Motivations of electronic word-of-mouth (eWOM) sharing in the automotive customer’s journey with an analysis of consumer sentiment in Singapore

Guo, Zhiwei (2020) Motivations of electronic word-of-mouth (eWOM) sharing in the automotive customer’s journey with an analysis of consumer sentiment in Singapore. [Dissertation (University of Nottingham only)]

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Abstract

Given the high cost of owning a car, many dealerships in Singapore look toward enhancing the consumer experience to make themselves stand out and gain a competitive advantage. In order to improve a customer’s ownership journey, understanding the various pain points from the perspective of the customer is crucial, as this will affect the customer’s repurchase intention and the spread of Word-Of-Mouth (WOM) either to their friends or relatives or through online channels.

This study looks firstly into the stage in the ownership journey where customers share their sentiments and experiences and their reasons for sharing them. Next, the study will look into the motivation behind why customers share their experiences online from the feelings generated from goal congruence or incontinence. Lastly, a general sentiment analysis of four popular brands of cars in Singapore, namely Honda, Mazda, Toyota, and Volkswagen, will be presented.

A mixed-method research approach combining focus groups, which are a qualitative method that gathers primary data, and sentiment analysis, which is a quantitative method that gathers textual information posted by online users and uses algorithms to determine forum users’ subjective expressions and opinions, will be used in this study.

The results from the findings will highlight the importance of giving customers the best experience at the stage of the customer ownership journey where it may elicit the highest levels of motivation toward sharing positive or negative WOM and repurchase intention. Actionable steps for dealerships to improve and evaluate the consumer experience are also listed.

Item Type: Dissertation (University of Nottingham only)
Keywords: eWOM, WOM, sentiment analysis, mixed-method research
Depositing User: Guo, Zhiwei
Date Deposited: 04 Aug 2020 08:00
Last Modified: 04 Aug 2020 08:00
URI: http://eprints.nottingham.ac.uk/id/eprint/60599

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