Designing a Data-Driven Model for Managing Media Processes in Online Home Appliance Marketplaces and Examining Its Effect on Improving Marketing Performance

Abstract

The present study was conducted with the aim of designing a data-driven model for managing media processes in online home appliance marketplaces and examining its effect on improving marketing performance. By identifying the relevant dimensions and components, the study sought to provide a scientific framework for enhancing the effectiveness of marketing activities in this industry. The research employed a mixed-methods approach using an exploratory sequential design. In the qualitative phase, semi-structured interviews were conducted with 22 managers of home appliance marketplaces, and the data were analyzed using the grounded theory method. Subsequently, a researcher-developed questionnaire derived from the qualitative model was distributed among 412 marketing managers and specialists. The validity and reliability of the instrument were confirmed using Cronbach’s alpha, composite reliability, convergent validity, and discriminant validity (Fornell–Larcker criterion). Due to the non-normal distribution of the data, the bootstrap method with 5,000 subsamples was employed within the framework of Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypotheses and examine direct, indirect, and moderating effects. The findings of the qualitative phase led to the identification of six main dimensions (data collection and integration, predictive analytics, dynamic personalization, real-time optimization, closed-loop feedback, and data governance) along with 24 components. In the quantitative phase, the results of structural equation modeling indicated that all six dimensions had significant effects on marketing performance (p < .001). The strongest effect was related to dynamic personalization (β = 0.28), whereas the weakest effect was associated with closed-loop feedback (β = 0.15). Indirect effects through data integration quality (β = 0.34) and decision-making speed (β = 0.29) were also significant. Digital maturity moderated the relationship between the model and marketing performance (β = 0.21), whereas organizational size did not demonstrate a significant moderating effect. The final model explained 67% of the variance in marketing performance, and Harman’s single-factor test indicated the absence of common method bias. The six-dimensional data-driven model developed in this study, with its emphasis on dynamic personalization and the enhancement of data integration quality and decision-making speed, can significantly improve the marketing performance of home appliance marketplaces.