The Influence of Network Embeddedness on Innovation Performance of MSMEs. The Mediating Role of Ambidextrous Organizational Learning

Abstract

DOI: 10.7176/EJBM/13-23-06

Publication date: December 31^st 2021

Abstract

Based on resource-based view (RBV) and organizational learning theories, the study has built theoretical conceptual model of multiple networks embedding influencing enterprises’ l innovation performance from the perspective of organizational learning. The work discussed the internal mechanism of ambidextrous organizational learning activities affected by embedded relationship, embedded structure and embedded resource influencing technological innovation performance. Through obtaining data form 305 respondents in management of MSMEs in Sierra Leone, the study systematically validated the conceptual model with the structural equation model. It showed that embedded relationship, embedded structure and embedded resource in enterprise organization network can effectively improve the enterprise’s organizational learning capability, bringing significant promotion in innovation performance. Wherein, the embedded relationship and embedded resource can promote not only the innovation performance of the enterprise, but also the performance by improving the knowledge management ability of the enterprise. While, the promotion effect of embedded structure to enterprises’ technological innovation majorly relies on the fully-mediated The macroscopical trend of modern enterprise networking development and the innovation performance model of Micro, Small and Medium Enterprises (MSME’s) also has gradually developed from the single model by taking a new paradigm of network embeddedness and regulated by ambidextrous organizational learning. Many studies (Zahra, Ireland et al. 2000, Yli‐Renko, Autio et al. 2001, Uzzi 2018)  have found that network embedding has generally become one of the most important factors that even determine the enterprise innovation portfolio. Networks enhance firms’ accessibility to new knowledge, external resources, technologies and new market opportunities (GF1). Polanyi proposed the concept of embeddedness in 1968 and defined it as the degree to which economic activity is constrained by noneconomic institutions. This phenomenon has seen diverse classification but still maintains its central core denotation of organizational relationship and their related economic activities. Morone and Taylor (2004) argues network embedding is one of the most important features in modern organization relationships. Echols and Tsai (2005) define Network embeddedness as a concept regarding “the structure of a firm’s relationship with other firms—specifically, the extent to which a firm is connected to other firms” and it was classified into three dimensions by Inkpen and Tsang (2005), namely, cognitive, structural and relational embeddedness. Cognitive embeddedness, on the other hand, represents the shared representations, goals, norms, faith and experience among network members (Le Breton-Miller and Miller 2009, Gölgeci, Ferraris et al. 2019).

Structural embeddedness is also considered as the amount of information the focal firm could obtain from its network, which largely depends on the position of the firm in the network and the number of members in the network (Mazzola, Perrone et al. 2015). Relational embeddedness founded on trust is explained as the degree of quality and cohesive social interaction among network members acting as a community of organizations (Lin, Fang et al. 2009, Wang and Chen 2012). We based this study on resource-based view (Barney 1991, Grant 1991) since network embeddedness provides MSME’s with external resources, which could be used to complement internal resources, for competitive advantage. knowledge-based view (KBV) suggested by Grant (1996) is applicable since Innovative knowledge largely represent resources. Network embeddedness concept has been used widely in innovation research to demonstrate its influence to various aspects of innovation in firms. The study of (Mazzola, Perrone et al. 2015) in assessing the effect of network embeddedness on new product development found that the relations between centrality and structural holes had an increasing effect on new product development, while structural holes had no significant effect. Different network positions yielded different payoffs in new technology exploration (Gilsing, Nooteboom et al. 2008) which implies the interaction of network embeddedness in innovation performance has a multiplicative effect on subsidiaries’ knowledge transfer  (Dezi, Ferraris et al. 2019). (2018). Balland, Dam et al. (2014) clearly illustrated that the business dynamic position, status and the extent of close to the center network in the cluster network directly affect enterprises’ ability in studying network embeddedness in industrial cluster innovation.

Organizational learning is also essential for MSME’s innovation with external partners (Al-Harrasi 2014). Scholars agree that firms across organizational boundaries to obtain resources and integrate capabilities of external partners (Yang, Liu et al. 2011) not only maintain moderate RE but also have a high level of learning capacity (Al-Harrasi 2014, Bahrami, Kiani et al. 2016, Bai, Wu et al. 2021). OLC absorb and integrate knowledge and technology needed for service innovation (Bai, Wu et al. 2021). Most study focus on knowledge sharing and trust as mediating variables. Qammach (2016) tested the mediating role of knowledge sharing in the relationship between IT ability and IT support to forecast the innovation performance in the mobile industry. Maciel and Chaves (2017) investigated the mediating role of knowledge sharing on informational status in intraorganizational networks. Akram, Lei et al. (2020) examined the mediating effect of knowledge sharing on the mechanism of organizational justice affecting employee innovative work behavior while Li and Kang (2019) argued that an enterprise, obtaining external resources through knowledge sharing is a passive process depending on the status quo of the partners. Ambidextrous organizational learning is a process of actively acquiring external resources (Bai, Wu et al. 2021) and knowledge which are more important for enterprise innovation. Ambidextrous organizational learning could influence network embeddedness to enhance technological innovation performance since. Li and Kang (2019) believes the deeper the degree of embedding among enterprises, the more advantages enterprises have in improving technological innovation performance through exploitative learning. Nevertheless, few studies have examined the mediating effect of ambidextrous organizational learning. Therefore, the main objective of this paper is to explore the influence of network embeddedness on the innovation performance of MSME’s in Sierra Leone, as well as the possible interdependencies among these knowledge linkages. This research will seek to help explain the dynamic role of ambidextrous organizational learning in MSME’s through explicit consideration of its influence on innovation outcomes. We adopt the SEM-PLS framework to study interdependences among the dual network embeddedness, innovation performance of enterprises and ambidextrous organizational learning. The rest of the study is arranged as literature review, methodology, results and conclusion.