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Semantic Learning-Based Innovation Framework for Social Media

Version 2 2024-06-04, 14:53
Version 1 2018-03-19, 16:25
journal contribution
posted on 2024-06-04, 14:53 authored by Kristijan MirkovskiKristijan Mirkovski, F Von Briel, PB Lowry
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.

History

Journal

IT Professional

Volume

18

Season

November/December 2016

Pagination

26-32

Location

Piscataway, N.J.

ISSN

1520-9202

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

6

Publisher

IEEE

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