The Impact of Big Data–Driven Dynamic Pricing Strategies on Purchase Behavior, Customer Satisfaction, and Repurchase Intention in Iranian Online Retail Stores Considering the Moderating Role of Customer Trust in the Platform - Khanali, Alireza; Moeini , Sina (Business, Marketing, and Finance Open, 2026)
Abstract: The present study aimed to examine the impact of big data–driven dynamic pricing strategies on purchase behavior, customer satisfaction, and repurchase intention in Iranian online retail stores while investigating the moderating role of customer trust in the platform. This research employed an applied quantitative approach using a correlational design based on structural equation modeling. The statistical population consisted of customers of major online retail platforms operating in Tehran, Iran. A sample of 384 online shoppers was selected using stratified convenience sampling. Data were collected through a standardized questionnaire measuring perceptions of dynamic pricing strategies, purchase behavior, customer satisfaction, repurchase intention, and customer trust in the platform using a five-point Likert scale. Content validity was confirmed by academic experts, and reliability indices exceeded acceptable thresholds. Data analysis was conducted using SPSS 27 for preliminary analysis and SmartPLS 4 for measurement and structural model evaluation, including moderation analysis through bootstrapping procedures. The results indicated that big data–driven dynamic pricing strategies had a significant positive effect on purchase behavior, customer satisfaction, and repurchase intention. Purchase behavior and customer satisfaction were found to significantly predict repurchase intention, demonstrating their mediating roles in translating pricing strategies into loyalty outcomes. Furthermore, customer trust in the platform significantly moderated the relationships between dynamic pricing strategies and all dependent variables, strengthening the effects of pricing strategies on behavioral and attitudinal outcomes. The structural model demonstrated strong explanatory power, confirming that trust-enhanced dynamic pricing contributes substantially to customer engagement and retention in online retail environments. The findings demonstrate that dynamic pricing supported by big data analytics represents a strategic mechanism for improving customer behavioral responses and long-term loyalty in online retail platforms. However, the effectiveness of algorithmic pricing depends heavily on customer trust, which amplifies positive consumer perceptions and acceptance of adaptive pricing practices. Integrating technological capability with trust-building strategies is therefore essential for sustainable competitive advantage in digital retail ecosystems.