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Abstract
Socially intelligent computing (SIC) is a broad term that describes large-scale systems that comprise heterogeneous and distributed computing elements (CEs), whether human-based computing element (HBCE) or machine-based computing element (MBCE). Any CE has its own individual characteristics, objectives, and actions; the aim is to plan collective intelligence systems. Our society increasingly depends on such systems, in which collections of heterogeneous CEs are tightly entangled with human and social structures. Therefore, one of the reasons behind the limited use of planned collective intelligence is the lack of available MBCEs for doing so and the little understanding of dependencies among different multifaceted problems. Crowd applications (CAs) are emerging trends that provide opportunities to overcome the lack of MBCEs. However, CAs are often analyzed following a somewhat disjointed approach rather than being assessed from an overarching design and management perspectives that can cover different multifaceted problems. This gap leaves room for revising the current approaches, identifying multidisciplinary approaches to multifaceted problems. In this thesis, the crowd-centered design approach (CCDA) is proposed to fill that gap. It captures crowds{u2019} perceptions to explore the characteristics, crowdsourcing jobs, and the human-machine work environment that collectively drive the crowd to participate in open source work, in open environment, guided by the job characteristic and work value perspectives