Using the collective intelligence of sports fans to improve professional football league customer service

This research investigates sports fans´ emotions and feelings toward a professional football game as a unique event offering related services, crowd interactions, performances, incidences, and outcomes. Critical incident surveys (open-ended, written text dialogues) were used to identify the most salient positive and negative phrases used to express football fans´ game experiences. The key term frequencies were first analyzed using text and data mining techniques to form the ontological base (with a focus on emotions, feeling, and events); second by building a theory based ontology tree structure; and third by experts abstracting the dialogues into consistent key terms and phases related to a formal ontology structure. The collective intelligence of 37 Green Bay Packers fans´ emotions, feelings, event related incidences, and outcomes were mapped to the derived ontology schema which in turn was re-submitted to the text mining algorithms. The ontology based, collective findings depicts the Green Bay Packers fans´ deep opinions. Given the structured text data results, clusters form a theoretical base for creating initial causal models for future verification. The initial research provides a new means for effectively improving professional sports services, particularly in defining the interrelation of feelings, emotions, events, and the related object properties.