This dissertation explores the significance of swarming concepts, particularly self-organization in human crowds, in harnessing collective intelligence through voluntary online participation. It provides practical strategies for managing large groups to enhance collective intelligence for scientific and business applications, including innovation development. With advancements in socio-technical systems like Web 2.0, individuals are leveraging the computing power of technology to collaboratively address complex problems, contributing human heuristics and insights. These diverse groups, often not composed of experts, tackle challenges previously unsolvable by individuals or smaller expert teams. The dissertation examines crowdsourcing and open innovation involving extensive Internet user participation, featuring an introductory section with a comprehensive literature review and nine research papers in the appendix. It advances the theory of collective intelligence by exploring distributed problem-solving characteristics in large groups across academic and business contexts. Additionally, it bridges disciplines by applying artificial intelligence techniques to phenomena typically studied by sociologists and business scholars. Key findings suggest that Internet-based human collaboration mirrors natural and artificial swarms, and specific management practices can enhance group output. The research culminates in a multi-agent syste
