On the path towards open innovation: Assessing the role of knowledge management capability and environmental dynamism in SMEs

Abstract

Purpose
This study aims to shed light on the internal and external antecedents of open innovation (OI) in the context of small- and medium-sized enterprises (SMEs), with a special focus on the role of knowledge management (KM) capability. The paper develops and tests an integrative research model which assesses the effect of internal factors on KM capability; the impact of organizational and external factors, namely, KM capability and environmental dynamism, on OI; and whether environmental dynamism moderates the relationship between KM capability and OI.

Design/methodology/approach
Drawing on the knowledge-based view and the social exchange and the contingency theories, this paper develops an integrative research model which analyzes several relations between organizational antecedents of KM capability and its effect on OI by using covariance-based structural equation modeling on a data set of Spanish SMEs.

Findings
Results confirm that information technology-supported operations and commitment-based human resource practices have a positive and significant influence on KM capability. In contrast, results do not find support for the relationship between interdepartmental connectedness and KM capability, whereas both KM capability and environmental dynamism have a direct influence on OI.

Originality/value
This paper adds to existing research on OI, as it is the first study that addresses the critical role of KM capability for the implementation of OI.

Full text

Journal of Knowledge Management
On the path towards open innovation: assessing the role of knowledge management capability and
environmental dynamism in SMEs
Isabel Martinez-Conesa, Pedro Soto-Acosta, Elias George Carayannis,
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Isabel Martinez-Conesa, Pedro Soto-Acosta, Elias George Carayannis, (2017) "On the path towards open innovation:
assessing the role of knowledge management capability and environmental dynamism in SMEs", Journal of Knowledge
Management, Vol. 21 Issue: 3, pp.553-570, https://doi.org/10.1108/JKM-09-2016-0403
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On the path towards open innovation:
assessing the role of knowledge
management capability and
environmental dynamism in SMEs
Isabel Martinez-Conesa, Pedro Soto-Acosta and Elias George Carayannis
Isabel Martinez-Conesa is
Professor at the
Department of Financial
Economics and
Accounting, University of
Murcia, Murcia, Spain.
Pedro Soto-Acosta is
Professor at the
Department of
Management and
Finance, University of
Murcia, Murcia, Spain.
Elias George Carayannis
is Professor at the
Department of
Information Systems
Technology Management,
The George Washington
University, Washington,
DC, USA.
Abstract
Purpose –This study aims to shed light on the internal and external antecedents of open innovation
(OI) in the context of small- and medium-sized enterprises (SMEs), with a special focus on the role of
knowledge management (KM) capability. The paper develops and tests an integrative research model
which assesses the effect of internal factors on KM capability; the impact of organizational and external
factors, namely, KM capability and environmental dynamism, on OI; and whether environmental
dynamism moderates the relationship between KM capability and OI.
Design/methodology/approach –Drawing on the knowledge-based view and the social exchange
and the contingency theories, this paper develops an integrative research model which analyzes
several relations between organizational antecedents of KM capability and its effect on OI by using
covariance-based structural equation modeling on a data set of Spanish SMEs.
Findings –Results confirm that information technology-supported operations and commitment-based
human resource practices have a positive and significant influence on KM capability. In contrast, results
do not find support for the relationship between interdepartmental connectedness and KM capability,
whereas both KM capability and environmental dynamism have a direct influence on OI.
Originality/value –This paper adds to existing research on OI, as it is the first study that addresses the
critical role of KM capability for the implementation of OI.
Keywords Information technology, Knowledge management capability, Open innovation,
Environmental dynamism, Human resource management practices
Paper type Research paper
1. Introduction
An increasing number of firms are nowadays relying more on external information and
research collaborations to innovate and gain competitive advantages, including small- and
medium-sized enterprises (SMEs) which must currently compete globally (Soto-Acosta
et al., 2017). Firms are building collaboration networks with external partners to access and
benefit from their new technologies, skills and expertise (Ahuja, 2000;Huggins and
Thompson, 2015;Lavie, 2006;Messeni Petruzzelli et al., 2007,2009). This emergent trend
toward opening the innovation process has been firstly identified by Chesbrough (2003) as
“open innovation” (OI). OI permits firms to explore outside knowledge and to externally
exploit existing internal resources to gain competitive advantages (Drechsler and Natter,
2012;Popa et al., 2017). In the specific context of SMEs, purposive inflows and outflows of
knowledge are even more relevant for sustainable competitiveness because they face
more severe resource constraints (Dahlander and Gann, 2010;Spithoven et al., 2013). At
the same time, SMEs have advantages over large firms in that they are more likely to benefit
Received 26 September 2016
Revised 21 November 2016
Accepted 19 December 2016
DOI 10.1108/JKM-09-2016-0403 VOL. 21 NO. 3 2017, pp. 553-570, © Emerald Publishing Limited, ISSN 1367-3270 JOURNAL OF KNOWLEDGE MANAGEMENT PAGE 553
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from outside knowledge. SMEs are comparatively less bureaucratic, more responsive to
market needs and more flexible (Chang et al., 2011;Moilanen et al., 2014).
Firms’ migration toward OI has been driven by a confluence of social, economical and
technological changes, such as globalization, increased labor division or the rise of
collaborative technologies (Huizingh, 2011;Soto-Acosta and Cegarra-Navarro, 2016).
Despite the great pressure of business environment trends, some authors found that many
firms are still reluctant to open up their innovation strategy through the use of OI practices
(de Wit et al., 2007;Lichtenthaler and Ernst, 2009). Along with resource and time limitations,
Huizingh (2011) suggest that the risk of failure is a critical factor that should be taken into
account as a potential barrier to implement OI practices. The basic premise of OI is
managing inflows and outflows of knowledge to improve internal innovation and maximize
the external exploitation of innovation (Cheng and Shiu, 2015). In OI processes, there is a
high risk of failure in managing the different knowledge processes. Consistent with this
perspective, Lichtenthaler and Lichtenthaler (2009) suggest that to successfully implement
OI, the complementary nature of internal and external knowledge processes requires an
integrative knowledge management (KM). Moreover, a firm needs to reconfigure and
realign its knowledge capabilities to fit changing environments. This draws attention on the
importance of KM capability for the successful implementation of OI. KM capability is a
dynamic capability that refers to “a firm’s ability to dynamically manage its knowledge base
over time by reconfiguring and realigning the processes of knowledge exploration,
retention, and exploitation inside and outside the organization” (Lichtenthaler and
Lichtenthaler, 2009, p. 1322).
Existing literature on OI are mainly descriptive by nature and based upon case studies and
in-depth interviews (Chesbrough, 2003;Dodgson et al., 2006;Huston and Sakkab, 2006).
In addition, a great part of the studies on OI have focused on high-tech large enterprises,
whereas it is widely accepted in literature that OI practices and consequences depend
heavily on firm size (Lee et al., 2010;Spithoven et al., 2013;van de Vrande et al., 2009).
Because of greater resource constrains, smaller firms are substantially different from larger
firms in perusing innovation (Cao et al., 2009;Ebben and Johnson, 2005; Martinez-Conesa,
et al., 2017). Nonetheless, only a few and recent studies have analyzed OI in the specific
context of SMEs (Laursen and Salter, 2006;Lee et al., 2010;Spithoven et al., 2013;van de
Vrande et al., 2009), with most of them contributing to the discussion about the differences
between OI in small and large firms. Meanwhile, SMEs are of key importance for economic
growth, employment and wealth creation, representing over 99 per cent of the total number
of firms in Europe (Jardim-Goncalves et al., 2012;Soto-Acosta et al., 2016).
Furthermore, although there is an increasing literature on the antecedents of OI, there is a
lack of empirical research on the role of KM for the implementation of OI practices. In this
sense, to the best of our knowledge, this is the first study to empirically address the
relationship between KM capability and OI. In addition, there is a need for further research
on how environmental moderators affect the firms’ disposal to open up their innovation
processes (Hung and Chou, 2013). There is therefore a need for further research on the
internal factors that may enhance the KM capability of firms and, at the same time, KM
capability role in promoting the use of OI practices considering the effect of contingent
factors are imperative.
The objective of this study is twofold. First, the paper assesses the effect of internal factors
on KM capability. Second, it analyzes the impact of organizational and external factors,
namely, KM capability and environmental dynamism, on OI and whether environmental
dynamism moderates the relationship between KM capability and OI. To address these
issues, this paper develops and tests a research model by considering literature that
covers internal antecedents of KM, OI and environmental moderators. The remainder of the
article is organized as follows. The next section describes the theoretical foundations and
the hypotheses of the study. Subsequent sections describe the research methods, the data
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analysis procedures and the empirical results. Finally, the paper ends with a discussion of
research findings, implications, limitations and concluding remarks.
2. Theoretical background and hypotheses
Over the past decade, OI has become one of the hottest topics in innovation management
research (Carayannis and Campbell, 2011;Spithoven et al., 2013). Although the use of the
concept of OI is relatively recent, the theoretical foundations of OI build upon
well-established concepts such as absorptive capacity, complementary assets or the
exploitation versus exploration dyad. Accordingly, academics and practitioners assert that
OI practices, such as scanning the external environment to seize business opportunities or
using external knowledge to improve in-house innovation processes, are not new to
companies (Messeni Petruzzelli et al., 2007,2009). OI has been broadly defined as “the
use of purposive inflows and outflows of knowledge to accelerate internal innovation and to
expand the markets for external use of innovation, respectively” (Chesbrough et al., 2006,
p. 1). Drawing on Chesbrough’s view of OI, most researchers describe OI along two
dimensions: inbound practices and outbound practices (Bianchi et al., 2010;Cheng and
Shiu, 2015;Tranekjer and Knudsen, 2012). This view of OI suggests that openness is
relevant for both internal innovation and its external exploitation.
Inbound dimension comprise purposive inflows of knowledge that permit firms to explore
new knowledge from outside sources such as customers, suppliers, competitors,
governments, consultants, universities or research organizations (Cheng and Shiu, 2015).
Inbound OI enables a firm to look beyond its boundaries and enrich its own knowledge
base. In this vein, firms that perform inbound OI may benefit from new ideas and
combinations of knowledge, new market opportunities and renewed problem-solving
capabilities (Hung and Chou, 2013;Zahra et al., 2006). In contrast, outbound OI implies the
exploitation of internal ideas or technological knowledge that flow out of the company
through licensing, patenting or contractual agreements to gain monetary of non-monetary
benefits (Hung and Chou, 2013;Lichtenthaler and Lichtenthaler, 2009). Previous studies
found that firms that pursue both inbound and outbound OI are more likely to obtain greater
competitive advantages (Lichtenthaler, 2008;van de Vrande et al., 2009).
The establishment of this new concept and its coincidence in time with a growing interest
for outsourcing, collaboration, organizational agility and flexibility permitted researchers
to reconsider innovation strategies in the light of an increasingly networked and
knowledge-intensive world (Huggins and Thompson, 2015;Huizingh, 2011;
Palacios-Marqués et al., 2015a). As part of the integration of firms in the knowledge society,
KM has become a major management concern and an active research area. Although
knowledge is a key resource with high strategic potential, KM capability has been identified
in literature as a prerequisite of firms’ innovation success (Eisenhardt and Martin, 2000;
Lichtenthaler and Lichtenthaler, 2009;Zahra et al., 2006). At the center of the concept of OI
is how firms manage the inflows and outflows of knowledge to enhance the innovation
process and maximize the external exploitation of innovation. Accordingly, the successful
implementation of OI may be dependent on the firms’ capability of managing purposive
inflows and outflows of knowledge.
Previous studies suggest that firms’ information technology (IT) have the potential to
improve knowledge exploration and exploitation (Chen et al., 2012;Sher and Lee, 2004).
Beside technological factors, coordination mechanisms are considered to enhance the
access, sharing and further refinement of existing knowledge within the firm’s boundaries
(Jansen et al., 2006). In addition, human resources (HR) practices have been shown to be
of key importance for KM (Choi et al., 2008;Chuang et al., 2013). For instance, internal
commitment has been pointed out as a key prerequisite for collaboration and knowledge
sharing (Palacios-Marqués et al., 2015b;Soto-Acosta et al., 2014). The social exchange
theory argues that employees balance their level of commitment with the company’s level
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of commitment to them (Wayne et al., 1997). Thus, building a positive social climate may be
crucial to motivate employees to work together and improve the knowledge flow (Popa et al.,
2017;Valkokari et al., 2012).
Furthermore, the degree of openness of innovation strategies depends on firm-specific
(internal) factors and environmental (external) factors (Drechsler and Natter, 2012,Messeni
Petruzzelli et al., 2007,2009). According to contingency theory, the competitiveness of
firms is contingent not only on the internal alignment of OI strategies to organizational
factors but also on the appropriate fit between organizational strategies and business
environments (Takeuchi, 2009). In a dynamic technological environment, firms rely more on
external technology as their current technological knowledge and infrastructures rapidly
become obsolete (Jansen et al., 2006;Teece, 2007). Furthermore, market turbulence
requires firms to seek constantly for new knowledge and technologies to satisfy customers’
new demands and preferences (Hung and Chou, 2013;Osakwe et al., 2016).
Drawing on the knowledge-based view (KBV), the contingency theory and the social
exchange theory, this paper develops and tests an integrative research model which
analyzes the effect of organizational antecedents on KM capability and its impact together
with external factors on OI. The network of relations and the associated hypotheses are
illustrated in Figure 1 and elaborated in the following subsections.
2.1 Organizational antecedents of knowledge management capability
Existing research offers extensive theoretical argumentation about the potential of IT for
efficient information and knowledge sharing with no distance limitations. For instance, IT
tools enable rapid search and access to information, and it can support knowledge transfer
and collaboration between organizational members (Pérez-López and Alegre, 2012,
Va˘ta˘ma˘nescu et al., 2016). IT infrastructure support the transformation of tacit knowledge
into explicit knowledge (Alavi and Leidner, 2001) allowing explicit knowledge to be
formalized and saved in documents, so it can facilitate knowledge retrieval (Al-Hawamdeh,
2002;Chen et al., 2012). Likewise, IT infrastructures support the automation of business
processes which facilitates the routinization of certain tasks and practices (García-Álvarez,
2015). However, IT-based resources should not be considered by firms a source of
competitive advantage because they are typically imitable or they can be acquired from IT
providers (Ardito et al., 2016;Popa et al., 2016a;Soto-Acosta and Meroño-Cerdan, 2008).
Previous studies suggest that to obtain a positive outcome in terms of efficient KM, firms
Figure 1 Research model
KM Capability
ICT-supported
operaons
Interdepartmental
Connecveness
Environmental Dynamism
H1(+)
H2(+)
H3(+)
H4(+)
H6(+)
H5(+)
Control Variables
•Firm age
•Firm size
Commitment-
based HR
CBHRP
(incenves)
CBHRP
(Selecon)
Open
Innovaon
Inbound
OI
Outbound
OI
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should complement IT infrastructures with IT operations (Pérez-López and Alegre, 2012). IT
operations regard a fit between IT-based resources and business processes, supporting
the management of inside and outside knowledge (Crawford et al., 2011). Accordingly, IT
operations can support KM capability by enhancing communication and collaboration
within and across firms’ boundaries. Building on these arguments, the following hypothesis
is formulated:
H1. There is a positive relationship between information technology operations and
knowledge management capability.
Although some firms make important investments in IT platforms that connect members in
the organization, this is not a sufficient condition for employees to collaborate and share
knowledge (Palacios-Marqués et al., 2016;Soto-Acosta et al., 2010). Promoting a positive
working environment may be crucial to motivate employees to work together and share
knowledge and experiences (Oparaocha, 2016;Valkokari et al., 2012). Previous studies
suggest that there are certain internal organizational structures such as inter-departmental
connectedness (IDC) that can result advantageous for developing collaboration network
and cooperation among organization members. IDC is an informal coordination mechanism
that increases the opportunities for informal social relations and supports accessibility,
deep understanding and further refinement of existing knowledge (Adler and Kwon, 2002;
Subramaniam and Youndt, 2005). In addition, dense social relations enable firm members
to share knowledge and experiences regarding the implement of innovations (Ardito et al.,
2015;Jansen et al., 2006; Martinez-Conesa, et al., 2017). IDC can support the
enhancement of KM capability by promoting trust and collaboration within the firm
boundaries. Hence, the second hypothesis posits a positive relationship between IDC and
KM capability:
H2. There is a positive relationship between inter-departmental connectedness and
knowledge management capability.
Previous literature suggests that HR practices are of key importance for KM (Choi et al.,
2008;Chuang et al., 2013). Knowledge creation and acquisition rarely occurs if individuals
do not interact. Thus, building a positive social climate may be crucial to motivate
employees to work together and improve the knowledge flow (Oparaocha, 2016;Popa
et al., 2017;Valkokari et al., 2012). This is even more important when sharing tacit
knowledge, which requires even more interaction between organizational members (Fox,
2000;Va˘ta˘ma˘nescu et al., 2016). Likewise, internal commitment has been pointed out as a
key prerequisite for collaboration and knowledge sharing (Palacios-Marqués et al., 2015b;
Soto-Acosta et al., 2014). The lack of commitment can result into resistance of employees
to take advantage of outside knowledge. In this sense, the not-invented-here (NIH)
syndrome has been pointed out as one of the main factors that may detract SMEs from
taking advantage of external knowledge (Chesbrough and Crowther, 2006;Lichtenthaler
and Lichtenthaler, 2009;Spithoven et al., 2013). The NIH syndrome reflects the internal
resistance of project groups of stable composition that reject outside knowledge because
of a conviction of possessing a monopoly of expertise in their field. Accordingly, the
principal focus of the NIH syndrome is on inflows of knowledge. However, resistance could
also descend from the only-used-here syndrome which results into barriers to purposive
knowledge outflows beyond firms’ boundaries (Lichtenthaler and Ernst, 2009).
According to social exchange theory, employees balance their level of commitment with
the company’s level of commitment to them (Oparaocha, 2016;Wayne et al., 1997).
Companies can effectively influence the behavior and motivation of employees through the
use of different HR practices (Collins and Smith, 2006). The literature distinguishes
between transaction-based HR practices, which concentrate on individual short-term
exchange relationships and commitment-based HR (CHR) practices, which draw their
attention to mutual long-term exchange relationships, suggesting that the latter may
contribute to a positive working environment (Tsui et al., 1997). In this regard, Collins and
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Smith (2006) found that CHR practices are positively related to knowledge exchange
among workers. Likewise, CHR practices can improve the KM capability of firms. Based on
these arguments, the following hypothesis is proposed:
H3. There is a positive relationship between commitment-based human resources
practices and knowledge management capability.
2.2 Knowledge management capability and open innovation
KBV considers knowledge as the most strategic resource of a firm with potential to
generate sustained competitive advantage and superior corporate performance because
it is socially complex and, usually, difficult to imitate (Nickerson and Zenger, 2004;
Soto-Acosta et al., 2016). The KBV is in reality an extension of the resource-based view,
which claims that resources possessed by a firm may be a source of competitive
advantage when they are valuable, rare, difficult to imitate and not substitutable by other
resources (Barney, 1991). However, although knowledge is a key resource with high
strategic potential, Laursen and Salter (2006) suggest that without effective KM systems
firms cannot fully take advantage of outside knowledge, facilitating only incremental
innovation instead of radical innovation.
OI relies on purposive inflows and outflows of knowledge to support internal innovation and
its external exploitation. Consistent with this perspective, Lichtenthaler and Lichtenthaler
(2009) suggest that the complementary nature of internal and external knowledge
processes requires an integrative KM to successfully implement OI. Likewise, firm must
possess KM capabilities to asses and respond rapidly to competitors’ actions (Liao et al.,
2011). The KM capability of a firm refers to the degree to which knowledge resources are
created, shared and utilized across firm’s functional boundaries. The KM capability is
dynamic in the sense that it permits firms to dynamically manage their knowledge bases by
reconfiguring and realigning the processes of knowledge exploration, retention and
exploitation inside and outside the firm (Lichtenthaler and Lichtenthaler, 2009).
Furthermore, KM capability determines which knowledge is appropriate for being
incorporated into the knowledge bases of firms. Absorbing external knowledge that is too
similar to the existing knowledge bases of the firm could diminish the positive outcomes of
exploring outside knowledge (Dahlander and Gann, 2010;Messeni Petruzzelli et al., 2007,
2009). In this sense, KM capability could minimize the detrimental outcomes of
over-searching the external environment. Based on these arguments, the following
hypothesis is formulated:
H4. There is a positive relationship between knowledge management capability and
open innovation.
2.3 Environmental moderators of the relationship between knowledge management
capability and open innovation
Consistent with the contingency theory, prior studies suggest that firms’ innovation
strategies are contingent on both internal and external factors (Gibson and Birkinshaw,
2004;Jansen et al., 2006). Accordingly, previous OI research suggests that firms’ migration
toward opening up innovation strategies is also influenced by internal and external contexts
(Huizingh, 2011). In this vein, existing literature that focus on the context in which OI
emerges suggest that opening up innovation strategies is more suitable in business
environments characterized by globalization, competitive intensity and market and
technological turbulence (Gassmann, 2006;Huizingh, 2011). Moreover, management
researchers agree on the fact that capabilities development and evolutionary processes
are dependent on the business environment (Lichtenthaler and Ernst, 2009;Teece, 2007).
For instance, Chesbrough (2006) states that firms scan their external environment prior to
initiating research and development (R&D) projects. Based on the comparison of firms’
current knowledge and the required knowledge for strategic objectives, strategic gaps
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might be identified. To fill these gaps and, subsequently, align their knowledge bases and
strategies, firms need to actively search for new business opportunities. Furthermore,
Laursen and Salter (2006) found that the extent of use of external knowledge is shaped in
part by external factors, such as environmental turbulence or technological opportunities
which underpin environmental dynamism. Environmental dynamism is characterized by
technological changes, variations in customer preferences, changes in product demand
and the unpredictability of change (Jansen et al., 2006).
In dynamic environments, the current knowledge bases of firms rapidly become obsolete
(Jansen et al., 2006;Teece, 2007). To remain competitive in dynamic environments, firms
adopt OI practices to benefit from the knowledge bases and technologies of external
partners (Cheng and Shiu, 2015). Moreover, the benefits that derive from strong KM
capabilities are expected to be even greater in dynamic environments (Lichtenthaler and
Lichtenthaler, 2009). In this sense, environmental dynamism may have a direct influence on
OI, whereas at the same time, the impact of KM capability on OI may be contingent on
environmental dynamism. The following hypotheses incorporate our expectations:
H5. There is a positive relationship between environmental dynamism and open
innovation.
H6. Environmental dynamism strengthens the positive effect of knowledge management
capability on open innovation.
3. Research methodology
3.1 Data and sample
The target population of our study is formed by manufacturing SMEs from Spain that
meet the following criteria: 20 ⱕemployees ⬍250, turnover ⱕ€50m and a balance sheet
total ⱕ€43m. Previous studies on the subject have used SMEs with at least 20 employees
for their research to ensure a minimum firm complexity (Carmeli and Shteigman, 2010;
Lubatkin et al., 2006). The study used a sample of 3,000 firms selected randomly from a list
of 10,460 manufacturing SMEs with at least 20 employees included in the SABI (Sistema de
Análisis de Balances Ibéricos) database. The sample drawn was a random sample of firms
from the respective sector population with the objective of fulfilling strata with respect to
business size and subsectors. In administering our survey, the questionnaire was assigned
to senior and middle managers whose primary responsibilities are related to strategic
innovation activities of the firms.
Data were collected in two stages. First, we performed a pilot study and following that we
conducted a questionnaire. In total, 15 SMEs were randomly selected from the SABI
database to perform the pilot study. Based on their responses and subsequent interviews
with participants in the pretest, minor modifications were made to the questionnaire.
Responses from the firms that participated in the pilot study were not included in the final
sample. The survey was administrated between May and June 2016 by using
computer-assisted telephone interviewing software. In total, a final data set of 429 valid
cases was obtained, yielding a response rate of 14.3 per cent, which was comparable to
other studies of similar scale. Data were examined for non-response-bias by comparing the
characteristics of early and late participants in the study. The results of this comparison
revealed that non-response bias does not represent a threat for the results obtained and
their interpretation.
3.2 Measurement
Measurement items were selected on the basis of a careful literature review. The research
instrument was pretested with 15 different researchers and managers. Our primary
objective was to detect inadequate wording and facilitate the ease of administering the
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instrument. The results from the pretest showed no particular bias. A description of the
constructs and the associated indicators is provided in Appendix.
All the variables used in the study were operationalized using multi-item instruments. Based
on the scales developed by Tippins and Sohi (2003),Popa et al. (2016b) and Soto-Acosta
et al. (2016) a construct was drawn up to measure IT-supported operations (ITO). Overall,
five items were adapted to measure the extent of use of IT to support firm operations. IDC
was operationalized using a two-item scale from Chang et al. (2011) which measures the
extent to which employees from different departments are networked. Using the scales
established by Collins and Smith (2006) and Ceylan (2013), a second-order construct was
drawn up to reflect CHR practices. Overall, ten items were adapted to measure the extent
of use of different commitment-based long-term-oriented practices along two dimensions:
selection policies and incentive and training and development policies. KM capability was
operationalized by using the nine-items scale of Liao et al. (2011), which measured the
extent of use of different KM practices across functional boundaries. Based on previous
work of Jansen et al. (2006), a three-item scale was used to measure environmental
dynamism. In measuring OI, the scale from Cheng and Shiu (2015) was adapted to
construct a second-order factor consisting of two dimensions: inbound OI and outbound
OI. Inbound OI focuses on practices that allow firms to gain and explore knowledge from
external sources, whereas outbound OI captures the firm’s approaches to commercialize
innovations developed internally. In addition, a number of control variables that may
influence innovation performance were included (firm size, age and industry). These
variables are commonly used as controls in innovation and OI studies (Chang et al., 2011).
3.3 Instrument validation
The unidimensionality and reliability of the data set were assessed by different procedures,
using SPSS 23.0 and AMOS 23.0 software. First, an initial exploration of unidimensionality
was done using maximum likelihood factor analyses with promax rotation. In each analysis,
the eigenvalues were greater than 1 and all factor loadings greater than 0.50 (with no
substantial cross-loadings), lending preliminary support to a claim of unidimensionality in
the constructs. Then, confirmatory factor analysis (CFA) was performed to establish the
convergent validity, discriminant validity and reliability of the constructs. The measurement
model presented a good fit to the data (
␹
2
(38) ⫽725.224; RMSEA ⫽0.046; CFI ⫽0.95;
IFI ⫽0.95; TLI ⫽0.95). All traditionally reported fit indexes were within the acceptable range.
Construct reliability assesses the degree to which items are free from random error and,
therefore, yield consistent results. This study calculated reliability of measures using
Bagozzi and Yi’s (1998) composite reliability index and Fornell and Larcker’s (1981)
average variance extracted index. For all the measures both indices were higher than the
evaluation criteria, namely, 0.6 for composite reliability and 0.5 for the average variance
extracted. Convergent validity assesses the consistency across multiple constructs. As
shown in Table I, after dropping insignificant items, all estimated standard loadings are
significant (p⬍0.01) and of acceptable magnitude, suggesting good convergent validity
(Sethi and King, 1994).
To assess the discriminant validity – the extent to which different constructs diverge from
one another – Fornell and Larcker’s (1981) criterion, that the square root of average
variance extracted for each construct (diagonal elements of the correlation matrix in
Table II) should be greater than the absolute value of interconstruct correlations
(off-diagonal elements), was used. All constructs met this criterion, suggesting that the
items share more variance with their respective constructs than with other constructs.
Table II also provides an overview of the means, standard deviations and correlations of the
constructs.
Most researchers agree that common method variance is a potentially serious bias threat
in behavioral research, especially with single informant surveys. Several steps to control for
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common method bias were adopted before data collection, such as assuring the
participants that there were no right or wrong answers and that their responses would
remain anonymous (Podsakoff et al., 2003). In addition, the extent of common method bias
was assessed after data collection by using three distinct methods. First, the Harman’s
one-factor test was used by entering all the indicators into a maximum likelihood factor
analysis (Podsakoff and Organ, 1986). Evidence for common method bias exists when a
general factor accounts for the majority of the covariance among all factors. With all
indicators entered, eight factors were extracted. The variance explained ranged from 19 to
2.9 per cent, indicating no substantial common method bias. Second, the “unmeasured
Table I Measurement model: confirmatory analysis and scale reliability
Construct
First order Second order
construct
Second order
ReliabilityIndicator Loading t-value Loading t-value
IT operations ITO1 0.72 – NA NA
ITO2 0.66 12.38 CR ⫽0.83
ITO3 0.80 14.62 AVE ⫽0.51
ITO4 0.67 12.46
ITO5 0.69 12.78
Interdepartmental
connectiveness
IDC1 0.82 – NA NA CR ⫽0.75
IDC2 0.72 9.45 AVE ⫽0.60
CBHRP selection CHR1 0.65 – Commitment-
based HR
0.813 –
CHR2 0.82 13.77
CHR3 0.83 13.92 CR ⫽0.74
CHR4 0.76 13.09 AVE ⫽0.59
CBHRP incentive and
development
CHR5 0.82 – 0.727 9.41
CHR6 0.78 16.46
CHR8 0.52 10.53
CHR9 0.76 16.04
KM capability KM2 0.82 – NA NA CR ⫽0.94
KM3 0.82 24.38 AVE ⫽0.75
KM4 0.83 20.59
KM5 0.92 23.92
KM6 0.92 24.01
KM7 0.90 23.28
Environmental dynamism ED1 0.65 – NA NA CR ⫽0.79
ED2 0.93 10.49 AVE ⫽0.57
ED3 0.73 10.89
Inbound open innovation OI1 0.80 – Open
innovation
0.798 –
OI2 0.84 15.78 CR ⫽0.71
OI3 0.67 13.14
Outbound open innovation OI5 0.75 – 0.692 7.38 AVE ⫽0.56
OI6 0.82 13.41
OI7 0.69 12.16
Notes: Fit statistics for measurement model:
␹
2
(38) ⫽725.224; RMSEA ⫽0.046; CFI ⫽0.95; IFI ⫽0.95; TLI ⫽0.95; insignificant factors
are dropped (CHR7, CHR10, KM1, KM8, KM9, OI4 and OI8); (⫺): fixed items; (NA) ⫽no applicable; CR ⫽composite reliability;
AVE ⫽average variance extracted
Table II Descriptive statistics and discriminant validity
Constructs Average SD (1) (2) (3) (4) (5) (6)
1. IT operations 4.06 1.44 0.71
2. Interdepartment connectiveness 5.78 1.06 0.25** 0.77
3. Commitment-based HR 4.94 1.10 0.48** 0.44** 0.77
4. KM capability 4.21 1.49 0.65** 0.28** 0.58** 0.87
5. Environmental dynamism 4.15 1.55 0.27** 0.18** 0.30** 0.29** 0.75
6. Open innovation 2.96 1.21 0.44** 0.18** 0.43** 0.47** 0.33** 0.74
Notes: Significance levels: *p⬍0.05; **p⬍0.01; NA variance extracted is not applicable to the
single-item constructs; diagonal values in italics represent the square root of the AVE
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latent factor method” suggested by Podsakoff et al. (2003) was used to extract the common
variance. This procedure requires the addition of an unmeasured latent factor to the
measurement model during CFA. This latent factor includes all indicators from all other
latent factors. This approach detects the variance common among all observed indicators.
The indicator loadings on this common latent factor are constrained to be equal to each
other to ensure that the unstandardized loadings will be equal. Squaring the
unstandardized loading (which for all indicators will be the same value) then gives the per
cent of common variance across all indicators in the model. The results of this test showed
that 27 per cent of the variance could be because of common method bias, showing no
evidence of common method bias. Third, the correlation matrix (Table II) did not indicate
any highly correlated variables, whereas evidence of common method bias usually results
in extremely high correlations (r⬎0.90) (Bagozzi et al., 1991). In summary, these tests
suggest that common method bias is not a serious threat in our study.
4. Results
This paper performs structural equation modeling to test the hypotheses, using AMOS 23.0
and maximum likelihood estimation techniques to test the model. The fit of the model is
satisfactory [
␹
2
(501) ⫽839.30; RMSEA ⫽0.044; CFI ⫽0.95; IFI ⫽0.95; TLI ⫽0.94],
suggesting that the nomological network of relations fits the data and the validity of the
measurement scales (Churchill, 1979).
Figure 2 shows the standardized path coefficients with their respective significant levels.
H1 and H3 were confirmed (H1: 0.44, p⬍0.01; H3: 0.51, p⬍0.01). These results show that
ITO and CHR practices have a positive and significant influence on KM capability. H2 did
not find support, indicating a non-significant relationship between interdepartmental
connectedness and KM capability. H4 found support (H1: 0.44, p⬍0.01), which suggests
that KM capability is positively associated with OI. In addition, H5 was confirmed (H5: 0.26,
p⬍0.01), whereas H6 was not supported. These past findings confirm that although
environmental dynamism has a direct influence on OI, it does not moderate the relationship
between KM capability and OI.
Figure 2 Results
KM Capability
Interdepartmental
Connecveness
Environmental Dynamism
Commitment-
based HR
CBHRP
(incenves)
CBHRP
(Selecon)
Open
Innovaon
Inbound
OI
Outbound
OI
χ2(460) = 839.30 RMSEA = 0.044
CFI = 0.95 IFI = 0.95 TLI = 0.94
H6: N.S. H5: 0.26***
H1: 0.44***
H3: 0.51***
H2: N.S.
H4: 0.54
***
R2 = 0.63
R2 = 0.47
ICT-supported
operaons
Notes: *** p < 0.01; ** p < 0.05; - - - - - = non significant path (N.S.)
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5. Discussion
Drawing on the KBV, the social exchange theory and the contingency theory, this study
sheds light on the internal and external antecedents of OI, with a special focus on the
role of KM capability for OI. Within the internal context, this study assesses the effect
of technological and organizational factors on KM capability. The results revealed that
internal factors have different impacts on the firms’ ability to dynamically manage
inflows and outflows of knowledge. Regarding technological factors, the results
revealed that ITO have a positive influence on the development of KM capability of
firms. Although IT-based resources alone do not have the potential to generate
competitive advantage (Ardito et al., 2016;Popa et al., 2016a), ITO enable firms to
efficiently manage knowledge within and beyond the firms’ boundaries (Crawford et al.,
2011). These findings are consistent with previous studies suggesting that
complimenting IT-based resources with IT operations may have positive outcomes in
terms of efficient KM (Pérez-López and Alegre, 2012).
Regarding the organizational factors, the effects of informal coordination mechanisms such
as interdepartmental connectedness seem to be non-significant. These findings depart
from existing studies stressing the benefits of building informal coordination mechanisms
for supporting the creation of social relations. Prior studies suggest that IDC increases the
opportunities for informal social relations and supports accessibility, deep understanding
and further refinement of existing knowledge bases of the firm (Adler and Kwon, 2002;
Subramaniam and Youndt, 2005). In this sense, Ardito et al. (2015) and Jansen et al. (2006)
suggest that dense social relations enable firm members to share knowledge and
experiences regarding the implement of innovations. However, previous studies have
focused mainly on large or multi-unit firms, whereas our study focuses on SMEs. A possible
explanation to this is that large firms have internal structures that over time become laden
with rules and procedures (Morris et al., 2008), whereas their smaller counterparts are less
bureaucratic and more internally adaptive (Chang et al., 2011;Moilanen et al., 2014).
Moreover, our results show that CHR practices have a positive influence on KM capability.
This finding echoed the prepositions of previous studies suggesting that firms can
effectively influence the behavior and motivation of employees through the use of different
HR practices (Collins and Smith, 2006). HR practices with commitment orientation help to
motivate employees to collaborate and share knowledge while developing their day-to-day
tasks (Camelo-Ordaz et al., 2011).
Furthermore, the results suggest that KM capability has a positive effect on OI, this
relationship being the strongest in our research model. This finding is consistent with
existing research which, although not focusing on OI, found that KM is a key
determinant of innovation (Andreeva and Kianto, 2011;Marqués and Simón, 2006). In
line with previous literature, our results show that SMEs with strong KM capability are
more capable of pursuing OI. However, there is extensive literature that agree on the
fact that firms’ innovation strategies are contingent on both internal and external factors
(Gibson and Birkinshaw, 2004;Jansen et al., 2006). In this sense, our findings provide
empirical support for studies suggesting that in dynamic environments firms tend to rely
more on outside knowledge. In dynamic environments, the existing knowledge of firms
rapidly becomes obsolete (Jansen et al., 2006;Teece, 2007). Therefore, firms adopt OI
to benefit from the knowledge bases and technologies of external partners (Cheng and
Shiu, 2015). Accordingly, our results show that SMEs operating in dynamic
environments are more likely to look beyond their boundaries and open up their
innovation strategies to OI. At the same time, the moderating effect of environmental
dynamism on the relationship between KM capability and OI was analyzed. The results
show that environmental dynamism does not strengthen the positive effect of KM
capability on OI. Thus, although environmental dynamism may foster OI in SMEs, it
does not boost the positive effect of KM capability on OI. A possible explanation of this
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finding is that building a strong KM capability for OI is both important in both low and
high dynamic environments.
6. Implications and conclusions
This study makes several contributions to the emerging research on the topic of OI and
provides important implications for managers involved in the firms’ migration toward OI.
Previous literature on OI has focused mainly on large or multi-unit firms, whereas it is widely
accepted that prescriptions for OI practices in large firms cannot be generalized to the
case of SMEs (Lee et al., 2010;Spithoven et al., 2013). In this sense, this paper adds to
existing research on OI, as it is the first study that addresses the critical role of KM
capability for the implementation of OI. More specifically, this study develops and
empirically tests an integrative research model which assesses the effect of internal factors
on KM capability; the impact of organizational and external factors, namely KM capability
and environmental dynamism, on OI; and whether environmental dynamism moderates the
relationship between KM capability and OI. In this sense, it was found that IT operations
and CHR practices strongly influence KM capability which, in turn, is crucial for improving
OI. At the same time, it was found that environmental dynamism enhances the development
of OI. This finding corroborates that OI is dependent on environmental dynamism. Overall,
this study’s findings shed light on the importance of KM capability as a prerequisite of OI.
Accordingly, managers should take into account that OI is a complex process that requires
an effective management of internal and external knowledge. In addition, our findings
suggest that firms should devote resources not only to adopt IT infrastructures but also to
improve the use of IT in business operations to improve the development of KM capability.
Also, managers should pay attention to the attitudes of employees toward openness, as in
many cases, employees can put up resistance against knowledge inflows and outflows
beyond firms’ boundaries. By implementing long-term CHR practices, SMEs may
encourage employees to socially interact and diminish their resistance to accept and
benefit from purposive inflows and outflows of knowledge. At the same time, SMEs
management should to be aware of the necessity of being responsive to environmental
dynamics. In this sense, SMEs need to recognize that there is a growing trend toward
opening the innovation strategy and that successful implementation of OI depends heavily
on firms’ ability to dynamically manage knowledge capabilities and their prompt response
to market changes.
Firms should devote extra efforts to develop IT capabilities and KM capabilities because
these capabilities are crucial when pursuing competing demands for exploration and
exploitation. Second, our results show that firms’ ability to deploy exploratory and
exploitative innovations in an ambidextrous manner may depend not only on the
development of internal capabilities but also on the quick response to external pressures
such as environmental dynamism. Thus, SMEs management should pay attention to both
the internal and the external business context when balancing exploratory and exploitative
innovations. Third, our findings also suggest that a dynamic environment creates
opportunities for ambidextrous firms to outperform their competitors. Accordingly,
ambidextrous SMEs should continuously scan and give a prompt response to changes in
the business environment to generate sustained competitive advantages.
Like most of empirical studies, ours suffers from some limitations which can be addressed
in future research. First, for data collection, this study has used the key informant method.
With this method, the data reflect the opinions of only one person. Although the
respondents were to senior and middle managers whose primary responsibilities are
related to strategic innovation activities of the firms, future studies could consider research
designs that allow data collection from multiple respondents within an organization.
Second, this research takes a static, cross-sectional picture of factors affecting OI, which
makes it difficult to address the issue of how these antecedents and their importance may
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change over time. A longitudinal study could enrich the findings. Third, future research
could consider other important organizational factors such as organizational culture,
leadership or organizational strategy. At the same time, future research could go deeper
into investigating the role of KM for OI by inquiring into the specific characteristics of
outside knowledge such as level of establishment, generality or complexity. These
suggestions should be taken into account in future studies to increase and expand the
validity of our findings.
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Appendix. Variable definitions
Independent variables
IT operations. Extent to which ICTs are used for [. . .]. (1-7):
ITO1: Extent to which ICTs are used for procurement and inventory management
activities.
ITO2: Extent to which ICTs are used for product design activities.
ITO3: Extent to which ICTs are used to support manufacturing activities.
ITO4: Extent to which ICTs are used to support marketing activities.
ITO5: Extent to which ICTs are used to support the distribution of products.
ITO6: Extent to which ICTs are used to support HR management.
Inter-departmental connectedness. Regarding your firm, to what extent do you agree with
the following statements? (1-7):
IDC1: In our company, employees from different departments feel comfortable calling
each other when the need arises.
IDC2: In our company, it is easy to talk with virtually anyone you need to, regardless of
rank or position.
Commitment-based HR practices. In regard with HR practices in your firm, to what extent
do you agree with the following statements? (1-7):
1. Selection policies:
CHR1: Internal candidates are given consideration over external candidates.
CHR2: We select employees based on an overall fit to the company.
CHR3: Our selection system focuses on the candidate’s potential to learn and grow
with the organization.
CHR4: We ensure that all employees are made aware of internal promotion
opportunities.
2. Incentive and training and development policies:
CHR5: Employee bonuses or incentive plans are based primarily on the
performance of the organization.
CHR6: Goals for incentive plans are based on business unit or company
performance.
CHR7: Salaries for employees in these positions are higher than those of our
competitors.
CHR8: Performance appraisals are used to plan skill development and training for
future advancement within the company.
CHR9: We provide multiple career path opportunities for employees to move across
multiple functional areas of the company.
CHR10: We provide training focused on team-building and teamwork skills.
Environmental dynamism (ED). Regarding your firm, to what extent do you agree with the
following statements? (1-7):
ED1: In a year, our market has changed a lot.
ED2: Our clients regularly ask for new products and services.
ED3: In our market, the volumes of products and services to be delivered change fast
and often.
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Dependent variables
Knowledge management capability. Regarding your firm, to what extent do you agree with
the following statements? (1-7):
KM1: Our company creates new knowledge for application across functional
boundaries.
KM2: Our company creates operation systems for application across functional
boundaries.
KM3: Our company creates managerial policies and processes for application across
functional boundaries.
KM4: Our company engages in the process of distributing knowledge among
departments.
KM5: Our company designs activities to facilitate knowledge sharing across functional
boundaries.
KM6: Our company engages in processes of integrating different sources of
knowledge across functional boundaries.
KM7: Our company engages in processes of transferring knowledge to employees
across functional boundaries.
KM8: Our company engages in processes which apply experiential knowledge across
functional boundaries.
KM9: Our company engages in processes which apply knowledge to solve new
problems across functional boundaries.
Open innovation. Regarding your firm, to what extent do you agree with the following
statements? (1-7):
1. Inbound practices:
OI1: External partners, such as customers, competitors, research institutes,
consultants, suppliers, government, or universities, are directly involved in all our
innovation projects.
OI2: All our innovation projects are highly dependent upon the contribution of
external partners, such as customers, competitors, research institutes, consultants,
suppliers, government or universities.
OI3: Our firm often buys R&D related products from external partners.
OI4: Our firm often buys intellectual property, such as patents, copyrights, or
trademarks, belonging from external partners to be used in our innovation projects.
2. Outbound practices:
OI5: Our firm often sells licenses, such as patents, copyrights, or trademarks, to
other firms to better benefit from our innovation efforts.
OI6: Our firm often offers royalty agreements to other firms to better benefit from our
innovation efforts.
OI7: Our firm strengthens every possible use of our own intellectual properties to
better benefit our firm.
OI8: Our firm founds spin-offs to better benefit from our innovation efforts.
Corresponding author
Pedro Soto-Acosta can be contacted at: psoto@um.es
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