Designing a Digital Marketing Model with an Emphasis on Artificial Intelligence in the Insurance Industry - Seraj, Neda; Rousta, Alireza; Asayesh, Farzad; Askari , Ahmad (Digital Transformation and Administration Innovation, 2025)
The present study aims to design a digital marketing model with an emphasis on artificial intelligence in the insurance industry. This research falls within the category of mixed-methods studies (qualitative and quantitative). The qualitative strategy employed is the grounded theory method, and the quantitative strategy is survey-based. The qualitative population includes 20 experts and managers from the insurance industry, while the quantitative section encompasses all managers and employees in the insurance sector. In the quantitative part, a non-random convenience sampling method was used, and the Cochran formula was applied to determine the sample size, resulting in the collection and analysis of 384 completed questionnaires. To assess the validity of the questionnaire, confirmatory factor analysis (CFA) was employed, and structural equation modeling (SEM) was used to test the research questions. In the qualitative phase, the data were analyzed using MAXQDA software and categorized into causal conditions, core phenomena, intervening conditions, contextual conditions, strategies, and outcomes. In the quantitative phase, using SmartPLS 3 software, two models—standard and significance models—were constructed. The path coefficient between causal conditions and the core phenomenon was 0.598 with a significance value of 7.644. The path coefficient between the core phenomenon and strategic factors was 0.635 with a significance value of 7.640. Additionally, the path coefficient between contextual conditions and strategic factors was 0.651 with a significance value of 8.021, and the coefficient between intervening conditions and strategic factors was 0.721 with a significance value of 9.458. Finally, the path coefficient between strategic factors and outcomes was 0.624 with a significance value of 8.450. These findings indicate that the components of the proposed executive model significantly and positively influence one another, and the overall model demonstrates a desirable level of fit.