Abstract

Purpose Smartphone demand has been driving people towards refurbished electronic products. However, a lack of transparency in refurbished product pricing makes purchases time-consuming and reduces customer trust. Thus, our research aims to help practitioners and researchers understand how product life and usage characteristics, technical attributes and crowdsourced product reviews and sentiments affect exchange prices for refurbished/remanufactured smartphones.Design/methodology/approach Our five-stage exchange price predictive framework begins with data gathering and predictor variable identification. Thereafter, customer review data were scraped to populate both customer ratings and textual content, enabling sentiment analysis for the various smartphone configurations. Stepwise regression was used to find statistically significant factors and validate the predictive model. Testing for nonlinear effects, normality, outliers and homoskedasticity warrants power transformation of the target variable. The analysis used data from GSMArena.com and Amazon.com.Findings Our study validates extant findings and provides several novel insights for functional yet hedonistic products like smartphones. Unlike other pure hedonistic products, refurbished phone buyers care more about usage duration than life. Besides having a strong affinity for the sleekness of the phone, such customers are strongly dissuaded by the presence of negative textual content in the customer reviews.Originality/value Our study augments the current understanding of exchange price modelling by bringing in perspectives from life cycle characteristics, technical attributes and product reviews.

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