A well-functioning supply chain management relationship cannot only develop seamless coordination with valuable members, but also improve operational efficiency to secure greater market share, increased profits and reduced costs. An accurate decision-making system considering multifactor relationship quality is highly desired. This study offers an alternative perspective and characterisation of the supply chain relationship quality and performance. A decision-making model is proposed with an artificial neural network approach for supply chain continuous performance improvement. Supply chain performance is analysed via a supervised learning back-propagation neural network. An ‘inverse’ neural network model is proposed to predict the supply chain relationship quality conditions. Optimal performance parameters can be obtained using the proposed neural network scheme, providing significant advantages in terms of improved relationship quality. This study demonstrates a new solution with the combination of qualitative and quantitative methods for performance improvement. The overall accuracy rate of the decision-making model is 88.703%. The results indicated that trust has the greatest influence on the supply chain performance. Relationship quality among supply chain partners impacts performance positively as the pace of technological turbulence increases.

Many firms undertake alliances as a tactic in their competitive strategy to gain technological benefits from their competitors. Little is known, however, about how simultaneous competition and cooperation between rivals (i.e. co-opetition) influence the alliance governance modes. To bridge this deficit, we integrated a variety of different theoretical perspectives into the concept of co-opetition. Based on the dynamic nature of co-opetitive relationships, we also consider the moderating effect of external technological turbulence on the choice of technology alliances. We tested our hypotheses on a sample of 405 international technology alliances in different industries. It was found that the inter-firm co-opetitive relationship results in alliance member preference to choose contractual agreements as the governance mode in new alliances. However, when external technological turbulence increases, firms tend to tighten alliance governance (i.e. through equity joint ventures) in response to the turbulence.

Previous studies have verified the relationship between entrepreneurial opportunity recognition and performance. However, the influences of a perceived gap between entrepreneurial opportunities before and after launching a new venture have been neglected. This study applied the partial least square method to analyse the entrepreneurial behaviours and performance of 214 Taiwanese new ventures, and adapted the bootstrap re-sampling method for estimating the variables. The empirical results showed that entrepreneurial resource and entrepreneurial alertness significantly influence the perceived opportunity, while the entrepreneurial resource significantly affects the actual opportunity. The results further demonstrated that both the perceived opportunity and actual opportunity considerably influence new venture performance. This study offered a research framework to better understand the cognition path of entrepreneurial opportunity, and suggested the factors affecting the recognition of perceived opportunity by longitudinal method can be further evaluated. To improve performance, entrepreneurs should enrich entrepreneurial resources and allocate them appropriately, and heighten and maintain their alertness to reduce the perceived gap arising from recognising entrepreneurial opportunities.