The Influence of User Recommendations and System Recommendations on Consumer Purchase Intention

Desi Arisandi (), Dyah Erny Herwindiati and Cokki Cokki
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Desi Arisandi: Universitas Tarumanagara
Dyah Erny Herwindiati: Universitas Tarumanagara
Cokki Cokki: Universitas Tarumanagara

A chapter in Proceedings of the Thirteenth International Conference on Entrepreneurship and Business Management UNTAR 2024 (ICEBM 2024), 2025, pp 142-152 from Springer

Abstract: Abstract Online markets have become the leading media for buying and selling activities in the digital era. E-commerce is also a company’s progress in providing more information and product choices that customers want. With the increasing amount of information and choices users receive, users need more time to evaluate and determine which products to buy. To help make it easier for customers to find the products they need, e-commerce platforms are now equipped with recommendation systems. Recommendation systems can help or influence consumers by showing them products that may be relevant to them. Recommendation systems work by providing recommendations automatically based on the results of specific algorithms used on e-commerce platforms. This aims to in-crease consumer buying interest and ultimately boost sales. However, apart from recommendation systems that can produce recommendations automatically, in e-commerce platforms, there are also recommendations that stand out in e-commerce, namely word of mouth or electronic word of mouth (e-WOM), which are generated by users, generally in the form of consumer reviews. These user-generated recommendations are believed to have a significant influence on the intentions of a product among other users, highlighting the power of their influence. This paper discusses automatic recommendation systems and user recommendations for a product based on the customer’s perspective. The aim of this study is to examine which has a greater impact on consumers between automatic recommendation systems and user recommendations.

Keywords: e-commerce; system recommendation; user recommendation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-809-7_11

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