Extended Abstract

Objectives: In recent years, repurchase intention—as a key indicator of customer loyalty—has garnered significant attention in marketing and management literature, given that retaining existing customers is far more cost-effective and profitable than acquiring new ones. The rapid expansion of e-commerce and digital platforms has driven organizations to adopt modern technologies, particularly artificial intelligence (AI), to establish a sustainable competitive advantage. With its capacity to analyze big data, predict consumer behavior, and personalize services, AI plays a pivotal role in transforming purchasing patterns and boosting customers' willingness to make repeat purchases. However, most prior research has been conducted in developed markets; the dynamics of these relationships in developing markets like Iran—which face infrastructure and technological constraints—have received less attention. Consequently, this study focuses on the Digikala platform to examine the impact of AI on customer repurchase intention, considering the mediating roles of customer agility, customer experience, and customer satisfaction.



Methods: This research is applied in terms of purpose and descriptive-survey in terms of method. This research was conducted among the users and buyers of the Digi-Kala application, which is an online marketplace and store, in the autumn and winter of 1403. Therefore, it is known as a cross-sectional research. Sampling was done by accessible method and the questionnaire was distributed online. Due to the unlimited size of the statistical population, the adequacy of the sample size was checked using statistical power analysis in G*Power software. The results of the analysis showed that to achieve the desired statistical power in the relationship test of the proposed model, the minimum required sample size is less than 284 people. Based on this, among the 299 questionnaires collected, 285 complete and usable questionnaires were selected for statistical analysis. Data were collected through a standard questionnaire with a five-point Likert scale. Structural equation modeling with partial least squares approach (PLS-SEM) and Smart-PLS software were used for data analysis. The validity of the instrument was confirmed by confirmatory factor analysis and divergent validity (Fornell & Larcker) and reliability with Cronbach's alpha and composite reliability.

Findings: The results of the structural model analysis indicated that all seven research hypotheses were supported at a 95% confidence level. Artificial intelligence has a positive and significant impact on customer agility, customer experience, and customer satisfaction; these findings align with the results of previous studies. Furthermore, customer agility demonstrated a positive and significant influence on customer experience and satisfaction. Customer experience also significantly enhances customer satisfaction, a finding consistent with the work of Yin and Qiu (2021). Finally, customer satisfaction exerted the strongest influence on repurchase intention—a result compatible with classic models of repurchase intention.

Conclusion: By examining the impact of artificial intelligence on customer repurchase intention on the Digikala platform—mediated by customer agility, customer experience, and customer satisfaction—this research offers a comprehensive framework for understanding consumer behavior in e-commerce. The findings indicate that artificial intelligence exerts a positive and significant effect on repurchase intention through the mediation of these three variables. Grounded in Stimulus-Organism-Response (S-O-R) theory, artificial intelligence acts as an environmental stimulus that influences customers' cognitive and emotional states by personalizing services, accelerating information processing, reducing uncertainty, and facilitating decision-making. These states manifest as customer agility, customer experience, and customer satisfaction, ultimately leading to the formation of a behavioral response: repurchase intention. Thus, the results of the present study support the theoretical logic of the S-O-R framework, demonstrating that the impact of artificial intelligence on customer behavior is transmitted primarily through cognitive and emotional mechanisms rather than merely through the technology's technical features.