Abstract:

Introduction: The rapid expansion of artificial intelligence in digital marketing, especially in social networks, has increased the need to rethink models for explaining consumer behavior. Instagram, as one of the most important platforms for B2C business interactions, has provided a space in which businesses can analyze customer data, personalize content, predict behavioral patterns, and improve the effectiveness of marketing communications by using smart tools. Despite the development of these capabilities, there is still a need for a coherent theoretical framework that explains the mechanism of the impact of AI capabilities on consumer behavior on Instagram business pages. Therefore, the purpose of this study was to design and validate a model for explaining consumer behavior on Instagram B2C business pages using AI capabilities. The research model was developed based on the stimulus-organism-response theory in such a way that natural language processing, computer vision, and machine learning were considered as technological drivers, customer satisfaction and brand trust as cognitive-affective states of the organism, and purchase intention and brand loyalty as behavioral responses.

Methods: The present study was conducted using a sequential exploratory mixed design because the research problem required that a conceptual model be first extracted from the qualitative data and then its validity be examined in the quantitative stage. In the qualitative stage, data were collected through in-depth semi-structured interviews with 12 experts (until theoretical saturation was reached). Participants were first selected purposively and then by the snowball method. Qualitative data were analyzed using a systematic approach of grounded theory and through open, axial, and selective coding in ATLAS.ti 9 software. The result of this stage was the design of a conceptual model of the research and the determination of the position of each component in the stimulus-organism-response framework. In the quantitative stage, to validate the model designed in the qualitative section, a researcher-made questionnaire was developed based on the findings of the previous stage and theoretical literature and distributed among the managers and activists of B2C Instagram business pages. The data from 150 complete questionnaires were analyzed using structural equation modeling based on covariance in AMOS 24 software.

Findings: The findings of the qualitative stage led to the extraction of a three-layer model based on the stimulus-organism-response theory. In this model, natural language processing, computer vision, and machine learning were placed in the position of technological drivers because these capabilities allow businesses to analyze users' textual and visual content, identify patterns of preferences and interactions, and make marketing decisions with greater accuracy. At the organismal level, customer satisfaction and brand trust were identified as two key cognitive-affective states that mediate between technological capabilities and behavioral outcomes. At the response level, purchase intention and brand loyalty were determined as the most important consumer behavioral outcomes in the context of Instagram business pages. The results of the quantitative phase showed that all six hypothesized relationships in the model were significant and confirmed. Specifically, perceived AI capabilities had a positive and significant effect on customer satisfaction and brand trust, and both constructs, in turn, had direct and significant effects on purchase intention and brand loyalty. The model fit indices also confirmed the adequacy of the proposed structure; the final model was able to explain 68% of the variance in brand loyalty, 53% of the variance in purchase intention, 50% of the variance in customer satisfaction, and 42% of the variance in brand trust.

Conclusion: The findings showed that the three-layer stimulus-organism-response model offers a coherent framework for explaining how perceived AI capabilities influence consumer behavior on Instagram business pages. The study contributes theoretically by integrating AI, social media marketing, and consumer behavior literature, and practically by providing a decision-making framework for managers to evaluate AI in relation to satisfaction, trust, and behavioral outcomes. The results suggest that AI creates value when it improves customer understanding, user experience, trust, and loyalty beyond mere content production. However, because the model was validated using managers’ perceptions rather than actual user behavioral data, the findings should not be interpreted as direct evidence of improvements in sales or conversion. Future research should test the model using real behavioral data, longitudinal designs, and direct measures of algorithm performance.