Vahid Mohseni Roudposhti

Faculty of Computing, Universiti Teknologi Malaysia (UTM), 

Malaysia

vahid.mohseni1987@gmail.com

A new model for customer purchase intention in e-commerce recommendation agents

Mehrbakhsh Nilashi

Faculty of Computing, Universiti Teknologi Malaysia (UTM), 

Malaysia 

nilashidotnet@hotmail.com

Abbas Mardani

Azman Hashim International Business School, 

Universiti Teknologi Malaysia (UTM), 

Malaysia

Dalia Streimikiene

Lithuanian Institute of Agrarian Economics, 

Vilnius, Lithuania

Sarminah Samad

CBA Research Centre, Department of Business Administration, Collage of Business and Administration, Princess Nourah Bint Abdulrahman University, Saudi Arabia 

sarminahsamad@hotmail.com

Othman Ibrahim

Faculty of Computing, Universiti Teknologi Malaysia (UTM), 

Malaysia

Abstract. Recommender systems were introduced to improve the online shopping experience by recommending appropriate products and services to customers according to their preferences. This research develops a new model by identifying the factors that influence customers’ purchase intention in recommender systems. The research model of this study was developed by reviewing the previous studies on web-based information systems, e-commerce and recommender systems. Quantitative data was collected from questionnaires conducted among the customers of online shopping websites. The questionnaires was adopted from the previous researches, and validated by the experts in the fields of information systems and recommender systems. Descriptive and hypotheses’ analyses were performed on the collected data using statistical analysis software and Partial Least Squares Structural Equation Modeling. 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. The developed model and the findings of this research will help e-commerce websites’ developers and e-commerce providers to enhance the recommender systems based on the factors that contribute to their quality.

Received: April, 2018

1st Revision: September, 2018

Accepted: November, 2018

DOI: 10.14254/2071-8330.2018/11-4/17

JEL Classification: E31, F31, Q4, Z32

Keywords: e-commerce, recommender systems, trust, satisfaction, purchase intention