Research on Chinese Consumers’ Attitudes Analysis of Big-Data Driven Price Discrimination Based on Machine Learning - Wang, Jun; Shu, Tao; Zhao, Wenjin; Zhou, Jixian (Frontiers in Psychology, 2022)
From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers’ attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the ffectionBehavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled LDA, LSTM, and Snow NLP, based on social media comments text dataset. Similar to questionnaires published results, this paper verified that 61% of consumers expressed negative sentiment towards BDPD in general. Differently, on a finer scale, this study further measured the negative sentiments that differ significantly among different topics. The measurement results show the topics “Regular Customers Priced High” (69%) and “Usage Intention” (67%) occupy the top two places of negative sentiment among consumers, and the topic “Precision Marketing” (42%) is at the bottom. Moreover, semantic recognition results that 49% of consumers’ comments involving multiple topics, indicating that consumers have a pretty clear cognition of the complex status of the BDPD. Importantly, this study found some topics that had not been focused on in previous studies, such as more than 8% of consumers calling for government and legal departments to regulate BDPD behavior, which indicates that quite enough consumers are losing confidence in the self-discipline of the platform enterprises. Another interesting result is that consumers who pursue solutions to the BDPD belong to two mutually exclusive groups: government protection and self-protection. The significance of this study is that reminds the e-commerce platforms to pay attention to the potential harm for consumers’ psychology while bringing additional profits through the BDPD. Otherwise, the negative consumer attitudes may cause damage to brand image, business reputation, and the sustainable development of the platforms themselves. It also provides government supervision departments an advanced analysis method reference for more effective administering to protect social fairness.