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Social media and sensemaking patterns in new product development : demystifying the customer sentiment

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Giannakis, Mihalis, Dubey, Rameshwar, Yan, Shishi, Spanaki, Konstantina and Papadopoulos, Thanos (2022) Social media and sensemaking patterns in new product development : demystifying the customer sentiment. Annals of Operations Research , 308 . pp. 145-175. doi:10.1007/s10479-020-03775-6 ISSN 0254-5330.

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Official URL: https://doi.org/10.1007/s10479-020-03775-6

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Abstract

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms.

Item Type: Journal Article
Subjects: H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HF Commerce
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Library of Congress Subject Headings (LCSH): New products -- Data processing, Decision making, Artificial intelligence, Social media -- Data processing, Consumer satisfaction
Journal or Publication Title: Annals of Operations Research
Publisher: Springer New York LLC
ISSN: 0254-5330
Official Date: January 2022
Dates:
DateEvent
January 2022Published
29 August 2020Available
Volume: 308
Page Range: pp. 145-175
DOI: 10.1007/s10479-020-03775-6
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 10 August 2022
Date of first compliant Open Access: 24 August 2022

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