Can user reviews on online shopping websites contribute to user-involved green product innovation: a case study of household refrigerators

ABSTRACT

User reviews (URs) have been regarded as a novel way of promoting user-involved green product innovation (GPI) in theory. However, the actual relationships between URs and GPI are still underexamined. This study adopted the method of content analysis to exemplify the effectiveness of user reviews on online shopping websites (OSURs) in user-involved GPI. Specifically, 62,909 effective OSURs on 24 green household refrigerators and 43 conventional refrigerators were collected and analyzed. The results show that 8.41% of all the pre-processed OSURs contain green information, which cannot be a dominant information source for promoting GPI. Yet, the identified green reviews (GRs) are still valuable for developing and diffusing GPI. On the one hand, three green topics related to energy efficiency, environmental friendliness, green materials and technologies are intensively generated, massive user experiences can objectively reflect the actual environmental performance of existing products. On the other hand, GRs account for 12.62% and 6.19% of all the OSURs on green refrigerators and conventional refrigerators, respectively. Users prefer to generate positive rather than negative GRs on green refrigerators. The implementation of GPI has a positive impact on OSURs in return. Firms can actively collect and analyze OSURs of extant green products, thus promoting the green innovation of their own products. This study adds to the literature of user-involved GPI by exploring the actual relationships between OSURs and GPI. A practical analysis framework and valuable implications are also proposed to advance GPI, green growth at firm level and sustainable development at social level.

KEYWORDS:

• Green product innovation
• user reviews
• content analysis
• natural language processing
• sustainable development
• green growth

Acknowledgments

This study is financially supported by Zhejiang Soft Science Programme(no.2023C25042&2023C25057), National Natural Science Foundation of China (no. 71901194), China Postdoctoral Science Foundation (no.2020M671775).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the China Postdoctoral Science Foundation [2020M671775]; National Natural Science Foundation of China [71901194].