Analysing product attributes of refurbished laptops based on customer reviews and ratings: machine learning approach to circular consumption

Abstract

Reviews and ratings of consumers towards a product impact consumer decision-making and their perceptions. Such information is key in measuring consumer satisfaction and net promoter scores. However, when the reviewed products are refurbished, consumer reviews become more important because information influences consumer behaviour and attitude toward looped products. This research explores the decision-influencing attributes of consumers while purchasing refurbished goods using quantitative and qualitative methods. Online after-sales 1986 laptop customers’ review and rating data in the public domain were analysed to reveal the decision-influencing attributes and their impact on potential consumers. The study envisions assisting the operations of sellers in the refurbished market by strengthening their businesses' value proposition and stimulating reverse logistics entrepreneurs to use the opportunity. Review data containing lifecycle valuation of old laptops induced feature extraction by machine learning applications. It is beneficial to sellers in the refurbished product segment. It provides information to strengthen their value proposition and is informative to entrepreneurs wanting to enter the segment. Based on the text analysis of consumer reviews, the study's results show that price, brand, design, performance, services, and utility influence consumers. The frequency analysis technique was used to extract attributes, followed by content analysis and feature selection using SHapley Additive exPlanations (SHAP) for exploring correlations between features and star ratings. Lastly, multinomial logistic regression was used to validate the generated model. The results show that brand, design, price, and utility are the most prominent attributes influencing consumers' decision-making with positive sentiments. In contrast, performance and services often generate neutral and negative sentiments.