Investigating the impact of networking capability on firm innovation performance: using the resource-action-performance framework
| Pages | 1009-1034 |
| DOI | https://doi.org/10.1108/JIC-01-2020-0005 |
| Date | 11 May 2020 |
| Published date | 11 May 2020 |
| Author | Nima Garousi Mokhtarzadeh,Hannan Amoozad Mahdiraji,Ismail Jafarpanah,Vahid Jafari-Sadeghi,Silvio Cardinali |
| Subject Matter | Organizational structure/dynamics,HR & organizational behaviour,Accounting/accountancy,Information & knowledge management |
Investigating the impact of
networking capability on firm
innovation performance: using the
resource-action-performance
framework
Nima Garousi Mokhtarzadeh
Faculty of Management, University of Tehran, Tehran, Iran
Hannan Amoozad Mahdiraji
School of Strategy and Leadership, Faculty of Business and Law, Coventry University,
Coventry, UK and
Faculty of Management, University of Tehran, Tehran, Iran
Ismail Jafarpanah
Faculty of Management, University of Tehran, Tehran, Iran
Vahid Jafari-Sadeghi
School of Strategy and Leadership, Faculty of Business and Law, Coventry University,
Coventry, UK, and
Silvio Cardinali
Universit
a Politecnica delle Marche, Ancona, Italy
Abstract
Purpose –The experience of successful firms has proven that one of the most importantways to promote co-
learning and create successful networked innovations is the proper application of inter-organizational
knowledge mechanisms. This study aims to use a resource-action-performance framework to open the black
box on the relationship between networking capability and innovation performance. The research population
embraces companies in the Iranian automotive industry.
Design/methodology/approach –Due to the latent nature of the variables studied, the required data are
collected through a web-based cross-sectional survey. First, the content validity of the measurement tool is
evaluated by experts. Then, a pre-test is conductedto assess the reliability of the measurement tool. All data are
gathered by the Iranian Vehicle Manufacturers Association (IVMA) and Iranian Auto Parts Manufacturers
Association (IAPMA) samples. The power analysis method and G*Power software are used to determine the
sample size. Moreover, SmartPLS 3 and IBM SPSS 25 software are used for data analysis of the conceptual
model and relating hypotheses.
Findings –The results of this study indicated that the relationships between networking capability, inter-
organizationalknowledge mechanisms and inter-organizational learning result in a self-reinforcing loop, with a
marked impact on firm innovation performance.
Originality/value –Since there is little understanding of the interdependencies of networking capability,
inter-organizational knowledge mechanisms, co-learning and their effect on firm innovation performance, most
previous research studies have focused on only one or two of the above-mentioned variables. Thus, their
cumulative effect has not examined yet. Looking at inter-organizational relationships from a network
perspective and knowledge-based view (KBV), and to consider the simultaneous effect of knowledge
mechanisms and learning as intermediary actions alongside, to consider the performance effect of the
capability-building process, are the main advantages of this research.
Keywords Networking capability, Inter-organizational knowledge mechanisms, Inter-organizational
learning, Innovation performance
Paper type Research paper
A framework
to investigate
networking
capability
1009
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1469-1930.htm
Received 10 January 2020
Revised 19 February 2020
Accepted 2 April 2020
Journal of Intellectual Capital
Vol. 21 No. 6, 2020
pp. 1009-1034
© Emerald Publishing Limited
1469-1930
DOI 10.1108/JIC-01-2020-0005
1. Introduction
In recent decades, the intensification of global competition has made firms’sustainable
competitive advantage and long-term growth dependent on continuous innovation
(Schumpeter, 1939;Griffiths-Hemans and Grover, 2006). However, research studies show
that the risk of development and failure rate for innovative activities is increasing as
technology becomes more sophisticated and specialized (Cooper et al., 2003). The wide range
of capabilities required to deliver new products to the market, especially new local markets,
has led firms to increasingly engage in co-innovation, seek to share R&D costs and risks,
accelerate innovation cycles and enhance flexibility, quality and productivity through inter-
organizational collaborations (Faems et al., 2005;Walter et al., 2006;Ford et al., 2018). Such
trends have led to the emergence of an open innovation paradigm. This theory emphasizes
the need for co-innovation in the context of collaborative networks based on processing the
inbound and outbound knowledge flows. Co-innovation is complementary to the firms’
internal innovation activities (Faems et al., 2005), and it reduces the risk of competency trap
because it broadens the range of partner firms’experiences (Knoppen et al., 2011). The
literature emphasizes that networked collaborations lead to co-learning and upgrading the
firm’s capability (Tsai, 2001). Networked innovations are much more likely to succeed
because of product complexity (Pittaway et al., 2004) and higher rates of innovation novelty
(Nieto and Santamaria, 2007). Co-innovation is the result of an interactive process in which
network members exchange, absorb and combine the knowledge. However, studies show
that most networked collaborations fail because of inconsistencies, inflexibilities and
conflicts (Kale et al., 2002;Pittaway et al., 2004;Rampersad et al., 2010) with rates ranging
between 60 and 70 percent (Gulati and Kletter, 2005). Increased complexity, lack of
independence, information asymmetry and the imposition of additional time and costs on the
firm are some risks of collaboration. On the one hand, this situation has drawn the attention of
innovation scholars to inter-organizational networks, and on the other hand, firms have been
thinking about how to establish good networked relationships and thereby successfully
enhance their innovation performance.
By adopting a knowledge-based view (KBV) approach to the firm, one of the most
important ways to promote co-learning and create successful networked innovations is the
proper application of inter-organizational knowledge mechanisms (Lavie et al., 2012). The
literature addresses a range of inter-organizational knowledge mechanisms, the most
important of which are physical relocation, concurrent product definition and design-build
teams (Nobeoka and Baba, 2001); supplier associations, voluntary learning and problem-
solving teams (Dyer and Nobeoka, 2000); forums, boundary-spanners and manufacturing
audits (Chai et al., 2003;Chai and Yap, 2004); supplier’s certification (Knoppen et al., 2011),
informal meetings and dynamic virtual processes (Spraggon and Bodolica, 2012) and
document transfer (Feller et al., 2009). Since these mechanisms can help both knowledge
sharing and knowledge co-creation, they have a significant impact on the firm’s co-learning
and innovation performance.
This study distinguishes itself from previous research studies by solving some of the
following theoretical dilemmas. On the one hand, in spite of the various emphases of the inter-
organizational relationship literature on the critical role of networking capability in a firm’s
innovation performance, the manner and causal mechanism of networking capability impact
on the performance has not been thoroughly analyzed (Kauppila, 2015;Jafari Sadeghi and
Biancone, 2017). On the other hand, although the previous studies of knowledge management
and organizational learning have identified numerous inter-organizational knowledge
mechanisms that have a significant impact on inter-organizational relationships and firm
innovation performance (Smeds et al., 2001;Edquist et al., 2002), the effect of these
mechanisms on enhancing co-learning has not been investigated. Therefore, although
previous research has separately explored the role of networking capability and inter-
JIC
21,6
1010
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