Modeling Brand Resilience in Iran's Handwoven Carpet Industry Using Background Knowledge and Data Mining in Critical Conditions

Brand resilience knowledge helps companies maintain customer trust and strengthen relationships through proper planning and strategies. This research was conducted to model brand resilience in Iran's handwoven carpet industry using background knowledge and data mining in critical conditions. In brand resilience, knowledge analysis is considered highly significant for identifying key factors and effective patterns. This mixed research has been done based on qualitative data techniques in NVIVO software and quantitative data mining method in MATLAB software. 12 people were selected purposefully from carpet industry experts. Interviews were analyzed, coded, according to Strauss and Corbin method, and compared with the data mining method of the trained model and the MLP method. Based on the proposed model, 6 categories, 15 core codes, and 41 primary codes were identified. The proposed model could predict 98% brand resilience in crisis conditions. This model can help brands to maintain their business interests and implement appropriate strategies for active development, internal resistance, creative support, and production under sanctions. Furthermore, this model can help brands strengthen their capabilities and brand value, and identity in critical situations.