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| Hi-index | 12.05 |
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 qualitative factors are hard to codify yet they have a significant effect on a customer's decision-making process in the form of causal relationships 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 recommendation 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.