Brand Feedback and eWOM Sentiment on Twitter : Evaluating the effect of company response strategies in online public conversations

Sammanfattning: Aim: The purpose of this study is to investigate how brand feedback response strategies impact eWOM sentiment in public conversations between consumers and businesses. Method: The study has been conducted by collecting over one million tweets from Twitter and performing automated sentiment analysis of these tweets. After processing the tweets, 39 773 conversations were identified containing brand feedback. Two hypotheses were formulated regarding brand feedback and eWOM sentiment, and the sentiment scores of the 39 773 conversations used to test the hypotheses. Result & Conclusions: There was, in general, a slight positive change in sentiment for conversations containing brand feedback. However, the results also showed that providing brand feedback does not always lead to a positive change in sentiment. The results showed no correlation between response speed and sentiment change in the conversations.  Contribution of the thesis: Previous studies within brand feedback and eWOM have to a large extent been experimental. This study contributes to the field by investigating brand feedback and eWOM from real data, using over 100 000 tweets in the analysis. The results of this study show that eWOM sentiment is not affected by response speed, which contradicts one previous study. This study also contributes by showing that brand feedback only has a slight positive effect on eWOM sentiment, which means that an improved eWOM alone may not be reason enough for companies to invest in brand feedback. Suggestions for future research: Further research could address limitations in the methodology, such as more sophisticated sentiment analysis or evaluating other social media platforms. Research could also address the lack of correlation between response speed and sentiment change and investigate intervening variables or moderating effects. Key words: eWOM, brand feedback, sentiment analysis, service recovery, social media, Twitter