In the online shopping context, brands aim to achieve a high level of profit by providing better customer satisfaction by using various artificial intelligence tools. They try creating a satisfactory customer experience by creating a system that provides never-ending customer support by using dialog-based chatbots, especially in the field of customer service. However, there is a lack of research investigating the impact of business and customer-based chatbot activities together on online purchase intention and the intention to reuse chatbots. This research considers the use of chatbots as a marketing tool from both customer and business perspectives and aims to determine the factors that affect the customers’ intention to purchase online and reuse chatbots. Accordingly, the impact on customer satisfaction with chatbot usage, which is based on chatbots’ communication quality and customers’ motivations to use chatbots, on online purchase intention and intention to reuse chatbots was examined. Through an online questionnaire with two hundred and ten participants, employing structural equation modeling, we revealed that customer satisfaction with chatbot usage has a greater impact on the intention to reuse chatbots than on online purchase intentions. In addition, chatbot communication quality has a greater impact on customer satisfaction with chatbot usage than customers’ motivation to use chatbots. To solidify these findings, confirmatory factor analysis, along with reliability and validity assessments, were implemented within the analytical framework to provide robust support for the study’s hypotheses. These findings not only provide empirical evidence and implications for companies in online shopping but also extend the understanding of AI tools in marketing, highlighting their subtle impact on consumer decision-making in the dynamic digital marketplace.

This study aims to make inferences about company performance by analyzing the financial performance of Amazon. com, Inc. over a period of fifteen years (2005-2019) using selected evaluation criteria in accordance with Multi-Criteria Decision-Making (MCDM) methods. For this purpose, CRiteria Importance Through Intercriteria Correlation (CRITIC) was used for weighting the evaluation criteria, and COmplex PRoportional ASsessment (COPRAS), Additive Ratio ASsessment (ARAS), Simple Additive Weighting (SAW), and BORDA Count and Copeland methods were used to rank financial performance by years. From the results obtained through the CRITIC method, it was found that the most crucial evaluation criterion was the debt-to-equity (DER) ratio, and the least important was the return-on-assets (ROA) ratio. The BORDA Count and Copeland methods were used to obtain an integrative and single ranking series due to varying results of the COPRAS, ARAS, SAW methods by years. According to the scores obtained using BORDA and Copeland methods 2005 was the best year and 2014 was the least successful year in terms of financial performance. During these years, developments experienced in the company were discussed, and an attempt was made to examine the reasons behind the financial performance.