Framework of open innovation in SMEs in an emerging economy: Firm characteristics, network openness, and network information

Abstract

As the argument regarding the effect of firm size on the effectiveness of innovation continues, the particularities of open innovation from the perspective of emerging market small and medium enterprises (EM SMEs) must be addressed. We propose a network framework by bridging the resource-based view and the social network perspective with their respective emphases on the importance of EM SME innovation capacity. Specifically, we address this gap by exploring the incidence of and trends toward open innovation, using a survey data of 420 innovative SMEs of China. Our study supports open innovation from the social network perspective and shows the open innovation of considering important choices for SMEs in the emerging market setting. Our analyses of Chinese SMEs data largely support our theoretical framework and demonstrate the importance of factors across firm characteristics (innovation capacity and innovation barriers), network openness, and network information in understanding open innovation in EM SMEs.

Full text

Int. J. Technology Management, Vol. 62, Nos. 2/3/4, 2013 223
Copyright © 2013 Inderscience Enterprises Ltd.
Framework of open innovation in SMEs in an
emerging economy: firm characteristics, network
openness, and network information
Peng Xiaobao, Song Wei* and Duan Yuzhen
School of Public Affairs,
University of Science and Technology of China,
Room 1131, Building of Management Academy,
No. 96, Jinzhai Rd, Hefei, 230026, Anhui Province, P.R.C.
E-mail: 57325723@qq.com
E-mail: songwei@ustc.edu.cn
E-mail: 381536287@qq.com
*Corresponding author
Abstract: As the argument regarding the effect of firm size on the
effectiveness of innovation continues, the particularities of open innovation
from the perspective of emerging market small and medium enterprises (EM
SMEs) must be addressed. We propose a network framework by bridging the
resource-based view and the social network perspective with their respective
emphases on the importance of EM SME innovation capacity. Specifically, we
address this gap by exploring the incidence of and trends toward open
innovation, using a survey data of 420 innovative SMEs of China. Our study
supports open innovation from the social network perspective and shows the
open innovation of considering important choices for SMEs in the emerging
market setting. Our analyses of Chinese SMEs data largely support our
theoretical framework and demonstrate the importance of factors across firm
characteristics (innovation capacity and innovation barriers), network openness,
and network information in understanding open innovation in EM SMEs.
Keywords: open innovation; emerging market small and medium enterprises;
EM SMEs; network framework; network information; competitive advantage;
technology management.
Reference to this paper should be made as follows: Xiaobao, P., Wei, S. and
Yuzhen, D. (2013) ‘Framework of open innovation in SMEs in an emerging
economy: firm characteristics, network openness, and network information’,
Int. J. Technology Management, Vol. 62, Nos. 2/3/4, pp.223–250.
Biographical notes: Peng Xiaobao received his BSc from Jilin University in
2005, MSc and PhD from University of Science and Technology of China in
2009 and 2012, respectively. He is an Associate Professor and the Dean of the
Innovation Management Research Center of USTC and NCKU. His main
research interests include innovation strategy, intellectual property, and
organisational learning.
Song Wei graduated from Beijing Institute of Technology and received his
Bachelor’s degree in 1983; he graduated from the University of Science and
Technology of China (USTC) and received his Master’s degree in 1990; and
he received his Doctor’s degree in 1998. He is a Professor in the School of
Public Affairs of USTC. He now holds the Director of Intellectual Property
Management Research Centre and the Director of Science and Technology Law

224 P. Xiaobao et al.
Research Centre. He also is a Senior Visiting Scholar of Michigan State
University (MSU) and Fulbright Scholar of the University of Washington
(UW). He hosts several national and provincial research topics.
Duan Yuzhen received her BSc degree from Fuyang Teachers College in 2008.
She is a Master of School of Public Affairs of USTC. Her main research
interests include intellectual property law, innovation management and patent
pool.
1 Introduction
Open innovation has been proposed as a new paradigm for management of innovation
(Chesbrough, 2003; Gassmann, 2006). In recent years, the analysis of open innovation
has subsequently been extended to various perspectives such as the following: studies on
the industrial dynamics of open innovation (Christensen et al., 2005), open innovation
processes in a particular industry sector (Cooke, 2005; Henkel, 2006), or techniques to
boost open innovation (Lichtenthaler, 2008). However, most open innovation activities
focus on specific industries. For instance, studies on open innovation and small and
medium-sized enterprise (SMEs) that are focused on the implication of open-source
strategies (Henkel, 2006; Lecocq and Demil, 2006), industrial dynamics (Christensen
et al, 2005), methodologies for technology out licensing (Bianchi et al., 2010), and effects
of intermediate organisations (Lee et al., 2010).
Furthermore, open innovation in large, high-tech multinational enterprises (MNEs) is
mainly analysed based on in-depth interviews and case studies (e.g., Chesbrough, 2003;
Kirschbaum, 2005). Studies that demonstrate the existence of open innovation in smaller
organisations have rarely been reported. To the best of our knowledge, van De Vrande
et al. (2009), Lichtenthaler (2008), and Laursen and Salter (2006) published three
prominent studies that encompass a broader set of open innovation activities in SMEs;
these studies used larger quantitative data. However, studies on the embeddedness of
innovation in SMEs are seldom conducted (Shaw, 1998; Paniccia, 1998).
The concept of open innovation in SMEs is excluded from the mainstream
discussions (West and Gallagher, 2006) for the following reasons: First, open innovation
is more easily examined in larger firm because SMEs have less ability to access external
resources and fewer technological assets that they can exchange compared with larger
firms (Narula, 2004). However, SMEs and larger firms have to be differentiated in open
innovation because they are generally considered satisfactory in various types of
innovation.
Second, SMEs use non-internal means of innovation compared with large firms
because the former consider alliances or network as ways to extend technological
competences (Edwards et al., 2005; Rothwell, 1991). This use of non-internal sources
suggests that innovation in SMEs focus externally and that the concept is not unfamiliar
to them. However, SME collaborations tend to be limited to strategic alliances with larger
firms (Rothwell, 1994) and outsource mainly through other SMEs (Rothwell, 1991). The
potential of various types of SMEs must be investigated in the context of open innovation
because firms involved in multiple-type ties are more innovative than those that use a
single-type tie (Baum et al., 2000; Finnegan et al., 1999).

Framework of open innovation in SMEs in an emerging economy 225
Finally, SMEs consider external network information as means of obtaining
access to marketing and sales channels at later stages of innovation (especially the
commercialisation stage), whereas open innovation normally focuses on external
technology and capital resources, addressing external technology sourcing and capital
with technology providers and venture capital companies. Network information is not
heavily considered in the existing literature at the commercialisation stage of open
innovation; however, the topic remains important because SMEs possess limited
information resources that can be used to obtain important information and capital
(Julien, 2002). Thus, SMEs find difficulty in network partner selection (Lee et al., 2010).
As the argument regarding the effect of firm size on the effectiveness of innovation
progresses, the particularities of open innovation that largely influence innovation, as
viewed by SMEs, should be addressed. The question is then directed to the process of
facilitating open innovation in SMEs to determine the factors that contribute to the
success (or failure) of their open innovation efforts. Specifically, as technology becomes
too complex for only one firm to handle and relevant knowledge becomes widely
distributed across various firms, a collaborative network among firms increasingly
becomes essential for success. SMEs have likewise engaged in various modes of
collaboration (Kleinknecht and Reijnen, 1992), and networking and alliances have drawn
considerable research interest.
Therefore, this article contributes in various ways to a growing body of research on
open innovation.
First, this paper attempts to address the above challenges and the complex
determinants of related factors. A comprehensive network framework is proposed, thus
bridging the resource-based view (RBV) and the social network perspective (SNP). The
RBV emphasises the importance of firms’ internal resource endowments (Barney, 1991),
whereas the SNP emphasises the importance of firms’ external resource opportunities
(Podolny and Page, 1998; Uzzi, 1996). We contend that firms’ boundary choices often
result from the tension between the need for external resources (to extend and
complement internal resources) and the need for risk control (to coordinate and maintain
external relations). We first assume that firms are not atomistic but rather, embedded in
network relations (Yang et al., 2010).
Second, almost all open innovation empirical studies are that they are all based on
samples of firms in developed economies (Chesbrough, 2003; Fleming and Waguespack,
2007; Terwiesch and Xu, 2008; Lichtenthaler and Lichtenthaler, 2009; Han et al., 2012),
which have to compete in both product innovation and intermediary markets. However,
throughout emerging economies, intermediary markets are underdeveloped, thus neither
asserting strong pressures for firms to be efficient nor enabling the efficient sell-off
of knowledge assets (Khanna and Palepu, 2000). On the other hand, during economic
transitions, market competition, participated not only by incumbent MNEs but also by
SMEs, is heating up (Peng and Luo, 2000). Therefore, the relationship between open
innovation condition and performance in emerging economies, under the condition of
strong product market competition and weak intermediary market infrastructure, is even
more ambiguous.
Third, we investigate technology innovation capacity and innovation barriers of
emerging market small and medium enterprise (EM SMEs) using data from data in
China. Accessing external resources through networks, SMEs consider both their own
resource endowments and potential external network sources, which they can access

226 P. Xiaobao et al.
through respective network positions. We consider EM SMEs’ network attributes such as
network information at the network openness level. These attributes affect firms’
relational context, channels for resource advantages, and strategic choices (e.g., Burt,
2000). We also investigate interactions, revealing their joint effects and contingencies
(Figure 1). Answers to these questions are critical for a more sophisticated understanding
of the network.
2 Theoretical background and hypotheses
This paper synthesises relevant research results to develop the initial model of the
mechanism of open innovation operation in EM SMEs based on network. During the
research process, we explore the influencing factors affecting open innovation and
determine a low commercial network factor loading. Thus, an effective research result
becomes difficult to obtain. Hence, we exclude from the model development the
commercial network factors that affect the review of theoretical framework. In the
theoretical model, innovation capacity, network openness, and network information are
established by drawing lessons from both domestic and foreign relevant research results.
We also identified innovation barriers through interviews and exploratory factor analysis
(EFA). The details are shown in the data analysis results.
2.1 Technology innovation capacity
Strategic alliance formations as firms’ strategic choices are subject to internal and
external constraints (Eisenhardt and Schoonhoven, 1996). The ability to use external
knowledge can lead to success under open innovation. However, the enterprise must
affirm and understand rich external resource and then connect with the resource to select
them. The enterprise has to combine internal and external technologies for a more
complicated technology portfolio that can be used to create a new system and framework
(Chesbrough, 2003).
The idea that external knowledge is essential to optimising in-house innovation has
been revitalised since the publication of Chesbrough’s book on open innovation
(Chesbrough, 2003). In the open innovation model, the enterprise must improve
surveillance, evaluation, absorption, and capability to use new technology, i.e., absorptive
capacity (Cohen and Levinthal, 1990; Garud and Nayyar, 1994; Mowery and Oxley,
1995; Zahra and George, 2002). Cohen and Levinthal (1990, p.128) argue that the ability
of a firm to appreciate assimilate, and apply new external information to commercial ends
is critical for its innovative capacity. Therefore, the concept of absorptive capacity is the
key to understanding successful open innovation, which is characterised by reliance on
external knowledge. External knowledge is distributed among various actors (Tether,
2002) and can be accessed through multiple channels (Coombs et al., 2003; Howells et
al., 2003). In such a context, firms constitute an environment characterised by distributed
knowledge, and innovation itself is distributed among a number of actors.
The internal research and development (R&D) ability can determine the accumulation
of technological capacity in the future and critically affect the search, identification, and
absorptive capacity of external resource (Cohen and Levinthal, 1989). Internal R&D is
vital in improving absorptive capacity. Increased internal R&D investment by the
enterprise indicates more positive interior activities. Hence, the enterprise tends to

Framework of open innovation in SMEs in an emerging economy 227
increase participation in technological cooperation programmes (Veugelers, 1997;
Bayona et a1., 2001; Fritsch and Lukas, 2001; Kaiser, 2002; Miotti and Sachwald, 2003;
Negassi, 2004). To seek a breakthrough in innovative solution on key and core
technologies with other enterprises, an enterprise must first possess a strong R&D ability
(Veugelers, 1997). Without such internal ability, the enterprise cannot become an
attractive partner and thus, cannot fully benefit from the external resource (Negassi,
2004).
H1 Technology innovation capacity has a remarkable positive effect on innovation
network openness between organisations.
Numerous empirical studies support that innovation capacity is the most important
determinant of firm performance. Literature on the diffusion of innovations confirms this
view and suggests that firms must be innovative to gain a competitive edge for survival.
Thus, in the open innovation system, innovation resources come from internal or external
firms. However, solutions that aim to meet customer demands must come from inside the
enterprise. Component factors may come from external sources but critical knowledge
must come from inside. The internal R&D section must search and integrate resources.
Innovators act as resource integration experts. The enterprise must possess two kinds of
ability to maintain competitive advantages: core and background competences (Prahalad
and Hamel, 1990; Granstrand et a1., 1997).
The enterprise must understand technical breakthroughs in the core field and obtain
external technologies from various technical fields. The enterprise must possess
background competence to obtain, absorb, use, and integrate external knowledge
(Christensen et al., 2006). Internal R&D is crucial in improving integration ability. R&D
ability can represent the enterprise in absorbing external knowledge, identify new
technological opportunities, and create new partnership abilities. Open innovation means
neither outsourcing R&D nor stopping internal R&D; rather, it suggests discovering and
importing new ideas that can mutually complement internal R&D (Caloghirou et al.,
2004; Chesbrough and Crowther, 2006). Internal R&D ability continues to be the key
element in open innovation. Nevertheless, the link between firm innovativeness and
performance has not been tested sufficiently. The following can be hypothesised:
H2 Technology innovation capacity has a remarkably positive effect on the Innovation
performance of SMEs.
2.2 Innovation barriers in SMEs
RBV reasoning would suggest that SMEs, often without the requisite resources for
survival and competitive advantage, must reach out both to the environment and alliance
relationship to obtain needed resources. We view, as do Das and Teng (2000), alliances
as facilitating the flow of resources between organisations. To the extent to which the
flow of resources is considered by an SME as being out of balance with expectations and
contributions, there may be the perception that alliance partners have behaved
opportunistically. Opportunistic behaviour, from an RBV, is seen as behaviour that while
designed to maximise the resources derived from an alliance by a participant to the
alliance is not necessarily in the best interest of the alliance.
Teece (1986) argues that it is difficult and costly to write and execute the kinds of
complex contracts that are necessary for controlling the potential for opportunism in

228 P. Xiaobao et al.
R&D alliances. Not only is the development of such contracts resource intense,
the enforcement, in the case of breaches of contract provisions, often falls upon the
aggrieved firm. The cost of enforcement, for SMEs, may often preclude their ability
to gain recourse from a partner’s behaviour. As the risks increase in alliances
where control rests largely upon equity investments and transaction specific assets, the
efficacy of the controls often depends upon continued investment (Colombo, 2003). For
resource-constrained SMEs, this may not be an option.
H3 Innovation barriers in SMEs have a remarkably negative effect on the innovation
performance.
EM SMEs basically rely on marketers and after-sales service employees and lack relevant
independent R&D capacity. They depend on external intelligence such as users,
suppliers, colleges, and universities, among others. Under fierce competition, SMEs must
establish an effective network and then search for an ideal mix ability between external
cooperation and internal R&D. According to Laursen and Salter (2004), “No data can
prove that large enterprises do better than SMEs in the first appearance of the model of
innovation.” This finding suggests that SMEs can innovate and realise ‘breakthrough’
innovation. If SMEs become proficient in applying open innovation by collaborating with
network partners, they can compensate for the scarcity of internal resources and
competences (Christensen et al, 2005; Kogut, 2000; Lichtenthaler and Ernst, 2008).
Knowledge diffusion and management between SMEs and network members are the
configuration and optimised conformity of value resources. SMEs must encourage
exploited innovation to develop self-innovation ability and enthusiasm in optimising the
innovation behaviour of the enterprise. Likewise, SMEs must emphasise that they should
make for modern cultural value innovation network organisation and technical
requirements. Through access to network partners’ external resources, SMEs can develop
new technological combinations and thus address or take advantage of a wider range of
market opportunities (Baum et al., 2000; Dyer and Singh, 1998). Insufficiency of
information and absence of infrastructure can be controlled through intermediate
organisations that help SMEs finish the innovation activity (Lee et al., 2010).
H4 Innovation barriers in SMEs have positive effect on innovation openness in the
network, because innovation network openness of SMEs is remarkably
complementary to innovation barriers in shaping Innovation performance.
2.3 Innovation network openness
The term ‘open’ is a central concept of the motives and roles of open innovation network.
However, Boudreau et al. (2008) found that the degree of openness may vary from one
open innovation network to another, depending on the level as well as locus of access and
decision authority provided to participants. On the basis of Boudreau’s framework, we
define access as the extent to which external partners are allowed to enter into the open
innovation network to use the existing resources and capabilities contributed by other
members. We define decision authority as the extent to which a member of an open
innovation network is authorised to participate in both day-to-day operational activities
and strategic decision making. Open innovation network provide full access and decision
authority to their members and limited access to others. Corporate alliances established in
an open form have multiple benefits, including the evasion of small-number bargaining,

Framework of open innovation in SMEs in an emerging economy 229
reduced transaction costs, and accumulated complementary resources and skills (Powell
et al., 1996). In this regard, the higher the degree of openness, the greater the benefits that
open innovation can produce.
Consequently, openness is crucial for the success of the intelligent enterprise (Greis
et al., 1995; Chesbrough, 2003). From the viewpoint of the innovator, the innovator
should obtain complementary resources through different strategic ways (Teece, 1986) or
the right to use the complementary resources to promote technical innovation (Sheen and
MacBryde, 1995; Funk, 2003). From the viewpoint of the enterprise in operation, despite
a relatively poor R&D, SMEs can absolutely adapt to the change caused by technological
innovation and obtain more benefits compared with the innovator if mature enterprise can
effectively use the complementary resources it owns to cooperate with the innovator
(Chesbrough, 2003). Therefore, the following is hypothesised:
H5 All else being equal, SMEs that participate in a network with full access and decision
authority will experience higher abnormal returns than do SMEs that join a network
with limited access and decision authority.
2.4 Network information variable
Both sociology and management studies acknowledge that firms are not isolated from
their external environment and that prior relations among firms, both direct and indirect,
create a network in which firms are embedded (Granovetter, 1985). The structure and
quality of social ties among firms shape economic actions by creating both unique
opportunities and access to those opportunities (Uzzi, 1996). The network represents
another possible form of collaboration, and its growing use by SMEs reflects a possible
catch-up by large firms (Narula, 2004). Many researchers claim that the success of SMEs
compared with their larger competitors is based on their ability to use external networks
more efficiently (Rothwell and Dodgson, 1992). Narula (2004) suggests that a firm’s
competitiveness is more efficiently determined by its external networks than its size.
SMEs can establish varied mechanisms to enhance the extent of innovation leverage
in the network; however, the opportunity for such leverage would likely be defined by the
inherent characteristics of both the innovation modularity and the network structure
(specifically, network openness) (Nambisan and Sawhney, 2011). The potential for
innovation leverage will be enhanced by the openness of the network – both structural
and decisional openness. First, structural openness (or more open network boundaries)
allows new members to join the network, thereby enhancing the degree to which existing
innovation assets can be leveraged (Iansiti and Levien, 2004). New members may also
contribute additional assets that existing members may leverage. Second, members are
also likely to be more willing to leverage other members’ innovation assets if they
perceive greater decisional openness (i.e., the potential to influence or shape the decisions
related to those assets).
Thus, to enhance innovation leverage, SMEs will need to maintain both structural and
decisional openness. We build on this network embeddedness perspective, positing that a
firm’s strategic choice of alliances can also be subject to network constraints. We also
focus on the effects of network information. Network information functions to seek
suitable partners. Some SMEs have limited information resources and lack the ability to
obtain important information and capital (Julien, 2002), Consequently, SMEs experience
difficulty in seeking potential partners to develop an effective network. With the network

230 P. Xiaobao et al.
established, SMEs can understand the network more deeply, thus keeping it for a
considerable time.
H6 Network information significantly affects the influence of network openness on
innovation performance.
SMEs use external network resources to shorten innovation time, reduce risk and cost,
and increase operation flexibility, among others (Hagedoorn, 1993). However, their use
must be carefully considered in strategic terms because inter-firm collaboration can lead
to new risks and threats, as well as transaction costs. Nevertheless, inter-firm
collaboration is particularly important for SMEs with limited complementary assets and
need to leverage their technology externally (Lichtenthaler, 2005).
In a network, innovation activities are enhanced by the effective sharing of
knowledge – typically, the greater the extent of knowledge that is shared among the
members, the greater the opportunities for individual members to build on that
knowledge and create value. Key to such rich network information sharing, however, is a
trust-based environment that encourages member interactions and strengthens the
appropriability regime (Uzzi, 1997). Inter-firm ties that define the structural
embeddedness of the network provide the backdrop for the hub firm to establish a secure
and trust-based environment for knowledge sharing (Davis, 1991). Hub firms can deploy
mechanisms that leverage the structural embeddedness in the network to minimise undue
appropriation of value without sacrificing the intensity of knowledge sharing. In the
integrator model, more effective information search of innovation network would likely
motivate SMEs to be more transparent in their innovation activities, thereby enhancing
network innovation performance.
H7 The more effective external Network information search will be in influencing
innovation performance.
In the next section, the proposed model is tested based on empirical data.
Figure 1 An illustration of the theoretical framework of open innovation network in SMEs
Innovation
capacity
Innovation
barriers
Networ
k
openness
Innovation
performance
Network
information
H1
H2
H4
H3
H6
H5
H7

Framework of open innovation in SMEs in an emerging economy 231
3 Sample and method
In this study, we follow the SME definition that SMEs as firms with fewer than 2,000
employees, total sales of less than 300 million yuan RMB, or total assets of less than
400 million yuan RMB.
3.1 Data and method
3.1.1 Data
We collected data from SMEs in China to test our model. As an emerging economy, there
are a number of compelling reasons why SMEs in China can be used as a test case. First,
China provides a rich setting for studying how open innovation contributes to
performance. In the past three decades, because of China’s growing importance in the
global economy, improved knowledge about Chinese firms has enormous practical
implications for Western firms that have to compete or open collaborate with them (Child
and Tse, 2001; Luo and Peng, 1999; Yan and Gray, 1994; Wright et al., 2008). Second,
given the enormous size of SMEs, it will be difficult to dismiss these SMEs as ‘outliers’
that can be ignored by researchers (Tan and Litschert, 1994; White, 2000). Finally,
because China shares an important common legacy with other emerging economies (e.g.,
those in Central and Eastern Europe), the Chinese experience can help answer critical
questions such as the role of firm characteristics, openness, and network information in
restructuring SMEs elsewhere (Peng and Luo, 2000). In sum, we believe that the Chinese
context represents a ‘viable research laboratory’ in which to examine the link between
firm characteristics, openness, network information and firm performance during
economic transitions.
The data for this study originated mainly from two sources: data on industrial
variables from the National Bureau of Statistics of China (2011, 2012), surveys among
top executives of 420 SMEs and 1504 high and new tech enterprises (HTEs) of China. To
check survey validity, the pilot test was conducted with 30 SME executives participating
in an advanced management training programme (2010) in Hefei, China. Some
modifications in the wording were made based on their feedback. The sample interviews
and the pilot study were obtained using the snowball approach. We distributed
questionnaires to executives in a random sample of 420 China SMEs, with focus on
Jiangsu, Zhejiang, Shanghai, Anhui, and Beijing provinces.
After two rounds of follow-up reminders, 264 useful questionnaires (N = 264) were
received, representing a 68.39% response rate. Among 264 effective samples, managers
and technical personnel constituted 69% and for 31% of the respondents, respectively.
The respondents comprised 75.3% males and 21.7% while females, which indicates the
relatively low proportion of female senior managers in SMEs. Young persons aged 26 to
45 years old represented 74.6% of the samples. This high percentage indicates a
reasonable managers’ age structure in SMEs, which is suitable for the medium- and
long-term development needs of the enterprises. Master’s or higher, bachelor’s and other
degree holders constituted 12.9%, 55.7%, and 31.4%, respectively, of the respondents.
This result indicates a satisfactory education structure of respondents.

232 P. Xiaobao et al.
3.1.2 Method
Data were analysed using structural equation modelling (SEM), a second-generation
statistical technique that allows greater flexibility in analysis and avoids problems with
multi-collinearity and correlated dependent variables present in many first-generation
techniques. All perceptual measures of this paper were subjected to assessments of
dimensionality, reliability, and validity. We conducted a two-step data analysis to first
assess the measurement model and then test the hypotheses by fitting the structural
model.
First, in this study, covariance-based SEM, as implemented in LISREL 8.70, was
chosen primarily because of its emphasis on the overall variance-covariance matrix and
the overall model fit (Fornell and Bookstein, 1982). The psychometric properties of the
five latent constructs (innovation capacity, innovation barriers, openness, network
information, innovation performance) involving 17 items (see Table 3) were evaluated
simultaneously in one confirmatory factor analysis (CFA).
Second, SEM serves purposes similar to multiple regressions; however, differences
exist between these two techniques. SEM can simultaneously examine a series of
dependence relationship (where a dependent variable becomes an independent variable in
subsequent relationship within the same analysis) and simultaneously analyse multiple
dependent variables (Jöreskog and Sörbom, 1999). This method simultaneously estimates
the latent variables and the relationship between them and other observable variables.
Because our research is aimed at testing the two-stage model and the network framework
as a whole, covariance-based SEM is appropriate.
Third, we conducted the SEM analysis using LISREL Version 8.70, a SEM software
program that is used frequently by researchers (Shook et al., 2004). SEM handles both
the measurement (i.e., how well the items fit their latent variables) and the structural (i.e.,
the path) models in the same analysis. We examined all of our model constructs and
believed that they are reflective in nature. Also, our sample size (N = 264) satisfies the
requirement of the covariance-based algorithm.
3.2 Variables and measurement
3.2.1 Innovation barriers in SMEs
Theorists share different views on IBs in SMEs. To understand the IBs of EM SMEs in
the network, we analysed 420 China SMEs to determine the barriers in the innovation
process and distinguish the interventions that intermediate organisation provide to assist
SMEs. The ten barriers reported most often by SMEs are listed in Table 1.
In China, the barriers of SMEs lie in the inadequacy of human resource, insufficiency
of information, absence of infrastructure, and lack of financial resources, labour shortage,
infrastructure deficiency, and financial resource can be obtained through cooperation.
However, insufficient information and absence of infrastructure can be mitigated to some
extent through intermediate organisations that help SMEs complete the innovation
implementation.

Framework of open innovation in SMEs in an emerging economy 233
Table 1 Innovation barriers of SMEs in China
Innovation barriers Score Rank
Lack of suitable human resource within the firm, and difficulties
in finding suitable manpower in a labour market
6.12 1
Lack of market information 5.10 2
Uncertainty of the market of innovation achievement 5.00 3
Possibility of technology innovation imitation 4.95 4
Lack of firm development strategy and management ability 4.61 5
Lack of information of technology market 4.35 6
Financial difficulty aroused by technology uncertainty and high
risk
4.02 7
Financial difficulty aroused by high technology innovation and
commercial cost
3.79 8
Difficulties in using external services (technology and business
services)
3.75 9
Frequent flowing of human resources (especially the research
staff)
3.64 10
3.2.2 Network information
In order to get further information about conditions of EM SMEs, through the
understanding of their cooperation demand to help them to promote innovation. This
paper analyses the network information activity of open innovation that using China data.
Details of external information sources for innovation show that Chinese SMEs
emphasise the importance of information acquired from collaboration with other firms;
however SMEs seem to have relied more heavily on internal and public information
(see Table 2).
Table 2 Information sources for technology innovation in Chinese SMEs
Information sources
(Lee, 2010)
Share of firms using
the information
source
Importance of the
information source
Development 74% 5.38
Manufacturing 72% 5.21
Research 76% 5.57
Marketing and sales 61% 5.26
Within the
firm
Purchase 79% 4.76
Customer and user 57% 5.59
Competitors in the industry 53% 5.22
Suppliers(raw materials) 45% 5.06
Non-competitors in industry 49% 4.98
Business service provider 44% 4.88
From other
firms and
market
Affiliates 38% 5.52

234 P. Xiaobao et al.
Table 2 Information sources for technology innovation in Chinese SMEs (continued)
Information sources
(Lee, 2010)
Share of firms using
the information
source
Importance of the
information source
University 33% 4.02
Government agencies 33% 3.01
Non-profit organisation 41% 3.61
From
university and
research centre
Private research centre 39% 354
Exhibition 73% 4.83
Internet 73% 4.76
Magazine 66% 3.98
Conference and meeting 65% 3.92
Mass media
(newspaper/TV) 54% 3.62
From public
information
Patent 56% 3.99
Looking at the details of external information sources for innovation, while SMEs
stressed the importance of information acquired from collaboration with other firms, they
seem to have relied more on internal and public information. Table 2 intensifies its focus
on SMEs. The first and second columns list the types of information source. The third
and fourth columns present the ratio of the SMEs that used the information source and
the evaluation of its importance respectively. SMEs are shown to recognise the
importance of information acquired from affiliates, competitors in the industry, as well as
customers and suppliers. However, few SMEs use the information in their innovation
process (see text in italics).
3.2.3 Innovation network openness
The focus on openness and interaction in innovation studies reflects a wider trend in
studies of firm behaviour. The network of relationship between the firm and its external
environment can influence performance. We introduce two variables reflecting openness
in terms of external search strategies of firms as determinants of innovation performance.
The first variable is breadth, which comprises a combination of 21 sources of
knowledge or information for innovation listed in Table 2. Each of the 21 sources are
coded as a binary variable; 0 for non-use and 1 if the given knowledge source is used.
The binary equivalents of the 21 sources are subsequently tallied. Each firm obtains a
score of 0 if no knowledge sources are used and 21 if all knowledge sources are used.
Firms that use more information sources are assumed to be more ‘open’ than other firms
with respect to search breadth.
External search depth is defined as the extent to which firms draw intensively from
different search channels or sources of innovative ideas. The second variable is called
depth, which comprises the same 21 sources of knowledge. Each of the 21 sources is
coded with 1 if the information source is intensively used and 0 if no, low, or medium use
of the same source is reported. As for breadth, the 21 sources are tallied. Each firm
obtains a score of 0 if no knowledge source is used intensively and 21 if all knowledge

Framework of open innovation in SMEs in an emerging economy 235
sources are used intensively. To reiterate, we assumed that firms using more sources are
more ‘open’ with respect to search depth compared with firms that do not.
To test the robustness of the results for external search depth, we calculate an
alternative measure by looking at whether the firm in question has formal innovation
collaboration links with different external sources. The survey lists five different external
partners:
1 suppliers
2 leader users
3 competitors
4 universities or other higher education institutes
5 government research organisations or private research institutes (Kogut and Zander,
1992).
The measure reflects a wide range innovation sources, including suppliers, clients, and
competitors, as well as general institutions operating within the innovation system, such
as regulations and standards (Nelson, 1993; Spencer, 2001).
3.2.4 Innovation performance
Innovation performance is a multidimensional construct (Hart, 1992). Therefore, we
assessed innovation performance along with two dimensions: firm innovativeness and
firm financial performance.
First, we were proxy firm financial performance by a SMEs’ return on equity (ROE).
ROE is a commonly used variable in strategic management research to proxy firm
financial performance because ROE assesses how a firm efficiently uses its resources.
We collected annual data for each firm during the five-year period between 2006 and
2010. Following an earlier research (Zahra and Bogner, 2000); we averaged the ROE
data for the said five-year period to create a performance index.
Second, we followed a prior research that proxy firm innovativeness by a firm’s
patents (Ahuja, 2000; Hagedoorn and Schakenraad, 1994; Henderson and Cockburn,
1994; Owen-Smith and Powell, 2004; Rothaermel and Hess, 2007; Rothaermel and
Thursby, 2007; Stuart 2000).
3.2.5 Measure variables
The respondents circled the number that best described the extent to which top executives
(general and deputy general managers) at his/her firm have utilised external resource,
networks information, and connections during the past three years with
1 top executives at supplier firms
2 top executives at buyer firms
3 top executives at competitor firms
4 top executives at distributor or marketer firms
5 officials in industrial departments (or bureaus)

236 P. Xiaobao et al.
6 officials in other governmental authorities.
These questions are more general than the name-generator approach often employed in
social network analysis, asking respondents to name their contacts (Burt, 1997). Most
items were derived from the literature. The results are as follows (see Table 3):
Table 3 Measure variables
Latent variables Measure variables Principle
sources
TIC 1 Company frequently tries out new ideas
TIC 2 Company seeks out new ways to do things
TIC 3 Company is creative in its methods of
operation
Technology
innovation
capacity (TIC)
TIC 4 Company is often the first to market with new
products and services
Calantone
et al.
(2002),
Cohen and
Levinthal
(1990)
IBs 1 Lack of suitable human resource within the
firm, and difficulties in finding suitable
manpower in a labour market
IBs 2 Lack of market information
IBs 3 Uncertainty of the market of innovation
achievement
Innovation
barriers (IBs)
IBs 4 Possibility of technology innovation imitation
Lee (2010)
INO 1 External search breadth
INO 2 External search depth
Innovation
network openness
(INO)
INO 3 Whether or not the firm in question has formal
innovation collaboration links with different
external sources
Laursen and
Salter
(2006)
NIV 1 Within the firm
NIV 2 From other firms and market
NIV 3 From university and research centre
Network
information
variable (NIV)
NIV 4 From public information
Lee (2010)
IPs 1 SMEs’ return on equity (ROE)
Innovation
performance (IPs) IPs 2 Firm innovativeness by a firm’s patents
Rothaermel
(2009)
4 Main results
4.1 Analysis
The responses in the questionnaire are measured using the seven-point Likert scale from
minimum 1 (‘very little/low’) to maximum 7 (‘very extensive/high’).
Analysis of questionnaire validity and reliability (measurement tools) was conducted
to ensure reliability and stability. Using SPSS 17.0 for data analysis, we obtained values
greater than 0.70 for all Cronbach’s alpha coefficient (see Table 4), which satisfies the
research requirements.

Framework of open innovation in SMEs in an emerging economy 237
Table 4 Descriptive statistics of observed variables
Cronbach’s
α Mean Standard
deviation Minimum Maximum
TIC
TIC 1 Company frequently tries out
new ideas
3.5303 1.70412 1.00 7.00
TIC 2 Company seeks out new ways to
do things
3.1477 1.64680 1.00 7.00
TIC 3 Company is creative in its
methods of operation
3.1894 1.67801 1.00 7.00
TIC 4 Company is often the first to
market with new products and
services
0.937
3.1818 1.72134 1.00 7.00
IBs
IBs 1 Lack of suitable human resource
within the firm, and difficulties
in finding suitable manpower in
a labour market
4.3258 1.66373 1.00 7.00
IBs 2 Lack of market information 4.1515 1.61967 1.00 7.00
IBs 3 Uncertainty of the market of
innovation achievement
4.1023 1.61532 1.00 7.00
IBs 4 Possibility of technology
innovation imitation
0.932
3.6098 1.57066 1.00 7.00
INO
INO 1 External search breadth 4.1705 1.52212 1.00 7.00
INO 2 External search depth 3.9053 1.65990 1.00 7.00
INO 3 whether or not the firm in
question has formal innovation
collaboration links with different
external sources
0.931
4.2083 1.51487 1.00 7.00
NIV
NIV 1 Within the firm 4.6326 1.22608 1.00 7.00
NIV 2 From other firms and market 4.4583 1.27500 1.00 7.00
NIV 3 From university and research
centre
4.2727 1.27920 1.00 7.00
NIV 4 From public information
0.933
4.3409 1.30124 1.00 7.00
IPs
IPs 1 SMEs’ return on equity (ROE) 4.1818 1.49939 1.00 7.00
IPs 2 firm innovativeness by a firm’s
patents
0.913
3.7083 1.50606 1.00 7.00

238 P. Xiaobao et al.
The implied precondition for principal component/factor analysis is that all variables
must be relevant. Otherwise, no shared information among variables or common factor
needs to be extracted, which renders this method inapplicable. This precondition is the
most stringent premise requirement of principal component/factor analysis. Specifically,
we can also use kaiser–meyer–olkin (KMO) statistics and Bartlett’s test of sphericity in
evaluating the conditions, in addition to obtaining an estimate based on expertise. The
KMO and Bartlett’s test results of scale obtained by SPSS analysis.
The results presented in Table 4 are used to evaluate whether factor analysis can be
performed. The value of KMO statistics is calculated at 0.904, which is greater than 0.9.
Thus, no obvious differences are indicated in the relevance among variables, and the data
are suitable for factor analysis. The second line in Table 6 shows the results of Bartlett’s
test of sphericity, which indicates that the hypothesis of Bartlett’s test of sphericity is
rejected. Therefore, the five variables are not the sole indicators, and the values are
relevant. This conclusion is consistent with the information provided by the above
correlation coefficient matrices.
Despite the lack of a single explanation index (R2) in SEM to continue the overall test
of the model, an overall inspection on the goodness of fit between the hypothesis and the
observation models can be conducted. After acceptance of the fitting degree, we must
aim at a certain individual factor quality for inspection. In essence, the fitting degree of a
model is a type of overall inspection, whereas the inspection of individual factors is an
ex-post evaluation programme. Hair et al. (2006) argue that in CFA, we must know
whether the individual parameter of measuring model is ideal (of item) and whether the
combination condition of latent variable is stable and reliable (validity and reliability of
concept), in addition to the index of reporting model. If some parameters are not ideal,
the modification programme of the model can remove the imperfect items or increase
some parameters to improve the inner fitting of the model.
In practice, the strategy adopted by most includes four tests: project quality,
composite reliability (ρc), and average variance extracted AVE (ρv). In the linear
structural relations analysis model, the reliability indices consist of project quality
inspection and composite reliability indices of each observed variable, requiring λ ≥ 0.71
(For the measurement model, Comrey and Lee (1992) suggest that factor loadings of 0.71
are excellent, 0.63 are very good, 0.55 are good, and 0.45 are fair and SMC > 0.5. The
validity indices are AVE (ρv) > 0.5 and the factor distinguish index. Tables 5 and 6
present the reliability and validity results.
Table 5 Analysis results
Latent variable SMC λ SE T-value CR (ρc) AVE (ρv)
TIC 1 0.86 0.93 0.14 19.61
TIC 2 0.89 0.95 0.11 20.35
TIC 3 0.70 0.83 0.30 16.53
TIC
TIC 4 0.68 0.82 0.32 16.13
0.93 0.78
IBs 1 0.64 0.80 0.36 15.57
IBs 2 0.79 0.89 0.21 18.37
IBs 3 0.83 0.91 0.17 19.07
IBs
IBs 4 0.89 0.94 0.11 21.16
0.94 0.82

Framework of open innovation in SMEs in an emerging economy 239
Table 5 Analysis results (continued)
Latent variable SMC λ SE T-value CR (ρc) AVE (ρv)
INO 1 0.80 0.89 0.20
INO 2 0.89 0.95 0.11 25.13
INO
INO 3 0.78 0.88 0.22 21.48
0.93 0.82
NIV 1 0.66 0.81 0.34
NIV 2 0.71 0.84 0.29 16.26
NIV 3 0.85 0.92 0.15 18.73
NIV
NIV 4 0.87 0.93 0.13 19.04
0.91 0.77
IPs 1 0.87 0.94 0.13 IPs
IPs 2 0.81 0.89 0.19 21.36
0.93 0.84
The results reveal that both the reliability and convergent validity indices of this model
are within the threshold range, with high reliability and convergent validity. Discriminant
validity index refers to AVE (ρv) of the item factor itself in measuring construction,
which is greater than the common variance of the other two constructing item factors (or
the square value of the correlation coefficient). This result indicates that the measurement
model possesses a satisfactory level of discriminant validity.
Table 6 Discriminant validity
INO NIV IPs IBs
NIV r(r2)
95%CI
Ave VE
0.73 (0.53)
(0.63, 0.83)
0.795
IPs r(r2)
95%CI
Ave VE
0.30 (0.09)
(0.20, 0.40)
0.83
0.72 (0.52)
(0.62, 0.82)
0.81
IBs r(r2)
95%CI
Ave VE
0.46 (0.2116)
(0.362,0.558)
0.82
0.16 (0.0256)
(0.063, 0.258)
0.83
TIC r(r2)
95%CI
Ave VE
0.63 (0.3969)
(0.532, 0.728)
0.80
0.08 (0.0064)
(–0.018, 0.178)
0.81
Using the comparative method proposed by Fornell and Larker (1981), we examine
whether the average of two potential variables is greater than the square of the correlation
coefficient of two latent variables. The results are listed on the third column of Table 6.
The average of every two AVE (ρv) of all factors is greater than the square of the
correlation coefficients, which also indicates that ideal distinction exists between each
concept.
In model evaluation, we need to examine:

240 P. Xiaobao et al.
1 whether the results of SEM are proper, or not, including whether the iterative
estimation is converging and parameter estimation value is within the range or not
(for example, the correlation coefficient is between +1 and –1)
2 whether the relationship of parameters and the default model are reasonable. Some
results of the data analysis may be unexpected; however, each parameter shall
neither contradict one another nor seriously conflict with prior assumptions
3 the overall fitness index of different types (including three types of indicators,
namely, absolute, relative, and simple fit indices) to measure the model fitness
(MacCallum et al., 1996).
The data were analysed using the LISREL 8.70 software, and the test results are
presented in Table 7.
Table 7 Fitting index of model
Type Index Judgment value Results
χ2/df 1 ≤ χ2/df ≤ 4 2.90
GFI > 0.90 0.87
Absolute fitting index
RMSEA < 0.08 0.085
NFI > 0.90 0.96
NNFI > 0.90 0.97
CFI > 0.90 0.98
IFI > 0.90 0.98
Relative fitting index
RFI > 0.90 0.96
PNFI > 0.50 0.79
Contracted fitting
index PGFI > 0.50 0.64
The parameters fall within the acceptable range. However, the results fail to cover the
most reasonable fitting value because the root mean square error of approximation
(RMSEA) confidence interval fails to satisfy p value ≥ 0.05 and the 95% confidence
interval of non-centralised parameter estimates (NCP) fail to contain 0. Therefore,
theoretical models do not always have the best fit and the hypothesis model can be
amended.
Continuing the previous step, we can use the T-value and the modified index to revise
the model, increasing or decreasing the model parameters. Removal of one path (the
effect of TIC to IPs, namely, the H2 assumption) and increasing the IBs→TIC 3
parameter, which belongs to the measurement mode, has been suggested. The CFA
analysis found in the first phase of this chapter is found to have a minimal effect on the
overall improvement of the model and lacking in theoretical basis. Hence, the revision is
discontinued. According to the estimation results, the fitness of the overall model slightly
improved after deletion of regression coefficients. We then examined the T-values and
obtained results within acceptable parameters. Therefore, this final model can be used as
a final analysis solution.
This study creates a comprehensive model analysis with SEM to verify whether the
data analysis results support the theory assumption relationship, developed during the
conceptualisation phase. The standardised path coefficient of each latent variable in SEM

Framework of open innovation in SMEs in an emerging economy 241
is the digital indicator, which can be used to measure the variables whether the
assumption is established or not. A larger path coefficient indicates a higher degree of
importance in a causal relationship. The verification results under the significance level
of the seven hypotheses of this study are shown in Table 8.
Table 8 Verification results of model hypothesis
Hypothesis Coefficient Results
H1 0.63 support (***)
H2 0.05 reject
H3 –0.46 support (***)
H4 0.13 support (**)
H5 0.21 support (**)
H6 0.73 support (***)
H7 0.73 support (***)
This article verifies the influencing factors of innovation performance. Six of the seven
assumptions were verified. H1 test results demonstrate significance, which indicates that
technology innovation capacity has a remarkable positive effect on the resource access in
the network. That is, the innovation ability of internal and external innovation of the main
subject in network represents the foundation of internal innovation diffusion, diffusion
among enterprises in the network, and total diffusion within the network. Enterprises in
innovation network are inclined to share motivity and consciousness. However, if
enterprises lack innovation ability, the network cannot operate. This finding suggests that
in open innovation, innovation resources lie in the internal or external sources of SMEs,
but solutions to meeting customer demands must come from the SMEs. Thus, SMEs must
enhance their innovation ability.
H3 and H4 are based on empirical research and trainee interview and are verified by
survey data analysis. The path coefficients of H3 and H4 are calculated at –0.46 and 0.13,
respectively, which are significant under the conditions p < 0.001 (***) and p < 0.01
(**).
H3 hypothesis indicates that innovation barriers have a remarkable negative effect on
innovation performance. SMEs are often wary of cooperation and exchange, which
results in the loss of core competitiveness for the enterprise. EM SMEs with very limited
resources often identify and acquire knowledge and market, which are related or similar
to existing products. EM SMEs are incapable of forming a virtuous circle of ‘R&D
work – accumulation of knowledge – R&D work’ because the search scope and the
likelihood of a successful search are relatively low.
H4 shows expected results that innovation barriers in EM SMEs have positive effect
on network openness, with an impact factor evaluated at 0.13. Under the environment of
competition, SMEs must pay attention to innovation opportunity. But the differences
between SMEs and MNEs are significant. From Table 2, we know the barriers of EM
SMEs lie in the inadequacy of human resource, insufficiency of information, absence of
infrastructure, and lack of financial resources. However, labour shortage, infrastructure
deficiency, and financial resource can be obtained through open cooperation.
Consequently, network openness of EM SMEs is remarkably complementary to
innovation barriers in shaping innovation performance.

242 P. Xiaobao et al.
Among all assumptions, H6 and H7 particularly show significance with a regression
coefficient of 0.73. This result indicates that in running EM SMEs network, the
intermediary role of the network information variables and the influence of the network
information on open innovation running are highest. H7 proved the necessity of a
network model.
Table 9 Coefficient values of innovative information sources
Open innovation network
Innovative information sources
Coefficient value p-value
Within the firm 0.81*** 0.000
From other firms and market 0.84*** 0.000
From university and research
centre 0.92*** 0.000
The external
information
sources
From public information 0.93*** 0.000
Review of the details of external information source (see Table 2 and Table 9) reveals
that EM SMEs emphasise the importance of information obtained from the external
information sources; they also seemingly depend more on public information.
For this reason, government offices should provide all kinds of information services
and fully implement fiscal measures such as tax preference and financial support to
encourage EM SMEs innovation. Thus, the government exerts influence to convey
innovation confidence. Government offices shall provide specific and detailed
information on cooperation channels as well as cooperation objects rather than slogans
through the establishment of intellectual property transaction and service centres,
incubation centres, and other institutions.
4.2 Findings
Then, going into some more details of the external information sources, this paper
analysed the correlation between types of innovation and the SMEs’ external information
usages. The data for this study originated mainly from 1504 HTEs of China.
Referencing the work by Laursen and Salter (2006), which identified the relationship
between the degree of novelty in innovation and the firms’ external search strategies,
several variables were designed for the analysis. Firstly, regarding the innovation
performance, we focused on the four types of innovation – major product innovation,
minor product innovation, service innovation and process innovation – and used the
numbers of innovation that the firm has made for the last five years in each type of
innovation. Secondly, regarding the SMEs’ external information usages, two variables
termed as breadth and depth were designed (Laursen and Salter, 2006). Finally, using the
variables, the correlation between the number of innovation and the external information
usages in SMEs was calculated.
Table 10 shows that four types of innovation are related to the depth and breadth of
external information usages at the significance level of 0.01 (**). Process innovation is
related only to the breadth at the significant level of 0.05 (*). Therefore, effective and
broad use of external information is significantly associated with the number of
innovation. In particular, the breadth is more closely linked to the product innovation
than the depth.

Framework of open innovation in SMEs in an emerging economy 243
Table 10 Correlation analysis between IPs in SMEs and external information usages
External information usages
Depth Breadth
IPs in SMEs
Coefficient
value p-value Coefficient value p-value
Radical product
innovation
0.735*** 0.001 0.901*** 0.000
Incremental product
innovation
0.612** 0.002 0.945*** 0.000
Service innovation 0.992*** 0.000 0.734*** 0.000
Process innovation 0.290 0.160 0.471* 0.021
Little differences in the correlation with external information usages are found between
radical product innovation and incremental product innovation. Intensive use of external
information seems to be slightly more related to radical product innovation than
incremental product innovation while searching information from various external
channels is more linked to incremental product innovation than radical product
innovation, which means that radical product innovation may require not only getting
information from external channels but also applying it actively to their innovation
process.
Another interesting finding is that service innovation is more closely related to the
depth than breadth. It seems that customer needs of target markets, as external
information sources, should be considered carefully and so having a deep relationship
with a few external sources are critical for service innovation. Unlike other types of
innovation, process innovation is not significantly linked to external information usages.
To summarise, the analysis results indicate that both the depth and breadth of external
information is linked to innovation in EM SMEs, but the role of external channels might
be different by innovation types.
5 Discussions
5.1 Contributions
This study aims not to contradict prior research but extend existing concepts while a new
approach is proposed, specifically through the theoretical thread on network. In our view,
at least four contributions emerge.
First, open innovation studies focus on MNEs, thus neglecting open innovation in
SMEs. This study addresses the gap by exploring the incidence and trends toward open
innovation in SMEs. Using a survey database of 420 innovative SMEs, we conclude that
SMEs extensively practice open innovation activities, and the trend is increasing. Our
results concur with the recent study by Lichtenthaler (2008), which demonstrated that
medium and large manufacturers embrace open innovation practices. We expect our
study to show researchers the importance of viewing SMEs open innovation from a new
and more sophisticated perspective.

244 P. Xiaobao et al.
Second, our study provides support for the open innovation paradigm in SMEs
through the SNP. The study also shows the open innovation network that considers
important innovation choices in the market setting. The study suggests that a
comprehensive framework is not only critical but also theoretically and methodologically
feasible for disentangling the factors driving closed or open innovation across SMEs
characteristics of innovation capacity and innovation barriers, network openness, and
network information. Open innovation through network participation has become an
increasingly widespread phenomenon. Issues pertaining to the design and organisation of
external innovation management tools such as networks are significant.
Third, we investigate SMEs innovation through a common theoretical thread,
the tension between firms’ simultaneous needs for both external resource and
network information. Prior research (White, 2000) in this area primarily examined
firm-perspective factors in boundary choices. As a comparison, our study does not only
enhance our understanding of this complex boundary choice phenomenon with a new
perspective and a sophisticated methodology. Our introduction of network information
variables significantly advances our understanding of SMEs innovation in an
interconnected world. The study also extends the conceptual work of Lavie (2006) by
empirically testing both internal resource endowments and external relations to show they
can critically affect firms’ tendency toward inter-firm collaboration. Our findings on the
interaction effects confirm that network information is a critical factor in understanding
how SMEs respond to network constraints and opportunities.
Finally, EM SMEs are in the process of economic structural adjustment. Adopting a
technical innovation strategy is imperative. EM SMEs must establish an open innovation
development model that relies on knowledge, science, and technology. The research
results support the notion of EM SMEs open innovation, by proposing network as a way
of facilitating this strategy and suggesting a network as an effective model to enable
SMEs collaboration and specialisation.
5.2 Limitations and future research directions
This study contributes to the literature in technology and innovation management as well
as small business management by improving our knowledge on the effects of open
innovation activities in the context of SMEs. Despite the attempt to reconcile this gap, the
study has several limitations.
First, given its small sample size, this study fails to explore fully the influence of
relevant factors on innovation commercialisation of EM SMEs and the influence of
knowledge-sharing model among different EM SMEs on technological innovation
achievements. Future research in this area should be based on a larger data sample;
extract medium-sized groups, small business groups, and so on by setting the threshold;
introduce the class variables, and conduct further confirmatory analysis using ANOVA.
Second, H6 and H7 are particularly significant, which confirmed the need for an
intermediary network model. However, this paper does not include the correlation
between types of innovation and the use of EM SMEs external information in the study.
In reference to the study by Laursen and Salter (2006), which identified the relationship
between the degree of novelty in innovation and the firms’ external search strategies,
several variables were designed for the analysis.

Framework of open innovation in SMEs in an emerging economy 245
5.3 Conclusions
First, open innovation has emerged as a new collaborative paradigm in SMEs settings and
varies substantially from traditional alliances. This paper sought to investigate the
business and strategic values of open innovation networks in EM SMEs through which
participating SMEs co-create economic value through the joint development and
co-marketing of innovations. Our theory is based on the unique characteristics of open
innovation in EM SMEs. Through an established methodology, the empirical validation
contributes to the growing body of literature on strategic alliances, specifically, on open
innovation network.
Second, open innovation networks in EM SMEs mainly depend on network openness
and network information. However, some EM SMEs have limited information resources
and lack the ability to obtain important information and capital. These inadequacies result
in difficulty in network partner selection. EM SMEs rely on self-improvement of
innovation ability and information, especially to strengthen the ability of obtaining
innovation information from leading users and competitors among others. EM SMEs also
outsource the design and development of new products and other services to other
organisations, form gradually an exploratory innovation model and strengthen
cooperation between SMEs and universities. We can actively promote the commercial
cooperation between EM SMEs in traditional business areas and jointly tap the
advantages of existing resources.
Acknowledgements
The authors would like to thank the editors (Dr. M.A. Dorgham) and two anonymous
referees for their valuable comments and suggestions, which led to significant
improvements in the paper. This work was supported by the National Natural Science
Foundation of China under Grant Nos. 71202054, the Natural Science Foundation of
Anhui under Grant Nos. 1208085MG124, and ‘the Fundamental Research Funds for the
Central Universities’.
References
Ahuja, G. (2000) ‘Collaboration networks, structural holes, and innovation: A longitudinal study’,
Administrative Science Quarterly, Vol. 45, No. 3, pp.425–455.
Barney, J. (1991) ‘Firm resources and sustained competitive advantage’, Journal of Management,
Vol. 17, No. 1, pp.99–120.
Baum, J.A.C., et al. (2000) ‘Don’t go it alone: alliance network composition and startups’
performance in Canadian biotechnology’, Strategic Management Journal, Vol. 21, No. 3,
pp.267–294.
Bayona, C., et al. (2001) ‘Firms’ motivations for cooperative R&D: an empirical analysis of
Spanish firms’, Research Policy, Vol. 30, No. 8, pp.1289–1307.
Bianchi, M., et al. (2010) ‘Enabling open innovation in small- and medium-sized enterprises: how
to find alternative applications for your technologies’, R & D Management, Vol. 40, No. 4,
pp.414–431.
Boudreau, M.C. et al. (2008) ‘Open source software research: an evolving endeavor’, Journal of
Database Management, Vol. 19, No. 2, pp.I–IV.

246 P. Xiaobao et al.
Burt, R. (2000) ‘Innovation or imitation? Technological dependency in the American nonferrous
mining industry’, Technology and Culture, Vol. 41, No. 2, pp.321–347.
Burt, R.S. (1997) ‘The contingent value of social capital’, Administrative Science Quarterly,
Vol. 42, No. 2, pp.339–365.
Calantone, R.J. et al. (2002) ‘Learning orientation, firm innovation capability, and firm
performance’, Industrial Marketing Management, Vol. 31, No. 6, pp.515–524.
Caloghirou, Y., et al. (2004) ‘Internal capabilities and external knowledge sources: complements or
substitutes for innovative performance?’, Technovation, Vol. 24, No. 1, pp.29–39.
Chesbrough, H. (2003) ‘The logic of open innovation: Managing intellectual property’, California
Management Review, Vol. 45, No. 3, pp.35–41.
Chesbrough, H. and Crowther, A.K. (2006) ‘Beyond high tech: early adopters of open innovation
in other industries’, R & D Management, Vol. 36, No. 3, pp.229–236.
Child, J. and Tse, D.K. (2001) ‘China’s transition and its implications for international business’,
Journal of International Business Studies, Vol. 32, No. 1, pp.5–21.
Christensen, C.M., et al. (2006) ‘Disruptive innovation for social change’, Harvard Business
Review, Vol. 84, No. 12, pp.94–101.
Christensen, J.F., et al. (2005) ‘The industrial dynamics of open innovation – evidence from the
transformation of consumer electronics’, Research Policy, Vol. 34, No. 10, pp.1533–1549.
Cohen, W.M. and Levinthal, D.A. (1989) ‘Innovation and learning – the 2 faces of R&D’,
Economic Journal, Vol. 99, No. 397, pp.569–596.
Cohen, W.M. and Levinthal, D.A. (1990) ‘Absorptive-capacity - a new perspective on learning and
innovation’, Administrative Science Quarterly, Vol. 35, No. 1, pp.128–152.
Colombo, M.G. (2003) ‘Alliance form: a test of the contractual and competence perspectives’,
Strategic Management Journal, Vol. 24, No. 12, pp.1209–1229.
Comrey, A.L. and Lee, H.B. (1992) A First Course in Factor Analysis, Erlbaum, Hillsdale, NJ.
Cooke, P. (2005) ‘Regionally asymmetric knowledge capabilities and open innovation exploring
‘Globalisation 2’ – a new model of industry organisation’, Research Policy, Vol. 34, No. 8,
pp.1128–1149.
Coombs, R., et al. (2003) ‘Analysing distributed processes of provision and innovation’, Industrial
and Corporate Change, Vol. 12, No. 6, pp.1125–1155.
Das, T.K. and Teng, B.S. (2000) ‘Instabilities of strategic alliances. An internal tensions
perspective’, Organization Science, Vol. 11, No. 1, pp.77–101.
Davis, G.F. (1991) ‘Agents without principles – the spread of the poison pill through the
intercorporate network’, Administrative Science Quarterly, Vol. 36, No. 4, pp.583–613.
Dyer, J.H. and Singh, H. (1998) ‘The relational view: cooperative strategy and sources of
interorganizational competitive advantage’, Academy of Management Review, Vol. 23, No. 4,
pp.660–679.
Edwards, T., et al. (2005) ‘Understanding innovation in small and medium-sized enterprises: a
process manifest’, Technovation, Vol. 25, No. 10, pp.1119–1127.
Eisenhardt, K.M. and Schoonhoven, C.B. (1996) ‘Resource-based view of strategic alliance
formation: strategic and social effects in entrepreneurial firms’, Organization Science, Vol. 7,
No. 2, pp.136–150.
Finnegan, P., et al. (1999) ‘Inter-organizational systems planning: learning from current practices’,
International Journal of Technology Management, Vol. 17, Nos. 1–2, pp.129–144.
Fleming, L. and Waguespack, D.M. (2007) ‘Brokerage, boundary spanning, and leadership in open
innovation communities’, Organization Science, Vol. 18, No. 2, pp.165–180.
Fornell, C. and Bookstein, F.L. (1982) ‘2 structural equation models – LISREL and PLS applied to
consumer exit – voice theory’, Journal of Marketing Research, Vol. 19, No. 4, pp.440–452.
Fornell, C. and Larcker, D.F. (1981) ‘Evaluating structural equation models with unobservable
variables and measurement error’, Journal of Marketing Research, Vol. 18, No. 1, pp.39–50.

Framework of open innovation in SMEs in an emerging economy 247
Fritsch, M. and Lukas, R. (2001) ‘Who cooperates on R&D?’, Research Policy, Vol. 30, No. 2,
pp.297–312.
Funk, J.L. (2003) ‘Standards, dominant designs and preferential acquisition of complementary
assets through slight information advantages’, Research Policy, Vol. 32, No. 8, pp.1325–1341.
Garud, R. and Nayyar, P.R. (1994) ‘Transformative capacity – continual structuring
by intertemporal technology-transfer’, Strategic Management Journal, Vol. 15, No. 5,
pp.365–385.
Gassmann, O. (2006) ‘Opening up the innovation process: towards an agenda’, R & D
Management, Vol. 36, No. 3, pp.223–228.
Granovetter, M. (1985) ‘Economic-action and social-structure – the problem of embeddedness’,
American Journal of Sociology, Vol. 91, No. 3, pp.481–510.
Granstrand, O., et al. (1997) ‘Multi-technology corporations: why they have “distributed” rather
than “distinctive core” competencies’, California Management Review, Vol. 39, No. 4,
pp.8–25.
Greis, N.P., et al. (1995) ‘External partnering as a response to innovation barriers and global
competition in biotechnology’, Research Policy, Vol. 24, No. 4, pp.609–630.
Hagedoorn, J. (1993) ‘Understanding the rationale of strategic technology partnering –
interorganizational modes of cooperation and sectoral differences’, Strategic Management
Journal, Vol. 14, No. 5, pp.371–385.
Hagedoorn, J. and Schakenraad, J. (1994) ‘The effect of strategic technology alliances on company
performance’, Strategic Management Journal, Vol. 15, No. 4, pp.291–309.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006) Multivariate Data
Analysis, 6th ed., Prentice-Hall, New Jersey.
Han, K., et al. (2012) ‘Value cocreation and wealth spillover in open innovation alliances’, MIS
Quarterly, Vol. 36, No. 1, pp.291–315.
Hart, S.L. (1992) ‘An integrative framework for strategy-making processes’, Academy of
Management Review, Vol. 17, No. 2, pp.327–351.
Henderson, R. and Cockburn, I. (1994) ‘Measuring competence – exploring firm effects in
pharmaceutical research’, Strategic Management Journal, Vol. 15, No. S1, pp.63–84.
Henkel, J. (2006) ‘Selective revealing in open innovation processes: the case of embedded Linux’,
Research Policy, Vol. 35, No. 7, pp.953–969.
Howells, J., et al. (2003) ‘The sourcing of technological knowledge: distributed innovation
processes and dynamic change’, R & D Management, Vol. 33, No. 4, pp.395–409.
Iansiti, M. and Levien, R. (2004) ‘Strategy as ecology – reply’, Harvard Business Review, Vol. 82,
No. 9, pp.132–133.
Jöreskog, K.G. and Sörbom, D. (1999) LISREL 8: Structural Equation Modelling with the SIMPLIS
Command Language, Scientific Software International, Lincolnwood, IL.
Julien, P. (2002) ‘The on-going challenge’, Journal of Hydraulic Engineering-Asce, Vol. 128,
No. 3, pp.250–251.
Kaiser, U. (2002) ‘Measuring knowledge spillovers in manufacturing and services: an empirical
assessment of alternative approaches’, Research Policy, Vol. 31, No. 1, pp.125–144.
Khanna, T. and Palepu, K. (2000) ‘The future of business groups in emerging markets: long-run
evidence from Chile’, Academy of Management Journal, Vol. 43, No. 3, pp.268–285.
Kirschbaum, R. (2005) ‘Open innovation in practice’, Research-Technology Management, Vol. 48,
No. 4, pp.24–28.
Kleinknecht, A. and Reijnen, J.O.N. (1992) ‘Why do firms cooperate on research-and-development
– an empirical-study’, Research Policy, Vol. 21, No. 4, pp.347–360.
Kogut, B. (2000) ‘The network as knowledge: generative rules and the emergence of structure’,
Strategic Management Journal, Vol. 21, No. 3, pp.405–425.

248 P. Xiaobao et al.
Kogut, B. and Zander, U. (1992) ‘Knowledge of the firm, combinative capabilities, and the
replication of technology’, Organization Science, Vol. 3, No. 3, pp.383–397.
Laursen, K. and Salter, A. (2004) ‘Searching high and low: what types of firms use universities as a
source of innovation?’, Research Policy, Vol. 33, No. 8, pp.1201–1215.
Laursen, K. and Salter, A. (2006) ‘Open for innovation: the role of openness in explaining
innovation performance among UK manufacturing firms’, Strategic Management Journal,
Vol. 27, No. 2, pp.131–150.
Lavie, D. (2006) ‘The competitive advantage of interconnected firms: an extension of the
resource-based view’, Academy of Management Review, Vol. 31, No. 3, pp.638–658.
Lecocq, X. and Demil, B. (2006) ‘Strategizing industry structure: the case of open systems in a
low-tech industry’, Strategic Management Journal, Vol. 27, No. 9, pp.891–898.
Lee, S., et al. (2010) ‘Open innovation in SMEs – an intermediated network model’, Research
Policy, Vol. 39, No. 2, pp.290–300.
Lichtenthaler, U. (2005) ‘External commercialization of knowledge: review and research agenda’,
International Journal of Management Reviews, Vol. 7, No. 4, pp.231–255.
Lichtenthaler, U. (2008) ‘Open innovation in practice: an analysis of strategic approaches to
technology transactions’, IEEE Transactions on Engineering Management, Vol. 55, No. 1,
pp.148–157.
Lichtenthaler, U. and Ernst, H. (2008) ‘Intermediary services in the markets for technology:
organizational antecedents and performance consequences’, Organization Studies, Vol. 29,
No. 7, pp.1003–1035.
Lichtenthaler, U. and Lichtenthaler, E. (2009) ‘A capability-based framework for open innovation:
complementing absorptive capacity’, Journal of Management Studies, Vol. 46, No. 8,
pp.1315–1338.
Luo, Y.D. and Peng, M.W. (1999) ‘Learning to compete in a transition economy: experience,
environment, and performance’, Journal of International Business Studies, Vol. 30, No. 2,
pp.269–295.
MacCallum, R.C., et al. (1996) ‘Power analysis and determination of sample size for covariance
structure modeling’, Psychological Methods, Vol. 1, No. 2, pp.130–149.
Miotti, L. and Sachwald, F. (2003) ‘Co-operative R&D: why and with whom? An integrated
framework of analysis’, Research Policy, Vol. 32, No. 8, pp.1481–1499.
Mowery, D.C. and Oxley, J.E. (1995) ‘Inward technology-transfer and competitiveness – the role
of national innovation systems’, Cambridge Journal of Economics, Vol. 19, No. 1, pp.67–93.
Nambisan, S. and Sawhney, M. (2011) ‘Orchestration processes in network-centric innovation:
evidence from the field’, Academy of Management Perspectives, Vol. 25, No. 3, pp.40–57.
Narula, R. (2004) ‘R&D collaboration by SMEs: new opportunities and limitations in the face of
globalisation’, Technovation, Vol. 24, No. 2, pp.153–161.
National Bureau of Statistics of China (2011) The China Statistical Yearbook 2011, China Statistics
Press, Beijing.
National Bureau of Statistics of China (2012) The China Statistical Yearbook 2012, China Statistics
Press, Beijing.
Negassi, S. (2004) ‘R&D co-operation and innovation a microeconometric study on French firms’,
Research Policy, Vol. 33, No. 3, pp.365–384.
Nelson, R.E. (1993) ‘Authority, organization, and societal context in multinational churches’,
Administrative Science Quarterly, Vol. 38, No. 4, pp.653–682.
Owen-Smith, J. and Powell, W.W. (2004) ‘Knowledge networks as channels and conduits: the
effects of spillovers in the Boston biotechnology community’, Organization Science, Vol. 15,
No. 1, pp.5–21.
Paniccia, I. (1998) ‘One, a hundred, thousands of industrial districts. Organizational variety in local
networks of small and medium-sized enterprises’, Organization Studies, Vol. 19, No. 4,
pp.667–699.

Framework of open innovation in SMEs in an emerging economy 249
Peng, M.W. and Luo, Y.D. (2000) ‘Managerial ties and firm performance in a transition economy:
the nature of a micro-macro link’, Academy of Management Journal, Vol. 43, No. 3,
pp.486–501.
Podolny, J.M. and Page, K.L. (1998) ‘Network forms of organization’, Annual Review of
Sociology, Vol. 24, No. 1, pp.57–76.
Powell, W.W., et al. (1996) ‘Interorganizational collaboration and the locus of innovation:
networks of learning in biotechnology’, Administrative Science Quarterly, Vol. 41, No. 1,
pp.116–145.
Prahalad, C.K. and Hamel, G. (1990) ‘The core competence of the corporation’, Harvard Business
Review, Vol. 68, No. 3, pp.79–91.
Rothaermel, F.T. and Hess, A.M. (2007) ‘Building dynamic capabilities: innovation driven
by individual-, firm-, and network-level effects’, Organization Science, Vol. 18, No. 6,
pp.898–921.
Rothaermel, F.T. and Thursby, M. (2007) ‘The nanotech versus the biotech revolution: sources of
productivity in incumbent firm research’, Research Policy, Vol. 36, No. 6, pp.832–849.
Rothwell, R. (1991) ‘External networking and innovation in small and medium-sized
manufacturing firms in Europe’, Technovation, Vol. 11, No. 2, pp.93–112.
Rothwell, R. (1994) ‘Issues in user producer relations in the innovation process – the role
of government’, International Journal of Technology Management, Vol. 9, Nos. 5–7,
pp.629–649.
Rothwell, R. and Dodgson, M. (1992) ‘European technology policy evolution – convergence
towards SMEs and regional technology-transfer’, Technovation, Vol. 12, No. 4, pp.223–238.
Shaw, B. (1998) ‘Innovation and new product development in the UK medical equipment industry’,
International Journal of Technology Management, Vol. 15, Nos. 3–5, pp.433–445.
Sheen, M.R. and Macbryde, J.C. (1995) ‘The importance of complementary assets in the
development of smart technology’, Technovation, Vol. 15, No. 2, pp.99–109.
Shook, C.L., et al. (2004) ‘An assessment of the use of structural equation modeling in strategic
management research’, Strategic Management Journal, Vol. 25, No. 4, pp.397–404.
Spencer, J.W. (2001) ‘How relevant is university-based scientific research to private
high-technology firms? A United States-Japan comparison’, Academy of Management
Journal, Vol. 44, No. 2, pp.432–440.
Stuart, T.E. (2000) ‘Interorganizational alliances and the performance of firms: a study of growth
and innovation rates in a high-technology industry’, Strategic Management Journal, Vol. 21,
No. 8, pp.791–811.
Tan, J.J. and Litschert, R.J. (1994) ‘Environment-strategy relationship and its performance
implications - an empirical-study of the Chinese electronics industry’, Strategic Management
Journal, Vol. 15, No. 1, pp.1–20.
Teece, D.J. (1986) ‘Profiting from technological innovation – implications for integration,
collaboration, licensing and public-policy’, Research Policy, Vol. 15, No. 6, pp.285–305.
Terwiesch, C. and Xu, Y. (2008) ‘Innovation contests, open innovation, and multiagent problem
solving’, Management Science, Vol. 54, No. 9, pp.1529–1543.
Tether, B.S. (2002) ‘Who co-operates for innovation, and why – an empirical analysis’, Research
Policy, Vol. 31, No. 6, pp.947–967.
Uzzi, B. (1996) ‘The sources and consequences of embeddedness for the economic performance
of organizations: the network effect’, American Sociological Review, Vol. 61, No. 4,
pp.674–698.
Uzzi, B. (1997) ‘Social structure and competition in interfirm networks: the paradox of
embeddedness’, Administrative Science Quarterly, Vol. 42, No. 1, pp.35–67.
van de Vrande, V., et al. (2009) ‘Open innovation in SMEs: Trends, motives and management
challenges’, Technovation, Vol. 29, Nos. 6–7, pp.423–437.

250 P. Xiaobao et al.
Veugelers, R. (1997) ‘Internal R&D expenditures and external technology sourcing’, Research
Policy, Vol. 26, No. 3, pp.303–315.
West, J. and Gallagher, S. (2006) ‘Challenges of open innovation: the paradox of firm investment
in open-source software’, R&D Management, Vol. 36, No. 3, pp.319–331.
White, S. (2000) ‘Competition, capabilities, and the make, buy, or ally decisions of Chinese
state-owned firms’, Academy of Management Journal, Vol. 43, No. 3, pp.324–341.
Wright, M., et al. (2008) ‘Returnee entrepreneurs, science park location choice and performance: an
analysis of high-technology SMEs in China’, Entrepreneurship Theory and Practice, Vol. 32,
No. 1, pp.131–155.
Yan, A.M. and Gray, B. (1994) ‘Bargaining power, management control, and performance in
United States-China joint ventures – a comparative case-study’, Academy of Management
Journal, Vol. 37, No. 6, pp.1478–1517.
Yang, H.B., et al. (2010) ‘A multilevel framework of firm boundaries: firm characteristics, dyadic
differences, and network attributes’, Strategic Management Journal, Vol. 31, No. 3,
pp.237–261.
Zahra, S.A. and Bogner, W.C. (2000) ‘Technology strategy and software new ventures’
performance: exploring the moderating effect of the competitive environment’, Journal of
Business Venturing, Vol. 15, No. 2, pp.135–173.
Zahra, S.A. and George, G. (2002) ‘Absorptive capacity: a review, reconceptualization, and
extension’, Academy of Management Review, Vol. 27, No. 2, pp.185–203.