Designing an Artificial Intelligence-Based Customer Relationship Management Model to Achieve Competitive Advantage in the Food Industry

The present study aims to design a customer relationship management (CRM) model based on artificial intelligence (AI) to achieve competitive advantage in the food industry. The research method is fundamental in terms of purpose. Data collection was conducted using the grounded theory approach, focusing on in-depth and semi-structured interviews. In this study, a systematic review of theoretical foundations and previous research was performed to extract relevant concepts and initial contextual factors related to the topic. Subsequently, in-depth and semi-structured interviews were conducted, and expert opinions were collected. The interviews with experts were meticulously analyzed. The statistical population of the study includes experts in the food industry, all managers and specialists from the Ministry of Health, Treatment, and Medical Education (Food and Drug Administration), managers and specialists from the Cultural Heritage, Handicrafts, and Tourism Organization, and private sector professionals active in the food industry. The sampling method employed was snowball sampling. The researcher achieved theoretical saturation after conducting 13 interviews. In fact, a few interviews before the 13th one, the researcher noticed that no new insights were being generated. However, to ensure the reliability of the findings and to review and refine the results, the interviews continued until the 15th interview. In each interview, concepts were coded using the grounded theory approach. This study applied grounded theory through three main stages: open coding, axial coding, and selective coding. The results indicated that in the open coding stage, more than 49 subcategories were identified. In the axial coding stage, a more precise examination of the identified codes was conducted, revealing that although many codes were expressed using different terms and interpretations, they conveyed the same meaning based on the research questions posed in this study. Further analysis of open codes and the aggregation and classification of related codes led to the identification of 10 main categories.