bought and sold) in a marketplace environment (Akolkar et al., 2012 Blasco et al., 2020 Menychtas et al., 2014). Sometimes, cloud services are made available and traded (i.e. The popularity of cloud services, driven by the pay-as-you-use billing model, underscores the commoditization of these IT services. Various IT services can be provided through the cloud to fulfil business objectives and usage scenarios based on traditional models. Traditionally, there are three primary cloud services categories classified as Software-, Platform-, and Infrastructure-as-a-Service denoted as SaaS, PaaS and IaaS, respectively (Odun-Ayo et al., 2020 Qaisar, 2012). The cloud’s advent has considerably impacted the computing landscape. Nowadays, cloud services have become a popular means to provide and consume IT services. The demonstration shows that our framework is a viable approach to rank and select SaaS in cloud e-marketplaces in a way that satisfactorily serves both the users of the platform and can potentially drive the business objectives of the e-marketplace. An illustrative case study of Customer-Relationship-Management-as-a-Service e-marketplace demonstrated the framework’s plausibility. 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. 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. There is a need to integrate mechanisms that handles the vagueness that characterises the human decision-making process when finding suitable services. 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. ![]() ![]() Most cloud service e-marketplaces incorporate basic features like search and billing but lack more sophisticated elements that optimise users’ experience.
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