Designing a Model of Factors Influencing Online Sales in AI Tool–Based Online Retail Stores
Keywords:
Online sales; online retail stores; artificial intelligence; AI toolsAbstract
Today, artificial intelligence (AI), as one of the most transformative technologies, plays a pivotal role in shaping and explaining the factors influencing sales in online retail stores through the deployment of intelligent tools. The present study aims to develop a localized model of factors affecting online sales in AI tool–based online retail stores. This research was conducted using a mixed-methods (qualitative–quantitative) approach with an exploratory–explanatory design.
In the qualitative phase, thematic analysis following the Horrocks & King approach was employed 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 who were selected through purposive and snowball sampling. Data collection and analysis continued until theoretical saturation was achieved.
In the quantitative phase, a survey method was utilized to empirically test the extracted model. The statistical population comprised consumers of online retail stores in Iran who had prior experience with AI-enabled online shopping. Stratified random sampling was employed, and the sample size was determined at 384 respondents based on Cochran’s formula. Data were gathered using a researcher-developed questionnaire and analyzed through structural equation modeling using the partial least squares approach (PLS-SEM).
The qualitative findings revealed that data-driven analytics, specialized human capital, and intelligent capabilities—such as personalization and prediction—emerged as core themes in explaining online sales in AI tool–based online retail stores, and their effects are manifested through interaction with organizational and cultural factors. By presenting a comprehensive and localized conceptual framework, the qualitative phase strengthens the study’s theoretical contribution by identifying and integrating contextual factors, intelligent mechanisms, and the outcomes of AI-enabled online sales.
The quantitative results empirically confirmed the relationships among the model constructs and the significance of the principal paths, demonstrating the critical role of data-driven decision-making and intelligent capabilities in enhancing customer experience, trust, and online sales performance. Through testing an integrated structural model, these findings advance the empirical contribution of the study by quantitatively validating the AI-enabled online sales model and providing actionable evidence for managerial decision-making.
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