Impact of Recommender Agents used in Online Retail on Customer Satisfaction and Purchase Intention

Abstract

Technology has changed how consumers live their lives and make purchases, which has forced businesses to adjust to a more competitive market environment. Artificial Intelligence (AI) is one example of a disruptive technology that has revolutionised corporate processes and offered creative ways to improve customer experiences. In contrast to conventional decisionmaking processes, this thesis examines the effects of AI-driven Recommendation Agents (RAs), on online retail, with a special emphasis on customer satisfaction and purchase intention. By evaluating customer data and forecasting their preferences, RAs use AI to customise the online purchasing experience, which enhances decision-making and reduces information overload. There is no empirical study on AI personalisation's substantial impact on customer behaviour, despite the industry's increased investment in this area. To close this gap, this study compares consumer responses when supported in making decisions by RAs vs traditional techniques. The main study topic looks at how customer satisfaction and purchase intention are affected by decision-making supported by RAs. Furthermore, the research explores how factors like algorithm aversion, perceived decision autonomy, and trust affect these results. This study attempts to advance knowledge of AI's revolutionary potential in marketing and its function in promoting improved customer interactions by offering insightful information about the strategic implications of RAs for online businesses. To accomplish the intended objective, quantitative analytic research using an online questionnaire with 150 replies was used to develop this thesis.