
Xie, & Tam, 2010; Vega-Jurado, Gutiérrez-Gracia, & Fernández-de-Lucio, 2009; Hagedoorn,
2002).
External collaborative efforts or partnerships are an important dimension of open
innovation (Chesbrough H. , 2003; Chesbrough, Vanhaverbeke, & West, 2006). To sustain in
the current globalised competitive environment, the enterprises are in search for and access to
external knowledge and resources through external partnerships (Qiao, Ju, & Fung, 2014).
The research on the influence of domestic and international firm collaborations on firm’s
innovation output in the Indian context is limited (Sasidharan & Kathuria, 2011; Kathuria,
2010). Thus we examined the following hypotheses on the effects of national and
international inter-firm collaborations on innovation.
Hypothesis 2a (H2A): Domestic inter-firm collaborations are positively associated with firm
innovation performance and enhance the firm innovativeness.
Hypothesis 2b (H2B): International inter-firm collaborations are positively related to
innovation output of firms and enhance the firm innovativeness.
2.4.4 Firm-research organisation linkages
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Firm’s linkages with the research organisations is considered as innovation stimulus
and the role of universities, technical institutes, and R&D centre’s as drivers of innovation
and change, has been emphasised by several authors as collaboration in these networks
integrate the firm with partners, reduce the transaction costs, risks and attempt to correct
market uncertainties as well as access to each other’s resources, leading to improved
productivity (Zeng, Xie, & Tam, 2010; Vega-Jurado, Gutiérrez-Gracia, & Fernández-de-
Lucio, 2009; Mention, 2011; Leiponen & Byma, 2009; Kang & Park, 2012). Generally, the
collaboration with research organisations is a vital source of new scientific and technological
knowledge for the enterprises in developing countries, the reason is they were endowed with
strong educational institutions, universities, which has direct impact on the firm’s innovation
activities (Zeng, Xie, & Tam, 2010; Liefner, Hennemann, & Xin, 2006). In their study on the
role of universities in Malaysia, Razak & Saad (2007) indicated that research organisations
became the “seedbed” in providing knowledge and skills to the new industries. The
relationship with the research organisations enables the firm to achieve sufficient knowledge
and competencies to succeed in the patenting activities (Fritsch & Franke, 2004) and lack of
linkages with these institutions could hamper the innovation performance of firms (Kaminski,
de Oliveira, & Lopes, 2008). Hence, we hypothesised that:
Hypothesis 3a (H3A): Collaborations between enterprises and domestic research
organisations are positively related with their innovation performance.
Hypothesis 3b (H3B): Collaborations between enterprises and international research
organisations are positively related with their innovation performance.
2.2 Conceptual framework
On the basis of the reviewed literature, a conceptual framework is designed by
integrating the theories of NIS and RBV in Figure 1. The framework indicates innovation is a
continuous process with separate but interacting, interdependent and inter-related various
internal, external and contextual factors linking together with a complex net of innovation
paths. The framework describes the influence of firm’s internal, external and contextual
factors on its level of innovative outputs. The major thrust of this research is there is a
positive relationship between inter-firm and extra-firm collaboration, firm’s internal
capabilities and external support and innovation performance of the enterprises. Furthermore,
it supposes that influence of government innovation support activities will have a positive
influence on cooperation networks and firm’s innovative output.
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Figure1: National Innovation System and Resource-based-view model of firm’s innovative behaviour
3. Data sources and variables
The main data sources for this study are (i) Indian Patent Office for information on
patents (http://www.ipindia.nic.in/: Accessed on 25 November 2014); (ii) Centre for
Monitoring Indian Economy’s (CMIE) Prowess database4 (for firm-level indicators); (iii)
Capitaline-2000 database5 (for number of external collaborations and firm-level indicators),
and (iv) National Innovation Council, Government of India on the government innovation
support schemes (http://innovationcouncilarchive.nic.in: Accessed on 15 January 2015).
Other sources comprised: websites of enterprises, annual reports of the enterprises and
national stock exchange reports. The website of Indian Patent Office administered by the
Controller General of Patent Design and Trade, Department of Industrial Policy and
Promotion, Ministry of Commerce and Industry, Government of India, provides information
on patents applied by and granted to the enterprises in the country. The variables, measures
and sources are shown in Table 1.
As indicated in the previous section, the study uses panel data for the period from
2010-2012. The number of firms in each year is 707, with a total of 2121 observations for the
3 years, covering the industries such as automotive, automobile ancillaries, biotechnology,
4 CMIE’s Prowess is a firm-level dataset created and maintained by Centre for Monitoring Indian Economy
(CMIE), an independent and largest private sector think-tank in India. It is similar to the Compustat (US firms
database) and the Financial Analysis Made Easy (FAME) (database on UK and Irish firms). The Prowess
database contains information of on around 28,000 companies (manufacturing, services and construction
companies) and 3,500 data fields per company. The database has been increasingly used in the literature for
firm-level analysis dealing with the issues like determinants of innovativeness, firm performance, etc (to cite a
few (Ghosh, 2012; Marin & Sasidharan, 2010; Ghosh, 2009)). 5 The data in the Capitaline-2000 database is compiled from the audited annual reports of more than 10,000
enterprises in India listed on the Bombay Stock Exchange.
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information technology, petroleum, drugs and pharmaceuticals, electrical and electronics,
machinery, equipment, agro products, etc., based on the National Industrial Classification
(NIC) 2008 codes. Pooling of data is carried out to take care of any year-specific
abnormalities in the data. Firm-level indicators on patents applied, R&D intensity, training &
development expenses, size, age, ownership status, presence or absence of diversification;
external indicators in the form of government innovation support measures like State and
Sectoral Innovation Councils, and contextual factors like domestic and international upstream
& downstream collaborations is collected for analysis.
Table 1: Variables, measures and data sources
Variable Definition Data Source
Dependent Variables
PATAPP (Patent
applications)
Total Number of applications to Controller
General of Patents, Designs & Trade
Marks (CGPDTM)
Controller General of Patents, Designs &
Trade Marks, Department of Industrial
Policy and Promotion, Government of
India
Independent Variables
R&D Intensity -R&DINT Ratio of the R&D spending to sales Prowess Database from CMIE; Capitaline
Databse
Training & Development
Expenditure - TRAINEXP
Staff training and development expenditure Prowess Database from CMIE; Capitaline
Databse
State Innovation Council -
SINC
Dummy variable capturing the presence of
State Innovation Council, where (States)
the enterprise is operating - 1 if the state
has Innovation Council, 0 otherwise
National Innovation Council, Government
of India
Sectoral Innovation Council
- SeINC
Dummy variable capturing the presence of
Sectoral Innovation Council in which
(Sectors) the enterprise is operating - 1 if
the Sector has Innovation Council, 0
otherwise
National Innovation Council, Government
of India
Domestic upstream
collaboration -
DOMUPCOL
Total Number of technical collaborations
with domestic Universities and Academic
& R&D Institutions
Enterprises websites, Annual Reports of
the enterprises, CRISIL Research Reports,
National Stock Exchange Reports,
International upstream
collaboration - INTUPCOL
Total Number of technical collaborations
with International Universities and
Academic & R&D Institutions
Enterprises websites, Annual Reports,
National Stock Exchange Reports,
Domestic downstream
collaboration - DOMFIRM
Total Number of technical collaborations
with domestic firms
Enterprises websites, Annual Reports,
National Stock Exchange Reports,
International downstream
collaboration - INTFIRM
Total Number of technical collaborations
with International firms (firms outside the
country)
Enterprises websites, Annual Reports,
National Stock Exchange Reports,
Control Variables
AGE (age of the firm) Number of years since established Prowess Database from CMIE
DIVERSIFIED
(diversification)
Dummy variable to capture the presence of
the firm in diversified businesses – 1 if
diversified, 0 otherwise
Prowess Database from CMIE
SIZE (size of the firm) Log of total assets Prowess Database from CMIE
OWNERSHIP1 Dummy variable to capture the
Government ownership – 1 if Government
Ownership, 0 otherwise
Prowess Database from CMIE, Capitaline
Database
OWNERSHIP2 Dummy variable to capture the presence of
Foreign Ownership – 1 if Foreign owned, 0
otherwise
Prowess Database from CMIE, Capitaline
Database
3.1 Definition of variables
15
The definitions of the studied variables with their abbreviations are presented in Table
1. These variables were drawn from the previous literature regarding innovativeness, national
innovation policies, and external collaborations.
Dependent variable: The firm innovativeness is often measured by the indicator patent
applications, which is a proxy for innovation and well-established source to measure
innovative activities of the firms (Akçomak & Ter Weel, 2009). Patent based indicators are
the most appropriate and frequently used measures in the firm innovation studies (Qiao, Ju, &
Fung, 2014; Cruz-Cázares, Bayona-Sáez, & García-Marco, 2013; Fu, 2012; Kang & Park,
2012; Banerjee & Cole, 2010; Leiponen & Byma, 2009; Álvarez, Marin, & Fonfría, 2009;
Blind, Edler, Frietsch, & Schmoch, 2006). Patent applications clearly capture the information
about the technologies, and products generated as an outcome of innovation activity (Blind,
Edler, Frietsch, & Schmoch, 2006). Hence, we used number of patent applications (PATAPP)
by the firms to the Indian Patent Office as a measure of innovation performance in our study
instead of patents granted. The patents granted data may undervalue the real innovations
attained by the firms (Qiao, Ju, & Fung, 2014). We measure the dependent variable,
𝑃𝐴𝑇𝐴𝑃𝑃𝑖𝑡 as the number of patent applications for firm 𝑖 in year 𝑡. We manually collected
the patent applications data for every singly company in the sample, and the major source of
the data is the website of Controller General of Patents, Designs & Trade Marks, Government
of India. The firm selection has been done based on the availability of R&D expenditures
data for those firms in the CMIE Prowess database. Only the firms that reported R&D
expenditures in at least three years have been included in the analysis.
Independent variables: The studies by Zeng et al., (2010), Hewitt-Dundas (2006), and
Smallbone et al., (2003) stressed the initiatives of the government in helping the firms to
promote their innovation capabilities through new policies and programmes. Furthermore, the
government plays an investor role through innovation policies to support firms’ financially in
R&D activities and encourages networking activities among firms involved in the innovation
development (Kang & Park, Influence of government R&D support and inter-firm
collaborations on innovation in Korean biotechnology SMEs, 2012). We used the
government innovation support policies is a proxy variable, which consists of state
innovation council (SINC) and sectoral innovation council (SEINC). If the firm operating in a
state where there is SINC, firm is coded 1, and 0 otherwise. Similarly, if the enterprise is
16
operating in a sector which has SEINC, such as cement, textiles, information technology,
pharmaceuticals, automotive, electronics, etc., the firm is coded as 1, and otherwise 0.
In the literature, R&D expenditure and the R&D intensity is often used as a measure
to represent the R&D investment of the firm. Kang & Park (2012), Griffiths & Webster
(2010), Cefis & Marsili (2006), and Romijn & Albaladejo (2002) calculated the R&D
intensity as ratio of R&D expenditure to the sale. In our study, we used the ratio of R&D
expenditure to sales as measure of R&D intensity (RNDINT). Generally, firms require a pool
of qualified human resources particularly engineers and scientists to absorb, modify and
create new technologies (Romijn & Albaladejo, 2002) and firm’s failure in recruiting
qualified human capital is a serious concern (Hoffman, Parejo, Bessant, & Perren, 1998).
Therefore, firms enrich their human capital through investment in on-the-job and internal or
external staff training (Romijn & Albaladejo, 2002). We selected the absolute values of the
firm’s training and development expenditure (TRAINEXP) as one of the internal indicators
of innovation.
During the last decade increasing number studies examined the impact of
collaborative efforts on firm innovation performance. The previous research suggests that
firms innovation activities advances by interaction with collaborators especially universities,
research institutes and competitors. The collaborative efforts integrate the firm with partners,
reduce the transaction costs and risks, and provide access to each other’s resources, leading to
increased productivity (Zeng, Xie, & Tam, 2010; Vega-Jurado, Gutiérrez-Gracia, &
Fernández-de-Lucio, 2009; Mention, 2011). To examine the different effects of collaboration,
we adopted the approach of Kang & Park (2012) and classified the external collaborations
into two categories: upstream & downstream and four sub-categories: domestic upstream &
international upstream and domestic downstream & international downstream collaborations.
The upstream alliances include number of collaborations with national and international
universities (DOMUPCOL & INTUPCOL), R&D institutions and laboratories, whereas,
downstream includes firm’s collaboration with domestic and international firms (DOMFIRM
& INTFIRM).
Control variables: Finally, we include a variety of control variables which has profound
implication on the innovation outcome of enterprises such as firm age, size, diversification,
and ownership status. Firm’s experience in knowledge accumulation and learning over a
17
period of time influences the innovativeness. The aged firms would have more effective
capacity for absorption to innovate than those of younger firms (Martín-de Castro, Delgado-
Verde, Navas-López, & Cruz-González, 2013; Wu, 2012; Love, Roper, & Bryson, 2011;
Kumar & Saqib, 1996). We calculated the age (AGE) based on the years and months of
firm’s operation since its establishment (Qiao, Ju, & Fung, 2014; Rhee, Park, & Lee, 2010).
Firm size is a well-researched determinant of the innovativeness of firms. Larger the firm
size, greater would be the possibility of using resources for innovative activities than the
smaller firms, and hence, size increases the nature of the innovativeness (Stock, Greis, &
Fischer, 2002; Beneito, 2003; Kannebley Jr, Porto, & Pazello, 2005; Blind, Edler, Frietsch, &
Schmoch, 2006; Coronado, Acosta, & Fernández, 2008; Banerjee & Cole, 2010; Galende &
de la Fuente, 2003; Tomlinson, 2010; Clausen, Korneliussen, & Madsen, 2013). We took
firm size (SIZE) as the natural log of total assets at the end of the year (Qiao, Ju, & Fung,
2014).
Research evidence shows both positive and negative effects of diversification on
innovation performance (Kafouros, Buckley, & Clegg, 2012; Hitt, Ireland, & Hoskisson,
2012; Ahuja, 2000; Hitt, Hoskisson, & Ireland, 1994). We do not make any prediction on the
effect of diversification on the innovation performance by including the variable. We used the
diversification as dummy variable (DIVERSIFIED) if the firm has diversified businesses,
coded as 1, otherwise it was 0. Finally, we control for ownership status in examining the
impact of the variables on firm’s innovation outcome. The ownership structure is considered
as an internal source and there are mixed results with regard to the effect of ownership on
innovation. We constructed the variable by including two dummy variables in our
estimations. First dummy variable captures the government ownership (OWNERSHIP1), if
the firm is government owned, coded as 1, otherwise 0. Second dummy variable captures the
private ownership and foreign presence. If the firm is privately owned or firm with foreign
participation (OWNERSHIP2), it was coded as 1, otherwise zero.
3.2 Econometric Model and estimation method
The dependent variable in this study is the count of patent applications (innovation
performance) by firms, which takes only non-negative integer values. Estimation of linear
regression model is inappropriate for modelling this type of variable because the model
assumes homoscedastic, normally distributed errors (Schilling & Phelps, 2007; Ahuja, 2000).
For this nature of variable, researchers suggest using Count Data models (Greene W. H.,
18
2008; 2003; Hausman, Hall, & Griliches, 1984) is appropriate and our analysis is based on
the Poisson and Negative Binomial models. The unconditional Poisson probability equation
is stated as:
Pr(𝑌𝑖𝑡 = 𝑦𝑖𝑡) =𝑒𝜆𝑖𝑡𝜆𝑖𝑡
𝑦𝑖𝑡
𝑦𝑖𝑡! for 𝑦𝑖 = 0, 1, 2, … … (1)
Where 𝑦 is indicating the number of times an event has occurred (number of patents applied
in firm 𝑖 in the year 𝑡) and 𝜆 is the observable expected (mean) rate of incidences of all 𝑖
firms during a specific period 𝑡 (in our study, 2010-2012); 𝜆𝑖 = 𝐸(𝑦𝑖) = 𝑉𝑎𝑟(𝑦𝑖). The
Poisson regression model assumes that the parameter 𝜆 for each 𝑖 is characterised by some
explanatory variables, 𝑋𝑠. Parameters 𝛽𝑠 are estimated by fitting the following equation:
𝜆𝑖 = exp(𝑋𝑖𝑡𝛽) (2)
Where 𝑋𝑠 are the independent variables defined in the Table and 𝛽𝑠 are the parameters to be
estimated. The model may be estimated by the maximum likelihood method (MLE). The
Poisson model provides a robustness property: 𝛽𝑠 are consistent (but inefficient) even if the
assumption of 𝜆𝑖 = E(𝑦|𝐱) = Var(𝑦|𝐱) does not hold (Wooldridge, 2010). In such cases, the
quasi-maximum likelihood estimation can be applied to retain some efficiency for certain
departures from the Poisson assumption (Wooldridge, 2010). The approach assumes the
variance is equal to the mean, i.e. E(𝑦|𝐱) = 𝜎2 Var(𝑦|𝐱) and thus adjustments to the standard
errors are allowed even 𝜎2 > 1 (over-dispersion) and 𝜎2 < 1 (under-dispersion).
The Poisson model is inappropriate to use when the variance of patent applications
data is significantly different from the mean. Patent related data frequently demonstrate over-
dispersion, where the variance is not proportional to the mean (Hausman, Hall, & Griliches,
1984), which appears true in our case, where we have observed an over-dispersion
phenomenon. The presence of over-dispersion leads to spurious high levels of significance
because of the consistently estimated coefficients and underestimated standard errors
(Cameron & Trivedi, 2013). Negative binomial (NB) model is an alternatively developed
model, which is also an extension of Poisson model that allows estimation of over-dispersed
count data. The over-dispersion of count data is normally due to the presence of non-
observable heterogeneity (Cameron & Trivedi, 2013). The NB model addresses this issue by
assuming that a degree of non-observable heterogeneity exists, which is distributed according
a Gamma function. The negative binomial model relaxes the assumption of Poisson model by
19
re-specifying E(𝑦𝑖) = 𝜆𝑖 and Var(𝑦𝑖) = 𝜆𝑖[1 + (1
𝜃) 𝜆𝑖]. As 𝜃 → 0, Var(𝑦𝑖) is inflated and
thus over-dispersion is addressed; as 𝜃 → ∞, Var(𝑦𝑖) → 𝜆𝑖 such that it returns to a simple
Poisson model if 𝜃 is significantly different from zero. Hence, the negative binomial model is
a valid extension of the Poisson model that takes into account the problem of over-dispersion.
The Negative binomial probability equation is stated as:
𝑃(𝑦𝑖) =𝑒
(−𝜆𝑖𝑒𝜀𝑖)(𝜆𝑖𝑒𝜀𝑖)𝑦𝑖
𝑦𝑖!, (3)
In the equation.3 𝜀𝑖 is as follows:
𝑃(𝑦𝑖) =Γ((
1
𝛼)+𝑦𝑖!)
Γ(1
𝛼)𝑦𝑖!
(1/𝛼
(1/𝛼)+𝜆𝑖) 1/α (
𝑦𝑖
(1/𝛼)+𝜆𝑖) 𝑦
𝑖 , (4)
In the equation. 4, Γ(. ) is a gamma function. The negative binomial model is also
estimated by the standard maximum likelihood method. The function is maximised to get
coefficient estimates for 𝛽 and 𝛼. The likelihood function is estimated as:
𝐿(𝜆𝑖) = ∏Γ((1/𝛼)+𝑦𝑖!
Γ(1/𝛼)𝑦𝑖!𝑖 (1/𝛼
(1/𝛼)+𝜆𝑖) 1/α (
𝑦𝑖
(1/𝛼)+𝜆𝑖) 𝑦𝑖 , (5)
In this study, the data is longitudinal with multiple observations for one firm on the
same unit over time. The repeated multiple observations on the same firm over time leads to
the problem of autocorrelation in the models. The traditional Poisson and negative binomial
models generally fail to account for unobserved heterogeneity. To address the issue of firm
heterogeneity, the generalized estimating equations (GEE) regression method is chosen to
model the data (Katila & Ahuja, 2002), which can account for autocorrelation (Liang &
Zeger, 1986). We considered Poisson and negative binomial link as the family function in the
GEE model to address the issue of over-dispersion of the data. The empirical analysis was
conducted on the basis of the following general specification (Wooldridge, 2010):
4. Results and analysis
4.1 Descriptive statistics
20
Descriptive statistics of the main variables of firms in our sample and correlations
between the independent variables are presented in Table 2 & 3. The research finds
significant correlations between the patent applications and firm’s training and development
initiatives & external collaborations. The variable government innovation support was
significantly correlated with innovation performance. It has been observed that some of the
independent variables are also correlated and the relationships obtained between the
independent variables are fairly low. The highest single correlation is being between the size
of the firm and international-firm collaborations and international upstream collaborations.
The indicated correlations do not show any serious problems of multicollinearity, as a rule-
of-thumb, multicollinearity problem arises most likely when inter-correlation values are
greater than 0.80 (Hair, Anderson, Tatham, & Black, 1998; Gujarati D. N., 1995; 2003).
Therefore, multicollinearity is not an issue in our study. We employed R software as a
statistical package to execute the econometric computations.
Table 2: Descriptive statistics (N=2121)
Min. value Max. value Mean Var Std. Dev
PATAPP 0.00 302.00 3.41 288.64 16.99
RNDINT 0.00 341.46 1.59 102.25 10.11
TRAINEXP 0.00 1421.20 14.90 4501.91 67.10
SINC 0.00 1.00 0.79 0.17 0.41
SEINC 0.00 1.00 0.89 0.10 0.31
DOMUPCOL 0.00 15.00 0.45 1.60 1.27
INTUPCOL 0.00 7.00 0.16 0.34 0.58
DOMFIRM 0.00 15.00 0.80 2.56 1.60
INTFIRM 0.00 29.00 2.02 9.88 3.14
AGE 1.00 115.00 37.60 450.94 21.24
DIVERSIFIED 0.00 1.00 0.03 0.03 0.17
OWNERSHIP1 0.00 1.00 0.06 0.05 0.23
OWNERSHIP2 0.00 1.00 0.59 0.24 0.49
It can be seen from the Table 3 that training & development expenses is positively
correlated to all the forms of collaborative efforts namely domestic upstream & downstream
and international upstream & downstream collaborations. This implies that firm’s spending
on training or employee skill development programmes provides access to the resources and
understanding of the technological innovation of the domestic or international competitors
which is inextricably linked with innovation performance. Firm size is also positively
correlated to training development and collaborative activities, which means larger the firm’s
size, larger the degree of association with different technological partners. Further, the
correlation coefficient between the state innovation policies and innovation performance is
21
positive and statistically significant. Therefore, the hypothesis (H1) that innovation policies
would positively influence the firm innovativeness is supported.
Table 3: Correlation coefficients of innovation, external & firm internal characteristics
1 2 3 4 5 6 7 8 9 10 11 12 13
1.PATAPP
2.RNDINT 0.00
3.TRAINEXP 0.37a -0.02
4.SINC 0.05b -0.02 0.05 b
5.SEINC 0.00 0.03 0.05 b -0.06
6.DOMUPCOL 0.11a 0.02 0.33a -0.03 0.06
7.INTUPCOL 0.35a 0.00 0.39a 0.06a 0.07 0.29a
8.DOMFIRM 0.22a -0.01 0.51a 0.04c 0.01 0.37a 0.24a
9.INTFIRM 0.40a -0.01 0.35a 0.04 0.04 0.21a 0.45a 0.46a
10.AGE 0.10a -0.09a 0.07 0.06b -0.05 0.03 0.03 0.13a 0.23a
11.SIZE 0.22a -0.07a 0.30a 0.04 -0.01 0.29a 0.28a 0.39a 0.40a 0.19
12.DIVERSIFIED -0.02 -0.02 0.00 -0.01 -0.02 0.06 0.02 0.03 0.03 0.02 0.12
13.OWNERSHIP1 0.00 -0.03 0.34a 0.04c 0.07 0.31a 0.11 0.42a 0.14a 0.10 0.28a -0.01
14.OWNERSHIP2 -0.11a -0.05b -0.18 0.05b 0.06 -0.30 -0.19 -0.38 -0.4 -0.05 -0.35 -0.01 -0.30a
Number of Observations = 2121, a, b, and c represent 1%, 5% and 10% significant level, respectively
4.2 Regression analysis and results
The minimum and maximum value of the dependent variable ranges from 0 to 302,
the Count Data model is considered as an appropriate model for our analysis as many firms in
the dataset do not have any patent applications in some years. We considered both Poisson
and negative binomial models, and the outcomes are quite similar. The results interpretation
is based on the GEE with negative binomial link (Table 4) because the tests for over-
dispersions for the Poisson models is insignificant (𝐻0: 𝛼 = 0), indicating that the
consideration of negative binomial models may be more appropriate. Table 4 shows the
results of GEE with negative binomial link models to show the influence of government
innovation policies and external collaborations on firm innovation performance. Model 1 is
the baseline model which includes all control variables. In Models 2 & 3 we introduced R&D
intensity and Training & Development expenses to access the individual effects on
innovation performance. Model 4 is the full model and we introduced the interaction of SINC
with international and domestic collaborations in Models 5 & 6 to test our hypothesis. The
Wald chi-square suggest that adding the SINC and its interaction with international upstream
and downstream collaborations significantly improves the model fit over the base model.
Model 5’s fit index is 131.8, shows a marginal improvement over the full Model 4 (the
difference is 9.0 at 1 degree of freedom).
22
The results indicate that the coefficient of State Innovation Council (SINC) is positive
and significant at 10% level for firm innovativeness. Whereas, in Models 4, 5 & 6 Sectoral
Innovation Council (SEINC) is negative and not significant. These results indicate that SINC
can affect the patent applications or innovativeness of enterprises and it seems having SEINC
has no impact on the firm’s innovation performance. Chung (2002) argues that NIS is a
matrix which includes regional and sectoral innovation systems/councils and Malerba (2002;
2005) averred that the relationship between NIS and regional and sectoral innovation systems
is not always one-way and their direction of impact on innovation is opposite. Because, the
sectoral innovation systems undergoes changes and transformation through various
idiosyncratic elements. The empirical analysis have shown that the sectoral innovation
systems are more predominant in developed countries such as France, USA, Japan, etc.,
whereas, in the context of developing countries the existing institutions provide innovative
stimulus to certain sectors not to others one hand and impact of policies drastically differ
from sector to sector on the other (Malerba, 2002; Dosi & Malerba, Organization and
Strategy in the Evolution of the Enterprise, 1996; Nelson, 1993). It is also observed from the
literature that sectoral innovation systems take different structures in different sectors and
countries.
Corroborating with the previous findings, the variable R&D intensity (RNDINT) is
positive and significant at 10% level. According to Chesbrough et al., (2006) and Chesbrough
(2003) the innovative firms spend on their internal R&D to draw knowledge and
technological know-how successfully from various external sources. According to Ahuja
(2000) when firms collaborate to develop new knowledge, potentially they receive greater
amount of knowledge form the collaborative projects. The indicator training expenditure
(TRAINEXP) has a positive effect on firm’s innovation outcome at the 5% level in Model 5
showing that more spending on training of human resources promotes greater innovative
capabilities of the firms. Domestic upstream (DOMUPCOL) and domestic firm
collaborations (DOMFIRM) shows insignificant results providing no support for Hypothesis
H2a & H3a, whereas, international upstream (INTUPCOL) and international firm
collaborations (INTFIRM) have significantly positive effect (at 10% and 1% level
respectively) on patent applications in all models. The results are corroborating with the
results of (Kang & Park (2012) who verified that international firm collaborations and ties
with foreign universities and R&D institutions play an important role in stimulating
23
innovation output. Marsh & Oxley (2005) in the context of New Zealand biotechnology
sector found a negative effect of domestic collaborations on firm innovativeness and
significantly positive effect of international firm collaborations. According to Keller (2001),
the international partnerships are the major sources of knowledge and technology diffusion
which promoted the productivity of enterprises in the OECD economies. In particular, in
developing economies, international partnerships enhance the innovation capabilities of firms
by providing them the advanced scientific knowledge and technological support to develop
new products and services and to enter into the new markets (Kang & Park, 2012).
In this research the diversification is negatively correlated with the innovation
outcome. The prior research suggests mixed result i.e., both positive and negative impact of
diversification on innovative activity (Cohen & Levin, 1989). The positive results argue that
diversification encourages innovation by providing impetus of multiple knowledge sources,
leading to commercialisation of innovative ideas (Ahuja, 2000). The results from the present
research infer that firms diversification in several business has a negative effect on the
patenting outcome. Among other control variables, the estimated coefficients of firm size is
significant in Models corroborating with Schumpeter (1942), whereas age is positive but not
significant. In studying the effect of size on innovation, Skuras, et al., (2008) found an
inverted U-shaped effect which indicate that (i) the conventional view of small firms as
followers or constrained firms, may not be true, (ii) as the firm size increases, the larger
firms might face acute deficit of resources needed for innovative activities. Research by
Cruz-Cázares et al., (2013), Clausen et al., (2013), Paunov (2012), Tomlinson (2010) and
Freel (2003) represent a non significant or negative impact of age upon the innovativeness of
firms.
Furthermore, the ownership status of the firms influences the innovation performance
of the firms. The variable state ownership (OWNERSHIP1) is negative and significant at 1%
level. Whereas the estimated coefficient of private or firms with foreign particiaption
(OWNERSHIP2) is positive and significant for innovation output in a range of 5-10% level
in the specifications. According to Tether (2002), compared to independent enterprises, firms
that belong to foreign participation were more innovative than their domestic peers. Due to
being more competitive, higher investments, less heterogenity in nature, workforce training
on technological innovaitons, and access to the resources from parent firms (in case of a
subsidiary firm) foreign presence firms present more innovativeness than the domestic firms.
24
Whereas, domestic firms in specific industries (automotive, electronic and chemicals) slightly
more innovative compared to other firms in domestic industries (food industry) (Álvarez,
Marin, & Fonfría, 2009; Gómez & Vargas, 2012; Griffiths & Webster, 2010; Paunov, 2012).
The likelyhood of innovativeness is reduces by more than 20% if the firm has state
ownership, it means public firms will have lesser incentives to improve innovation
performance compared to the private firms owing to their social welfare objectives (Huergo,
2006).
Table 4: GEE negative binomial regression analysis of innovative performance for the
PATAPP as the dependent variable
DV: PATAPP (1) (2) (3) (4) (5) (6)
RNDINT 0.026 * (0.012)
0.028 * (0.013)
0.030 * (0.014)
0.034 * (0.018)
0.028 * (0.014)
TRAINEXP 0.068 * (0.028)
0.068 * (0.028)
0.092 ** (0.032)
0.065 * (0.026)
SINC 0.014 * (0.021)
0.100 * (0.547)
0.462 * (0.509)
SEINC -1.493 (1.237)
-0.038 (1.250)
-1.550 (1.230)
DOMUPCOL -0.119 (0.238)
-0.126 (0.238)
-0.472 * (0.263)
-0.439 * (0.267)
-0.225 (0.347)
0.241 (0.275)
INTUPCOL 6.202 * (2.498)
6.203 * (2.499)
4.259 * (1.743)
4.278 * (1.745)
4.617 ** (1.581)
4.180 * (1.760)
DOMFIRM 0.731 * (0.408)
0.730 * (0.408)
-0.255 (0.501)
-0.268 (0.500)
0.456 (0.524)
2.489 (1.283)
INTFIRM 1.533 *** (0.418)
1.532 *** (0.418)
1.428 *** (0.390)
1.440 *** (0.390)
0.381 *** (0.094)
1.410 *** (0.382)
AGE 0.017 (0.014)
0.018 (0.014)
0.022 (0.014)
0.020 (0.013)
0.050 ** (0.017)
0.021 (0.014)
SIZE 0.002 *** (0.001)
0.002 *** (0.001)
0.002 ** (0.001)
0.002 ** (0.001)
0.004 *** (0.001)
0.002 * (0.001)
DIVERSIFIED -4.101 *** (0.957)
-4.088 *** (0.957)
-3.458 *** (0.895)
-3.493 *** (0.915)
-3.540 *** (0.921)
-3.660 *** (0.919)
OWNERSHIP1 -6.457 *** (1.575)
-6.423 *** (1.575)
-9.486 *** (1.884)
-9.321 *** (1.854)
-13.900 (2.160)
-10.100 *** (2.200)
OWNERSHIP2 2.407 ** (0.914)
2.448 ** (0.919)
1.380 (0.953)
1.472 (0.986)
1.320 (0.903)
1.790 * (0.910)
Interaction1 SINC x INTUPCOL SINC x INTFIRM
9.683 ** (3.661) 1.306 ** (0.436)
Interaction 2 SINC x DOMUPCOL SINC x DOMFIRM
2.041 * (0.878) 0.591 (1.427)
Intercept -5.058 *** (1.327)
-5.197 *** (1.346)
-3.779 ** (1.403)
-2.774 * (1.568)
1.127 * (1.179)
-3.020 * (1.570)
N 2121 2121 2121 2121 2121 2122
25
Wald chi-square 100.7 *** 106.3 *** 121.9 *** 122.8 *** 131.8 *** 108.1 DF 9 10 11 13 14 14
Notes: Values in parentheses are std.err for the coefficient estimates. ***, **, and * represent significant at 1%, 5%, and 10% level, respectively.
Table 5: GEE Poisson regression analysis of innovative performance for the PATAPP as the dependent variable
DV: PATAPP (1) (2) (3) (4) (5) (6)
RNDINT 0.011 *** (0.003)
0.012 *** (0.003)
0.012 *** (0.003)
0.012 *** (0.003)
0.012 *** (0.003)
TRAINEXP 0.002 *** (0.000)
0.002 *** (0.000)
0.002 *** (0.000)
0.002 *** (0.000)
SINC 0.088 * (0.266)
0.282 (0.273)
0.121 (0.245)
SEINC -0.425 (0.340)
-0.186 (0.337)
-0.435 (0.345)
DOMUPCOL 0.025 (0.043)
0.023 (0.044)
0.025 (0.049)
0.034 (0.053)
0.062 (0.059)
0.379 ** (0.145)
INTUPCOL 0.194 * (0.082)
0.193 * (0.083)
0.127 * (0.059)
0.125 * (0.061)
0.917 ** (0.726)
0.118 * (0.049)
DOMFIRM 0.141 ** (0.049)
0.141 ** (0.049)
0.082 (0.061)
0.078 (0.064)
0.165 *** (0.049)
0.778 *** (0.221)
INTFIRM 0.059 ** (0.019)
0.059 ** (0.019)
0.065 *** (0.015)
0.067 *** (0.015)
0.168 *** (0.034)
0.075 *** (0.010)
AGE 0.009 ** (0.003)
0.009 ** (0.003)
0.010 ** (0.003)
0.010 ** (0.003)
0.010 ** (0.003)
0.010 *** (0.003)
SIZE 0.002 *** (0.000)
0.002 *** (0.000)
0.002 *** (0.000)
0.002 *** (0.000)
0.002 *** (0.000)
0.002 *** (0.000)
DIVERSIFIED -1.175 *** (0.318)
-1.172 *** (0.317)
-1.127 *** (0.320)
-1.168 *** (0.337)
-1.233 *** (0.360)
-1.141 *** (0.332)
OWNERSHIP1 -1.760 ** (0.571)
-1.749 ** (0.568)
-1.987 ** (0.618)
-1.958 ** (0.613)
-3.084 *** (0.720)
-1.546 *** (0.384)
OWNERSHIP2 0.016 (0.246)
0.030 (0.246)
0.096 (0.252)
0.117 (0.251)
-0.151 (0.247)
0.086 (0.254)
Interaction1 SINC x INTUPCOL SINC x INTFIRM
2.638 *** (0.731) 0.007 * (0.036)
Interaction 2 SINC x DOMUPCOL SINC x DOMFIRM
-0.114 (0.151) -0.444 * (0.226)
Intercept -2.123 *** (0.378)
-2.200 *** (0.384)
-2.130 *** (0.386)
-1.857 *** (0.493)
-2.331 *** (0.501)
-1.954 *** (0.458)
N 2121 2121 2121 2121 2121 2121
26
Notes: values in parentheses are std.err for the coefficient estimates. ***, **, and * represent significant at 1%, 5%, and 10% level, respectively
4.3 Discussion
The results of the GEE model regressions results shows that most of the variables
considered as determinants of firm’s innovation output as anticipated. It can be observed
from the Table 4 that the Wald chi-square statistics are also statistically significant at 1%
level. Hence, the regression results obtained can be inferred meaningfully. First, the
government innovation support policies (SINC) had a direct positive effect on the patent
outcomes, supporting hypothesis H1 (p < 0.01). Further, the interaction of government
innovation support policies with domestic and international collaborations are positively
significant showing R&D policies play a vital role and are positively associated with external
collaborations especially with international upstream partners and domestic and international
firm collaborations, which means government innovation policies indirectly promotes
external international collaborations (𝐻1 → 𝐻2𝐵). The results are consistent with the
findings of George et al., (2002) and Kang & Park (2012).
The TRAINEXP has significantly positive effect (p < 0.05) for patent applications,
which means most innovative firms train more employees to enhance the human resource
competencies, because in modern economics innovation and training are intricately linked.
The researchers have argued that highly trained and qualified workers in the firms will have
understanding and access to the resources from collaborative partners, which enhances the
likelihood of successful partnerships and innovativeness (Álvarez, Marin, & Fonfría, 2009;
Gómez & Vargas, 2012; Griffiths & Webster, 2010; Paunov, 2012).
The indicator for external collaboration is significant and positive for INTUPCOL and
INTFIRM at 5% and 1% level respectively, showing that firm’s partnership with
international collaborators is an important factor explaining innovative performance,
supporting hypothesis H2B & H3B. Secondly, DOMUPCOL & DOMFIRM is not supported,
because the coefficient of DOMUPCOL is negative and insignificant and DOMFIRM is not
significant on innovation outcome. The results indicate that international collaborations are
much stronger than domestic ones and domestic-based alliances do not have significant effect
on firm innovativeness, because in the context of a developing country like India,
27
international technology dissemination is the major source of innovation (Marsh & Oxley,
2005; Siddharthan & Safarian, 1997; Kumar & Saqib, 1996).
With regard to the other indicators of innovation success, the firm size is positive and
statistically significant (p < 0.05-0.001) on PATAPP. This implies that firms with larger size
acquire more innovative capabilities and have significant impact on innovation. The
coefficient of OWNERSHIP2 is significant and positive (p < 0.05), indicating that firms in
private and with foreign participation in India are mainly innovative comapred to the state-
owned firms due to the innovative activities carried out by their parent firms and foreign
subsidiaries. The estimated coefficients and hypothesis confirmation is presented in Table 5.
Table 5: Estimated coefficients and hypothesis confirmation
Indicator Coefficients Std.err Hypothesis Result
SINC 0.100 * 0.547 H1A Supported
SEINC -0.038 1.250 H1B Negatively significant
DOMUPCOL -0.225 0.347 H2A Negatively significant
INTUPCOL 4.617 ** 1.581 H2B Supported
DOMFIRM 0.456 0.524 H3A Not significant
INTFIRM 0.381 *** 0.094 H3B Supported
Consistent with the finding of the previous studies, the present finding support the
influence of government innovation support policies and external collaborative efforts on
innovation performance is positive. We found that government innovation support policies
plays a main role in encouraging international upstream and downstream collaborations,
which are vital for an emerging country like India in developing innovations using advanced
scientific knowledge and technologies available from international upstream and downstream
partners.
5. Conclusion and implications
The NIS and RBV theories help in identifying the innovation determinants in terms of
the firm’s internal resources and contextual determinants. Innovation is a complex process,
which is influenced by several interrelated factors. Importantly, Dosi (1998) described the
interrelatedness of those factors as complementary, which means indicators act collectively
and support each other. The findings from this study reveal that external collaboration
enhances the innovation performance of the firms. Further, the existing literature highlights
that collaboration with distant partners or international collaborators are more beneficial and
strong, which increases the probability of accessing novel, up-to-date scientific knowledge
28
and resources (Berchicci, de Jong, & Freel, 2013). Our research confirms that international
collaborations boost firm innovative performance, importantly, we found that the relationship
between training expenditure and international collaborations and innovation performance are
interrelated and more strongly significant. When we classified the enterprises on the basis of
their ownership status and technology levels, the results suggest the in the cases of SOEs and
non-high technology industries, government innovation support is not a panacea which
directly promotes innovativeness among the firms and the direct contribution of government
innovation is substituted by the firm’s strong internal innovation capabilities. The research
also highlights several important areas for enhancing the effectiveness of government
innovation support in terms of sectoral innovation councils where the government should
formulate policies that create favourable sectoral conditions that enhance the innovativeness
and provide resources for successful conversion of internal and collaborative efforts into
innovation outputs.
The firms in the Indian context do not show interest in domestic collaborations. The
study does not find any evidence of effect of domestic upstream and downstream
collaborations on the innovative outcome of Indian enterprises. In fact, in most cases the
coefficients are insignificant or negatively significant. At the same time, R&D intensity for
patent output is also found to be insignificant. Research by Lall (1986; 1983) and Siddharthan
(1992; 1988) analysed that Indian R&D is not innovative rather it is more adaptive in nature.
The first practical implication of this study is that emerging economies can benefit from
collaborations with international firm’s (international downstream partners). The findings
suggest that governments in emerging economies like India formulate and implement policies
which encourage development of innovation support institutions domestically and create an
atmosphere of development of sectoral specific technology policies which focuses on the
effects of inter-firm and inter-sectoral collaborations. Secondly, the collaboration with
international research organisations acts as a main source of international upstream
knowledge. Governments should also formulate policies in such a way that which improve
the research quality of domestic or regional universities and research think-tanks by fostering
an environment which enables the firms to utilise the talent of domestic upstream partners
(Hess & Rothaermel, 2011). In summary, depending on the firm level and sector level
internal, external and contextual determinants, the firms in an emerging country like India
can enhance their innovative performance to become more competitive in the globalised
world.
29
30
Appendix 1: Comparison of previous & present research findings Reference Variable Literature findings Our findings Relation
Kang & Park (2012);
Zeng et al., (2010);
Paunov (2012); Wu
(2012); Lokshin, et al.,
(2011); Vega-Jurado et
al., (2008); Cruz-Cázares
et al., (2013); Mention
(2011); (Trigo & Vence
(2012)
External
collaborations There is a linear relationship between cooperation
and innovation performance.
Firm’s collaboration with international universities
and research institutes and overseas firms is
significantly positively associated with the
innovation performance.
Firms involved in innovation networks innovated
more frequently.
Like developed markets, emerging market firms
equally benefit from technological collaborations,
especially when the firms operating in high-tech
industries.
Collaboration or interaction with the customers,
suppliers, R&D institutions help the firm to fill the
gaps in the information, scientific knowledge,
resources and competencies.
Collaboration with international
firms, overseas universities,
R&D institutes and firm’s
innovation performance is
significantly positively
associated.
International collaborations are
much stronger than domestic
partnerships.
Supported
Kang & Park (2012);
Kanwar (2013);
Hadjimanolis (1999);
Freel & De Jong (2009);
Simeth & Raffo (2013);
Tether (2002); Falk
(2007); Beneito (2003);
Ganter & Hecker (2013);
Tang (2006)
Government
innovation
support
measures
Positive influence of innovation policy on the
innovative activity of firms
The government R&D support directly and
indirectly affects firm’s innovation by stimulating
international and domestic collaborations.
Government support in the form of R&D tax credits
and grants are important and highly significant for
innovation
Government innovation
initiatives stimulate
international collaborations.
Government innovation support
policies play a main role in
encouraging national and
international collaborations.
Supported
Ambrammal & Sharma
(2014); Laursen & Salter
(2006); Vega-Jurado et
al., (2008);
R&D intensity There is a positive association between R&D
intensity and innovativeness and the external
technological opportunities induce investment in
R&D.
R&D intensity is not significant
in the case of state-owned
enterprises.
It is significant with firms in
high-technology industries.
Mixed
Romijn & Albaladejo
(2002)
Training &
development
expenditure
Training expenditure does not appear to have a
significant effect on innovation.
Firm’s training and development
expenditure is highly and positively
significantly associated with the
innovation performance.
Not supported
31
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