Abstract
A new model for customer purchase intention in e-commerce recommendation agents
Highlights
The results reveal that Accuracy, Diversity, Ease of Use, Recommendation Quality, Satisfaction, Trust and Usefulness have significant influence on customers’ intention to purchase a product recommended by the recommender systems
Recommender systems are defined as techniques and software tools which offer products in online shopping websites to users according to their preferences (Pursel et al, 2016)
Motivation for this study is that there has been little research assessing the effect of customers’ objective opinions and satisfaction in the presence of recommender system’s usage. This leads us to the following research questions: What are the factors influencing customer purchase intention in e-commerce recommender systems? What are the relationships among the factors behind customer purchase intention in e-commerce recommender systems? What is the suitable model of customer purchase intention in e-commerce recommender systems?
Summary
Recommender systems are defined as techniques and software tools which offer products in online shopping websites to users according to their preferences (Pursel et al, 2016) They offer recommendations on different procedures of decision-making. As a decision-making system, recommender systems have been used by numerous online commerce websites, such as Amazon.com (Lu et al, 2015) They have brought benefits both to service providers and users. Already 10% of these online customers purchase items in the e-commerce website every day (Baier & Stüber, 2010) This survey showed the significance of online shopping among German Internet users and the role of recommendation systems in their purchase decision-making.
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