Ying Dai

In this paper, we put forward a data-driven supply chain consisting of a manufacturer, a retailer, and a third-party data service provider and explore the two stages of the manufacturer in making investment decisions. Given the convenience of recycling information sources, we divided the structure of the closed-loop supply chain into three investment models, including (1) the manufacturer concentrates on the two stages through big data technology; (2) the manufacturer can stimulate the retailer, who is close to reused products, to attend data collection activities; (3) the manufacturer subcontracts data collection activities to the third-party. The result shows that the manufacturer as the leader of Stackelberg will have alternative models in different situations. Specifically, the retailer is more inclusive than the third-party in the information market due to the positive influence of the price sensitivity coefficient, with the manufacturer always choosing to cooperate with the retailer when the cost advantages of big data investment are less than or equal to that of the third-party. In the event that the third-party data provider, seeing its cost advantage coefficient 0.5 times bigger than that of the retailer, will dominate the information market. Although the third-party has the upper hand, the information market will be shared by the retailer when the cost advantage coefficient of the former is two times bigger or more than that of the latter, with the share shinking with the advantage of the retailer. In addition, there is an interesting finding that the expanding cost advantage of the third-party will have a positive impact on the optimal profit of the manufacturer from two perspectives, including a direct rise in profit and a narrower range of dropping profit.

Purpose: The purpose of this paper is to provide a R&D outsourcing contract design framework to incent R&D outsourcing service providers sharing tacit and explicit knowledge. Design/methodology/approach: The author uses the principal-agent theory to builds multitask principal-agent model which focuses on two cases. One case is that the effort costs of explicit and tacit knowledge sharing are complementary, but another is the effort costs are substitutable. Findings: It was found that when the effort costs of explicit and tacit knowledge sharing are mutually complementary the multitask R&D outsourcing contract can incent tacit knowledge sharing. Moreover, the multitask R&D outsourcing contract can motivate the effort of explicit knowledge sharing to achieve system optimization, but it fails to motivate the effort of tacit knowledge sharing to achieve system optimization. Research limitations/implications: In this paper we only consider that the outsourcing relationship is short-term, so the multitask R&D outsourcing contract is formal. In fact, the outsourcing relationship may be long-term, so the multitask R&D outsourcing contract is informal. In the long-term cooperation the buyer can promise some reward which cannot be verified by court. service providers choose to trust the promise of the buyer first. Once the buyer don’t fulfill the promise, they not only lose the trust of service providers in this cooperation, but also lose social trust and are seen as dishonest enterprises. Practical implications: Our study provides a theoretical model for formulating an effective R&D outsourcing contract and promoting the transfer of explicit and tacit knowledge sharing. Originality/value: The paper extends prior literature by designing multitask R&D outsourcing contract to share the tacit knowledge. We not only consider the cost of substitution relationship between tacit knowledge and explicit knowledge, but also considered the cost of complementary relationship.

Using LMDI approach to analyze changes in carbon dioxide emissions of China’s logistics industry