Sourcing product innovation intelligence from online reviews

In recent years, online reviews have offered a rich new medium for consumers to express their opinions and feedback. Product designers frequently aim to consider consumer preferences in their work, but many firms are unsure of how best to harness this online feedback given that textual data is both...

Full description

Saved in:
Bibliographic Details
Published in:Decision Support Systems 2022-06, Vol.157, p.113751, Article 113751
Main Authors: Goldberg, David M., Abrahams, Alan S.
Format: Article
Language:English
Subjects:
Business intelligence
Data mining
Feedback
Innovation
Innovations
Online reviews
Outdoors
Text mining
Unstructured data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent years, online reviews have offered a rich new medium for consumers to express their opinions and feedback. Product designers frequently aim to consider consumer preferences in their work, but many firms are unsure of how best to harness this online feedback given that textual data is both unstructured and voluminous. In this study, we use text mining tools to propose a method for rapid prioritization of online reviews, differentiating the reviews pertaining to innovation opportunities that are most useful for firms. We draw from the innovation and entrepreneurship literature and provide an empirical basis for the widely accepted attribute mapping framework, which delineates between desirable product attributes that firms may want to capitalize upon and undesirable attributes that they may need to remedy. Based on a large sample of reviews in the countertop appliances industry, we demonstrate the performance of our technique, which offers statistically significant improvements relative to existing methods. We validate the usefulness of our technique by asking senior managers at a large manufacturing firm to rate a selection of online reviews, and we show that the selected attribute types are more useful than alternative reviews. Our results offer insight in how firms may use online reviews to harness vital consumer feedback. •Online reviews are studied for innovation opportunities.•Reviews are categorized as irritators, feature requests, and compliments.•Text mining techniques are developed to curate reviews of interest.•A Tabu search heuristic outperforms competing text mining approaches.•The value of innovation-related reviews is confirmed by industry practitioners.
ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2022.113751