Purpose The antecedents and cost management methods and their effects on the value creation of inter-organization are essential topics of inter-organizational cost management (IOCM) in a hybrid relational perspective. This study aims to develop a synthesis coordinating mechanism theory which combines supply network theory and transaction cost economics. Using this modified theory, a structural model of IOCM and its hypotheses are developed by considering the organic connection among hybrid relational context, capabilities, methods and the effects of IOCM. Design/methodology/approach The data were collected under convenient sampling using the questionnaire survey method and analyzed using principal component analysis and structural equation modeling. Findings The results (significant at p < 0.01 and p < 0.05 level) show that there is a positive correlation among the hybrid relational context, capabilities (antecedents), methods and the effects of IOCM. Capabilities (antecedents) and cost management methods are also found to have a positive impact on synergic effect value and on improving collaborative efficiency in IOCM. Practical implications The managers of inter-firm networks may use the results of this study to ensure competitive advantages through collaboration with each other and building and retaining a long-term relationship. Originality/value This study demonstrates that the coordinating mechanism of IOCM can create the synergic effect value and improve the collaborative efficiency of the inter-organization relationship.

No abstract

No abstract

No abstract

Applying case-based reasoning (CBR) to analyze the effect of collaborative cost management (CCM) in supply chain is a new research topic. This paper illustrates a new method which combining four steps of CCM and procedures of CBR with fuzzy inference model. The method improves the traditional similarity assessment and obtains the most suitable case to solve new problems. Moreover, this study gives a new definition of information set on the CCM problem and emphasizes self-learning in CBR for CCM in order to make the selected case solution more suitable and obtain the more collaborative effect value of CCM.