Providing a Knowledge Sharing Model with Suppliers Based on White, Grey, and Black Box Using the Interpretive Structural Modeling (ISM) Approach

Document Type : Original Article

Authors
Seifallah Andayesh 1
Zahra Kianrad 2
1 Assistant Prof., Department of knowledge and Information Science, Persian Gulf University, Bushehr, Iran
2 PhD., Department of knowledge and Information Science, University of Tehran, Tehran, Iran
10.22034/aimj.2025.497908.1622
Abstract
Knowledge sharing is one of the critical topics in management science in the new millennium, playing a vital role in enhancing organizational performance. The suppliers of the National Iranian South Oil Company (NISOC) have been considered one of the most promising organizations for identifying key indicators of knowledge sharing with suppliers. This study aims to identify and rank the factors influencing knowledge sharing and to propose a knowledge-sharing model in these organizations. This research was conducted using a mixed-methods approach. Data collection tools included library studies, semi-structured interviews, and questionnaires. The study population consisted of engineers from the Sahand Forklift Manufacturing Company in Tabriz, who are suppliers to the National Iranian South Oil Company. A random sampling method was employed to select a sample of 28 participants. The findings revealed that through meta-synthesis, 86 codes, 12 indicators, and 3 main dimensions—knowledge transfer, knowledge translation, and knowledge transformation—were identified. In the knowledge transfer dimension, four indicators were recognized, including quality planning, meetings, production planning, and reports. The knowledge transformation dimension included indicators such as pricing, supplier capability, production guarantee, and model clarification. The knowledge translation dimension encompassed three indicators: technical specifications, functional specifications, and prototype approval.This study demonstrated that identifying key indicators in knowledge sharing could contribute to developing effective models in supply chain management.

Keywords

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  • Receive Date 05 January 2025
  • Revise Date 22 March 2025
  • Accept Date 07 April 2025