Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce

Abstract

With the acceleration of China's urbanization and the increasing number of household appliances, the promotion of energy-saving products has attracted widespread attention. However, the influence of online reviews on energy-saving products, especially the influence of energy-efficiency-related textual reviews, has not been elucidated. Drawing upon product information and consumer comments from one of China's largest e-commerce platforms, we covered 1188 products and over 600,000 corresponding online textual reviews. After categorizing the textual reviews, we constructed a dictionary of energy-saving attitude. Based on an elaboration likelihood model (ELM), this study analyzes the influence of energy-saving related e-WOM on product sales by a linear regression model. With an intermediary role in consideration, this study also demonstrates the influence mechanism of online reviews on purchase decision through the central route (the effects of gross sentiment, e-WOM and review tag on product sales) and peripheral route (the effects of star rating and review numbers on product sales) in ELM. The results show that both the online reviews and the rating scores of energy star have a strong influence on product sales in a short period of time. The gross sentiment of an online review can influence product sales, which is wholly affected by the intermediary role of the star rating. The central route can also influence product sales indirectly through the peripheral route.