Using the Collective Intelligence of Sports Fans to Improve Professional Football League Customer Service

Abstract

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.