Purpose The purpose of this study is to adopt the perspective of congruence to explore how organizational unlearning facilitates knowledge transfer in cross-border mergers and acquisitions (M&A). Design/methodology/approach Drawing on the congruence theory, this study built a theoretical model and examined it with survey data from 212 firms in China. Findings Organizational unlearning has no direct influence on knowledge transfer. In contrast, it promotes knowledge and routine compatibility that facilitate knowledge transfer. Routine and knowledge compatibility have different mechanisms on knowledge transfer. Specifically, the higher routine compatibility, the more effective is knowledge transfer. When knowledge compatibility is at a medium level, the effectiveness of knowledge transfer is optimal. Practical implications Firms should regard organizational unlearning as a crucial facilitator to knowledge and routine compatibility that promote knowledge transfer. Originality/value This study provides a specific understanding of the relationships between organizational unlearning and knowledge transfer by focusing on knowledge and routine compatibility as the crucial links, and enriches existing literature regarding knowledge transfer.

Purpose This paper aims to explore the relationships between organizational unlearning and knowledge transfer in cross-border mergers and acquisitions (M&As) from a routine-based view. The study also stresses the mediating role that knowledge integration capability plays in this relationship. Design/methodology/approach In all, 178 samples were collected from Chinese multinational corporations that experienced cross-border M&As. In addition, the bootstrap method was used to test the mediating role of knowledge integration capability. Findings The empirical results indicate that knowledge integration capability is the crucial link between organizational unlearning and knowledge transfer. Specifically, this capability goes beyond the direct effect of organizational unlearning on knowledge transfer and points to the importance of enhancing knowledge integration capability. In turn, knowledge integration capability has a significant influence on knowledge transfer. The study finds that knowledge integration capability mediates the relationship between organizational unlearning and knowledge transfer. Originality/value This study adopts a routine-based view to develop a theoretical model for examining the relationship between organizational unlearning, knowledge integration capability and knowledge transfer in the context of cross-border M&As. This model provides new insights for a routine-based understanding of the important mediating role of knowledge integration capability for knowledge transfer and the effects of this role on the specific knowledge transfer, i.e. technological, marketing and managerial knowledge.

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Facing the sustainable use of electric power resources, many countries in the world focus on the R&D investment and application of electrochemical energy storage projects (i.e., EESP). However, the high R&D cost of EESP has been hindering large-scale industrial promotion in the energy-intensive manufacturing industry represented by the tobacco industry. Reducing and controlling the R&D cost has become an urgent problem to be solved. In this context, this paper innovatively proposes a multi-technology driven R&D cost improvement scheme, which integrates WBS (i.e., Work Breakdown Structure), EVM (i.e., Earned Value Method), BD (i.e., Big Data), and ML (i.e., Machine Learning) methods. Especially, the influence of R&D cost improvement on EESP application performance is discussed through mathematical model analysis. The research indicates that reducing EESP R&D costs can significantly improve the stability of EESP power supply, and ultimately improve the application value of EESP in energy-intensive manufacturing industries. The R&D cost management scheme and technical method proposed in this paper have important theoretical guiding values and practical significance for accelerating the large-scale application of EESP.