Most cloud service e-marketplaces incorporate basic features like search and billing but lack more sophisticated elements that optimise users’ experience.
The cognitive demands of searching for and evaluating multiple cloud SaaS along multiple QoS criteria can be overwhelming, giving rise to what Alvin Toffler called
choice overload. There is a need to integrate mechanisms that handles the vagueness that characterises the human decision-making process when finding suitable
services. The objective of this paper is to reduce cognitive overload during cloud
service selection in e-marketplaces by employing low cognitive demanding tools
that leverage the dynamics of human expressions. We proposed a QoS-aware SaaS ranking and selection framework that integrates fuzzy theory and information
visualisation for optimal decision-making in cloud e-marketplaces. An illustrative case study of Customer-Relationship-Management-as-a-Service e-marketplace
demonstrated the framework’s plausibility. The demonstration shows that our framework is a viable approach to rank and select SaaS in cloud e-marketplaces ina way that satisfactorily serves both the users of the platform and can potentially drive the business objectives of the e-marketplace
