Designing an Interpretive Structural Model of Artificial Intelligence Applications in the International Marketing Process (Case Study: Food Industry)
Keywords:
artificial intelligence, international marketing, interpretive structural modeling (ISM)Abstract
Artificial intelligence (AI), by transforming data analytics and content personalization, has become a strategic tool in international marketing, rapidly reshaping traditional marketing approaches. The primary objective of this study is to identify AI applications in marketing based on the stages of the marketing process and then to prioritize them by considering their reciprocal influences and establishing a hierarchical structure. To identify relevant factors, the study draws upon the existing research literature. For this purpose, a mixed-methods approach (qualitative–quantitative) is employed. In the qualitative phase, the focus is on identifying factors, while in the quantitative phase, the emphasis is on their stratification and ranking. The research paradigm is interpretive, the study is applied in nature, and its reasoning combines deductive and inductive logic. The research strategy follows a survey design. The statistical population consists of experts from leading brands in Iran’s food industry. A sample of 15 experts was selected and analyzed. To examine data and the causal relationships among variables influencing the application of AI in the international marketing process of the food industry, the Interpretive Structural Modeling (ISM) method was utilized. Data were collected using a specialized questionnaire and analyzed with the ISM technique. The findings from the data analysis, which include 16 selected AI applications, resulted in the creation of a six-level hierarchical structure. In this structure, the sixth level, encompassing big data analytics, emerged as the most influential, while the first level, including customer satisfaction evaluation, sales forecasting, and similar metrics, was identified as the most dependent and impacted. Additionally, the market trend forecasting criterion, positioned at the third level, was recognized as a pivotal turning point within the model. Using this model enables a deeper understanding of the interrelationships among these components, facilitating the design of more effective and efficient strategies to enhance performance in the food industry. Considering the growing significance of AI in marketing and its pivotal role in improving productivity and competitiveness for companies in international markets, the outcomes of this study can serve as a valuable guide for managers and decision-makers across various industries, particularly the food sector.
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