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

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.

A Confirmation Bias View on Social Media Induced Polarisation During Covid-19

“…Social media is increasingly being used by organisations to manage business activities and relationships among supply-chain stakeholders (Choi et al, 2020;Huang et al, 2020;Singh et al, 2018) including, new product development (Giannakis et al, 2020), service improvement (Fan & Niu, 2016), daily sales forecasts (Cui et al, 2019), and information dissemination (Kanagarajoo et al, 2019). The prevalence and speed at which social media platforms (e.g., WeChat, Facebook, LinkedIn, Twitter, Flickr, What-sApp) facilitate the exchange of real-time data (Gorwa & Guilbeault, 2020;Rousidis et al, 2020) has made it a popular tool for effective supply chain management (Choi et al, 2020).…”

The role of alliance management, big data analytics and information visibility on new-product development capability

“…Furthermore. existing studies have either focused on the 3Vs-volume, velocity, and variety of data and their influence on the NPD (Johnson et al, 2017) or the role played by social media in NPD (Giannakis et al, 2020;Zhan et al, 2018). Rothaermel and Deeds (2006) found that the alliance type and experience act to moderate the relationship between a high-technology venture's R&D alliances and its performance in NPD.…”

“…This generated competitive advantage for firms competing in dynamic turbulent environments. NPD in the age of big data has gained significant momentum (see, Johnson et al, 2017;Zhan et al, 2018;Sun & Liu, 2020;Giannakis et al, 2020;Liu et al, 2020;Cappa et al, 2021). Johnson et al (2017) further argue that organizations are increasingly investing in big data analytics capability to transform their NPD activities.…”

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

“…System development is carried out using system design techniques, namely prototyping. This technique aims to model a system for detecting sentiment based on social media emotions [36], [37]. The prototyping process uses three main stages in text processing: preprocessing, emotion detection based on sentiment analysis, and cross-validation testing.…”