A Two-Phased AI-Enabled Framework for Innovation with User-Generated Data from Consumer Review Sites
Files
Date
2025-01-07
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
682
Ending Page
Alternative Title
Abstract
User-generated data from consumer review sites holds immense potential for driving product innovation, yet actionable research in this area remains limited. This paper addresses this gap by proposing a two-phased AI-enabled framework for leveraging consumer reviews throughout the idea selection and generation processes. Our framework utilizes advanced AI approaches to automate idea selection and generation in open innovation settings. The proposed framework aims to extract product innovation ideas from raw online reviews, employing cutting-edge machine learning for natural language processing and generative pre-trained transformers for natural language generation. This paper offers a novel AI-enabled approach for organizations to drive open innovation and improve product development processes.
Description
Keywords
Responsible Innovation in Collaborative, Connected, and Intelligent Systems: Design, Implementation, and Governance, ai-enabled innovation., idea generation, idea selection, user-generated data
Citation
Extent
10
Format
Geographic Location
Time Period
Related To
Proceedings of the 58th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.