Online shopping recommendation mechanism and its influence on consumer decisions and behaviors: A causal map approach Kun Chang Lee, Soonjae Kwon * School of Business Administration, Sungkyunkwan University, Myung Ryun 3-53, Chong No-Ku, Seoul 110-745, Republic of Korea Abstract Purpose of this paper: Online product recommendation mechanism (agents) are becoming increasingly available on websites to assist consumers with reducing information overload, provide advice in finding suitable products, and facilitate online consumer decision- making. Central of these services is consumers’ satisfaction with recommendation results. Traditional recommendation mechanism (TRM) is based content and/or collaborative filtering approach. However, the remaining problem concerning TRM is how to analyze the causal relationships between quantitative and qualitative factors, and investigate their impact on the central routes and peripheral routes through which both quantitative and qualitative factors can affect customer online shopping decisions. It is well known that qual- itative factors are hard to codify yet they have a significant effect on a customer’s decision-making process in the form of causal rela- tionships with quantitative factors. Thus, a new online recommendation mechanism is required that incorporates qualitative factors systematically with quantitative factors to analyze their combined influence on customers’ purchasing decision-making process. So, our study suggest that causal maps based recommendation mechanism (CMRM). Design/methodology/approach: ELM was applied to build hypotheses concerning how consumers’ decision satisfaction and online shopping behavior are affected by CMRM. Specifically, the performance of the proposed CMRM is analyzed empirically by garnering the experiment data from 250 qualified respondents who were asked to refer to the proposed CMRM before making purchasing decisions on mobile phones. Findings: Statistical results proved that the proposed CMRM could enhance consumers’ decision satisfaction, attitude towards the recommended products, as well as positive purchase intentions and actual purchase. Practical implications: CMRM can be easily implemented on the web, allowing target consumers to experience a real recommenda- tion process. And, a wide variety of qualitative factors that seem crucial to most consumers can be pre-defined through a survey, and incorporated into causal maps. Thus, such causal maps will improve the personalization effect on the target consumer’s purchase intentions. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Causal map; Traditional recommendation mechanism (TRM); Elaboration likelihood model (ELM) 1. Introduction Recently, information technology has been utilized to help companies maintain competitive advantage (Nissen & Sengupta, 2006). Data mining techniques with recom- mendation systems are a widely used information technol- ogy for extracting customer’s knowledge and further supporting marketing decisions (Balabanovic & Shoham, 1997). The buying patterns of individual customers and groups can be identified via analyzing customer data (Maes, Guttman, & Moukas, 1999), but also allows a company to develop one-to-one marketing strategies that provide individual marketing decisions for each customer (Lampel & Mintzberg, 1996; Murthi & Sarkar, 2003). Recommendation systems are technologies that assist busi- nesses to implement such strategies, and provide a type of mass customization that is becoming increasingly popular on the internet (Ansari, Essegaier, & Kohli, 2000; Lee & Lee, 2005). They have emerged in e-commerce applications 0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.08.109 * Corresponding author. Tel: +82 16 722 8757; fax: +82 2 760 0440. E-mail address: kwonsoonjae@naver.com (S. Kwon). www.elsevier.com/locate/eswa Available online at www.sciencedirect.com Expert Systems with Applications 35 (2008) 1567–1574 Expert Systems with Applications