Purpose Guanxi, an invisible aspect of many levels of interaction in economic life, has increased in importance in recent years; however, this subject has been underappreciated in high-tech industries. High-tech firms can choose to use guanxi with their partners as a key component of their competitive advantage or to view guanxi as necessary evil that is required to complete business exchanges. This paper aims to evaluate how guanxi contributes to or impedes a high-tech firm’s innovation performance. Design/methodology/approach This study proposes and tests a nonlinear model that explores the positive and negative impact of guanxi on the innovation performance using a sample of 184 high-tech firms located in Taiwan Science Park. Findings In contrast to the mainstream of findings in the previous literature, the authors find that guanxi is a competitive advantage in constructing new business networks; however, additional increase of guanxi may even decrease innovation performance. This view opens up new research avenues in the context of high-tech industries and suggests directions for future research and practice. Research limitations/implications Academics and practitioners know little about the unfavorable aspects of this variable. Future studies might develop specific measurement scales that capture the negative side of strong guanxi. Beyond the positive side of variables that have been amply studied, such as affection and kindness, other interesting negative side of variables that may be considered in future studies are blood (Tsang, 1998), extortion (Dunfee and Warren, 2001) and favoritism (Hwang et al., 2009). Originality/value The authors contribute to the research on guanxi and innovation performance, in that this study extends guanxi literature beyond the intuitive conclusion that innovation performance can be significantly enhanced in Asian countries’ business networks by leveraging guanxi as competitive advantage, and further demonstrating when and how guanxi can be harmful for high-tech firms’ innovation performance.

This study mainly probed into the factors influencing consumers’ OGB intention on community e-commerce platforms, built models that included facets of conformity behaviors, perceived risks, customer engagement, and OGB intention, and focused on whether there is a mediating effect on customer engagement and an interference effect on perceived risks. The subjects of this study were consumers with OGB experience on community e-commerce platforms. The results show that conformity behaviors have a significant positive impact on customer engagement and OGB intention; thus, when customers have insufficient information or relevant knowledge of OGB, the comments or recommendations of their relatives and friends would influence their engagement and OGB intention. Moreover, this study found that customer engagement has a complete mediating effect, which indicates that conformity behaviors can influence OGB intention through customer engagement. In addition, this study found that perceived risks have an interference effect, meaning that when consumers sense high risks, the OGB intention is weakened through customer engagement. This shows that in the OGB organizations of the community e-commerce platforms, it is recommended that more attention be paid to customer engagement; it is also a topic worthy of discussion in practical business applications.

It is widely held that good relationships between partners are beneficial for firms' performance, and the literature on strategic management has consistently promoted the bright side of these relationships. This study extends that stream of investigation by investigating the degree to which three components of relationships (trust, affective commitment, and calculative commitment) can contribute to or impede innovation performance in high-tech industries. Thus, a theoretical model is developed and tested using data from 173 Taiwanese high-tech firms. The findings reveal that the relationships between trust and commitment and innovation performance can be depicted as an inverted curve.