Potential buyer identification and purchase likelihood quantification by mining user-generated content on social media

Xu, Zhaoguang; Dang, Yanzhong; Wang, Qianwen

doi:10.1016/j.eswa.2021.115899

Cited by 10 publications

(2 citation statements)

References 85 publications

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“…In China, auto forums such as Autohome and DCar are popular, where many consumers gather to exchange, discuss, and evaluate products, etc. Hence, there is a large amount of real user-generated content on auto forums [9]. When choosing a new energy vehicle, consumers prefer to browse the post-purchase reviews and star ratings of various new energy vehicles on platforms such as DCar.…”

Section: Introductionmentioning

confidence: 99%

A New Multi-Attribute Decision Making Method for Overvalued Star Ratings Adjustment and Its Application in New Energy Vehicle Selection

Zhang

Chen

2023

Mathematics

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Under the global consensus of carbon peaking and carbon neutrality, new energy vehicles have gradually become mainstream, driven by the dual crises regarding the atmospheric environment and energy security. When choosing new energy vehicles, consumers prefer to browse the post-purchase reviews and star ratings of various new energy vehicles on platforms such as DCar.com. However, it is easy for consumers to become lost in the high-star text reviews and mismatched reviews. To solve the above two issues, this study selected nine new energy vehicles and used a multi-attribute decision making method to rank the vehicles. We first designed adjustment rules based on star ratings and text reviews to cope with the issue of high star ratings but negative text reviews. Secondly, we classified consumers and recommended the optimal alternative for each type of consumer to deal with the issue of mismatched demands between review writers and viewers. Finally, this study compared the ranking results with the sales charts of the past year to verify the feasibility of the proposed method initially. The feasibility and stability of the proposed method were further verified through comparative and sensitivity analyses.

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Section: Introductionmentioning

confidence: 99%

A New Multi-Attribute Decision Making Method for Overvalued Star Ratings Adjustment and Its Application in New Energy Vehicle Selection

Zhang

Chen

2023

Mathematics

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“…The online reputation of a company, brand, product, or service is composed of a set of opinions, experiences, and evaluations that customers share through different social media [1]. Online reputation is extremely important in a company's strategy because customers increasingly use information obtained on the Internet to make decisions about buying services such as those provided by hotels or airlines [2][3][4][5][6][7][8][9]. Moreover, the information available on the Internet about the quality of services and the value perceived by customers, both of a given company and its competitors, is quite relevant for measuring the degree of compliance with the objectives established [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Methodology for Machine-Learning Content Analysis to Define the Key Labels in the Titles of Online Customer Reviews with the Rating Evaluation

Ahmed

Rodríguez‐Díaz

2022

Sustainability

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Online reputation is of great strategic importance to companies today. Customers share their emotions and experiences about the service received or the product acquired through online opinions in the form of quantitative variables or text comments. Although quantitative variables can be analyzed using different statistical methods, the main limitation of comment content analysis lies in the statistical analysis because the texts are qualitative. This study proposes and applies a methodology to develop a machine learning designed to identify the key labels related to the quantitative variables in the general rating of the service received from an airline. To this end, we create a quantitative dichotomous variable from zero to one from a database of comment title labels, thus facilitating the conversion of titles into quantitative variables. On this basis, we carry out a multiple regression analysis where the dependent variable is the overall rating and the independent variables are the labels. The results obtained are satisfactory, and the significant labels are determined, as well as their signs and coefficients with the general ratings. Findings show that the significant labels detected in titles positively influence the prediction of the overall rating of airline. This paper is a new approach to applying cluster analysis to the text content of customers’ online reviews in an airline. Thus, the proposed methodology results in a quantitative value for the labels that determines the direction and intensity of customers’ opinions. Moreover, it has important practical implications for managers to identify the weakness and the strengths of their services in order to increase their positioning in the market by developing meaningful strategies.

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An action–reaction influence model relying on OSN user-generated content

et al. 2023

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Potential buyer identification and purchase likelihood quantification by mining user-generated content on social media

Cited by 10 publications

References 85 publications

A New Multi-Attribute Decision Making Method for Overvalued Star Ratings Adjustment and Its Application in New Energy Vehicle Selection

A New Multi-Attribute Decision Making Method for Overvalued Star Ratings Adjustment and Its Application in New Energy Vehicle Selection

A Methodology for Machine-Learning Content Analysis to Define the Key Labels in the Titles of Online Customer Reviews with the Rating Evaluation

An action–reaction influence model relying on OSN user-generated content

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