![]() The study involved reviewing literature resources comprising of scientific research reports, journals, theses, and conference papers. This paper embarked on using desk-research to address the following objectives: to analyze the technological concepts of big data analytics to establish application and uses of big data and its effects on business decisions to examine the challenges of big data in decision making by smart firms. Today, big data has captured an extensive interest from researchers and smart firms. At the end, the study presents theoretical and practical implications along with the scope for future research.īig data designates huge data-sets which demand unique advanced tools and techniques to collect, analyze, control and envision. The study highlights how cases from emerging markets can be useful for other firms and ecosystems, ranging from e-commerce to manufacturing, that employ large number of decision makers with the aim of creating a circular economy. The findings of our study indicate a four-step design (enabling technologies, business significance, deriving value, and circular goals) to implement the 10R's of the circular economy through emerging technologies such as big data and related mobile applications along with cloud-based platforms. In these cases, different linear economy problems are addressed that further utilizes the integration of big data and large-scale group decision making by stakeholders to achieve circularity. This research is designed based on five case studies from emerging markets with a focus on circular models to propose a framework for large scale decision making. This study is focused on presenting a unique landscape for big data-enabled circular economy that involves stakeholders as important decision makers. This article focuses on this mechanism in the specific context of crisis management. Besides, this metamodel can be extended for some precise application domains. This metamodel (describing collaborative situation between organizations) is structured according to four complementary dimensions: the context (social, physical and geographical environment), the partners (the involved organizations, their capabilities resources and relations), the objectives (the aims of the network, the goals to be the achieved and the risks to avoid, etc.) and the behaviour (the collaborative processes to be implemented by the partners to achieve the objectives in the considered context). This article presents a model‐based AI framework for describing collaborative situations and the associated formal metamodel dedicated to be instantiated to characterize collaborative situations in a very wide range of application domains. ![]() Formal descriptions of collaborative situations, that could be used, transformed, computed and exploited would be of great benefit for the quality of such collaborative networks. However, there is a lack regarding formal approaches dedicated to characterize collaborative networks of organizations. responders in a crisis situation) are key activities in nowadays ecosystems. Identifying, designing, deploying and maintaining accurate collaborative networks of organizations (e.g.
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