An Ontology-based Co-creation Enhancing System for Idea Recommendation in an Online Community

Companies have been collecting innovative ideas that can help them to develop new products and services through co-creation with their customers. As more customers participate in suggesting ideas, companies are likely to acquire more valuable ones. At the same time, however, some fundamental problems occur such as managing and selecting useful ideas from a large number of collected ideas. Semantic web mining techniques allow us to manage a large number of customers' ideas effectively, extract meaningful information from the ideas, and provide useful information for idea selection. In order to cope with such problems and enhance the value of co-creation, we propose an ontology-based co-creation enhancing system (OnCES) developed using semantic web mining techniques. To this end, we 1) defined a co-creation idea ontology (CCIO) that includes common concepts related to customers' ideas from MyStarbucksIdea.com, their attributes, and relationships between them; 2) transformed the customers' ideas into semantic data in RDF format according to the CCIO; 3) conducted text mining to extract new knowledge from the ideas such as keywords, the number of positive words, the number of negative words, and the sentiment score; and 4) built prediction models using keywords and other features such as those about customer and idea in order to predict the adoptability of each idea. The results of text mining and prediction analysis were also added to the semantic data. We implemented the OnCES system, which provides useful services such as idea navigation, idea recommendation, semantic information retrieval, and idea clustering, utilizing the stored semantic data while saving the time and effort required to process a huge number of customers' ideas.