PRESENTATION OF A NEW APPROACH FOR ROBUST ESTIMATION OF VAR: A COMPARATIVE APPROACH

The aim of this paper is to survey the relation of knowledge for supplier with their capabilities and willingness as the development criteria. There are many context to have knowledge ow. What are more benecial contexts? After developing the research hypothesis, a structured questionnaire was adopted to gather primary data from suppliers. The draft questionnaire was sent to ve academic and industry experts to comment on the content. Received feedback was utilized to change the layout of the questionnaire. The selected participants were the managers' purchasing managers or equivalent position. Sampling is purposive and dened before the data collection commences. 105 questionnaires were sent back. Six questionnaires were rejected. 99 complete and clean questionnaires were analyzed. The questionnaires received within the rst 15 days and questionnaires received later were compared with a subset of 25 non-respondents to test of non-response bias. The non-signicance of these tests shows that non-response bias does not matter to the validity of ndings. When PLS is applied to re- ective constructs, it leads to inconsistency of PLS path coecient estimates. PLSc provides a correction for this estimate. The state of the art consistent PLS technique is utilized instead of traditional PLS. With regard to denition, knowledge for supplier has a large eect on supplier capabilities. The Important- Performance Matrix Analysis prioritizes the indicators and suggests to focus on knowledge ow about manufacturing processes, development expertise and marketing expertise. Results showed that supplier willingness for sharing condential knowledge and longtime relationship depends on development programs. Having a communication mechanism for each of partnership's specic contexts is suggested to maximize the knowledge ow. The main contribution of the paper is to survey the supplier knowledge in depth for maximizing the knowledge ow and study the eect on the segmentation criteria such as capability and willingness. Important-Performance Matrix Analysis is utilized to prioritize the indicators.