Journal Article10.1111/jpim.12676
Capturing product/service improvement ideas from social media based on lead user theory
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TL;DR: In this article , an innovative framework based on lead user theory for capturing product/service improvement ideas from user-generated content on social media (henceforth called "chatter") is proposed.
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About: This article is published in Journal of Product Innovation Management. The article was published on 19 May 2023. The article focuses on the topics: Automatic summarization & Computer science.
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Peningkatan Kinerja Pemasaran: Media Sosial, Inovasi Produk, dan pemasaran Afiliasi
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References
Netnography as a Method of Lead User Identification
Frank-Martin Belz,Wenke Baumbach +1 more
TL;DR: It is concluded that netnography is a viable method of lead user identification, which relies on external assessments instead of self-assessments and is probably less costly than mass screening.
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From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets
TL;DR: The present study aims to provide a comprehensive understanding of the main challenges related to user privacy that affect DDI, and identifies 14 topics related to the study of DDI and UGD strategies.
184
Understanding User-Generated Content and Customer Engagement on Facebook Business Pages
TL;DR: A mobile application that automates the very labor-intensive and therefore time-heavy and expensive and expensive process of manually cataloging and cleaning up after a person has used the app.
173
The role of lead users in knowledge sharing
Lars Bo Jeppesen,Keld Laursen +1 more
TL;DR: The results support the hypothesis that lead users – due to their characteristics – are likely to possess more relevant solution knowledge and thus be centrally involved in contributing knowledge and find that search and integration of knowledge from different external sources of relevance to the community positively moderates knowledge contributions by lead users.
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“Pyramiding: Efficient search for rare subjects”
TL;DR: It is found that pyramiding on average identified the most expert individual in a group on a specific topic with only 28.4% of the group interviewed - a great efficiency gain relative to mass screening.
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