Influence of Consumer Decisions by Recommendar system in fashion e-commerce website

The Study aims at providing deeper insights on influence of recommender systems and various personalized suggestions in Fashion e-commerce websites. It gives information regarding how recommender systems impact the purchasing decision of a consumer to formulate better systems with data. The data obtained from the consumers who have shopped various fashion products in e-commerce websites infers that the suggestions listed below when they browse a product based on brand, colour or patterns shows it is time-saving and is more likely to get products that they are looking for. The data collected and inferences made from customer on the driving factors for the purchase of products from the recommendations helps us to customize and create better recommender systems for improved personalized experience. A Quantitative Method of approach has been adopted for this study. A preliminary set of five-point Likert-style questions with several subscales was designed and distributed online to a sample of 165 participants. The results were then evaluated using exploratory factor analysis and multiple regression techniques to test the hypotheses. Factor Analysis of the survey underlined several parameters of an individual's perception towards their personal information like- accountability and trust issues, geopolitical and data sharing concerns, and cyber security cognizance. It also provided an empirical understanding of the consumer-perceived model for an e-commerce transaction. Based on the findings, better industry-standard e-security frameworks and models can be implemented towards the consumers' decision-making process. Knowledge from all these aspects can be applied to serve the research and practitioner groups.