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The access to external expertise and collaboration initiatives became an undeniable strategy for highly innovative sectors. Innovation intermediaries have gained a prominent role in this scenario, and crowdsourcing platforms reached notable results. However, identifying right competencies with demanded innovation is a critical issue. Research is needed in the domain of matchmaking mechanisms for identifying the necessary skills to fulfil different kinds of problems in order to build effective collaborative teams. This paper investigates different approaches adopted in crowdsourcing and main characteristics of teams operating in these contexts. Thus, a set of critical issues is highlighted and a structured team-building methodology for finding suitable solvers in crowdsourcing challenges is presented. Such method is grounded on natural language processing (NLP) and semantic ontologies fulfilling some of the identified criticalities. The description is supported by a case study conducted within a self-developed crowdsourcing platform.
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Montelisciani et al. (2014) studied this question.
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