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

Abstract

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 cocreation 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.

The social side of innovation: peer influence in online brand communities

“…The findings help enrich the research on factors affecting idea quality in OBCs. While previous studies on idea quality in OBCs have focused mainly on individual ideators' characteristics, idea characteristics and community policy guarantees (Aksoy et al, 2021;Bayus, 2013;Camacho et al, 2019;Li et al, 2016;Ma et al, 2019;Yoo et al, 2015), our study recognizes that OBCs are social networks where consumer interactions are equally if not more important for idea generation (Gowing, 2019;Resch and Kock, 2021), and such interactions play a crucial role in the sustainable development of such social networks. Although some studies have examined the impact of consumer feedback on innovation behavior and idea implementation in OBCs (Yang and Han, 2021;Zhu et al, 2019), few have explored how peer influence affects idea quality in OBCs.…”

“…, 2016; Ma et al. , 2019; Yoo et al. , 2015), our study recognizes that OBCs are social networks where consumer interactions are equally if not more important for idea generation (Gowing, 2019; Resch and Kock, 2021), and such interactions play a crucial role in the sustainable development of such social networks.…”

“…Other scholars have examined factors that affect idea quality, such as the number of ideas (Camacho et al. , 2019), emotional characteristics (Yoo et al. , 2015) and idea popularity (Li et al.…”

“…Previous studies on the factors influencing the quality of ideas generated in OBCs have primarily focused on factors at the individual level, such as ideators' characteristics, ideas' characteristics, member feedback and community policies (Aksoy et al, 2021;Bayus, 2013;Camacho et al, 2019;Li et al, 2016;Ma et al, 2019;Yoo et al, 2015;Zhu et al, 2019). However, one major role of OBCs as digitalized social networks is to allow for close consumer interactions that are previously difficult or even impossible to achieve, and the impact of such interactions in OBCs has not been well studied (Resch and Kock, 2021).…”

Discovering the Sweet Spot of Human-Computer Configurations

“…Enabling the annotation of concepts in idea texts would have multiple advantages. The concepts could be used, for example, as an input for a faceted search tool (e.g., finding all ideas that talk about kinds of plants), they enable aggregation and cluster visualization of the solution space [63], and they could potentially enable effective inspiration mechanisms such as analogical transfer [23] (via a 'has-usage' relationship between concepts).…”