Semantic Learning-Based Innovation Framework for Social Media

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.

Open Innovation and Social Media

The concept of involving users in the innovation process, which is referred to as in-bound open innovation, is at the core of the shift around how the Internet is used and should be used—from top-down content consumption to bottom-up participation and user-centric content creation.² The evolution of the Internet has simultaneously created new opportunities and reduced costs for organizations to collaborate with external actors, providing SMEs in particular with cost-efficient ways to open up their innovation processes. Online open innovation approaches give SMEs a cost-efficient way to integrate external contributors from all over the world into new product development processes, and the increasing pervasiveness of social media provides opportunities for listening to and engaging with customers.⁴