Designing an interpretive structural model of artificial intelligence applications in the international marketing process (food industry case study)

Abstract

Artificial intelligence, with its transformation in data analysis and content personalization, has become a strategic tool in international marketing and is rapidly changing traditional marketing approaches. The main objective of this study was to identify the applications of artificial intelligence in marketing based on the stages of the marketing process and then prioritize them by considering their mutual influence and impact on each other and creating a hierarchy. The research literature was used to identify relevant factors. For this purpose, a mixed research method (qualitative-quantitative) was used. In the qualitative part, the emphasis is on identifying factors, and in the quantitative part, their leveling and ranking are discussed. The paradigm of this research is interpretive, its type is applied, and its reasoning is a combination of deductive and inductive. The research strategy is also survey. The statistical population of this study consists of experts from the best brands in the Iranian food industry. From this population, 15 samples were selected and analyzed. To analyze the data and the causal relationships between the variables affecting the application of artificial intelligence in the international marketing process of the food industry, the structural-interpretive model (ISM) was used. The data were collected based on a special questionnaire and analyzed using the ISM method. The results of the data analysis, which included 16 selected applications, led to the creation of six hierarchical levels. In this hierarchy, the sixth level, which includes the criterion of big data analysis, is the most influential level, and the first level, which includes customer satisfaction assessment, sales forecasting, etc., is influenced by other levels (the most influential). Also, the criterion of market trend prediction at the third level is known as the turning point of this model. Using this model, a deeper understanding of the mutual influence of these components can be achieved and, as a result, more effective and efficient strategies can be designed to improve performance in the food industry. Given the increasing importance of artificial intelligence in the field of marketing and its key role in increasing the productivity and competitiveness of companies in international markets, the results of this research can be used as a valuable guide for managers and decision-makers in various industries, especially the food industry.