Rapid industrialization with attendant higher consumption of fossil energy has led to sharp increases in CO2 emissions. Under the new trend of economic globalization, low-carbon production (systems of production and delivering of goods and services with application of less energy per output) has become a critical strategy for competitiveness of firms. Hence, low-carbon technology innovation (LTI) plays a key role in the performance of industrial enterprises (economic and environmental), improves energy efficiency, and helps reduce carbon emissions (Dou, 2017).
With the global crusade for emission limitation, enterprises now face a complex task of economic-environmental maximization. To this end, experts recommend more collaborative approaches towards technological innovation rather than low-carbon innovation attempts of single enterprises (Bagherzadeh et al., 2019). Low-carbon technologies reduce greenhouse gas emissions through strategic investments in manufacturing systems. This may involve application of knowledge from several fields of discipline, technology in product design, manufacturing processes, sewage treatment and energy saving (Li et al., 2021). For an enterprise to achieve successful LTI by relying only on its own technical experience and knowledge is daunting (Chen et al.,2021). Therefore, looking beyond organizational boundaries towards a wide range of heterogeneous knowledge through networking and alliances is the more effective way to carry out LTI (Chen et al., 2021). This kind of collaborative efforts for mutual benefits has been termed as alliance innovation (Lin et al., 2012).
The concept of alliance innovation is an innovation paradigm which emphasizes that technological innovation encapsulates the ability of enterprises to seek external information, resources, and reallocating innovation resources with third parties mainly to obtain a competitive edge (Lin et al., 2012). Alliance innovation network is an important network characteristic that affects the technological innovation behavior and innovation performance of the allied members. Good innovation alliance relationship is beneficial for enterprises to obtain high-quality information and knowledge, thereby promoting LTI (Jiang et al., 2020). Zhou and Ren (2021) pointed out that cooperation between enterprises and noncompetitive organizations such as suppliers and scientific research institutions is beneficial for LTI. The impact of different forms of innovation alliance on LTI is different. The binary strong alliance relationship is likely to cause knowledge and information redundancy, and the multi-dimensional alliance weak relationship will lead to the prevalence of opportunism in the process of cooperation. Both alliance relationships are not conducive to knowledge sharing and technological innovation (Wang et al., 2019)
Recently, the use of Simmelian Ties (ST) theory in social network research has gained popularity, and some scholars have introduced it into the field of management (Xu et al., 2020). Simmel (1950) suggested that relations embedded in a triad are stronger, more durable, and more able to produce agreement between actors than relations not so embedded. Based on Simmel’s pioneering work, we refer to dyadic ties embedded in three-person and/or firms cliques as ST in this study (Krackhardt and Kilduff, 2002).
Weak ST can bring new information to enterprises, but it also brings the challenge of how to transfer and integrate that new information. On the other hand, strong ST can bring relational capital such as trust to enterprises, but it also brings challenges such as information redundancy (Mors, 2010). Tortoriello and Krackhardt (2010) proposed that the idea that weak bridge ties have an impact on individual technological innovation depends on whether the tie is ST. Wu et al. (2016) pointed out that strong ST is beneficial to the technological innovation of enterprises. Jayaraj et al. (2017) also opined that cross-border ST are strong bridge ties, which can integrate the resources of the participants to promote technological innovation of enterprises. Soda et al. (2004) espoused that the closeness of ST can make the network relationship more stable. Therefore, the ultimate effect of ST on technological innovation is greater than that of non-ST.
Similarly, Sheng and Li (2012) pointed out that the triangular relationship structure of ST forms the cooperation and knowledge sharing pattern of the highest trust among enterprises. It is therefore beneficial to harness heterogeneous knowledge and innovation strategies among enterprises. Cunningham et al. (2018) opined that ST addresses the problem of contradiction between the sharing and integration of external knowledge needed for the innovation process. Although, existing studies have analyzed the impact of strong and weak STs on enterprise technological innovation, they failed to reach consistent conclusions. Moreover, available studies did not consider their impact on different types of technological innovation. Therefore, it is very important to study the influence of strong and weak ST on LTI.
The network relationship embedded in enterprises can help them obtain resources and benefits that would have been difficult to achieve individually (Jayaraj et al.,2017). Inspired by the theory of social network, some scholars studying low-carbon innovation have pointed out the need to establish a stable and a lasting inter-organizational network with external organizations. They equally advocated for a mechanism to obtain the knowledge needed for low-carbon innovation initiatives by enterprises (Gu and Su,2018). When using ST to study enterprise low-carbon innovation, it is imperative to understand which organizational network is more conducive to low-carbon innovation. Therefore, to apply ST to promote low-carbon innovation, we need to consider how the relevant personnel of the enterprise choose the appropriate information processing strategy at the cognitive level to control and process the knowledge and information from the external network (Krackhardt and Kilduff, 2002).
In view of this, the attention-based view of upper echelon gives us inspiration. According to the theory, the behavior and decision-making of enterprises are to some extent a reflection of TMEA, and managers will consciously or unconsciously bring their own concerns and preferences into the decision-making process (Kammerlander and Ganter, 2015). It can be inferred that the effect of enterprise low-carbon innovation activities largely depends on the TMEA. Therefore, the attention of top managers may be a key factor in exploring the impact mechanism of ST on low-carbon innovation. Jiang et al. (2020) pointed out that one of the defects in the existing field of low-carbon innovation research is the lack of attention to individual top managers, especially at the cognitive level. In the process of implementing LTI by enterprises, the question of whether the attention factor of top managers play a role and what effect a combination of the former and ST will have on enterprise low-carbon innovation come to bare.
It is difficult to obtain economic benefits from LTI investments in a short time. This implies that the key driving force of low-carbon innovation from ST and stakeholder pressure is more psychological than economic motivation (Xu et al., 2020). According to the stakeholder theory, the main purpose of low-carbon innovation is to enhance organizational legitimacy. It also points out that the pressure of key stakeholders and secondary stakeholders pressure are the key institutional factors to promote low-carbon innovation (Jiang et al., 2020). At present, from the perspective of stakeholder pressure, there has been a lot of achievements in innovation research. Particularly, innovation studies from the perspective of stakeholder pressure keeps expanding (Ahsa et al., 2019). However, few studies have discussed how the interaction between TMEA and LTI can be moderated by stakeholder pressure. Thus, we explore moderation role of stakeholder pressure in the relationship between ST, TMEA and LTI.
Herein, this paper develops a framework from the ST, alliance innovation network and stakeholder theories to investigate the interaction between ST, TMEA, LTI, and stakeholder pressure. Consequently, the study addresses the following questions.
- How does ST affect LTI?
- What is the relationship between TMEA and LTI?
- What role does stakeholder pressure play in ST and LTI?
A key contribution of this study is the development of a conceptual model for evaluation of the impact of ST on LTI. This, in effect opens the "black box" of ST, and further contributes to the strand of research on the relationship between alliance innovation network and low-carbon innovation in theory. In addition, this study enhances understanding of low-carbon innovation from the perspective of top managers.
The remaining components of this paper are organized as follows. Section 2 provides a theoretical framework; Section 3 describes the research methodology; Section 4 focuses on the results. Finally, in Sections 5 and 6, we focus on the discussion, implications as well as the conclusion and limitations of the study.