Product improvement in a big data environment: A novel method based on text mining and large group decision making

Abstract

Product improvement has become a multifaceted and uncertain endeavour for manufacturers in an increasingly competitive business environment. Online platforms have emerged to solicit consumer opinions and product feedback. However, product improvement requires a critical yet complex decision-making approach for manufacturers. Managers face the challenge of identifying the most effective decision-making methodology for product improvement, especially in a big data environment. In this research, we comprehensively evaluate different product improvement decision-making methodologies through a series of experimental investigations. Specifically, three different experiments are conducted, including: i) an initial selection guided by intuitive perception, ii) expert decision-making, and iii) a hybrid method that incorporates consumer big data and large-scale group decision-making. Product criteria sets are categorized using the Latent Dirichlet Allocation (LDA) method, while the importance of these criteria is determined by applying the TextRank and Word2Vec algorithms. Our empirical results show that the mixed method, which utilizes text-mining techniques in conjunction with large-group decision-making, provides a more reliable and effective approach to facilitating product improvement.