Knowledge Sharing Strategy and Emission Reduction Benefits of Low Carbon Technology Collaborative Innovation in the Green Supply Chain

Knowledge sharing (KS) in the green supply chain (GSC) is jointly determined by the KS efforts of suppliers and manufacturers. This study uses the differential game method to explore the dynamic strategy of KS and the benefits of emission reduction in the process of low carbon (LC) technology in the GSC. The optimal trajectory of the knowledge stock and emission reduction benefits of suppliers and manufacturers under different strategies are obtained. The validity of the model and the results are verified by numerical simulation analysis, and the sensitivity analysis of the main parameters in the case of collaborative sharing is carried out. The results show that in the case of centralized decision-making, the KS efforts of suppliers and manufacturers are the highest, and the knowledge stock and emission reduction benefits of GSC are also the best. The cost-sharing mechanism can realize the Pareto improvement of GSC’s knowledge stock and emission reduction benefits, but the cost-sharing mechanism can only increase the supplier’s KS effort level. In addition, this study found that the price of carbon trading and the rate of knowledge decay have a significant impact on KS. The study provides a theoretical basis for promoting KS in the GSC and LC technology innovation.

Some Context: From First to Fourth Generation Universities

The evolution and governance of the modern university, the balance between the needs of the consumer (students and employers) and the community (knowledge stock and needs of society).

Toward a Business Resilience Model: The Case of Sharia Property in Surabaya Raya Area during COVID-19 Pandemic

This study aims at examining how sharia property can build resilience during COVID-19 pandemic and to provide a business resilience model by exploring its capabilities to be resilient during COVID-19 pandemic. The empirical results from a case study of sharia developer located in Surabaya Area, show that the strengthening of the developers’ commitment, strengthening cash flow management, enhancing, and deploying knowledge stock about sharia property, and emerging dynamic capability to respond to the shock and maintain the business continuity of sharia property during COVID-19 pandemic. We contribute to extant business and management literature about the concept of business resilience of sharia property by proposing a model about how sharia property builds its business resilience during COVID-19 pandemic.

A system dynamic approach for simulation of a knowledge transfer model of heterogeneous senders in mega project innovation

PurposeThe owners of mega projects typically assemble multiple academic research units and enterprises to form an innovation alliance, which carries out knowledge transfer and knowledge creation targeting technical challenges in the process of engineering construction. Due to high technical and management complexity of mega projects, factors affecting knowledge transfer among innovation subjects are complex and diverse. This study proposes a mixed system dynamics (SD) method to build and simulate the process of knowledge transfer in mega projects innovation and analyzes the driving mechanism that enhances knowledge stock of enterprises and engineering innovation results.Design/methodology/approachFirst, this paper proposes a conceptual model for knowledge transfer in mega projects by adopting event analysis of the data gained from investigations and interviews. Then, a qualitative model of knowledge transfer that considers mutual influences of the owner, academic research unit and enterprises is developed. Based on that, mathematical relationship among variables of the qualitative model is determined and a quantitative model of knowledge transfer that considers heterogeneity of knowledge sender is built. Finally, simulation is achieved using Vensim software.FindingsThe factors affecting knowledge stock of enterprises are analyzed from three aspects: (1) the individual motives and capability of academic research units and enterprises; (2) the gap between academic research units and enterprises; (3) the heterogeneity of academic research units. The results show that the willingness and capability of knowledge reception by enterprises, specific knowledge transfer context such as relational distance and organization distance between academic research units and enterprises and academic research units with high knowledge stock have key influences on the knowledge stock of enterprises.Research limitations/implicationsFactors affecting knowledge transfer within the alliance of innovation in mega projects and their correlations are highly complicated and difficult to determine. Despite massive investigations and interviews on many long-span bridges in China in this study, it is barely possible to directly obtain accurate data for all variables in the model. Limitations of historical data result in limitations on applications of the proposed model.Practical implicationsBy building the mega projects knowledge transfer model and conducting simulation analysis, this paper has generated practical values for the owners of mega projects on fostering, organizing, coordinating and managing of innovations. Especially, this study provides specific strategies and suggestions on selection of innovation subjects, motivation and guaranteed efficiency of knowledge transfer and knowledge creation of academic research units and enterprises.Originality/valueThis study proposes a conceptual model for factors affecting knowledge transfer that applies to innovations in mega project context, which fills the gap in the research of knowledge management in mega project innovations. Additionally, combining with the method of SD, the unique role of owner in knowledge transfer of mega projects and the differences among various knowledge senders and their influences on knowledge stocks of enterprises are thoroughly considered, and the research method of modeling and simulation of knowledge transfer mechanism is supplemented and extended.

Exploring Efficient Network Structure of Interfirm Knowledge Sharing From Perspective of Optimal Node Degree

Background: Network structure is a critical issue for efficient interfirm knowledge sharing. The optimal node degree turns out to be decisive because it is generally regarded as a core proxy of network structural characteristics. This paper is to examine what is the optimal node degree for an efficient network structure. Methods: Based on an interaction rule combining the barter rule and the gift rule, we first describe and then build a knowledge diffusion process. Then using four factors, namely network size, network randomness, knowledge endowment of network, and knowledge stock of each firm, we examine the factors that influence the optimal node degree for efficient knowledge sharing. Results: The simulation results show that the optimal node degree can be determined along the change in outer factors. Furthermore, changing the network randomness and network size has little impact on node degree. Instead, knowledge endowment of network and knowledge stock of each firm both have significant impact on the node degree. Conclusion: We find that an optimal node degree can always be found in any condition, which confirms the existence of a balanced state. Thus, policymakers can determine the appropriate number of links to avoid redundancy and thus reduce cost in interfirm networks. We also examine how different factors influence the size of the optimal node degree, and as a result, policymakers can set an appropriate number of links under different situations.

Optimal time allocation in active retirement

We set up a lifecycle model of a retired scholar who chooses optimally the time devoted to different activities including physical activity, continued work and social engagement. While time spent in physical activity increases life expectancy, continued scientific publications increases the knowledge stock. We show the optimal trade off between these activities in retirement and its sensitivity with respect to alternative settings of the preference parameters.