Semantic Learning-Based Innovation Framework for Social Media

Abstract

Small- and medium-sized enterprises (SMEs) typically face resource and capability constraints that inhibit their innovation activities. One way SMEs can overcome these constraints is by complementing internal resources and capabilities with external knowledge, referred to as open innovation. With the proliferation of the Internet, SMEs have added social media to their traditional marketing activities. However, they rarely embrace social media's analytical capabilities for innovation. The authors propose the semantic-learning-based innovation framework (SLBIF) to guide SMEs in using social media's analytical capabilities to innovate their products or services. Their framework includes three consecutive stages innovators should follow--idea selection, idea refinement, and idea diffusion--that explain how to analyze customer preferences through semantic analysis of customer posts and identify lead users and opinion leaders using user-directed social network analysis.