2. Polanyi came with the concept of embeddedness and defined it as the degree to which
economic activity is constrained by noneconomic institutions. Network embeddedness as a
concept is thus defined by Echols and Tsai (2005, p. 221) as the “the structure of a firm’s
relationship with other firms—specifically, the extent to which a firm is connected to other
firms.” Inkpen and Tsang (2005) classified network embeddedness into three dimensions,
namely, relational, structural and cognitive embeddedness. Relational embeddedness
founded on trust is explained as the degree of quality and cohesive social interaction
among network members acting as a community of organizations (Wang and Chen, 2012; Lin
et al., 2009). Structural embeddedness is also considered as the amount of information the
focal firm could obtain from its network, which largely depends on the position of the firm in
the network and the number of members in the network (Mazzola et al., 2015). Cognitive
embeddedness, on the other hand, represents the shared representations, goals, norms, faith
and experience among network members (Breton-Miller and Miller, 2009; G€
olgeci et al., 2019).
This study is founded on resource-based view (RBV) (Barney, 1991; Grant, 1991), because
network embeddedness provides small and medium-sized enterprises (SMEs) with external
resources, which could be used to complement internal resources, for competitive advantage.
The resource here largely represents innovation knowledge (which is an intangible asset),
thereby making knowledge-based view (KBV) also applicable (Grant, 1996).
The concept of network embeddedness has been used widely in innovation research to
demonstrate its contribution to various aspects of innovation in firms. In the biopharmaceutical
industry, Mazzola et al. (2015) focused on the effect of network embeddedness on new product
development (NPD) and found out that the interactions between centrality and structural holes
had an increasing effect on NPD, while structural holes had no significant effect. In the same
biopharmaceutical sector, Mazzola et al. (2016) found that interlocking directorate network
enhances the interfirm network and NPD relationship. With panel data generated from
chemicals, automotive and pharmaceutical industries, Gilsing et al. (2008) found that different
network positions yielded different payoffs in new technology exploration. Focusing on
European MNE subsidiaries, G€
olgeci et al. (2019) found that the degree of knowledge transfer
from other MNE units mediates the relationship between subsidiaries’ internal embeddedness
and their innovation performance. In a multinational corporation context, Ferraris et al. (2018)
found that the interaction between internal and external network embeddedness had a
multiplicative effect on subsidiaries’ knowledge transfer. Ferraris et al. (2017) also demonstrate
that MNE subsidiaries’ innovation performance could be significantly enhanced through
external research and development activities.
Inthesteeland semiconductor industries, Rowleyet al. (2000) arguedthattheroles relational
embeddedness and structural embeddedness play in firm performance can only be understood
with reference to the other. In the information technology (IT) sector, Grewal et al. (2006) found
that networkembeddedness has significant effects on both technical andcommercial success of
open-source systems. In the high-tech industry, Lyu et al. (2019) found that at the initial and
growth stages of industrial technology development, the centrality and network reach of a
firm’s network embeddedness exert a positive impact on the inbound open-innovation practice.
Studying manufacturing firms, Xie et al. (2019) found that network embeddedness significantly
enhanced the relationship between research and development intensity and new product
performance. On a regional level, Rutten and Boekema (2007) found that norms, values and
customs of regional networks facilitate collaboration for mutual benefit of innovation firms. By
concentrating on IT, automobile, environmental protection firms and pharmaceutical firms,
Wang and Chen (2012) found that network embeddedness promotes disruptive innovation
performance through absorptive capacity. In the food and beverage industry, Santoro et al.
(2017) found that external knowledge sourcing significantly influenced incremental innovation
performance. In the healthcare sector, Pauget and Wald (2018) demonstrated that informal
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3. networks support the early phase of the invention and development of organizational
innovation, while later phases depend more on the formal structure.
From the earlier discussions, it is realized that despite substantial work on network
embeddedness, very limited attention has been paid specifically to the SMEs. This is not to
say that the concept of network embeddedness is not applicable or relevant to SMEs. Xiaobao
et al. (2013) in their study demonstrated that SMEs consider external network information as
means of obtaining access to marketing and sales channels at later stages of innovation.
Given that size and resource capacity put SMEs at a disadvantage when it comes to
innovation, their managerial approach is likely to take an external and boundary-spanning
component, thus exploiting knowledge sourcing strategies ( Jansen et al., 2006; Brunswicker
and Vanhaverbeke, 2015; Festa et al., 2017). Being embedded in a network will therefore grant
SMEs access to external resources, which they could not have hitherto generated internally
for innovation (Raisch et al., 2009). In meeting this research gap, this current study seeks to
throw more light on the effect of network embeddedness on SMEs’ innovation performance.
Innovation performance in this study is measured in terms of NPD speed, on-time product
launch and new product innovativeness (Abdallah et al., 2019).
Another concept that is closely related to network embeddedness is open innovation.
Open innovation represents the purposeful knowledge exchange with members outside
the organization, to leverage on the external technologies for competitive advantage
(Chesbrough, 2003; Chesbrough and Crowther, 2006). From this concept, Laursen and
Salter (2006) develop the concept of innovation openness (openness in innovation), which
they defined as the firm’s external search breadth and external search depth strategies.
External search breadth represents the number of external entities firms interact with,
while the external search depth also represents the frequency of contact with a particular
entity or group (Laursen and Salter, 2006). The external entities that firms could contact
for knowledge are customers, suppliers, competitors, consultants, contracted R&D or
design firms, distributors/retailers, universities or other research institutes, regulatory
and standards bodies, industry technical/trade associations and investors (Laursen and
Salter, 2014; Stanko and Henard, 2017). Some studies have attempted to explore the
relationship between network embeddedness and open innovation. Lyu et al. (2019), for
example, showed that network embeddedness had a positive effect on open innovation,
although this relationship is further enhanced by technology cluster. Openness enriches
firms’ pool of knowledge, which helps to identify and leverage complementary external
asset (Ren et al., 2015). It is therefore expected that the effect of network embeddedness on
SMEs’ innovation performance be further enhanced when firms engage in external
knowledge search (G€
olgeci et al., 2019; Mazzola et al., 2015; Xiaobao et al., 2013). We
therefore present SMEs’ level of openness to moderate the relationship between the
network embeddedness and innovation performance.
2. Literature review
2.1 Network embeddedness and innovation performance
A network comprises of many actors that interact among themselves and whose activities are
influenced not by a single actor but by multiple actors through exchange relationships
(H
akansson and Johanson, 1993). A firm’s relationship with one actor in the network therefore
influences the firm’s other relationships with members within the same network (Ryu et al.,
2013). A network comprises of social ties, value systems and trusting relationship (Tsai and
Ghoshal, 1998; Zaheer et al., 2010; Moran, 2005). Social relations among actors are essential in
promoting knowledge and resource exchange (Adler and Kwon, 2002). Inkpen and Tsang
(2005) classified knowledge exchange in a social context into relational, structural and
cognitive embeddedness. Relational embeddedness is experienced in the tie strength and
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4. trust among network actors (Tsai and Ghoshal, 1998). Tie strength increases the level of
communication and interactions among network members, due to closeness in relationship
(Hsueh et al., 2010). Strong tie enhances knowledge transfer among network members
(Reagans and McEvily, 2003), which increases knowledge accessibility for NPD and
innovation activities (Nooteboom et al., 2006). As indicated by Hansen (1999), strong ties
among members encourage knowledge seekers to invest in assimilating and exploiting the
externally acquired knowledge. Trust, as a characteristic of relational embeddedness, also
encourages the free flow of knowledge among network members (Szulanski et al., 2004). High
levels of trust among network actors increase the willingness of members to help each other
in the acquisition and use of externally acquired knowledge for innovation (Lane et al., 2001).
High levels of trust mean that network members’ words are dependable, as obligations are
fulfilled as expected (Inkpen, 2000).
The second dimension of network embeddedness is structural embeddedness, which is
reflected in the number of relations and centrality of a network member (Mazzola et al., 2015;
Inkpen and Tsang, 2005). High number of relations increases accessibility to innovative
ideas, knowledge and resources, as well as enhancing the chances of knowledge transfer
between the focal firm and network members (Reagans and McEvily, 2003; Mazzola et al.,
2015). High relations increase information processing capacity, which enables knowledge
flow among the relationships (Gilsing et al., 2008). The number of relations increase
knowledge accessibility; however, firms that occupy centralized positions in the network
have greater access to relevant knowledge needed for innovation (Gilsing et al., 2008; Mazzola
et al., 2016). As indicated by Burt (2009), firms that occupy centralized position in the network
create brokerage position, which helps them in identifying and acquiring the relevant
knowledge needed for innovation.
Cognitive embeddedness represents the last dimension of network embeddedness, which
is defined by firms’ shared interpretations, representations, values, norms and meanings
(Nahapiet and Ghoshal, 1998). Inkpen and Tsang (2005) demonstrated that shared cultural
distance and shared vision are important social network characteristics, which enhance the
smooth transfer of knowledge in the network. Common systems and visions enhance mutual
understanding and act as a bonding mechanism that facilitates knowledge integration
among network members (Van Wijk et al., 2008). When network members have a low cultural
distance through shared norms and visions, it facilitates critical innovation knowledge
exchange even among internal network members, because there is common understanding
(Nooteboom et al., 2006; Szulanski et al., 2004). The focal firm could thus exploit these
externally accessed knowledge, to enhance its NPD speed.
Empirical literature has suggested that network embeddedness influenced various
aspects of firms’ innovation (Xie et al., 2019; Pauget and Wald, 2018; Wang and Chen, 2012;
Rutten and Boekema, 2007; Grewal et al., 2006). Even in a clutter with geographical proximity
and homogeneity, the innovation performances of the members varied, and network
embeddedness was found to be a key contributor (Gebreeyesus and Mohnen, 2013). SMEs in a
country may tend to possess some similar characteristics; however, the level of network
embeddedness peculiar to each firm will have great impact on its innovation performance.
From an emerging economy perspective, Liu et al. (2019) found that network embeddedness
positively influenced NPD through joint innovation. In the service sector, Hsueh et al. (2010)
found network embeddedness to positively influence innovation performance. Network
embeddedness is therefore not limited to only the manufacturing sector. SMEs from different
sectors may exhibit different network characteristics; however, it is expected that network
embeddedness would have significant effect on SMEs from any sector (be it manufacturing or
service). The use of knowledge generated from both external and internal sources gives
superior advantage to firms in their innovation performance (Ferraris et al., 2018; Santoro
et al., 2017). Based on this, we set the first hypothesis as:
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5. H1. Network embeddedness has a positive effect on SMEs’ innovation performance.
2.2 Moderating role of innovation openness
Innovation openness concerns firm’s external knowledge search strategy for commercial
innovation, and Laursen and Salter (2006) classified openness into differentiated (search
breadth) and intensively exploited (search depth). Search breadth represents the number of
external actors that firm engages with, and search depth represents the intensity or
frequency with which knowledge is sourced from a particular actor (Chesbrough, 2006).
External sources of innovation knowledge may include customers, suppliers, competitors,
universities, government institutions or research institutions (Martinez-Conesa et al., 2017).
Pursuing external search breadth comes with cost of time needed to assimilate and exploit the
wide range of knowledge acquired externally (Dahlander et al., 2016). However, as indicated
by Granovetter (1973), search breath in a strong tie network generates variety of new
understandings. External search depth on the other hand represents attention to details and
appreciation of relevant knowledge (G€
olgeci et al., 2019). Openness depth enhances deeper
relational involvement, increases trust and commitment with external actors (Wuebker et al.,
2015). With high levels of embeddedness and external search depth, firms will have a superior
advantage in their NPD processes (G€
olgeci et al., 2019). In the pursuit of innovation
performance, deep tie with particular knowledge source enhanced critical knowledge transfer
(Wang, 2015). Although studies such as Lyu et al. (2019) seem to suggest that network
embeddedness has a direct effect on innovation openness, we propose openness to rather
moderate the relationship between network embeddedness and SMEs’ innovation
performance. This falls on the backdrop that SMEs that are well networked with other
business units get external resources for innovation agenda; however, having multiple and
intensive sources of external knowledge grants SMEs the extra knowledge resource for
competitive advantage in their innovation performance (Xiaobao et al., 2013; Santoro et al.,
2019; Mazzola et al., 2015; Popa et al., 2017; Scuotto et al., 2017). Among MNE subsidiaries, for
example, G€
olgeci et al. (2019) found that the positive effect of network embeddedness on
innovation performance was further enhanced or improved among subsidiaries with high
levels of external knowledge search depth. Based on this, we state the last hypothesis as:
H2. Openness positively moderates the relationship between network embeddedness
and SMEs’ innovation performance.
Figure 1 presents the theoretical framework for the study.
3. Methods
3.1 Sample and data collection
The paper adopts the definition of SME by the National Board for Small-Scale Industries
(NBSSI) in Ghana. Using the number of employee criteria, NBSSI (1990) defined a small
enterprise as having 6–29 employees, medium enterprise as having 30–99 employees. As
indicated in Table I, the firms sampled for this study had employees ranging from 6 to 99, but
dominated by medium-sized firms. Firms were classified into manufacturing and service, and
Innovation
Openness
H2
H1
Network
Embeddedness
SMEs’
Innovation
Performance
Figure 1.
Theoretical framework
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6. manufacturing firms dominated the sample. SMEs sampled had at least five years of
operating experience, although the majority had 11–15 years of operating experience.
The list of registered SMEs was obtained from NBSSI, which had name of business,
year of registration, nature of business, contact and location. With purposive sampling,
1,000 SMEs that had operated for at least five years with full contact details (email, phone
and postal address) were selected. First, a printed version of the questionnaire, cover letter
and a postage-paid return envelope were sent to the general managers of these SMEs.
Second, the Web link to the online questionnaire and a cover letter were also emailed to the
SMEs. This was to give firms the flexibility of responding to the questionnaire in any
preferred format. Random phone calls followed after four weeks of not getting response
from some of the SMEs. The data collection began on February 4, 2019 and ended on
March 16, 2019. After six weeks of data collection process, 388 questionnaires were
appropriately filled and returned. In Ghana, there are quite a number of SMEs operating
without duly registering, making it difficult to have a very accurate estimation of SMEs
number. This notwithstanding, Kirby et al. (2002) showed that for a population figure of
10,000,000, with 95 percent confidence level and a 5 percent margin of error, a sample size
of 384 is enough. Our sample of 388 SMEs was therefore deemed to fairly represent the
SMEs population in Ghana.
3.2 Survey questionnaire and measures
Structured questionnaire was used in the data collection, which was first pretested with 20
SMEs. This helped to clear any ambiguity in the questions. Table II presents the various
observed items used in measuring the variables for this study. There were three variables in
the study framework, which were network embeddedness (independent variable), innovation
openness (moderator) and innovation performance (dependent variable). Network
embeddedness was a second-order variable with three first-order latent variables, which
were relational, structural and cognitive embeddedness. For all the measures under network
embeddedness, respondents were asked to respond on a Likert scale of 1 – strongly disagree
to 5 – strongly agree. Relational embeddedness and structural embeddedness had four
observed variables each. Cognitive embeddedness also had five observed variables, but one
was deleted due to poor factor loading. The observed items measuring the three dimensions
of network embeddedness were adapted from Lin et al. (2009).
The moderating variable, innovation openness, was defined as firms’ external search
strategies, in terms of its breadth and depth (Laursen and Salter, 2006). The possible sources
of external innovation knowledge are customers, suppliers, competitors, consultants,
contracted RD or design firms, distributors/retailers, universities or other research
Firms and respondent background Frequency Percentages (%)
Industry 388 100%
Manufacturing 236 60.82
Service 152 39.18
Size 388 100%
6–29 employees 156 40.21
30–99 employees 232 59.79
Age of firm 388 100%
5–10 years 97 25.00
11–15 years 136 35.05
15–20 years 93 23.97
Above 20 years 62 15.98
Table I.
Firms and respondent
background
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7. institutes, regulatory and standards bodies, industry technical/trade associations and
investors (Laursen and Salter, 2014; Stanko and Henard, 2017). To simultaneously capture
both openness breadth and depth, the respondents were asked to indicate the frequency with
which their firms sourced knowledge from each of these 10 sources, using a Likert scale of 1 –
not at all to 5 – very often. Ticking 1 or 2 (Likert scale) for more external sources listed
indirectly implies that SME sourced external knowledge from limited sources (low openness
breadth) and also had less frequent contact with external sources (low openness depth).
Observed and latent variables Std. factor loading
Network embeddedness (NET): CA 5 0.948; CR 5 0.943; AVE 5 0.846
Source: Lin et al. (2009)
Relational embeddedness (RE): CA 5 0.921; CR 5 0.914; AVE 5 0.728 0.914
Our partners highly trust each other 0.886
We believe all of the partner firms will not act against the law of mutual benefits 0.914
It is believed that all partners act with high transparency 0.785
There is no abuse of power among our partners 0.822
Structural embeddedness (SE): CA 5 0.891; CR 5 0.900; AVE 5 0.694 0.951
We interact with other firms on a high frequency 0.816
There is a long-standing interaction among our partners 0.763
Network ties generate significant influences on partners’ behavior during alliance 0.750
The partners with which we maintain frequent relationships, in general, know each
other
0.983
Cognitive embeddedness (CE): CA 5 0.849; CR 5 0.681; AVE 5 0.608 0.893
All partners respect and act upon the shared goals 0.729
Patterns of coordination are clear during the alliance 0.853
Partner behaviors are not mainly restricted by regulation, but norms 0.795
Partners shared norms of behaviors 0.735
Partners have clear motives during alliance and interactions ∞
Openness (OP): CA 5 0.910; CR 5 0.912; AVE 5 0.537
Source: Stanko and Henard (2017); Laursen and Salter (2014)
In your organization, to what extent are new product development ideas drawn from:
Customers 0.743
Suppliers 0.753
Competitors 0.778
Consultants 0.724
Contracted RD or design firms 0.701
Distributors/retailers 0.641
Universities or other research institutes 0.859
Regulatory and standards bodies 0.731
Industry technical/trade associations 0.637
Investors ∞
Innovation performance (IP): CA 5 0.824; CR 5 0.896; AVE 5 0.745
Source: Abdallah et al. (2019)
We are able to develop new products/services with speed 0.953
We are able to launch new products/services on time 0.700
Our new products/services are innovativeness 0.915
Our new products/services improve corporate image ∞
Model fitness CMIN DF CMIN/DF CFI SRMR RMSEA P-close
NET 131.158 89 1.474 0.953 0.051 0.045 0.210
OP 108.608 56 1.939 0.971 0.048 0.051 0.176
IP 96.677 51 1.896 0.961 0.064 0.048 0.128
Overall model 126.011 71 1.775 0.986 0.014 0.034 0.314
Note(s): ∞ ∼ Item deleted due to poor factor loading
Table II.
Confirmatory factor
analysis
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8. Innovation performance had four measurement variables, which were adapted from
Abdallah et al. (2019). One variable was, however, deleted due to poor factor loading, and the
remaining three generally measured innovation speed and new product innovativeness.
The study controlled for some firm-specific variables such as industry, age and size
(measured by number of employees). The type of industry coded as 0 – service and 1 –
manufacturing was controlled for because every industry may have some unique
characteristics that could influence innovation performance. Similarly, Boso et al. (2013)
found that firm size could significantly influence innovation activities in a firm. The
relationship between firm age and innovation has not been consistent across studies. While
some studies found positive relationship, others found negative relationship. But in any case,
Wu et al. (2016) indicated that the RD activities across younger and older firms are different,
which results in different innovation performances.
3.3 Evaluation of common method variance (CMV)
For firm-level analysis, where senior management members respond to the questionnaire, it is
important to check for potential CMV (MacKenzie and Podsakoff, 2012). Firstly,
confidentiality and anonymity were assured for the respondents, so as to reduce
evaluation anxiety. As indicated earlier, we also pretested the questionnaire to correct any
ambiguity in the questionnaire. Fuller et al. (2016) recommended Harman’s single-factor test
through exploratory factor analysis (EFA). In accordance with our constructs, the EFA
performed in SPSS (v.20) presented five extracted factors, with each factor having eigenvalue
greater than 1, and the variance explained by the largest factor was 31.52 percent. It was
concluded that no single factor accounted for most of the covariance among the study
variables.
As suggested by Lindell and Whitney (2001), we also conducted partial correlations, to
assess whether there existed any significant difference in the correlations between variables
after restricting for a marker variable (theoretically unrelated to at least one of the other
constructs). Results showed that the zero-order and partial correlations were similar after
restricting for the marker variables and therefore also conclude that CMV was not a problem.
3.4 Reliability and validity of the constructs
Confirmatory factor analysis (CFA) was performed in Amos (v.23), using maximum
likelihood to assess how well the data fit our model (results presented in Table II). Based on
Hair et al.’s (2010) recommended fit indices criteria, we conclude that our CFA model for the
constructs appropriately fit the data (for both individual dimensions and overall models).
CMIN/DF is supposed to be less than 3, CFI is all expected to be greater than 0.9, while
RMSEA and SRMR are also expected to be less than 0.08. P-close is also expected to be
statistically insignificant at 5 percent. The average variance extracted (AVE) for all the
constructs was greater than 0.5 (the recommended threshold by Fornell and Larcker, 1981);
composite reliability (CR) and Cronbach’s alpha (CA) were also greater than 0.7 as expected
(Bamfo et al., 2018; Brown, 2014). Figure 2 presents the CFA in a diagrammatic form.
As presented by Bamfo et al. (2018), the discriminant validity for the constructs were
assessed by comparing the squared-root of the AVEs (√AVEs) with the intercorrelation
scores. To conclude there was discriminant validity, the √AVEs are expected to be greater
than the respective intercorrelation scores. From Table III, results showed √AVEs were
greater in all cases, and it was therefore concluded that there existed discriminant validity
among the constructs studied.
4. Results
Structural equation modeling (SEM) was run in Amos (v.23) to assess the various paths
hypothesized in the study. Results as presented in Table IV indicate that none of the control
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9. Variables Mean Std. Dev. Industry Age Size NET OP IP
Industry – – –
Age – – 0.043 –
Size – – 0.055 0.540**
–
NET 3.664 1.041 0.107 0.198*
0.292*
0.920
OP 3.015 1.096 0.145*
0.167*
0.220*
0.414**
0.733
IP 3.436 0.930 0.030 0.136 0.142 0.571**
0.453**
0.863
Note(s): **
∼ p-value significant at 1% (0.01); *
∼ p-value significant at 5% (0.05); √AVE are bold and
underlined
Path Std. Estimate C.R.
IP — NET 0.523 7.646**
IP — OP 0.431 3.679**
IP — NETxOP 0.564 9.006**
IP — Size 0.029 0.557
IP — Age 0.068 1.057
IP — Industry 0.092 1.099
Model fitness CMIN DF CMIN/DF CFI SRMR RMSEA PClose
Overall model 50.898 27 1.885 0.965 0.035 0.044 0.217
Note(s): **
∼ p-value significant at 1% (0.01)
Figure 2.
Confirmatory factor
analysis
Table III.
Discriminant validity
and descriptive
analysis
Table IV.
Path summary
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10. variables (size, age and industry) had a significant effect on innovation performance of SMEs.
The analysis pointed out that innovation openness had a positive significant effect on SMEs’
innovation performance. The paths for the two hypotheses of this study (H1: Network
embeddedness has a positive effect on SMEs’ innovation performance; and H2: Openness
positively moderates the relationship between network embeddedness and SMEs’ innovation
performance) were also statistically significant. Figure 3 presents the structural model, while
Figure 4 also presents the two-way interaction diagram, which showed that at higher levels of
both network embeddedness and innovation openness, SMEs achieve greater innovation
performance.
Figure 3.
Structure
equation model
Figure 4.
Two-way interaction
effect model
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11. 5. Discussions
From the analysis presented, we conclude that network embeddedness had a positive
significant effect on SMEs’ innovation performance. This effect is generated from the
cumulative roles of relational, structural and cognitive network embeddedness. Strong ties in a
network lead to effective interfirm connections, because this kind of relationship is governed by
trust (Reagans and McEvily, 2003; Wang and Chen, 2012). This builds norms of reciprocity and
mutual gains, which increase firms’ willingness to share innovation ideas for the benefit of all
(Van Wijk et al., 2008; Gr€
onroos and Helle, 2012). SMEs, which are embedded in its industry’s
strategic alliance networks, are able to draw innovative ideas for integration in its innovation
operations. When network is based on trust, there is limited room for opportunistic behavior,
and as such, SMEs are able to receive the necessary resources (human, financial and
technological) from their networks, to enhance their innovation performance. As indicated by
Dyer and Singh (1998), business cooperation processes that are founded on trust lead to a sense
of belongingness to the clan or community, which is devoid of self-interest. This community
identity accelerates the willingness to share knowledge and other resources to help in other
partners’ innovation agenda, because of the reciprocal benefit (Blyler and Coff, 2003).
As explained by Hansen et al. (2005), the structure of network tie significantly
influences the opportunities that are available for knowledge sharing. Having high levels
of interaction with network members through long-term network relationship reduces
opportunism and increases knowledge sharing propensity (Mazzola et al., 2015; Reagans
and McEvily, 2003). Having strong tie leads to shared risk through effective collaborations
and also facilitates the development of skills needed by SMEs to develop and
commercialize new products (Vidal and Mitchell, 2013). Networks that are not solely
controlled by regulations, but also by behavioral norms with clear patterns of
coordination during alliance, have greater influence on the innovation performance of
its members (Osmonbekov et al., 2016). Shared cognition accelerates communication
process among network members, leading to high knowledge exchange (Anderson and
Narus, 1990; Rampersad et al., 2010). Having shared cognition, norms and values
facilitates trust among members (relational) and also defines the network tie (structural)
(Li et al., 2013). SMEs in an industrial network and alliance will tend to trust each other
more, when they share similar values, vision, goals and norms (Nooteboom et al., 2006). In
a shared cognition, the interest of the network is supreme, so opportunistic or
uncooperative member could be asked to exit the network, so the network could
function with oneness (Li et al., 2013). Network embeddedness on a whole thus facilitates
the sharing of innovation ideas among network members (G€
olgeci et al., 2019; Mazzola
et al., 2016) and also increases mutual support for each other’s innovation activities (Lyu
et al., 2019), thereby facilitating SMEs’ innovation performance.
Finally, it is realized from the analysis (Table IV and Figure 3) that although SME’s level of
network embeddedness and openness both had a significant effect on innovation
performance, the interaction between these two variables had the greatest impact on
innovation performance. The interaction (NETxOP) had a coefficient of 0.56, which was
larger than that of network embeddedness (0.52) and openness (0.43). While network
embeddedness in this case focused on horizontal embeddedness (i.e. alliance and
collaboration with industry members) (Rowley et al., 2000), innovation openness focused
on drawing innovation knowledge from a wider group of external entities, which include
customers, suppliers, competitors, consultants, contracted RD or design firms, distributors/
retailers, universities or other research institutes, regulatory and standards bodies, industry
technical/trade associations and investors (Laursen and Salter, 2014; Stanko and Henard,
2017). Innovation openness opens up SMEs to multiple sources of external innovative ideas,
which grants greater competitive advantage in innovation performance. High levels of both
network embeddedness and innovation openness thus grant SMEs greater opportunity for
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12. innovation performance, compared with SMEs that focused solely on network
embeddedness.
6. Conclusion and contributions
First, the study concludes that SMEs’ network embeddedness positively impacted their
innovation performance, in terms of NPD innovativeness, NPD speed and speed of launching
new products into the market. Finally, it is concluded that the interaction between high levels
of network embeddedness and innovation openness had a greater impact on SMEs’
innovation performance.
6.1 Theoretical implications
The paper contributes to literature in two main ways. Firstly, prior studies on network
embedded and innovation performance have largely focused on MNEs and large
corporations. Realizing the need for SMEs to consider external network information as
means of obtaining access to marketing and sales channels in diverse stages of innovation
(Xiaobao et al., 2013), this study was positioned in the context of SMEs. This study therefore
advanced the academic argument in the areas of network embeddedness and innovation
performance, to include SMEs. Trust, network tie and shared cognition in a network place
knowledge resources at the disposal of SMEs, for their innovation agenda. The exchange of
resources among network members also makes this study relevant to the RBV (Barney, 1991;
Grant, 1991). The resource here in question is largely knowledge transfer, which according to
the KBV represents an intangible asset of firms (Grant, 1996).
Secondly, this study contributes to literature by identifying innovative openness to
moderate network embeddedness and SMEs’ innovation performance relationship. Openness
has been studied in the context of SMEs (e.g. Xiaobao et al., 2013), and some have also studied
the varied relationships between network embeddedness and openness (Lyu et al., 2019;
G€
olgeci et al., 2019). This study departs from previous studies, by identifying that the positive
relationship between network embeddedness and SMEs’ innovation performance is further
strengthened by SMEs’ innovation openness. That is, SMEs’ with both high levels of network
embeddedness and innovation openness have greater innovation performance than SMEs
who solely rely on network embeddedness. Innovation openness grants SMEs with
additional knowledge resources needed for effective innovation.
6.2 Managerial implications
As SMEs engage in industry collaborations and alliances, they draw from the available
innovation knowledge in these networks, for their innovation activities. From KBV,
knowledge plays a vital role in competitive advantage of firms (Grant, 1996), including SMEs.
With this perspective, vis-
a-vis our findings, management of SMEs is advised to pay
particular attention to knowledge transfer practices, by making investments in the
transformation of external innovative inputs to achieve successful innovation
performance. Management of SMEs is advised to develop conducive organizational
structures based on trust, openness to collaboration and so on, for effective innovative
knowledge transfer and transformation.
Secondly, our study shows that network embeddedness and innovation openness had a
superior effect on SMEs’ innovation performance. Management of SMEs should therefore
take advantage of the combined effect of network embeddedness and innovation openness.
Firms should focus on occupying key position in the network, which gives them greater
advantage. Network participation and open innovation have become very important
business concepts, to which SMEs should pay particular attention. Issues regarding
organization and design of external innovation management tools such as network are very
crucial. In this dispensation of volatile and complex market demands, SMEs can no longer
JSMA
13,2
192
13. solely rely on closed innovation, if they want to gain superior innovation performance. SMEs
by their nature are less bureaucratic, more proactive in adapting to market changes, easily
take risk and so on, which are qualities that make the adoption of innovation openness more
feasible (Zhou et al., 2018).
6.3 Limitations and future research suggestions
This study just as any other study had some limitations. The study was based on cross-
sectional data obtained from top management members. Cross-sectional data was necessarily
important because of the detailed information needed on firms’ network embeddedness. The
responses of these respondents may not necessarily be objective, and so a number of steps
were taken to curb that. One of such steps is the analysis of CMV presented under the
methodology of this study. This notwithstanding, it is recommended that future studies focus
on longitudinal data if possible, to assess if there will be any disparity in results.
The study also presents network embeddedness to directly influence innovation
performance of SMEs, as well as using innovation openness as a moderating variable.
However, there may be some other mediating and moderating variables facilitating the effect
of network embeddedness on innovation performance. Future studies could pay attention to
some other influential variables such as absorptive capacity, organizational learning,
knowledge transfer mechanisms and so on.
This study did not also place much emphasis on the levels of network embeddedness and
innovation openness. For example, we found that at high levels of both network
embeddedness and innovation openness, SMEs’ innovation performance much more
increases. However, the various levels of cognitive embeddedness of the SMEs were not
critically assessed. Future studies could pay much attention on the various levels.
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Corresponding author
Courage Simon Kofi Dogbe can be contacted at: 5103181233@stmail.ujs.edu.cn
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