Process of innovation knowledge increase in supply chain network from the perspective of sustainable development

DOIhttps://doi.org/10.1108/IMDS-06-2017-0243
Publication Date14 May 2018
Pages873-888
AuthorDan Zhang,Ching-Hsin Wang,Dengpan Zheng,Xianyun Yu
SubjectInformation & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems
Process of innovation knowledge
increase in supply chain network
from the perspective of
sustainable development
Dan Zhang
School of Business and Administration,
Zhejiang University of Finance & Economics, Hangzhou, China
Ching-Hsin Wang
Institute of Project Management, National Chin-Yi University of Technology,
Taichung City, Taiwan, and
Dengpan Zheng and Xianyun Yu
School of Management, Hangzhou Dianzi University, Hangzhou, China
Abstract
Purpose The purpose of this paper is to extend prior supply chain research by describing the process of
innovation knowledg e increase in supply chain network. Mo re specifically, this study invest igates the role
of network density, and views the kn owledge increase as the proces s of knowledge diffusion and
knowledge innovation .
Design/methodology/approach A multi-agent model, which demonstrates the process of knowledge
increase in supply chain network, was established, and simulated by using NetLogo simulation platform.
Findings The results indicate that the network density will promote the knowledge increase of the supply
chain when it is high or low. In the meantime, these results show that the inhibition of knowledge diffusion
and knowledge innovation will appear when network density is moderate.
Originality/value Although previous resear ch has identified the importance of knowledge increase in
promoting sustainabl e development of supply chain, far les s attention was given to the study of t he effect
of network structure on the k nowledge increase in supply chain . This study thus fulfills the rese arch gap by
providing a description of the process of knowledge increase with the consideration of network density.
The conclusion is of gre at significance for the choice of network dens ity for sustainable de velopment of
supply chain.
Keywords Network density, Agent-based model and simulation, Innovation knowledge increase,
Supply chain network
Paper type Research paper
1. Introduction
The existing research considers the supply chain as a knowledge alliance, and believes that
the knowledge increase in the supply chain is important for its sustainable development
(Wowak et al., 2013; Ikem, 2013; Sambasivan et al., 2009). Driven by increased market
competition and rapid technological changes, the supply chain needs to continuously
increase innovative knowledge to strengthen its core competitiveness (Liang and Chen,
2015; Min et al., 2015). In order to promote the growth of knowledge, the structure of supply
chain also gradually changes from the original chain structure into a network structure, that
is, the close innovation cooperation also exists between suppliers and users in addition to
the cooperation between the enterprise and its upstream and downstream (Shi et al., 2012;
Tseng et al., 2013; Zhao, 2016; Zhou et al., 2013). With the help of modern information
technology, this complex network structure can meet the innovation demand quickly in the
Industrial Management & Data
Systems
Vol. 118 No. 4, 2018
pp. 873-888
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-06-2017-0243
Received 12 June 2017
Revised 2 October 2017
Accepted 13 November 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
This research has been sponsored by NSFC (No. 71402160; 71502046; 71273078).
873
Sustainable
development
market (Xu et al., 2015; Liao et al., 2016; Katja, 2011). Some scholars believe that such
network structure is beneficial to the acquisition of heterogeneity knowledge, and reduces
the transmission costs caused by redundant connections, so as to ensure the sustainable
increase of innovation knowledge in supply chain (Chen et al., 2015; Chih-Hsing, 2011; Scott,
2012; Burt, 1992).
However, few studies have analyzed the innovation knowledge increase of supply chain
with this network structure, and have not proved that the network structure is beneficial to
the innovation knowledge increase in supply chain. Several studies, based on social network
theory, have analyzed the characteristics of the supply chain network structure, and
proposed the factors which affect the formation of the network structure (Gnyawali and
Madhavan, 2001; Zhou and Zhou, 2016; Yi and Xue, 2016). A part of studies have found the
influence of knowledge integration and flow in obtaining the core competitive advantage of
supply chain; however, few have analyzed the innovation knowledge increase process of
supply chain with different network structures. Indeed, the influence of network structure
on the innovation knowledge increase has been studied in the existing innovation literature
(Katja, 2011; Kieron and Mark, 2004), and network density is regarded as an important
factor that affect innovation knowledge increase based on the complex network theory
(Katja, 2011; Wang, 2016; Long, 2016; Kühne et al., 2013). Nevertheless, some studies suggest
that network density has a positive influence on the innovative knowledge increase (Wang
et al., 2010; Xiong and Li, 2011), others suggest that network density has a negative
influence on the innovative knowledge increase (Zhang et al., 2011; Ma and Zhang, 2017).
Nonaka et al.s (2000) argument on Socialization-Externalization-Combination-Internalization
(SECI) model helps to explain the inconsistent results of prior research. According to Nonaka
et al. (2000), knowledge increase is viewed as the process of knowledge diffusion and knowledge
innovation. However, the innovation knowledge increase in the supply chain network is
equivalent to the knowledge diffusion or knowledge innovation in the existing researches,
without considering the two as a continuous process (Cao et al., 2016; Wang, 2016). When the
network density becomes greater, the social relations and the goals of the enterprises
in the supply chain are more and more unified through long-term cooperation and
communication, thus conducive to the knowledge diffusion. But the deepening of
interconnection can also lead to the homogeneity of knowledge, which is adverse to
knowledge innovation (Xu et al., 2015; Liao et al., 2016).
Based on the research of Nonaka et al. (2000), the process of innovation knowledge
increase in the supply chain network can be understood accurately only through the
analysis of the knowledge diffusion and knowledge innovation as a whole process (Tse et al.,
2016). We argue that, in the supply chain network, enterprises acquire the outside
knowledge through knowledge diffusion in the network (Love and Roper, 2009; Chen et al.,
2015), and make an innovation based on the existing knowledge, to increase their own
knowledge. When the knowledge quantity of an enterprise exceeds that of others, it will
trigger a new round of knowledge diffusion and knowledge innovation, and ultimately lead
to the entire network knowledge increase (Tse et al., 2016). As a continuous, interconnecting
and constant process, knowledge innovation and knowledge diffusion push each other and
jointly facilitate the innovation knowledge increase of supply chain network (Argote and
Guo, 2016; Battistella et al., 2016).
Using the analysis of knowledge diffusion and knowledge innovation of the supply chain
with different network density structures, in this study we describe the process of
innovation knowledge increase based on the innovation theory, complex network theory
and SECI model in knowledge creation theory. In the application of methods, it is difficult to
carry out the follow-up research on network density structure and innovation knowledge
increase of supply chain for a long time in different periods by adopting case study method.
In addition, if econometric model or other empirical research methods which need to collect
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