The influence of energy-saving information in online reviews on green home appliance purchase behavior based on machine learning

Abstract

The renewal of green home appliances is a crucial measure for households to save energy and reduce emissions. However, how online reviews, especially those relate to energy-saving, affect green home appliance purchase behavior (GHAPB) lacks exploration. In this paper, we investigate over 1 million online reviews on about 3,116 types of air conditioner from JD. By applying word2vec, we divide energy-saving related information in the following three types: norm information, environmental health information and price information, and construct dictionaries for each. Then, the effect value of energy-saving information is quantified from perspectives of breadth, depth and intensity through sentiment analysis. The influence of energy-saving information in online reviews on GHAPB is finally analyzed by linear regression and machine learning models. The results show that all energy-saving information has positive impact on GHAPB, and environmental health information is the most important one. In addition, the attributes of online reviews impose a greater influence on GHAPB, comparing with those of products. The in-depth exploration of energy-saving information in online reviews provides targeted recommendations for the manufacturer and the retailer to promote the adoption of green home appliances