Transactive memory system and green innovation: a cross-level mediation of social network
| DOI | https://doi.org/10.1108/IMDS-04-2021-0254 |
| Published date | 17 October 2022 |
| Date | 17 October 2022 |
| Pages | 2737-2761 |
| Subject Matter | Information & 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 |
| Author | Yao Xiao,Jie Cen,Jie Hao |
Transactive memory system and
green innovation: a cross-level
mediation of social network
Yao Xiao, Jie Cen and Jie Hao
Zhejiang Gongshang University, Hangzhou, China
Abstract
Purpose –The aim of this study was to investigate the impact of the transactive memory system (TMS) on
green innovation and examine the mediation role of the social network at all hierarchical levels.
Design/methodology/approach –Three hypotheses were examined by performing regression analyses on
survey data from manufacturing firms in China. Especially, the nested sets of data from 389 individual
observations nested in 53 work teams, including individual level and collective level have been investigated.
Findings –The study results show that the TMS has a positive effect on green innovation. Furthermore, the
results indicate that at the team level, structure holes’mediation in this relationship is stronger than degree
centrality; at the individual level, weak ties mediation in the relationship of specialization and green innovation
is stronger than strong ties, conversely, strong ties mediation in the relationship of credibility and green
innovation is stronger than weak ties.
Originality/value –This study expands previous research by highlighting the significance of multilevel
social network elements in the context of the TMS and sustainable development and enriches the present
research on green innovation.
Keywords Transactive memory system, Social network, Green innovation, Multilevel
Paper type Research paper
1. Introduction
As a win-win solution for reducing the conflicts between economic development and
environmental protection, green innovation has become a very popular concept in both
academia and practice (Wang, 2019). Firms are aware of the importance of environmental
protection and start to pay more attention to promote green innovation especially in the
manufacturing industry of China. Global warming, environmental pollution and energy
consumption have brought systemic negative impacts on the earth’s ecology and threaten the
sustainable development of human society. From the perspective of green practice, on the one
hand, green innovation is in line with the sustainable development needs of enterprises,
balancing energy consumption and reducing carbon emissions while innovating; on the other
hand, the balance between low energy consumption and economic benefits brought by green
innovation is more likely to bring organizational performance growth to the manufacturing
and energy industries in the long run compared to ordinary innovation. Literatures also point
out that lean is beneficial for Green practices and the implementation of Green practices in
turn also has a positive influence on existing Lean business practices (D€
ues et al., 2013).
Therefore, promoting energy conservation and emission reduction and transforming the
economic development mode has become the key to developing country especially for China’s
sustainable development. Since proposing the strategy for carbon neutrality, the low-carbon
economy characterized by low energy consumption, low pollution and low emissions has
become the top priority of the whole world’s future economic development. In order to cope
with the problem of high energy consumption, China’s 11th Five-Year Plan adopted by the
A cross-level
mediation of
social network
2737
This paper has been funded by the Zhejiang Provincial Social Science Foundation; 21NDQN244YB, the
Zhejiang Province Soft Science Project; 2022C35019 and the NSFC Project; 72072162.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 25 June 2021
Revised 3 December 2021
18 March 2022
8 August 2022
Accepted 2 September 2022
Industrial Management & Data
Systems
Vol. 122 No. 12, 2022
pp. 2737-2761
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-04-2021-0254
National People’s Congress in 2006 formally incorporated reducing energy consumption into
the national energy development strategy, and put forward units’constraint indicators of
reducing GDP (Gross Domestic Product) energy consumption by 20%. What’s more, carbon
emissions in China reached 9.15 billion tons in 2015, accounting for 27.3% of global carbon
emissions (Xu et al., 2021). Under this pressure, China’s dual carbon targets call for peaking
carbon emissions by 2030 and achieving carbon neutrality by 2060. Therefore, in order to
achieve the goals of economic efficiency and sustainable development, companies need to
balance the process of innovation with carbon emission reduction, and green innovation is
considered by manufacturing and energy companies as the key factor to solving the dilemma
of economic growth and carbon emission reduction (Jiang and Bai, 2022). As the main body of
energy consumption and greenhouse gas emissions, enterprises choose the green
development strategy and actively promote green technology innovation under the
pressure of government environmental regulation.
Besides, green innovation refers to the development or improvement of products and
processes about saving energy, controlling pollution, recycling waste and implementing
environmental management (Chen and Liu, 2019). Meanwhile, facing an increasingly
complex task and decision issue, firms have to use teams at all hierarchical levels seeking
diversity resources to deal with dynamics challenges and improve green innovation. In a
pioneer study Rezende et al., 2019, noted that green product innovation and green process
innovation are influenced by green knowledge and collaboration elements from social
networks at both individual and collective level. Furthermore, evidence shows that
transactive memory system (TMS) reduce the cognitive load of each member and enhance the
efficiency of green knowledge searching which may play a vital role to impact green
innovation (Li et al., 2019a,b)(Li and Huang, 2013).
However, there are remaining gaps in the prior literature that deserve more empirical
examination. First, although researchers have studied the antecedents of green innovation,
including knowledge management (Abbas and Sa
gsan, 2019), knowledge acquisition (Wang
et al., 2020) and green strategic (Liu et al., 2020a,b), there is little research about the link
between TMS and green innovation. Second, sociologists have made considerable social
network may play a mediation role in explaining team knowledge memory and green
innovation (Ali et al., 2019)(Michinov et al., 2008), but they have seldom examined how social
network that measured by network structure and social tie mediating the link between TMS
and green innovation. Third, with few exceptions, largely limited to research on the formation
mechanism of green innovation merely considerate organization elements (Aron and Molina,
2019), however increasingly complex task and decision issues have motivated organizations
to use team resources at all hierarchical levels. Individual and team members performing
complex tasks can be effective because it increases the pool of green knowledge resources
available to deal with dynamics challenges faced by organization green innovation in rapid
and unpredictable changes in the environment (Cao and Ali, 2018).
To fill the gaps mentioned above, this study takes insights from TMS and social network
theory to explore the relationship between TMS, social network and green innovation at
hierarchical levels. First of all, the process of green innovation requires more integration of
knowledge from different disciplines than ordinary innovation. The balance of resources
between energy saving, low carbon, emission reduction and efficient management require not
only individual competence but also distribution of the knowledge in group. Therefore,
cooperation, specialization and credibility in the TMS are particularly important in green
innovation compared to ordinary innovation. Secondly, the pursuit of cooperation in green
innovation is more dependent on the degree of embedded in social networks than in
ordinary innovation. Meanwhile, TMS is inseparable from the nesting of network positions
for both individual and collective behaviors. More structural holes may capture more creative
activities through extensive information combining, but also reduce the cohesiveness within
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