Framework of early adopters’ incipient and innovative ideas and chance discovery

Abstract

The innovative diffusion theory indicates that the key to success of businesses is the innovative ideas of the early adopters. Furthermore, the early adopters’ documents on the Internet were extremely rare; the traditional associative analyses in text mining tend to ignore these useful ideas of the early adopters. In this study, a framework was proposed, which uses low-term frequency (TF), low-term frequency with inverse document frequency and low TF with the inverse clusters frequency, to acquire rare connections between low-frequency terms, to extract early adopters’ incipient and innovative ideas. This new proposed framework amplifies the rare chance to find potential terms which are valuable for businesses’ future. Finally, some observed data obtained from the passengers on airplanes or trains were used to extract the innovative ideas from early adopters. By putting the data into a business scenario, a case study was presented and the feasibility of the framework can therefore be checked by experts. A comparison has been made between the proposed framework and chance discovery. The experimental result evidences that the results in the new framework are more effective than the outcomes of chance discovery method to sift out incipient ideas.

Keywords

• Early adopter
• innovation diffusion model
• chance discovery
• innovation
• text mining