Research Background: With the rapid expansion of digital technologies and the growing volume of market data, big data and marketing analytics have emerged as strategic resources for enhancing marketing effectiveness and improving new product success. However, the causal mechanisms through which big data contributes to new product performance, particularly through analytical and managerial capabilities, remain insufficiently understood.
ResearchObjective: This study aims to develop and empirically test an integrated conceptual model examining the impact of big data on new product performance, with particular emphasis on the sequential mediating roles of marketing analytics, customer relationship management (CRM), marketing decision-making, and product development management in food industry companies located in Tehran Province, Iran.
Methodology: This research is applied in terms of purpose and adopts a descriptive-survey, cross-sectional design. The statistical population consisted of companies operating in the food industry in Tehran Province. The minimum sample size was estimated at 263 firms using G*Power software. Ultimately, 271 valid questionnaires were collected through a non-probability convenience sampling method. Data were gathered using a standardized 45-item questionnaire and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3 software.
Findings: The results indicate that the use of big data has a positive and significant effect on marketing analytics; however, its direct effect on customer relationship management is not statistically significant. In contrast, marketing analytics exerts positive and significant effects on customer relationship management, marketing decision-making, and product development management. Furthermore, customer relationship management positively and significantly influences both marketing decision-making and new product performance. The findings also reveal that marketing decision-making is indirectly associated with new product performance through its effect on product development management. In addition, product development management demonstrates a positive and significant impact on new product performance.
Conclusion: Investment in big data alone does not necessarily lead to direct improvements in customer relationship management or new product performance. Rather, the realization of data-driven value requires the enhancement of marketing analytics capabilities and the institutionalization of data-driven decision-making processes. These capabilities improve customer relationship management and product development management, ultimately leading to superior new product performance.
Research Originality/Value: Drawing upon the dynamic capabilities perspective, this study contributes to the literature in three important ways. First, it proposes a comprehensive causal model explaining both direct and indirect pathways through which big data influences new product performance. Second, it empirically examines the sequential mediating roles of key organizational capabilities, including customer relationship management, marketing decision-making, and product development management. Third, it provides context-specific evidence from the food industry in Tehran Province—an underexplored research setting—offering practical insights for prioritizing investments in analytical infrastructures and organizational processes.