Abstarct: Given the growing trend of e-commerce and online shopping, identifying the factors that influence customers' intention to purchase from online stores is of particular importance. This is because it can lead to improving the online shopping experience for customers, increasing their satisfaction, better designing sales and marketing processes by companies, aligning services better with customer needs, formulating more suitable marketing strategies, and ultimately increasing the success and profitability of companies active in this area. The goal of the current study is to identify the concepts and present a model of online purchase intention. 865 articles in the field of online purchase intention were evaluated, and ultimately 46 articles were selected and analyzed using the mexta-synthesis tool, which includes the seven-step approach of Sandelowski and Barroso (2006) for content analysis and the extraction of 7 categories, 20 dimensions, and 80 unique codes. Ultimately, 46 selected studies were analyzed using coding, and 7 categories were classified, Individual factors, technical and security factors, motivational factors, marketing factors, product-related factors, supportive and relational factors, and social factors. In order to understand the importance and priority of each of the identified codes, the Shannon Entropy method was utilized, revealing that the codes of "customer trust, perceived risk, perceived ease of use and perceived utility" have the highest importance in influencing customer online purchase intention, ranking first to third respectively. The codes of "product price and perceived enjoyment of online shopping" were ranked fourth.