Yes / This paper aims to explore the important qualitative aspects of online user-generated-content that reflects customers’ brand-attitudes. Additionally, the qualitative aspects can help service-providers understand customers’ brand-attitudes by focusing on the important aspects rather than reading the entire review, which will save both their time and effort. We have utilised a total of 10,000 reviews from TripAdvisor (an online-travel-agency provider). This study has analysed the data using statistical-technique (logistic regression), predictive-model (artificial-neural-networks) and structural-modelling technique to understand the most important aspects (i.e. sentiment, emotion or parts-of-speech) that can help to predict customers’ brand-attitudes. Results show that sentiment is the most important aspect in predicting brand-attitudes. While total sentiment content and content polarity have significant positive association, negative high-arousal emotions and low-arousal emotions have significant negative association with customers’ brand attitudes. However, parts-of-speech aspects have no significant impact on brand attitude. The paper concludes with implications, limitations and future research directions.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18339 |
| Date | 14 February 2021 |
| Creators | Ray, A., Bala, P.K., Rana, Nripendra P. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted manuscript |
| Rights | © 2021 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
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