KMAN Publication Inc. AI and Tech in Behavioral and Social Sciences 4 Serial Number 15 2026 07 01 Designing a Model of Factors Affecting Online Sales in AI Tool-Based Online Retail Stores Designing a Model of Factors Affecting Online Sales in AI Tool-Based Online Retail Stores 1 13 EN Journal Article 2026 03 10 <p>Artificial intelligence (AI) has become a major technological driver of online retailing and can influence sales through data-driven, automated, and personalized decision-support tools. The present study aimed to develop a localized model of factors affecting online sales in AI tool-based online retail stores. The research was conducted using a mixed-methods qualitative-quantitative approach with an exploratory sequential mixed-methods design. In the qualitative phase, thematic analysis based on the King and Horrocks approach was used to identify the dimensions and components of the conceptual model. Qualitative data were collected through semi-structured interviews with 15 experts in e-commerce, artificial intelligence, and digital marketing, selected through purposive and snowball sampling. Data collection and analysis continued until thematic saturation was achieved. In the quantitative phase, a survey method was used to empirically test the extracted model. The statistical population consisted of consumers of online retail stores in Iran who had prior experience with AI-enabled online shopping. A stratified sampling strategy was used, and the sample size was determined as 384 respondents based on Cochran’s formula. Data were gathered through a researcher-made questionnaire and analyzed by structural equation modeling using the partial least squares approach (PLS-SEM). The qualitative findings showed that data-driven analysis, specialized human capital, and intelligent capabilities such as personalization and prediction emerged as core themes in explaining AI-enabled online sales, and that their effects are realized through interaction with organizational and cultural factors. By presenting a comprehensive localized conceptual framework, the qualitative phase strengthens the theoretical contribution of the study by identifying and integrating contextual factors, intelligent mechanisms, and outcomes of AI-enabled online sales. The quantitative results statistically supported the relationships among the model constructs and the significance of the principal paths. These results demonstrate the critical role of data-driven decision-making and intelligent capabilities in improving customer experience, trust, and online sales performance. By testing an integrated structural model, the findings also advance the empirical contribution of the study through the quantitative validation of an AI-enabled online sales model and the provision of actionable evidence for managerial decision-making.</p> https://www.journals.kmanpub.com/index.php/aitechbesosci/article/download/5604/10626