New energy vehicle demand forecasting via an improved Bass model with perceived quality identified from online reviews

Abstract

As one source of user-generated content, online reviews embed vast quantities of important business information, significantly affecting consumer demand. In this study, we aim to propose a new forecasting approach to predict the demand for new energy vehicles (NEVs) by incorporating perceived quality measures extracted from online reviews into the traditional Bass model. To this end, we consider three crucial dimensions (i.e., emotional experience, defect perception, and brand/product image) and adopt text analysis techniques to mine perceived quality information from online reviews for NEVs comprehensively. Coping with the limited datasets, we further dynamically incorporate the mined perceived quality into the Bass model to improve the accuracy of new energy vehicle (NEV) demand forecasting. Finally, we meticulously conduct a series of experiments with crawled online reviews and historical sales of distinct NEV models. The experimental results demonstrate that the perceived quality measures identified from online reviews jointly affect the consumers’ purchasing decisions, and effectively enhance the performance of the NEV demand forecasting. Furthermore, some interesting and important findings are achieved based on the proposed methodology, including the time-lag effect of perceived quality on consumers’ purchasing decisions and the formulation of specific product strategies based on demand trends.