Semantic learning-based innovation framework for social media

Mirkovski, Kristijan, von Briel, Frederik, & Lowry, Paul Benjamin (2016) Semantic learning-based innovation framework for social media. IT Professional, 18(6), pp. 26-32.

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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 the analytical capabilities of social media for innovation. Hence, we propose the semantic learning-based innovation framework (SLBIF) to guide SMEs in using the analytical capabilities of social media to innovate their products or services. Our framework includes three consecutive stages innovators should follow —idea selection, idea refinement, and idea diffusion—and which 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.

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ID Code: 97726
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Open Innovation, Semantic Learning, Big Data, Social Media, SME
DOI: 10.1109/MITP.2016.104
ISSN: 1520-9202
Subjects: Australian and New Zealand Standard Research Classification > COMMERCE MANAGEMENT TOURISM AND SERVICES (150000) > BUSINESS AND MANAGEMENT (150300) > Business Information Systems (150302)
Australian and New Zealand Standard Research Classification > COMMERCE MANAGEMENT TOURISM AND SERVICES (150000) > BUSINESS AND MANAGEMENT (150300) > Innovation and Technology Management (150307)
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Management
Copyright Owner: Copyright 2016 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Deposited On: 29 Jul 2016 01:59
Last Modified: 14 Dec 2016 06:54

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