Knowledge based management support systems
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
A psychological approach to decision support systems
Management Science
Assessing IT usage: the role of prior experience
MIS Quarterly
Fab: content-based, collaborative recommendation
Communications of the ACM
Communications of the ACM
Supporting business process redesign using cognitive maps
Decision Support Systems
Understanding software operations support expertise: a revealed causal mapping approach
MIS Quarterly - Special issue on Intensive research in information systems: using qualitative, interpretive, and case methods to study information technology—third installment
Building an agent-mediated electronic commerce system with decision analysis features
Decision Support Systems - Decision-making and E-commerce systems
Sales Configuration in Business Processes
IEEE Intelligent Systems
Adaptive Assistants for Customized E-Shopping
IEEE Intelligent Systems
Developing and Validating Trust Measures for e-Commerce: An Integrative Typology
Information Systems Research
The Role of the Management Sciences in Research on Personalization
Management Science
Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective
Information Systems Research
The Use of Cognitive Maps and Case-Based Reasoning for B2B Negotiation
Journal of Management Information Systems
A cognitive map-driven avatar design recommendation DSS and its empirical validity
Decision Support Systems
An effective customization procedure with configurable standard models
Decision Support Systems
A contextual fuzzy cognitive map framework for geographic information systems
IEEE Transactions on Fuzzy Systems
Contextual fuzzy cognitive map for decision support in geographic information systems
IEEE Transactions on Fuzzy Systems
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The use of the Internet in the daily activities of individuals and firms has become entrenched, and online shopping has become commonplace. However, debates about how online shopping recommendation mechanisms (OREMs) should be designed have not been completely resolved. The challenge with traditional online shopping recommendation mechanisms (TR-OREMs) is that they focus too much on quantitative factors. Thus, they ignore causal interrelationships with qualitative factors that are believed to significantly impact quantitative factors. Considering only quantitative factors and ignoring qualitative ones likely distorts the final recommendation results. Another problem with TR-OREMs is that they ignore the perceived psychological reactance of consumers against the recommended products. Such consumer reactance may be reduced when the causal interrelationships among all the quantitative and qualitative factors are analyzed and incorporated properly into the OREM. To overcome these problems, we propose a causal map - online shopping recommendation mechanisms (CM-OREMs) based on a causal map. We analyzed possible causal relationships among quantitative and qualitative factors and incorporated them in the recommendation process to reduce consumer reactance against the recommendation results. Furthermore, an elaboration likelihood model (ELM) was used to build hypotheses about how the online shopping behavior of consumers is affected by OREMs based on the proposed causal map. Specifically, the performance of the proposed OREM was empirically analyzed by gathering experiment data from qualified respondents who were asked to refer to the proposed OREM before making purchasing decisions via mobile phones. Statistical results showed that the proposed OREMs could enhance consumer decision satisfaction, decision confidence, and attitude toward the recommended products. It could also positively affect consumer purchasing intentions. The OREM had a greater effect on the high-reactance group of participants than on the low-reactance group as well as on a high-involvement product versus a low-involvement product.