Abstract:
As global competition becomes more and more fierce in the business world, Supplier Relationships Management (SRM) is gathering more and more attention from both practitioners and the academic community. Supply chain management professionals argue that commitment and trust, information transferring and sharing, and IT use are the main factors influencing SRM. However, previous studies merely investigated these factors from a linear and static perspective, which is far from the dynamic nature of the real world. Motivated by the lack of systematic and dynamic research in this field, this research aims to combine multiple related factors into an integrated system and analyse them in a dynamic manner. Therefore, a System Dynamic Framework is developed based on an extensive literature review and two rounds of intensive interviews with six large organizations in New Zealand (three pairs of buyers and suppliers) to examine the significance of major factors identified in previous studies and explore how the relationship between SRM and IT use evolves over time. Qualitative findings support the argument that the relationships in a collaboration of business partners and IT use are dynamic. Specifically, there are two dynamic loops, one a reinforcing loop between „collaboration‟ and „IT use‟ that works on the strategic level and operational level, the other a balance loop centred on long term transaction costs. Data analysis shows that „information sharing‟, „trust‟, „relationship‟ and „information systems‟ are the top four most influential factors according to the six research participants, and organisations of different collaborative stages may prioritise the factors differently. In general, buyer pays more attention to „Cost‟ in the early stage. The main contribution of this study is the introduction of an integrated model that evolves dynamically over time, where the utilisation of system dynamic tools has not been seen before. However, the research is flawed by limited time and the small sample size underpinning it, which makes the conclusion less statistically significant. Simulation analysis with more data could be conducted in the future to test and evaluate this system dynamic framework.